WorldWideScience

Sample records for network classifiers constructed

  1. Constructing and Classifying Email Networks from Raw Forensic Images

    Science.gov (United States)

    2016-09-01

    below in section 2.1.1) in which users are able to correspond with other users of their choosing. The brain can be thought of as a network in which...the number of nodes and edges can be in the billions such as a network of the neurons in the brain and their connections. A graph’s density is the...main difference was the computation time. For example, on a 1275-node jazz musician network, the fast algorithm ran to completion in about one

  2. Analysis of the Earth's magnetosphere states using the algorithm of adaptive construction of hierarchical neural network classifiers

    Science.gov (United States)

    Dolenko, Sergey; Svetlov, Vsevolod; Isaev, Igor; Myagkova, Irina

    2017-10-01

    This paper presents analysis of the results of clusterization of the array of increases in the flux of relativistic electrons in the outer radiation belt of the Earth by two clustering algorithms. One of them is the algorithm for adaptive construction of hierarchical neural network classifiers developed by the authors, applied in clustering mode; the other one is the well-known k-means clusterization algorithm. The obtained clusters are analysed from the point of view of their possible matching to characteristic types of events, the partitions obtained by both methods are compared with each other.

  3. Analysis of the Earth's magnetosphere states using the algorithm of adaptive construction of hierarchical neural network classifiers

    Directory of Open Access Journals (Sweden)

    Dolenko Sergey

    2017-01-01

    Full Text Available This paper presents analysis of the results of clusterization of the array of increases in the flux of relativistic electrons in the outer radiation belt of the Earth by two clustering algorithms. One of them is the algorithm for adaptive construction of hierarchical neural network classifiers developed by the authors, applied in clustering mode; the other one is the well-known k-means clusterization algorithm. The obtained clusters are analysed from the point of view of their possible matching to characteristic types of events, the partitions obtained by both methods are compared with each other.

  4. Clustering signatures classify directed networks

    Science.gov (United States)

    Ahnert, S. E.; Fink, T. M. A.

    2008-09-01

    We use a clustering signature, based on a recently introduced generalization of the clustering coefficient to directed networks, to analyze 16 directed real-world networks of five different types: social networks, genetic transcription networks, word adjacency networks, food webs, and electric circuits. We show that these five classes of networks are cleanly separated in the space of clustering signatures due to the statistical properties of their local neighborhoods, demonstrating the usefulness of clustering signatures as a classifier of directed networks.

  5. Face detection by aggregated Bayesian network classifiers

    NARCIS (Netherlands)

    Pham, T.V.; Worring, M.; Smeulders, A.W.M.

    2002-01-01

    A face detection system is presented. A new classification method using forest-structured Bayesian networks is used. The method is used in an aggregated classifier to discriminate face from non-face patterns. The process of generating non-face patterns is integrated with the construction of the

  6. Neural Classifier Construction using Regularization, Pruning

    DEFF Research Database (Denmark)

    Hintz-Madsen, Mads; Hansen, Lars Kai; Larsen, Jan

    1998-01-01

    In this paper we propose a method for construction of feed-forward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme, we derive a modified form of the entropic error measure and an algebraic estimate of the test error. In conjunction...

  7. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Design of Robust Neural Network Classifiers

    DEFF Research Database (Denmark)

    Larsen, Jan; Andersen, Lars Nonboe; Hintz-Madsen, Mads

    1998-01-01

    This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present...... a modified likelihood function which incorporates the potential risk of outliers in the data. This leads to the introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We...

  9. Revealing effective classifiers through network comparison

    Science.gov (United States)

    Gallos, Lazaros K.; Fefferman, Nina H.

    2014-11-01

    The ability to compare complex systems can provide new insight into the fundamental nature of the processes captured, in ways that are otherwise inaccessible to observation. Here, we introduce the n-tangle method to directly compare two networks for structural similarity, based on the distribution of edge density in network subgraphs. We demonstrate that this method can efficiently introduce comparative analysis into network science and opens the road for many new applications. For example, we show how the construction of a “phylogenetic tree” across animal taxa according to their social structure can reveal commonalities in the behavioral ecology of the populations, or how students create similar networks according to the University size. Our method can be expanded to study many additional properties, such as network classification, changes during time evolution, convergence of growth models, and detection of structural changes during damage.

  10. Revealing effective classifiers through network comparison

    CERN Document Server

    Gallos, Lazaros K

    2014-01-01

    The ability to compare complex systems can provide new insight into the fundamental nature of the processes captured in ways that are otherwise inaccessible to observation. Here, we introduce the $n$-tangle method to directly compare two networks for structural similarity, based on the distribution of edge density in network subgraphs. We demonstrate that this method can efficiently introduce comparative analysis into network science and opens the road for many new applications. For example, we show how the construction of a phylogenetic tree across animal taxa according to their social structure can reveal commonalities in the behavioral ecology of the populations, or how students create similar networks according to the University size. Our method can be expanded to study a multitude of additional properties, such as network classification, changes during time evolution, convergence of growth models, and detection of structural changes during damage.

  11. Stochastic margin-based structure learning of Bayesian network classifiers.

    Science.gov (United States)

    Pernkopf, Franz; Wohlmayr, Michael

    2013-02-01

    The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.

  12. Feature selection for Bayesian network classifiers using the MDL-FS score

    NARCIS (Netherlands)

    Drugan, Madalina M.; Wiering, Marco A.

    When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features

  13. One pass learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2016-01-01

    Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance. Copyright

  14. Classifying patient portal messages using Convolutional Neural Networks.

    Science.gov (United States)

    Sulieman, Lina; Gilmore, David; French, Christi; Cronin, Robert M; Jackson, Gretchen Purcell; Russell, Matthew; Fabbri, Daniel

    2017-10-01

    Patients communicate with healthcare providers via secure messaging in patient portals. As patient portal adoption increases, growing messaging volumes may overwhelm providers. Prior research has demonstrated promise in automating classification of patient portal messages into communication types to support message triage or answering. This paper examines if using semantic features and word context improves portal message classification. Portal messages were classified into the following categories: informational, medical, social, and logistical. We constructed features from portal messages including bag of words, bag of phrases, graph representations, and word embeddings. We trained one-versus-all random forest and logistic regression classifiers, and convolutional neural network (CNN) with a softmax output. We evaluated each classifier's performance using Area Under the Curve (AUC). Representing the messages using bag of words, the random forest detected informational, medical, social, and logistical communications in patient portal messages with AUCs: 0.803, 0.884, 0.828, and 0.928, respectively. Graph representations of messages outperformed simpler features with AUCs: 0.837, 0.914, 0.846, 0.884 for informational, medical, social, and logistical communication, respectively. Representing words with Word2Vec embeddings, and mapping features using a CNN had the best performance with AUCs: 0.908 for informational, 0.917 for medical, 0.935 for social, and 0.943 for logistical categories. Word2Vec and graph representations improved the accuracy of classifying portal messages compared to features that lacked semantic information such as bag of words, and bag of phrases. Furthermore, using Word2Vec along with a CNN model, which provide a higher order representation, improved the classification of portal messages. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. A Convolutional Neural Network Neutrino Event Classifier

    CERN Document Server

    Aurisano, A; Rocco, D; Himmel, A; Messier, M D; Niner, E; Pawloski, G; Psihas, F; Sousa, A; Vahle, P

    2016-01-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  16. Automatic speech recognition using a predictive echo state network classifier.

    Science.gov (United States)

    Skowronski, Mark D; Harris, John G

    2007-04-01

    We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

  17. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    Geng, S.; Ren, G.; Ogihara, M.

    2017-05-01

    Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

  18. Classifying Radio Galaxies with the Convolutional Neural Network

    Science.gov (United States)

    Aniyan, A. K.; Thorat, K.

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  19. Recurrent neural networks for classifying relations in clinical notes.

    Science.gov (United States)

    Luo, Yuan

    2017-08-01

    We proposed the first models based on recurrent neural networks (more specifically Long Short-Term Memory - LSTM) for classifying relations from clinical notes. We tested our models on the i2b2/VA relation classification challenge dataset. We showed that our segment LSTM model, with only word embedding feature and no manual feature engineering, achieved a micro-averaged f-measure of 0.661 for classifying medical problem-treatment relations, 0.800 for medical problem-test relations, and 0.683 for medical problem-medical problem relations. These results are comparable to those of the state-of-the-art systems on the i2b2/VA relation classification challenge. We compared the segment LSTM model with the sentence LSTM model, and demonstrated the benefits of exploring the difference between concept text and context text, and between different contextual parts in the sentence. We also evaluated the impact of word embedding on the performance of LSTM models and showed that medical domain word embedding help improve the relation classification. These results support the use of LSTM models for classifying relations between medical concepts, as they show comparable performance to previously published systems while requiring no manual feature engineering. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Sequential Construction of Costly Networks

    Energy Technology Data Exchange (ETDEWEB)

    Gutfraind, Alexander [Los Alamos National Laboratory

    2011-01-01

    Natural disasters or attacks often disrupt infrastructure networks requiring a costly recovery. This motivates an optimization problem where the objecitve is to construct the nodes of a graph G(V;E), and the cost of each node is dependent on the number of its neighbors previously constructed, or more generally, any properties of the previously-completed subgraph. In this optimization problem the objective is to find a permutation of the nodes which results in the least construction cost. We prove that in the case where the cost of nodes is a convex function in the number of neighbors, the optimal construction sequence is to start at a single node and move outwards. We also introduce algorithms and heuristics for solving various instances of the problem. Those methods can be applied to help reduce the cost of recovering from disasters as well as to plan the deployment of new network infrastructure.

  1. Learning Bayesian network classifiers for credit scoring using Markov Chain Monte Carlo search

    NARCIS (Netherlands)

    Baesens, B.; Egmont-Petersen, M.; Castelo, R.; Vanthienen, J.

    2001-01-01

    In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search.

  2. Evolutionary biosemiotics and multilevel construction networks.

    Science.gov (United States)

    Sharov, Alexei A

    2016-12-01

    In contrast to the traditional relational semiotics, biosemiotics decisively deviates towards dynamical aspects of signs at the evolutionary and developmental time scales. The analysis of sign dynamics requires constructivism (in a broad sense) to explain how new components such as subagents, sensors, effectors, and interpretation networks are produced by developing and evolving organisms. Semiotic networks that include signs, tools, and subagents are multilevel, and this feature supports the plasticity, robustness, and evolvability of organisms. The origin of life is described here as the emergence of simple self-constructing semiotic networks that progressively increased the diversity of their components and relations. Primitive organisms have no capacity to classify and track objects; thus, we need to admit the existence of proto-signs that directly regulate activities of agents without being associated with objects. However, object recognition and handling became possible in eukaryotic species with the development of extensive rewritable epigenetic memory as well as sensorial and effector capacities. Semiotic networks are based on sequential and recursive construction, where each step produces components (i.e., agents, scaffolds, signs, and resources) that are needed for the following steps of construction. Construction is not limited to repair and reproduction of what already exists or is unambiguously encoded, it also includes production of new components and behaviors via learning and evolution. A special case is the emergence of new levels of organization known as metasystem transition . Multilevel semiotic networks reshape the phenotype of organisms by combining a mosaic of features developed via learning and evolution of cooperating and/or conflicting subagents.

  3. Text Classification: Classifying Plain Source Files with Neural Network

    Directory of Open Access Journals (Sweden)

    Jaromir Veber

    2010-10-01

    Full Text Available The automated text file categorization has an important place in computer engineering, particularly in the process called data management automation. A lot has been written about text classification and the methods allowing classification of these files are well known. Unfortunately most studies are theoretical and for practical implementation more research is needed. I decided to contribute with a research focused on creating of a classifier for different kinds of programs (source files, scripts…. This paper will describe practical implementation of the classifier for text files depending on file content.

  4. Functional Interaction Network Construction and Analysis for Disease Discovery.

    Science.gov (United States)

    Wu, Guanming; Haw, Robin

    2017-01-01

    Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

  5. Managing drivers of innovation in construction networks

    NARCIS (Netherlands)

    Bossink, B.A.G.

    2004-01-01

    Various drivers of construction innovation are distinguished and classified in four distinctive categories: environmental pressure, technological capability, knowledge exchange, and boundary spanning. Innovation drivers in these categories are active at the transfirm, intrafirm, and interfirm level

  6. Classifying pairs with trees for supervised biological network inference.

    Science.gov (United States)

    Schrynemackers, Marie; Wehenkel, Louis; Babu, M Madan; Geurts, Pierre

    2015-08-01

    Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as a classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the latter for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.

  7. Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images.

    Science.gov (United States)

    Jisu Hong; Bo-Yong Park; Hyunjin Park

    2017-07-01

    Barrett's esophagus is a diseased condition with abnormal changes of the cells in the esophagus. Intestinal metaplasia (IM) and gastric metaplasia (GM) are two sub-classes of Barrett's esophagus. As IM can progress to the esophageal cancer, the neoplasia (NPL), developing methods for classifying between IM and GM are important issues in clinical practice. We adopted a deep learning (DL) algorithm to classify three conditions of IM, GM, and NPL based on endimicroscopy images. We constructed a convolutional neural network (CNN) architecture to distinguish among three classes. A total of 262 endomicroscopy imaging data of Barrett's esophagus were obtained from the international symposium on biomedical imaging (ISBI) 2016 challenge. 155 IM, 26 GM and 55 NPL cases were used to train the architecture. We implemented image distortion to augment the sample size of the training data. We tested our proposed architecture using the 26 test images that include 17 IM, 4 GM and 5 NPL cases. The classification accuracy was 80.77%. Our results suggest that CNN architecture could be used as a good classifier for distinguishing endomicroscopy imaging data of Barrett's esophagus.

  8. Classifying epilepsy diseases using artificial neural networks and genetic algorithm.

    Science.gov (United States)

    Koçer, Sabri; Canal, M Rahmi

    2011-08-01

    In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.

  9. Strategies for Transporting Data Between Classified and Unclassified Networks

    Science.gov (United States)

    2016-03-01

    Sustainment Logistics CPC System mission command (S2MC) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18...analyze the national enterprise data portal (NEDP), a foundational component of the sustainment system mission command. The analysis focused on...Taps,” Network Performance Channel GmbH, Langen, Germany , July 2011. 7. United States Navy, “Tactical Command System – MIP,” Defense Technical

  10. Construction of the NIFS campus information network, NIFS-LAN

    Energy Technology Data Exchange (ETDEWEB)

    Tsuda, Kenzo; Yamamoto, Takashi; Kato, Takeo; Nakamura, Osamu; Watanabe, Kunihiko; Watanabe, Reiko; Tsugawa, Kazuko; Kamimura, Tetsuo

    2000-10-01

    The advanced NIFS campus information network, NIFS-LAN, was designed and constructed as an informational infrastructure in 1996, 1997 and 1998 fiscal year. NIFS-LAN was composed of three autonomous clusters classified from research purpose; Research Information cluster, Large Helical Device Experiment cluster and Large-Scale Computer Simulation Research cluster. Many ATM(Asychronous Transfer Mode) switching systems and switching equipments were used for NIFS-LAN. Here, the outline of NIFS-LAN is described. (author)

  11. Fuzzy Naive Bayesian for constructing regulated network with weights.

    Science.gov (United States)

    Zhou, Xi Y; Tian, Xue W; Lim, Joon S

    2015-01-01

    In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights. Then, we can use regulated relations and weights to construct a regulated network. Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN. The experiment results show that the FNB has a higher classification rate than Naive Bayesian and TAN.

  12. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.

    Science.gov (United States)

    Xu, Jun; Luo, Xiaofei; Wang, Guanhao; Gilmore, Hannah; Madabhushi, Anant

    2016-05-26

    Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

  13. Classifying chemical mode of action using gene networks and machine learning: a case study with the herbicide linuron.

    Science.gov (United States)

    Ornostay, Anna; Cowie, Andrew M; Hindle, Matthew; Baker, Christopher J O; Martyniuk, Christopher J

    2013-12-01

    The herbicide linuron (LIN) is an endocrine disruptor with an anti-androgenic mode of action. The objectives of this study were to (1) improve knowledge of androgen and anti-androgen signaling in the teleostean ovary and to (2) assess the ability of gene networks and machine learning to classify LIN as an anti-androgen using transcriptomic data. Ovarian explants from vitellogenic fathead minnows (FHMs) were exposed to three concentrations of either 5α-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12h. Ovaries exposed to DHT showed a significant increase in 17β-estradiol (E2) production while FLUT and LIN had no effect on E2. To improve understanding of androgen receptor signaling in the ovary, a reciprocal gene expression network was constructed for DHT and FLUT using pathway analysis and these data suggested that steroid metabolism, translation, and DNA replication are processes regulated through AR signaling in the ovary. Sub-network enrichment analysis revealed that FLUT and LIN shared more regulated gene networks in common compared to DHT. Using transcriptomic datasets from different fish species, machine learning algorithms classified LIN successfully with other anti-androgens. This study advances knowledge regarding molecular signaling cascades in the ovary that are responsive to androgens and anti-androgens and provides proof of concept that gene network analysis and machine learning can classify priority chemicals using experimental transcriptomic data collected from different fish species. © 2013.

  14. A deep convolutional neural network model to classify heartbeats.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San

    2017-10-01

    The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

    Directory of Open Access Journals (Sweden)

    M. Njah

    2017-06-01

    Full Text Available This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.

  16. A neural network-based electromyography motion classifier for upper limb activities

    Directory of Open Access Journals (Sweden)

    Karan Veer

    2016-11-01

    Full Text Available The objective of the work is to investigate the classification of different movements based on the surface electromyogram (SEMG pattern recognition method. The testing was conducted for four arm movements using several experiments with artificial neural network classification scheme. Six time domain features were extracted and consequently classification was implemented using back propagation neural classifier (BPNC. Further, the realization of projected network was verified using cross validation (CV process; hence ANOVA algorithm was carried out. Performance of the network is analyzed by considering mean square error (MSE value. A comparison was performed between the extracted features and back propagation network results reported in the literature. The concurrent result indicates the significance of proposed network with classification accuracy (CA of 100% recorded from two channels, while analysis of variance technique helps in investigating the effectiveness of classified signal for recognition tasks.

  17. Comparision of Clustering Algorithms usingNeural Network Classifier for Satellite Image Classification

    OpenAIRE

    S.Praveena; Dr. S.P Singh

    2015-01-01

    This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid genetic-Artificial Bee Colony(ABC) algorithm that is developed by hybridizing the ABC and FCM to obtain the effective segmentation in satellite image and classified using neural n...

  18. Hybrid vector quantization/neural tree network classifiers for speaker recognition

    Science.gov (United States)

    Farrell, Kevin R.; Mammone, Richard J.

    1994-10-01

    A new classification system for text-independent speaker recognition is presented. Text- independent speaker recognition systems generally model each speaker with a single classifier. The traditional methods use unsupervised training algorithms, such as vector quantization (VQ), to model each speaker. Such methods base their decision on the distortion between an observation and the speaker model. Recently, supervised training algorithms, such as neural networks, have been successfully applied to speaker recognition. Here, each speaker is represented by a neural network. Due to their discriminative training, neural networks capture the differences between speakers and use this criteria for decision making. Hence, the output of a neural network can be considered as an interclass measure. The VQ classifier, on the other hand, uses a distortion which is independent of the other speaker models, and can be considered as an intraclass measure. Since these two measures are based on different criteria, they can be effectively combined to yield improved performance. This paper uses data fusion concepts to combine the outputs of the neural tree network and VQ classifiers. The combined system is evaluated for text-independent speaker identification and verification and is shown to outperform either classifier when used individually.

  19. Carbon classified?

    DEFF Research Database (Denmark)

    Lippert, Ingmar

    2012-01-01

    . Using an actor- network theory (ANT) framework, the aim is to investigate the actors who bring together the elements needed to classify their carbon emission sources and unpack the heterogeneous relations drawn on. Based on an ethnographic study of corporate agents of ecological modernisation over...... and corporations construing themselves as able and suitable to manage their emissions, and, additionally, given that the construction of carbon emissions has performative consequences, the underlying practices need to be declassified, i.e. opened for public scrutiny. Hence the paper concludes by arguing...

  20. Constructive autoassociative neural network for facial recognition.

    Directory of Open Access Journals (Sweden)

    Bruno J T Fernandes

    Full Text Available Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network. CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer. Neurons in the CANet output layer present lateral inhibitory connections that improve the recognition rate. Experiments in face recognition and facial expression recognition show that the CANet outperforms other methods presented in the literature.

  1. A comparative study of support vector machine, artificial neural network and bayesian classifier for mutagenicity prediction.

    Science.gov (United States)

    Sharma, Anju; Kumar, Rajnish; Varadwaj, Pritish Kumar; Ahmad, Ausaf; Ashraf, Ghulam Md

    2011-09-01

    Mutagenicity is the capability of a chemical to carry out mutations in genetic material of an organism. In order to curtail expensive drug failures due to mutagenicity found in late development or even in clinical trials, it is crucial to determine potential mutagenicity problems as early as possible. In this work we have proposed three different classifiers, i.e. Support Vector Machine (SVM), Artificial Neural Network (ANN) and bayesian classifiers, for the prediction of mutagenicity of compounds based on seventeen descriptors. Among the three classifiers Radial Basis Function (RBF) kernel based SVM classifier appeared to be more accurate for classifying the compounds under study on mutagens and non-mutagens. The overall prediction accuracy of SVM model was found to be 71.73% which was appreciably higher than the accuracy of ANN based classifier (59.72%) and bayesian classifier (66.61%). It suggests that SVM based prediction model can be used for predicting mutagenicity more accurately compared to ANN and bayesian classifier for data under consideration.

  2. A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data

    Directory of Open Access Journals (Sweden)

    Wulamu Aziguli

    2017-01-01

    Full Text Available Text classification has always been an interesting issue in the research area of natural language processing (NLP. While entering the era of big data, a good text classifier is critical to achieving NLP for scientific big data analytics. With the ever-increasing size of text data, it has posed important challenges in developing effective algorithm for text classification. Given the success of deep neural network (DNN in analyzing big data, this article proposes a novel text classifier using DNN, in an effort to improve the computational performance of addressing big text data with hybrid outliers. Specifically, through the use of denoising autoencoder (DAE and restricted Boltzmann machine (RBM, our proposed method, named denoising deep neural network (DDNN, is able to achieve significant improvement with better performance of antinoise and feature extraction, compared to the traditional text classification algorithms. The simulations on benchmark datasets verify the effectiveness and robustness of our proposed text classifier.

  3. Feature extraction using convolutional neural network for classifying breast density in mammographic images

    Science.gov (United States)

    Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

    2017-03-01

    Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

  4. Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging

    Science.gov (United States)

    Halicek, Martin; Lu, Guolan; Little, James V.; Wang, Xu; Patel, Mihir; Griffith, Christopher C.; El-Deiry, Mark W.; Chen, Amy Y.; Fei, Baowei

    2017-06-01

    Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.

  5. Neural-network classifiers for automatic real-world aerial image recognition.

    Science.gov (United States)

    Greenberg, S; Guterman, H

    1996-08-10

    We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

  6. Neural-network classifiers for automatic real-world aerial image recognition

    Science.gov (United States)

    Greenberg, Shlomo; Guterman, Hugo

    1996-08-01

    We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

  7. Infrared dim moving target tracking via sparsity-based discriminative classifier and convolutional network

    Science.gov (United States)

    Qian, Kun; Zhou, Huixin; Wang, Bingjian; Song, Shangzhen; Zhao, Dong

    2017-11-01

    Infrared dim and small target tracking is a great challenging task. The main challenge for target tracking is to account for appearance change of an object, which submerges in the cluttered background. An efficient appearance model that exploits both the global template and local representation over infrared image sequences is constructed for dim moving target tracking. A Sparsity-based Discriminative Classifier (SDC) and a Convolutional Network-based Generative Model (CNGM) are combined with a prior model. In the SDC model, a sparse representation-based algorithm is adopted to calculate the confidence value that assigns more weights to target templates than negative background templates. In the CNGM model, simple cell feature maps are obtained by calculating the convolution between target templates and fixed filters, which are extracted from the target region at the first frame. These maps measure similarities between each filter and local intensity patterns across the target template, therefore encoding its local structural information. Then, all the maps form a representation, preserving the inner geometric layout of a candidate template. Furthermore, the fixed target template set is processed via an efficient prior model. The same operation is applied to candidate templates in the CNGM model. The online update scheme not only accounts for appearance variations but also alleviates the migration problem. At last, collaborative confidence values of particles are utilized to generate particles' importance weights. Experiments on various infrared sequences have validated the tracking capability of the presented algorithm. Experimental results show that this algorithm runs in real-time and provides a higher accuracy than state of the art algorithms.

  8. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

    Science.gov (United States)

    Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

    2015-10-01

    Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

  9. Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

    Directory of Open Access Journals (Sweden)

    Er-Yang Huan

    2017-01-01

    Full Text Available Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

  10. ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.

    Science.gov (United States)

    Liu, George S; Zhu, Michael H; Kim, Jinkyung; Raphael, Patrick; Applegate, Brian E; Oghalai, John S

    2017-10-01

    Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.

  11. Identification of functional modules by integration of multiple data sources using a Bayesian network classifier.

    Science.gov (United States)

    Wang, Jinlian; Zuo, Yiming; Liu, Lun; Man, Yangao; Tadesse, Mahlet G; Ressom, Habtom W

    2014-04-01

    Prediction of functional modules is indispensable for detecting protein deregulation in human complex diseases such as cancer. Bayesian network is one of the most commonly used models to integrate heterogeneous data from multiple sources such as protein domain, interactome, functional annotation, genome-wide gene expression, and the literature. In this article, we present a Bayesian network classifier that is customized to (1) increase the ability to integrate diverse information from different sources, (2) effectively predict protein-protein interactions, (3) infer aberrant networks with scale-free and small-world properties, and (4) group molecules into functional modules or pathways based on the primary function and biological features. Application of this model in discovering protein biomarkers of hepatocellular carcinoma leads to the identification of functional modules that provide insights into the mechanism of the development and progression of hepatocellular carcinoma. These functional modules include cell cycle deregulation, increased angiogenesis (eg, vascular endothelial growth factor, blood vessel morphogenesis), oxidative metabolic alterations, and aberrant activation of signaling pathways involved in cellular proliferation, survival, and differentiation. The discoveries and conclusions derived from our customized Bayesian network classifier are consistent with previously published results. The proposed approach for determining Bayesian network structure facilitates the integration of heterogeneous data from multiple sources to elucidate the mechanisms of complex diseases.

  12. Junction trees constructions in Bayesian networks

    Science.gov (United States)

    Smail, Linda

    2017-10-01

    Junction trees are used as graphical structures over which propagation will be carried out through a very important property called the ruining intersection property. This paper examines an alternative method for constructing junction trees that are essential for the efficient computations of probabilities in Bayesian networks. The new proposed method converts a sequence of subsets of a Bayesian network into a junction tree, in other words, into a set of cliques that has the running intersection property. The obtained set of cliques and separators coincide with the junction trees obtained by the moralization and triangulation process, but it has the advantage of adapting to any computational task by adding links to the Bayesian network graph.

  13. Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

    Science.gov (United States)

    Hramov, Alexander E.; Maksimenko, Vladimir A.; Pchelintseva, Svetlana V.; Runnova, Anastasiya E.; Grubov, Vadim V.; Musatov, Vyacheslav Yu.; Zhuravlev, Maksim O.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

    2017-01-01

    In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces. PMID:29255403

  14. Automating the construction of scene classifiers for content-based video retrieval

    NARCIS (Netherlands)

    Khan, L.; Israël, Menno; Petrushin, V.A.; van den Broek, Egon; van der Putten, Peter

    2004-01-01

    This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a

  15. Analysis of feature selection with Probabilistic Neural Network (PNN) to classify sources influencing indoor air quality

    Science.gov (United States)

    Saad, S. M.; Shakaff, A. Y. M.; Saad, A. R. M.; Yusof, A. M.; Andrew, A. M.; Zakaria, A.; Adom, A. H.

    2017-03-01

    There are various sources influencing indoor air quality (IAQ) which could emit dangerous gases such as carbon monoxide (CO), carbon dioxide (CO2), ozone (O3) and particulate matter. These gases are usually safe for us to breathe in if they are emitted in safe quantity but if the amount of these gases exceeded the safe level, they might be hazardous to human being especially children and people with asthmatic problem. Therefore, a smart indoor air quality monitoring system (IAQMS) is needed that able to tell the occupants about which sources that trigger the indoor air pollution. In this project, an IAQMS that able to classify sources influencing IAQ has been developed. This IAQMS applies a classification method based on Probabilistic Neural Network (PNN). It is used to classify the sources of indoor air pollution based on five conditions: ambient air, human activity, presence of chemical products, presence of food and beverage, and presence of fragrance. In order to get good and best classification accuracy, an analysis of several feature selection based on data pre-processing method is done to discriminate among the sources. The output from each data pre-processing method has been used as the input for the neural network. The result shows that PNN analysis with the data pre-processing method give good classification accuracy of 99.89% and able to classify the sources influencing IAQ high classification rate.

  16. CLASSIFYING RAILWAY PASSENGER STATIONS FOR USE TRANSPORT PLANNING – APPLICATION TO BULGARIAN RAILWAY NETWORK

    Directory of Open Access Journals (Sweden)

    Svetla STOILOVA

    2016-06-01

    Full Text Available A methodology for the classification of railway passenger stations was developed in this study. Four groups of factors are defined to study the characteristics of the station: potential of the town, importance of the town, infrastructural factors, and characteristics of passengers. In the research we investigated 18 factors and studied 98 passenger stations of railway network in Bulgaria. The method of principal components has been applied for grouping the factors and cluster analysis has been applied to classify the stations. The factors have been classified into 4 components by the method of principal components. The stations have been classified into 6 groups using hierarchical cluster analysis. The methods, Average linkage between group and Within-groups linkage, and distance-type measures Euclidean distance and Squared Euclidean distance were compared to verify the results of cluster analysis. The grouping of the stations has been used for the determination of the stops for categories intercity passenger trains. The main groups of stations for servicing express trains are the first, second, third, fourth and fifth groups. In the sixth group are stations for servicing fast passenger trains. The methodology can be applied to the study of all stations and stops in the rail network.

  17. A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification

    Directory of Open Access Journals (Sweden)

    Jing Zhao

    2016-01-01

    Full Text Available The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural network (GFCMNN. To obtain more information about uncertainty, an intuitionistic fuzzy linguistic term is employed to describe medical features. The solution of classification is obtained by a similarity measurement. The advantages of the novel classifier proposed here are drawn out by comparing the same medical example under the methods of intuitionistic fuzzy sets (IFSs and intuitionistic fuzzy cross-entropy (IFCE with different score functions. Cross verification experiments are also taken to further test the classification ability of the GFCMNN multidimensional classifier. All of these experimental results show the effectiveness of the proposed GFCMNN multidimensional classifier and point out that it can assist in supporting for correct medical diagnoses associated with multiple categories.

  18. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    Science.gov (United States)

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  19. Detecting Cyber-Attacks on Wireless Mobile Networks Using Multicriterion Fuzzy Classifier with Genetic Attribute Selection

    Directory of Open Access Journals (Sweden)

    El-Sayed M. El-Alfy

    2015-01-01

    Full Text Available With the proliferation of wireless and mobile network infrastructures and capabilities, a wide range of exploitable vulnerabilities emerges due to the use of multivendor and multidomain cross-network services for signaling and transport of Internet- and wireless-based data. Consequently, the rates and types of cyber-attacks have grown considerably and current security countermeasures for protecting information and communication may be no longer sufficient. In this paper, we investigate a novel methodology based on multicriterion decision making and fuzzy classification that can provide a viable second-line of defense for mitigating cyber-attacks. The proposed approach has the advantage of dealing with various types and sizes of attributes related to network traffic such as basic packet headers, content, and time. To increase the effectiveness and construct optimal models, we augmented the proposed approach with a genetic attribute selection strategy. This allows efficient and simpler models which can be replicated at various network components to cooperatively detect and report malicious behaviors. Using three datasets covering a variety of network attacks, the performance enhancements due to the proposed approach are manifested in terms of detection errors and model construction times.

  20. A Neural Network Classifier Model for Forecasting Safety Behavior at Workplaces

    Directory of Open Access Journals (Sweden)

    Fakhradin Ghasemi

    2017-07-01

    Full Text Available The construction industry is notorious for having an unacceptable rate of fatal accidents. Unsafe behavior has been recognized as the main cause of most accidents occurring at workplaces, particularly construction sites. Having a predictive model of safety behavior can be helpful in preventing construction accidents. The aim of the present study was to build a predictive model of unsafe behavior using the Artificial Neural Network approach. A brief literature review was conducted on factors affecting safe behavior at workplaces and nine factors were selected to be included in the study. Data were gathered using a validated questionnaire from several construction sites. Multilayer perceptron approach was utilized for constructing the desired neural network. Several models with various architectures were tested to find the best one. Sensitivity analysis was conducted to find the most influential factors. The model with one hidden layer containing fourteen hidden neurons demonstrated the best performance (Sum of Squared Errors=6.73. The error rate of the model was approximately 21 percent. The results of sensitivity analysis showed that safety attitude, safety knowledge, supportive environment, and management commitment had the highest effects on safety behavior, while the effects from resource allocation and perceived work pressure were identified to be lower than those of others. The complex nature of human behavior at workplaces and the presence of many influential factors make it difficult to achieve a model with perfect performance.

  1. SCIENCE FACILITIES. A CLASSIFIED LIST OF LITERATURE RELATED TO DESIGN, CONSTRUCTION AND OTHER ARCHITECTURAL MATTERS.

    Science.gov (United States)

    National Science Foundation, Washington, DC.

    A CLASSIFIED LIST OF ARTICLES, PAPERS AND CATALOGS IN THE SCIENCE FACILITIES COLLECTION OF THE ARCHITECTURAL SERVICES STAFF OF THE NATIONAL SCIENCE FOUNDATION IS PRESENTED WHICH MAY BE USEFUL IN SEARCHING FOR PERTINENT LITERATURE ON PROBLEMS IN THE DESIGN OF SCIENCE FACILITIES. CITATIONS COVER SUCH AREAS AS GENERAL PLANNING, SPACE UTILIZATION AND…

  2. Classifying Multi-year Land Use and Land Cover using Deep Convolutional Neural Networks

    Science.gov (United States)

    Seo, B.

    2015-12-01

    Cultivated ecosystems constitute a particularly frequent form of human land use. Long-term management of a cultivated ecosystem requires us to know temporal change of land use and land cover (LULC) of the target system. Land use and land cover changes (LUCC) in agricultural ecosystem is often rapid and unexpectedly occurs. Thus, longitudinal LULC is particularly needed to examine trends of ecosystem functions and ecosystem services of the target system. Multi-temporal classification of land use and land cover (LULC) in complex heterogeneous landscape remains a challenge. Agricultural landscapes often made up of a mosaic of numerous LULC classes, thus spatial heterogeneity is large. Moreover, temporal and spatial variation within a LULC class is also large. Under such a circumstance, standard classifiers would fail to identify the LULC classes correctly due to the heterogeneity of the target LULC classes. Because most standard classifiers search for a specific pattern of features for a class, they fail to detect classes with noisy and/or transformed feature data sets. Recently, deep learning algorithms have emerged in the machine learning communities and shown superior performance on a variety of tasks, including image classification and object recognition. In this paper, we propose to use convolutional neural networks (CNN) to learn from multi-spectral data to classify agricultural LULC types. Based on multi-spectral satellite data, we attempted to classify agricultural LULC classes in Soyang watershed, South Korea for the three years' study period (2009-2011). The classification performance of support vector machine (SVM) and CNN classifiers were compared for different years. Preliminary results demonstrate that the proposed method can improve classification performance compared to the SVM classifier. The SVM classifier failed to identify classes when trained on a year to predict another year, whilst CNN could reconstruct LULC maps of the catchment over the study

  3. Multilayer cellular neural network and fuzzy C-mean classifiers: comparison and performance analysis

    Science.gov (United States)

    Trujillo San-Martin, Maite; Hlebarov, Vejen; Sadki, Mustapha

    2004-11-01

    Neural Networks and Fuzzy systems are considered two of the most important artificial intelligent algorithms which provide classification capabilities obtained through different learning schemas which capture knowledge and process it according to particular rule-based algorithms. These methods are especially suited to exploit the tolerance for uncertainty and vagueness in cognitive reasoning. By applying these methods with some relevant knowledge-based rules extracted using different data analysis tools, it is possible to obtain a robust classification performance for a wide range of applications. This paper will focus on non-destructive testing quality control systems, in particular, the study of metallic structures classification according to the corrosion time using a novel cellular neural network architecture, which will be explained in detail. Additionally, we will compare these results with the ones obtained using the Fuzzy C-means clustering algorithm and analyse both classifiers according to its classification capabilities.

  4. Modeling the Merger of the Classified Networks of the DDN (Defense Data Network): BLACKER

    Science.gov (United States)

    1988-11-01

    Cain (1983) presented ’The DoD Internet Architecture Model" approach where a common internal host-to-host packet or datagram service is proposed for...The baseline model was a simulation similar to the model of *The DoD Internet Architecture Model" presented by Cerf and Cain. (1983) Parameters for...in each network can be redirected, what would be the optimal method for allocation? Third, what are the impacts of interdomain messages? What happens

  5. Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.

    Science.gov (United States)

    Luo, Yuan; Cheng, Yu; Uzuner, Özlem; Szolovits, Peter; Starren, Justin

    2018-01-01

    We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying relations from clinical notes. Seg-CNNs use only word-embedding features without manual feature engineering. Unlike typical CNN models, relations between 2 concepts are identified by simultaneously learning separate representations for text segments in a sentence: preceding, concept1, middle, concept2, and succeeding. We evaluate Seg-CNN on the i2b2/VA relation classification challenge dataset. We show that Seg-CNN achieves a state-of-the-art micro-average F-measure of 0.742 for overall evaluation, 0.686 for classifying medical problem-treatment relations, 0.820 for medical problem-test relations, and 0.702 for medical problem-medical problem relations. We demonstrate the benefits of learning segment-level representations. We show that medical domain word embeddings help improve relation classification. Seg-CNNs can be trained quickly for the i2b2/VA dataset on a graphics processing unit (GPU) platform. These results support the use of CNNs computed over segments of text for classifying medical relations, as they show state-of-the-art performance while requiring no manual feature engineering. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  6. Use of artificial neural networks and geographic objects for classifying remote sensing imagery

    Directory of Open Access Journals (Sweden)

    Pedro Resende Silva

    2014-06-01

    Full Text Available The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1 to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2 to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3 to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

  7. Classifying Sources Influencing Indoor Air Quality (IAQ Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Shaharil Mad Saad

    2015-05-01

    Full Text Available Monitoring indoor air quality (IAQ is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC, base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

  8. A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns.

    Science.gov (United States)

    Andrade, Andre; Costa, Marcelo; Paolucci, Leopoldo; Braga, Antônio; Pires, Flavio; Ugrinowitsch, Herbert; Menzel, Hans-Joachim

    2015-01-01

    The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.

  9. Binary tissue classification on wound images with neural networks and bayesian classifiers.

    Science.gov (United States)

    Veredas, Francisco; Mesa, Héctor; Morente, Laura

    2010-02-01

    A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, or friction. Diagnosis, treatment, and care of pressure ulcers are costly for health services. Accurate wound evaluation is a critical task for optimizing the efficacy of treatment and care. Clinicians usually evaluate each pressure ulcer by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. In this paper, a hybrid approach based on neural networks and Bayesian classifiers is used in the design of a computational system for automatic tissue identification in wound images. A mean shift procedure and a region-growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multilayer perceptrons is trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes which are determined by clinical experts. This training procedure is driven by a k-fold cross-validation method. Finally, a Bayesian committee machine is formed by training a Bayesian classifier to combine the classifications of the k neural networks. Specific heuristics based on the wound topology are designed to significantly improve the results of the classification. We obtain high efficiency rates from a binary cascade approach for tissue identification. Results are compared with other similar machine-learning approaches, including multiclass Bayesian committee machine classifiers and support vector machines. The different techniques analyzed in this paper show high global classification accuracy rates. Our binary cascade approach gives high global performance rates (average sensitivity =78.7% , specificity =94.7% , and accuracy =91.5% ) and shows the highest average sensitivity score ( =86.3%) when detecting

  10. Classifying region of interests from mammograms with breast cancer into BIRADS using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Estefanía D. Avalos-Rivera

    2017-05-01

    Full Text Available Breast cancer is one of the most common cancers among female diseases all over the world. Early diagnosis and treatment is particularly important in reducing the mortality rate. This research is focused on the prevention of breast cancer, therefore it is important to detect micro-calcifications (MCs which are a sign of early stage breast cancer. Micro-calcifications are tiny deposits of calcium which are visible on mammograms as they present as tiny white spots. A computer-aided diagnosis system (CAD is created with the development of computer technology that way radiologists are aided improving their diagnostics while using CAD as a second reader. We are aiming to classify into BIRADS 2, 3 and 4 which are the stages when the cancer can be prevented and a fourth category called No lesion which are veins and tissue that our high pass Gaussian filter detects. This research focuses on classification using ANN (Artificial Neural Network. Experimenting with the categories to classify into using ANN, the results were the following: into the four mentioned before an overall accuracy of 71% was obtained, then joining categories BIRADS 2 and 3 into one and classifying into 3 categories gave an 80% of accuracy. Joining this two categories was the result of analizing the ROC curve and observation of the ROI images of the MCs as the regions measured are very alike in this two categories and variation is that MCs are more present in BIRADS 3 than in BIRADS 2. Data matrix was reduced using PCA (Principal Component Analysis but it did not gave better results so it was discarded as the ANN accuracy to classify was reduced to a 69.8%.

  11. From gesture to sign language: conventionalization of classifier constructions by adult hearing learners of British Sign Language.

    Science.gov (United States)

    Marshall, Chloë R; Morgan, Gary

    2015-01-01

    There has long been interest in why languages are shaped the way they are, and in the relationship between sign language and gesture. In sign languages, entity classifiers are handshapes that encode how objects move, how they are located relative to one another, and how multiple objects of the same type are distributed in space. Previous studies have shown that hearing adults who are asked to use only manual gestures to describe how objects move in space will use gestures that bear some similarities to classifiers. We investigated how accurately hearing adults, who had been learning British Sign Language (BSL) for 1-3 years, produce and comprehend classifiers in (static) locative and distributive constructions. In a production task, learners of BSL knew that they could use their hands to represent objects, but they had difficulty choosing the same, conventionalized, handshapes as native signers. They were, however, highly accurate at encoding location and orientation information. Learners therefore show the same pattern found in sign-naïve gesturers. In contrast, handshape, orientation, and location were comprehended with equal (high) accuracy, and testing a group of sign-naïve adults showed that they too were able to understand classifiers with higher than chance accuracy. We conclude that adult learners of BSL bring their visuo-spatial knowledge and gestural abilities to the tasks of understanding and producing constructions that contain entity classifiers. We speculate that investigating the time course of adult sign language acquisition might shed light on how gesture became (and, indeed, becomes) conventionalized during the genesis of sign languages. Copyright © 2014 Cognitive Science Society, Inc.

  12. Unexpected properties of bandwidth choice when smoothing discrete data for constructing a functional data classifier

    KAUST Repository

    Carroll, Raymond J.

    2013-12-01

    The data functions that are studied in the course of functional data analysis are assembled from discrete data, and the level of smoothing that is used is generally that which is appropriate for accurate approximation of the conceptually smooth functions that were not actually observed. Existing literature shows that this approach is effective, and even optimal, when using functional data methods for prediction or hypothesis testing. However, in the present paper we show that this approach is not effective in classification problems. There a useful rule of thumb is that undersmoothing is often desirable, but there are several surprising qualifications to that approach. First, the effect of smoothing the training data can be more significant than that of smoothing the new data set to be classified; second, undersmoothing is not always the right approach, and in fact in some cases using a relatively large bandwidth can be more effective; and third, these perverse results are the consequence of very unusual properties of error rates, expressed as functions of smoothing parameters. For example, the orders of magnitude of optimal smoothing parameter choices depend on the signs and sizes of terms in an expansion of error rate, and those signs and sizes can vary dramatically from one setting to another, even for the same classifier.

  13. Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure

    Directory of Open Access Journals (Sweden)

    Xiaodong Zeng

    2014-01-01

    Full Text Available A weighted accuracy and diversity (WAD method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases.

  14. Analysis of regulatory networks constructed based on gene ...

    Indian Academy of Sciences (India)

    Gene coexpression patterns can reveal gene collections with functional consistency. This study systematically constructs regulatory networks for pituitary tumours by integrating gene coexpression, transcriptional and posttranscriptional regulation. Through network analysis, we elaborate the incidence mechanism of pituitary ...

  15. Analysis of regulatory networks constructed based on gene ...

    Indian Academy of Sciences (India)

    2013-12-09

    Dec 9, 2013 ... Abstract. Gene coexpression patterns can reveal gene collections with functional consistency. This study systematically constructs regulatory networks for pituitary tumours by integrating gene coexpression, transcriptional and posttranscriptional regulation. Through network analysis, we elaborate the ...

  16. Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

    Science.gov (United States)

    Nicandro, Cruz-Ramírez; Efrén, Mezura-Montes; María Yaneli, Ameca-Alducin; Enrique, Martín-Del-Campo-Mena; Héctor Gabriel, Acosta-Mesa; Nancy, Pérez-Castro; Alejandro, Guerra-Hernández; Guillermo de Jesús, Hoyos-Rivera; Rocío Erandi, Barrientos-Martínez

    2013-01-01

    Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool. PMID:23762182

  17. Performance Evaluation of Neural Network Based Pulse-Echo Weld Defect Classifiers

    Science.gov (United States)

    Seyedtabaii, S.

    2012-10-01

    Pulse-echo ultrasonic signal is used to detect weld defects with high probability. However, utilizing echo signal for defects classification is another issue that has attracted attention of many researchers who have devised algorithms and tested them against their own databases. In this paper, a study is conducted to score the performance of various algorithms against a single echo signal database. Algorithms tested the use of Wavelet Transform (WT), Fast Fourier Transform (FFT) and time domain echo signal features and employed several NN’s architectures such as Multi-Layer Perceptron Neural Network (MLP), Self Organizing Map (SOM) and others known to be good classifiers. The average performance of all can be viewed fair (90%) while some algorithms render success rate of about 94%. It seems that acquiring higher success rates out of a single fixed angle probe pulseecho set up needs new arrangements of data collection, which is under investigation.

  18. WAVELET ANALYSIS AND NEURAL NETWORK CLASSIFIERS TO DETECT MID-SAGITTAL SECTIONS FOR NUCHAL TRANSLUCENCY MEASUREMENT

    Directory of Open Access Journals (Sweden)

    Giuseppa Sciortino

    2016-04-01

    Full Text Available We propose a methodology to support the physician in the automatic identification of mid-sagittal sections of the fetus in ultrasound videos acquired during the first trimester of pregnancy. A good mid-sagittal section is a key requirement to make the correct measurement of nuchal translucency which is one of the main marker for screening of chromosomal defects such as trisomy 13, 18 and 21. NT measurement is beyond the scope of this article. The proposed methodology is mainly based on wavelet analysis and neural network classifiers to detect the jawbone and on radial symmetry analysis to detect the choroid plexus. Those steps allow to identify the frames which represent correct mid-sagittal sections to be processed. The performance of the proposed methodology was analyzed on 3000 random frames uniformly extracted from 10 real clinical ultrasound videos. With respect to a ground-truth provided by an expert physician, we obtained a true positive, a true negative and a balanced accuracy equal to 87.26%, 94.98% and 91.12% respectively.

  19. Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

    Directory of Open Access Journals (Sweden)

    P. Kumudha

    2016-01-01

    Full Text Available Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN and the novel adaptive dimensional biogeography based optimization (ADBBO model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

  20. Analyzing animal behavior via classifying each video frame using convolutional neural networks

    Science.gov (United States)

    Stern, Ulrich; He, Ruo; Yang, Chung-Hui

    2015-01-01

    High-throughput analysis of animal behavior requires software to analyze videos. Such software analyzes each frame individually, detecting animals’ body parts. But the image analysis rarely attempts to recognize “behavioral states”—e.g., actions or facial expressions—directly from the image instead of using the detected body parts. Here, we show that convolutional neural networks (CNNs)—a machine learning approach that recently became the leading technique for object recognition, human pose estimation, and human action recognition—were able to recognize directly from images whether Drosophila were “on” (standing or walking) or “off” (not in physical contact with) egg-laying substrates for each frame of our videos. We used multiple nets and image transformations to optimize accuracy for our classification task, achieving a surprisingly low error rate of just 0.072%. Classifying one of our 8 h videos took less than 3 h using a fast GPU. The approach enabled uncovering a novel egg-laying-induced behavior modification in Drosophila. Furthermore, it should be readily applicable to other behavior analysis tasks. PMID:26394695

  1. An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

    Science.gov (United States)

    He, Jian; Bai, Shuang; Wang, Xiaoyi

    2017-06-16

    Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.

  2. Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    GEMAN, O.

    2014-02-01

    Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.

  3. Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction.

    Science.gov (United States)

    Kumudha, P; Venkatesan, R

    Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

  4. Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines.

    Science.gov (United States)

    Pérez-Magariño, S; Ortega-Heras, M; González-San José, M L; Boger, Z

    2004-04-19

    Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepeñas and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view.

  5. Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers

    Directory of Open Access Journals (Sweden)

    Nilufer Ozdemir

    2014-01-01

    Full Text Available The aim of this paper is to develop an automated system for epileptic seizure prediction from intracranial EEG signals based on Hilbert-Huang transform (HHT and Bayesian classifiers. Proposed system includes decomposition of the signals into intrinsic mode functions for obtaining features and use of Bayesian networks with correlation based feature selection for binary classification of preictal and interictal recordings. The system was trained and tested on Freiburg EEG database. 58 hours of preictal data, 40-minute data blocks prior to each of 87 seizures collected from 21 patients, and 503.1 hours of interictal data were examined resulting in 96.55% sensitivity with 0.21 false alarms per hour, 13.896% average proportion of time spent in warning, and 33.21 minutes of average detection latency using 30-second EEG segments with 50% overlap and a simple postprocessing technique resulting in a decision (a seizure is expected/not expected every 5 minutes. High sensitivity and low false positive rate with reasonable detection latency show that HHT based features are acceptable for patient specific seizure prediction from intracranial EEG data. Time spent for testing an EEG segment was 4.1451 seconds on average, which makes the system viable for use in real-time seizure control systems.

  6. Constructing and drawing regular planar split networks.

    Science.gov (United States)

    Spillner, Andreas; Nguyen, Binh T; Moulton, Vincent

    2012-01-01

    Split networks are commonly used to visualize collections of bipartitions, also called splits, of a finite set. Such collections arise, for example, in evolutionary studies. Split networks can be viewed as a generalization of phylogenetic trees and may be generated using the SplitsTree package. Recently, the NeighborNet method for generating split networks has become rather popular, in part because it is guaranteed to always generate a circular split system, which can always be displayed by a planar split network. Even so, labels must be placed on the “outside” of the network, which might be problematic in some applications. To help circumvent this problem, it can be helpful to consider so-called flat split systems, which can be displayed by planar split networks where labels are allowed on the inside of the network too. Here, we present a new algorithm that is guaranteed to compute a minimal planar split network displaying a flat split system in polynomial time, provided the split system is given in a certain format. We will also briefly discuss two heuristics that could be useful for analyzing phylogeographic data and that allow the computation of flat split systems in this format in polynomial time.

  7. Influence of Acoustic Feedback on the Learning Strategies of Neural Network-Based Sound Classifiers in Digital Hearing Aids

    Directory of Open Access Journals (Sweden)

    Lorena Álvarez

    2009-01-01

    Full Text Available Sound classifiers embedded in digital hearing aids are usually designed by using sound databases that do not include the distortions associated to the feedback that often occurs when these devices have to work at high gain and low gain margin to oscillation. The consequence is that the classifier learns inappropriate sound patterns. In this paper we explore the feasibility of using different sound databases (generated according to 18 configurations of real patients, and a variety of learning strategies for neural networks in the effort of reducing the probability of erroneous classification. The experimental work basically points out that the proposed methods assist the neural network-based classifier in reducing its error probability in more than 18%. This helps enhance the elderly user's comfort: the hearing aid automatically selects, with higher success probability, the program that is best adapted to the changing acoustic environment the user is facing.

  8. Convolutional Neural Networks with Batch Normalization for Classifying Hi-hat, Snare, and Bass Percussion Sound Samples

    DEFF Research Database (Denmark)

    Gajhede, Nicolai; Beck, Oliver; Purwins, Hendrik

    2016-01-01

    After having revolutionized image and speech processing, convolu- tional neural networks (CNN) are now starting to become more and more successful in music information retrieval as well. We compare four CNN types for classifying a dataset of more than 3000 acoustic and synthesized samples...

  9. A Survey of Linear Network Coding and Network Error Correction Code Constructions and Algorithms

    Directory of Open Access Journals (Sweden)

    Michele Sanna

    2011-01-01

    Full Text Available Network coding was introduced by Ahlswede et al. in a pioneering work in 2000. This paradigm encompasses coding and retransmission of messages at the intermediate nodes of the network. In contrast with traditional store-and-forward networking, network coding increases the throughput and the robustness of the transmission. Linear network coding is a practical implementation of this new paradigm covered by several research works that include rate characterization, error-protection coding, and construction of codes. Especially determining the coding characteristics has its importance in providing the premise for an efficient transmission. In this paper, we review the recent breakthroughs in linear network coding for acyclic networks with a survey of code constructions literature. Deterministic construction algorithms and randomized procedures are presented for traditional network coding and for network-control network coding.

  10. Constructing an Intelligent Patent Network Analysis Method

    OpenAIRE

    Chao-Chan Wu; Ching-Bang Yao

    2012-01-01

    Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks...

  11. Implementing an Integrated Network Defense Construct

    Science.gov (United States)

    2013-06-01

    to create an easily defendable avenue for ingress and egress. In medieval times, castles leveraged this principle. If an attacker was brazen enough...of an encompassing defensive structure for network security (Small, 2012). 2.1. The Cyber Defense Dilemma While the philosophy of network...defense mirrors that of the physical world, its application has some significant drawbacks. Whereas the medieval castle defenders had the high ground

  12. Constructing an Intelligent Patent Network Analysis Method

    Directory of Open Access Journals (Sweden)

    Chao-Chan Wu

    2012-11-01

    Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.

  13. An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data

    Directory of Open Access Journals (Sweden)

    Evangelos Stromatias

    2017-06-01

    Full Text Available This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77% and Poker-DVS (100% real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

  14. An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

    Science.gov (United States)

    Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

    2017-01-01

    This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

  15. Seeded Bayesian Networks: Constructing genetic networks from microarray data

    Directory of Open Access Journals (Sweden)

    Quackenbush John

    2008-07-01

    Full Text Available Abstract Background DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. Results Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. Conclusion The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

  16. Application of a Hidden Bayes Naive Multiclass Classifier in Network Intrusion Detection

    Science.gov (United States)

    Koc, Levent

    2013-01-01

    With increasing Internet connectivity and traffic volume, recent intrusion incidents have reemphasized the importance of network intrusion detection systems for combating increasingly sophisticated network attacks. Techniques such as pattern recognition and the data mining of network events are often used by intrusion detection systems to classify…

  17. PBO Facility Construction: GPS Network Completed

    Science.gov (United States)

    Feaux, F.; Jackson, M.; Blume, F.; Coyle, B.; Walls, C.; Friesen, B.; Austin, K.; Basset, A.; Williams, T.; Jenkins, F.; Kasmer, D.; Lawrence, S.; Enders, M.

    2008-12-01

    The Plate Boundary Observatory (PBO), part of the larger NSF-funded EarthScope project, will study the three-dimensional strain field resulting from active plate boundary deformation across the Western United States. The PBO construction phase is now completed, which involved the reconnaissance, permitting, installation, documentation, and maintenance of 891 permanent GPS stations and the upgrade of 209 existing stations in five years. Some of the GPS construction highlights from the project will be presented. These highlights include the San Simeon earthquake response, Mount Saint Helens volcano emergency response, the Magnitude 6.0 Parkfield earthquake emergency response, and GPS and tiltmeter installations on Augustine, Akutan, and Unimak Island in Alaska.

  18. Multicast Tree Construction in Directed Networks

    Science.gov (United States)

    1996-01-01

    an architecture for scalable interdomain multicast routing", in ACM SIGCOMM, September 1993 [2] B. Cain, S. Deering, and A. Thyagarajan, Internet ... internet There are two basic approaches to multicast tree construction. The first is a shared multicast tree [1] and the other is a source rooted tree... Internet Working Group, November 1988. [14] B. Waxman, "Routing of Multipoint Connections, " IEEE Selected Areas in Communications, December 1988.

  19. Properties of healthcare teaming networks as a function of network construction algorithms.

    Science.gov (United States)

    Zand, Martin S; Trayhan, Melissa; Farooq, Samir A; Fucile, Christopher; Ghoshal, Gourab; White, Robert J; Quill, Caroline M; Rosenberg, Alexander; Barbosa, Hugo Serrano; Bush, Kristen; Chafi, Hassan; Boudreau, Timothy

    2017-01-01

    Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106-108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and

  20. Constructing and Analyzing Uncertain Social Networks from Unstructured Textual Data

    Science.gov (United States)

    Johansson, Fredrik; Svenson, Pontus

    Social network analysis and link diagrams are popular tools among intelligence analysts for analyzing and understanding criminal and terrorist organizations. A bottleneck in the use of such techniques is the manual effort needed to create the network to analyze from available source information. We describe how text mining techniques can be used for extraction of named entities and the relations among them, in order to enable automatic construction of networks from unstructured text. Since the text mining techniques used, viz. algorithms for named entity recognition and relation extraction, are not perfect, we also describe a method for incorporating information about uncertainty when constructing the networks and when doing the social network analysis. The presented approach is applied on text documents describing terrorist activities in Indonesia.

  1. Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels

    Directory of Open Access Journals (Sweden)

    Yi Sun

    2017-12-01

    Full Text Available Bayesian network classifiers (BNCs have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead.

  2. Constructing Neuronal Network Models in Massively Parallel Environments

    Directory of Open Access Journals (Sweden)

    Tammo Ippen

    2017-05-01

    Full Text Available Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

  3. Constructing Neuronal Network Models in Massively Parallel Environments.

    Science.gov (United States)

    Ippen, Tammo; Eppler, Jochen M; Plesser, Hans E; Diesmann, Markus

    2017-01-01

    Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

  4. The Presentation of a New Method for Image Distinction with Robot by Using Rough Fuzzy Sets and Rough Fuzzy Neural Network Classifier

    OpenAIRE

    Maryam Shahabi Lotfabadi

    2011-01-01

    Distinguishing different images by robots and classifying them in distinct groups is an important issue in robot vision. In this paper we want to propose a new method for distinguishing images by robot via using Rough fuzzy sets' decreases method and Rough fuzzy neural network classifier. In this method, the image features like color, texture and shape are excluded and the redundant features are decreased by Rough fuzzy sets method. Then the Rough fuzzy neural network classifier is educated b...

  5. Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks

    NARCIS (Netherlands)

    Kooi, T.; Karssemeijer, N.

    2017-01-01

    Neural networks, in particular deep Convolutional Neural Networks (CNN), have recently gone through a renaissance sparked by the introduction of more efficient training procedures and massive amounts of raw annotated data. Barring a handful of modalities, medical images are typically too large to

  6. Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks

    NARCIS (Netherlands)

    Zwartjes, G.J.; Havinga, Paul J.M.; Smit, Gerardus Johannes Maria; Hurink, Johann L.

    2012-01-01

    Online processing is essential for many sensor network applications. Sensor nodes can sample far more data than what can practically be transmitted using state of the art sensor network radios. Online processing, however, is complicated due to limited resources of individual nodes. The naive Bayes

  7. A constructive algorithm for unsupervised learning with incremental neural network

    OpenAIRE

    Wang, Jenq-Haur; Wang, Hsin-Yang; Chen, Yen-Lin; Liu, Chuan-Ming

    2015-01-01

    Artificial neural network (ANN) has wide applications such as data processing and classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to integrate the ideas of human learning mechanism with the existing models of ANN. We propose an incremental neural network construction framework for unsupervised learning. In this framework, a neur...

  8. A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

    Science.gov (United States)

    Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

    2017-03-01

    Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust

  9. Characterization of Antimicrobial Resistance Dissemination across Plasmid Communities Classified by Network Analysis

    Directory of Open Access Journals (Sweden)

    Akifumi Yamashita

    2014-04-01

    Full Text Available The global clustering of gene families through network analysis has been demonstrated in whole genome, plasmid, and microbiome analyses. In this study, we carried out a plasmidome network analysis of all available complete bacterial plasmids to determine plasmid associations. A blastp clustering search at 100% aa identity cut-off and sharing at least one gene between plasmids, followed by a multilevel community network analysis revealed that a surprisingly large number of the plasmids were connected by one largest connected component (LCC, with dozens of community sub-groupings. The LCC consisted mainly of Bacilli and Gammaproteobacteria plasmids. Intriguingly, horizontal gene transfer (HGT was noted between different phyla (i.e., Staphylococcus and Pasteurellaceae, suggesting that Pasteurellaceae can acquire antimicrobial resistance (AMR genes from closely contacting Staphylococcus spp., which produce the external supplement of V-factor (NAD. Such community network analysis facilitate displaying possible recent HGTs like a class 1 integron, str and tet resistance markers between communities. Furthermore, the distribution of the Inc replicon type and AMR genes, such as the extended-spectrum ß-lactamase (ESBL CTX-M or the carbapenemases KPC NDM-1, implies that such genes generally circulate within limited communities belonging to typical bacterial genera. Thus, plasmidome network analysis provides a remarkable discriminatory power for plasmid-related HGT and evolution.

  10. A Constructive Neural-Network Approach to Modeling Psychological Development

    Science.gov (United States)

    Shultz, Thomas R.

    2012-01-01

    This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…

  11. A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization.

    Science.gov (United States)

    Li, Wei; Wang, Miao; Li, Yapeng; Huang, Yue; Chen, Xi

    2016-01-01

    The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the "intrinsic edges" to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting (SNV) method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases.

  12. A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization

    Directory of Open Access Journals (Sweden)

    Wei Li

    2016-01-01

    Full Text Available The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the “intrinsic edges” to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting (SNV method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases.

  13. Using Expectation Maximization and Resource Overlap Techniques to Classify Species According to Their Niche Similarities in Mutualistic Networks

    Directory of Open Access Journals (Sweden)

    Hugo Fort

    2015-11-01

    Full Text Available Mutualistic networks in nature are widespread and play a key role in generating the diversity of life on Earth. They constitute an interdisciplinary field where physicists, biologists and computer scientists work together. Plant-pollinator mutualisms in particular form complex networks of interdependence between often hundreds of species. Understanding the architecture of these networks is of paramount importance for assessing the robustness of the corresponding communities to global change and management strategies. Advances in this problem are currently limited mainly due to the lack of methodological tools to deal with the intrinsic complexity of mutualisms, as well as the scarcity and incompleteness of available empirical data. One way to uncover the structure underlying complex networks is to employ information theoretical statistical inference methods, such as the expectation maximization (EM algorithm. In particular, such an approach can be used to cluster the nodes of a network based on the similarity of their node neighborhoods. Here, we show how to connect network theory with the classical ecological niche theory for mutualistic plant-pollinator webs by using the EM algorithm. We apply EM to classify the nodes of an extensive collection of mutualistic plant-pollinator networks according to their connection similarity. We find that EM recovers largely the same clustering of the species as an alternative recently proposed method based on resource overlap, where one considers each party as a consuming resource for the other party (plants providing food to animals, while animals assist the reproduction of plants. Furthermore, using the EM algorithm, we can obtain a sequence of successfully-refined classifications that enables us to identify the fine-structure of the ecological network and understand better the niche distribution both for plants and animals. This is an example of how information theoretical methods help to systematize and

  14. Hydrogel Bioprinted Microchannel Networks for Vascularization of Tissue Engineering Constructs

    Science.gov (United States)

    Bertassoni, Luiz E.; Cecconi, Martina; Manoharan, Vijayan; Nikkhah, Mehdi; Hjortnaes, Jesper; Cristino, Ana Luiza; Barabaschi, Giada; Demarchi, Danilo; Dokmeci, Mehmet R.; Yang, Yunzhi; Khademhosseini, Ali

    2014-01-01

    Vascularization remains a critical challenge in tissue engineering. The development of vascular networks within densely populated and metabolically functional tissues facilitate transport of nutrients and removal of waste products, thus preserving cellular viability over a long period of time. Despite tremendous progress in fabricating complex tissue constructs in the past few years, approaches for controlled vascularization within hydrogel based engineered tissue constructs have remained limited. Here, we report a three dimensional (3D) micromolding technique utilizing bioprinted agarose template fibers to fabricate microchannel networks with various architectural features within photo cross linkable hydrogel constructs. Using the proposed approach, we were able to successfully embed functional and perfusable microchannels inside methacrylated gelatin (GelMA), star poly (ethylene glycol-co-lactide) acrylate (SPELA), poly (ethylene glycol) dimethacrylate (PEGDMA) and poly (ethylene glycol) diacrylate (PEGDA) hydrogels at different concentrations. In particular, GelMA hydrogels were used as a model to demonstrate the functionality of the fabricated vascular networks in improving mass transport, cellular viability and differentiation within the cell-laden tissue constructs. In addition, successful formation of endothelial monolayers within the fabricated channels was confirmed. Overall, our proposed strategy represents an effective technique for vascularization of hydrogel constructs with useful applications in tissue engineering and organs on a chip. PMID:24860845

  15. A modular neural network classifier for the recognition of occluded characters in automatic license plate reading

    NARCIS (Netherlands)

    Nijhuis, JAG; Broersma, A; Spaanenburg, L; Ruan, D; Dhondt, P; Kerre, EE

    2002-01-01

    Occlusion is the most common reason for lowered recognition yield in free-flow license-plate reading systems. (Non-)occluded characters can readily be learned in separate neural networks but not together. Even a small proportion of occluded characters in the training set will already significantly

  16. Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

    Science.gov (United States)

    Choi, Joon Yul; Yoo, Tae Keun; Seo, Jeong Gi; Kwak, Jiyong; Um, Terry Taewoong; Rim, Tyler Hyungtaek

    2017-01-01

    Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

  17. Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers

    Directory of Open Access Journals (Sweden)

    Ryan Henderson

    2017-09-01

    Full Text Available Picasso is a free open-source (Eclipse Public License web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks. Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend. Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures. Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.

  18. Properties of healthcare teaming networks as a function of network construction algorithms

    Science.gov (United States)

    Trayhan, Melissa; Farooq, Samir A.; Fucile, Christopher; Ghoshal, Gourab; White, Robert J.; Quill, Caroline M.; Rosenberg, Alexander; Barbosa, Hugo Serrano; Bush, Kristen; Chafi, Hassan; Boudreau, Timothy

    2017-01-01

    Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106–108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and

  19. Constructing Battery-Aware Virtual Backbones in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chi Ma

    2007-05-01

    Full Text Available A critical issue in battery-powered sensor networks is to construct energy efficient virtual backbones for network routing. Recent study in battery technology reveals that batteries tend to discharge more power than needed and reimburse the over-discharged power if they are recovered. In this paper we first provide a mathematical battery model suitable for implementation in sensor networks. We then introduce the concept of battery-aware connected dominating set (BACDS and show that in general the minimum BACDS (MBACDS can achieve longer lifetime than the previous backbone structures. Then we show that finding a MBACDS is NP-hard and give a distributed approximation algorithm to construct the BACDS. The resulting BACDS constructed by our algorithm is at most (8+Δopt size, where Δ is the maximum node degree and opt is the size of an optimal BACDS. Simulation results show that the BACDS can save a significant amount of energy and achieve up to 30% longer network lifetime than previous schemes.

  20. A constructive algorithm for unsupervised learning with incremental neural network

    Directory of Open Access Journals (Sweden)

    Jenq-Haur Wang

    2015-04-01

    In our experiment, Reuters-21578 was used as the dataset to show the effectiveness of the proposed method on text classification. The experimental results showed that our method can effectively classify texts with the best F1-measure of 92.5%. It also showed the learning algorithm can enhance the accuracy effectively and efficiently. This framework also validates scalability in terms of the network size, in which the training and testing times both showed a constant trend. This also validates the feasibility of the method for practical uses.

  1. Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network

    Directory of Open Access Journals (Sweden)

    Nick J. Pizzi

    2012-01-01

    Full Text Available Although many classification techniques exist to analyze patterns possessing straightforward characteristics, they tend to fail when the ratio of features to patterns is very large. This “curse of dimensionality” is especially prevalent in many complex, voluminous biomedical datasets acquired using the latest spectroscopic modalities. To address this pattern classification issue, we present a technique using an adaptive network of fuzzy logic connectives to combine class boundaries generated by sets of discriminant functions. We empirically evaluate the effectiveness of this classification technique by comparing it against two conventional benchmark approaches, both of which use feature averaging as a preprocessing phase.

  2. Robust Template Decomposition without Weight Restriction for Cellular Neural Networks Implementing Arbitrary Boolean Functions Using Support Vector Classifiers

    Directory of Open Access Journals (Sweden)

    Yih-Lon Lin

    2013-01-01

    Full Text Available If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.

  3. Backpropagation artificial neural network classifier to detect changes in heart sound due to mitral valve regurgitation.

    Science.gov (United States)

    Sinha, Rakesh Kumar; Aggarwal, Yogender; Das, Barda Nand

    2007-06-01

    The phonocardiograph (PCG) can provide a noninvasive diagnostic ability to the clinicians and technicians to compare the heart acoustic signal obtained from normal and that of pathological heart (cardiac patient). This instrument was connected to the computer through the analog to digital (A/D) converter. The digital data stored for the normal and diseased (mitral valve regurgitation) heart in the computer were decomposed through the Coifman 4th order wavelet kernel. The decomposed phonocardiographic (PCG) data were tested by backpropagation artificial neural network (ANN). The network was containing 64 nodes in the input layer, weighted from the decomposed components of the PCG in the input layer, 16 nodes in the hidden layer and an output node. The ANN was found effective in differentiating the wavelet components of the PCG from mitral valve regurgitation confirmed person (93%) to normal subjects (98%) with an overall performance of 95.5%. This system can also be used to detect the defects in cardiac valves especially, and other several cardiac disorders in general.

  4. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Directory of Open Access Journals (Sweden)

    Mark D McDonnell

    Full Text Available Recent advances in training deep (multi-layer architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM approach, which also enables a very rapid training time (∼ 10 minutes. Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  5. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Science.gov (United States)

    McDonnell, Mark D; Tissera, Migel D; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

    2015-01-01

    Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  6. Scalable brain network construction on white matter fibers

    Science.gov (United States)

    Chung, Moo K.; Adluru, Nagesh; Dalton, Kim M.; Alexander, Andrew L.; Davidson, Richard J.

    2011-03-01

    DTI offers a unique opportunity to characterize the structural connectivity of the human brain non-invasively by tracing white matter fiber tracts. Whole brain tractography studies routinely generate up to half million tracts per brain, which serves as edges in an extremely large 3D graph with up to half million edges. Currently there is no agreed-upon method for constructing the brain structural network graphs out of large number of white matter tracts. In this paper, we present a scalable iterative framework called the ɛ-neighbor method for building a network graph and apply it to testing abnormal connectivity in autism.

  7. The Construction of Corporate Social Responsibility in Network Societies

    DEFF Research Database (Denmark)

    Schultz, Friederike; Castello, Itziar; Morsing, Mette

    2013-01-01

    The paper introduces the communication view on Corporate Social Responsibility (CSR), which regards CSR as communicatively constructed in dynamic interaction processes in today's networked societies. Building on the idea that communication constitutes organizations we discuss the potentially......-normative view on CSR, which highlights the societal conditions and role of corporations in creating norms. We argue that both the established views, by not sufficiently acknowledging communication dynamics in networked societies, remain biased in three ways: control-biased, consistency-biased, and consensus......-biased. We discuss implications of these biases and propose a future research agenda for the communication view on CSR....

  8. Analysis of unintended events in hospitals : inter-rater reliability of constructing causal trees and classifying root causes

    NARCIS (Netherlands)

    Smits, M.; Janssen, J.; Vet, R. de; Zwaan, L.; Groenewegen, P.P.; Timmermans, D.

    2009-01-01

    Background. Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

  9. Analysis of unintended events in hospitals: inter-rater reliability of constructing causal trees and classifying root causes

    NARCIS (Netherlands)

    Smits, M.; Janssen, J.; Vet, de H.C.W.; Zwaan, L.; Timmermans, D.R.M.; Groenewegen, P.P.; Wagner, C.

    2009-01-01

    BACKGROUND: Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis: is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

  10. Analysis of unintended events in hospitals: inter-rater reliability of constructing causal trees and classifying root causes.

    NARCIS (Netherlands)

    Smits, M.; Janssen, J.; Vet, R. de; Zwaan, L.; Timmermans, D.; Groenewegen, P.; Wagner, C.

    2009-01-01

    Background: Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

  11. Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier

    Energy Technology Data Exchange (ETDEWEB)

    Rocco, Dominick Rosario [Minnesota U.

    2016-03-01

    The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of Δm2 and θ23 and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74×1020 POT, 14 kt equivalent) to provide new best- fit measurements of sin2(θ23) = 0.43 (with a statistically-degenerate compliment near 0.60) and |Δm2 | = 2.48 × 10-3 eV2.

  12. Signalling network construction for modelling plant defence response.

    Directory of Open Access Journals (Sweden)

    Dragana Miljkovic

    Full Text Available Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2 triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be

  13. Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks

    DEFF Research Database (Denmark)

    S. Nadimi, Esmaeil; Jørgensen, Rasmus Nyholm; Blanes-Vidal, Victoria

    2012-01-01

    Animal welfare is an issue of great importance in modern food production systems. Because animal behavior provides reliable information about animal health and welfare, recent research has aimed at designing monitoring systems capable of measuring behavioral parameters and transforming them...... into their corresponding behavioral modes. However, network unreliability and high-energy consumption have limited the applicability of those systems. In this study, a 2.4-GHz ZigBee-based mobile ad hoc wireless sensor network (MANET) that is able to overcome those problems is presented. The designed MANET showed high...... communication reliability, low energy consumption and low packet loss rate (14.8%) due to the deployment of modern communication protocols (e.g. multi-hop communication and handshaking protocol). The measured behavioral parameters were transformed into the corresponding behavioral modes using a multilayer...

  14. Classifying and profiling Social Networking Site users: a latent segmentation approach.

    Science.gov (United States)

    Alarcón-del-Amo, María-del-Carmen; Lorenzo-Romero, Carlota; Gómez-Borja, Miguel-Ángel

    2011-09-01

    Social Networking Sites (SNSs) have showed an exponential growth in the last years. The first step for an efficient use of SNSs stems from an understanding of the individuals' behaviors within these sites. In this research, we have obtained a typology of SNS users through a latent segmentation approach, based on the frequency by which users perform different activities within the SNSs, sociodemographic variables, experience in SNSs, and dimensions related to their interaction patterns. Four different segments have been obtained. The "introvert" and "novel" users are the more occasional. They utilize SNSs mainly to communicate with friends, although "introverts" are more passive users. The "versatile" user performs different activities, although occasionally. Finally, the "expert-communicator" performs a greater variety of activities with a higher frequency. They tend to perform some marketing-related activities such as commenting on ads or gathering information about products and brands as well as commenting ads. The companies can take advantage of these segmentation schemes in different ways: first, by tracking and monitoring information interchange between users regarding their products and brands. Second, they should match the SNS users' profiles with their market targets to use SNSs as marketing tools. Finally, for most business, the expert users could be interesting opinion leaders and potential brand influencers.

  15. Constructing general partial differential equations using polynomial and neural networks.

    Science.gov (United States)

    Zjavka, Ladislav; Pedrycz, Witold

    2016-01-01

    Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Construction of Individual Morphological Brain Networks with Multiple Morphometric Features

    Directory of Open Access Journals (Sweden)

    Chunlan Yang

    2017-04-01

    Full Text Available In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28 each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20–40%, and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78 indicates

  17. 3FGLzoo: classifying 3FGL unassociated Fermi-LAT γ-ray sources by artificial neural networks

    Science.gov (United States)

    Salvetti, D.; Chiaro, G.; La Mura, G.; Thompson, D. J.

    2017-09-01

    In its first four years of operation, the Fermi-Large Area Telescope (LAT) detected 3033 γ-ray emitting sources. In the Fermi-LAT Third Source Catalogue (3FGL) about 50 per cent of the sources have no clear association with a likely γ-ray emitter. We use an artificial neural network algorithm aimed at distinguishing BL Lacs from FSRQs to investigate the source subclass of 559 3FGL unassociated sources characterized by γ-ray properties very similar to those of active galactic nuclei. Based on our method, we can classify 271 objects as BL Lac candidates, 185 as FSRQ candidates, leaving only 103 without a clear classification. We suggest a new zoo for γ-ray objects, where the percentage of sources of uncertain type drops from 52 per cent to less than 10 per cent. The result of this study opens up new considerations on the population of the γ-ray sky, and it will facilitate the planning of significant samples for rigorous analyses and multiwavelength observational campaigns.

  18. A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

    Science.gov (United States)

    Lee, Yu-Hao; Hsieh, Ya-Ju; Shiah, Yung-Jong; Lin, Yu-Huei; Chen, Chiao-Yun; Tyan, Yu-Chang; GengQiu, JiaCheng; Hsu, Chung-Yao; Chen, Sharon Chia-Ju

    2017-04-01

    To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant normalizing scaling are manipulated as the evaluating variables for the detection of accuracy. Thereafter, by modulating the sliding window (the period of the analyzed data) and shifting interval of the window (the time interval to shift the analyzed data), the effect of immediate analysis for the 2 methods is compared. This analysis system is performed on 3 meditation groups, categorizing their meditation experiences in 10-year intervals from novice to junior and to senior. After an exhausted calculation and cross-validation across all variables, the high accuracy rate >98% is achievable under the criterion of 0.5-minute sliding window and 2 seconds shifting interval for both methods. In a word, the minimum analyzable data length is 0.5 minute and the minimum recognizable temporal resolution is 2 seconds in the decision of meditative classification. Our proposed classifier of the meditation experience promotes a rapid evaluation system to distinguish meditation experience and a beneficial utilization of artificial techniques for the big-data analysis.

  19. [Regional ecological planning and ecological network construction: a case study of "Ji Triangle" Region].

    Science.gov (United States)

    Li, Bo; Han, Zeng-Lin; Tong, Lian-Jun

    2009-05-01

    By the methods of in situ investigation and regional ecological planning, the present ecological environment, ecosystem vulnerability, and ecological environment sensitivity in "Ji Triangle" Region were analyzed, and the ecological network of the study area was constructed. According to the ecological resources abundance degree, ecological recovery, farmland windbreak system, environmental carrying capacity, forestry foundation, and ecosystem integrity, the study area was classified into three regional ecological function ecosystems, i. e., east low hill ecosystem, middle plain ecosystem, and west plain wetland ecosystem. On the basis of marking regional ecological nodes, the regional ecological corridor (Haerbin-Dalian regional axis, Changchun-Jilin, Changchun-Songyuan, Jilin-Songyuan, Jilin-Siping, and Songyuan-Siping transportation corridor) and regional ecological network (one ring, three links, and three belts) were constructed. Taking the requests of regional ecological security into consideration, the ecological environment security system of "Ji Triangle" Region, including regional ecological conservation district, regional ecological restored district, and regional ecological management district, was built.

  20. On Study of Construction of New Generation Intelligent Communication Network for Distribution System

    Science.gov (United States)

    Wu, Dan; Zhang, Xueyan

    2017-09-01

    many new technological means are integrated in the new network of smart electricity distribution including electric power technology, control technology and information technology, and in this area, people carry out profound research to the construction of communication network and power distribution network. This paper analyzes specific structures of the communication network for distribution system, discusses the development trend of it, explores its technical system in depth, and finally proposes some concrete constructive strategies, hoping to be a valuable reference for related research persons.

  1. Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data.

    Science.gov (United States)

    Luque-Baena, Rafael Marcos; Urda, Daniel; Subirats, Jose Luis; Franco, Leonardo; Jerez, Jose M

    2014-05-07

    Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results

  2. Classifying Microorganisms

    DEFF Research Database (Denmark)

    Sommerlund, Julie

    2006-01-01

    This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... characteristics. The coexistence of the classification systems does not lead to a conflict between them. Rather, the systems seem to co-exist in different configurations, through which they are complementary, contradictory and inclusive in different situations-sometimes simultaneously. The systems come...

  3. Propagation of New Innovations: An Approach to Classify Human Behavior and Movement from Available Social Network Data

    Science.gov (United States)

    Mahmud, Faisal; Samiul, Hasan

    2010-01-01

    It is interesting to observe new innovations, products, or ideas propagating into the society. One important factor of this propagation is the role of individual's social network; while another factor is individual's activities. In this paper, an approach will be made to analyze the propagation of different ideas in a popular social network. Individuals' responses to different activities in the network will be analyzed. The properties of network will also be investigated for successful propagation of innovations.

  4. Modeling social influence through network autocorrelation : constructing the weight matrix

    NARCIS (Netherlands)

    Leenders, Roger Th. A. J.

    Many physical and social phenomena are embedded within networks of interdependencies, the so-called 'context' of these phenomena. In network analysis, this type of process is typically modeled as a network autocorrelation model. Parameter estimates and inferences based on autocorrelation models,

  5. CNMO: Towards the Construction of a Communication Network Modelling Ontology

    Science.gov (United States)

    Rahman, Muhammad Azizur; Pakstas, Algirdas; Wang, Frank Zhigang

    Ontologies that explicitly identify objects, properties, and relationships in specific domains are essential for collaboration that involves sharing of data, knowledge or resources. A communications network modelling ontology (CNMO) has been designed to represent a network model as well as aspects related to its development and actual network operation. Network nodes/sites, link, traffic sources, protocols as well as aspects of the modeling/simulation scenario and operational aspects are defined with their formal representation. A CNMO may be beneficial for various network design/simulation/research communities due to the uniform representation of network models. This ontology is designed using terminology and concepts from various network modeling, simulation and topology generation tools.

  6. An examination of electronic file transfer between host and microcomputers for the AMPMODNET/AIMNET (Army Material Plan Modernization Network/Acquisition Information Management Network) classified network environment

    Energy Technology Data Exchange (ETDEWEB)

    Hake, K.A.

    1990-11-01

    This report presents the results of investigation and testing conducted by Oak Ridge National Laboratory (ORNL) for the Project Manager -- Acquisition Information Management (PM-AIM), and the United States Army Materiel Command Headquarters (HQ-AMC). It concerns the establishment of file transfer capabilities on the Army Materiel Plan Modernization (AMPMOD) classified computer system. The discussion provides a general context for micro-to-mainframe connectivity and focuses specifically upon two possible solutions for file transfer capabilities. The second section of this report contains a statement of the problem to be examined, a brief description of the institutional setting of the investigation, and a concise declaration of purpose. The third section lays a conceptual foundation for micro-to-mainframe connectivity and provides a more detailed description of the AMPMOD computing environment. It gives emphasis to the generalized International Business Machines, Inc. (IBM) standard of connectivity because of the predominance of this vendor in the AMPMOD computing environment. The fourth section discusses two test cases as possible solutions for file transfer. The first solution used is the IBM 3270 Control Program telecommunications and terminal emulation software. A version of this software was available on all the IBM Tempest Personal Computer 3s. The second solution used is Distributed Office Support System host electronic mail software with Personal Services/Personal Computer microcomputer e-mail software running with IBM 3270 Workstation Program for terminal emulation. Test conditions and results are presented for both test cases. The fifth section provides a summary of findings for the two possible solutions tested for AMPMOD file transfer. The report concludes with observations on current AMPMOD understanding of file transfer and includes recommendations for future consideration by the sponsor.

  7. 76 FR 63811 - Structural Reforms To Improve the Security of Classified Networks and the Responsible Sharing and...

    Science.gov (United States)

    2011-10-13

    ... (PM- ISE) with respect to the functions to be performed by the Classified Information Sharing and..., and the Omnibus Diplomatic Security and Antiterrorism Act of 1986; the Director of ISOO under...: (1) the authority granted by law to an agency, or the head thereof; or (2) the functions of the...

  8. Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

    Science.gov (United States)

    Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

    2015-07-15

    While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

  9. Construction of road network vulnerability evaluation index based on general travel cost

    Science.gov (United States)

    Leng, Jun-qiang; Zhai, Jing; Li, Qian-wen; Zhao, Lin

    2018-03-01

    With the development of China's economy and the continuous improvement of her urban road network, the vulnerability of the urban road network has attracted increasing attention. Based on general travel cost, this work constructs the vulnerability evaluation index for the urban road network, and evaluates the vulnerability of the urban road network from the perspective of user generalised travel cost. Firstly, the generalised travel cost model is constructed based on vehicle cost, travel time, and traveller comfort. Then, the network efficiency index is selected as an evaluation index of vulnerability: the network efficiency index is composed of the traffic volume and the generalised travel cost, which are obtained from the equilibrium state of the network. In addition, the research analyses the influence of traffic capacity decrease, road section attribute value, and location of road section, on vulnerability. Finally, the vulnerability index is used to analyse the local area network of Harbin and verify its applicability.

  10. A new method for constructing networks from binary data

    Science.gov (United States)

    van Borkulo, Claudia D.; Borsboom, Denny; Epskamp, Sacha; Blanken, Tessa F.; Boschloo, Lynn; Schoevers, Robert A.; Waldorp, Lourens J.

    2014-08-01

    Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.

  11. A comparison of Spectral Angle Mapper and Artificial Neural Network classifiers combined with Landsat TM imagery analysis for obtaining burnt area mapping.

    Science.gov (United States)

    Petropoulos, George P; Vadrevu, Krishna Prasad; Xanthopoulos, Gavriil; Karantounias, George; Scholze, Marko

    2010-01-01

    Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM) classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878) the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795). Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ≈ 1% for ANN and ≈ 6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

  12. A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

    Directory of Open Access Journals (Sweden)

    Marko Scholze

    2010-03-01

    Full Text Available Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN and Spectral Angle Mapper (SAM classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878 the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795. Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ~1% for ANN and ~6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

  13. Determination of priority of the construction of irrigation network

    Directory of Open Access Journals (Sweden)

    Subandiyah Azis

    2016-12-01

    Full Text Available Development of the irrigation network is one of the factors that give the great impact to economic growth in Indonesia. The Indonesian government has the authority on this development. However, the handling of the irrigation network has not been maximum and cannot meet the expectations/needs during this time. This is because the limitation of government funding which is not in a good proportion to the increasing of the damage of the irrigation network from year to year and the priority scale is only based on the proposed community and the desire of some of the stakeholders. Therefore, a research is important to determine priority scale, which is appropriate to the real needs in the field. The method applied in this research was descriptive research by spread over the questioner to the stakeholders related to the development of the irrigation network and analyzed the data using Analytic Hierarchy Process (AHP. The location of the research was in Sidoarjo Regency that covers 10 locations. The analysis results showed the priority order execution of irrigation network development in accordance with the requirements of the irrigation network. It is expected that this method can be applied to the entire development of the irrigation networks and other infrastructures, therefore, limited government funds can be used efficiently and effectively.

  14. Integrative Analysis of DCE-MRI and Gene Expression Profiles in Construction of a Gene Classifier for Assessment of Hypoxia-Related Risk of Chemoradiotherapy Failure in Cervical Cancer

    DEFF Research Database (Denmark)

    Fjeldbo, Christina S; Julin, Cathinka H; Lando, Malin

    2016-01-01

    PURPOSE: A 31-gene expression signature reflected in dynamic contrast enhanced (DCE)-MR images and correlated with hypoxia-related aggressiveness in cervical cancer was identified in previous work. We here aimed to construct a dichotomous classifier with key signature genes and a predefined...... platforms. The prognostic value was independent of existing clinical markers, regardless of clinical endpoints. CONCLUSIONS: A robust DCE-MRI-associated gene classifier has been constructed that may be used to achieve an early indication of patients' risk of hypoxia-related chemoradiotherapy failure....

  15. Using the Network Metaphor to Design, Deliver, and Maintain a Construction Management Curriculum

    National Research Council Canada - National Science Library

    Saad, Ihab M. H

    2015-01-01

    .... Using a network metaphor, with a critical path consisting of critical activities, activity codes, and constraints, can be a successful methodology to develop/align a construction management curriculum...

  16. The construction of corporate social responsibility in network societies: A communication view

    NARCIS (Netherlands)

    Schultz, F.; Castello, I.; Morsing, M.

    2013-01-01

    The paper introduces the communication view on Corporate Social Responsibility (CSR), which regards CSR as communicatively constructed in dynamic interaction processes in today's networked societies. Building on the idea that communication constitutes organizations we discuss the potentially

  17. Comparison of two neural network classifiers in the differential diagnosis of essential tremor and Parkinson's disease by {sup 123}I-FP-CIT brain SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Palumbo, Barbara [University of Perugia, Nuclear Medicine Section, Department of Surgical, Radiological and Odontostomatological Sciences, Ospedale S. Maria della Misericordia, Perugia (Italy); Fravolini, Mario Luca [University of Perugia, Department of Electronic and Information Engineering, Perugia (Italy); Nuvoli, Susanna; Spanu, Angela; Madeddu, Giuseppe [University of Sassari, Department of Nuclear Medicine, Sassari (Italy); Paulus, Kai Stephan [University of Sassari, Department of Neurology, Sassari (Italy); Schillaci, Orazio [University Tor Vergata, Department of Biopathology and Diagnostic Imaging, Rome (Italy); IRCSS Neuromed, Pozzilli (Italy)

    2010-11-15

    To contribute to the differentiation of Parkinson's disease (PD) and essential tremor (ET), we compared two different artificial neural network classifiers using {sup 123}I-FP-CIT SPECT data, a probabilistic neural network (PNN) and a classification tree (ClT). {sup 123}I-FP-CIT brain SPECT with semiquantitative analysis was performed in 216 patients: 89 with ET, 64 with PD with a Hoehn and Yahr (H and Y) score of {<=}2 (early PD), and 63 with PD with a H and Y score of {>=}2.5 (advanced PD). For each of the 1,000 experiments carried out, 108 patients were randomly selected as the PNN training set, while the remaining 108 validated the trained PNN, and the percentage of the validation data correctly classified in the three groups of patients was computed. The expected performance of an ''average performance PNN'' was evaluated. In analogy, for ClT 1,000 classification trees with similar structures were generated. For PNN, the probability of correct classification in patients with early PD was 81.9{+-}8.1% (mean{+-}SD), in patients with advanced PD 78.9{+-}8.1%, and in ET patients 96.6{+-}2.6%. For ClT, the first decision rule gave a mean value for the putamen of 5.99, which resulted in a probability of correct classification of 93.5{+-}3.4%. This means that patients with putamen values >5.99 were classified as having ET, while patients with putamen values <5.99 were classified as having PD. Furthermore, if the caudate nucleus value was higher than 6.97 patients were classified as having early PD (probability 69.8{+-}5.3%), and if the value was <6.97 patients were classified as having advanced PD (probability 88.1%{+-}8.8%). These results confirm that PNN achieved valid classification results. Furthermore, ClT provided reliable cut-off values able to differentiate ET and PD of different severities. (orig.)

  18. Quantum Google algorithm. Construction and application to complex networks

    Science.gov (United States)

    Paparo, G. D.; Müller, M.; Comellas, F.; Martin-Delgado, M. A.

    2014-07-01

    We review the main findings on the ranking capabilities of the recently proposed Quantum PageRank algorithm (G.D. Paparo et al., Sci. Rep. 2, 444 (2012) and G.D. Paparo et al., Sci. Rep. 3, 2773 (2013)) applied to large complex networks. The algorithm has been shown to identify unambiguously the underlying topology of the network and to be capable of clearly highlighting the structure of secondary hubs of networks. Furthermore, it can resolve the degeneracy in importance of the low-lying part of the list of rankings. Examples of applications include real-world instances from the WWW, which typically display a scale-free network structure and models of hierarchical networks. The quantum algorithm has been shown to display an increased stability with respect to a variation of the damping parameter, present in the Google algorithm, and a more clearly pronounced power-law behaviour in the distribution of importance among the nodes, as compared to the classical algorithm.

  19. Digging into construction: social networks and their potential impact on knowledge transfer.

    Science.gov (United States)

    Carlan, N A; Kramer, D M; Bigelow, P; Wells, R; Garritano, E; Vi, P

    2012-01-01

    A six-year study is exploring the most effective ways to disseminate ideas to reduce musculoskeletal disorders (MSDs) in the construction sector. The sector was targeted because MSDs account for 35% of all lost time injuries. This paper reports on the organization of the construction sector, and maps potential pathways of communication, including social networks, to set the stage for future dissemination. The managers, health and safety specialists, union health and safety representatives, and 28 workers from small, medium and large construction companies participated. Over a three-year period, data were collected from 47 qualitative interviews. Questions were guided by the PARIHS (Promoting Action on Research Implementation in Health Services) knowledge-transfer conceptual framework and adapted for the construction sector. The construction sector is a complex and dynamic sector, with non-linear reporting relationships, and divided and diluted responsibilities. Four networks were identified that can potentially facilitate the dissemination of new knowledge: worksite-project networks; union networks; apprenticeship program networks; and networks established by the Construction Safety Association/Infrastructure Health and Safety Association. Flexible and multi-directional lines of communication must be used in this complex environment. This has implications for the future choice of knowledge transfer strategies.

  20. Comparisons of topological properties in autism for the brain network construction methods

    Science.gov (United States)

    Lee, Min-Hee; Kim, Dong Youn; Lee, Sang Hyeon; Kim, Jin Uk; Chung, Moo K.

    2015-03-01

    Structural brain networks can be constructed from the white matter fiber tractography of diffusion tensor imaging (DTI), and the structural characteristics of the brain can be analyzed from its networks. When brain networks are constructed by the parcellation method, their network structures change according to the parcellation scale selection and arbitrary thresholding. To overcome these issues, we modified the Ɛ -neighbor construction method proposed by Chung et al. (2011). The purpose of this study was to construct brain networks for 14 control subjects and 16 subjects with autism using both the parcellation and the Ɛ-neighbor construction method and to compare their topological properties between two methods. As the number of nodes increased, connectedness decreased in the parcellation method. However in the Ɛ-neighbor construction method, connectedness remained at a high level even with the rising number of nodes. In addition, statistical analysis for the parcellation method showed significant difference only in the path length. However, statistical analysis for the Ɛ-neighbor construction method showed significant difference with the path length, the degree and the density.

  1. The construction of multimedia teaching resource base based on campus network

    Science.gov (United States)

    Yuan, Bin

    2017-03-01

    Multimedia teaching is the important embodiment of modern education, is an important approach to improve the quality of teaching. In the current environment, campus network construction and application of multimedia teaching resource must be combined with school practice, explore the resources integration of multimedia technology in the campus network environment, in order to realize the long-term development of higher education.

  2. Real-Time Water Vapor Maps from a GPS Surface Network: Construction, Validation, and Applications

    NARCIS (Netherlands)

    Haan, de S.; Holleman, I.; Holtslag, A.A.M.

    2009-01-01

    In this paper the construction of real-time integrated water vapor (IWV) maps from a surface network of global positioning system (GPS) receivers is presented. The IWV maps are constructed using a twodimensional variational technique with a persistence background that is 15 min old. The background

  3. Multimedia Classifier

    Science.gov (United States)

    Costache, G. N.; Gavat, I.

    2004-09-01

    Along with the aggressive growing of the amount of digital data available (text, audio samples, digital photos and digital movies joined all in the multimedia domain) the need for classification, recognition and retrieval of this kind of data became very important. In this paper will be presented a system structure to handle multimedia data based on a recognition perspective. The main processing steps realized for the interesting multimedia objects are: first, the parameterization, by analysis, in order to obtain a description based on features, forming the parameter vector; second, a classification, generally with a hierarchical structure to make the necessary decisions. For audio signals, both speech and music, the derived perceptual features are the melcepstral (MFCC) and the perceptual linear predictive (PLP) coefficients. For images, the derived features are the geometric parameters of the speaker mouth. The hierarchical classifier consists generally in a clustering stage, based on the Kohonnen Self-Organizing Maps (SOM) and a final stage, based on a powerful classification algorithm called Support Vector Machines (SVM). The system, in specific variants, is applied with good results in two tasks: the first, is a bimodal speech recognition which uses features obtained from speech signal fused to features obtained from speaker's image and the second is a music retrieval from large music database.

  4. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells.

    Science.gov (United States)

    Boland, M V; Murphy, R F

    2001-12-01

    Assessment of protein subcellular location is crucial to proteomics efforts since localization information provides a context for a protein's sequence, structure, and function. The work described below is the first to address the subcellular localization of proteins in a quantitative, comprehensive manner. Images for ten different subcellular patterns (including all major organelles) were collected using fluorescence microscopy. The patterns were described using a variety of numeric features, including Zernike moments, Haralick texture features, and a set of new features developed specifically for this purpose. To test the usefulness of these features, they were used to train a neural network classifier. The classifier was able to correctly recognize an average of 83% of previously unseen cells showing one of the ten patterns. The same classifier was then used to recognize previously unseen sets of homogeneously prepared cells with 98% accuracy. Algorithms were implemented using the commercial products Matlab, S-Plus, and SAS, as well as some functions written in C. The scripts and source code generated for this work are available at http://murphylab.web.cmu.edu/software. murphy@cmu.edu

  5. Construction of Course Ubiquitous Learning Based on Network

    Science.gov (United States)

    Wang, Xue; Zhang, Wei; Yang, Xinhui

    2017-01-01

    Ubiquitous learning has been more and more recognized, which describes a new generation of learning from a new point of view. Ubiquitous learning will bring the new teaching practice and teaching reform, which will become an essential way of learning in 21st century. Taking translation course as a case study, this research constructed a system of…

  6. Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.

    Science.gov (United States)

    Zhu, Fei; Liu, Quan; Zhang, Xiaofang; Shen, Bairong

    2015-08-01

    Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well.

  7. A new method based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for recognition of urine cells from microscopic images independent of rotation and scaling.

    Science.gov (United States)

    Avci, Derya; Leblebicioglu, Mehmet Kemal; Poyraz, Mustafa; Dogantekin, Esin

    2014-02-01

    So far, analysis and classification of urine cells number has become an important topic for medical diagnosis of some diseases. Therefore, in this study, we suggest a new technique based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling. Some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of this ADWEENN in this study. Nowadays, the image processing and pattern recognition topics have come into prominence. The image processing concludes operation and design of systems that recognize patterns in data sets. In the past years, very difficulty in classification of microscopic images was the deficiency of enough methods to characterize. Lately, it is seen that, multi-resolution image analysis methods such as Gabor filters, discrete wavelet decompositions are superior to other classic methods for analysis of these microscopic images. In this study, the structure of the ADWEENN method composes of four stages. These are preprocessing stage, feature extraction stage, classification stage and testing stage. The Discrete Wavelet Transform (DWT) and adaptive wavelet entropy and energy is used for adaptive feature extraction in feature extraction stage to strengthen the premium features of the Artificial Neural Network (ANN) classifier in this study. Efficiency of the developed ADWEENN method was tested showing that an avarage of 97.58% recognition succes was obtained.

  8. Problem-Solving Skills Among Precollege Students in Clinical Immunology and Microbiology: Classifying Strategies with a Rubric and Artificial Neural Network Technology

    Science.gov (United States)

    KANOWITH-KLEIN, SUSAN; STAVE, MEL; STEVENS, RON; CASILLAS, ADRIAN M.

    2001-01-01

    Educators emphasize the importance of problem solving that enables students to apply current knowledge and understanding in new ways to previously unencountered situations. Yet few methods are available to visualize and then assess such skills in a rapid and efficient way. Using a software system that can generate a picture (i.e., map) of students’ strategies in solving problems, we investigated methods to classify problem-solving strategies of high school students who were studying infectious and noninfectious diseases. Using maps that indicated items students accessed to solve a software simulation as well as the sequence in which items were accessed, we developed a rubric to score the quality of the student performances and also applied artificial neural network technology to cluster student performances into groups of related strategies. Furthermore, we established that a relationship existed between the rubric and neural network results, suggesting that the quality of a problem-solving strategy could be predicted from the cluster of performances in which it was assigned by the network. Using artificial neural networks to assess students’ problem-solving strategies has the potential to permit the investigation of the problem-solving performances of hundreds of students at a time and provide teachers with a valuable intervention tool capable of identifying content areas in which students have specific misunderstandings, gaps in learning, or misconceptions. PMID:23653541

  9. Artificial neural network classifier for the diagnosis of Parkinson's disease using [99mTc]TRODAT-1 and SPECT

    Science.gov (United States)

    Acton, Paul D.; Newberg, Andrew

    2006-06-01

    Imaging the dopaminergic neurotransmitter system with positron emission tomography (PET) or single photon emission tomography (SPECT) is a powerful tool for the diagnosis of Parkinson's disease (PD). Previous studies have indicated that human observers have a diagnostic accuracy similar to conventional ROI analysis of SPECT imaging data. Consequently, it has been hypothesized that an artificial neural network (ANN), which can mimic the pattern recognition skills of human observers, may provide similar results. A set of patients with PD, and normal healthy control subjects, were studied using the dopamine transporter tracer [99mTc]TRODAT-1 and SPECT. The sample was comprised of 81 patients (mean age ± SD: 63.4 ± 10.4 years; age range: 39.0-84.2 years) and 94 healthy controls (mean age ± SD: 61.8 ± 11.0 years; age range: 40.9-83.3 years). The images were processed to extract the striatum and the striatal pixel values were used as inputs to a three-layer ANN. The same set of data was used to both train and test the ANN, in a 'leave one out' procedure. The diagnostic accuracy of the ANN was higher than any previous analysis method applied to the same data (94.4% total accuracy, 97.5% specificity and 91.4% sensitivity). However, it should be stressed that, as with all applications of an ANN, it was difficult to interpret precisely what triggers in the images were being detected by the network.

  10. Classifying Membrane Proteins in the Proteome by Using Artificial Neural Networks Based on the Preferential Parameters of Amino Acids

    Science.gov (United States)

    Bose, Subrata K.; Browne, Antony; Kazemian, Hassan; White, Kenneth

    Membrane proteins (MPs) are large set of biological macromolecules that play a fundamental role in physiology and pathophysiology for survival. From a pharma-economical perspective, though it is the fact that MPs constitute ˜75% of possible targets for novel drugs but MPs are one of the most understudied groups of proteins in biochemical research. This is mainly because of the technical difficulties of obtaining structural information about trans-membrane regions (these are small sequences that crossways the bilayer lipid membrane). It is quite useful to predict the location of transmembrane segments down the sequence, since these are the elementary structural building blocks defining their topology. There have been several attempts over the last 20 years to develop tools for predicting membrane-spanning regions but current tools are far away from achieving a considerable reliability in prediction. This study aims to exploit the knowledge and current understanding in the field of artificial neural networks (ANNs) in particular data representation through the development of a system to identify and predict membrane-spanning regions by analysing primary amino acids sequence. In this paper we present a novel neural network (NNs) architecture and algorithms for predicting membrane spanning regions from primary amino acids sequences by using their preference parameters.

  11. Review of pore network modelling of porous media: experimental characterisations, network constructions and applications to reactive transport

    OpenAIRE

    Xiong, Qingrong; Baychev, Todor; Jivkov, Andrey

    2016-01-01

    Pore network models have been applied widely for simulating a variety of different physical and chemical processes, including phase exchange, non-Newtonian displacement, non-Darcy flow, reactive transport and thermodynamically consistent oil layers. The realism of such modelling, i.e. the credibility of their predictions, depends to a large extent on the quality of the correspondence between the pore space of a given medium and the pore network constructed as its representation. The main expe...

  12. Databases and tools for constructing signal transduction networks in cancer.

    Science.gov (United States)

    Nam, Seungyoon

    2017-01-01

    Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, highthroughput data, too complex for conventional processing methods (i.e., "big data"), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called "systems biology". One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets. [BMB Reports 2017; 50(1): 12-19].

  13. The DEEP-South: Network Construction and Test Operations

    Science.gov (United States)

    Moon, Hong-Kyu; Kim, Myung-Jin; Yim, Hong-Suh; Choi, Young-Jun; Bae, Youngho; Roh, Dong-Goo; the DEEP-South Team

    2015-08-01

    Korea Astronomy and Space Science Institute achieved completion of a network of optical telescopes called the KMTNet (Korea Micro-lensing Telescope Network) in the end of 2014. The KMTNet is comprised of three 1.6-m prime focus wide-field optics and 18K×18K mosaic CCDs, each providing 2×2 degrees field of view. This network facilities located at CTIO (Chile), SAAO (South Africa), and SSO (Australia) are expected to be on line in mid-2015 with their CCDs fully functional. While its primary objective is discovery and characterization of extrasolar planets, it is also being used for “Deep Ecliptic Patrol of the Southern Sky (DEEP-South)” aiming at asteroid and comet studies as one of its secondary science projects. The KMTNet telescopes are almost equally separated in longitude, and hence enable a 24-hour uninterrupted monitoring of the southern sky. The DEEP-South will thus provide a prompt solution to a demand from the scientific community to bridge the gaps in global sky coverage with a coordinated use of a network of ground-based telescopes in the southern hemisphere. Thanks to round-the-clock capability orbits, spin states and three dimensional shape of an object will be systematically investigated and archived for the first time. Based on SDSS and BVRI colors, we will also constrain their surface mineralogy, with an emphasis on targeted photometry of km-sized Potentially Hazardous Asteroids (PHAs) in the first stage (2015-2019). In the end of 2015, we plan to complete implementing dedicated software subsystem made of an automated observation scheduler and data pipeline for the sake of an increased discovery rate, rapid follow-up, timely phase coverage, and more efficient data reduction and analysis. We will give a brief introduction to a series of test operations conducted at the KMTNet-CTIO in February, March and April in 2015 with experimental data processing. Preliminary scientific results will also be presented.

  14. A large number of stepping motor network construction by PLC

    Science.gov (United States)

    Mei, Lin; Zhang, Kai; Hongqiang, Guo

    2017-11-01

    In the flexible automatic line, the equipment is complex, the control mode is flexible, how to realize the large number of step and servo motor information interaction, the orderly control become a difficult control. Based on the existing flexible production line, this paper makes a comparative study of its network strategy. After research, an Ethernet + PROFIBUSE communication configuration based on PROFINET IO and profibus was proposed, which can effectively improve the data interaction efficiency of the equipment and stable data interaction information.

  15. Dynamic system classifier

    CERN Document Server

    Pumpe, Daniel; Müller, Ewald; Enßlin, Torsten A

    2016-01-01

    Stochastic differential equations describe well many physical, biological and sociological systems, despite the simplification often made in their derivation. Here the usage of simple stochastic differential equations to characterize and classify complex dynamical systems is proposed within a Bayesian framework. To this end, we develop a dynamic system classifier (DSC). The DSC first abstracts training data of a system in terms of time dependent coefficients of the descriptive stochastic differential equation. Thereby the DSC identifies unique correlation structures within the training data. For definiteness we restrict the presentation of DSC to oscillation processes with a time dependent frequency {\\omega}(t) and damping factor {\\gamma}(t). Although real systems might be more complex, this simple oscillator captures many characteristic features. The {\\omega} and {\\gamma} timelines represent the abstract system characterization and permit the construction of efficient signal classifiers. Numerical experiment...

  16. Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory

    Science.gov (United States)

    Isaacson, Sven; Luo, Feng; Feltus, Frank A.; Smith, Melissa C.

    2013-01-01

    The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust. PMID:23409071

  17. Using an artificial neural network to classify multicomponent emission lines with integral field spectroscopy from SAMI and S7

    Science.gov (United States)

    Hampton, E. J.; Medling, A. M.; Groves, B.; Kewley, L.; Dopita, M.; Davies, R.; Ho, I.-T.; Kaasinen, M.; Leslie, S.; Sharp, R.; Sweet, S. M.; Thomas, A. D.; Allen, J.; Bland-Hawthorn, J.; Brough, S.; Bryant, J. J.; Croom, S.; Goodwin, M.; Green, A.; Konstantantopoulos, I. S.; Lawrence, J.; López-Sánchez, Á. R.; Lorente, N. P. F.; McElroy, R.; Owers, M. S.; Richards, S. N.; Shastri, P.

    2017-09-01

    Integral field spectroscopy (IFS) surveys are changing how we study galaxies and are creating vastly more spectroscopic data available than before. The large number of resulting spectra makes visual inspection of emission line fits an infeasible option. Here, we present a demonstration of an artificial neural network (ANN) that determines the number of Gaussian components needed to describe the complex emission line velocity structures observed in galaxies after being fit with lzifu. We apply our ANN to IFS data for the S7 survey, conducted using the Wide Field Spectrograph on the ANU 2.3 m Telescope, and the SAMI Galaxy Survey, conducted using the SAMI instrument on the 4 m Anglo-Australian Telescope. We use the spectral fitting code lzifu (Ho et al. 2016a) to fit the emission line spectra of individual spaxels from S7 and SAMI data cubes with 1-, 2- and 3-Gaussian components. We demonstrate that using an ANN is comparable to astronomers performing the same visual inspection task of determining the best number of Gaussian components to describe the physical processes in galaxies. The advantage of our ANN is that it is capable of processing the spectra for thousands of galaxies in minutes, as compared to the years this task would take individual astronomers to complete by visual inspection.

  18. Review of pore network modelling of porous media: Experimental characterisations, network constructions and applications to reactive transport

    Science.gov (United States)

    Xiong, Qingrong; Baychev, Todor G.; Jivkov, Andrey P.

    2016-09-01

    Pore network models have been applied widely for simulating a variety of different physical and chemical processes, including phase exchange, non-Newtonian displacement, non-Darcy flow, reactive transport and thermodynamically consistent oil layers. The realism of such modelling, i.e. the credibility of their predictions, depends to a large extent on the quality of the correspondence between the pore space of a given medium and the pore network constructed as its representation. The main experimental techniques for pore space characterisation, including direct imaging, mercury intrusion porosimetry and gas adsorption, are firstly summarised. A review of the main pore network construction techniques is then presented. Particular focus is given on how such constructions are adapted to the data from experimentally characterised pore systems. Current applications of pore network models are considered, with special emphasis on the effects of adsorption, dissolution and precipitation, as well as biomass growth, on transport coefficients. Pore network models are found to be a valuable tool for understanding and predicting meso-scale phenomena, linking single pore processes, where other techniques are more accurate, and the homogenised continuum porous media, used by engineering community.

  19. Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data.

    Science.gov (United States)

    Raith, Stefan; Vogel, Eric Per; Anees, Naeema; Keul, Christine; Güth, Jan-Frederik; Edelhoff, Daniel; Fischer, Horst

    2017-01-01

    Chairside manufacturing based on digital image acquisition is gainingincreasing importance in dentistry. For the standardized application of these methods, it is paramount to have highly automated digital workflows that can process acquired 3D image data of dental surfaces. Artificial Neural Networks (ANNs) arenumerical methods primarily used to mimic the complex networks of neural connections in the natural brain. Our hypothesis is that an ANNcan be developed that is capable of classifying dental cusps with sufficient accuracy. This bears enormous potential for an application in chairside manufacturing workflows in the dental field, as it closes the gap between digital acquisition of dental geometries and modern computer-aided manufacturing techniques.Three-dimensional surface scans of dental casts representing natural full dental arches were transformed to range image data. These data were processed using an automated algorithm to detect candidates for tooth cusps according to salient geometrical features. These candidates were classified following common dental terminology and used as training data for a tailored ANN.For the actual cusp feature description, two different approaches were developed and applied to the available data: The first uses the relative location of the detected cusps as input data and the second method directly takes the image information given in the range images. In addition, a combination of both was implemented and investigated.Both approaches showed high performance with correct classifications of 93.3% and 93.5%, respectively, with improvements by the combination shown to be minor.This article presents for the first time a fully automated method for the classification of teeththat could be confirmed to work with sufficient precision to exhibit the potential for its use in clinical practice,which is a prerequisite for automated computer-aided planning of prosthetic treatments with subsequent automated chairside manufacturing. Copyright

  20. Cropping Pattern Detection and Change Analysis in Central Luzon, Philippines Using Multi-Temporal MODIS Imagery and Artificial Neural Network Classifier

    Science.gov (United States)

    dela Torre, D. M.; Perez, G. J. P.

    2016-12-01

    Cropping practices in the Philippines has been intensifying with greater demand for food and agricultural supplies in view of an increasing population and advanced technologies for farming. This has not been monitored regularly using traditional methods but alternative methods using remote sensing has been promising yet underutilized. This study employed multi-temporal data from MODIS and neural network classifier to map annual land use in agricultural areas from 2001-2014 in Central Luzon, the primary rice growing area of the Philippines. Land use statistics derived from these maps were compared with historical El Nino events to examine how land area is affected by drought events. Fourteen maps of agricultural land use was produced, with the primary classes being single-cropping, double-cropping and perennial crops with secondary classes of forests, urban, bare, water and other classes. Primary classes were produced from the neural network classifier while secondary classes were derived from NDVI threshold masks. The overall accuracy for the 2014 map was 62.05% and a kappa statistic of 0.45. 155.56% increase in single-cropping systems from 2001 to 2014 was observed while double cropping systems decreased by 14.83%. Perennials increased by 76.21% while built-up areas decreased by 12.22% within the 14-year interval. There are several sources of error including mixed-pixels, scale-conversion problems and limited ground reference data. An analysis including El Niño events in 2004 and 2010 demonstrated that marginally irrigated areas that usually planted twice in a year resorted to single cropping, indicating that scarcity of water limited the intensification allowable in the area. Findings from this study can be used to predict future use of agricultural land in the country and also examine how farmlands have responded to climatic factors and stressors.

  1. A project plan for construction and cabling of picture archiving and communication system network.

    Science.gov (United States)

    Luo, Min; Wang, Xiaolin; Luo, Song; Lei, Wenyong; Wang, Xuejian; Wen, Hongyu; Wu, Hongxing

    2003-05-01

    To determine a network solution to meet the network requirements of the heavy data flow, load balance, and potential network storms from expansion of picture archiving and communication system (PACS) application. Intel Netstructure 480T Giga Switch was used as the main switch and connected to each building by fiber channel at 1 Giga speed to archive 100 MB/s to each port. At the same time, the in-dependence of the original network construction was physically kept. The layer 3 and 4 switchers were used as load balance to reduce the heavy load of the network, and all the cabling for PACS used the super CAT5 along with the Intel NetStructure 1520 to prepare for potential network storms. An advanced intranet was set up to fully meet the high standard requirement of the PACS. The foundation for upgrading the whole network system to 1 Giga application was built to achieve sharing and transmission of images, information, and patient data within the hospital. The base was established for the standardized management of the hospital. Good planning is the first step in setting up PACS, and the equipment forms the necessary platform to run PACS and all kinds of hospital information system (HIS). The networking construction is the foundation of e-hospital.

  2. Construction of Network Management Information System of Agricultural Products Supply Chain Based on 3PLs

    OpenAIRE

    Gao, Shujin; Yang, Qiangxian

    2010-01-01

    The necessity to construct the network management information system of 3PLs agricultural supply chain is analyzed, showing that 3PLs can improve the overall competitive advantage of agricultural supply chain. 3PLs changes the homogeneity management into specialized management of logistics service and achieves the alliance of the subjects at different nodes of agricultural products supply chain. Network management information system structure of agricultural products supply chain based on 3PL...

  3. Systematic construction and control of stereo nerve vision network in intelligent manufacturing

    Science.gov (United States)

    Liu, Hua; Wang, Helong; Guo, Chunjie; Ding, Quanxin; Zhou, Liwei

    2017-10-01

    A system method of constructing stereo vision by using neural network is proposed, and the operation and control mechanism in actual operation are proposed. This method makes effective use of the neural network in learning and memory function, by after training with samples. Moreover, the neural network can learn the nonlinear relationship in the stereoscopic vision system and the internal and external orientation elements. These considerations are Worthy of attention, which includes limited constraints, the scientific of critical group, the operating speed and the operability in technical aspects. The results support our theoretical forecast.

  4. The research of constructing dynamic cognition model based on brain network

    Directory of Open Access Journals (Sweden)

    Fang Chunying

    2017-03-01

    Full Text Available Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function

  5. A computational framework for the automated construction of glycosylation reaction networks.

    Directory of Open Access Journals (Sweden)

    Gang Liu

    Full Text Available Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS data. The features described above are illustrated using three case studies that examine: i O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii automated N-linked glycosylation pathway construction; and iii the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme

  6. A Hybrid Constructive Algorithm for Single-Layer Feedforward Networks Learning.

    Science.gov (United States)

    Wu, Xing; Rózycki, Paweł; Wilamowski, Bogdan M

    2015-08-01

    Single-layer feedforward networks (SLFNs) have been proven to be a universal approximator when all the parameters are allowed to be adjustable. It is widely used in classification and regression problems. The SLFN learning involves two tasks: determining network size and training the parameters. Most current algorithms could not be satisfactory to both sides. Some algorithms focused on construction and only tuned part of the parameters, which may not be able to achieve a compact network. Other gradient-based optimization algorithms focused on parameters tuning while the network size has to be preset by the user. Therefore, trial-and-error approach has to be used to search the optimal network size. Because results of each trial cannot be reused in another trial, it costs much computation. In this paper, a hybrid constructive (HC)algorithm is proposed for SLFN learning, which can train all the parameters and determine the network size simultaneously. At first, by combining Levenberg-Marquardt algorithm and least-square method, a hybrid algorithm is presented for training SLFN with fixed network size. Then,with the hybrid algorithm, an incremental constructive scheme is proposed. A new randomly initialized neuron is added each time when the training entrapped into local minima. Because the training continued on previous results after adding new neurons, the proposed HC algorithm works efficiently. Several practical problems were given for comparison with other popular algorithms. The experimental results demonstrated that the HC algorithm worked more efficiently than those optimization methods with trial and error, and could achieve much more compact SLFN than those construction algorithms.

  7. Construction of citrus gene coexpression networks from microarray data using random matrix theory

    Science.gov (United States)

    Du, Dongliang; Rawat, Nidhi; Deng, Zhanao; Gmitter, Fred G.

    2015-01-01

    After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 37 633 edges among 6256 nodes (genes), which accounts for 52.11% measurable genes of the citrus microarray. Then, these networks were partitioned into functional modules using the Markov Cluster Algorithm. Significantly enriched Gene Ontology biological process terms and KEGG pathway terms were detected for 343 and 60 modules, respectively. Finally, independent verification of these networks was performed using another expression data of 371 genes. This study provides new targets for further functional analyses in citrus. PMID:26504573

  8. Constructing an integrated gene similarity network for the identification of disease genes.

    Science.gov (United States)

    Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

    2017-09-20

    Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

  9. The Discursive Construction of Teachers’ Desirable Identity on a Social Networking Site

    Directory of Open Access Journals (Sweden)

    Radzuwan Ab Rashid

    2016-09-01

    Full Text Available This study is situated in the broader identity-construction literature. Bringing discourse community theory to examine teachers’ postings on Facebook Timelines, we explored how teachers discursively construct socially desirable identities to fit into the Timeline community. Data were gathered from the Status updates and Comments on 29 Timelines belonged to Malaysian English language teachers who were purposively chosen as they often posted and commented on teaching-related issues on their Timelines. The analysis shows that the commonest form of identity construction on the teachers’ Timelines was as an engager which had been carefully constructed to portray positive self-image. This paper concludes that when participating on a public networking site, the teachers were being strategic as not to construct identities which could tarnish their professional image.

  10. A greedy construction heuristic for the liner service network design problem

    DEFF Research Database (Denmark)

    Brouer, Berit Dangaard

    is challenging due to the size of a global liner shipping operation and due to the hub-and-spoke network design, where a high percentage of the total cargo is transshipped. We present the first construction heuristic for large scale instances of the LSN-DP. The heuristic is able to find a solution for a real...

  11. Atom-centered symmetry functions for constructing high-dimensional neural network potentials

    Science.gov (United States)

    Behler, Jörg

    2011-02-01

    Neural networks offer an unbiased and numerically very accurate approach to represent high-dimensional ab initio potential-energy surfaces. Once constructed, neural network potentials can provide the energies and forces many orders of magnitude faster than electronic structure calculations, and thus enable molecular dynamics simulations of large systems. However, Cartesian coordinates are not a good choice to represent the atomic positions, and a transformation to symmetry functions is required. Using simple benchmark systems, the properties of several types of symmetry functions suitable for the construction of high-dimensional neural network potential-energy surfaces are discussed in detail. The symmetry functions are general and can be applied to all types of systems such as molecules, crystalline and amorphous solids, and liquids.

  12. A Method of DTM Construction Based on Quadrangular Irregular Networks and Related Error Analysis.

    Science.gov (United States)

    Kang, Mengjun; Wang, Mingjun; Du, Qingyun

    2015-01-01

    A new method of DTM construction based on quadrangular irregular networks (QINs) that considers all the original data points and has a topological matrix is presented. A numerical test and a real-world example are used to comparatively analyse the accuracy of QINs against classical interpolation methods and other DTM representation methods, including SPLINE, KRIGING and triangulated irregular networks (TINs). The numerical test finds that the QIN method is the second-most accurate of the four methods. In the real-world example, DTMs are constructed using QINs and the three classical interpolation methods. The results indicate that the QIN method is the most accurate method tested. The difference in accuracy rank seems to be caused by the locations of the data points sampled. Although the QIN method has drawbacks, it is an alternative method for DTM construction.

  13. A Method of DTM Construction Based on Quadrangular Irregular Networks and Related Error Analysis.

    Directory of Open Access Journals (Sweden)

    Mengjun Kang

    Full Text Available A new method of DTM construction based on quadrangular irregular networks (QINs that considers all the original data points and has a topological matrix is presented. A numerical test and a real-world example are used to comparatively analyse the accuracy of QINs against classical interpolation methods and other DTM representation methods, including SPLINE, KRIGING and triangulated irregular networks (TINs. The numerical test finds that the QIN method is the second-most accurate of the four methods. In the real-world example, DTMs are constructed using QINs and the three classical interpolation methods. The results indicate that the QIN method is the most accurate method tested. The difference in accuracy rank seems to be caused by the locations of the data points sampled. Although the QIN method has drawbacks, it is an alternative method for DTM construction.

  14. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

    Science.gov (United States)

    Kumari, Sapna; Nie, Jeff; Chen, Huann-Sheng; Ma, Hao; Stewart, Ron; Li, Xiang; Lu, Meng-Zhu; Taylor, William M; Wei, Hairong

    2012-01-01

    Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

  15. Evidence reasoning method for constructing conditional probability tables in a Bayesian network of multimorbidity.

    Science.gov (United States)

    Du, Yuanwei; Guo, Yubin

    2015-01-01

    The intrinsic mechanism of multimorbidity is difficult to recognize and prediction and diagnosis are difficult to carry out accordingly. Bayesian networks can help to diagnose multimorbidity in health care, but it is difficult to obtain the conditional probability table (CPT) because of the lack of clinically statistical data. Today, expert knowledge and experience are increasingly used in training Bayesian networks in order to help predict or diagnose diseases, but the CPT in Bayesian networks is usually irrational or ineffective for ignoring realistic constraints especially in multimorbidity. In order to solve these problems, an evidence reasoning (ER) approach is employed to extract and fuse inference data from experts using a belief distribution and recursive ER algorithm, based on which evidence reasoning method for constructing conditional probability tables in Bayesian network of multimorbidity is presented step by step. A multimorbidity numerical example is used to demonstrate the method and prove its feasibility and application. Bayesian network can be determined as long as the inference assessment is inferred by each expert according to his/her knowledge or experience. Our method is more effective than existing methods for extracting expert inference data accurately and is fused effectively for constructing CPTs in a Bayesian network of multimorbidity.

  16. Constructing regional climate networks in the Amazonia during recent drought events.

    Science.gov (United States)

    Guo, Heng; Ramos, Antônio M T; Macau, Elbert E N; Zou, Yong; Guan, Shuguang

    2017-01-01

    Climate networks are powerful approaches to disclose tele-connections in climate systems and to predict severe climate events. Here we construct regional climate networks from precipitation data in the Amazonian region and focus on network properties under the recent drought events in 2005 and 2010. Both the networks of the entire Amazon region and the extreme networks resulted from locations severely affected by drought events suggest that network characteristics show slight difference between the two drought events. Based on network degrees of extreme drought events and that without drought conditions, we identify regions of interest that are correlated to longer expected drought period length. Moreover, we show that the spatial correlation length to the regions of interest decayed much faster in 2010 than in 2005, which is because of the dual roles played by both the Pacific and Atlantic oceans. The results suggest that hub nodes in the regional climate network of Amazonia have fewer long-range connections when more severe drought conditions appeared in 2010 than that in 2005.

  17. Novel methodology for construction and pruning of quasi-median networks

    Directory of Open Access Journals (Sweden)

    Brown Terence A

    2008-02-01

    Full Text Available Abstract Background Visualising the evolutionary history of a set of sequences is a challenge for molecular phylogenetics. One approach is to use undirected graphs, such as median networks, to visualise phylogenies where reticulate relationships such as recombination or homoplasy are displayed as cycles. Median networks contain binary representations of sequences as nodes, with edges connecting those sequences differing at one character; hypothetical ancestral nodes are invoked to generate a connected network which contains all most parsimonious trees. Quasi-median networks are a generalisation of median networks which are not restricted to binary data, although phylogenetic information contained within the multistate positions can be lost during the preprocessing of data. Where the history of a set of samples contain frequent homoplasies or recombination events quasi-median networks will have a complex topology. Graph reduction or pruning methods have been used to reduce network complexity but some of these methods are inapplicable to datasets in which recombination has occurred and others are procedurally complex and/or result in disconnected networks. Results We address the problems inherent in construction and reduction of quasi-median networks. We describe a novel method of generating quasi-median networks that uses all characters, both binary and multistate, without imposing an arbitrary ordering of the multistate partitions. We also describe a pruning mechanism which maintains at least one shortest path between observed sequences, displaying the underlying relations between all pairs of sequences while maintaining a connected graph. Conclusion Application of this approach to 5S rDNA sequence data from sea beet produced a pruned network within which genetic isolation between populations by distance was evident, demonstrating the value of this approach for exploration of evolutionary relationships.

  18. Connection with seismic networks and construction of real time earthquake monitoring system

    Energy Technology Data Exchange (ETDEWEB)

    Chi, Heon Cheol; Lee, H. I.; Shin, I. C.; Lim, I. S.; Park, J. H.; Lee, B. K.; Whee, K. H.; Cho, C. S. [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    2000-12-15

    It is natural to use the nuclear power plant seismic network which have been operated by KEPRI(Korea Electric Power Research Institute) and local seismic network by KIGAM(Korea Institute of Geology, Mining and Material). The real time earthquake monitoring system is composed with monitoring module and data base module. Data base module plays role of seismic data storage and classification and the other, monitoring module represents the status of acceleration in the nuclear power plant area. This research placed the target on the first, networking the KIN's seismic monitoring system with KIGAM and KEPRI seismic network and the second, construction the KIN's Independent earthquake monitoring system.

  19. Developmental self-construction and -configuration of functional neocortical neuronal networks.

    Directory of Open Access Journals (Sweden)

    Roman Bauer

    2014-12-01

    Full Text Available The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.

  20. An Efficient Distributed Algorithm for Constructing Spanning Trees in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rosana Lachowski

    2015-01-01

    Full Text Available Monitoring and data collection are the two main functions in wireless sensor networks (WSNs. Collected data are generally transmitted via multihop communication to a special node, called the sink. While in a typical WSN, nodes have a sink node as the final destination for the data traffic, in an ad hoc network, nodes need to communicate with each other. For this reason, routing protocols for ad hoc networks are inefficient for WSNs. Trees, on the other hand, are classic routing structures explicitly or implicitly used in WSNs. In this work, we implement and evaluate distributed algorithms for constructing routing trees in WSNs described in the literature. After identifying the drawbacks and advantages of these algorithms, we propose a new algorithm for constructing spanning trees in WSNs. The performance of the proposed algorithm and the quality of the constructed tree were evaluated in different network scenarios. The results showed that the proposed algorithm is a more efficient solution. Furthermore, the algorithm provides multiple routes to the sensor nodes to be used as mechanisms for fault tolerance and load balancing.

  1. Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China

    Science.gov (United States)

    Gao, Yu; Ma, Lei; Liu, Jiaxun; Zhuang, Zhuzhou; Huang, Qiuhao; Li, Manchun

    2017-04-01

    Fragmentation and reduced continuity of habitat patches threaten the environment and biodiversity. Recently, ecological networks are increasingly attracting the attention of researchers as they provide fundamental frameworks for environmental protection. This study suggests a set of procedures to construct an ecological network. First, we proposed a method to construct a landscape resistance surface based on the assessment of habitat quality. Second, to analyze the effect of the resistance surface on corridor simulations, we used three methods to construct resistance surfaces: (1) the method proposed in this paper, (2) the entropy coefficient method, and (3) the expert scoring method. Then, we integrated habitat patches and resistance surfaces to identify potential corridors using graph theory. These procedures were tested in Changzhou, China. Comparing the outputs of using different resistance surfaces demonstrated that: (1) different landscape resistance surfaces contribute to how corridors are identified, but only slightly affect the assessment of the importance of habitat patches and potential corridors; (2) the resistance surface, which is constructed based on habitat quality, is more applicable to corridor simulations; and (3) the assessment of the importance of habitat patches is fundamental for ecological network optimization in the conservation of critical habitat patches and corridors.

  2. Dynamic Construction Scheme for Virtualization Security Service in Software-Defined Networks.

    Science.gov (United States)

    Lin, Zhaowen; Tao, Dan; Wang, Zhenji

    2017-04-21

    For a Software Defined Network (SDN), security is an important factor affecting its large-scale deployment. The existing security solutions for SDN mainly focus on the controller itself, which has to handle all the security protection tasks by using the programmability of the network. This will undoubtedly involve a heavy burden for the controller. More devastatingly, once the controller itself is attacked, the entire network will be paralyzed. Motivated by this, this paper proposes a novel security protection architecture for SDN. We design a security service orchestration center in the control plane of SDN, and this center physically decouples from the SDN controller and constructs SDN security services. We adopt virtualization technology to construct a security meta-function library, and propose a dynamic security service composition construction algorithm based on web service composition technology. The rule-combining method is used to combine security meta-functions to construct security services which meet the requirements of users. Moreover, the RETE algorithm is introduced to improve the efficiency of the rule-combining method. We evaluate our solutions in a realistic scenario based on OpenStack. Substantial experimental results demonstrate the effectiveness of our solutions that contribute to achieve the effective security protection with a small burden of the SDN controller.

  3. A novel constructive-optimizer neural network for the traveling salesman problem.

    Science.gov (United States)

    Saadatmand-Tarzjan, Mahdi; Khademi, Morteza; Akbarzadeh-T, Mohammad-R; Moghaddam, Hamid Abrishami

    2007-08-01

    In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.

  4. Time for a real shift to relations: appraisal of Social Network Analysis applications in the UK construction industry

    Directory of Open Access Journals (Sweden)

    Ximing Ruan

    2013-03-01

    Full Text Available The Social Network Analysis (SNA has been adopted in the UK construction management research and generated meaningful insights in analysing project management organisations from network perspectives. As an effective tool, social network analysis has been used to analyse information and knowledge flow between construction project teams which is considered as foundation for collaborative working and subsequently improving overall performance. Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models and applications that are expressed in terms of relational concepts or processes. Many believe, moreover, that the success or failure of organisations often depends on the patterning of their internal structure. This paper reviewed existing literatures on SNA applications in construction industry from three leading construction management journals.  From the review, the research proposed some advance in the application of SNA in the construction industry.

  5. Dynamic Response Genes in CD4+ T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Sandra Hellberg

    2016-09-01

    Full Text Available Multiple sclerosis (MS is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers.

  6. Par@Graph - a parallel toolbox for the construction and analysis of large complex climate networks

    Science.gov (United States)

    Ihshaish, H.; Tantet, A.; Dijkzeul, J. C. M.; Dijkstra, H. A.

    2015-10-01

    In this paper, we present Par@Graph, a software toolbox to reconstruct and analyze complex climate networks having a large number of nodes (up to at least 106) and edges (up to at least 1012). The key innovation is an efficient set of parallel software tools designed to leverage the inherited hybrid parallelism in distributed-memory clusters of multi-core machines. The performance of the toolbox is illustrated through networks derived from sea surface height (SSH) data of a global high-resolution ocean model. Less than 8 min are needed on 90 Intel Xeon E5-4650 processors to reconstruct a climate network including the preprocessing and the correlation of 3 × 105 SSH time series, resulting in a weighted graph with the same number of vertices and about 3.2 × 108 edges. In less than 14 min on 30 processors, the resulted graph's degree centrality, strength, connected components, eigenvector centrality, entropy and clustering coefficient metrics were obtained. These results indicate that a complete cycle to construct and analyze a large-scale climate network is available under 22 min Par@Graph therefore facilitates the application of climate network analysis on high-resolution observations and model results, by enabling fast network reconstruct from the calculation of statistical similarities between climate time series. It also enables network analysis at unprecedented scales on a variety of different sizes of input data sets.

  7. Persistent homology of time-dependent functional networks constructed from coupled time series

    Science.gov (United States)

    Stolz, Bernadette J.; Harrington, Heather A.; Porter, Mason A.

    2017-04-01

    We use topological data analysis to study "functional networks" that we construct from time-series data from both experimental and synthetic sources. We use persistent homology with a weight rank clique filtration to gain insights into these functional networks, and we use persistence landscapes to interpret our results. Our first example uses time-series output from networks of coupled Kuramoto oscillators. Our second example consists of biological data in the form of functional magnetic resonance imaging data that were acquired from human subjects during a simple motor-learning task in which subjects were monitored for three days during a five-day period. With these examples, we demonstrate that (1) using persistent homology to study functional networks provides fascinating insights into their properties and (2) the position of the features in a filtration can sometimes play a more vital role than persistence in the interpretation of topological features, even though conventionally the latter is used to distinguish between signal and noise. We find that persistent homology can detect differences in synchronization patterns in our data sets over time, giving insight both on changes in community structure in the networks and on increased synchronization between brain regions that form loops in a functional network during motor learning. For the motor-learning data, persistence landscapes also reveal that on average the majority of changes in the network loops take place on the second of the three days of the learning process.

  8. Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP

    Directory of Open Access Journals (Sweden)

    Kihara Daisuke

    2010-05-01

    Full Text Available Abstract Background A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. Results Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria. The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. Conclusion The analyses demonstrate that applying high confidence predictions from PFP

  9. Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

    Science.gov (United States)

    Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

    2015-03-01

    The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

  10. Methanol steam reforming in microreactor with constructal tree-shaped network

    Science.gov (United States)

    Chen, Yongping; Zhang, Chengbin; Wu, Rui; Shi, Mingheng

    2011-08-01

    The construcal tree-shaped network is introduced into the design of a methanol steam microreactor in the context of optimization of the flow configuration. A three-dimensional model for methanol steam reaction in this designed microreactor is developed and numerically analyzed. The methanol conversion, CO concentration in the product and the total pressure drop of the gases in the microreactor with constructal tree-shaped network are evaluated and compared with those in the serpentine reactor. It is found that the reaction of methanol steam reforming is enhanced in the constructal tree-shaped microreactor, since the tree-shaped reactor configuration, which acts an optimizer for the reactant distribution, provides a reaction space with larger surface-to-volume ratio and the reduction of reactant velocities in the branches. Compared with the serpentine microreactor, the constructal reactor possesses a higher methanol conversion rate accompanied with a higher CO concentration. The conversion rate of the constructal microreactor is more than 10% over that of serpentine reactor. More particularly, the reduction of flow distance makes the constructal microreactor still possess almost the same pressure drop as the corresponding serpentine reactor, despite that the bifurcations induce extra local pressure loss, and the reduction of channel size in branches also causes pressure losses.

  11. EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

    Directory of Open Access Journals (Sweden)

    Chun-Chuan Chen

    Full Text Available Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation. In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

  12. Influence of soil compaction on tunnel network construction by the eastern subterranean termite (Isoptera: Rhinotermitidae).

    Science.gov (United States)

    Tucker, Cynthia L; Koehler, Philip G; Oi, Faith M

    2004-02-01

    Eastern subterranean termites, Reticulitermes flavipes (Kollar), workers were introduced into arenas containing low, moderate, and high compaction builder's sand (1.05 g/cm3, 1.18 g/cm3 or 1.35 g/cm3 bulk densities, respectively), and they immediately began tunneling. Termites built the tunnel network significantly fastest in soil of low compaction compared with moderately or highly compacted soil. In soil of low compaction, 221.67 +/- 4.73 cm of total tunnel distance was constructed in 1 d compared with only 96 cm of tunneling in highly compacted soil. At 14 d, total tunnel distance averaged 216.83 +/- 4.56 cm in soil of low compaction compared with 169.70 +/- 4.10 and 181.18 +/- 6.13 cm in moderately and highly compacted soil, respectively. Decreases in total tunnel distance between 1 and 14 d were caused by backfilling of seldom-used tunnels. Termites did the majority of tunneling during the first day of introduction into arenas. In soil of low and moderate compaction, termites essentially constructed the entire tunnel network within the first day, only modifying it by backfilling or maintaining tunnels. In highly compacted soil, 53% of the final tunnel network was constructed during the first day, 87% was constructed by the third day, and 97% was constructed by the seventh day. Soil compaction did not affect the number of primary tunnels or the number and diameter of secondary tunnels. The angle between the secondary tunnel and primary tunnel also was not significantly affected by soil compaction. However, the number of secondary tunnels in soil of low compaction (5.89 +/- 0.51) was significantly greater than in moderately (2.74 +/- 0.36) and highly (3.58 +/- 0.59) compacted soils.

  13. Efficient Geo-Computational Algorithms for Constructing Space-Time Prisms in Road Networks

    Directory of Open Access Journals (Sweden)

    Hui-Ping Chen

    2016-11-01

    Full Text Available The Space-time prism (STP is a key concept in time geography for analyzing human activity-travel behavior under various Space-time constraints. Most existing time-geographic studies use a straightforward algorithm to construct STPs in road networks by using two one-to-all shortest path searches. However, this straightforward algorithm can introduce considerable computational overhead, given the fact that accessible links in a STP are generally a small portion of the whole network. To address this issue, an efficient geo-computational algorithm, called NTP-A*, is proposed. The proposed NTP-A* algorithm employs the A* and branch-and-bound techniques to discard inaccessible links during two shortest path searches, and thereby improves the STP construction performance. Comprehensive computational experiments are carried out to demonstrate the computational advantage of the proposed algorithm. Several implementation techniques, including the label-correcting technique and the hybrid link-node labeling technique, are discussed and analyzed. Experimental results show that the proposed NTP-A* algorithm can significantly improve STP construction performance in large-scale road networks by a factor of 100, compared with existing algorithms.

  14. An instruction language for self-construction in the context of neural networks

    Directory of Open Access Journals (Sweden)

    Frederic eZubler

    2011-12-01

    Full Text Available Biological systems are based on an entirely different concept of construction than human artifacts. They construct themselves by a process of self-organization that is a systematic spatio-temporal generation of, and interaction between, various specialized cell types. We propose a framework for designing gene-like codes for guiding the self-construction of neural networks. The description of neural development is formalized by defining a set of primitive actions taken locally by neural precursors during corticogenesis. These primitives can be combined into networks of instructions similar to biochemical pathways, capable of reproducing complex developmental sequences in a biologically plausible way. Moreover, the conditional activation and deactivation of these instruction networks can also be controlled by these primitives, allowing for the design of a `genetic code' containing both coding and regulating elements. We demonstrate in a simulation of physical cell development how this code can be incorporated into a single progenitor, which then by replication and differentiation, reproduces important aspects of corticogenesis.

  15. Extended evolution: A conceptual framework for integrating regulatory networks and niche construction.

    Science.gov (United States)

    Laubichler, Manfred D; Renn, Jürgen

    2015-11-01

    This paper introduces a conceptual framework for the evolution of complex systems based on the integration of regulatory network and niche construction theories. It is designed to apply equally to cases of biological, social and cultural evolution. Within the conceptual framework we focus especially on the transformation of complex networks through the linked processes of externalization and internalization of causal factors between regulatory networks and their corresponding niches and argue that these are an important part of evolutionary explanations. This conceptual framework extends previous evolutionary models and focuses on several challenges, such as the path-dependent nature of evolutionary change, the dynamics of evolutionary innovation and the expansion of inheritance systems. © 2015 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution published by Wiley Periodicals, Inc.

  16. Early-life exposure to caffeine affects the construction and activity of cortical networks in mice.

    Science.gov (United States)

    Fazeli, Walid; Zappettini, Stefania; Marguet, Stephan Lawrence; Grendel, Jasper; Esclapez, Monique; Bernard, Christophe; Isbrandt, Dirk

    2017-09-01

    The consumption of psychoactive drugs during pregnancy can have deleterious effects on newborns. It remains unclear whether early-life exposure to caffeine, the most widely consumed psychoactive substance, alters brain development. We hypothesized that maternal caffeine ingestion during pregnancy and the early postnatal period in mice affects the construction and activity of cortical networks in offspring. To test this hypothesis, we focused on primary visual cortex (V1) as a model neocortical region. In a study design mimicking the daily consumption of approximately three cups of coffee during pregnancy in humans, caffeine was added to the drinking water of female mice and their offspring were compared to control offspring. Caffeine altered the construction of GABAergic neuronal networks in V1, as reflected by a reduced number of somatostatin-containing GABA neurons at postnatal days 6-7, with the remaining ones showing poorly developed dendritic arbors. These findings were accompanied by increased synaptic activity in vitro and elevated network activity in vivo in V1. Similarly, in vivo hippocampal network activity was altered from the neonatal period until adulthood. Finally, caffeine-exposed offspring showed increased seizure susceptibility in a hyperthermia-induced seizure model. In summary, our results indicate detrimental effects of developmental caffeine exposure on mouse brain development. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Multi-input distributed classifiers for synthetic genetic circuits.

    Directory of Open Access Journals (Sweden)

    Oleg Kanakov

    Full Text Available For practical construction of complex synthetic genetic networks able to perform elaborate functions it is important to have a pool of relatively simple modules with different functionality which can be compounded together. To complement engineering of very different existing synthetic genetic devices such as switches, oscillators or logical gates, we propose and develop here a design of synthetic multi-input classifier based on a recently introduced distributed classifier concept. A heterogeneous population of cells acts as a single classifier, whose output is obtained by summarizing the outputs of individual cells. The learning ability is achieved by pruning the population, instead of tuning parameters of an individual cell. The present paper is focused on evaluating two possible schemes of multi-input gene classifier circuits. We demonstrate their suitability for implementing a multi-input distributed classifier capable of separating data which are inseparable for single-input classifiers, and characterize performance of the classifiers by analytical and numerical results. The simpler scheme implements a linear classifier in a single cell and is targeted at separable classification problems with simple class borders. A hard learning strategy is used to train a distributed classifier by removing from the population any cell answering incorrectly to at least one training example. The other scheme implements a circuit with a bell-shaped response in a single cell to allow potentially arbitrary shape of the classification border in the input space of a distributed classifier. Inseparable classification problems are addressed using soft learning strategy, characterized by probabilistic decision to keep or discard a cell at each training iteration. We expect that our classifier design contributes to the development of robust and predictable synthetic biosensors, which have the potential to affect applications in a lot of fields, including that of

  18. Forecasting Construction Cost Index based on visibility graph: A network approach

    Science.gov (United States)

    Zhang, Rong; Ashuri, Baabak; Shyr, Yu; Deng, Yong

    2018-03-01

    Engineering News-Record (ENR), a professional magazine in the field of global construction engineering, publishes Construction Cost Index (CCI) every month. Cost estimators and contractors assess projects, arrange budgets and prepare bids by forecasting CCI. However, fluctuations and uncertainties of CCI cause irrational estimations now and then. This paper aims at achieving more accurate predictions of CCI based on a network approach in which time series is firstly converted into a visibility graph and future values are forecasted relied on link prediction. According to the experimental results, the proposed method shows satisfactory performance since the error measures are acceptable. Compared with other methods, the proposed method is easier to implement and is able to forecast CCI with less errors. It is convinced that the proposed method is efficient to provide considerably accurate CCI predictions, which will make contributions to the construction engineering by assisting individuals and organizations in reducing costs and making project schedules.

  19. Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences.

    Science.gov (United States)

    Zhong, Suyu; He, Yong; Gong, Gaolang

    2015-05-01

    Using diffusion MRI, a number of studies have investigated the properties of whole-brain white matter (WM) networks with differing network construction methods (node/edge definition). However, how the construction methods affect individual differences of WM networks and, particularly, if distinct methods can provide convergent or divergent patterns of individual differences remain largely unknown. Here, we applied 10 frequently used methods to construct whole-brain WM networks in a healthy young adult population (57 subjects), which involves two node definitions (low-resolution and high-resolution) and five edge definitions (binary, FA weighted, fiber-density weighted, length-corrected fiber-density weighted, and connectivity-probability weighted). For these WM networks, individual differences were systematically analyzed in three network aspects: (1) a spatial pattern of WM connections, (2) a spatial pattern of nodal efficiency, and (3) network global and local efficiencies. Intriguingly, we found that some of the network construction methods converged in terms of individual difference patterns, but diverged with other methods. Furthermore, the convergence/divergence between methods differed among network properties that were adopted to assess individual differences. Particularly, high-resolution WM networks with differing edge definitions showed convergent individual differences in the spatial pattern of both WM connections and nodal efficiency. For the network global and local efficiencies, low-resolution and high-resolution WM networks for most edge definitions consistently exhibited a highly convergent pattern in individual differences. Finally, the test-retest analysis revealed a decent temporal reproducibility for the patterns of between-method convergence/divergence. Together, the results of the present study demonstrated a measure-dependent effect of network construction methods on the individual difference of WM network properties. © 2015 Wiley

  20. New PBO GPS Station Construction: Eastern Region Network Enhancements and Multiple-Monument Stability Comparisons

    Science.gov (United States)

    Dittmann, S. T.; Austin, K. E.; Berglund, H. T.; Blume, F.; Feaux, K.; Mann, D.; Mattioli, G. S.; Walls, C. P.

    2013-12-01

    The Plate Boundary Observatory (PBO) network consists of 1100 continuously operating, permanent GPS stations throughout the United States. The majority of this network was constructed using NSF-MREFC funding as part of the EarthScope Project during FY2003-FY2008. Since FY2009, UNAVCO has operated and maintained PBO through a Cooperative Agreement (CA) with NSF. Construction of new, permanent GPS monuments in the PBO network was the result of two change orders to the original PBO O&M CA. Change Order 33 (CO33) allocated funds to construct additional GPS stations at six locations in the Eastern Region of PBO. Three of these locations were designed to replace poorly performing existing GPS monuments in Georgia, Texas and New York. The remaining three new locations were selected to fill in gaps in network coverage in Pennsylvania, Wisconsin and North Dakota. Construction of all six new sites was completed in September 2013. Important scientific goals for CO33 include improvement of the stable North American reference frame, measurement of the vertical signal associated with the Glacial Isostatic Adjustment, and improved constraints on surface deformation and possible earthquakes, which occur in the low-strain tectonic setting of the eastern North American Plate. Change Order 35 (CO35) allocated funds to construct two additional geodetic monuments at five existing PBO stations in order to test and compare the long-term stability of various monument designs under near-identical geologic conditions. Sites were chosen to yield a variety of geographic, hydrologic and geologic conditions, including both fine-grained alluvium and crystalline bedrock. At each location, three different monuments (deep drill braced, short drill braced/driven-braced, mast/pillar) were built with 10 meter spacing, with shared power systems and data telemetry infrastructure. Construction of these multi-monument test locations began in October 2012 and finished in September 2013. See G010- Berglund

  1. Construction and analysis of an integrated regulatory network derived from high-throughput sequencing data.

    Directory of Open Access Journals (Sweden)

    Chao Cheng

    2011-11-01

    Full Text Available We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3'UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications.

  2. Construction and evaluation of yeast expression networks by database-guided predictions

    Directory of Open Access Journals (Sweden)

    Katharina Papsdorf

    2016-05-01

    Full Text Available DNA-Microarrays are powerful tools to obtain expression data on the genome-wide scale. We performed microarray experiments to elucidate the transcriptional networks, which are up- or down-regulated in response to the expression of toxic polyglutamine proteins in yeast. Such experiments initially generate hit lists containing differentially expressed genes. To look into transcriptional responses, we constructed networks from these genes. We therefore developed an algorithm, which is capable of dealing with very small numbers of microarrays by clustering the hits based on co-regulatory relationships obtained from the SPELL database. Here, we evaluate this algorithm according to several criteria and further develop its statistical capabilities. Initially, we define how the number of SPELL-derived co-regulated genes and the number of input hits influences the quality of the networks. We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. We find that this approach is useful for our own experimental data sets and also for many other data sets which we tested from the SPELL microarray database. Furthermore, the clusters obtained by the described algorithm greatly improve the assignment to biological processes and transcription factors for the individual clusters. Thus, the described clustering approach, which will be available through the ClusterEx web interface, and the evaluation parameters derived from it represent valuable tools for the fast and informative analysis of yeast microarray data.

  3. Construction

    Science.gov (United States)

    2002-01-01

    San Juan, PR Tren Urbano Subway Project, San Juan, PR U.S. Army South, San Juan, PR U.S. Coast Guard Housing Project, San Juan, PR U.S. Coast Guard...construction?, Foresight/CRISP Workshop on Nanotechnology, Royal Society of Arts . Cheltenham, England: 2001, p.5. 56 Concrete Proposals, Economist, July 24

  4. The construction of a public key infrastructure for healthcare information networks in Japan.

    Science.gov (United States)

    Sakamoto, N

    2001-01-01

    The digital signature is a key technology in the forthcoming Internet society for electronic healthcare as well as for electronic commerce. Efficient exchanges of authorized information with a digital signature in healthcare information networks require a construction of a public key infrastructure (PKI). In order to introduce a PKI to healthcare information networks in Japan, we proposed a development of a user authentication system based on a PKI for user management, user authentication and privilege management of healthcare information systems. In this paper, we describe the design of the user authentication system and its implementation. The user authentication system provides a certification authority service and a privilege management service while it is comprised of a user authentication client and user authentication serves. It is designed on a basis of an X.509 PKI and is implemented with using OpenSSL and OpenLDAP. It was incorporated into the financial information management system for the national university hospitals and has been successfully working for about one year. The hospitals plan to use it as a user authentication method for their whole healthcare information systems. One implementation of the system is free to the national university hospitals with permission of the Japanese Ministry of Education, Culture, Sports, Science and Technology. Another implementation is open to the other healthcare institutes by support of the Medical Information System Development Center (MEDIS-DC). We are moving forward to a nation-wide construction of a PKI for healthcare information networks based on it.

  5. Constructing a Bayesian network model for improving safety behavior of employees at workplaces.

    Science.gov (United States)

    Mohammadfam, Iraj; Ghasemi, Fakhradin; Kalatpour, Omid; Moghimbeigi, Abbas

    2017-01-01

    Unsafe behavior increases the risk of accident at workplaces and needs to be managed properly. The aim of the present study was to provide a model for managing and improving safety behavior of employees using the Bayesian networks approach. The study was conducted in several power plant construction projects in Iran. The data were collected using a questionnaire composed of nine factors, including management commitment, supporting environment, safety management system, employees' participation, safety knowledge, safety attitude, motivation, resource allocation, and work pressure. In order for measuring the score of each factor assigned by a responder, a measurement model was constructed for each of them. The Bayesian network was constructed using experts' opinions and Dempster-Shafer theory. Using belief updating, the best intervention strategies for improving safety behavior also were selected. The result of the present study demonstrated that the majority of employees do not tend to consider safety rules, regulation, procedures and norms in their behavior at the workplace. Safety attitude, safety knowledge, and supporting environment were the best predictor of safety behavior. Moreover, it was determined that instantaneous improvement of supporting environment and employee participation is the best strategy to reach a high proportion of safety behavior at the workplace. The lack of a comprehensive model that can be used for explaining safety behavior was one of the most problematic issues of the study. Furthermore, it can be concluded that belief updating is a unique feature of Bayesian networks that is very useful in comparing various intervention strategies and selecting the best one form them. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. A Hierarchical Approach to Persistent Scatterer Network Construction and Deformation Time Series Estimation

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    2014-12-01

    Full Text Available This paper presents a hierarchical approach to network construction and time series estimation in persistent scatterer interferometry (PSI for deformation analysis using the time series of high-resolution satellite SAR images. To balance between computational efficiency and solution accuracy, a dividing and conquering algorithm (i.e., two levels of PS networking and solution is proposed for extracting deformation rates of a study area. The algorithm has been tested using 40 high-resolution TerraSAR-X images collected between 2009 and 2010 over Tianjin in China for subsidence analysis, and validated by using the ground-based leveling measurements. The experimental results indicate that the hierarchical approach can remarkably reduce computing time and memory requirements, and the subsidence measurements derived from the hierarchical solution are in good agreement with the leveling data.

  7. Integrated Approach to Construction of Benchmarking Network in DEA-Based Stepwise Benchmark Target Selection

    Directory of Open Access Journals (Sweden)

    Jaehun Park

    2016-06-01

    Full Text Available Stepwise benchmark target selection in data envelopment analysis (DEA is a realistic and effective method by which inefficient decision-making units (DMUs can choose benchmarks in a stepwise manner. We propose, for the construction of a benchmarking network (i.e., a network structure consisting of an alternative sequence of benchmark targets, an approach that integrates the cross-efficiency DEA, K-means clustering and context-dependent DEA methods to minimize resource improvement pattern inconsistency in the selection of the intermediate benchmark targets (IBTs of an inefficient DMU. The specific advantages and overall effectiveness of the proposed method were demonstrated by application to a case study of 34 actual container terminal ports and the successful determination of the stepwise benchmarking path of an inefficient DMU.

  8. Use of limited data to construct Bayesian networks for probabilistic risk assessment.

    Energy Technology Data Exchange (ETDEWEB)

    Groth, Katrina M.; Swiler, Laura Painton

    2013-03-01

    Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.

  9. Construction of diabatic energy surfaces for LiFH with artificial neural networks.

    Science.gov (United States)

    Guan, Yafu; Fu, Bina; Zhang, Dong H

    2017-12-14

    A new set of diabatic potential energy surfaces (PESs) for LiFH is constructed with artificial neural networks (NNs). The adiabatic PESs of the ground state and the first excited state are directly fitted with NNs. Meanwhile, the adiabatic-to-diabatic transformation (ADT) angles (mixing angles) are obtained by simultaneously fitting energy difference and interstate coupling gradients. No prior assumptions of the functional form of ADT angles are used before fitting, and the ab initio data including energy difference and interstate coupling gradients are well reproduced. Converged dynamical results show remarkable differences between adiabatic and diabatic PESs, which suggests the significance of non-adiabatic processes.

  10. Autonomous construction agents: An investigative framework for large sensor network self-management

    Energy Technology Data Exchange (ETDEWEB)

    Koch, Joshua Bruce [Iowa State Univ., Ames, IA (United States)

    2008-01-01

    Recent technological advances have made it cost effective to utilize massive, heterogeneous sensor networks. To gain appreciable value from these informational systems, there must be a control scheme that coordinates information flow to produce meaningful results. This paper will focus on tools developed to manage the coordination of autonomous construction agents using stigmergy, in which a set of basic low-level rules are implemented through various environmental cues. Using VE-Suite, an open-source virtual engineering software package, an interactive environment is created to explore various informational configurations for the construction problem. A simple test case is developed within the framework, and construction times are analyzed for possible functional relationships pertaining to performance of a particular set of parameters and a given control process. Initial experiments for the test case show sensor saturation occurs relatively quickly with 5-7 sensors, and construction time is generally independent of sensor range except for small numbers of sensors. Further experiments using this framework are needed to define other aspects of sensor performance. These trends can then be used to help decide what kinds of sensing capabilities are required to simultaneously achieve the most cost-effective solution and provide the required value of information when applied to the development of real world sensor applications.

  11. Hybrid classifiers methods of data, knowledge, and classifier combination

    CERN Document Server

    Wozniak, Michal

    2014-01-01

    This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

  12. Linear programming model to construct phylogenetic network for 16S rRNA sequences of photosynthetic organisms and influenza viruses.

    Science.gov (United States)

    Mathur, Rinku; Adlakha, Neeru

    2014-06-01

    Phylogenetic trees give the information about the vertical relationships of ancestors and descendants but phylogenetic networks are used to visualize the horizontal relationships among the different organisms. In order to predict reticulate events there is a need to construct phylogenetic networks. Here, a Linear Programming (LP) model has been developed for the construction of phylogenetic network. The model is validated by using data sets of chloroplast of 16S rRNA sequences of photosynthetic organisms and Influenza A/H5N1 viruses. Results obtained are in agreement with those obtained by earlier researchers.

  13. Assessment of the capacity of the national ecological network elements for road construction and operation

    Directory of Open Access Journals (Sweden)

    Kicošev Vesna

    2014-01-01

    Full Text Available Road construction and usage have a wide range of direct and indirect negative effects on protected areas. The impact of state roads on protected areas in Vojvodina was reviewed in this article, based on the orientation values of habitat loss and secondary negative effects originating from traffic functioning. Results of the assessment indicate that the use of existing roads constructed on habitats within the national ecological network exceeded the capacity of individual PA-protected areas (e.g., in case of Straža Natural Monument. Recorded capacity overflow on other PAs occurs solely as a consequence of overlapping between protected areas and areas of influence of roads routed along the borders of protected areas (which is the case with Slano Kopovo Special Nature Reserve and Selevenjske pustare Special Nature Reserve. The aim of this article is to show that even with the smallest values of the parameters related to the width of roads and critical distance from the habitat, the vulnerability of certain core areas of the national ecological network is evident.

  14. Using GeneReg to construct time delay gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Qian Ziliang

    2010-05-01

    Full Text Available Abstract Background Understanding gene expression and regulation is essential for understanding biological mechanisms. Because gene expression profiling has been widely used in basic biological research, especially in transcription regulation studies, we have developed GeneReg, an easy-to-use R package, to construct gene regulatory networks from time course gene expression profiling data; More importantly, this package can provide information about time delays between expression change in a regulator and that of its target genes. Findings The R package GeneReg is based on time delay linear regression, which can generate a model of the expression levels of regulators at a given time point against the expression levels of their target genes at a later time point. There are two parameters in the model, time delay and regulation coefficient. Time delay is the time lag during which expression change of the regulator is transmitted to change in target gene expression. Regulation coefficient expresses the regulation effect: a positive regulation coefficient indicates activation and negative indicates repression. GeneReg was implemented on a real Saccharomyces cerevisiae cell cycle dataset; more than thirty percent of the modeled regulations, based entirely on gene expression files, were found to be consistent with previous discoveries from known databases. Conclusions GeneReg is an easy-to-use, simple, fast R package for gene regulatory network construction from short time course gene expression data. It may be applied to study time-related biological processes such as cell cycle, cell differentiation, or causal inference.

  15. Construction of an integrated gene regulatory network link to stress-related immune system in cattle.

    Science.gov (United States)

    Behdani, Elham; Bakhtiarizadeh, Mohammad Reza

    2017-10-01

    The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

  16. Construction and development of IGP DMC of China National Seismological Network

    Science.gov (United States)

    Zheng, X.; Zheng, J.; Lin, P.; Yao, Z.; Liang, J.

    2011-12-01

    In 2003, CEA (China Earthquake Administration) commenced the construction of China Digital Seismological Observation Network. By the end of 2007, a new-generation digital seismological observation system had been established, which consists of 1 National Seismic Network, 32 regional seismic networks, 2 small-aperture seismic arrays, 6 volcano monitoring networks and 19 mobile seismic networks, as well as CENC (China Earthquake Network Center) DMC (Data Management Centre) and IGP (Institute of Geophysics) DMC. Since then, the seismological observation system of China has completely entered a digital time. For operational, data backup and data security considerations, the DMC at the Institute of Geophysics (IGP), CEA was established at the end of 2007. IGP DMC now receives and archives waveform data from more than 1000 permanent seismic stations around China in real-time. After the great Wenchuan and Yushu earthquakes, the real-time waveform data from 56 and 8 portable seismic stations deployed in the aftershock area are added to IGP DMC. The technical system of IGP DMC is designed to conduct data management, processing and service through the network of CEA. We developed and integrated a hardware system with high-performance servers, large-capacity disc arrays, tape library and other facilities, as well as software packages for real-time waveform data receiving, storage, quality control, processing and service. Considering the demands from researchers for large quantities of seismic event waveform data, IGP DMC adopts an innovative "user order" method to extract event waveform data. Users can specify seismic stations, epicenter distance and record length. In a short period of 3 years, IGP DMC has supplied about 350 Terabytes waveform data to over 200 researches of more than 40 academic institutions. According to incomplete statistics, over 40 papers have been published in professional journals, in which 30 papers were indexed by SCI. Now, IGP DMC has become an

  17. Statistical identification of gene association by CID in application of constructing ER regulatory network

    Directory of Open Access Journals (Sweden)

    Lien Huang-Chun

    2009-03-01

    Full Text Available Abstract Background A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID, is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs (X and their downstream genes (Y based on clinical data. More specifically, we use estrogen receptor α (ERα as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A. Results The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC, Student's t-test (STT, coefficient of determination (CoD, and mutual information (MI. When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y against a discrete variable (X, it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. Conclusion CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the

  18. Synergistic effect in conductive networks constructed with carbon nanofillers in different dimensions

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    Q. Fu

    2012-02-01

    Full Text Available Herein, investigation on synergistic effect during network formation for conductive network constructed with carbon nanofillers in different dimensions is conducted. Multi-walled carbon nanotubes (MWNTs and carbon black (CB are employed as conductive fillers in this system. Morphological control of the conductive network is realized by adjusting the ratio between different fillers. Classical percolation threshold theory and adjusted excluded volume theory are used to analyze the electrical percolating behavior of these systems. It is observed that the percolation threshold of hybrid fillers filled conductive polymer composites (CPCs is much lower than that of MWNTs or CB filled CPCs, and it can be reduced from 2.4 to 0.21 wt% by replacing half of the MWNTs with CB. Possible mechanism of this phenomenon is discussed together with morphological observation. A model is proposed to understand the mechanism of the percolation behavior in the composites containing various proportions of nanofillers. Our work is important for the design and preparation of low cost conductive polymer composites with novel electrical property.

  19. Online identity: constructing interpersonal trust and openness through participating in hospitality social networks

    Directory of Open Access Journals (Sweden)

    Alexander Ronzhyn

    2013-06-01

    Full Text Available The present article describes the results of research on online identity construction during the participation in the hospitality social networks. Specifically the user references are analysed to understand patterns that form the image of a member. CouchSurfing service (couchsurfing.org allows users to leave short texts where the experience of hosting/being hosted by a CS member is described, is an evaluation of the CS members of each other’s personal traits, skills and common experience. Therefore references can become a good instrument for portraying a CouchSurfing member and understanding his or her particular traits. References form an important part of a user’s virtual identity in the network. Using a sample of references of Spanish CouchSurfing users, the research established main characteristics of the references, which are the openness, readiness to share ideas and experiences and trustworthiness. These concepts illustrate the typical traits associated with a user of the network and also shed light on the activities common during offl ine CS meetings

  20. Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks.

    Science.gov (United States)

    Meng, X Flora; Baetica, Ania-Ariadna; Singhal, Vipul; Murray, Richard M

    2017-05-01

    Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces. © 2017 The Author(s).

  1. Context-Aware Community Construction in Proximity-Based Mobile Networks

    Directory of Open Access Journals (Sweden)

    Na Yu

    2015-01-01

    Full Text Available Sensor-equipped mobile devices have allowed users to participate in various social networking services. We focus on proximity-based mobile social networking environments where users can share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom as the connection and the space and time related to the shared information as the contexts. To model the potential information influences, we construct an influence graph by integrating the space and time contexts into the proximity-based contacts of mobile users. Further, we propose a two-phase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications.

  2. A computational model for the item-based induction of construction networks.

    Science.gov (United States)

    Gaspers, Judith; Cimiano, Philipp

    2014-04-01

    According to usage-based approaches to language acquisition, linguistic knowledge is represented in the form of constructions--form-meaning pairings--at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item-based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous context. To determine meanings of constructions starting from ambiguous contexts, we rely on the principle of cross-situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model, we show how cross-situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences. Copyright © 2014 Cognitive Science Society, Inc.

  3. In Vitro Construction of Scaffold-Free Bilayered Tissue-Engineered Skin Containing Capillary Networks

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    Yuan Liu

    2013-01-01

    Full Text Available Many types of skin substitutes have been constructed using exogenous materials. Angiogenesis is an important factor for tissue-engineered skin constructs. In this study, we constructed a scaffold-free bilayered tissue-engineered skin containing a capillary network. First, we cocultured dermal fibroblasts with dermal microvascular endothelial cells at a ratio of 2 : 1. A fibrous sheet was formed by the interactions between the fibroblasts and the endothelial cells, and capillary-like structures were observed after 20 days of coculture. Epithelial cells were then seeded on the fibrous sheet to assemble the bilayered tissue. HE staining showed that tissue-engineered skin exhibited a stratified epidermis after 7 days. Immunostaining showed that the epithelium promoted the formation of capillary-like structures. Transmission electron microscopy (TEM analysis showed that the capillary-like structures were typical microblood vessels. ELISA demonstrated that vascularization was promoted by significant upregulation of vascularization associated growth factors due to interactions among the 3 types of cells in the bilayer, as compared to cocultures of fibroblast and endothelial cells and monocultures.

  4. Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

    Science.gov (United States)

    Arabzadeh, Vida; Niaki, S. T. A.; Arabzadeh, Vahid

    2017-10-01

    One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg-Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

  5. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  6. Intelligent sensor positioning and orientation through constructive neural network-embedded INS/GPS integration algorithms.

    Science.gov (United States)

    Chiang, Kai-Wei; Chang, Hsiu-Wen

    2010-01-01

    Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks--the Cascade Correlation Neural Network (CCNNs)--to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN

  7. Development of Hybrid Model for Estimating Construction Waste for Multifamily Residential Buildings Using Artificial Neural Networks and Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Dongoun Lee

    2016-09-01

    Full Text Available Due to the increasing costs of construction waste disposal, an accurate estimation of the amount of construction waste is a key factor in a project’s success. Korea has been burdened by increasing construction waste as a consequence of the growing number of construction projects and a lack of construction waste management (CWM strategies. One of the problems associated with predicting the amount of waste is that there are no suitable estimation strategies currently available. Therefore, we developed a hybrid estimation model to predict the quantity and cost of waste in the early stage of construction. The proposed approach can be used to address cost overruns and improve CWM in the subsequent stages of construction. The proposed hybrid model uses artificial neural networks (ANNs and ant colony optimization (ACO. It is expected to provide an accurate waste estimate by applying historical data from multifamily residential buildings.

  8. A new constructive algorithm for architectural and functional adaptation of artificial neural networks.

    Science.gov (United States)

    Islam, Md Monirul; Sattar, Md Abdus; Amin, Md Faijul; Yao, Xin; Murase, Kazuyuki

    2009-12-01

    The generalization ability of artificial neural networks (ANNs) is greatly dependent on their architectures. Constructive algorithms provide an attractive automatic way of determining a near-optimal ANN architecture for a given problem. Several such algorithms have been proposed in the literature and shown their effectiveness. This paper presents a new constructive algorithm (NCA) in automatically determining ANN architectures. Unlike most previous studies on determining ANN architectures, NCA puts emphasis on architectural adaptation and functional adaptation in its architecture determination process. It uses a constructive approach to determine the number of hidden layers in an ANN and of neurons in each hidden layer. To achieve functional adaptation, NCA trains hidden neurons in the ANN by using different training sets that were created by employing a similar concept used in the boosting algorithm. The purpose of using different training sets is to encourage hidden neurons to learn different parts or aspects of the training data so that the ANN can learn the whole training data in a better way. In this paper, the convergence and computational issues of NCA are analytically studied. The computational complexity of NCA is found to be O(W xP(t) xtau), where W is the number of weights in the ANN, P(t) is the number of training examples, and tau is the number of training epochs. This complexity has the same order as what the backpropagation learning algorithm requires for training a fixed ANN architecture. A set of eight classification and two approximation benchmark problems was used to evaluate the performance of NCA. The experimental results show that NCA can produce ANN architectures with fewer hidden neurons and better generalization ability compared to existing constructive and nonconstructive algorithms.

  9. Concept mapping and network analysis: an analytic approach to measure ties among constructs.

    Science.gov (United States)

    Goldman, Alyssa W; Kane, Mary

    2014-12-01

    Group concept mapping is a mixed-methods approach that helps a group visually represent its ideas on a topic of interest through a series of related maps. The maps and additional graphics are useful for planning, evaluation and theory development. Group concept maps are typically described, interpreted and utilized through points, clusters and distances, and the implications of these features in understanding how constructs relate to one another. This paper focuses on the application of network analysis to group concept mapping to quantify the strength and directionality of relationships among clusters. The authors outline the steps of this analysis, and illustrate its practical use through an organizational strategic planning example. Additional benefits of this analysis to evaluation projects are also discussed, supporting the overall utility of this supplemental technique to the standard concept mapping methodology. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. The parametric modified limited penetrable visibility graph for constructing complex networks from time series

    Science.gov (United States)

    Li, Xiuming; Sun, Mei; Gao, Cuixia; Han, Dun; Wang, Minggang

    2018-02-01

    This paper presents the parametric modified limited penetrable visibility graph (PMLPVG) algorithm for constructing complex networks from time series. We modify the penetrable visibility criterion of limited penetrable visibility graph (LPVG) in order to improve the rationality of the original penetrable visibility and preserve the dynamic characteristics of the time series. The addition of view angle provides a new approach to characterize the dynamic structure of the time series that is invisible in the previous algorithm. The reliability of the PMLPVG algorithm is verified by applying it to three types of artificial data as well as the actual data of natural gas prices in different regions. The empirical results indicate that PMLPVG algorithm can distinguish the different time series from each other. Meanwhile, the analysis results of natural gas prices data using PMLPVG are consistent with the detrended fluctuation analysis (DFA). The results imply that the PMLPVG algorithm may be a reasonable and significant tool for identifying various time series in different fields.

  11. Integrated assembly of 3D graphene networks for construction of all-in-one supercapacitor electrodes

    DEFF Research Database (Denmark)

    Dey, Ramendra Sundar; Chi, Qijin

    intrinsic resistance and high ion conductance is still a challenging issue. I n this work, we have undertaken the challenge and used electrochemically generated copper foams (CuF) as an effective template to directly integrate reduced graphene oxide (rGO) 3D networks . This has led to the construction...... efficient and cost - effective n ovel materials. Because of their ultrahigh specific surface areas and excellent conductivity , t hree - dimensional (3D) graphene materials hold great promises for supercapacitors. However, the assembly of graphene building blocks into the supercapacitor electrodes with low...... of all - in - one supercapacitor ele ctrodes (3DrGO@CuF) [1] . The overall procedure in clude s two step s : self - assembly of graphene oxide (GO) on Cu F and electrochemical reduction of GO into rGO. The resulting electrodes are capable of delivering a specific capacitance as high as 623 F g - 1...

  12. Mastering the political Process of Building Innovation Networks - A Case from the Danish Construction Industry

    DEFF Research Database (Denmark)

    Stissing Jensen, Jens; Koch, Christian; Thomassen, Mikkel

    2008-01-01

    Drawing on network of innovation and organizational politics perspectives this paper analyzes the role of an innovation broker organization in developing and supporting an inter-organizational innovation process in the Danish construction industry. The aim is to implement an ICT-based product...... synchronization. These minimal structures were both conceptual and processual. In the development phase these structures facilitated a processes of experimentation in which the utilization of the configurator were utilized to organize the development of several aspects and activities such as branding, product...... configuration tool to support the production, sale, and installation of balconies. It is suggested that the innovation broker was successful in stabilizing the innovation process by supplying minimal structures which provided a template which facilitated a combination of individual flexibility and overall...

  13. A fuzzy neural network to estimate at completion costs of construction projects

    Directory of Open Access Journals (Sweden)

    Morteza Bagherpour

    2012-04-01

    Full Text Available In construction cost management system, normally earned value management (EVM is applied as an efficient control approach in both status detection and estimation at completion (EAC cost forecasting. The traditional approaches in EAC predictions normally extend the current situation of a project to the future by employing pervious performance factor. The proposed approach of this paper considers both qualitative and quantitative factors affecting the EAC prediction. The proposed approach of this research not only estimates the completion of the project, but also it can generate accurate forecast for the entire future periods using a fuzzy neural network model. The model is also implemented for a real-world case study and yields encouraging preliminary results.

  14. Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks

    DEFF Research Database (Denmark)

    Tan, Chris Soon Heng; Linding, Rune

    2009-01-01

    The explosion of site- and context-specific in vivo phosphorylation events presents a potentially rich source of biological knowledge and calls for novel data analysis and modeling paradigms. Perhaps the most immediate challenge is delineating detected phosphorylation sites to their effector...... kinases. This is important for (re)constructing transient kinase-substrate interaction networks that are essential for mechanistic understanding of cellular behaviors and therapeutic intervention, but has largely eluded high-throughput protein-interaction studies due to their transient nature and strong...... dependencies on cellular context. Here, we surveyed some of the computational approaches developed to dissect phosphorylation data detected in systematic proteomic experiments and reviewed some experimental and computational approaches used to map phosphorylation sites to their effector kinases in efforts...

  15. Construction of an interatomic potential for zinc oxide surfaces by high-dimensional neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Artrith, Nongnuch; Morawietz, Tobias; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany)

    2011-07-01

    Zinc oxide (ZnO) is a technologically important material with many applications, e.g. in heterogeneous catalysis. For theoretical studies of the structural properties of ZnO surfaces, defects, and crystal structures it is necessary to simulate large systems over long time-scales with sufficient accuracy. Often, the required system size is not accessible by computationally rather demanding density-functional theory (DFT) calculations. Recently, artificial Neural Networks (NN) trained to first principles data have shown to provide accurate potential-energy surfaces (PESs) for condensed systems. We present the construction and analysis of a NN PES for ZnO. The structural and energetic properties of bulk ZnO and ZnO surfaces are investigated using this potential and compared to DFT calculations.

  16. Feasibility of Construction of the Continuously Operating Geodetic GPS Network of Sinaloa, Mexico

    Science.gov (United States)

    Vazquez, G. E.; Jacobo, C.

    2011-12-01

    This research is based on the study and analysis of feasibility for the construction of the geodetic network for GPS continuous operation for Sinaloa, hereafter called (RGOCSIN). A GPS network of continuous operation is defined as that materialized structure physically through permanent monuments where measurements to the systems of Global Positioning (GPS) is performed continuously throughout a region. The GPS measurements in this network are measurements of accuracy according to international standards to define its coordinates, thus constituting the basic structure of geodetic referencing for a country. In this context is that in the near future the RGOCSIN constitutes a system state only accurate and reliable georeferencing in real-time (continuous and permanent operation) and will be used for different purposes; i.e., in addition to being fundamental basis for any lifting topographic or geodetic survey, and other areas such as: (1) Different construction processes (control and monitoring of engineering works); (2) Studies of deformation of the Earth's crust (before and after a seismic event); (3) GPS meteorology (weather forecasting); (4) Demarcation projects (natural and political); (5) Establishment of bases to generate mapping (necessary for the economic and social development of the state); (6) Precision agriculture (optimization of economic resources to the various crops); (7) Geographic information systems (Organization and planning activities associated with the design and construction of public services); (8) Urban growth (possible settlements in the appropriate form and taking care of the environmental aspect), among others. However there are criteria and regulations according to the INEGI (Instituto Nacional de Estadística y Geografía, http://www.inegi.org.mx/) that must be met; even for this stage of feasibility of construction that sees this project as a first phase. The fundamental criterion to be taken into account according to INEGI is a

  17. A neural network construction method for surrogate modeling of physics-based analysis

    Science.gov (United States)

    Sung, Woong Je

    In this thesis existing methodologies related to the developmental methods of neural networks have been surveyed and their approaches to network sizing and structuring are carefully observed. This literature review covers the constructive methods, the pruning methods, and the evolutionary methods and questions about the basic assumption intrinsic to the conventional neural network learning paradigm, which is primarily devoted to optimization of connection weights (or synaptic strengths) for the pre-determined connection structure of the network. The main research hypothesis governing this thesis is that, without breaking a prevailing dichotomy between weights and connectivity of the network during learning phase, the efficient design of a task-specific neural network is hard to achieve because, as long as connectivity and weights are searched by separate means, a structural optimization of the neural network requires either repetitive re-training procedures or computationally expensive topological meta-search cycles. The main contribution of this thesis is designing and testing a novel learning mechanism which efficiently learns not only weight parameters but also connection structure from a given training data set, and positioning this learning mechanism within the surrogate modeling practice. In this work, a simple and straightforward extension to the conventional error Back-Propagation (BP) algorithm has been formulated to enable a simultaneous learning for both connectivity and weights of the Generalized Multilayer Perceptron (GMLP) in supervised learning tasks. A particular objective is to achieve a task-specific network having reasonable generalization performance with a minimal training time. The dichotomy between architectural design and weight optimization is reconciled by a mechanism establishing a new connection for a neuron pair which has potentially higher error-gradient than one of the existing connections. Interpreting an instance of the absence of

  18. Ecological Network Construction Based on Minimum Cumulative Resistance for the City of Nanjing, China

    Directory of Open Access Journals (Sweden)

    Jihong Dong

    2015-10-01

    Full Text Available With economic growth and the improvement of the urbanization level, human activities have constantly interfered with landscape patterns, resulting in serious threats to regional ecological security. Therefore, it is of great significance to study the evolution and optimization of the landscape patterns. Based on three TM images from 1990, 2000, and 2010, and selected landscape pattern indexes, the changes in the landscape pattern of Nanjing in the past twenty years were studied based on landscape ecology theory using Remote Sensing (RS and a Geographical Information System (GIS. The ecological network was built on the basis of extracted ecological nodes and the minimum cumulative resistance. The results show that changes in the landscape pattern of the city of Nanjing were notable. Class-level indexes indicate that the farmland landscape area decreased and the degree of patch fragmentation increased. The construction land area increased, and it tended to show dispersed distribution. The proportion of forest land increased and the shape of patches became more complex. The proportion of water firstly showed a decrease, followed by an increase, and the shape of the water became more regular. Landscape-level indexes indicate that biological diversity and the degree of fragmentation increased. Spatial heterogeneity of the natural landscape increased, and the patch shape of each landscape type developed similarly. The results also call for stepping-stones to enhance the connectivity and optimization of the ecological network, which will help improve ecological services and improve the landscape pattern of the city.

  19. An investigation into the authorship of scientific data webs of a network under construction

    Directory of Open Access Journals (Sweden)

    Jackson da Silva Medeiros

    2016-05-01

    Full Text Available This work aims to analyze the scientific authorship from the data sharing. For this purpose, it uses the Actor-Network Theory (ANT, which seeks, from an idea of symmetry between human and non-human, do not assume a division between those entities, allowing to view the establishment and dissolution of a network from the relationships that are created at different levels. The data collected, which were the basis for description and analysis from the literature that also emerged from what ware collected by observation, interviews and bibliographic materials. The study was carried out by the Repositório de Dados de Estudos Ecológicos do Programa de Pesquisa em Biodiversidade (PPBio. Concludes that conceptual and pragmatic objects can not be seen in isolate way, but like actors directly affected by the technology, should be considered important actants in the process of production, sharing and use of digital research data. The construction of scientific and social fact authorship is coordinated by a number of elements, such as data collection, curation, scientific data and its metadata, funding sources, data policy, repository and its management software for data and metadata, licensing, ownership and responsibility, acting like a kind of exploitation of a phenomena arrangement.

  20. Using a Backpropagation Artificial Neural Network to Predict Nutrient Removal in Tidal Flow Constructed Wetlands

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    Wei Li

    2018-01-01

    Full Text Available Nutrient removal in tidal flow constructed wetlands (TF-CW is a complex series of nonlinear multi-parameter interactions. We simulated three tidal flow systems and a continuous vertical flow system filled with synthetic wastewater and compared the influent and effluent concentrations to examine (1 nutrient removal in artificial TF-CWs, and (2 the ability of a backpropagation (BP artificial neural network to predict nutrient removal. The nutrient removal rates were higher under tidal flow when the idle/reaction time was two, and reached 90 ± 3%, 99 ± 1%, and 58 ± 13% for total nitrogen (TN, ammonium nitrogen (NH4+-N, and total phosphorus (TP, respectively. The main influences on nutrient removal for each scenario were identified by redundancy analysis and were input into the model to train and verify the pollutant effluent concentrations. Comparison of the actual and model-predicted effluent concentrations showed that the model predictions were good. The predicted and actual values were correlated and the margin of error was small. The BP neural network fitted best to TP, with an R2 of 0.90. The R2 values of TN, NH4+-N, and nitrate nitrogen (NO3−-N were 0.67, 0.73, and 0.69, respectively.

  1. Construction of an miRNA-Regulated Pathway Network Reveals Candidate Biomarkers for Postmenopausal Osteoporosis

    Directory of Open Access Journals (Sweden)

    Min Shao

    2017-01-01

    Full Text Available We aimed to identify risk pathways for postmenopausal osteoporosis (PMOP via establishing an microRNAs- (miRNA- regulated pathway network (MRPN. Firstly, we identified differential pathways through calculating gene- and pathway-level statistics based on the accumulated normal samples using the individual pathway aberrance score (iPAS. Significant pathways based on differentially expressed genes (DEGs using DAVID were extracted, followed by identifying the common pathways between iPAS and DAVID methods. Next, miRNAs prediction was implemented via calculating TargetScore values with precomputed input (log fold change (FC, TargetScan context score (TSCS, and probabilities of conserved targeting (PCT. An MRPN construction was constructed using the common genes in the common pathways and the predicted miRNAs. Using false discovery rate (FDR < 0.05, 279 differential pathways were identified. Using the criteria of FDR < 0.05 and log⁡FC≥2, 39 DEGs were retrieved, and these DEGs were enriched in 64 significant pathways identified by DAVID. Overall, 27 pathways were the common ones between two methods. Importantly, MAPK signaling pathway and PI3K-Akt signaling pathway were the first and second significantly enriched ones, respectively. These 27 common pathways separated PMOP from controls with the accuracy of 0.912. MAPK signaling pathway and PI3K/Akt signaling pathway might play crucial roles in PMOP.

  2. Construction of high-dimensional neural network potentials using environment-dependent atom pairs.

    Science.gov (United States)

    Jose, K V Jovan; Artrith, Nongnuch; Behler, Jörg

    2012-05-21

    An accurate determination of the potential energy is the crucial step in computer simulations of chemical processes, but using electronic structure methods on-the-fly in molecular dynamics (MD) is computationally too demanding for many systems. Constructing more efficient interatomic potentials becomes intricate with increasing dimensionality of the potential-energy surface (PES), and for numerous systems the accuracy that can be achieved is still not satisfying and far from the reliability of first-principles calculations. Feed-forward neural networks (NNs) have a very flexible functional form, and in recent years they have been shown to be an accurate tool to construct efficient PESs. High-dimensional NN potentials based on environment-dependent atomic energy contributions have been presented for a number of materials. Still, these potentials may be improved by a more detailed structural description, e.g., in form of atom pairs, which directly reflect the atomic interactions and take the chemical environment into account. We present an implementation of an NN method based on atom pairs, and its accuracy and performance are compared to the atom-based NN approach using two very different systems, the methanol molecule and metallic copper. We find that both types of NN potentials provide an excellent description of both PESs, with the pair-based method yielding a slightly higher accuracy making it a competitive alternative for addressing complex systems in MD simulations.

  3. Construction of high-dimensional neural network potentials using environment-dependent atom pairs

    Science.gov (United States)

    Jose, K. V. Jovan; Artrith, Nongnuch; Behler, Jörg

    2012-05-01

    An accurate determination of the potential energy is the crucial step in computer simulations of chemical processes, but using electronic structure methods on-the-fly in molecular dynamics (MD) is computationally too demanding for many systems. Constructing more efficient interatomic potentials becomes intricate with increasing dimensionality of the potential-energy surface (PES), and for numerous systems the accuracy that can be achieved is still not satisfying and far from the reliability of first-principles calculations. Feed-forward neural networks (NNs) have a very flexible functional form, and in recent years they have been shown to be an accurate tool to construct efficient PESs. High-dimensional NN potentials based on environment-dependent atomic energy contributions have been presented for a number of materials. Still, these potentials may be improved by a more detailed structural description, e.g., in form of atom pairs, which directly reflect the atomic interactions and take the chemical environment into account. We present an implementation of an NN method based on atom pairs, and its accuracy and performance are compared to the atom-based NN approach using two very different systems, the methanol molecule and metallic copper. We find that both types of NN potentials provide an excellent description of both PESs, with the pair-based method yielding a slightly higher accuracy making it a competitive alternative for addressing complex systems in MD simulations.

  4. Intelligent Sensor Positioning and Orientation Through Constructive Neural Network-Embedded INS/GPS Integration Algorithms

    Directory of Open Access Journals (Sweden)

    Kai-Wei Chiang

    2010-10-01

    Full Text Available Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS as the major positioning sensor and an Inertial Navigation System (INS as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs, one of the most famous Artificial Neural Networks (ANNs, and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks––the Cascade Correlation Neural Network (CCNNs––to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms

  5. Bayesian state space models for dynamic genetic network construction across multiple tissues.

    Science.gov (United States)

    Liang, Yulan; Kelemen, Arpad

    2016-08-01

    Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

  6. Minimum Constructive Back Propagation Neural Network Based on Fuzzy Logic for Pattern Recognition of Electronic Nose System

    Directory of Open Access Journals (Sweden)

    Radi Radi

    2011-08-01

    Full Text Available Constructive Back Propagation Neural Network (CBPNN is a kind of back propagation neural network trained with constructive algorithm. Training of CBPNN is mainly conducted by developing the network’s architecture which commonly done by adding a number of new neuron units on learning process. Training of the network usually implements fixed method to develop its structure gradually by adding new units constantly. Although this method is simple and able to create an adaptive network for data pattern complexity, but it is wasteful and inefficient for computing. New unit addition affects directly to the computational load of training, speed of convergence, and structure of the final neural network. While increases training load significantly, excessive addition of units also tends to generate a large size of final network. Moreover, addition pattern with small unit number tends to drop off the adaptability of the network and extends time of training. Therefore, there is important to design an adaptive structure development pattern for CBPNN in order to minimize computing load of training. This study proposes Fuzzy Logic (FL algorithm to manage and develop structure of CBPNN. FL method was implemented on two models of CBPNN, i.e. designed with one and two hidden layers, used to recognize aroma patterns on an electronic nose system. The results showed that this method is effective to be applied due to its capability to minimize time of training, to reduce load of computational learning, and generate small size of network.

  7. A gene expression signature classifying telomerase and ALT immortalization reveals an hTERT regulatory network and suggests a mesenchymal stem cell origin for ALT

    DEFF Research Database (Denmark)

    Lafferty-Whyte, K; Cairney, C J; Will, M B

    2009-01-01

    Telomere length is maintained by two known mechanisms, the activation of telomerase or alternative lengthening of telomeres (ALT). The molecular mechanisms regulating the ALT phenotype are poorly understood and it is unknown how the decision of which pathway to activate is made at the cellular...... level. We have shown earlier that active repression of telomerase gene expression by chromatin remodelling of the promoters is one mechanism of regulation; however, other genes and signalling networks are likely to be required to regulate telomerase and maintain the ALT phenotype. Using gene expression...... this signature revealed a regulatory signalling network consistent with a model of human telomerase reverse transcriptase (hTERT) repression in ALT cell lines and liposarcomas. This network expands on our existing knowledge of hTERT regulation and provides a platform to understand differential regulation of h...

  8. Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Hindol Rakshit

    Full Text Available BACKGROUND: Parkinson's Disease (PD is one of the most prevailing neurodegenerative diseases. Improving diagnoses and treatments of this disease is essential, as currently there exists no cure for this disease. Microarray and proteomics data have revealed abnormal expression of several genes and proteins responsible for PD. Nevertheless, few studies have been reported involving PD-specific protein-protein interactions. RESULTS: Microarray based gene expression data and protein-protein interaction (PPI databases were combined to construct the PPI networks of differentially expressed (DE genes in post mortem brain tissue samples of patients with Parkinson's disease. Samples were collected from the substantia nigra and the frontal cerebral cortex. From the microarray data, two sets of DE genes were selected by 2-tailed t-tests and Significance Analysis of Microarrays (SAM, run separately to construct two Query-Query PPI (QQPPI networks. Several topological properties of these networks were studied. Nodes with High Connectivity (hubs and High Betweenness Low Connectivity (bottlenecks were identified to be the most significant nodes of the networks. Three and four-cliques were identified in the QQPPI networks. These cliques contain most of the topologically significant nodes of the networks which form core functional modules consisting of tightly knitted sub-networks. Hitherto unreported 37 PD disease markers were identified based on their topological significance in the networks. Of these 37 markers, eight were significantly involved in the core functional modules and showed significant change in co-expression levels. Four (ARRB2, STX1A, TFRC and MARCKS out of the 37 markers were found to be associated with several neurotransmitters including dopamine. CONCLUSION: This study represents a novel investigation of the PPI networks for PD, a complex disease. 37 proteins identified in our study can be considered as PD network biomarkers. These network

  9. The construction of digital terain model by using neural networks with optimised radial basis functions

    Science.gov (United States)

    Stateczny, A.; Lubczonek, J.

    2003-04-01

    The basic problem in the construction of a numerical spatial sea chart is such transformation of the sounding data that it should be possible to determine the depth at any point of the bottom area. In recent years, much attention has been devoted to the problem of modelling the seabed shape in a numerical three-dimensional sea chart. Various methods for modelling the seabed shape are applied. These methods can be divided into analytical and neural. In the case of applying the model for navigational tasks, the selection of a suitable method should ensure high accuracy of surface projection. The model should be conformed to the surface shape, spatial distribution of the measurement points and their number. The application of universal methods like 'multiquadric' or 'kriging' does not ensure an optimal result either, as each of these methods can have a certain number of options and parameters, which frequently play a significant role during surface modelling. It is often difficult to optimise these factors and even experience does not guarantee a satisfactory result. This applies especially to modelling irregular surfaces, when it is difficult to select the method suitable for the surface shape that is sometimes unpredictable. It has been suggested that the method of selecting the shape parameter of the radial basis functions should be applied which makes it possible to minimise the mean square error of the approximated surface. The paper presents a new method of optimising the parameters of radial functions used for modelling the bottom surface. The accuracy of the surface projection obtained was the criterion for optimisation. The properties of self-organizing networks created the possibility of selecting testing points out of any set of measurement points and the determination of the minimum value of RMS error by means of the GRNN network. Optimisation of the shape parameter required building the proper polygon of the test points. For building such kind of polygon

  10. Research on the Application of Artificial Neural Networks in Tender Offer for Construction Projects

    Science.gov (United States)

    Minli, Zhang; Shanshan, Qiao

    The BP model in artificial neural network is used in this paper. Various factors that affect the tender offer is identified and these factors as the input nodes of network to conduct iterated operation in the network is applied in this paper. Through taking advantage of the self-learning function of network, this paper constantly modifies the weight matrix to achieve the objective error of the network error to achieve the function of predicting offer. As a software support tool, MATLAB is used in artificial neural network, the neural network toolbox helps to reduce the workload of writing code greatly and make the application of neural network more widely.

  11. The influence of management and construction methods in the repair costs of Spain’s low-volume road network

    Directory of Open Access Journals (Sweden)

    Eutiquio Gallego

    2016-06-01

    Full Text Available This paper describes the entire process of the implementation of the Spanish low volume road network, including the design criteria, the construction techniques and the management policies during all the periods. The current situation of low volume roads in Spain was analyzed with respect to the legal framework and their actual condition. In addition, the budget required for the repair of 41 low volume roads throughout Spain was calculated in order to statistically analyze the influence of the pavement materials and the period of construction. The main conclusions were that low volume roads constructed during the 1970´s are currently those in the best state of repair and those requiring the lower repair costs, even lower than those constructed after 1980´s. In addition, low volume roads constructed with higher quality materials and using standardized techniques required five times lower repair costs than those made of lower quality materials.

  12. Content-rich biological network constructed by mining PubMed abstracts

    Directory of Open Access Journals (Sweden)

    Sharp Burt M

    2004-10-01

    Full Text Available Abstract Background The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality biological interactions. Programs with natural language processing (NLP capability have been created to address these limitations, however, they are in general not readily accessible to the public. Results We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses. Conclusions Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot http://www.chilibot.net can be accessed free of charge to academic users.

  13. Quantum Minimum Distance Classifier

    OpenAIRE

    Enrica Santucci

    2017-01-01

    We propose a quantum version of the well known minimum distance classification model called Nearest Mean Classifier (NMC). In this regard, we presented our first results in two previous works. First, a quantum counterpart of the NMC for two-dimensional problems was introduced, named Quantum Nearest Mean Classifier (QNMC), together with a possible generalization to any number of dimensions. Secondly, we studied the n-dimensional problem into detail and we showed a new encoding for arbitrary n-...

  14. Classifying Returns as Extreme

    DEFF Research Database (Denmark)

    Christiansen, Charlotte

    2014-01-01

    I consider extreme returns for the stock and bond markets of 14 EU countries using two classification schemes: One, the univariate classification scheme from the previous literature that classifies extreme returns for each market separately, and two, a novel multivariate classification scheme...... that classifies extreme returns for several markets jointly. The new classification scheme holds about the same information as the old one, while demanding a shorter sample period. The new classification scheme is useful....

  15. Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

    Science.gov (United States)

    Valdés, Julio J; Barton, Alan J

    2007-05-01

    A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

  16. Stepwise construction of a metabolic network in Event-B: The heat shock response.

    Science.gov (United States)

    Sanwal, Usman; Petre, Luigia; Petre, Ion

    2017-12-01

    There is a high interest in constructing large, detailed computational models for biological processes. This is often done by putting together existing submodels and adding to them extra details/knowledge. The result of such approaches is usually a model that can only answer questions on a very specific level of detail, and thus, ultimately, is of limited use. We focus instead on an approach to systematically add details to a model, with formal verification of its consistency at each step. In this way, one obtains a set of reusable models, at different levels of abstraction, to be used for different purposes depending on the question to address. We demonstrate this approach using Event-B, a computational framework introduced to develop formal specifications of distributed software systems. We first describe how to model generic metabolic networks in Event-B. Then, we apply this method for modeling the biological heat shock response in eukaryotic cells, using Event-B refinement techniques. The advantage of using Event-B consists in having refinement as an intrinsic feature; this provides as a final result not only a correct model, but a chain of models automatically linked by refinement, each of which is provably correct and reusable. This is a proof-of-concept that refinement in Event-B is suitable for biomodeling, serving for mastering biological complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Bayesian-network-based safety risk assessment for steel construction projects.

    Science.gov (United States)

    Leu, Sou-Sen; Chang, Ching-Miao

    2013-05-01

    There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Efficient Regularized Regression with L0 Penalty for Variable Selection and Network Construction

    Directory of Open Access Journals (Sweden)

    Zhenqiu Liu

    2016-01-01

    Full Text Available Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology. One appealing approach is the L0 regularized regression which penalizes the number of nonzero features in the model directly. However, it is well known that L0 optimization is NP-hard and computationally challenging. In this paper, we propose efficient EM (L0EM and dual L0EM (DL0EM algorithms that directly approximate the L0 optimization problem. While L0EM is efficient with large sample size, DL0EM is efficient with high-dimensional (n≪m data. They also provide a natural solution to all Lp  p∈[0,2] problems, including lasso with p=1 and elastic net with p∈[1,2]. The regularized parameter λ can be determined through cross validation or AIC and BIC. We demonstrate our methods through simulation and high-dimensional genomic data. The results indicate that L0 has better performance than lasso, SCAD, and MC+, and L0 with AIC or BIC has similar performance as computationally intensive cross validation. The proposed algorithms are efficient in identifying the nonzero variables with less bias and constructing biologically important networks with high-dimensional big data.

  19. Stratified construction of neural network based interatomic models for multicomponent materials

    Science.gov (United States)

    Hajinazar, Samad; Shao, Junping; Kolmogorov, Aleksey N.

    2017-01-01

    Recent application of neural networks (NNs) to modeling interatomic interactions has shown the learning machines' encouragingly accurate performance for select elemental and multicomponent systems. In this study we explore the possibility of building a library of NN-based models by introducing a hierarchical NN training. In such a stratified procedure NNs for multicomponent systems are obtained by sequential training from the bottom up: first unaries, then binaries, and so on. Advantages of constructing NN sets with shared parameters include acceleration of the training process and intact description of the constituent systems. We use an automated generation of diverse structure sets for NN training on density functional theory-level reference energies. In the test case of Cu, Pd, Ag, Cu-Pd, Cu-Ag, Pd-Ag, and Cu-Pd-Ag systems, NNs trained in the traditional and stratified fashions are found to have essentially identical accuracy for defect energies, phonon dispersions, formation energies, etc. The models' robustness is further illustrated via unconstrained evolutionary structure searches in which the NN is used for the local optimization of crystal unit cells.

  20. A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

    Directory of Open Access Journals (Sweden)

    Ozgur Orcay

    2011-01-01

    Full Text Available Inefficient utilization of the frequency spectrum due to conventional regulatory limitations and physical performance limiting factors, mainly the Signal to Noise Ratio (SNR, are prominent restrictions in digital wireless communication. Pattern Based Communication System (PBCS is an adaptive and perceptual communication method based on a Cognitive Radio (CR approach. It intends an SNR oriented cognition mechanism in the physical layer for improvement of Link Spectral Efficiency (LSE. The key to this system is construction of optimal communication signals, which consist of encoded data in different pattern forms (waveforms depending on spectral availabilities. The signals distorted in the communication medium are recovered according to the pre-trained pattern glossary by the perceptual receiver. In this study, we have shown that it is possible to improve the bandwidth efficiency when largely uncorrelated signal patterns are chosen in order to form a glossary that represents symbols for different length data groups and the information can be recovered by the Artificial Neural Network (ANN in the receiver site.

  1. Construction and testing of five 25 kW elektrOmat wind energy generators with network-controlled inverter for network parallel operation

    Energy Technology Data Exchange (ETDEWEB)

    Frees, H.H.

    1988-01-01

    It is planned to construct and test 5 WPP type elektrOmat (25 kW) with network-controlled inverter for network-parallel operation in various applications. The novel wind generators operate in pure isolated operation for power production without network connection. Control, excitation and wind tracking are effected independently without power supplied by either network or other stations. Synchronous generator excitation is controlled by a microprocessor. The three-phase current produced has variable frequency and voltage. A special wing profile allows to effect rotor start-up at wind speeds as low as 3.5 m/s at an output of 1.5 kWh. Hence, these converters are particularly suitable for weak wind regions. Power is supplied to a water works, an electrical company, a marzipan manufacturer, a utility and an industrial entreprise. (HWJ).

  2. Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

    Science.gov (United States)

    Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

    2017-12-01

    State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with

  3. Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

    Directory of Open Access Journals (Sweden)

    Bogdan Gliwa

    2011-01-01

    Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

  4. Constructing more informative plant-pollinator networks: visitation and pollen deposition networks in a heathland plant community.

    Science.gov (United States)

    Ballantyne, G; Baldock, Katherine C R; Willmer, P G

    2015-09-07

    Interaction networks are widely used as tools to understand plant-pollinator communities, and to examine potential threats to plant diversity and food security if the ecosystem service provided by pollinating animals declines. However, most networks to date are based on recording visits to flowers, rather than recording clearly defined effective pollination events. Here we provide the first networks that explicitly incorporate measures of pollinator effectiveness (PE) from pollen deposition on stigmas per visit, and pollinator importance (PI) as the product of PE and visit frequency. These more informative networks, here produced for a low diversity heathland habitat, reveal that plant-pollinator interactions are more specialized than shown in most previous studies. At the studied site, the specialization index [Formula: see text] was lower for the visitation network than the PE network, which was in turn lower than [Formula: see text] for the PI network. Our study shows that collecting PE data is feasible for community-level studies in low diversity communities and that including information about PE can change the structure of interaction networks. This could have important consequences for our understanding of threats to pollination systems. © 2015 The Authors.

  5. Constructing more informative plant–pollinator networks: visitation and pollen deposition networks in a heathland plant community

    Science.gov (United States)

    Ballantyne, G.; Baldock, Katherine C. R.; Willmer, P. G.

    2015-01-01

    Interaction networks are widely used as tools to understand plant–pollinator communities, and to examine potential threats to plant diversity and food security if the ecosystem service provided by pollinating animals declines. However, most networks to date are based on recording visits to flowers, rather than recording clearly defined effective pollination events. Here we provide the first networks that explicitly incorporate measures of pollinator effectiveness (PE) from pollen deposition on stigmas per visit, and pollinator importance (PI) as the product of PE and visit frequency. These more informative networks, here produced for a low diversity heathland habitat, reveal that plant–pollinator interactions are more specialized than shown in most previous studies. At the studied site, the specialization index was lower for the visitation network than the PE network, which was in turn lower than for the PI network. Our study shows that collecting PE data is feasible for community-level studies in low diversity communities and that including information about PE can change the structure of interaction networks. This could have important consequences for our understanding of threats to pollination systems. PMID:26336181

  6. Analysis of brain fMRI time-series using HRF knowledge-based correlation classifier on unsupervised self-organizing neural network map

    Science.gov (United States)

    Erberich, Stephan G.; Bluml, Stefan; Nelson, Marvin D.

    2003-05-01

    Brain imaging and particular functional MRI (fMRI), which acquires brain volumes in time, reveals new understanding of the functional/structural relation in neuroscience. During fMRI imaging physiological state changes occur in the brain regions activated from the task paradigm which the subject performs in the scanner. These state changes can be depicted in the small veins of the activated region due to the blood oxygen level dependent (BOLD) effect. For each brain voxel in the fMRI experiment one accumulates a time series vector which has to be analyzed for similarity to the original task paradigm vector and its characteristic hemodynamic response function (HRF). Various analysis methods have been discussed for fMRI analysis, model-based statistical or unsupervised data-driven techniques. The purpose of this paper is to introduce a new method which combines two different approaches. We use an unsupervised self-organizing map (SOM) neural network to reduce the time series vector space by non-linear pattern recognition into a 2D table of representative time series wave-forms. Using a-priori knowledge of the HRF, either derived from a theoretical wave-form model or estimated from a brain region of interest (ROI), one can use correlation analysis between the time series patterns of the SOM table and the HRF to depict regions of activation specific to the HRF. An optional second SOM training with a reduce number of neurons of the best-matching time series to the HRF classification refines the second neural network pattern table. The learned time series pattern of each neuron and the corresponding brain voxels are superimposed onto the subject's brain image for visual investigation.

  7. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining

    Directory of Open Access Journals (Sweden)

    Lan Chung-Yu

    2008-09-01

    Full Text Available Abstract Background Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions. Results In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs such as NF-κB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin. Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network

  8. Constructing Integrated Networks for Identifying New Secondary Metabolic Pathway Regulators in Grapevine: Recent Applications and Future Opportunities.

    Science.gov (United States)

    Wong, Darren C J; Matus, José Tomás

    2017-01-01

    Representing large biological data as networks is becoming increasingly adopted for predicting gene function while elucidating the multifaceted organization of life processes. In grapevine ( Vitis vinifera L.), network analyses have been mostly adopted to contribute to the understanding of the regulatory mechanisms that control berry composition. Whereas, some studies have used gene co-expression networks to find common pathways and putative targets for transcription factors related to development and metabolism, others have defined networks of primary and secondary metabolites for characterizing the main metabolic differences between cultivars throughout fruit ripening. Lately, proteomic-related networks and those integrating genome-wide analyses of promoter regulatory elements have also been generated. The integration of all these data in multilayered networks allows building complex maps of molecular regulation and interaction. This perspective article describes the currently available network data and related resources for grapevine. With the aim of illustrating data integration approaches into network construction and analysis in grapevine, we searched for berry-specific regulators of the phenylpropanoid pathway. We generated a composite network consisting of overlaying maps of co-expression between structural and transcription factor genes, integrated with the presence of promoter cis -binding elements, microRNAs, and long non-coding RNAs (lncRNA). This approach revealed new uncharacterized transcription factors together with several microRNAs potentially regulating different steps of the phenylpropanoid pathway, and one particular lncRNA compromising the expression of nine stilbene synthase (STS) genes located in chromosome 10. Application of network-based approaches into multi-omics data will continue providing supplementary resources to address important questions regarding grapevine fruit quality and composition.

  9. Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction.

    Directory of Open Access Journals (Sweden)

    Zitong Zhang

    Full Text Available Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT, wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families, and wavelet length (2 to 24-each essential parameters in wavelet-based methods-on the estimated values of graph metrics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of graph metrics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.

  10. Neural network computing model for highway construction project scheduling and management : final report, October 1999.

    Science.gov (United States)

    1999-10-01

    A general mathematical formulation has been developed for scheduling of construction projects and applied to the problem of highway construction scheduling. Repetitive and non-repetitive tasks, work continuity considerations, multiple-crew strategies...

  11. Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site

    Energy Technology Data Exchange (ETDEWEB)

    Carta, Jose A. [Department of Mechanical Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Canary Islands (Spain); Velazquez, Sergio [Department of Electronics and Automatics Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Canary Islands (Spain); Matias, J.M. [Department of Statistics, University of Vigo, Lagoas Marcosende, 36200 Vigo (Spain)

    2011-02-15

    Due to the interannual variability of wind speed a feasibility analysis for the installation of a Wind Energy Conversion System at a particular site requires estimation of the long-term mean wind turbine energy output. A method is proposed in this paper which, based on probabilistic Bayesian networks (BNs), enables estimation of the long-term mean wind speed histogram for a site where few measurements of the wind resource are available. For this purpose, the proposed method allows the use of multiple reference stations with a long history of wind speed and wind direction measurements. That is to say, the model that is proposed in this paper is able to involve and make use of regional information about the wind resource. With the estimated long-term wind speed histogram and the power curve of a wind turbine it is possible to use the method of bins to determine the long-term mean energy output for that wind turbine. The intelligent system employed, the knowledgebase of which is a joint probability function of all the model variables, uses efficient calculation techniques for conditional probabilities to perform the reasoning. This enables automatic model learning and inference to be performed efficiently based on the available evidence. The proposed model is applied in this paper to wind speeds and wind directions recorded at four weather stations located in the Canary Islands (Spain). Ten years of mean hourly wind speed and direction data are available for these stations. One of the conclusions reached is that the BN with three reference stations gave fewer errors between the real and estimated long-term mean wind turbine energy output than when using two measure-correlate-predict algorithms which were evaluated and which use a linear regression between the candidate station and one reference station. (author)

  12. Classifying Cereal Data

    Science.gov (United States)

    The DSQ includes questions about cereal intake and allows respondents up to two responses on which cereals they consume. We classified each cereal reported first by hot or cold, and then along four dimensions: density of added sugars, whole grains, fiber, and calcium.

  13. Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

    Directory of Open Access Journals (Sweden)

    Saket Navlakha

    2015-07-01

    Full Text Available Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

  14. Deconvolution When Classifying Noisy Data Involving Transformations

    KAUST Repository

    Carroll, Raymond

    2012-09-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  15. LCC: Light Curves Classifier

    Science.gov (United States)

    Vo, Martin

    2017-08-01

    Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.

  16. Three-dimensional fabrication of thick and densely populated soft constructs with complex and actively perfused channel network

    DEFF Research Database (Denmark)

    Pimentel, C Rodrigo; Ko, Suk Kyu; Caviglia, Claudia

    2017-01-01

    cm) and densely populated (e.g. 10 million cells·mL-1) tissue constructs with a three-dimensional (3D) four arm branch network and stiffness in the range of soft tissues (1-10 kPa), which can be directly perfused on a fluidic platform for long time periods (> 14 days). Specifically, we co-print a 3D...... four-arm branch using water-soluble Poly(vinyl alcohol) (PVA) as main material and Poly(lactic acid) (PLA) as the support structure. The PLA support structure was selectively removed, and the water soluble PVA structure was used for creating a 3D vascular network within a customized extracellular...... that are not perfused. This study shows the creation of densely populated tissue constructs with a 3D four arm branch network and stiffness in the range of soft tissues, which can be directly perfused. The cells encapsulated within the construct showed proliferation as a function of the vasculature distance...

  17. A Holistic And Integrative Concept For Strong-Motion Records On Constructions Of The URBAN-INCERC National Seismic Network

    Directory of Open Access Journals (Sweden)

    Dragomir Claudiu-Sorin

    2015-10-01

    Full Text Available The main objective of the National Seismic Network for Constructions, operated by the National Institute for Research and Development in Constructions, Urban Planning and Spatial Territorial Development “URBAN-INCERC”, is the monitoring of situations generated by earthquakes or other dangerous sources of vibrations induced in constructions on the entire Romanian territory. The NIRD URBAN-INCERC seismic records obtained in-situ and on buildings were and are extremely important for designers, especially in 1977, 1986 and 1990. It is the largest network in Romania, consisting of some 60 digital acceleration recorders distributed in Bucharest and in the country. This network is strategic from the population safety point of view. Due to the specific seismic hazard and vulnerability, our country shall be in preparation for the impact of a possible earthquake, which cannot be predicted in time domain, but it is possible to occur anytime. To prevent and mitigate negative consequences of such an event, urgent actions are required to ensure structural safety. Given the facts, Romania is in a critical time on strategic options regarding the seismic risks.

  18. A Real-Time Construction Safety Monitoring System for Hazardous Gas Integrating Wireless Sensor Network and Building Information Modeling Technologies.

    Science.gov (United States)

    Cheung, Weng-Fong; Lin, Tzu-Hsuan; Lin, Yu-Cheng

    2018-02-02

    In recent years, many studies have focused on the application of advanced technology as a way to improve management of construction safety management. A Wireless Sensor Network (WSN), one of the key technologies in Internet of Things (IoT) development, enables objects and devices to sense and communicate environmental conditions; Building Information Modeling (BIM), a revolutionary technology in construction, integrates database and geometry into a digital model which provides a visualized way in all construction lifecycle management. This paper integrates BIM and WSN into a unique system which enables the construction site to visually monitor the safety status via a spatial, colored interface and remove any hazardous gas automatically. Many wireless sensor nodes were placed on an underground construction site and to collect hazardous gas level and environmental condition (temperature and humidity) data, and in any region where an abnormal status is detected, the BIM model will alert the region and an alarm and ventilator on site will start automatically for warning and removing the hazard. The proposed system can greatly enhance the efficiency in construction safety management and provide an important reference information in rescue tasks. Finally, a case study demonstrates the applicability of the proposed system and the practical benefits, limitations, conclusions, and suggestions are summarized for further applications.

  19. Optimization of ITS Construction Scheme for Road Network under the Restriction of Different Transports’ Passenger Person-Kilometers

    Directory of Open Access Journals (Sweden)

    Ming-wei Li

    2017-01-01

    Full Text Available Diversified transport modes and increased personal transportation demands have increased in urban traffic problems such as traffic congestion and environmental pollution. To cope with traffic problems, advanced transportation technologies are being developed as intelligent transportation system (ITS. There is a growing trend to coordinate varying kinds of transportation modes. However, the effective construction and application of ITS in urban traffic can be affected by many factors, such as transport mode. Therefore, how to reasonably construct ITS by consideration of different transport modes’ characteristics and requirements is an important research challenge. Additionally, both costs and negative effects must be minimized and application efficiency is required to be optimal in the construction process. To address these requirements, a multiobjective optimization model and a fuzzy selecting optimum model were combined to study the construction scheme based on optimization results. The empirical analysis of Beijing, China, suggested several considerations for improvements to future road network ITS construction with controlled costs. Finally, guidelines are proposed to facilitate ITS construction, improve ITS application efficiency, and transform and innovate strategies to cope with urban traffic.

  20. Dynamic Construction Scheme for Virtualization Security Service in Software-Defined Networks

    OpenAIRE

    Lin, Zhaowen; Tao, Dan; Wang, Zhenji

    2017-01-01

    For a Software Defined Network (SDN), security is an important factor affecting its large-scale deployment. The existing security solutions for SDN mainly focus on the controller itself, which has to handle all the security protection tasks by using the programmability of the network. This will undoubtedly involve a heavy burden for the controller. More devastatingly, once the controller itself is attacked, the entire network will be paralyzed. Motivated by this, this paper proposes a novel s...

  1. Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle costing of construction projects

    Directory of Open Access Journals (Sweden)

    Ayedh Alqahtani

    2013-09-01

    Full Text Available Industrial application of life-cycle cost analysis (LCCA is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client the advantages to be gained from objective (LCCA comparison of (subcomponent material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs to compute the whole-cost(s of construction and uses the concept of cost significant items (CSI to identify the main cost factors affecting the accuracy of estimation. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method was adopted (using MATLAB SOFTWARE; and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver. The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver and 2% (via back-propagation respectively.

  2. Constructing an urban green belt network : the target area of this research is Daegu City in Korea

    Energy Technology Data Exchange (ETDEWEB)

    Lee, D.H.; Hong, K.P. [Dongguk Univ., Seoul (Korea, Republic of). Dept. of Landscape Architecture

    2008-07-01

    Local governments, urban planners, architects, and landscape architects are planning new cities in response to rapidly growing urbanization. Many propose the use of more parks and unused open spaces to solve the problem of diminishing green tracts of land in cities. This paper reported on the results of a study that was conducted to find ways to build cities in harmony with ecology by using green tracts of land in a growing new urban development. This study selected Daegu City in Korea as the research area and analyzed forest vegetations and their functions, geographical features and the conditions of green tracts of land. This study also intended to contribute to the creation of a pleasant urban environment by making use of networks through inland water systems. The green network system between forests, rivers, parks and green zones were examined along with ways to construct a green network connecting neighbouring greenbelts and urban greenbelts with Nagdong River, Kumho River and Sin Cheon River. The study showed that neighbouring greenbelts can be used as space to preserve natural vegetation and study ecology. It was concluded that in order to construct a greenbelt network in Daegu City, solutions should be devised which maximize the use of green zones such as parks, neighbouring parks, green roads through street trees, rooftop greening and perpendicular greening. 18 refs., 7 tabs., 8 figs.

  3. Method of construction of rational corporate network using the simulation model

    Directory of Open Access Journals (Sweden)

    V.N. Pakhomovа

    2013-06-01

    Full Text Available Purpose. Search for new options of the transition from Ethernet technology. Methodology. Physical structuring of the Fast Ethernet network based on hubs and logical structuring of Fast Ethernet network using commutators. Organization of VLAN based on ports grouping and in accordance with the standard IEEE 802 .1Q. Findings. The options for improving of the Ethernet network are proposed. According to the Fast Ethernet and VLAN technologies on the simulation models in packages NetCraker and Cisco Packet Traker respectively. Origiality. The technique of designing of local area network using the VLAN technology is proposed. Practical value.Each of the options of "Dniprozaliznychproekt" network improving has its advantages. Transition from the Ethernet to Fast Ethernet technology is simple and economical, it requires only one commutator, when the VLAN organization requires at least two. VLAN technology, however, has the following advantages: reducing the load on the network, isolation of the broadcast traffic, change of the logical network structure without changing its physical structure, improving the network security. The transition from Ethernet to the VLAN technology allows you to separate the physical topology from the logical one, and the format of the ÌEEE 802.1Q standard frames allows you to simplify the process of virtual networks implementation to enterprises.

  4. Intelligent Garbage Classifier

    Directory of Open Access Journals (Sweden)

    Ignacio Rodríguez Novelle

    2008-12-01

    Full Text Available IGC (Intelligent Garbage Classifier is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.

  5. Quantum Minimum Distance Classifier

    Directory of Open Access Journals (Sweden)

    Enrica Santucci

    2017-12-01

    Full Text Available We propose a quantum version of the well known minimum distance classification model called Nearest Mean Classifier (NMC. In this regard, we presented our first results in two previous works. First, a quantum counterpart of the NMC for two-dimensional problems was introduced, named Quantum Nearest Mean Classifier (QNMC, together with a possible generalization to any number of dimensions. Secondly, we studied the n-dimensional problem into detail and we showed a new encoding for arbitrary n-feature vectors into density operators. In the present paper, another promising encoding is considered, suggested by recent debates on quantum machine learning. Further, we observe a significant property concerning the non-invariance by feature rescaling of our quantum classifier. This fact, which represents a meaningful difference between the NMC and the respective quantum version, allows us to introduce a free parameter whose variation provides, in some cases, better classification results for the QNMC. The experimental section is devoted: (i to compare the NMC and QNMC performance on different datasets; and (ii to study the effects of the non-invariance under uniform rescaling for the QNMC.

  6. NetRaVE: constructing dependency networks using sparse linear regression

    DEFF Research Database (Denmark)

    Phatak, A.; Kiiveri, H.; Clemmensen, Line Katrine Harder

    2010-01-01

    NetRaVE is a small suite of R functions for generating dependency networks using sparse regression methods. Such networks provide an alternative to interpreting 'top n lists' of genes arising out of an analysis of microarray data, and they provide a means of organizing and visualizing the resulting...... information in a manner that may suggest relationships between genes....

  7. Network design and operational modelling for construction green supply chain management

    OpenAIRE

    Pengfei Zhou Dong Chen; Qiuliang Wang

    2013-01-01

    Based on studying organizational structure of Construction Green Supply Chain Management (CGSCM), a mathematical programming model of CGSCM was proposed. The model aimed to maximize the aggregate profits of normalized construction logistics, the reverse logistics and the environmental performance. Numerical experiments show that the proposed approach can improve the aggregate profit effectively. In addition, return ratio, subsidies from governmental organizations, and environmental performanc...

  8. The role of “network of cities” in construction of global urban culture

    OpenAIRE

    Baycan-Levent, Tüzin; Kundak, Seda; Gülümser, Aliye Ahu

    2004-01-01

    The globalization process has led to an increased interaction between cities and to a new urban system/network in which they need to be competitive and complementary at the same time. “Network of cities”, such as World Cities, Eurocities or Sister Cities are among the well known examples of interaction and cooperation of the cities at the regional and global level. The cities of different regions and countries tend to share their experiences and their cultures within these networks in order t...

  9. Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales

    Science.gov (United States)

    Deza, J. I.; Barreiro, M.; Masoller, C.

    2013-06-01

    We study global climate networks constructed by means of ordinal time series analysis. Climate interdependencies among the nodes are quantified by the mutual information, computed from time series of monthly-averaged surface air temperature anomalies, and from their symbolic ordinal representation (OP). This analysis allows identifying topological changes in the network when varying the time-interval of the ordinal pattern. We consider intra-season time-intervals (e.g., the patterns are formed by anomalies in consecutive months) and inter-annual time-intervals (e.g., the patterns are formed by anomalies in consecutive years). We discuss how the network density and topology change with these time scales, and provide evidence of correlations between geographically distant regions that occur at specific time scales. In particular, we find that an increase in the ordinal pattern spacing (i.e., an increase in the timescale of the ordinal analysis), results in climate networks with increased connectivity on the equatorial Pacific area. On the contrary, the number of significant links decreases when the ordinal analysis is done with a shorter timescale (by comparing consecutive months), and interpret this effect as due to more stochasticity in the time-series in the short timescale. As the equatorial Pacific is known to be dominated by El Niño-Southern Oscillation (ENSO) on scales longer than several months, our methodology allows constructing climate networks where the effect of ENSO goes from mild (monthly OP) to intense (yearly OP), independently of the length of the ordinal pattern and of the thresholding method employed.

  10. Network design and operational modelling for construction green supply chain management

    Directory of Open Access Journals (Sweden)

    Pengfei Zhou Dong Chen

    2013-01-01

    Full Text Available Based on studying organizational structure of Construction Green Supply Chain Management (CGSCM, a mathematical programming model of CGSCM was proposed. The model aimed to maximize the aggregate profits of normalized construction logistics, the reverse logistics and the environmental performance. Numerical experiments show that the proposed approach can improve the aggregate profit effectively. In addition, return ratio, subsidies from governmental organizations, and environmental performance were analyzed for CGSCM performance. Herein, the proper return, subsidy and control strategy could optimize construction green supply chain.

  11. Mining for constructions in texts using N-gram and network analysis

    DEFF Research Database (Denmark)

    Shibuya, Yoshikata; Jensen, Kim Ebensgaard

    2015-01-01

    N-gram analysis to Lewis Carroll's novel Alice's Adventures in Wonderland and Mark Twain's novelThe Adventures of Huckleberry Finn and extrapolate a number of likely constructional phenomena from recurring N-gram patterns in the two texts. In addition to simple N-gram analysis, the following....... The main premise is that, if constructions are functional units, then configurations of words that tend to recur together in discourse are likely to have some sort of function that speakers utilize in discourse. Writers of fiction, for instance, may use constructions in characterizations, mind-styles, text...

  12. Shanghai Connection: The Construction and Collapse of the Comintern Network in East and Southeast Asia

    National Research Council Canada - National Science Library

    Onimaru, Takeshi

    2016-01-01

    As East and Southeast Asia’s communist parties, founded between 1920 and 1930, strove to promote communism regionally from the 1920s to the 1930s, they were linked in an international network of communist movements formed...

  13. NEW CANDIDATE GENES FOR SUSCEPTIBILITY TO TUBERCULOSIS IDENTIFIED THROUGH THE CONSTRUCTION AND ANALYSIS OF ASSOCIATIVE NETWORKS

    Directory of Open Access Journals (Sweden)

    Ye. Yu. Bragina

    2015-01-01

    Full Text Available Tuberculosis (TB is a common disease caused by infection of Mycobacterium tuberculosis and influenced by host hereditary and environmental factors. Accumulated genomic data obtained through the use of new methodological approaches, including analysis of associative networks, contribute to the understanding of the hereditary basis of the disease. In the current study, we carried out the reconstruction and analysis of associative network representing molecular genetic links between proteins/genes involved in the development of TB. In the associative network, well studied proteins and genes with a decisive importance in the efficiency of the human immune response against a pathogen predominated. However, this approach identified 12 new genes encoding for the respective proteins in the associative network polymorphismsof which has not been studied regarding the development of TB.

  14. Unification and mechanistic detail as drivers of model construction: models of networks in economics and sociology.

    Science.gov (United States)

    Kuorikoski, Jaakko; Marchionni, Caterina

    2014-12-01

    We examine the diversity of strategies of modelling networks in (micro) economics and (analytical) sociology. Field-specific conceptions of what explaining (with) networks amounts to or systematic preference for certain kinds of explanatory factors are not sufficient to account for differences in modelling methodologies. We argue that network models in both sociology and economics are abstract models of network mechanisms and that differences in their modelling strategies derive to a large extent from field-specific conceptions of the way in which a good model should be a general one. Whereas the economics models aim at unification, the sociological models aim at a set of mechanism schemas that are extrapolatable to the extent that the underlying psychological mechanisms are general. These conceptions of generality induce specific biases in mechanistic explanation and are related to different views of when knowledge from different fields should be seen as relevant.

  15. Classifying TDSS Stellar Variables

    Science.gov (United States)

    Amaro, Rachael Christina; Green, Paul J.; TDSS Collaboration

    2017-01-01

    The Time Domain Spectroscopic Survey (TDSS), a subprogram of SDSS-IV eBOSS, obtains classification/discovery spectra of point-source photometric variables selected from PanSTARRS and SDSS multi-color light curves regardless of object color or lightcurve shape. Tens of thousands of TDSS spectra are already available and have been spectroscopically classified both via pipeline and by visual inspection. About half of these spectra are quasars, half are stars. Our goal is to classify the stars with their correct variability types. We do this by acquiring public multi-epoch light curves for brighter stars (rclassifications and parameters in the Catalina Surveys Periodic Variable Star Catalog. Variable star classifications include RR Lyr, close eclipsing binaries, CVs, pulsating white dwarfs, and other exotic systems. The key difference between our catalog and others is that along with the light curves, we will be using TDSS spectra to help in the classification of variable type, as spectra are rich with information allowing estimation of physical parameters like temperature, metallicity, gravity, etc. This work was supported by the SDSS Research Experience for Undergraduates program, which is funded by a grant from Sloan Foundation to the Astrophysical Research Consortium.

  16. Classifying Facial Actions

    Science.gov (United States)

    Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.

    2010-01-01

    The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284

  17. Classifying network attack scenarios using an ontology

    CSIR Research Space (South Africa)

    Van Heerden, RP

    2012-03-01

    Full Text Available ). ?Spear Phishing? refers to targeted social engineering-type email attacks (Jagatic, 2007). ?Physical? refers to manual methods to gain access, for example physically removing the hard drive or breaking the access door to enter a secure server room... the size and utility of the target. The "Target" class is the physical device or entity targeted by an attack. The "Vulnerability" class describes a target vulnerability used by the attacker. The "Phase" class represents an attack model that subdivides...

  18. A Simple Neural Network Contextual Classifier

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Tidemann, J.

    1997-01-01

    I. Kanellopoulos, G.G. Wilkinson, F. Roli and J. Austin (Eds.)Proceedings of European Union Environment and Climate Programme Concerted Action COMPARES (COnnectionist Methods in Pre-processing and Analysis of REmote Sensing data).......I. Kanellopoulos, G.G. Wilkinson, F. Roli and J. Austin (Eds.)Proceedings of European Union Environment and Climate Programme Concerted Action COMPARES (COnnectionist Methods in Pre-processing and Analysis of REmote Sensing data)....

  19. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha

    2013-11-25

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  20. Construction, visualisation, and clustering of transcription networks from microarray expression data.

    Directory of Open Access Journals (Sweden)

    Tom C Freeman

    2007-10-01

    Full Text Available Network analysis transcends conventional pairwise approaches to data analysis as the context of components in a network graph can be taken into account. Such approaches are increasingly being applied to genomics data, where functional linkages are used to connect genes or proteins. However, while microarray gene expression datasets are now abundant and of high quality, few approaches have been developed for analysis of such data in a network context. We present a novel approach for 3-D visualisation and analysis of transcriptional networks generated from microarray data. These networks consist of nodes representing transcripts connected by virtue of their expression profile similarity across multiple conditions. Analysing genome-wide gene transcription across 61 mouse tissues, we describe the unusual topography of the large and highly structured networks produced, and demonstrate how they can be used to visualise, cluster, and mine large datasets. This approach is fast, intuitive, and versatile, and allows the identification of biological relationships that may be missed by conventional analysis techniques. This work has been implemented in a freely available open-source application named BioLayout Express(3D.

  1. General and Local: Averaged k-Dependence Bayesian Classifiers

    Directory of Open Access Journals (Sweden)

    Limin Wang

    2015-06-01

    Full Text Available The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB classifier can construct at arbitrary points (values of k along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB, tree augmented naive Bayes (TAN, Averaged one-dependence estimators (AODE, and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance.

  2. A GIS analysis of suitability for construction aggregate recycling sites using regional transportation network and population density features

    Science.gov (United States)

    Robinson, G.R.; Kapo, K.E.

    2004-01-01

    Aggregate is used in road and building construction to provide bulk, strength, support, and wear resistance. Reclaimed asphalt pavement (RAP) and reclaimed Portland cement concrete (RPCC) are abundant and available sources of recycled aggregate. In this paper, current aggregate production operations in Virginia, Maryland, and the District of Columbia are used to develop spatial association models for the recycled aggregate industry with regional transportation network and population density features. The cost of construction aggregate to the end user is strongly influenced by the cost of transporting processed aggregate from the production site to the construction site. More than 60% of operations recycling aggregate in the mid-Atlantic study area are located within 4.8 km (3 miles) of an interstate highway. Transportation corridors provide both sites of likely road construction where aggregate is used and an efficient means to move both materials and on-site processing equipment back and forth from various work sites to the recycling operations. Urban and developing areas provide a high market demand for aggregate and a ready source of construction debris that may be processed into recycled aggregate. Most aggregate recycling operators in the study area are sited in counties with population densities exceeding 77 people/km2 (200 people/mile 2). No aggregate recycling operations are sited in counties with less than 19 people/km2 (50 people/mile2), reflecting the lack of sufficient long-term sources of construction debris to be used as an aggregate source, as well as the lack of a sufficient market demand for aggregate in most rural areas to locate a recycling operation there or justify the required investment in the equipment to process and produce recycled aggregate. Weights of evidence analyses (WofE), measuring correlation on an area-normalized basis, and weighted logistic regression (WLR), are used to model the distribution of RAP and RPCC operations relative

  3. Classifying Partner Femicide

    Science.gov (United States)

    Dixon, Louise; Hamilton-Giachritsis, Catherine; Browne, Kevin

    2008-01-01

    The heterogeneity of domestic violent men has long been established. However, research has failed to examine this phenomenon among men committing the most severe form of domestic violence. This study aims to use a multi-dimensional approach to empirically construct a classification system of men who are incarcerated for the murder of their female…

  4. S-net project: Construction of large scale seafloor observatory network for tsunamis and earthquakes in Japan

    Science.gov (United States)

    Mochizuki, M.; Kanazawa, T.; Uehira, K.; Shimbo, T.; Shiomi, K.; Kunugi, T.; Aoi, S.; Matsumoto, T.; Sekiguchi, S.; Yamamoto, N.; Takahashi, N.; Shinohara, M.; Yamada, T.

    2016-12-01

    National Research Institute for Earth Science and Disaster Resilience ( NIED ) has launched the project of constructing an observatory network for tsunamis and earthquakes on the seafloor. The observatory network was named "S-net, Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench". The S-net consists of 150 seafloor observatories which are connected in line with submarine optical cables. The total length of submarine optical cable is about 5,700 km. The S-net system extends along Kuril and Japan trenches around Japan islands from north to south covering the area between southeast off island of Hokkaido and off the Boso Peninsula, Chiba Prefecture. The project has been financially supported by MEXT Japan. An observatory package is 34cm in diameter and 226cm long. Each observatory equips two units of a high sensitive water-depth sensor as a tsunami meter and four sets of three-component seismometers. The water-depth sensor has measurement resolution of sub-centimeter level. Combination of multiple seismometers secures wide dynamic range and robustness of the observation that are needed for early earthquake warning. The S-net is composed of six segment networks that consists of about 25 observatories and 800-1,600km length submarine optical cable. Five of six segment networks except the one covering the outer rise area of the Japan Trench has been already installed. The data from the observatories on those five segment networks are being transferred to the data center at NIED on a real-time basis, and then verification of data integrity are being carried out at the present moment. Installation of the last segment network of the S-net, that is, the outer rise one is scheduled to be finished within FY2016. Full-scale operation of the S-net will start at FY2017. We will report construction and operation of the S-net submarine cable system as well as the outline of the obtained data in this presentation.

  5. Stack filter classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory

    2009-01-01

    Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.

  6. AN INITIATIVE FOR CONSTRUCTION OF NEW-GENERATION LUNAR GLOBAL CONTROL NETWORK USING MULTI-MISSION DATA

    Directory of Open Access Journals (Sweden)

    K. Di

    2017-07-01

    Full Text Available A lunar global control network provides geodetic datum and control points for mapping of the lunar surface. The widely used Unified Lunar Control Network 2005 (ULCN2005 was built based on a combined photogrammetric solution of Clementine images acquired in 1994 and earlier photographic data. In this research, we propose an initiative for construction of a new-generation lunar global control network using multi-mission data newly acquired in the 21st century, which have much better resolution and precision than the old data acquired in the last century. The new control network will be based on a combined photogrammetric solution of an extended global image and laser altimetry network. The five lunar laser ranging retro-reflectors, which can be identified in LROC NAC images and have cm level 3D position accuracy, will be used as absolute control points in the least squares photogrammetric adjustment. Recently, a new radio total phase ranging method has been developed and used for high-precision positioning of Chang’e-3 lander; this shall offer a new absolute control point. Systematic methods and key techniques will be developed or enhanced, including rigorous and generic geometric modeling of orbital images, multi-scale feature extraction and matching among heterogeneous multi-mission remote sensing data, optimal selection of images at areas of multiple image coverages, and large-scale adjustment computation, etc. Based on the high-resolution new datasets and developed new techniques, the new generation of global control network is expected to have much higher accuracy and point density than the ULCN2005.

  7. Construction and analysis of a human testis/sperm-enriched interaction network: Unraveling the PPP1CC2 interactome.

    Science.gov (United States)

    Silva, Joana Vieira; Yoon, Sooyeon; De Bock, Pieter-Jan; Goltsev, Alexander V; Gevaert, Kris; Mendes, José Fernando F; Fardilha, Margarida

    2017-02-01

    Phosphoprotein phosphatase 1 catalytic subunit gamma 2 (PPP1CC2), a PPP1CC tissue-specific alternative splice restricted to testicular germ cells and spermatozoa, is essential for spermatogenesis and spermatozoa motility. The key to understand PPP1CC2 regulation lies on the characterization of its interacting partners. We construct a testis/sperm-enriched protein interaction network and analyzed the topological properties and biological context of the network. Further the interaction of a potential target for pharmacological intervention was validated in human spermatozoa. A total of 1778 proteins and 32,187 interactions between them were identified in the testis/sperm-enriched network. The network analysis revealed the members of functional modules that interact more tightly with each other. In the network, PPP1CC was located in the fourth maximum core part (k=41) and had 106 direct interactors. Sixteen PPP1CC interactors were involved in spermatogenesis-related categories. Also, PPP1CC had 50 direct interactors, highly interconnected and many of them part of the network maximum core (k=44), associated with motility-related annotations, including several previously uncharacterized interactors, such as, LMNA, JAK2 and RIPK3. In this study we integrated tissue-specific protein expression and protein-protein interaction data in order to identify key PPP1CC2 complexes for male reproductive functions. One of the most intriguing interactors was A-kinase anchor protein 4 (AKAP4), a testis-specific protein related to infertility phenotypes and involved in all major motility-related annotations. We demonstrated for the first time the interaction between PPP1CC2 and AKAP4 in human spermatozoa and the potential of the complex as contraceptive target. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. An Initiative for Construction of New-Generation Lunar Global Control Network Using Multi-Mission Data

    Science.gov (United States)

    Di, K.; Liu, B.; Peng, M.; Xin, X.; Jia, M.; Zuo, W.; Ping, J.; Wu, B.; Oberst, J.

    2017-07-01

    A lunar global control network provides geodetic datum and control points for mapping of the lunar surface. The widely used Unified Lunar Control Network 2005 (ULCN2005) was built based on a combined photogrammetric solution of Clementine images acquired in 1994 and earlier photographic data. In this research, we propose an initiative for construction of a new-generation lunar global control network using multi-mission data newly acquired in the 21st century, which have much better resolution and precision than the old data acquired in the last century. The new control network will be based on a combined photogrammetric solution of an extended global image and laser altimetry network. The five lunar laser ranging retro-reflectors, which can be identified in LROC NAC images and have cm level 3D position accuracy, will be used as absolute control points in the least squares photogrammetric adjustment. Recently, a new radio total phase ranging method has been developed and used for high-precision positioning of Chang'e-3 lander; this shall offer a new absolute control point. Systematic methods and key techniques will be developed or enhanced, including rigorous and generic geometric modeling of orbital images, multi-scale feature extraction and matching among heterogeneous multi-mission remote sensing data, optimal selection of images at areas of multiple image coverages, and large-scale adjustment computation, etc. Based on the high-resolution new datasets and developed new techniques, the new generation of global control network is expected to have much higher accuracy and point density than the ULCN2005.

  9. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

    Science.gov (United States)

    Luo, Feng; Yang, Yunfeng; Zhong, Jianxin; Gao, Haichun; Khan, Latifur; Thompson, Dorothea K; Zhou, Jizhong

    2007-01-01

    Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the

  10. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory.

    Science.gov (United States)

    Luo, Feng; Yang, Yunfeng; Zhong, Jianxin; Gao, Haichun; Khan, Latifur; Thompson, Dorothea K; Zhou, Jizhong

    2007-08-14

    Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the recovery of gene

  11. Construction of pancreatic cancer double-factor regulatory network based on chip data on the transcriptional level.

    Science.gov (United States)

    Zhao, Li-Li; Zhang, Tong; Liu, Bing-Rong; Liu, Tie-Fu; Tao, Na; Zhuang, Li-Wei

    2014-05-01

    Transcription factor (TF) and microRNA (miRNA) have been discovered playing crucial roles in cancer development. However, the effect of TFs and miRNAs in pancreatic cancer pathogenesis remains vague. We attempted to reveal the possible mechanism of pancreatic cancer based on transcription level. Using GSE16515 datasets downloaded from gene expression omnibus database, we first identified the differentially expressed genes (DEGs) in pancreatic cancer by the limma package in R. Then the DEGs were mapped into DAVID to conduct the kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. TFs and miRNAs that DEGs significantly enriched were identified by Fisher's test, and then the pancreatic cancer double-factor regulatory network was constructed. In our study, total 1117 DEGs were identified and they significantly enriched in 4 KEGG pathways. A double-factor regulatory network was established, including 29 DEGs, 24 TFs, 25 miRNAs. In the network, LAMC2, BRIP1 and miR155 were identified which may be involved in pancreatic cancer development. In conclusion, the double-factor regulatory network was found to play an important role in pancreatic cancer progression and our results shed new light on the molecular mechanism of pancreatic cancer.

  12. Self-constructed tree-shape high thermal conductivity nanosilver networks in epoxy

    Science.gov (United States)

    Pashayi, Kamyar; Fard, Hafez Raeisi; Lai, Fengyuan; Iruvanti, Sushumna; Plawsky, Joel; Borca-Tasciuc, Theodorian

    2014-03-01

    We report the formation of high aspect ratio nanoscale tree-shape silver networks in epoxy, at low temperatures (polyvinylpyrrolidone (PVP) coated nanoparticles, removal of PVP coating from the surface, and sintering of silver nanoparticles in high aspect ratio networked structures. Controlling self-assembly and sintering by carefully designed multistep temperature and time processing leads to κ of our silver nanocomposites that are up to 300% of the present state of the art polymer nanocomposites at similar volume fractions. Our investigation of the κ enhancements enabled by tree-shaped network nanocomposites provides a basis for the development of new polymer nanocomposites for thermal transport and storage applications.We report the formation of high aspect ratio nanoscale tree-shape silver networks in epoxy, at low temperatures (polyvinylpyrrolidone (PVP) coated nanoparticles, removal of PVP coating from the surface, and sintering of silver nanoparticles in high aspect ratio networked structures. Controlling self-assembly and sintering by carefully designed multistep temperature and time processing leads to κ of our silver nanocomposites that are up to 300% of the present state of the art polymer nanocomposites at similar volume fractions. Our investigation of the κ enhancements enabled by tree-shaped network nanocomposites provides a basis for the development of new polymer nanocomposites for thermal transport and storage applications. Electronic supplementary information (ESI) available: Raman spectroscopy of particles with and without PVP coating, the morphology of uncoated particles, the curing time of samples presented in Fig. 2, optical microscopy images studying the effect of processing time and temperature on nanocomposite agglomeration, measurement results of viscosity versus time for pure epoxy and the 20 vol% silver-epoxy nanocomposite, theoretical determination of the agglomeration time, and the DSC curve of pure bulk PVP are presented in the

  13. Artificial microtubule cytoskeleton construction, manipulation, and modeling via holographic trapping of network nodes

    Science.gov (United States)

    Bergman, J.; Doval, F.; Vershinin, M.

    2016-09-01

    Cytoskeletal networks are 3D arrangements of filaments whose complex spatial structure contributes significantly to their intracellular functions, e.g. biomechanics and cargo motility. Microtubule networks in cells are a particular challenge for in vitro modeling because they are sparse and possess overall structure and so cannot be approximated experimentally as a random hydrogel. We have used holographic optical trapping to precisely position and hold multiple microtubule filaments in an in vitro assay, where chemical and environmental variables can be carefully controlled. Below we describe the relevant practical details of the approach and demonstrate how our approach can scale to accommodate modeling of molecular motor transport and biomechanics experiments.

  14. The Construction of Higher Education Entrepreneur Services Network System a Research Based on Ecological Systems Theory

    Science.gov (United States)

    Xue, Jingxin

    The article aims to completely, systematically and objectively analyze the current situation of Entrepreneurship Education in China with Ecological Systems Theory. From this perspective, the author discusses the structure, function and its basic features of higher education entrepreneur services network system, and puts forward the opinion that every entrepreneurship organization in higher education institution does not limited to only one platform. Different functional supporting platforms should be combined closed through composite functional organization to form an integrated network system, in which each unit would impels others' development.

  15. Construction and Application of a National Data-Sharing Service Network of Material Environmental Corrosion

    Directory of Open Access Journals (Sweden)

    Xiaogang Li

    2007-12-01

    Full Text Available This article discusses the key features of a newly developed national data-sharing online network for material environmental corrosion. Written in Java language and based on Oracle database technology, the central database in the network is supported with two unique series of corrosion failure data, both of which were accumulated during a long period of time. The first category of data, provided by national environment corrosion test sites, is corrosion failure data for different materials in typical environments (atmosphere, seawater and soil. The other category is corrosion data in production environments, provided by a variety of firms. This network system enables standardized management of environmental corrosion data, an effective data sharing process, and research and development support for new products and after-sale services. Moreover this network system provides a firm base and data-service platform for the evaluation of project bids, safety, and service life. This article also discusses issues including data quality management and evaluation in the material corrosion data sharing process, access authority of different users, compensation for providers of shared historical data, and finally, the related policy and law legal processes, which are required to protect the intellectual property rights of the database.

  16. Par@Graph - a parallel toolbox for the construction and analysis of large complex climate networks

    NARCIS (Netherlands)

    Tantet, A.J.J.

    2015-01-01

    In this paper, we present Par@Graph, a software toolbox to reconstruct and analyze complex climate networks having a large number of nodes (up to at least 106) and edges (up to at least 1012). The key innovation is an efficient set of parallel software tools designed to leverage the inherited hybrid

  17. Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

    Science.gov (United States)

    Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

  18. A bayesian belief networks approach to risk control in construction projects

    NARCIS (Netherlands)

    Chivatá Cárdenas, Ibsen; Al-Jibouri, Saad H.S.; Halman, Johannes I.M.; Telichenko, V.; Volkov, A.; Bilchuk, I.

    2012-01-01

    Although risk control is a key step in risk management of construction projects, very often risk measures used are based merely on personal experience and engineering judgement rather than analysis of comprehensive information relating to a specific risk. This paper deals with an approach to provide

  19. Academe-Industry Collaboration through the Experience of a Construction IT Membership-Based Network

    Science.gov (United States)

    Underwood, Jason; Khosrowshahi, Farzad

    2005-01-01

    Traditionally, the construction industry has been renowned for its deep fragmentation, with various stakeholders and disciplines brought together as virtual teams, in many instances for one-off projects. In addition, the industry is extremely information-intensive and document-driven. Such traits have been major contributors to the poor…

  20. Construction and analysis of protein-protein interaction network correlated with ankylosing spondylitis.

    Science.gov (United States)

    Kanwal, Attiya; Fazal, Sahar

    2018-01-05

    Ankylosing spondylitis, a systemic illness is a foundation of progressing joint swelling that for the most part influences the spine. However, it frequently causes aggravation in different joints far from the spine, and in addition organs, for example, the eyes, heart, lungs, and kidneys. It's an immune system ailment that may be activated by specific sorts of bacterial or viral diseases that initiate an invulnerable reaction that don't close off after the contamination is recuperated. The particular reason for ankylosing spondylitis is obscure, yet hereditary qualities assume a huge part in this condition. The rising apparatuses of network medicine offer a stage to investigate an unpredictable illness at framework level. In this study, we meant to recognize the key proteins and the biological regulator pathways including in AS and further investigating the molecular connectivity between these pathways by the topological examination of the Protein-protein communication (PPI) system. The extended network including of 93 nodes and have 199 interactions respectively scanned from STRING database and some separated small networks. 24 proteins with high BC at the threshold of 0.01 and 55 proteins with large degree at the threshold of 1 have been identified. CD4 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for AS and the crosstalk between them. The finding of this research suggests that AS variation is orchestrated by an integrated PPI network centered on CD4 out of 93 nodes. Ankylosing spondylitis, a systemic disease is an establishment of advancing joint swelling that generally impacts the spine. Be that as it may, it as often as possible causes disturbance in various joints a long way from the spine, and what's more organs. It's a resistant framework affliction that might be actuated by particular sorts

  1. Analysis and Construction of Full-Diversity Joint Network-LDPC Codes for Cooperative Communications

    Directory of Open Access Journals (Sweden)

    Capirone Daniele

    2010-01-01

    Full Text Available Transmit diversity is necessary in harsh environments to reduce the required transmit power for achieving a given error performance at a certain transmission rate. In networks, cooperative communication is a well-known technique to yield transmit diversity and network coding can increase the spectral efficiency. These two techniques can be combined to achieve a double diversity order for a maximum coding rate on the Multiple-Access Relay Channel (MARC, where two sources share a common relay in their transmission to the destination. However, codes have to be carefully designed to obtain the intrinsic diversity offered by the MARC. This paper presents the principles to design a family of full-diversity LDPC codes with maximum rate. Simulation of the word error rate performance of the new proposed family of LDPC codes for the MARC confirms the full diversity.

  2. Constructing Cost-Effective and Targetable ICS Honeypots Suited for Production Networks

    Science.gov (United States)

    2015-03-26

    attacker connects to a target computer using a secure shell (i.e., ssh ) to the IP ad- dress 10.1.10.12. If the attacker were to then query the target...Privacy , vol. 2, no. 2, pp.77-79, March-April 2004. 9. The CIP Networks Library Volume 2: EtherNet/IP Adaptation of CIP. Open DeviceNet Vendor

  3. Transcription network construction for large-scale microarray datasets using a high-performance computing approach

    OpenAIRE

    Wu Qishi; Zhu Mengxia

    2008-01-01

    Abstract Background The advance in high-throughput genomic technologies including microarrays has demonstrated the potential of generating a tremendous amount of gene expression data for the entire genome. Deciphering transcriptional networks that convey information on intracluster correlations and intercluster connections of genes is a crucial analysis task in the post-sequence era. Most of the existing analysis methods for genome-wide gene expression profiles consist of several steps that o...

  4. Automatic construction of image inspection algorithm by using image processing network programming

    Science.gov (United States)

    Yoshimura, Yuichiro; Aoki, Kimiya

    2017-03-01

    In this paper, we discuss a method for automatic programming of inspection image processing. In the industrial field, automatic program generators or expert systems are expected to shorten a period required for developing a new appearance inspection system. So-called "image processing expert system" have been studied for over the nearly 30 years. We are convinced of the need to adopt a new idea. Recently, a novel type of evolutionary algorithms, called genetic network programming (GNP), has been proposed. In this study, we use GNP as a method to create an inspection image processing logic. GNP develops many directed graph structures, and shows excellent ability of formulating complex problems. We have converted this network program model to Image Processing Network Programming (IPNP). IPNP selects an appropriate image processing command based on some characteristics of input image data and processing log, and generates a visual inspection software with series of image processing commands. It is verified from experiments that the proposed method is able to create some inspection image processing programs. In the basic experiment with 200 test images, the success rate of detection of target region was 93.5%.

  5. Scalable Approach To Construct Free-Standing and Flexible Carbon Networks for Lithium–Sulfur Battery

    KAUST Repository

    Li, Mengliu

    2017-02-21

    Reconstructing carbon nanomaterials (e.g., fullerene, carbon nanotubes (CNTs), and graphene) to multidimensional networks with hierarchical structure is a critical step in exploring their applications. Herein, a sacrificial template method by casting strategy is developed to prepare highly flexible and free-standing carbon film consisting of CNTs, graphene, or both. The scalable size, ultralight and binder-free characteristics, as well as the tunable process/property are promising for their large-scale applications, such as utilizing as interlayers in lithium-sulfur battery. The capability of holding polysulfides (i.e., suppressing the sulfur diffusion) for the networks made from CNTs, graphene, or their mixture is pronounced, among which CNTs are the best. The diffusion process of polysulfides can be visualized in a specially designed glass tube battery. X-ray photoelectron spectroscopy analysis of discharged electrodes was performed to characterize the species in electrodes. A detailed analysis of lithium diffusion constant, electrochemical impedance, and elementary distribution of sulfur in electrodes has been performed to further illustrate the differences of different carbon interlayers for Li-S batteries. The proposed simple and enlargeable production of carbon-based networks may facilitate their applications in battery industry even as a flexible cathode directly. The versatile and reconstructive strategy is extendable to prepare other flexible films and/or membranes for wider applications.

  6. Classifying Entropy Measures

    Directory of Open Access Journals (Sweden)

    Angel Garrido

    2011-07-01

    Full Text Available Our paper analyzes some aspects of Uncertainty Measures. We need to obtain new ways to model adequate conditions or restrictions, constructed from vague pieces of information. The classical entropy measure originates from scientific fields; more specifically, from Statistical Physics and Thermodynamics. With time it was adapted by Claude Shannon, creating the current expanding Information Theory. However, the Hungarian mathematician, Alfred Rényi, proves that different and valid entropy measures exist in accordance with the purpose and/or need of application. Accordingly, it is essential to clarify the different types of measures and their mutual relationships. For these reasons, we attempt here to obtain an adequate revision of such fuzzy entropy measures from a mathematical point of view.

  7. Evolving extended naive Bayes classifiers

    OpenAIRE

    Klawonn, Frank; Angelov, Plamen

    2006-01-01

    Naive Bayes classifiers are a very simple, but often effective tool for classification problems, although they are based on independence assumptions that do not hold in most cases. Extended naive Bayes classifiers also rely on independence assumptions, but break them down to artificial subclasses, in this way becoming more powerful than ordinary naive Bayes classifiers. Since the involved computations for Bayes classifiers are basically generalised mean value calculations, they easily render ...

  8. Effect of bridge construction on floodplain hydrology—assessment by using monitored data and artificial neural network models

    Science.gov (United States)

    Gautam, Mahesh R.; Watanabe, Kunio; Ohno, Hiroyuki

    2004-06-01

    Study of floodplain hydrology of urban areas is important for its implications to flood and pollution control and ecology of the floodplain areas. In this study, the effect of bridge-pier-strengthening work over Tama River at Nagata district, Japan on the general hydrological condition of the floodplain was investigated by analysing monitored hydrogeological data and results of artificial neural network (ANN) models. A conceptualization of surface and subsurface of flow condition based on field observations was made and linked with the behavior of monitored data at various monitoring wells. The spatio-temporal variation of hydrological variables in the study area was found to have influenced by the morphological change of the river and its flood plain in the last 120 years. The old river-channels have served as preferential flow pathways in the floodplain, which caused rapid change in groundwater level in some groundwater monitoring wells. The presence of some form of organized heterogeneity in the electrical conductivity in the study area was indicative of one of the effects of historic changes of the floodplain and river channel-pathways. ANN-based models formulated using the pre-construction period data offered benchmarks to analyse the effect of construction on the floodplain hydrology. Based on the interpretation of monitored data and results of ANN modeling, it was clearly found that the effect of construction on the floodplain hydrology was significant.

  9. Evaluating the Impacts of Health, Social Network and Capital on Craft Efficiency and Productivity: A Case Study of Construction Workers in China.

    Science.gov (United States)

    Yuan, Jingfeng; Yi, Wen; Miao, Mengyi; Zhang, Lei

    2018-02-15

    The construction industry has been recognized, for many years, as among those having a high likelihood of accidents, injuries and occupational illnesses. Such risks of construction workers can lead to low productivity and social problems. As a result, construction workers' well-being should be highly addressed to improve construction workers' efficiency and productivity. Meanwhile, the social support from a social network and capital (SNC) of construction workers has been considered as an effective approach to promote construction workers' physical and mental health (P&M health), as well as their work efficiency and productivity. Based on a comprehensive literature review, a conceptual model, which aims to improve construction workers' efficiency and productivity from the perspective of health and SNC, was proposed. A questionnaire survey was conducted to investigate the construction workers' health, SNC and work efficiency and productivity in Nanjing, China. A structural equation model (SEM) was employed to test the three hypothetical relationships among construction workers' P&M health, SNC and work efficiency and productivity. The results indicated that the direct impacts from construction workers' P&M health on work efficiency and productivity were more significant than that from the SNC. In addition, the construction workers' social capital and the network can indirectly influence the work efficiency and productivity by affecting the construction workers' P&M health. Therefore, strategies for enhancing construction workers' efficiency and productivity were proposed. Furthermore, many useable suggestions can be drawn from the research findings from the perspective of a government. The identified indicators and relationships would contribute to the construction work efficiency and productivity assessment and health management from the perspective of the construction workers.

  10. Classifiers of quantity and quality in Romanian

    Directory of Open Access Journals (Sweden)

    Mihaela Tănase-Dogaru

    2013-11-01

    Full Text Available The present paper proposes that classifiers in Romanian pertain to two distinct categories: classifiers of quantity or “massifiers” and classifiers of quality or “count-classifiers”, to borrow the terms from Cheng and Sybesma (1999. The first category is represented by the first nominal in a pseudopartitive construction of the type o bucată de brânză / a piece of cheese (Tănase-Dogaru 2009. The second category is represented by the first nominal in the so-called restrictive appositives, an example of which is Planeta Venus / the planet Venus (van Riemsdijk 1998, Cornilescu 2007. An important result of the paper is the unification under a similar treatment of concepts which are generally offered different analyses in the literature.

  11. Actor networks and the construction of applicable knowledge: the case of the Timbre Brownfield Prioritization Tool

    Czech Academy of Sciences Publication Activity Database

    Alexandrescu, F.; Klusáček, Petr; Bartke, S.; Osman, Robert; Frantál, Bohumil; Martinát, Stanislav; Kunc, Josef; Pizzol, L.; Zabeo, A.; Giubilato, E.; Critto, A.; Bleicher, A.

    2017-01-01

    Roč. 19, č. 5 (2017), s. 1323-1334 ISSN 1618-954X R&D Projects: GA MŠk(CZ) 7E11035; GA ČR(CZ) GA17-26934S Institutional support: RVO:68145535 Keywords : actor network theory * applicable knowledge * brownfield prioritization * four moments of translation * end-users Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 3.331, year: 2016 https://link.springer.com/article/10.1007/s10098-016-1331-8

  12. Gearbox Condition Monitoring Using Advanced Classifiers

    Directory of Open Access Journals (Sweden)

    P. Večeř

    2010-01-01

    Full Text Available New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS. Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing. 

  13. Construction and comparison of gene co-expression networks shows complex plant immune responses

    Directory of Open Access Journals (Sweden)

    Luis Guillermo Leal

    2014-10-01

    Full Text Available Gene co-expression networks (GCNs are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA. Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses.

  14. Construction and comparison of gene co-expression networks shows complex plant immune responses.

    Science.gov (United States)

    Leal, Luis Guillermo; López, Camilo; López-Kleine, Liliana

    2014-01-01

    Gene co-expression networks (GCNs) are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA). Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses.

  15. Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

    Science.gov (United States)

    Hargrave, C.; Moores, M.; Deegan, T.; Gibbs, A.; Poulsen, M.; Harden, F.; Mengersen, K.

    2014-03-01

    A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific subregions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

  16. Neural Networks as a Tool for Constructing Continuous NDVI Time Series from AVHRR and MODIS

    Science.gov (United States)

    Brown, Molly E.; Lary, David J.; Vrieling, Anton; Stathakis, Demetris; Mussa, Hamse

    2008-01-01

    The long term Advanced Very High Resolution Radiometer-Normalized Difference Vegetation Index (AVHRR-NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non-stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor-specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at one degree is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.

  17. Detecting and classifying faults on transmission systems using a backpropagation neural network; Deteccion y clasificacion de fallas en sistemas de transmision empleando una red neuronal con retropropagacion del error

    Energy Technology Data Exchange (ETDEWEB)

    Rosas Ortiz, German

    2000-01-01

    Fault detection and diagnosis on transmission systems is an interesting area of investigation to Artificial Intelligence (AI) based systems. Neurocomputing is one of fastest growing areas of research in the fields of AI and pattern recognition. This work explores the possible suitability of pattern recognition approach of neural networks for fault detection and classification on power systems. The conventional detection techniques in modern relays are based in digital processing of signals and it need some time (around 1 cycle) to send a tripping signal, also they are likely to make incorrect decisions if the signals are noisy. It's desirable to develop a fast, accurate and robust approach that perform accurately for changing system conditions (like load variations and fault resistance). The aim of this work is to develop a novel technique based on Artificial Neural Networks (ANN), which explores the suitability of a pattern classification approach for fault detection and diagnosis. The suggested approach is based in the fact that when a fault occurs, a change in the system impedance take place and, as a consequence changes in amplitude and phase of line voltage and current signals take place. The ANN-based fault discriminator is trained to detect this changes as indicators of the instant of fault inception. This detector uses instantaneous values of these signals to make decisions. Suitability of using neural network as pattern classifiers for transmission systems fault diagnosis is described in detail a neural network design and simulation environment for real-time is presented. Results showing the performance of this approach are presented and indicate that it is fast, secure and exact enough, and it can be used in high speed fault detection and classification schemes. [Spanish] El diagnostico y la deteccion de fallas en sistemas de transmision es una area de interes en investigacion para sistemas basados en Inteligencia Artificial (IA). El calculo neuronal

  18. A Classifier Learning Method through Data Summaries

    Science.gov (United States)

    Suematsu, Nobuo; Nakayasu, Toshiko; Hayashi, Akira

    Knowledge discovery in databases (KDD) has been studied intensively recent years. In KDD, inductive classifier learning methods which were developed in statistics and machine learning have been used to extract classification rules from databases. Although in KDD we have to deal with large databases in many cases, many of the previous classifier learning methods are not suitable for large databases. They were designed under assumption that any data in databases is accessible on demand and they usually need to access a datum several times in a process of learning. So, they require a huge memory space or a large I/O cost to access storage devices. In this paper, we propose a classifier learning method, we call CIDRE, in which data summaries are constructed and classifiers are learned from the summaries. This learning method is realized by using a clustering method, we call MCF-tree, which is an extension of CF-tree proposed by Zhang et al. In the method, we can specify the size of memory space occupied by data summaries, and databases are swept only once to construct the summaries. In addition, new instances can be inserted into the summaries incrementally. Thus, the method possesses important properties which are desirable to deal with large databases. We also show empirical results, which indicate that our method performs very well in comparison to C4.5 and naive Bayes, and the extension from CF-tree to MCF-tree is indispensable to achieve high classification accuracy.

  19. Hailstone classifier based on Rough Set Theory

    Science.gov (United States)

    Wan, Huisong; Jiang, Shuming; Wei, Zhiqiang; Li, Jian; Li, Fengjiao

    2017-09-01

    The Rough Set Theory was used for the construction of the hailstone classifier. Firstly, the database of the radar image feature was constructed. It included transforming the base data reflected by the Doppler radar into the bitmap format which can be seen. Then through the image processing, the color, texture, shape and other dimensional features should be extracted and saved as the characteristic database to provide data support for the follow-up work. Secondly, Through the Rough Set Theory, a machine for hailstone classifications can be built to achieve the hailstone samples’ auto-classification.

  20. POSSIBILITIES OF THE USE OF GRP PIPING IN THE CONSTRUCTION AND RECONSTRUCTION OF ENGINEERING NETWORKS

    Directory of Open Access Journals (Sweden)

    ikitina Irina Nikolaevna

    2015-12-01

    Full Text Available Today in modern construction new technologies and materials are used for the manufacture of pipelines for water supply and sanitation. They are supposed to operate for at least 50 years. Unlike plastic pipes, fiberglass ones may be made of larger sizes — up to 3700 mm in diameter. They are produced using the technology of optical fiber winding, which is carried out according to modern international standards of quality. The basic raw materials — fiberglass and resin — are produced in Russia, but their production is limited, so they are purchased abroad, which increases the cost of manufacture of this type of piping. However, due to the necessity of laying pipelines of large diameter, which cannot be made with plastic pipes, the manufacture of GRP pipes will increase. The experience of laying and constructing this type of pipelines, for example, in the areas of hot water supply allows concluding that they are able to withstand the temperatures of up to 150 °C, while their weight is four times less than the weight of steel pipes (they are easily installed with the help of small lifting equipment and by a team of six people. It should be noted that the use of fiberglass pipes helps to reduce the costs of system operation, because this type of piping is not subject to corrosion and encrustation of the inner surface, since it has a low level of roughness, which, for example, is 0.013 for a steel pipe, and 0.01 for fiberglass pipe. Thus, it is not necessary to put protective corrosion-resistant coatings and to provide an expensive protection against electrochemical corrosion. Piping made of fiberglass pipes can be designed as underground, above-ground with stacking or raised on poles. It is possible to combine these options.

  1. A Nonlinear Multiobjective Bilevel Model for Minimum Cost Network Flow Problem in a Large-Scale Construction Project

    Directory of Open Access Journals (Sweden)

    Jiuping Xu

    2012-01-01

    Full Text Available The aim of this study is to deal with a minimum cost network flow problem (MCNFP in a large-scale construction project using a nonlinear multiobjective bilevel model with birandom variables. The main target of the upper level is to minimize both direct and transportation time costs. The target of the lower level is to minimize transportation costs. After an analysis of the birandom variables, an expectation multiobjective bilevel programming model with chance constraints is formulated to incorporate decision makers’ preferences. To solve the identified special conditions, an equivalent crisp model is proposed with an additional multiobjective bilevel particle swarm optimization (MOBLPSO developed to solve the model. The Shuibuya Hydropower Project is used as a real-world example to verify the proposed approach. Results and analysis are presented to highlight the performances of the MOBLPSO, which is very effective and efficient compared to a genetic algorithm and a simulated annealing algorithm.

  2. The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations

    Directory of Open Access Journals (Sweden)

    Hyeim Jung

    2015-09-01

    Full Text Available Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.

  3. The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations.

    Science.gov (United States)

    Jung, Hyeim; Han, Seonggyun; Kim, Sangsoo

    2015-09-01

    Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.

  4. Predictive structural dynamic network analysis.

    Science.gov (United States)

    Chen, Rong; Herskovits, Edward H

    2015-04-30

    Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data. Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models. We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity. For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002). We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Implications of physical symmetries in adaptive image classifiers

    DEFF Research Database (Denmark)

    Sams, Thomas; Hansen, Jonas Lundbek

    2000-01-01

    It is demonstrated that rotational invariance and reflection symmetry of image classifiers lead to a reduction in the number of free parameters in the classifier. When used in adaptive detectors, e.g. neural networks, this may be used to decrease the number of training samples necessary to learn...

  6. SAINT: Supervised Actor Identification for Network Tuning

    Science.gov (United States)

    Farrugia, Michael; Hurley, Neil; Quigley, Aaron

    Whenever the actors of a social network are not uniquely identifiable in the data, then entity resolution in the form of actor identification becomes a critical facet of a social network construction process. Here we develop SAINT, a pipeline for supervised entity resolution that uses relational information to improve, or tune, the quality of the constructed network. The first phase of SAINT uses attribute only based entity resolution to create an initial social network. Relational information between actors, actor network properties and other relational output of the first classification phase, are used in a second phase to improve the results of the original entity resolution. When compared to single phased approaches, the results from this two phased approach are consistently superior in both recall and precision measures. Embedded within SAINT are a series of evaluation checkpoints designed to measure both the quality of the individual classifiers and their impact within the entire pipeline. Our evaluation results provide insight on the potential propagation of error and open research questions for further improvement of the individual classifiers within the entire pipeline. As the main application of the process is to improve actor identification in social networks, we characterise the impact that entity resolution has on the final constructed network. We compare the network constructed using SAINT with a ground truth network using perfect entity resolution and use global and local network measures to study the differences.

  7. Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

    Science.gov (United States)

    Yang, Yi; Hu, Xiao-Pan; Ma, Bin-Guang

    2017-02-28

    Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

  8. Emergent behaviors of classifier systems

    Energy Technology Data Exchange (ETDEWEB)

    Forrest, S.; Miller, J.H.

    1989-01-01

    This paper discusses some examples of emergent behavior in classifier systems, describes some recently developed methods for studying them based on dynamical systems theory, and presents some initial results produced by the methodology. The goal of this work is to find techniques for noticing when interesting emergent behaviors of classifier systems emerge, to study how such behaviors might emerge over time, and make suggestions for designing classifier systems that exhibit preferred behaviors. 20 refs., 1 fig.

  9. Construction of transport and energy networks in the Baltic region as an impetus for regional development

    Directory of Open Access Journals (Sweden)

    Kuznetsov Alexey

    2013-11-01

    Full Text Available In light of some new aspects of the EU functioning, particularly, the recovery from the 2008-2009 global crisis, transportation and energy development projects are coming to the forefront in the Baltic region. At the same time, there is a need to consider EU’s recent adoption of a common seven-year financial program (2014—2020, which serves, in effect, as the Union’s budget. Given that, one may conclude that the countries of the Baltic region are entering a new stage of development. We look at the role and significance of transportation and energy projects as an instrument of economic development. Having studied the largest transport and energy projects in the Baltic region, we were able to show that the new infrastructure networks supported the investment expansion of Swedish and Finnish companies into the post-communist countries of the Baltic Region. Which, in its turn, allowed the Nordic investors to expand their domestic markets. The analysis also shows that the experience of private businesses proves a recent theoretical concept — the pyramid of regional development factors. As a result, the actual regional policy of the EU cannot be considered in the narrow sense of the Cohesion Policy alone.

  10. Construction of transport and energy networks in the Baltic region as an impetus for regional development

    Directory of Open Access Journals (Sweden)

    Kuznetsov Alexey

    2013-12-01

    Full Text Available In light of some new aspects of the EU functioning, particularly, the recovery from the 2008-2009 global crisis, transportation and energy development projects are coming to the forefront in the Baltic region. At the same time, there is a need to consider EU’s recent adoption of a common seven-year financial program (2014—2020, which serves, in effect, as the Union’s budget. Given that, one may conclude that the countries of the Baltic region are entering a new stage of development. We look at the role and significance of transportation and energy projects as an instrument of economic development. Having studied the largest transport and energy projects in the Baltic region, we were able to show that the new infrastructure networks supported the investment expansion of Swedish and Finnish companies into the post-communist countries of the Baltic Region. Which, in its turn, allowed the Nordic investors to expand their domestic markets. The analysis also shows that the experience of private businesses proves a recent theoretical concept — the pyramid of regional development factors. As a result, the actual regional policy of the EU cannot be considered in the narrow sense of the Cohesion Policy alone.

  11. Construction of transport and energy networks in the Baltic region as an impetus for regional development

    Directory of Open Access Journals (Sweden)

    Kuznetsov Alexey

    2013-01-01

    Full Text Available In light of some new aspects of the EU functioning, particularly, the recovery from the 2008-2009 global crisis, transportation and energy development projects are coming to the forefront in the Baltic region. At the same time, there is a need to consider EU’s recent adoption of a common seven-year financial program (2014—2020, which serves, in effect, as the Union’s budget. Given that, one may conclude that the countries of the Baltic region are entering a new stage of development. We look at the role and significance of transportation and energy projects as an instrument of economic development. Having studied the largest transport and energy projects in the Baltic region, we were able to show that the new infrastructure networks supported the investment expansion of Swedish and Finnish companies into the post-communist countries of the Baltic Region. Which, in its turn, allowed the Nordic investors to expand their domestic markets. The analysis also shows that the experience of private businesses proves a recent theoretical concept — the pyramid of regional development factors. As a result, the actual regional policy of the EU cannot be considered in the narrow sense of the Cohesion Policy alone.

  12. Ganoderma lucidum polysaccharides in human monocytic leukemia cells: from gene expression to network construction

    Directory of Open Access Journals (Sweden)

    Ou Chern-Han

    2007-11-01

    Full Text Available Abstract Background Ganoderma lucidum has been widely used as a herbal medicine for promoting health and longevity in China and other Asian countries. Polysaccharide extracts from Ganoderma lucidum have been reported to exhibit immuno-modulating and anti-tumor activities. In previous studies, F3, the active component of the polysaccharide extract, was found to activate various cytokines such as IL-1, IL-6, IL-12, and TNF-α. This gave rise to our investigation on how F3 stimulates immuno-modulating or anti-tumor effects in human leukemia THP-1 cells. Results Here, we integrated time-course DNA microarray analysis, quantitative PCR assays, and bioinformatics methods to study the F3-induced effects in THP-1 cells. Significantly disturbed pathways induced by F3 were identified with statistical analysis on microarray data. The apoptosis induction through the DR3 and DR4/5 death receptors was found to be one of the most significant pathways and play a key role in THP-1 cells after F3 treatment. Based on time-course gene expression measurements of the identified pathway, we reconstructed a plausible regulatory network of the involved genes using reverse-engineering computational approach. Conclusion Our results showed that F3 may induce death receptor ligands to initiate signaling via receptor oligomerization, recruitment of specialized adaptor proteins and activation of caspase cascades.

  13. Exchanging expertise and constructing boundaries: The development of a transnational knowledge network around heroin-assisted treatment.

    Science.gov (United States)

    Duke, Karen

    2016-05-01

    Over the last 20 years, supervised injectable and inhalable heroin prescribing has been developed, tested and in some cases introduced as a second line treatment for limited groups of entrenched heroin users in a number of European countries and Canada. Based on documentary analyses and eleven key informant interviews, this paper investigates the growth of 'expertise' and the sharing of knowledge between scientific stakeholders from different countries involved in researching and developing this area of treatment. Drawing on Stone's concept of the 'knowledge network' (Stone, 2013) and Gieryn's theory of 'boundary-work' (Gieryn, 1983), the analysis demonstrates the collective power of this group of scientists in producing a particular form of knowledge and expertise which has accrued and been exchanged over time. It also illustrates the ways in which this type of science has gained credibility and authority and become legitimised, reinforced and reproduced by those who employ it in both scientific and political debates. Boundaries were constructed by the knowledge network between different types of professions/disciplines, different forms of science and between the production of science and its consumption by non-scientists. The uniformity of the knowledge network in terms of their professional and disciplinary backgrounds, methodological expertise and ideological perspectives has meant that alternative forms of knowledge and perspectives have been neglected. This limits the nature and scope of the scientific evidence on which to base policy and practice decisions impacting on the work of policy makers and practitioners as well as the experiences of those in treatment who are most affected by this research and policy development. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. Classified

    CERN Multimedia

    Computer Security Team

    2011-01-01

    In the last issue of the Bulletin, we have discussed recent implications for privacy on the Internet. But privacy of personal data is just one facet of data protection. Confidentiality is another one. However, confidentiality and data protection are often perceived as not relevant in the academic environment of CERN.   But think twice! At CERN, your personal data, e-mails, medical records, financial and contractual documents, MARS forms, group meeting minutes (and of course your password!) are all considered to be sensitive, restricted or even confidential. And this is not all. Physics results, in particular when being preliminary and pending scrutiny, are sensitive, too. Just recently, an ATLAS collaborator copy/pasted the abstract of an ATLAS note onto an external public blog, despite the fact that this document was clearly marked as an "Internal Note". Such an act was not only embarrassing to the ATLAS collaboration, and had negative impact on CERN’s reputation --- i...

  15. Telepresence teacher professional development for physics and math constructs focused on US and Thai classrooms' TC-1 slinky seismometer networks

    Science.gov (United States)

    Livelybrooks, D.; Parris, B. A.; Cook, A.; Kant, M.; Wogan, N.; Zeryck, A.; Tulyatid, D.; Toomey, D. R.

    2015-12-01

    As part of the Broader Impacts of the Cascadia Initiative, a seismic study of the Cascadia margin, and the Magnetotelluric Observations of Cascadia using a Huge Array (MOCHA) collaboration we have developed school- and museum/library-based networks of TC-1 educational seismometers. The TC-1 is constructed such that its 'guts' are visible through an transparent acrylic outer cylinder, thus it is an excellent demonstration of how fundamental physics constructs can be leveraged to design and operate a vertical-channel seismometer capable of recording signals from large earthquakes world-wide. TC-1 (aka 'slinky seismometer') networks therefore serve as the application for projects-based learning (PBL) physics and data science instruction in Oregon and Thai classrooms. The TC-1 acts as a simple harmonic oscillator, employing electromagnetic induction of a moving magnet within a wire coil. Movement of the lower magnet within an electrically conductive pipe dampens motion such that P-, S- and Surface wave phases can be identified. Further, jAmaSeis software can be configured to simultaneously show live signals from three TC-1s and has tools necessary to pick phases for earthquake signals and, thus, locate earthquake epicenters. Leveraging a long-standing collaboration between the Royal Thai Distance Learning Foundation and the University of Oregon, we developed five, 2-hour, two-way teacher professional development sessions that were transmitted live to Thai K-12 teachers and others starting mid-August, 2015. As an example, one session emphasized hands-on activities to analyze the effect of spring stiffness, inertial mass and initial displacement on the resonance frequency of a simple oscillator. Another pedagogical goal was to elucidate how math is important to understanding the analysis of seismic data, for example, how cross-correlation is useful for distinguishing between genuine earthquake signals and, say, a truck rolling by a TC-1 station. UO graduate and

  16. Classifying objects in LWIR imagery via CNNs

    Science.gov (United States)

    Rodger, Iain; Connor, Barry; Robertson, Neil M.

    2016-10-01

    The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave Infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.

  17. A receiver-initiated WDM multicast tree construction protocol to support IP dense mode multicast routing in all-optical Lambda-switched networks

    NARCIS (Netherlands)

    Niemegeers, I.G.M.M.; Salvador, M.R.; Heemstra de Groot, S.M.; Dey, D.

    We describe a source-rooted WDM multicast tree construction protocol to support IP dense mode multicast routing protocols in all-optical lambda-switched networks. Using a receiver-initiated approach, the protocol relies on a mechanism that collects information on the capabilities of WDM nodes. This

  18. [Construction of urban green space ecosystem by using corridor network: a case study in west urban area of Dongying City, Shandong Province].

    Science.gov (United States)

    Huang, Yi; Chen, Hui; Huang, Zhiji; Cai, Mantang; Kang, Junshui

    2006-09-01

    This paper discussed the ecological significance of urban green corridors network in urban green space ecosystem, analyzed the present status of green space ecosystem in the west urban area of Dongying in Shangdong Province, and figured out the ways of constructing urban green corridors network in this area to strengthen the linkage between its fragmented greenbelts, and between these greenbelts and rural natural environment. Through the construction of this network, the greenbelt area in the west urban area of Dongying would increase 1400 hm2, greenbelt area per capita would increase to 66 m2, and urban and rural greenbelts would be integrated into a whole system to serve the whole city, giving a powerful support to enhance the life quality of local people and the stability of urban ecosystem.

  19. A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network.

    Science.gov (United States)

    Guo, Hongliang; Meng, Yan; Jin, Yaochu

    2009-12-01

    A major research challenge of multi-robot systems is to predict the emerging behaviors from the local interactions of the individual agents. Biological systems can generate robust and complex behaviors through relatively simple local interactions in a world characterized by rapid changes, high uncertainty, infinite richness, and limited availability of information. Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and development of biological organisms from cells. In this paper, inspired by biological organisms, we propose a distributed GRN-based algorithm for a multi-robot construction task. Through this algorithm, multiple robots can self-organize autonomously into different predefined shapes, and self-reorganize adaptively under dynamic environments. This developmental process is evolved using a multi-objective optimization algorithm to achieve a shorter travel distance and less convergence time. Furthermore, a theoretical proof of the system's convergence is also provided. Various case studies have been conducted in the simulation, and the results show the efficiency and convergence of the proposed method.

  20. Exploring the Acquisition and Production of Grammatical Constructions Through Human-Robot Interaction with Echo State Networks

    Directory of Open Access Journals (Sweden)

    Xavier eHinaut

    2014-05-01

    Full Text Available One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot’s execution of these events in time. Second, this learning mechanism functions in the inverse sense, i.e. in a language production mode, where rather than executing commanded actions, the robot will describe the results of human generated actions. Finally, we collect data from naïve subjects who interact with the robot via spoken language, and demonstrate significant learning and generalization results. This allows us to conclude that such a neural language learning system not only helps to characterize and understand some aspects of human language acquisition, but also that it can be useful in adaptive human-robot interaction.

  1. Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks.

    Science.gov (United States)

    Hinaut, Xavier; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

    2014-01-01

    One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot's execution of these events in time. Second, this learning mechanism functions in the inverse sense, i.e., in a language production mode, where rather than executing commanded actions, the robot will describe the results of human generated actions. Finally, we collect data from naïve subjects who interact with the robot via spoken language, and demonstrate significant learning and generalization results. This allows us to conclude that such a neural language learning system not only helps to characterize and understand some aspects of human language acquisition, but also that it can be useful in adaptive human-robot interaction.

  2. Gender identity construction in virtual political communication: Discourse strategies and self-representation practices in social networks

    Directory of Open Access Journals (Sweden)

    O B Maximova

    2016-12-01

    Full Text Available The article considers the specifics of gender construction in political virtual communication. The author employs the discursive approach combined with the content analysis to study gender communicative strategies and self-representation practices in social networks. Facebook postings within a viral flash-mob “The Island of the‘90’s” provided the data for the study, and the article explains the rationale for their attribution to political communication. The analysis based on the cognitive model of discourse revealed the three-level structure of flash-mob discourse that reflects different levels of publications involvement into the political context. The comparative analysis of male and female participation in the development of flash-mob discourse helped to identify a pronounced gender asymmetry in both flash-mob discourse structure and its structural levels. The content analysis showed no signs of gender differences disappearance, traditional gender-role patterns weakening or androgyne model manifestation. The results lead to the conclusion that women seem to be more flexible in their representation strategies, and women’s participation in the discourse has less degree of political involvement at all structural levels. The author highlights the theoretical importance of the Internet flash-mob investigation methodology development for the study of virtual political communication.

  3. Development of Hybrid Model for Estimating Construction Waste for Multifamily Residential Buildings Using Artificial Neural Networks and Ant Colony Optimization

    OpenAIRE

    Dongoun Lee; Seungho Kim; Sangyong Kim

    2016-01-01

    Due to the increasing costs of construction waste disposal, an accurate estimation of the amount of construction waste is a key factor in a project’s success. Korea has been burdened by increasing construction waste as a consequence of the growing number of construction projects and a lack of construction waste management (CWM) strategies. One of the problems associated with predicting the amount of waste is that there are no suitable estimation strategies currently available. Therefore, we d...

  4. 76 FR 34761 - Classified National Security Information

    Science.gov (United States)

    2011-06-14

    ... Classified National Security Information AGENCY: Marine Mammal Commission. ACTION: Notice. SUMMARY: This..., ``Classified National Security Information,'' and 32 CFR part 2001, ``Classified National Security Information.... Executive Order 13526, ``Classified National Security Information,'' December 29, 2009 b. 32 CFR part 2001...

  5. Método para classificação de tipos de erros humanos: estudo de caso em acidentes em canteiros de obras An algorithm for classifying error types of front-line workers: a case study in accidents in construction sites

    Directory of Open Access Journals (Sweden)

    Tarcisio Abreu Saurin

    2012-04-01

    Full Text Available Este trabalho tem como objetivo principal desenvolver melhorias em um método de classificação de tipos de erros humanos de operadores de linha de frente. Tais melhorias foram desenvolvidas com base no teste do método em canteiros de obras, um ambiente no qual ele ainda não havia sido aplicado. Assim, foram investigados 19 acidentes de trabalho ocorridos em uma construtora de pequeno porte, sendo classificados os tipos de erros dos trabalhadores lesionados e de colegas de equipe que se encontravam no cenário do acidente. Os resultados indicaram que não houve nenhum erro em 70,5% das 34 vezes em que o método foi aplicado, evidenciando que as causas dos acidentes estavam fortemente associadas a fatores organizacionais. O estudo apresenta ainda recomendações para a interpretação das perguntas que constituem o método, bem como modificações em algumas dessas perguntas em comparação às versões anteriores.The objective of this study is to propose improvements in the algorithm for classifying error types of front-line workers. The improvements have been identified on the basis of testing the algorithm in construction sites, an environment where it had not been implemented it. To this end, 19 occupational accidents which occurred in a small construction company were investigated, and the error types of both injured workers and team members were classified. The results indicated that there was no error in 70.5% of the 34 times the algorithm was applied, providing evidence that the causes were strongly linked to organizational factors. Moreover, the study presents not only recommendations to facilitate the interpretation of the questions that constitute the algorithm, but also changes in some questions in comparison to the previous versions of the tool.

  6. Classifying Variable Sources in SDSS Stripe 82

    Science.gov (United States)

    Willecke Lindberg, Christina

    2018-01-01

    SDSS (Sloan Digital Sky Survey) Stripe 82 is a well-documented and researched region of the sky that does not have all of its ~67,500 variable objects labeled. By collecting data and consulting different catalogs such as the Catalina Survey, we are able to slowly cross-match more objects and add classifications within the Stripe 82 catalog. Such matching is performed either by pairing SDSS identification numbers, or by converting and comparing the coordinates of every object within the Stripe 82 catalog to every object within the classified catalog, such as the Catalina Survey catalog. If matching is performed with converted coordinates, a follow-up check is performed to ascertain that the magnitudes of the paired objects are within a reasonable margin of error and that objects have not been mismatched. Once matches have been confirmed, the light curves of classified objects can then be used to determine features that most effectively separate the different types of variable objects in feature spaces. By classifying variable objects, we can construct a reference for subsequent large research surveys, such as LSST (the Large Synoptic Survey Telescope), that could utilize SDSS data as a training set for its own classifications.

  7. Construction of a lncRNA-mediated feed-forward loop network reveals global topological features and prognostic motifs in human cancers.

    Science.gov (United States)

    Ning, Shangwei; Gao, Yue; Wang, Peng; Li, Xiang; Zhi, Hui; Zhang, Yan; Liu, Yue; Zhang, Jizhou; Guo, Maoni; Han, Dong; Li, Xia

    2016-07-19

    Long non-coding RNAs (lncRNAs), transcription factors and microRNAs can form lncRNA-mediated feed-forward loops (L-FFLs), which are functional network motifs that regulate a wide range of biological processes, such as development and carcinogenesis. However, L-FFL network motifs have not been systematically identified, and their roles in human cancers are largely unknown. In this study, we computationally integrated data from multiple sources to construct a global L-FFL network for six types of human cancer and characterized the topological features of the network. Our approach revealed several dysregulated L-FFL motifs common across different cancers or specific to particular cancers. We also found that L-FFL motifs can take part in other types of regulatory networks, such as mRNA-mediated FFLs and ceRNA networks, and form the more complex networks in human cancers. In addition, survival analyses further indicated that L-FFL motifs could potentially serve as prognostic biomarkers. Collectively, this study elucidated the roles of L-FFL motifs in human cancers, which could be beneficial for understanding cancer pathogenesis and treatment.

  8. 3D Bayesian contextual classifiers

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    2000-01-01

    We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....

  9. In-situ construction of three-dimensional titania network on Ti foil toward enhanced performance of flexible dye-sensitized solar cells

    DEFF Research Database (Denmark)

    Rui, Yichuan; Wang, Yuanqiang; Zhang, Qinghong

    2016-01-01

    Three-dimensional titania network was in-situ constructed on Ti foil via sequential acid and hydrogen peroxide treatments. The titania network was pure anatase phase and homogeneously covered on the titanium grain surface, which largely enhanced the roughness of the Ti foil. The as-received Ti foil...... pathways and recombination inhibition of electrons in Ti substrate with triiodide ions in electrolyte. Flexible DSSCs based on the treated Ti foil showed relatively good mechanical stability, which exhibited 97.3% retention of the initial efficieny after twenty consecutive bending....

  10. Classifying Cereal Data (Earlier Methods)

    Science.gov (United States)

    The DSQ includes questions about cereal intake and allows respondents up to two responses on which cereals they consume. We classified each cereal reported first by hot or cold, and then along four dimensions: density of added sugars, whole grains, fiber, and calcium.

  11. The networked student: A design-based research case study of student constructed personal learning environments in a middle school science course

    Science.gov (United States)

    Drexler, Wendy

    This design-based research case study applied a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted emerging Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. The personal learning environments constructed used Application Programming Interface (API) widgets to access, organize, and synthesize content from a number of educational Internet resources and social network connections. This study examined the nature of personal learning environments; the processes students go through during construction, and patterns that emerged. The project was documented from both an instructional and student-design perspective. Findings revealed that students applied the processes of: practicing digital responsibility; practicing digital literacy; organizing content; collaborating and socializing; and synthesizing and creating. These processes informed a model of the networked student that will serve as a framework for future instructional designs. A networked learning approach that incorporates these processes into future designs has implications for student learning, teacher roles, professional development, administrative policies, and delivery. This work is significant in that it shifts the focus from technology innovations based on tools to student empowerment based on the processes required to support learning. It affirms the need for greater attention to digital literacy and responsibility in K12 schools as well as consideration for those skills students will need to achieve success in the 21st century. The design-based research case study provides a set of design principles for teachers to follow when facilitating student construction of personal learning environments.

  12. In-situ construction of three-dimensional titania network on Ti foil toward enhanced performance of flexible dye-sensitized solar cells

    Energy Technology Data Exchange (ETDEWEB)

    Rui, Yichuan [State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Donghua University, Shanghai 201620 (China); College of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620 (China); Wang, Yuanqiang [State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Donghua University, Shanghai 201620 (China); Zhang, Qinghong, E-mail: zhangqh@dhu.edu.cn [State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Donghua University, Shanghai 201620 (China); Chi, Qijin; Zhang, Minwei [Department of Chemistry, Technical University of Denmark, DK-2800 Kongens Lyngby (Denmark); Wang, Hongzhi; Li, Yaogang [State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Donghua University, Shanghai 201620 (China); Hou, Chengyi, E-mail: chehou@kemi.dtu.dk [State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Donghua University, Shanghai 201620 (China); Department of Chemistry, Technical University of Denmark, DK-2800 Kongens Lyngby (Denmark)

    2016-09-01

    Graphical abstract: - Highlights: • Three-dimensional titania network was in-situ constructed on Ti foil via sequential acid and hydrogen peroxide treatments. • All-flexible DSSCs based on the treated Ti foil showed significantly improved energy conversion efficiency. • Large-area flexible DSSCs exhibited good bending-stability due to the existence of the three-dimensional titania network. - Abstract: Three-dimensional titania network was in-situ constructed on Ti foil via sequential acid and hydrogen peroxide treatments. The titania network was pure anatase phase and homogeneously covered on the titanium grain surface, which largely enhanced the roughness of the Ti foil. The as-received Ti foil and the treated one were used as the flexible substrates of DSSCs, and energy conversion efficiencies of 3.74% and 4.98% were obtained, respectively. Such remarkable increment can be ascribed to the good electrical contact between the nanocrystalline TiO{sub 2} and the Ti foil, the improved electron percolation pathways and recombination inhibition of electrons in Ti substrate with triiodide ions in electrolyte. Flexible DSSCs based on the treated Ti foil showed relatively good mechanical stability, which exhibited 97.3% retention of the initial efficieny after twenty consecutive bending.

  13. Fingerprint prediction using classifier ensembles

    CSIR Research Space (South Africa)

    Molale, P

    2011-11-01

    Full Text Available for improving fingerprint prediction accuracy. The study is organized as follows. The next section briefly gives details of the five classifiers used in this study, followed by a description of different types of MCS architectures. Then the robustness... discrimination (LgDA): Logistic Discrimination Analysis (LgDA), due to Cox (1966) is related to logistic regression analysis. The dependent variable can only take values of 0 and 1, say, given two classes. This technique is partially parametric...

  14. Multiple classifier integration for the prediction of protein structural classes.

    Science.gov (United States)

    Chen, Lei; Lu, Lin; Feng, Kairui; Li, Wenjin; Song, Jie; Zheng, Lulu; Yuan, Youlang; Zeng, Zhenbin; Feng, Kaiyan; Lu, Wencong; Cai, Yudong

    2009-11-15

    Supervised classifiers, such as artificial neural network, partition trees, and support vector machines, are often used for the prediction and analysis of biological data. However, choosing an appropriate classifier is not straightforward because each classifier has its own strengths and weaknesses, and each biological dataset has its own characteristics. By integrating many classifiers together, people can avoid the dilemma of choosing an individual classifier out of many to achieve an optimized classification results (Rahman et al., Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variation, Springer, Berlin, 2002, 167-178). The classification algorithms come from Weka (Witten and Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, 2005) (a collection of software tools for machine learning algorithms). By integrating many predictors (classifiers) together through simple voting, the correct prediction (classification) rates are 65.21% and 65.63% for a basic training dataset and an independent test set, respectively. These results are better than any single machine learning algorithm collected in Weka when exactly the same data are used. Furthermore, we introduce an integration strategy which takes care of both classifier weightings and classifier redundancy. A feature selection strategy, called minimum redundancy maximum relevance (mRMR), is transferred into algorithm selection to deal with classifier redundancy in this research, and the weightings are based on the performance of each classifier. The best classification results are obtained when 11 algorithms are selected by mRMR method, and integrated together through majority votes with weightings. As a result, the prediction correct rates are 68.56% and 69.29% for the basic training dataset and the independent test dataset, respectively. The web-server is available at http

  15. A deep learning method for classifying mammographic breast density categories.

    Science.gov (United States)

    Mohamed, Aly A; Berg, Wendie A; Peng, Hong; Luo, Yahong; Jankowitz, Rachel C; Wu, Shandong

    2018-01-01

    Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples

  16. Classification of wheat varieties: Use of two-dimensional gel electrophoresis for varieties that can not be classified by matrix assisted laser desorption/ionization-time of flight-mass spectrometry and an artificial neural network

    DEFF Research Database (Denmark)

    Jacobsen, Susanne; Nesic, Ljiljana; Petersen, Marianne Kjerstine

    2001-01-01

    Analyzing a gliadin extract by matrix assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI- TOF-MS) combined with an artificial neural network (ANN) is a suitable method for identification of wheat varieties. However, the ANN can not distinguish between all different wheat...

  17. Fcoused crawler bused on Bayesian classifier

    Directory of Open Access Journals (Sweden)

    JIA Haijun

    2013-12-01

    Full Text Available With the rapid development of the network,its information resources are increasingly large and faced a huge amount of information database,search engine plays an important role.Focused crawling technique,as the main core portion of search engine,is used to calculate the relationship between search results and search topics,which is called correlation.Normally,focused crawling method is used only to calculate the correlation between web content and search related topics.In this paper,focused crawling method is used to compute the importance of links through link content and anchor text,then Bayesian classifier is used to classify the links,and finally cosine similarity function is used to calculate the relevance of web pages.If the correlation value is greater than the threshold the page is considered to be associated with the predetermined topics,otherwise not relevant.Experimental results show that a high accuracy can be obtained by using the proposed crawling approach.

  18. Comparing parametric and non-parametric classifiers for remote sensing of tree species across a land use gradient in a Savanna landscape

    CSIR Research Space (South Africa)

    Cho, Moses A

    2012-11-01

    Full Text Available were classified for two sites in the vicinity of the Kruger National Park, South Africa using two parametric (ML and Mahalanobis distance classifiers) and three non-parametric classifiers (spectral angle mapper (SAM), artificial neural networks (ANN...

  19. Learning ensemble classifiers for diabetic retinopathy assessment.

    Science.gov (United States)

    Saleh, Emran; Błaszczyński, Jerzy; Moreno, Antonio; Valls, Aida; Romero-Aroca, Pedro; de la Riva-Fernández, Sofia; Słowiński, Roman

    2017-10-06

    Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doctors to determine the risk of each patient to attain this condition, so that patients with a low risk may be screened less frequently and the use of resources can be improved. This paper explores the use of two kinds of ensemble classifiers learned from data: fuzzy random forest and dominance-based rough set balanced rule ensemble. These classifiers use a small set of attributes which represent main risk factors to determine whether a patient is in risk of developing diabetic retinopathy. The levels of specificity and sensitivity obtained in the presented study are over 80%. This study is thus a first successful step towards the construction of a personalized decision support system that could help physicians in daily clinical practice. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. The role of “network of cities” in construction of global urban culture

    OpenAIRE

    Tüzin Baycan-Levent; Seda Kundak; Aliye Ahu Gülümser

    2004-01-01

    The globalization process has led to an increased interaction between cities and to a new urban system/network in which they need to be competitive and complementary at the same time. “Network of cities”, such as World Cities, Eurocities or Sister Cities are among the well known examples of interaction and cooperation of the cities at the regional and global level. The cities of different regions and countries tend to share their experiences and their cultures within these networks in order...

  1. Classifying patents based on their semantic content

    Science.gov (United States)

    2017-01-01

    In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information. PMID:28445550

  2. Diagnosis of Broiler Livers by Classifying Image Patches

    DEFF Research Database (Denmark)

    Jørgensen, Anders; Fagertun, Jens; Moeslund, Thomas B.

    2017-01-01

    The manual health inspection are becoming the bottleneck at poultry processing plants. We present a computer vision method for automatic diagnosis of broiler livers. The non-rigid livers, of varying shape and sizes, are classified in patches by a convolutional neural network, outputting maps...

  3. Soft- to network hard-material for constructing both ion- and electron-conductive hierarchical porous structure to significantly boost energy density of a supercapacitor.

    Science.gov (United States)

    Yang, Pingping; Xie, Jiale; Guo, Chunxian; Li, Chang Ming

    2017-01-01

    Soft-material PEDOT is used to network hard Co3O4 nanowires for constructing both ion- and electron-conductive hierarchical porous structure Co3O4/PEDOT to greatly boost the capacitor energy density than sum of that of plain Co3O4 nanowires and PEDOT film. Specifically, the networked hierarchical porous structure of Co3O4/PEDOT is synthesized and tailored through hydrothermal method and post-electrochemical polymerization method for the PEDOT coating onto Co3O4 nanowires. Typically, Co3O4/PEDOT supercapacitor gets a highest areal capacitance of 160mFcm-2 at a current density of 0.2mAcm-2, which is about 2.2 times larger than the sum of that of plain Co3O4 NWs (0.92mFcm-2) and PEDOT film (69.88mFcm-2). Besides, if only PEDOT as active mass is counted, Co3O4/PEDOT cell can achieve a highest capacitance of 567.21Fg-1, this is the highest capacitance value obtained by PEDOT-based supercapacitors. Furthermore, this soft-hard network porous structure also achieves a high cycling stability of 93% capacitance retention after the 20,000th cycle. This work demonstrates a new approach to constructing both ion and electron conductive hierarchical porous structure to significantly boost energy density of a supercapacitor. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Evaluation of diagnostic classifiers using artificial clinical cases

    Directory of Open Access Journals (Sweden)

    Antczak Karol

    2017-01-01

    Full Text Available Evaluation of classifiers in diagnosis support systems is a non-trivial task. It can be done in a form of controlled and blinded clinical trial, which is often difficult and costly. We propose a new method for generating artificial medical cases from a knowledge base, utilizing the concept of so-called medical diamonds. Cases generated using this method have features analogous to that of double-blinded trial and, thus, can be used for measuring sensitivity and specificity of diagnostic classifiers. This is easy and low-cost method of evaluation and comparison of classifiers in diagnosis support systems. We demonstrate that this method is able to produce valuable results when used for evaluation of similarity-based classifiers as well as shallow and deep neural networks.

  5. Construction of monitoring model and algorithm design on passenger security during shipping based on improved Bayesian network.

    Science.gov (United States)

    Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

    2014-01-01

    A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.

  6. Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

    Directory of Open Access Journals (Sweden)

    Jiali Wang

    2014-01-01

    Full Text Available A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.

  7. Dimensionality Reduction Through Classifier Ensembles

    Science.gov (United States)

    Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)

    1999-01-01

    In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

  8. Neural classifiers using one-time updating.

    Science.gov (United States)

    Diamantaras, K I; Strintzis, M G

    1998-01-01

    The linear threshold element (LTE), or perceptron, is a linear classifier with limited capabilities due to the problems arising when the input pattern set is linearly nonseparable. Assuming that the patterns are presented in a sequential fashion, we derive a theory for the detection of linear nonseparability as soon as it appears in the pattern set. This theory is based on the precise determination of the solution region in the weight space with the help of a special set of vectors. For this region, called the solution cone, we present a recursive computation procedure which allows immediate detection of nonseparability. The separability-violating patterns may be skipped so that, at the end, we derive a totally separable subset of the original pattern set along with its solution cone. The intriguing aspect of this algorithm is that it can be directly cast into a simple neural-network implementation. In this model the synaptic weights are committed (they are updated only once, and the only change that may happen after that is their destruction). This bears resemblance to the behavior of biological neural networks, and it is a feature unlike those of most other artificial neural techniques. Finally, by combining many such neural models we develop a learning procedure capable of separating convex classes.

  9. GenCLiP 2.0: a web server for functional clustering of genes and construction of molecular networks based on free terms.

    Science.gov (United States)

    Wang, Jia-Hong; Zhao, Ling-Feng; Lin, Pei; Su, Xiao-Rong; Chen, Shi-Jun; Huang, Li-Qiang; Wang, Hua-Feng; Zhang, Hai; Hu, Zhen-Fu; Yao, Kai-Tai; Huang, Zhong-Xi

    2014-09-01

    Identifying biological functions and molecular networks in a gene list and how the genes may relate to various topics is of considerable value to biomedical researchers. Here, we present a web-based text-mining server, GenCLiP 2.0, which can analyze human genes with enriched keywords and molecular interactions. Compared with other similar tools, GenCLiP 2.0 offers two unique features: (i) analysis of gene functions with free terms (i.e. any terms in the literature) generated by literature mining or provided by the user and (ii) accurate identification and integration of comprehensive molecular interactions from Medline abstracts, to construct molecular networks and subnetworks related to the free terms. http://ci.smu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  10. Defining and Classifying Interest Groups

    DEFF Research Database (Denmark)

    Baroni, Laura; Carroll, Brendan; Chalmers, Adam

    2014-01-01

    The interest group concept is defined in many different ways in the existing literature and a range of different classification schemes are employed. This complicates comparisons between different studies and their findings. One of the important tasks faced by interest group scholars engaged...... in large-N studies is therefore to define the concept of an interest group and to determine which classification scheme to use for different group types. After reviewing the existing literature, this article sets out to compare different approaches to defining and classifying interest groups with a sample...... of lobbying actors coded according to different coding schemes. We systematically assess the performance of different schemes by comparing how actor types in the different schemes differ with respect to a number of background characteristics. This is done in a two-stage approach where we first cluster actors...

  11. Adaptive Regularization of Neural Classifiers

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Larsen, Jan; Hansen, Lars Kai

    1997-01-01

    We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermore......, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method...

  12. On-the-fly neural network construction for repairing F-16 flight control panel using thermal imaging

    Science.gov (United States)

    Allred, Lloyd G.; Howard, Tom R.; Serpen, Gursel

    1996-03-01

    When the card-level tester for the F-16 flight control panel (FLCP) had been dysfunctional for over 18 months, infrared thermography was investigated as an alternative for diagnosing and repairing the 7 cards in the FLCP box. Using thermal imaging alone, over 20 FLCP boxes were made serviceable, effectively bringing the FLCP out of awaiting parts (AWP) status. Through the incorporation of a novel on-the-fly neural network paradigm, the neural radiant energy detection system (NREDS) now has the capability to make correct fault classification from a large history of repair data. By surveying the historical data, the network makes assessments about relevant repair actions and probable component malfunctions. On one of the circuit cards, a repair accuracy of 11 out of 12 was achieved during the first repair attempt. By operating on the raw repair data and doing the network calculations on the fly, the network becomes virtual, thus eliminating the need to retain intermediate calculations in trained network files. Erroneous classifications are correctable via a text editor. Erroneous training of neural networks has been a chronic problem with prior implementations. In view of the current environment of downsizing, the likelihood of obtaining functionality at the card-level tester is remote. Success of the imager points to corresponding inadequacies of the automatic test equipment (ATE) to detect certain kinds of failure. In particular, we were informed that one particular relay had never been ordered in the life of the F-16 system, whereas some cards became functional when the relay was the sole component replaced.

  13. On-chip constructive cell-network study (I): contribution of cardiac fibroblasts to cardiomyocyte beating synchronization and community effect.

    Science.gov (United States)

    Kaneko, Tomoyuki; Nomura, Fumimasa; Yasuda, Kenji

    2011-05-23

    To clarify the role of cardiac fibroblasts in beating synchronization, we have made simple lined-up cardiomyocyte-fibroblast network model in an on-chip single-cell-based cultivation system. The synchronization phenomenon of two cardiomyocyte networks connected by fibroblasts showed (1) propagation velocity of electrophysiological signals decreased a magnitude depending on the increasing number of fibroblasts, not the lengths of fibroblasts; (2) fluctuation of interbeat intervals of the synchronized two cardiomyocyte network connected by fibroblasts did not always decreased, and was opposite from homogeneous cardiomyocyte networks; and (3) the synchronized cardiomyocytes connected by fibroblasts sometimes loses their synchronized condition and recovered to synchronized condition, in which the length of asynchronized period was shorter less than 30 beats and was independent to their cultivation time, whereas the length of synchronized period increased according to cultivation time. The results indicated that fibroblasts can connect cardiomyocytes electrically but do not significantly enhance and contribute to beating interval stability and synchronization. This might also mean that an increase in the number of fibroblasts in heart tissue reduces the cardiomyocyte 'community effect', which enhances synchronization and stability of their beating rhythms.

  14. Understanding the Construction of Personal Learning Networks to Support Non-Formal Workplace Learning of Training Professionals

    Science.gov (United States)

    Manning, Christin

    2013-01-01

    Workers in the 21st century workplace are faced with rapid and constant developments that place a heavy demand on them to continually learn beyond what the Human Resources and Training groups can meet. As a consequence, professionals must rely on non-formal learning approaches through the development of a personal learning network to keep…

  15. Conceptualizing cancer drugs as classifiers.

    Directory of Open Access Journals (Sweden)

    Patrick Nathan Lawlor

    Full Text Available Cancer and healthy cells have distinct distributions of molecular properties and thus respond differently to drugs. Cancer drugs ideally kill cancer cells while limiting harm to healthy cells. However, the inherent variance among cells in both cancer and healthy cell populations increases the difficulty of selective drug action. Here we formalize a classification framework based on the idea that an ideal cancer drug should maximally discriminate between cancer and healthy cells. More specifically, this discrimination should be performed on the basis of measurable cell markers. We divide the problem into three parts which we explore with examples. First, molecular markers should discriminate cancer cells from healthy cells at the single-cell level. Second, the effects of drugs should be statistically predicted by these molecular markers. Third, drugs should be optimized for classification performance. We find that expression levels of a handful of genes suffice to discriminate well between individual cells in cancer and healthy tissue. We also find that gene expression predicts the efficacy of some cancer drugs, suggesting that these cancer drugs act as suboptimal classifiers using gene profiles. Finally, we formulate a framework that defines an optimal drug, and predicts drug cocktails that may target cancer more accurately than the individual drugs alone. Conceptualizing cancer drugs as solving a discrimination problem in the high-dimensional space of molecular markers promises to inform the design of new cancer drugs and drug cocktails.

  16. Measurement of the Constructs of Health Belief Model related to Self-care during Pregnancy in Women Referred to South Tehran Health Network

    Directory of Open Access Journals (Sweden)

    Yalda Soleiman Ekhtiari

    2016-03-01

    Full Text Available Background and Objective: Self-care activities during pregnancy can be effective in reducing adverse pregnancy outcomes. Health Belief Model (HBM is one of the most applicable models in educational need assessment for planning and implementation of educational interventions. The purpose of this study was to measurement of the constructs of HBM related to self-care during pregnancy in women referred to South Tehran health network.Materials and Methods: In this cross-sectional study 270 pregnant women who referred to health centers of South Tehran Health Networks participated. Demographic, knowledge and attitude questionnaires based on constructs of HBM was used to measure the status of knowledge and attitude of women. Data were analyzed using statistical software SPSS18.Results: Results showed that 92.2% of women had the knowledge scores in good level. The scores of perceived severity, perceived self-efficacy and cues to action were in good level in almost of women but almost of women obtained weak point in perceived susceptibility, perceived benefits and barriersConclusion: HBM can be used as an appropriate tool for assessment the status of pregnant women in the field of self-care behaviors during pregnancy and planning and implementation of educational interventions.

  17. MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition

    Directory of Open Access Journals (Sweden)

    King Hann Lim

    2012-01-01

    Full Text Available Lyapunov theory-based radial basis function neural network (RBFNN is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO classification. Multidimensional input is inserted into RBF nodes and these nodes are linked with multiple weights. An iterative weight adaptation scheme is hence designed with regards to the Lyapunov stability theory to obtain a set of optimum weights. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Weight gain is formed later to obey the Lyapunov stability theory. Detail analysis and discussion on the proposed classifier’s properties are included in the paper. The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations.

  18. Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach.

    Science.gov (United States)

    Surkis, Alisa; Hogle, Janice A; DiazGranados, Deborah; Hunt, Joe D; Mazmanian, Paul E; Connors, Emily; Westaby, Kate; Whipple, Elizabeth C; Adamus, Trisha; Mueller, Meridith; Aphinyanaphongs, Yindalon

    2016-08-05

    Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications. Based on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier. The definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating

  19. Managed Networks of Competence in Distributed Organizations - The role of ICT and Identity Construction in Knowledge Sharing

    OpenAIRE

    Hermanrud, Inge

    2014-01-01

    Knowledge is seen as a main driving force for current public organizations to fulfill their mission in changing environments, and for some organizations the response is to design managed networks for knowledge sharing and learning. Distributed organizations, which this study examines, are particularly challenged to develop knowledge sharing and learning across distance to strengthen their operative units. Communities of practice have become a central notion for the management o...

  20. Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle) costing of construction projects

    OpenAIRE

    Alqahtani, Ayedh; Whyte, Andrew

    2013-01-01

    Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective (LCCA) comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges A...

  1. Artificial Neural Network-Based Constitutive Relationship of Inconel 718 Superalloy Construction and Its Application in Accuracy Improvement of Numerical Simulation

    Directory of Open Access Journals (Sweden)

    Junya Lv

    2017-01-01

    Full Text Available The application of accurate constitutive relationship in finite element simulation would significantly contribute to accurate simulation results, which play critical roles in process design and optimization. In this investigation, the true stress-strain data of an Inconel 718 superalloy were obtained from a series of isothermal compression tests conducted in a wide temperature range of 1153–1353 K and strain rate range of 0.01–10 s−1 on a Gleeble 3500 testing machine (DSI, St. Paul, DE, USA. Then the constitutive relationship was modeled by an optimally-constructed and well-trained back-propagation artificial neural network (ANN. The evaluation of the ANN model revealed that it has admirable performance in characterizing and predicting the flow behaviors of Inconel 718 superalloy. Consequently, the developed ANN model was used to predict abundant stress-strain data beyond the limited experimental conditions and construct the continuous mapping relationship for temperature, strain rate, strain and stress. Finally, the constructed ANN was implanted in a finite element solver though the interface of “URPFLO” subroutine to simulate the isothermal compression tests. The results show that the integration of finite element method with ANN model can significantly promote the accuracy improvement of numerical simulations for hot forming processes.

  2. Hierarchical mixtures of naive Bayes classifiers

    OpenAIRE

    Wiering, M.A.

    2002-01-01

    Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this pa- per we study combining multiple naive Bayes classifiers by using the hierar- chical mixtures of experts system. This system, which we call hierarchical mixtures of naive Bayes classifiers, is compared to a simple naive Bayes classifier and to using bagging and boosting for combining ...

  3. 15 CFR 4.8 - Classified Information.

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Classified Information. 4.8 Section 4... INFORMATION Freedom of Information Act § 4.8 Classified Information. In processing a request for information..., the information shall be reviewed to determine whether it should remain classified. Ordinarily the...

  4. Aggregation Operator Based Fuzzy Pattern Classifier Design

    DEFF Research Database (Denmark)

    Mönks, Uwe; Larsen, Henrik Legind; Lohweg, Volker

    2009-01-01

    This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable. The perfor...

  5. Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

    Science.gov (United States)

    Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

    2015-01-01

    The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

  6. Fault diagnosis with the Aladdin transient classifier

    Science.gov (United States)

    Roverso, Davide

    2003-08-01

    The purpose of Aladdin is to assist plant operators in the early detection and diagnosis of faults and anomalies in the plant that either have an impact on the plant performance, or that could lead to a plant shutdown or component damage if allowed to go unnoticed. The kind of early fault detection and diagnosis performed by Aladdin is aimed at allowing more time for decision making, increasing the operator awareness, reducing component damage, and supporting improved plant availability and reliability. In this paper we describe in broad lines the Aladdin transient classifier, which combines techniques such as recurrent neural network ensembles, Wavelet On-Line Pre-processing (WOLP), and Autonomous Recursive Task Decomposition (ARTD), in an attempt to improve the practical applicability and scalability of this type of systems to real processes and machinery. The paper focuses then on describing an application of Aladdin to a Nuclear Power Plant (NPP) through the use of the HAMBO experimental simulator of the Forsmark 3 boiling water reactor NPP in Sweden. It should be pointed out that Aladdin is not necessarily restricted to applications in NPPs. Other types of power plants, or even other types of processes, can also benefit from the diagnostic capabilities of Aladdin.

  7. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network

    Science.gov (United States)

    Lim, Woohyung; Kim, Myoung Shin; Na, Jung Im; Park, Ilwoo

    2018-01-01

    Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study. PMID:29352285

  8. Integrating ChIP-sequencing and digital gene expression profiling to identify BRD7 downstream genes and construct their regulating network.

    Science.gov (United States)

    Xu, Ke; Xiong, Wei; Zhou, Ming; Wang, Heran; Yang, Jing; Li, Xiayu; Chen, Pan; Liao, Qianjin; Deng, Hao; Li, Xiaoling; Li, Guiyuan; Zeng, Zhaoyang

    2016-01-01

    BRD7 is a single bromodomain-containing protein that functions as a subunit of the SWI/SNF chromatin-remodeling complex to regulate transcription. It also interacts with the well-known tumor suppressor protein p53 to trans-activate genes involved in cell cycle arrest. In this paper, we report an integrative analysis of genome-wide chromatin occupancy of BRD7 by chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and digital gene expression (DGE) profiling by RNA-sequencing upon the overexpression of BRD7 in human cells. We localized 156 BRD7-binding peaks representing 184 genes by ChIP-sequencing, and most of these peaks were co-localized with histone modification sites. Four novel motifs were significantly represented in these BRD7-enriched regions. Ingenuity pathway analysis revealed that 22 of these BRD7 target genes were involved in a network regulating cell death and survival. DGE profiling identified 560 up-regulated genes and 1088 down-regulated genes regulated by BRD7. Using Gene Ontology and pathway analysis, we found significant enrichment of the cell cycle and apoptosis pathway genes. For the integrative analysis of the ChIP-seq and DEG data, we constructed a regulating network of BRD7 downstream genes, and this network suggests multiple feedback regulations of the pathways. Furthermore, we validated BIRC2, BIRC3, TXN2, and NOTCH1 genes as direct, functional BRD7 targets, which were involved in the cell cycle and apoptosis pathways. These results provide a genome-wide view of chromatin occupancy and the gene regulation network of the BRD7 signaling pathway.

  9. Grinding wheel condition monitoring with boosted minimum distance classifiers

    Science.gov (United States)

    Liao, T. Warren; Tang, Fengming; Qu, J.; Blau, P. J.

    2008-01-01

    Grinding wheels get dull as more material is removed. This paper presents a methodology to detect a 'dull' wheel online based on acoustic emission (AE) signals. The methodology has three major steps: preprocessing, signal analysis and feature extraction, and constructing boosted classifiers using the minimum distance classifier (MDC) as the weak learner. Two booting algorithms, i.e., AdaBoost and A-Boost, were implemented. The methodology was tested with signals obtained in grinding of two ceramic materials with a diamond wheel under different grinding conditions. The results of cross-validation tests indicate that: (i) boosting greatly improves the effectiveness of the basic MDC; (ii) over all A-Boost does not outperform AdaBoost in terms of classification accuracy; and (iii) the performance of the boosted classifiers improves as the ensemble size increases.

  10. A History of Classified Activities at Oak Ridge National Laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Quist, A.S.

    2001-01-30

    The facilities that became Oak Ridge National Laboratory (ORNL) were created in 1943 during the United States' super-secret World War II project to construct an atomic bomb (the Manhattan Project). During World War II and for several years thereafter, essentially all ORNL activities were classified. Now, in 2000, essentially all ORNL activities are unclassified. The major purpose of this report is to provide a brief history of ORNL's major classified activities from 1943 until the present (September 2000). This report is expected to be useful to the ORNL Classification Officer and to ORNL's Authorized Derivative Classifiers and Authorized Derivative Declassifiers in their classification review of ORNL documents, especially those documents that date from the 1940s and 1950s.

  11. Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.

    Directory of Open Access Journals (Sweden)

    Nolan Priedigkeit

    2015-02-01

    Full Text Available Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC, is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

  12. Classifier fusion for VoIP attacks classification

    Science.gov (United States)

    Safarik, Jakub; Rezac, Filip

    2017-05-01

    SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

  13. Two Stage Comparison of Classifier Performances for Highly Imbalanced Datasets

    Directory of Open Access Journals (Sweden)

    Goran Oreški

    2015-12-01

    Full Text Available During the process of knowledge discovery in data, imbalanced learning data often emerges and presents a significant challenge for data mining methods. In this paper, we investigate the influence of class imbalanced data on the classification results of artificial intelligence methods, i.e. neural networks and support vector machine, and on the classification results of classical classification methods represented by RIPPER and the Naïve Bayes classifier. All experiments are conducted on 30 different imbalanced datasets obtained from KEEL (Knowledge Extraction based on Evolutionary Learning repository. With the purpose of measuring the quality of classification, the accuracy and the area under ROC curve (AUC measures are used. The results of the research indicate that the neural network and support vector machine show improvement of the AUC measure when applied to balanced data, but at the same time, they show the deterioration of results from the aspect of classification accuracy. RIPPER results are also similar, but the changes are of a smaller magnitude, while the results of the Naïve Bayes classifier show overall deterioration of results on balanced distributions. The number of instances in the presented highly imbalanced datasets has significant additional impact on the classification performances of the SVM classifier. The results have shown the potential of the SVM classifier for the ensemble creation on imbalanced datasets.

  14. Examining the significance of fingerprint-based classifiers

    Directory of Open Access Journals (Sweden)

    Collins Jack R

    2008-12-01

    Full Text Available Abstract Background Experimental examinations of biofluids to measure concentrations of proteins or their fragments or metabolites are being explored as a means of early disease detection, distinguishing diseases with similar symptoms, and drug treatment efficacy. Many studies have produced classifiers with a high sensitivity and specificity, and it has been argued that accurate results necessarily imply some underlying biology-based features in the classifier. The simplest test of this conjecture is to examine datasets designed to contain no information with classifiers used in many published studies. Results The classification accuracy of two fingerprint-based classifiers, a decision tree (DT algorithm and a medoid classification algorithm (MCA, are examined. These methods are used to examine 30 artificial datasets that contain random concentration levels for 300 biomolecules. Each dataset contains between 30 and 300 Cases and Controls, and since the 300 observed concentrations are randomly generated, these datasets are constructed to contain no biological information. A modest search of decision trees containing at most seven decision nodes finds a large number of unique decision trees with an average sensitivity and specificity above 85% for datasets containing 60 Cases and 60 Controls or less, and for datasets with 90 Cases and 90 Controls many DTs have an average sensitivity and specificity above 80%. For even the largest dataset (300 Cases and 300 Controls the MCA procedure finds several unique classifiers that have an average sensitivity and specificity above 88% using only six or seven features. Conclusion While it has been argued that accurate classification results must imply some biological basis for the separation of Cases from Controls, our results show that this is not necessarily true. The DT and MCA classifiers are sufficiently flexible and can produce good results from datasets that are specifically constructed to contain no

  15. Applying deep learning to classify pornographic images and videos

    OpenAIRE

    Moustafa, Mohamed

    2015-01-01

    It is no secret that pornographic material is now a one-click-away from everyone, including children and minors. General social media networks are striving to isolate adult images and videos from normal ones. Intelligent image analysis methods can help to automatically detect and isolate questionable images in media. Unfortunately, these methods require vast experience to design the classifier including one or more of the popular computer vision feature descriptors. We propose to build a clas...

  16. Mal-Netminer: Malware Classification Approach Based on Social Network Analysis of System Call Graph

    OpenAIRE

    Jae-wook Jang; Jiyoung Woo; Aziz Mohaisen; Jaesung Yun; Huy Kang Kim

    2015-01-01

    As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a ...

  17. Telecommunication networks

    CERN Document Server

    Iannone, Eugenio

    2011-01-01

    Many argue that telecommunications network infrastructure is the most impressive and important technology ever developed. Analyzing the telecom market's constantly evolving trends, research directions, infrastructure, and vital needs, Telecommunication Networks responds with revolutionized engineering strategies to optimize network construction. Omnipresent in society, telecom networks integrate a wide range of technologies. These include quantum field theory for the study of optical amplifiers, software architectures for network control, abstract algebra required to design error correction co

  18. [Sharing patient information using iPads in the introduction of IT for home medical care-construction of a network for home care].

    Science.gov (United States)

    Morishima, Atsutomo; Kijima, Yasuaki

    2012-12-01

    In hospitals, information technology(IT)has been a natural part of care for some time. However, IT has not yet been widely introduced into home care. While performing home care, healthcare providers must carry numerous patients' medical records, and are unable to share the information included in those records with other providers. Thus, we introduced the system of sharing patient information using iPads. This system enables access to patient information regardless of time and place. In addition, we can share information such as X-ray images and computed tomography(CT)scans between different clinics. Thus, we are able to give clearer instructions to patients and other providers in a smoother way. This system could be used to construct a network for home care. In the future, we could aim to share patient information within a much wider network, including families and other kinds of organizations, for optimal care. This system will aid in the development of IT use in home care.

  19. A social network analysis of social cohesion in a constructed pride: implications for ex situ reintroduction of the African lion (Panthera leo).

    Science.gov (United States)

    Abell, Jackie; Kirzinger, Morgan W B; Gordon, Yvonne; Kirk, Jacqui; Kokeŝ, Rae; Lynas, Kirsty; Mandinyenya, Bob; Youldon, David

    2013-01-01

    Animal conservation practices include the grouping of captive related and unrelated individuals to form a social structure which is characteristic of that species in the wild. In response to the rapid decline of wild African lion (Panthera leo) populations, an array of conservational strategies have been adopted. Ex situ reintroduction of the African lion requires the construction of socially cohesive pride structures prior to wild release. This pilot study adopted a social network theory approach to quantitatively assess a captive pride's social structure and the relationships between individuals within them. Group composition (who is present in a group) and social interaction data (social licking, greeting, play) was observed and recorded to assess social cohesion within a released semi-wild pride. UCINET and SOCPROG software was utilised to represent and analyse these social networks. Results indicate that the pride is socially cohesive, does not exhibit random associations, and the role of socially influential keystone individuals is important for maintaining social bondedness within a lion pride. These results are potentially informative for the structure of lion prides, in captivity and in the wild, and could have implications for captive and wild-founder reintroductions.

  20. A social network analysis of social cohesion in a constructed pride: implications for ex situ reintroduction of the African lion (Panthera leo.

    Directory of Open Access Journals (Sweden)

    Jackie Abell

    Full Text Available Animal conservation practices include the grouping of captive related and unrelated individuals to form a social structure which is characteristic of that species in the wild. In response to the rapid decline of wild African lion (Panthera leo populations, an array of conservational strategies have been adopted. Ex situ reintroduction of the African lion requires the construction of socially cohesive pride structures prior to wild release. This pilot study adopted a social network theory approach to quantitatively assess a captive pride's social structure and the relationships between individuals within them. Group composition (who is present in a group and social interaction data (social licking, greeting, play was observed and recorded to assess social cohesion within a released semi-wild pride. UCINET and SOCPROG software was utilised to represent and analyse these social networks. Results indicate that the pride is socially cohesive, does not exhibit random associations, and the role of socially influential keystone individuals is important for maintaining social bondedness within a lion pride. These results are potentially informative for the structure of lion prides, in captivity and in the wild, and could have implications for captive and wild-founder reintroductions.

  1. Investigation of neuronal pathfinding and construction of artificial neuronal networks on 3D-arranged porous fibrillar scaffolds with controlled geometry.

    Science.gov (United States)

    Kim, Dongyoon; Kim, Seong-Min; Lee, Seyeong; Yoon, Myung-Han

    2017-08-10

    Herein, we investigated the neurite pathfinding on electrospun microfibers with various fiber densities, diameters, and microbead islands, and demonstrated the development of 3D connected artificial neuronal network within a nanofiber-microbead-based porous scaffold. The primary culture of rat hippocampal embryonic neurons was deposited on geometry-controlled polystyrene (PS) fiber scaffolds while growth cone morphology, neurite outgrowth patterns, and focal adhesion protein expression were cautiously examined by microscopic imaging of immunostained and live neuronal cells derived from actin-GFP transgenic mice. It was demonstrated that the neurite outgrowth was guided by the overall microfiber orientation, but the increase in fiber density induced the neurite path alteration, thus, the reduction in neurite linearity. Indeed, we experimentally confirmed that growth cone could migrate to a neighboring, but, spatially disconnected microfiber by spontaneous filopodium extrusion, which is possibly responsible for the observed neurite steering. Furthermore, thinner microfiber scaffolds showed more pronounced expression of focal adhesion proteins than thicker ones, suggesting that the neuron-microfiber interaction can be delicately modulated by the underlying microfiber geometry. Finally, 3D connected functional neuronal networks were successfully constructed using PS nanofiber-microbead scaffolds where enhanced porosity and vertical fiber orientation permitted cell body inclusion within the scaffold and substantial neurite outgrowth in a vertical direction, respectively.

  2. Adaptation in P300 braincomputer interfaces: A two-classifier cotraining approach

    DEFF Research Database (Denmark)

    Panicker, Rajesh C.; Sun, Ying; Puthusserypady, Sadasivan

    2010-01-01

    A cotraining-based approach is introduced for constructing high-performance classifiers for P300-based braincomputer interfaces (BCIs), which were trained from very little data. It uses two classifiers: Fishers linear discriminant analysis and Bayesian linear discriminant analysis progressively...

  3. Error minimizing algorithms for nearest eighbor classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory; Zimmer, G. Beate [TEXAS A& M

    2011-01-03

    Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. We use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.

  4. Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma

    Directory of Open Access Journals (Sweden)

    Kobayashi Takeshi

    2004-09-01

    Full Text Available Abstract Background Screening of various gene markers such as single nucleotide polymorphism (SNP and correlation between these markers and development of multifactorial disease have previously been studied. Here, we propose a susceptible marker-selectable artificial neural network (ANN for predicting development of allergic disease. Results To predict development of childhood allergic asthma (CAA and select susceptible SNPs, we used an ANN with a parameter decreasing method (PDM to analyze 25 SNPs of 17 genes in 344 Japanese people, and select 10 susceptible SNPs of CAA. The accuracy of the ANN model with 10 SNPs was 97.7% for learning data and 74.4% for evaluation data. Important combinations were determined by effective combination value (ECV defined in the present paper. Effective 2-SNP or 3-SNP combinations were found to be concentrated among the 10 selected SNPs. Conclusion ANN can reliably select SNP combinations that are associated with CAA. Thus, the ANN can be used to characterize development of complex diseases caused by multiple factors. This is the first report of automatic selection of SNPs related to development of multifactorial disease from SNP data of more than 300 patients.

  5. Construction and implications of structural equation modeling network for pediatric cataract: a data mining research of rare diseases.

    Science.gov (United States)

    Long, Erping; Xu, Shuangjuan; Liu, Zhenzhen; Wu, Xiaohang; Zhang, Xiayin; Wang, Jinghui; Li, Wangting; Liu, Runzhong; Chen, Zicong; Chen, Kexin; Yu, Tongyong; Wu, Dongxuan; Zhao, Xutu; Chen, Jingjing; Lin, Zhuoling; Cao, Qianzhong; Lin, Duoru; Li, Xiaoyan; Cai, Jingheng; Lin, Haotian

    2017-05-19

    The majority of rare diseases are complex diseases caused by a combination of multiple morbigenous factors. However, uncovering the complex etiology and pathogenesis of rare diseases is difficult due to limited clinical resources and conventional statistical methods. This study aims to investigate the interrelationship and the effectiveness of potential factors of pediatric cataract, for the exploration of data mining strategy in the scenarios of rare diseases. We established a pilot rare disease specialized care center to systematically record all information and the entire treatment process of pediatric cataract patients. These clinical records contain the medical history, multiple structural indices, and comprehensive functional metrics. A two-layer structural equation model network was applied, and eight potential factors were filtered and included in the final modeling. Four risk factors (area, density, location, and abnormal pregnancy experience) and four beneficial factors (axis length, uncorrected visual acuity, intraocular pressure, and age at diagnosis) were identified. Quantifiable results suggested that abnormal pregnancy history may be the principle risk factor among medical history for pediatric cataracts. Moreover, axis length, density, uncorrected visual acuity and age at diagnosis served as the dominant factors and should be emphasized in regular clinical practice. This study proposes a generalized evidence-based pattern for rare and complex disease data mining, provides new insights and clinical implications on pediatric cataract, and promotes rare-disease research and prevention to benefit patients.

  6. REEXPORT OF SCIENTIFIC COMPETENCIES IN THE LIGHT OF THE RE-CONSTRUCTION OF A NETWORK OF SCIENTIFIC-RESEARCH BODIES

    Directory of Open Access Journals (Sweden)

    О. A. Yeremchenko

    2016-01-01

    Full Text Available One of the primary challengesRussiais currently facing is the need for diversification of the Russian economy and its increase in the share of manufacturing and exported scientific-driven work products. In this light, improving the effectiveness of the scientific-technological complex of the country is becoming increasingly important. The article considers two scalable, developed in parallel, projects for increasing effectiveness of the scientificresearch sector: restructurization of the scientific organizations network and the project for bringing back home 15 thousand Russian scientists reverse immigration. A conclusion is made about the adequacy of a refusal from a large-scale change in the personnel of scientists in circumstances of when the budget for research and development and the number of scientific-research organizations is cut. It is proposed to create comfortable conditions for scientific search for all parties involved in the process of new knowledge creation, both for the scientists returning toRussiaand those that remain working in the country. 

  7. To the Issue of Historical Prerequisites and the Significance of the Construction of the Railway Network in the Caucasus (the late 19th and early 20th Century era

    Directory of Open Access Journals (Sweden)

    Vladimir B. Karataev

    2015-12-01

    Full Text Available The article discusses the historical prerequisites of the construction of the railway network in the Caucasus in the late 19th and early 20th century era. The attention is paid to the interest of the foreign capital to the Russian railways. This article utilizes the records of Georgian national archive. A great part of materials is introduced into scientific circulation for the first time. The authors come to the conclusion that to the development of railway network in the Caucasus was paid the great attention. In the period of 1905 to 1913 years the railway network in the Caucasus has increased by 50 %. Almost the entire network was built by the funds from private entrepreneurs, and in the construction of the third road took part the local Caucasian population.

  8. Novel inorganic-organic hybrids constructed from multinuclear copper cluster and Keggin polyanions: from 1D wave-like chain to 2D network.

    Science.gov (United States)

    Wang, Xiuli; Wang, Yufei; Liu, Guocheng; Tian, Aixiang; Zhang, Juwen; Lin, Hongyan

    2011-09-28

    Two novel inorganic-organic hybrids constructed from Keggin-type polyanions and multinuclear copper clusters based on 1-H-1,2,3-benzotriazole (HBTA), [Cu(I)(8)(BTA)(4)(HBTA)(8)(SiMo(12)O(40))]·2H(2)O (1) and [Cu(II)(6)(OH)(4)(BTA)(4)(SiW(12)O(40))(H(2)O)(6)]·6H(2)O (2), have been hydrothermally synthesized and structurally characterized by single crystal X-ray diffraction, elemental analyses, IR spectra and thermogravimetric (TG) analyses. In compound 1, eight Cu(I) ions were linked by twelve HBTA/BTA ligands to form an octanuclear Cu(I) cluster, which is connected by SiMo(12)O(40)(4-) anion with two bridging O atoms and two terminal O atoms to construct a one-dimensional (1D) wave-like chain. The octanuclear copper unit represents the maximum subunit linked just by amine ligands in the POMs system. In 2, four BTA ligands linked five Cu(II) ions constructing a pentanuclear "porphyrin-like" subunit, which is connected by another Cu(II) ion to form a 1D metal-organic band. The SiW(12)O(40)(4-) polyanions as tetradentate inorganic linkages extend the 1D band into a two-dimensional (2D) network with (8(3))(2)(8(5)·10) topology. To the best of our knowledge, compounds 1 and 2 represent the first examples of inorganic-organic hybrids based on metal-HBTA multinuclear subunits and polyoxometalates. The photocatalysis and electrochemical properties have been investigated in this paper. This journal is © The Royal Society of Chemistry 2011

  9. Evaluating Machine Learning Classifiers for Hybrid Network Intrusion Detection Systems

    Science.gov (United States)

    2015-03-26

    like Perl, Python, Java, C#, or Ruby [20]. Winpcap is the Windows equivalent. • Packet Decoder : Receives the Ethernet frame from libpcap and analyzes... falls between the linear to cubic range with regards to the training set size. WEKA utilizes the SMO training process for its SVM implementation. 2.5.4...minimal error in DRK falls into the unknown threat detection metric, explained next. 3.9.3 Unknown Threat Detection. The unknown threat detection rate

  10. Using Conjugate Gradient Network to Classify Stress Level of Patients.

    Directory of Open Access Journals (Sweden)

    Er. S. Pawar

    2013-02-01

    Full Text Available Diagnosis of stress is important because it can cause many diseases e.g., heart disease, headache, migraine, sleep problems, irritability etc. Diagnosis of stress in patients often involves acquisition of biological signals for example heart rate, electrocardiogram (ECG, electromyography signals (EMG etc. Stress diagnosis using biomedical signals is difficult and since the biomedical signals are too complex to generate any rule an experienced person or expert is needed to determine stress levels. Also, it is not feasible to use all the features that are available or possible to extract from the signal. So, relevant features should be chosen from the extracted features that are capable to diagnose stress. Electronics devices are increasingly being seen in the field of medicine for diagnosis, therapy, checking of stress levels etc. The research and development work of medical electronics engineers leads to the manufacturing of sophisticated diagnostic medical equipment needed to ensure good health care. Biomedical engineering combines the design and problem solving skills of engineering with medical and biological sciences to improve health care diagnosis and treatment.

  11. APHRODITE: Constructing a Long-term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges

    Science.gov (United States)

    Yatagai, A.; Yasutomi, N.; Hamada, A.; Kitoh, A.; Kamiguchi, K.; Arakawa, O.

    2012-04-01

    A daily gridded precipitation dataset for the period 1951-2007 was created by collecting and analyzing rain-gauge observation data across Asia through the activities of the Asian Precipitation - Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE's water resources) project. APHRODITE's daily gridded precipitation is presently the only long-term continental-scale high-resolution daily product. Our product is based on data collected at 5000 to 12,000 stations, which represents 2.3 to 4.5 times the data available through the Global Telecommunication System (GTS) network that are used for most daily gridded precipitation products. Hence, the APHRODITE project has substantially improved the depiction of the areal distribution and variability of precipitation around the Himalayas, Southeast Asia and mountainous regions of the Middle East. The APHRODITE project now contributes to studies such as the determination of Asian monsoon precipitation change, evaluation of water resources, verification of high-resolution model simulations and satellite precipitation estimates, and improvement of forecasts. We released APHRO_V1101 datasets for Monsoon Asia, the Middle East and Russia (on 0.5 × 0.5 degree and 0.25 × 0.25 degree grids) and the APHRO_JP_V1005 dataset for Japan (on a 0.05 × 0.05 degree grid) on the website (http://www.chikyu.ac.jp/precip/ and http://aphrodite.suiri.tsukuba.ac.jp/). The major differences of APHRO_V1101 to that of the previous version (APHRO_V1003R1) are 1) improved quality control (QC) scheme and more input data (Belarus, Bhutan, South Korea, Saudi Arabia, Thailand, Taiwan and E-Obs). We are developing a daily gridded temperature dataset for Asia and a flag to discriminate between rain and snow will be added to the APHRODITE daily precipitation product. The combination of daily mean temperature, precipitation and rain/snow information in this high-resolution gridded format would be useful as input to river

  12. A fuzzy classifier system for process control

    Science.gov (United States)

    Karr, C. L.; Phillips, J. C.

    1994-01-01

    A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

  13. Data characteristics that determine classifier performance

    CSIR Research Space (South Africa)

    Van der Walt, Christiaan M

    2006-11-01

    Full Text Available The relationship between the distribution of data, on the one hand, and classifier performance, on the other, for non-parametric classifiers has been studied. It is shown that predictable factors such as the available amount of training data...

  14. Hierarchical mixtures of naive Bayes classifiers

    NARCIS (Netherlands)

    Wiering, M.A.

    2002-01-01

    Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this pa- per we study combining multiple naive Bayes classifiers by using the hierar- chical

  15. Congestive heart failure detection using random forest classifier.

    Science.gov (United States)

    Masetic, Zerina; Subasi, Abdulhamit

    2016-07-01

    Automatic electrocardiogram (ECG) heartbeat classification is substantial for diagnosing heart failure. The aim of this paper is to evaluate the effect of machine learning methods in creating the model which classifies normal and congestive heart failure (CHF) on the long-term ECG time series. The study was performed in two phases: feature extraction and classification phase. In feature extraction phase, autoregressive (AR) Burg method is applied for extracting features. In classification phase, five different classifiers are examined namely, C4.5 decision tree, k-nearest neighbor, support vector machine, artificial neural networks and random forest classifier. The ECG signals were acquired from BIDMC Congestive Heart Failure and PTB Diagnostic ECG databases and classified by applying various experiments. The experimental results are evaluated in several statistical measures (sensitivity, specificity, accuracy, F-measure and ROC curve) and showed that the random forest method gives 100% classification accuracy. Impressive performance of random forest method proves that it plays significant role in detecting congestive heart failure (CHF) and can be valuable in expressing knowledge useful in medicine. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. Counting, Measuring And The Semantics Of Classifiers

    Directory of Open Access Journals (Sweden)

    Susan Rothstein

    2010-12-01

    Full Text Available This paper makes two central claims. The first is that there is an intimate and non-trivial relation between the mass/count distinction on the one hand and the measure/individuation distinction on the other: a (if not the defining property of mass nouns is that they denote sets of entities which can be measured, while count nouns denote sets of entities which can be counted. Crucially, this is a difference in grammatical perspective and not in ontological status. The second claim is that the mass/count distinction between two types of nominals has its direct correlate at the level of classifier phrases: classifier phrases like two bottles of wine are ambiguous between a counting, or individuating, reading and a measure reading. On the counting reading, this phrase has count semantics, on the measure reading it has mass semantics.ReferencesBorer, H. 1999. ‘Deconstructing the construct’. In K. Johnson & I. Roberts (eds. ‘Beyond Principles and Parameters’, 43–89. Dordrecht: Kluwer publications.Borer, H. 2008. ‘Compounds: the view from Hebrew’. In R. Lieber & P. Stekauer (eds. ‘The Oxford Handbook of Compounds’, 491–511. Oxford: Oxford University Press.Carlson, G. 1977b. Reference to Kinds in English. Ph.D. thesis, University of Massachusetts at Amherst.Carlson, G. 1997. Quantifiers and Selection. Ph.D. thesis, University of Leiden.Carslon, G. 1977a. ‘Amount relatives’. Language 53: 520–542.Chierchia, G. 2008. ‘Plurality of mass nouns and the notion of ‘semantic parameter”. In S. Rothstein (ed. ‘Events and Grammar’, 53–103. Dordrecht: Kluwer.Danon, G. 2008. ‘Definiteness spreading in the Hebrew construct state’. Lingua 118: 872–906.http://dx.doi.org/10.1016/j.lingua.2007.05.012Gillon, B. 1992. ‘Toward a common semantics for English count and mass nouns’. Linguistics and Philosophy 15: 597–640.http://dx.doi.org/10.1007/BF00628112Grosu, A. & Landman, F. 1998. ‘Strange relatives of the third kind

  17. Battlefield Tourism at Gallipoli: The Revival of Collective Memory, the Construction of National Identity and the Making of a Long-distance Tourism Network

    Directory of Open Access Journals (Sweden)

    Elif Yeneroglu Kutbay

    2016-11-01

    Full Text Available The Battle of the Dardanelles (Çanakkale, also known as the Gallipoli Campaign, played a crucial role in the construction and endorsement of national identity, irrespective of the immediate consequences such as the prolongation of the war or the resignation of Winston Churchill upon failure. The Battle of the Dardanelles is commemorated every year in Turkey, Australia and New Zealand, as a day of remembrance. The battlefields at Dardanelles were reinstated as the Gallipoli Peninsula Historical National Park in 1973. The park covers numerous cemeteries of soldiers from both sides, memorials, museums and the battlefields in an area of 33,000 hectares. The park provides a vivid setting and depiction of the war experience, and stands out as the most important battlefield site in Turkey.The aim of this paper is to analyze battlefield tourism in Çanakkale in terms of its components and its impact on domestic and international tourism in Turkey. Battlefield tourism in Çanakkale encompasses not only the battlefield itself, but also the Çanakkale Victory Day in Turkey, March 18th, and the Anzac Day in Australia, April 25th. While domestic tourism contributes to the revival of collective memory and to the building of national identity, international tourism provides representations of national heritage as a source of political legitimacy. Unique to this case, battlefield tourism plays a significant role in the construction of a long-distance tourism network between Australia, and Turkey. The annual flow of descendants of ANZAC (Australian and New Zealand Army Corps soldiers is an important source of tourism activity in the area.

  18. Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes

    Directory of Open Access Journals (Sweden)

    Kerem Altun

    2009-10-01

    Full Text Available This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM, a rule-based algorithm (RBA or decision tree, least-squares method (LSM, k-nearest neighbor algorithm (k-NN, dynamic time warping (DTW, support vector machines (SVM, and artificial neural networks (ANN. A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost.

  19. Deep Feature Learning and Cascaded Classifier for Large Scale Data

    DEFF Research Database (Denmark)

    Prasoon, Adhish

    from data rather than having a predefined feature set. We explore deep learning approach of convolutional neural network (CNN) for segmenting three dimensional medical images. We propose a novel system integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D......This thesis focuses on voxel/pixel classification based approaches for image segmentation. The main application is segmentation of articular cartilage in knee MRIs. The first major contribution of the thesis deals with large scale machine learning problems. Many medical imaging problems need huge...... amount of training data to cover sufficient biological variability. Learning methods scaling badly with number of training data points cannot be used in such scenarios. This may restrict the usage of many powerful classifiers having excellent generalization ability. We propose a cascaded classifier which...

  20. A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING

    Directory of Open Access Journals (Sweden)

    JORGE FLORES CRUZ

    2017-01-01

    Full Text Available Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered.

  1. Classifier ensemble selection based on affinity propagation clustering.

    Science.gov (United States)

    Meng, Jun; Hao, Han; Luan, Yushi

    2016-04-01

    A small number of features are significantly correlated with classification in high-dimensional data. An ensemble feature selection method based on cluster grouping is proposed in this paper. Classification-related features are chosen using a ranking aggregation technique. These features are divided into unrelated groups by an affinity propagation clustering algorithm with a bicor correlation coefficient. Some diversity and distinguishing feature subsets are constructed by randomly selecting a feature from each group and are used to train base classifiers. Finally, some base classifiers that have better classification performance are selected using a kappa coefficient and integrated using a majority voting strategy. The experimental results based on five gene expression datasets show that the proposed method has low classification error rates, stable classification performance and strong scalability in terms of sensitivity, specificity, accuracy and G-Mean criteria. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Nonlinear interpolation fractal classifier for multiple cardiac arrhythmias recognition

    Energy Technology Data Exchange (ETDEWEB)

    Lin, C.-H. [Department of Electrical Engineering, Kao-Yuan University, No. 1821, Jhongshan Rd., Lujhu Township, Kaohsiung County 821, Taiwan (China); Institute of Biomedical Engineering, National Cheng-Kung University, Tainan 70101, Taiwan (China)], E-mail: eechl53@cc.kyu.edu.tw; Du, Y.-C.; Chen Tainsong [Institute of Biomedical Engineering, National Cheng-Kung University, Tainan 70101, Taiwan (China)

    2009-11-30

    This paper proposes a method for cardiac arrhythmias recognition using the nonlinear interpolation fractal classifier. A typical electrocardiogram (ECG) consists of P-wave, QRS-complexes, and T-wave. Iterated function system (IFS) uses the nonlinear interpolation in the map and uses similarity maps to construct various data sequences including the fractal patterns of supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. Grey relational analysis (GRA) is proposed to recognize normal heartbeat and cardiac arrhythmias. The nonlinear interpolation terms produce family functions with fractal dimension (FD), the so-called nonlinear interpolation function (NIF), and make fractal patterns more distinguishing between normal and ill subjects. The proposed QRS classifier is tested using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Compared with other methods, the proposed hybrid methods demonstrate greater efficiency and higher accuracy in recognizing ECG signals.

  3. Security Enrichment in Intrusion Detection System Using Classifier Ensemble

    Directory of Open Access Journals (Sweden)

    Uma R. Salunkhe

    2017-01-01

    Full Text Available In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

  4. Channel Division Based Multiple Classifiers Fusion for Emotion Recognition Using EEG signals

    Directory of Open Access Journals (Sweden)

    Li Xian

    2017-01-01

    Full Text Available With the rapid development of computer technology, pervasive computing and wearable devices, EEG-based emotion recognition has gradually attracted much attention in affecting computing (AC domain. In this paper, we propose an approach of emotion recognition using EEG signals based on the weighted fusion of multiple base classifiers. These base classifiers based on SVM are constructed using a channel division mechanism according to the neuropsychological theory that different brain areas are differ in processing intensity of emotional information. The outputs of channel base classifiers are integrated by a weighted fusion strategy which is based on the confidence estimation on each emotional label by each base classifier. The evaluation on the DEAP dataset shows that our proposed multiple classifiers fusion method outperforms individual channel base classifiers and the feature fusion method for EEG-based emotion recognition.

  5. Classifying the conflict: a soldier's dilemma

    National Research Council Canada - National Science Library

    Carswell, Andrew J

    2009-01-01

    .... Classifying these various scenarios to determine the applicable international law is rendered difficult by both the lack of clarity inherent in the law and the political factors that tend to enter...

  6. Combining multiple classifiers for age classification

    CSIR Research Space (South Africa)

    Van Heerden, C

    2009-11-01

    Full Text Available The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector...

  7. Quality Classifiers for Open Source Software Repositories

    OpenAIRE

    Tsatsaronis, George; Halkidi, Maria; Giakoumakis, Emmanouel A.

    2009-01-01

    Open Source Software (OSS) often relies on large repositories, like SourceForge, for initial incubation. The OSS repositories offer a large variety of meta-data providing interesting information about projects and their success. In this paper we propose a data mining approach for training classifiers on the OSS meta-data provided by such data repositories. The classifiers learn to predict the successful continuation of an OSS project. The `successfulness' of projects is defined in terms of th...

  8. Complex Network for Solar Active Regions

    Science.gov (United States)

    Daei, Farhad; Safari, Hossein; Dadashi, Neda

    2017-08-01

    In this paper we developed a complex network of solar active regions (ARs) to study various local and global properties of the network. The values of the Hurst exponent (0.8-0.9) were evaluated by both the detrended fluctuation analysis and the rescaled range analysis applied on the time series of the AR numbers. The findings suggest that ARs can be considered as a system of self-organized criticality (SOC). We constructed a growing network based on locations, occurrence times, and the lifetimes of 4227 ARs recorded from 1999 January 1 to 2017 April 14. The behavior of the clustering coefficient shows that the AR network is not a random network. The logarithmic behavior of the length scale has the characteristics of a so-called small-world network. It is found that the probability distribution of the node degrees for undirected networks follows the power law with exponents of about 3.7-4.2. This indicates the scale-free nature of the AR network. The scale-free and small-world properties of the AR network confirm that the system of ARs forms a system of SOC. Our results show that the occurrence probability of flares (classified by GOES class C> 5, M, and X flares) in the position of the AR network hubs takes values greater than that obtained for other nodes.

  9. Pixel Classification of SAR ice images using ANFIS-PSO Classifier

    Directory of Open Access Journals (Sweden)

    G. Vasumathi

    2016-12-01

    Full Text Available Synthetic Aperture Radar (SAR is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN, Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS classifier and proposed ANFIS with Particle Swarm Optimization (PSO classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages.

  10. On-line prediction of the feeding phase in high-cell density cultivation of rE. coli using constructive neural networks.

    Science.gov (United States)

    Nicoletti, M C; Bertini, J R; Tanizaki, M M; Zangirolami, T C; Gonçalves, V M; Horta, A C L; Giordano, R C

    2013-07-01

    Streptococcus pneumoniae (pneumococcus) is a bacterium responsible for a wide spectrum of illnesses. The surface of the bacterium consists of three distinctive membranes: plasmatic, cellular and the polysaccharide (PS) capsule. PS capsules may mediate several biological processes, particularly invasive infections of human beings. Prevention against pneumococcal related illnesses can be provided by vaccines. There is a sound investment worldwide in the investigation of a proteic antigen as a possible alternative to pneumococcal vaccines based exclusively on PS. A few proteins which are part of the membrane of the pneumococcus seem to have antigen potential to be part of a vaccine, particularly the PspA. A vital aspect in the production of the intended conjugate pneumococcal vaccine is the efficient production (in industrial scale) of both, the chosen PS serotypes as well as the PspA protein. Growing recombinant Escherichia coli (rE. coli) in high-cell density cultures (HCDC) under a fed-batch regime requires a refined continuous control over various process variables where the on-line prediction of the feeding phase is of particular relevance and one of the focuses of this paper. The viability of an on-line monitoring software system, based on constructive neural networks (CoNN), for automatically detecting the time to start the fed-phase of a HCDC of rE. coli that contains a plasmid used for PspA expression is investigated. The paper describes the data and methodology used for training five different types of CoNNs, four of them suitable for classification tasks and one suitable for regression tasks, aiming at comparatively investigate both approaches. Results of software simulations implementing five CoNN algorithms as well as conventional neural networks (FFNN), decision trees (DT) and support vector machines (SVM) are also presented and discussed. A modified CasCor algorithm, implementing a data softening process, has shown to be an efficient candidate to be

  11. Mapping and discrimination of networks in the complexity-entropy plane

    Science.gov (United States)

    Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

    2017-10-01

    Complex networks are usually characterized in terms of their topological, spatial, or information-theoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it still remains a subject of current research to appropriately quantify a network's complexity and correspondingly discriminate between different types of complex networks, like infrastructure or social networks, on such a basis. Here we explore the possibility to classify complex networks by means of a statistical complexity measure that has formerly been successfully applied to distinguish different types of chaotic and stochastic time series. It is composed of a network's averaged per-node entropic measure characterizing the network's information content and the associated Jenson-Shannon divergence as a measure of disequilibrium. We study 29 real-world networks and show that networks of the same category tend to cluster in distinct areas of the resulting complexity-entropy plane. We demonstrate that within our framework, connectome networks exhibit among the highest complexity while, e.g., transportation and infrastructure networks display significantly lower values. Furthermore, we demonstrate the utility of our framework by applying it to families of random scale-free and Watts-Strogatz model networks. We then show in a second application that the proposed framework is useful to objectively construct threshold-based networks, such as functional climate networks or recurrence networks, by choosing the threshold such that the statistical network complexity is maximized.

  12. Self-organizing map classifier for stressed speech recognition

    Science.gov (United States)

    Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav

    2016-05-01

    This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.

  13. Entropy based classifier for cross-domain opinion mining

    Directory of Open Access Journals (Sweden)

    Jyoti S. Deshmukh

    2018-01-01

    Full Text Available In recent years, the growth of social network has increased the interest of people in analyzing reviews and opinions for products before they buy them. Consequently, this has given rise to the domain adaptation as a prominent area of research in sentiment analysis. A classifier trained from one domain often gives poor results on data from another domain. Expression of sentiment is different in every domain. The labeling cost of each domain separately is very high as well as time consuming. Therefore, this study has proposed an approach that extracts and classifies opinion words from one domain called source domain and predicts opinion words of another domain called target domain using a semi-supervised approach, which combines modified maximum entropy and bipartite graph clustering. A comparison of opinion classification on reviews on four different product domains is presented. The results demonstrate that the proposed method performs relatively well in comparison to the other methods. Comparison of SentiWordNet of domain-specific and domain-independent words reveals that on an average 72.6% and 88.4% words, respectively, are correctly classified.

  14. Classifier-Guided Sampling for Complex Energy System Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Backlund, Peter B. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

    2015-09-01

    This report documents the results of a Laboratory Directed Research and Development (LDRD) effort enti tled "Classifier - Guided Sampling for Complex Energy System Optimization" that was conducted during FY 2014 and FY 2015. The goal of this proj ect was to develop, implement, and test major improvements to the classifier - guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for perform ing search and optimization over a set of discrete design variables in the face of one or more objective functions. E xisting evolutionary algorithms, such as genetic algorithms , may require a large number of o bjecti ve function evaluations to identify optimal or near - optimal solutions . Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduce s the evaluation count by us ing a Bayesian network classifier to filter out non - promising candidate designs , prior to evaluation, based on their posterior probabilit ies . In this project, b oth the single - objective and multi - objective version s of the CGS are developed and tested on a set of benchm ark problems. As a domain - specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.

  15. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  16. CLASSIFIED BY SUBJECT IN SPORT SCIENCES

    Directory of Open Access Journals (Sweden)

    Petar Protić

    2007-05-01

    Full Text Available High school and academic libraries users need precise classifi cation and subject access review of printed and electronic resources. In library catalogue since, Universal Decimal Classifi cation (UDC -similar to Dewey system - ex classifi es research and scientifi c areas. in subject areas of 796 Sport and 371 Teaching. Nowadays, users need structure of subjects by disciplines in science. Full-open resources of library must be set for users in subject access catalogue, because on the example of bachelors degree thesis in Faculty of Physical Education in Novi Sad they reaches for disciplines in database with 36 indexes sort by fi rst letters in names (Athletics, Boxing, Cycling, etc. This database have single and multiplied index for each thesis. Users in 80% cases of research according to the subject access catalogue of this library.

  17. Experimental study on imbibition displacement mechanisms of two-phase fluid using micromodel: Fracture network, distribution of pore size, and matrix construction

    Science.gov (United States)

    Jafari, Iman; Masihi, Mohsen; Nasiri Zarandi, Masoud

    2017-12-01

    In this study, the effect of different parameters on the fluid transport in a fractured micromodel has been investigated. All experiments in this study have been conducted in a glass micromodel. Since the state of wetting is important in the micromodel, the wetting experiments have been conducted to determine the state of wetting in the micromodel. The used micromodel was wet by water and non-wet regarding normal decane. The fracture network, distribution of pore size, matrix construction, and injection rate are the most important parameters affecting the process. Therefore, the influence of these parameters was studied using five different patterns (A to E). The obtained results from pattern A showed that increasing water injection the flow rate results in both higher rate of imbibition and higher ultimate recovery. Pattern B, which was characterized with higher porosity and permeability, was employed to study the effect of matrix pore size distribution on the imbibition process. Compared to pattern A, a higher normal decane production was observed in this pattern. Patterns C and D were designed to understand the impact of lateral fractures on the displacement process. Higher ultimate recoveries were obtained in these patterns. A system of matrix-fracture was designed (pattern E) to evaluate water injection performance in a multi-block system. Injection of water with the flow rate of 0.01 cc/min could produce 15% of the oil available in the system. While in the test with the flow rate of 0.1 cc/min, a normal decane recovery of 0.28 was achieved.

  18. A survey of decision tree classifier methodology

    Science.gov (United States)

    Safavian, S. R.; Landgrebe, David

    1991-01-01

    Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

  19. A survey of decision tree classifier methodology

    Science.gov (United States)

    Safavian, S. Rasoul; Landgrebe, David

    1990-01-01

    Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issue. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

  20. Evaluating Microarray-based Classifiers: An Overview

    Directory of Open Access Journals (Sweden)

    M. Daumer

    2008-01-01

    Full Text Available For the last eight years, microarray-based class prediction has been the subject of numerous publications in medicine, bioinformatics and statistics journals. However, in many articles, the assessment of classification accuracy is carried out using suboptimal procedures and is not paid much attention. In this paper, we carefully review various statistical aspects of classifier evaluation and validation from a practical point of view. The main topics addressed are accuracy measures, error rate estimation procedures, variable selection, choice of classifiers and validation strategy.

  1. A Customizable Text Classifier for Text Mining

    Directory of Open Access Journals (Sweden)

    Yun-liang Zhang

    2007-12-01

    Full Text Available Text mining deals with complex and unstructured texts. Usually a particular collection of texts that is specified to one or more domains is necessary. We have developed a customizable text classifier for users to mine the collection automatically. It derives from the sentence category of the HNC theory and corresponding techniques. It can start with a few texts, and it can adjust automatically or be adjusted by user. The user can also control the number of domains chosen and decide the standard with which to choose the texts based on demand and abundance of materials. The performance of the classifier varies with the user's choice.

  2. Measuring and Classifying Land-Based and Water-Based Daily Living Activities Using Inertial Sensors

    Directory of Open Access Journals (Sweden)

    Koichi Kaneda

    2018-02-01

    Full Text Available This study classified motions of typical daily activities in both environments using inertial sensors attached at the chest and thigh to determine the optimal site to attach the sensors. Walking, chair standing and sitting, and step climbing were conducted both in water and on land. A mean, variance and skewness for acceleration data was calculated. A Neural Network and Decision Tree algorithm was applied for classifying each motion in both environments. In total, 126 and 144 samples of thigh and chest data sets were obtained for analysis in each condition. For the chest data, the algorithm correctly classified 80% of the water-based activities, and 90% of the land-based. Whilst the thigh sensor correctly classified 97% of water-based and 100% of land-based activities. The inertial sensor placed on the thigh provided the most appropriate protocol for classifying motions for land-based and water-based typical daily life activities.

  3. Integrating conventional classifiers with a GIS expert system to increase the accuracy of invasive species mapping

    Science.gov (United States)

    Masocha, Mhosisi; Skidmore, Andrew K.

    2011-06-01

    Mapping the cover of invasive species using remotely sensed data alone is challenging, because many invaders occur as mid-level canopy species or as subtle understorey species and therefore contribute little to the spectral signatures captured by passive remote sensing devices. In this study, two common non-parametric classifiers namely, the neural network and support vector machine were used to map four cover classes of the invasive shrub Lantana camara in a protected game reserve and the adjacent area under communal land management in Zimbabwe. These classifiers were each combined with a geographic information system (GIS) expert system, in order to test whether the new hybrid classifiers yielded significantly more accurate invasive species cover maps than the single classifiers. The neural network, when used on its own, mapped the cover of L. camara with an overall accuracy of 71% and a Kappa index of agreement of 0.61. When the neural network was combined with an expert system, the overall accuracy and Kappa index of agreement significantly increased to 83% and 0.77, respectively. Similarly, the support vector machine achieved an overall accuracy of 64% with a Kappa index of agreement of 0.52, whereas the hybrid support vector machine and expert system classifier achieved a significantly higher overall accuracy of 76% and a Kappa index of agreement of 0.67. These results suggest that integrating conventional image classifiers with an expert system increases the accuracy of invasive species mapping.

  4. Classifying and quantifying basins of attraction

    Energy Technology Data Exchange (ETDEWEB)

    Sprott, J. C.; Xiong, Anda [Physics Department, University of Wisconsin-Madison, 1150 University Avenue, Madison, Wisconsin 53706 (United States)

    2015-08-15

    A scheme is proposed to classify the basins for attractors of dynamical systems in arbitrary dimensions. There are four basic classes depending on their size and extent, and each class can be further quantified to facilitate comparisons. The calculation uses a Monte Carlo method and is applied to numerous common dissipative chaotic maps and flows in various dimensions.

  5. Feature selection based classifier combination approach for ...

    Indian Academy of Sciences (India)

    Conditional mutual information based feature selection when driving the ensemble of classifier produces improved recognition results for most of the benchmarking datasets. The improve- ment is also observed with maximum relevance minimum redundancy based feature selection when used in combination with ensemble ...

  6. Classifying bicrossed products of two Taft algebras

    OpenAIRE

    Agore, A. L.

    2016-01-01

    We classify all Hopf algebras which factorize through two Taft algebras $\\mathbb{T}_{n^{2}}(\\bar{q})$ and respectively $T_{m^{2}}(q)$. To start with, all possible matched pairs between the two Taft algebras are described: if $\\bar{q} \

  7. Multilevel Growth Mixture Models for Classifying Groups

    Science.gov (United States)

    Palardy, Gregory J.; Vermunt, Jeroen K.

    2010-01-01

    This article introduces a multilevel growth mixture model (MGMM) for classifying both the individuals and the groups they are nested in. Nine variations of the general model are described that differ in terms of categorical and continuous latent variable specification within and between groups. An application in the context of school effectiveness…

  8. On the interpretation of number and classifiers

    NARCIS (Netherlands)

    Cheng, L.L.; Doetjes, J.S.; Sybesma, R.P.E.; Zamparelli, R.

    2012-01-01

    Mandarin and Cantonese, both of which are numeral classifier languages, present an interesting puzzle concerning a compositional account of number in the various forms of nominals. First, bare nouns are number neutral (or vague in number). Second, cl-noun combinations appear to have different

  9. MScanner: a classifier for retrieving Medline citations.

    Science.gov (United States)

    Poulter, Graham L; Rubin, Daniel L; Altman, Russ B; Seoighe, Cathal

    2008-02-19

    Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance. Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance. A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains. MScanner is an implementation of a Bayesian classifier that provides a simple web interface for submitting a corpus of relevant training examples in the form of PubMed IDs and returning results ranked by decreasing probability of relevance. For maximum speed it uses the Medical Subject Headings (MeSH) and journal of publication as a concise document representation, and takes roughly 90 seconds to return results against the 16 million records in Medline. The web interface provides interactive exploration of the results, and cross validated performance evaluation on the relevant input against a random subset of Medline. We describe the classifier implementation, cross validate it on three domain-specific topics, and compare its performance to that of an expert PubMed query for a complex topic. In cross validation on the three sample topics against 100,000 random articles, the classifier achieved excellent separation of relevant and irrelevant article score distributions, ROC areas between 0.97 and 0.99, and averaged precision between 0.69 and 0.92. MScanner is an effective non-domain-specific classifier that operates on the entire Medline database, and is suited to retrieving topics for which

  10. Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

    Science.gov (United States)

    Janaki Sathya, D.; Geetha, K.

    2017-12-01

    Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

  11. Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech

    Science.gov (United States)

    Álvarez, Aitor; Sierra, Basilio; Arruti, Andoni; López-Gil, Juan-Miguel; Garay-Vitoria, Nestor

    2015-01-01

    In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS)) and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one. PMID:26712757

  12. Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech

    Directory of Open Access Journals (Sweden)

    Aitor Álvarez

    2015-12-01

    Full Text Available In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking, is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one.

  13. Constructing criticality by classification

    DEFF Research Database (Denmark)

    Machacek, Erika

    2017-01-01

    This paper explores the role of expertise, the nature of criticality, and their relationship to securitisation as mineral raw materials are classified. It works with the construction of risk along the liberal logic of security to explore how "key materials" are turned into "critical materials......" in the bureaucratic practice of classification: Experts construct material criticality in assessments as they allot information on the materials to the parameters of the assessment framework. In so doing, they ascribe a new set of connotations to the materials, namely supply risk, and their importance to clean energy...

  14. Constructed Wetlands

    Science.gov (United States)

    these systems can improve water quality, engineers and scientists construct systems that replicate the functions of natural wetlands. Constructed wetlands are treatment systems that use natural processes

  15. Network workshop

    DEFF Research Database (Denmark)

    Bruun, Jesper; Evans, Robert Harry

    2014-01-01

    This paper describes the background for, realisation of and author reflections on a network workshop held at ESERA2013. As a new research area in science education, networks offer a unique opportunity to visualise and find patterns and relationships in complicated social or academic network data...... research community. With this workshop, participants were offered a way into network science based on authentic educational research data. The workshop was constructed as an inquiry lesson with emphasis on user autonomy. Learning activities had participants choose to work with one of two cases of networks...... network methodology in one’s research might supersede the perceived benefits of doing so. As a response to that problem, we argue that workshops can act as a road towards meaningful engagement with networks and highlight that network methodology promises new ways of interpreting data to answer questions...

  16. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  17. Comparing cosmic web classifiers using information theory

    Science.gov (United States)

    Leclercq, Florent; Lavaux, Guilhem; Jasche, Jens; Wandelt, Benjamin

    2016-08-01

    We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.

  18. N-Zero Integrated Analog Classifier (NINA)

    Science.gov (United States)

    2017-03-01

    detected signals. For physical- asset detection and classification, passive piezoelectric MEMS resonant seismic sensors will be utilized to produce...computational circuits operating deep in the weak inversion regime and non-volatile analog memories to classify seismic signals at extremely low...frequencies to maximize the sensor output voltage with generator signal. Figure 3. Vibration signal spectrum measured from by a sensor located atop the

  19. Robot Learning Using Learning Classifier Systems Approach

    OpenAIRE

    Jabin, Suraiya

    2010-01-01

    In this chapter, I have presented Learning Classifier Systems, which add to the classical Reinforcement Learning framework the possibility of representing the state as a vector of attributes and finding a compact expression of the representation so induced. Their formalism conveys a nice interaction between learning and evolution, which makes them a class of particularly rich systems, at the intersection of several research domains. As a result, they profit from the accumulated extensions of ...

  20. Classifying and Evaluating Architecture Design Methods

    OpenAIRE

    Tekinerdogan, B.; Aksit, Mehmet; Aksit, Mehmet

    2002-01-01

    The concept of software architecture has gained a wide popularity and is generally considered to play a fundamental role in coping with the inherent difficulties of the development of large-scale and complex software systems. This chapter first gives a definition of architecture. Second, a meta-model for architecture design methods is presented. This model is used for classifying and evaluating various architecture design approaches. The chapter concludes with the description of the identifie...