Learning sorting algorithms through visualization construction
Cetin, Ibrahim; Andrews-Larson, Christine
2016-01-01
Recent increased interest in computational thinking poses an important question to researchers: What are the best ways to teach fundamental computing concepts to students? Visualization is suggested as one way of supporting student learning. This mixed-method study aimed to (i) examine the effect of instruction in which students constructed visualizations on students' programming achievement and students' attitudes toward computer programming, and (ii) explore how this kind of instruction supports students' learning according to their self-reported experiences in the course. The study was conducted with 58 pre-service teachers who were enrolled in their second programming class. They expect to teach information technology and computing-related courses at the primary and secondary levels. An embedded experimental model was utilized as a research design. Students in the experimental group were given instruction that required students to construct visualizations related to sorting, whereas students in the control group viewed pre-made visualizations. After the instructional intervention, eight students from each group were selected for semi-structured interviews. The results showed that the intervention based on visualization construction resulted in significantly better acquisition of sorting concepts. However, there was no significant difference between the groups with respect to students' attitudes toward computer programming. Qualitative data analysis indicated that students in the experimental group constructed necessary abstractions through their engagement in visualization construction activities. The authors of this study argue that the students' active engagement in the visualization construction activities explains only one side of students' success. The other side can be explained through the instructional approach, constructionism in this case, used to design instruction. The conclusions and implications of this study can be used by researchers and
Learning Sorting Algorithms through Visualization Construction
Cetin, Ibrahim; Andrews-Larson, Christine
2016-01-01
Recent increased interest in computational thinking poses an important question to researchers: What are the best ways to teach fundamental computing concepts to students? Visualization is suggested as one way of supporting student learning. This mixed-method study aimed to (i) examine the effect of instruction in which students constructed…
Spike sorting based upon machine learning algorithms (SOMA).
Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F
2007-02-15
We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.
A Computer Environment for Beginners' Learning of Sorting Algorithms: Design and Pilot Evaluation
Kordaki, M.; Miatidis, M.; Kapsampelis, G.
2008-01-01
This paper presents the design, features and pilot evaluation study of a web-based environment--the SORTING environment--for the learning of sorting algorithms by secondary level education students. The design of this environment is based on modeling methodology, taking into account modern constructivist and social theories of learning while at…
Binar Sort: A Linear Generalized Sorting Algorithm
Gilreath, William F.
2008-01-01
Sorting is a common and ubiquitous activity for computers. It is not surprising that there exist a plethora of sorting algorithms. For all the sorting algorithms, it is an accepted performance limit that sorting algorithms are linearithmic or O(N lg N). The linearithmic lower bound in performance stems from the fact that the sorting algorithms use the ordering property of the data. The sorting algorithm uses comparison by the ordering property to arrange the data elements from an initial perm...
Akl, Selim G
1985-01-01
Parallel Sorting Algorithms explains how to use parallel algorithms to sort a sequence of items on a variety of parallel computers. The book reviews the sorting problem, the parallel models of computation, parallel algorithms, and the lower bounds on the parallel sorting problems. The text also presents twenty different algorithms, such as linear arrays, mesh-connected computers, cube-connected computers. Another example where algorithm can be applied is on the shared-memory SIMD (single instruction stream multiple data stream) computers in which the whole sequence to be sorted can fit in the
International Nuclear Information System (INIS)
Dinev, D.
1996-01-01
Several new algorithms for sorting of dipole and/or quadrupole magnets in synchrotrons and storage rings are described. The algorithms make use of a combinatorial approach to the problem and belong to the class of random search algorithms. They use an appropriate metrization of the state space. The phase-space distortion (smear) is used as a goal function. Computational experiments for the case of the JINR-Dubna superconducting heavy ion synchrotron NUCLOTRON have shown a significant reduction of the phase-space distortion after the magnet sorting. (orig.)
Simple sorting algorithm test based on CUDA
Meng, Hongyu; Guo, Fangjin
2015-01-01
With the development of computing technology, CUDA has become a very important tool. In computer programming, sorting algorithm is widely used. There are many simple sorting algorithms such as enumeration sort, bubble sort and merge sort. In this paper, we test some simple sorting algorithm based on CUDA and draw some useful conclusions.
Algorithm Sorts Groups Of Data
Evans, J. D.
1987-01-01
For efficient sorting, algorithm finds set containing minimum or maximum most significant data. Sets of data sorted as desired. Sorting process simplified by reduction of each multielement set of data to single representative number. First, each set of data expressed as polynomial with suitably chosen base, using elements of set as coefficients. Most significant element placed in term containing largest exponent. Base selected by examining range in value of data elements. Resulting series summed to yield single representative number. Numbers easily sorted, and each such number converted back to original set of data by successive division. Program written in BASIC.
Algorithm 426 : Merge sort algorithm [M1
Bron, C.
1972-01-01
Sorting by means of a two-way merge has a reputation of requiring a clerically complicated and cumbersome program. This ALGOL 60 procedure demonstrates that, using recursion, an elegant and efficient algorithm can be designed, the correctness of which is easily proved [2]. Sorting n objects gives
ALGORITHM FOR SORTING GROUPED DATA
Evans, J. D.
1994-01-01
It is often desirable to sort data sets in ascending or descending order. This becomes more difficult for grouped data, i.e., multiple sets of data, where each set of data involves several measurements or related elements. The sort becomes increasingly cumbersome when more than a few elements exist for each data set. In order to achieve an efficient sorting process, an algorithm has been devised in which the maximum most significant element is found, and then compared to each element in succession. The program was written to handle the daily temperature readings of the Voyager spacecraft, particularly those related to the special tracking requirements of Voyager 2. By reducing each data set to a single representative number, the sorting process becomes very easy. The first step in the process is to reduce the data set of width 'n' to a data set of width '1'. This is done by representing each data set by a polynomial of length 'n' based on the differences of the maximum and minimum elements. These single numbers are then sorted and converted back to obtain the original data sets. Required input data are the name of the data file to read and sort, and the starting and ending record numbers. The package includes a sample data file, containing 500 sets of data with 5 elements in each set. This program will perform a sort of the 500 data sets in 3 - 5 seconds on an IBM PC-AT with a hard disk; on a similarly equipped IBM PC-XT the time is under 10 seconds. This program is written in BASIC (specifically the Microsoft QuickBasic compiler) for interactive execution and has been implemented on the IBM PC computer series operating under PC-DOS with a central memory requirement of approximately 40K of 8 bit bytes. A hard disk is desirable for speed considerations, but is not required. This program was developed in 1986.
Engineering a Cache-Oblivious Sorting Algorithm
DEFF Research Database (Denmark)
Brodal, Gerth Stølting; Fagerberg, Rolf; Vinther, Kristoffer
2007-01-01
This paper is an algorithmic engineering study of cache-oblivious sorting. We investigate by empirical methods a number of implementation issues and parameter choices for the cache-oblivious sorting algorithm Lazy Funnelsort, and compare the final algorithm with Quicksort, the established standard...
Algorithm Animations for Teaching and Learning the Main Ideas of Basic Sortings
Végh, Ladislav; Stoffová, Veronika
2017-01-01
Algorithms are hard to understand for novice computer science students because they dynamically modify values of elements of abstract data structures. Animations can help to understand algorithms, since they connect abstract concepts to real life objects and situations. In the past 30-35 years, there have been conducted many experiments in the…
NeatSort - A practical adaptive algorithm
La Rocca, Marcello; Cantone, Domenico
2014-01-01
We present a new adaptive sorting algorithm which is optimal for most disorder metrics and, more important, has a simple and quick implementation. On input $X$, our algorithm has a theoretical $\\Omega (|X|)$ lower bound and a $\\mathcal{O}(|X|\\log|X|)$ upper bound, exhibiting amazing adaptive properties which makes it run closer to its lower bound as disorder (computed on different metrics) diminishes. From a practical point of view, \\textit{NeatSort} has proven itself competitive with (and of...
6. Algorithms for Sorting and Searching
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 2; Issue 3. Algorithms - Algorithms for Sorting and Searching. R K Shyamasundar. Series Article ... Author Affiliations. R K Shyamasundar1. Computer Science Group, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005, India ...
Energy efficient data sorting using standard sorting algorithms
Bunse, Christian; Hö pfner, Hagen; Roychoudhury, Suman; Mansour, Essam
2011-01-01
Protecting the environment by saving energy and thus reducing carbon dioxide emissions is one of todays hottest and most challenging topics. Although the perspective for reducing energy consumption, from ecological and business perspectives is clear, from a technological point of view, the realization especially for mobile systems still falls behind expectations. Novel strategies that allow (software) systems to dynamically adapt themselves at runtime can be effectively used to reduce energy consumption. This paper presents a case study that examines the impact of using an energy management component that dynamically selects and applies the "optimal" sorting algorithm, from an energy perspective, during multi-party mobile communication. Interestingly, the results indicate that algorithmic performance is not key and that dynamically switching algorithms at runtime does have a significant impact on energy consumption. © Springer-Verlag Berlin Heidelberg 2011.
Fixing the Sorting Algorithm for Android, Java and Python
C.P.T. de Gouw (Stijn); F.S. de Boer (Frank)
2015-01-01
htmlabstractTim Peters developed the Timsort hybrid sorting algorithm in 2002. TimSort was first developed for Python, a popular programming language, but later ported to Java (where it appears as java.util.Collections.sort and java.util.Arrays.sort). TimSort is today used as the default sorting
Enhancement of Selection, Bubble and Insertion Sorting Algorithm
Muhammad Farooq Umar; Ehsan Ullah Munir; Shafqat Ali Shad; Muhammad Wasif Nisar
2014-01-01
In everyday life there is a large amount of data to arrange because sorting removes any ambiguities and make the data analysis and data processing very easy, efficient and provides with cost less effort. In this study a set of improved sorting algorithms are proposed which gives better performance and design idea. In this study five new sorting algorithms (Bi-directional Selection Sort, Bi-directional bubble sort, MIDBiDirectional Selection Sort, MIDBidirectional bubble sort and linear insert...
Learning banknote fitness for sorting
Geusebroek, J.M.; Markus, P.; Balke, P.
2011-01-01
In this work, a machine learning method is proposed for banknote soiling determination. We apply proven techniques from computer vision to come up with a robust and effective method for automatic sorting of banknotes. The proposed method is evaluated with respect to various invariance classes. The
Categorizing Variations of Student-Implemented Sorting Algorithms
Taherkhani, Ahmad; Korhonen, Ari; Malmi, Lauri
2012-01-01
In this study, we examined freshmen students' sorting algorithm implementations in data structures and algorithms' course in two phases: at the beginning of the course before the students received any instruction on sorting algorithms, and after taking a lecture on sorting algorithms. The analysis revealed that many students have insufficient…
Unsupervised spike sorting based on discriminative subspace learning.
Keshtkaran, Mohammad Reza; Yang, Zhi
2014-01-01
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.
Denni Algorithm An Enhanced Of SMS (Scan, Move and Sort) Algorithm
Aprilsyah Lubis, Denni; Salim Sitompul, Opim; Marwan; Tulus; Andri Budiman, M.
2017-12-01
Sorting has been a profound area for the algorithmic researchers, and many resources are invested to suggest a more working sorting algorithm. For this purpose many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. Efficient sorting is important to optimize the use of other algorithms that require sorted lists to work correctly. Sorting has been considered as a fundamental problem in the study of algorithms that due to many reasons namely, the necessary to sort information is inherent in many applications, algorithms often use sorting as a key subroutine, in algorithm design there are many essential techniques represented in the body of sorting algorithms, and many engineering issues come to the fore when implementing sorting algorithms., Many algorithms are very well known for sorting the unordered lists, and one of the well-known algorithms that make the process of sorting to be more economical and efficient is SMS (Scan, Move and Sort) algorithm, an enhancement of Quicksort invented Rami Mansi in 2010. This paper presents a new sorting algorithm called Denni-algorithm. The Denni algorithm is considered as an enhancement on the SMS algorithm in average, and worst cases. The Denni algorithm is compared with the SMS algorithm and the results were promising.
An empirical study on SAJQ (Sorting Algorithm for Join Queries
Directory of Open Access Journals (Sweden)
Hassan I. Mathkour
2010-06-01
Full Text Available Most queries that applied on database management systems (DBMS depend heavily on the performance of the used sorting algorithm. In addition to have an efficient sorting algorithm, as a primary feature, stability of such algorithms is a major feature that is needed in performing DBMS queries. In this paper, we study a new Sorting Algorithm for Join Queries (SAJQ that has both advantages of being efficient and stable. The proposed algorithm takes the advantage of using the m-way-merge algorithm in enhancing its time complexity. SAJQ performs the sorting operation in a time complexity of O(nlogm, where n is the length of the input array and m is number of sub-arrays used in sorting. An unsorted input array of length n is arranged into m sorted sub-arrays. The m-way-merge algorithm merges the sorted m sub-arrays into the final output sorted array. The proposed algorithm keeps the stability of the keys intact. An analytical proof has been conducted to prove that, in the worst case, the proposed algorithm has a complexity of O(nlogm. Also, a set of experiments has been performed to investigate the performance of the proposed algorithm. The experimental results have shown that the proposed algorithm outperforms other Stable–Sorting algorithms that are designed for join-based queries.
Queue and stack sorting algorithm optimization and performance analysis
Qian, Mingzhu; Wang, Xiaobao
2018-04-01
Sorting algorithm is one of the basic operation of a variety of software development, in data structures course specializes in all kinds of sort algorithm. The performance of the sorting algorithm is directly related to the efficiency of the software. A lot of excellent scientific research queue is constantly optimizing algorithm, algorithm efficiency better as far as possible, the author here further research queue combined with stacks of sorting algorithms, the algorithm is mainly used for alternating operation queue and stack storage properties, Thus avoiding the need for a large number of exchange or mobile operations in the traditional sort. Before the existing basis to continue research, improvement and optimization, the focus on the optimization of the time complexity of the proposed optimization and improvement, The experimental results show that the improved effectively, at the same time and the time complexity and space complexity of the algorithm, the stability study corresponding research. The improvement and optimization algorithm, improves the practicability.
Sorting on STAR. [CDC computer algorithm timing comparison
Stone, H. S.
1978-01-01
Timing comparisons are given for three sorting algorithms written for the CDC STAR computer. One algorithm is Hoare's (1962) Quicksort, which is the fastest or nearly the fastest sorting algorithm for most computers. A second algorithm is a vector version of Quicksort that takes advantage of the STAR's vector operations. The third algorithm is an adaptation of Batcher's (1968) sorting algorithm, which makes especially good use of vector operations but has a complexity of N(log N)-squared as compared with a complexity of N log N for the Quicksort algorithms. In spite of its worse complexity, Batcher's sorting algorithm is competitive with the serial version of Quicksort for vectors up to the largest that can be treated by STAR. Vector Quicksort outperforms the other two algorithms and is generally preferred. These results indicate that unusual instruction sets can introduce biases in program execution time that counter results predicted by worst-case asymptotic complexity analysis.
An introduction to three algorithms for sorting in situ
Dijkstra, E.W.; Gasteren, van A.J.M.
1982-01-01
The purpose of this paper is to give a crisp introduction to three algorithms for sorting in situ, viz. insertion sort, heapsort and smoothsort. The more complicated the algorithm, the more elaborate the justification for the design decisions embodied by it. In passing we offer a style for the
Tradeoffs Between Branch Mispredictions and Comparisons for Sorting Algorithms
DEFF Research Database (Denmark)
Brodal, Gerth Stølting; Moruz, Gabriel
2005-01-01
Branch mispredictions is an important factor affecting the running time in practice. In this paper we consider tradeoffs between the number of branch mispredictions and the number of comparisons for sorting algorithms in the comparison model. We prove that a sorting algorithm using O(dnlog n......) comparisons performs Omega(nlogd n) branch mispredictions. We show that Multiway MergeSort achieves this tradeoff by adopting a multiway merger with a low number of branch mispredictions. For adaptive sorting algorithms we similarly obtain that an algorithm performing O(dn(1+log (1+Inv/n))) comparisons must...... perform Omega(nlogd (1+Inv/n)) branch mispredictions, where Inv is the number of inversions in the input. This tradeoff can be achieved by GenericSort by Estivill-Castro and Wood by adopting a multiway division protocol and a multiway merging algorithm with a low number of branch mispredictions....
A Simple Deep Learning Method for Neuronal Spike Sorting
Yang, Kai; Wu, Haifeng; Zeng, Yu
2017-10-01
Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.
An efficient non-dominated sorting method for evolutionary algorithms.
Fang, Hongbing; Wang, Qian; Tu, Yi-Cheng; Horstemeyer, Mark F
2008-01-01
We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.
A Low-Tech, Hands-On Approach To Teaching Sorting Algorithms to Working Students.
Dios, R.; Geller, J.
1998-01-01
Focuses on identifying the educational effects of "activity oriented" instructional techniques. Examines which instructional methods produce enhanced learning and comprehension. Discusses the problem of learning "sorting algorithms," a major topic in every Computer Science curriculum. Presents a low-tech, hands-on teaching method for sorting…
Sort-Mid tasks scheduling algorithm in grid computing
Directory of Open Access Journals (Sweden)
Naglaa M. Reda
2015-11-01
Full Text Available Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan.
Sort-Mid tasks scheduling algorithm in grid computing.
Reda, Naglaa M; Tawfik, A; Marzok, Mohamed A; Khamis, Soheir M
2015-11-01
Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan.
A 1.375-approximation algorithm for sorting by transpositions.
Elias, Isaac; Hartman, Tzvika
2006-01-01
Sorting permutations by transpositions is an important problem in genome rearrangements. A transposition is a rearrangement operation in which a segment is cut out of the permutation and pasted in a different location. The complexity of this problem is still open and it has been a 10-year-old open problem to improve the best known 1.5-approximation algorithm. In this paper, we provide a 1.375-approximation algorithm for sorting by transpositions. The algorithm is based on a new upper bound on the diameter of 3-permutations. In addition, we present some new results regarding the transposition diameter: we improve the lower bound for the transposition diameter of the symmetric group and determine the exact transposition diameter of simple permutations.
A novel automated spike sorting algorithm with adaptable feature extraction.
Bestel, Robert; Daus, Andreas W; Thielemann, Christiane
2012-10-15
To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.
Comparison of spike-sorting algorithms for future hardware implementation.
Gibson, Sarah; Judy, Jack W; Markovic, Dejan
2008-01-01
Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.
A real time sorting algorithm to time sort any deterministic time disordered data stream
Saini, J.; Mandal, S.; Chakrabarti, A.; Chattopadhyay, S.
2017-12-01
In new generation high intensity high energy physics experiments, millions of free streaming high rate data sources are to be readout. Free streaming data with associated time-stamp can only be controlled by thresholds as there is no trigger information available for the readout. Therefore, these readouts are prone to collect large amount of noise and unwanted data. For this reason, these experiments can have output data rate of several orders of magnitude higher than the useful signal data rate. It is therefore necessary to perform online processing of the data to extract useful information from the full data set. Without trigger information, pre-processing on the free streaming data can only be done with time based correlation among the data set. Multiple data sources have different path delays and bandwidth utilizations and therefore the unsorted merged data requires significant computational efforts for real time manifestation of sorting before analysis. Present work reports a new high speed scalable data stream sorting algorithm with its architectural design, verified through Field programmable Gate Array (FPGA) based hardware simulation. Realistic time based simulated data likely to be collected in an high energy physics experiment have been used to study the performance of the algorithm. The proposed algorithm uses parallel read-write blocks with added memory management and zero suppression features to make it efficient for high rate data-streams. This algorithm is best suited for online data streams with deterministic time disorder/unsorting on FPGA like hardware.
Web page sorting algorithm based on query keyword distance relation
Yang, Han; Cui, Hong Gang; Tang, Hao
2017-08-01
In order to optimize the problem of page sorting, according to the search keywords in the web page in the relationship between the characteristics of the proposed query keywords clustering ideas. And it is converted into the degree of aggregation of the search keywords in the web page. Based on the PageRank algorithm, the clustering degree factor of the query keyword is added to make it possible to participate in the quantitative calculation. This paper proposes an improved algorithm for PageRank based on the distance relation between search keywords. The experimental results show the feasibility and effectiveness of the method.
Performance evaluation of PCA-based spike sorting algorithms.
Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George
2008-09-01
Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.
Umbarkar, A. J.; Balande, U. T.; Seth, P. D.
2017-06-01
The field of nature inspired computing and optimization techniques have evolved to solve difficult optimization problems in diverse fields of engineering, science and technology. The firefly attraction process is mimicked in the algorithm for solving optimization problems. In Firefly Algorithm (FA) sorting of fireflies is done by using sorting algorithm. The original FA is proposed with bubble sort for ranking the fireflies. In this paper, the quick sort replaces bubble sort to decrease the time complexity of FA. The dataset used is unconstrained benchmark functions from CEC 2005 [22]. The comparison of FA using bubble sort and FA using quick sort is performed with respect to best, worst, mean, standard deviation, number of comparisons and execution time. The experimental result shows that FA using quick sort requires less number of comparisons but requires more execution time. The increased number of fireflies helps to converge into optimal solution whereas by varying dimension for algorithm performed better at a lower dimension than higher dimension.
A New Algorithm Using the Non-Dominated Tree to Improve Non-Dominated Sorting.
Gustavsson, Patrik; Syberfeldt, Anna
2018-01-01
Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This article presents a new, more efficient algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the article, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.
Comparison Of Hybrid Sorting Algorithms Implemented On Different Parallel Hardware Platforms
Directory of Open Access Journals (Sweden)
Dominik Zurek
2013-01-01
Full Text Available Sorting is a common problem in computer science. There are lot of well-known sorting algorithms created for sequential execution on a single processor. Recently, hardware platforms enable to create wide parallel algorithms. We have standard processors consist of multiple cores and hardware accelerators like GPU. The graphic cards with their parallel architecture give new possibility to speed up many algorithms. In this paper we describe results of implementation of a few different sorting algorithms on GPU cards and multicore processors. Then hybrid algorithm will be presented which consists of parts executed on both platforms, standard CPU and GPU.
Optimization of magnet sorting in a storage ring using genetic algorithms
International Nuclear Information System (INIS)
Chen Jia; Wang Lin; Li Weimin; Gao Weiwei
2013-01-01
In this paper, the genetic algorithms are applied to the optimization problem of magnet sorting in an electron storage ring, according to which the objectives are set so that the closed orbit distortion and beta beating can be minimized and the dynamic aperture maximized. The sorting of dipole, quadrupole and sextupole magnets is optimized while the optimization results show the power of the application of genetic algorithms in magnet sorting. (authors)
Krasichkov, A S; Grigoriev, E B; Nifontov, E M; Shapovalov, V V
The paper presents an algorithm of cardio complex classification as part of processing the data of continuous cardiac monitoring. R-wave detection concurrently with cardio complex sorting is discussed. The core of this approach is the use of prior information about. cardio complex forms, segmental structure, and degree of kindness. Results of the sorting algorithm testing are provided.
The Methods and Goals of Teaching Sorting Algorithms in Public Education
Bernát, Péter
2014-01-01
The topic of sorting algorithms is a pleasant subject of informatics education. Not only is it so because the notion of sorting is well known from our everyday life, but also because as an algorithm task, whether we expect naive or practical solutions, it is easy to define and demonstrate. In my paper I will present some of the possible methods…
Algorithms for Reinforcement Learning
Szepesvari, Csaba
2010-01-01
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'
Keshtkaran, Mohammad Reza; Yang, Zhi
2017-06-01
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.
Keshtkaran, Mohammad Reza; Yang, Zhi
2017-06-01
Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.
Opposition-Based Memetic Algorithm and Hybrid Approach for Sorting Permutations by Reversals.
Soncco-Álvarez, José Luis; Muñoz, Daniel M; Ayala-Rincón, Mauricio
2018-02-21
Sorting unsigned permutations by reversals is a difficult problem; indeed, it was proved to be NP-hard by Caprara (1997). Because of its high complexity, many approximation algorithms to compute the minimal reversal distance were proposed until reaching the nowadays best-known theoretical ratio of 1.375. In this article, two memetic algorithms to compute the reversal distance are proposed. The first one uses the technique of opposition-based learning leading to an opposition-based memetic algorithm; the second one improves the previous algorithm by applying the heuristic of two breakpoint elimination leading to a hybrid approach. Several experiments were performed with one-hundred randomly generated permutations, single benchmark permutations, and biological permutations. Results of the experiments showed that the proposed OBMA and Hybrid-OBMA algorithms achieve the best results for practical cases, that is, for permutations of length up to 120. Also, Hybrid-OBMA showed to improve the results of OBMA for permutations greater than or equal to 60. The applicability of our proposed algorithms was checked processing permutations based on biological data, in which case OBMA gave the best average results for all instances.
Towards understanding and managing the learning process in mail sorting.
Berglund, M; Karltun, A
2012-01-01
This paper was based on case study research at the Swedish Mail Service Division and it addresses learning time to sort mail at new districts and means to support the learning process on an individual as well as organizational level. The study population consisted of 46 postmen and one team leader in the Swedish Mail Service Division. Data were collected through measurements of time for mail sorting, interviews and a focus group. The study showed that learning to sort mail was a much more complex process and took more time than expected by management. Means to support the learning process included clarification of the relationship between sorting and the topology of the district, a good work environment, increased support from colleagues and management, and a thorough introduction for new postmen. The identified means to support the learning process require an integration of human, technological and organizational aspects. The study further showed that increased operations flexibility cannot be reinforced without a systems perspective and thorough knowledge about real work activities and that ergonomists can aid businesses to acquire this knowledge.
Faster magnet sorting with a threshold acceptance algorithm
International Nuclear Information System (INIS)
Lidia, S.; Carr, R.
1995-01-01
We introduce here a new technique for sorting magnets to minimize the field errors in permanent magnet insertion devices. Simulated annealing has been used in this role, but we find the technique of threshold acceptance produces results of equal quality in less computer time. Threshold accepting would be of special value in designing very long insertion devices, such as long free electron lasers (FELs). Our application of threshold acceptance to magnet sorting showed that it converged to equivalently low values of the cost function, but that it converged significantly faster. We present typical cases showing time to convergence for various error tolerances, magnet numbers, and temperature schedules
Faster magnet sorting with a threshold acceptance algorithm
International Nuclear Information System (INIS)
Lidia, S.
1994-08-01
The authors introduce here a new technique for sorting magnets to minimize the field errors in permanent magnet insertion devices. Simulated annealing has been used in this role, but they find the technique of threshold acceptance produces results of equal quality in less computer time. Threshold accepting would be of special value in designing very long insertion devices, such as long FEL's. Their application of threshold acceptance to magnet sorting showed that it converged to equivalently low values of the cost function, but that it converged significantly faster. They present typical cases showing time to convergence for various error tolerances, magnet numbers, and temperature schedules
Unsupervised learning algorithms
Aydin, Kemal
2016-01-01
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering,...
An Empirical Derivation of the Run Time of the Bubble Sort Algorithm.
Gonzales, Michael G.
1984-01-01
Suggests a moving pictorial tool to help teach principles in the bubble sort algorithm. Develops such a tool applied to an unsorted list of numbers and describes a method to derive the run time of the algorithm. The method can be modified to run the times of various other algorithms. (JN)
Magnet sorting algorithms for insertion devices for the Advanced Light Source
International Nuclear Information System (INIS)
Humphries, D.; Hoyer, E.; Kincaid, B.; Marks, S.; Schlueter, R.
1994-01-01
Insertion devices for the Advanced Light Source (ALS) incorporate up to 3,000 magnet blocks each for pole energization. In order to minimize field errors, these magnets must be measured, sorted and assigned appropriate locations and orientation in the magnetic structures. Sorting must address multiple objectives, including pole excitation and minimization of integrated multipole fields from minor field components in the magnets. This is equivalent to a combinatorial minimization problem with a large configuration space. Multi-stage sorting algorithms use ordering and pairing schemes in conjunction with other combinatorial methods to solve the minimization problem. This paper discusses objective functions, solution algorithms and results of application to magnet block measurement data
Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.
Chah, E; Hok, V; Della-Chiesa, A; Miller, J J H; O'Mara, S M; Reilly, R B
2011-02-01
This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.
Efficient Out of Core Sorting Algorithms for the Parallel Disks Model.
Kundeti, Vamsi; Rajasekaran, Sanguthevar
2011-11-01
In this paper we present efficient algorithms for sorting on the Parallel Disks Model (PDM). Numerous asymptotically optimal algorithms have been proposed in the literature. However many of these merge based algorithms have large underlying constants in the time bounds, because they suffer from the lack of read parallelism on PDM. The irregular consumption of the runs during the merge affects the read parallelism and contributes to the increased sorting time. In this paper we first introduce a novel idea called the dirty sequence accumulation that improves the read parallelism. Secondly, we show analytically that this idea can reduce the number of parallel I/O's required to sort the input close to the lower bound of [Formula: see text]. We experimentally verify our dirty sequence idea with the standard R-Way merge and show that our idea can reduce the number of parallel I/Os to sort on PDM significantly.
Algorithms for the Automatic Classification and Sorting of Conifers in the Garden Nursery Industry
DEFF Research Database (Denmark)
Petri, Stig
with the classification and sorting of plants using machine vision have been discussed as an introduction to the work reported here. The use of Nordmann firs as a basis for evaluating the developed algorithms naturally introduces a bias towards this species in the algorithms, but steps have been taken throughout...... was used as the basis for evaluating the constructed feature extraction algorithms. Through an analysis of the construction of a machine vision system suitable for classifying and sorting plants, the needs with regard to physical frame, lighting system, camera and software algorithms have been uncovered......The ultimate purpose of this work is the development of general feature extraction algorithms useful for the classification and sorting of plants in the garden nursery industry. Narrowing the area of focus to bare-root plants, more specifically Nordmann firs, the scientific literature dealing...
Design and Large-Scale Evaluation of Educational Games for Teaching Sorting Algorithms
Battistella, Paulo Eduardo; von Wangenheim, Christiane Gresse; von Wangenheim, Aldo; Martina, Jean Everson
2017-01-01
The teaching of sorting algorithms is an essential topic in undergraduate computing courses. Typically the courses are taught through traditional lectures and exercises involving the implementation of the algorithms. As an alternative, this article presents the design and evaluation of three educational games for teaching Quicksort and Heapsort.…
Algorithms for sorting unsigned linear genomes by the DCJ operations.
Jiang, Haitao; Zhu, Binhai; Zhu, Daming
2011-02-01
The double cut and join operation (abbreviated as DCJ) has been extensively used for genomic rearrangement. Although the DCJ distance between signed genomes with both linear and circular (uni- and multi-) chromosomes is well studied, the only known result for the NP-complete unsigned DCJ distance problem is an approximation algorithm for unsigned linear unichromosomal genomes. In this article, we study the problem of computing the DCJ distance on two unsigned linear multichromosomal genomes (abbreviated as UDCJ). We devise a 1.5-approximation algorithm for UDCJ by exploiting the distance formula for signed genomes. In addition, we show that UDCJ admits a weak kernel of size 2k and hence an FPT algorithm running in O(2(2k)n) time.
Oh, Cheolhwan; Huang, Xiaodong; Regnier, Fred E; Buck, Charles; Zhang, Xiang
2008-02-01
We report a novel peak sorting method for the two-dimensional gas chromatography/time-of-flight mass spectrometry (GC x GC/TOF-MS) system. The objective of peak sorting is to recognize peaks from the same metabolite occurring in different samples from thousands of peaks detected in the analytical procedure. The developed algorithm is based on the fact that the chromatographic peaks for a given analyte have similar retention times in all of the chromatograms. Raw instrument data are first processed by ChromaTOF (Leco) software to provide the peak tables. Our algorithm achieves peak sorting by utilizing the first- and second-dimension retention times in the peak tables and the mass spectra generated during the process of electron impact ionization. The algorithm searches the peak tables for the peaks generated by the same type of metabolite using several search criteria. Our software also includes options to eliminate non-target peaks from the sorting results, e.g., peaks of contaminants. The developed software package has been tested using a mixture of standard metabolites and another mixture of standard metabolites spiked into human serum. Manual validation demonstrates high accuracy of peak sorting with this algorithm.
A novel unsupervised spike sorting algorithm for intracranial EEG.
Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R
2011-01-01
This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.
Validation of neural spike sorting algorithms without ground-truth information.
Barnett, Alex H; Magland, Jeremy F; Greengard, Leslie F
2016-05-01
The throughput of electrophysiological recording is growing rapidly, allowing thousands of simultaneous channels, and there is a growing variety of spike sorting algorithms designed to extract neural firing events from such data. This creates an urgent need for standardized, automatic evaluation of the quality of neural units output by such algorithms. We introduce a suite of validation metrics that assess the credibility of a given automatic spike sorting algorithm applied to a given dataset. By rerunning the spike sorter two or more times, the metrics measure stability under various perturbations consistent with variations in the data itself, making no assumptions about the internal workings of the algorithm, and minimal assumptions about the noise. We illustrate the new metrics on standard sorting algorithms applied to both in vivo and ex vivo recordings, including a time series with overlapping spikes. We compare the metrics to existing quality measures, and to ground-truth accuracy in simulated time series. We provide a software implementation. Metrics have until now relied on ground-truth, simulated data, internal algorithm variables (e.g. cluster separation), or refractory violations. By contrast, by standardizing the interface, our metrics assess the reliability of any automatic algorithm without reference to internal variables (e.g. feature space) or physiological criteria. Stability is a prerequisite for reproducibility of results. Such metrics could reduce the significant human labor currently spent on validation, and should form an essential part of large-scale automated spike sorting and systematic benchmarking of algorithms. Copyright © 2016 Elsevier B.V. All rights reserved.
Performance comparison of extracellular spike sorting algorithms for single-channel recordings.
Wild, Jiri; Prekopcsak, Zoltan; Sieger, Tomas; Novak, Daniel; Jech, Robert
2012-01-30
Proper classification of action potentials from extracellular recordings is essential for making an accurate study of neuronal behavior. Many spike sorting algorithms have been presented in the technical literature. However, no comparative analysis has hitherto been performed. In our study, three widely-used publicly-available spike sorting algorithms (WaveClus, KlustaKwik, OSort) were compared with regard to their parameter settings. The algorithms were evaluated using 112 artificial signals (publicly available online) with 2-9 different neurons and varying noise levels between 0.00 and 0.60. An optimization technique based on Adjusted Mutual Information was employed to find near-optimal parameter settings for a given artificial signal and algorithm. All three algorithms performed significantly better (psorting algorithm, receiving the best evaluation score for 60% of all signals. OSort operated at almost five times the speed of the other algorithms. In terms of accuracy, OSort performed significantly less well (palgorithms was optimal in general. The accuracy of the algorithms depended on proper choice of the algorithm parameters and also on specific properties of the examined signal. Copyright © 2011 Elsevier B.V. All rights reserved.
A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.
Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P
2013-01-01
We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.
Cascade Error Projection Learning Algorithm
Duong, T. A.; Stubberud, A. R.; Daud, T.
1995-01-01
A detailed mathematical analysis is presented for a new learning algorithm termed cascade error projection (CEP) and a general learning frame work. This frame work can be used to obtain the cascade correlation learning algorithm by choosing a particular set of parameters.
An algorithm for 4D CT image sorting using spatial continuity.
Li, Chen; Liu, Jie
2013-01-01
4D CT, which could locate the position of the movement of the tumor in the entire respiratory cycle and reduce image artifacts effectively, has been widely used in making radiation therapy of tumors. The current 4D CT methods required external surrogates of respiratory motion obtained from extra instruments. However, respiratory signals recorded by these external makers may not always accurately represent the internal tumor and organ movements, especially when irregular breathing patterns happened. In this paper we have proposed a novel automatic 4D CT sorting algorithm that performs without these external surrogates. The sorting algorithm requires collecting the image data with a cine scan protocol. Beginning with the first couch position, images from the adjacent couch position are selected out according to spatial continuity. The process is continued until images from all couch positions are sorted and the entire 3D volume is produced. The algorithm is verified by respiratory phantom image data and clinical image data. The primary test results show that the 4D CT images created by our algorithm have eliminated the motion artifacts effectively and clearly demonstrated the movement of tumor and organ in the breath period.
A New Efficient Algorithm for the All Sorting Reversals Problem with No Bad Components.
Wang, Biing-Feng
2016-01-01
The problem of finding all reversals that take a permutation one step closer to a target permutation is called the all sorting reversals problem (the ASR problem). For this problem, Siepel had an O(n (3))-time algorithm. Most complications of his algorithm stem from some peculiar structures called bad components. Since bad components are very rare in both real and simulated data, it is practical to study the ASR problem with no bad components. For the ASR problem with no bad components, Swenson et al. gave an O (n(2))-time algorithm. Very recently, Swenson found that their algorithm does not always work. In this paper, a new algorithm is presented for the ASR problem with no bad components. The time complexity is O(n(2)) in the worst case and is linear in the size of input and output in practice.
Machine Learning an algorithmic perspective
Marsland, Stephen
2009-01-01
Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement le
A faster 1.375-approximation algorithm for sorting by transpositions.
Cunha, Luís Felipe I; Kowada, Luis Antonio B; Hausen, Rodrigo de A; de Figueiredo, Celina M H
2015-11-01
Sorting by Transpositions is an NP-hard problem for which several polynomial-time approximation algorithms have been developed. Hartman and Shamir (2006) developed a 1.5-approximation [Formula: see text] algorithm, whose running time was improved to O(nlogn) by Feng and Zhu (2007) with a data structure they defined, the permutation tree. Elias and Hartman (2006) developed a 1.375-approximation O(n(2)) algorithm, and Firoz et al. (2011) claimed an improvement to the running time, from O(n(2)) to O(nlogn), by using the permutation tree. We provide counter-examples to the correctness of Firoz et al.'s strategy, showing that it is not possible to reach a component by sufficient extensions using the method proposed by them. In addition, we propose a 1.375-approximation algorithm, modifying Elias and Hartman's approach with the use of permutation trees and achieving O(nlogn) time.
How many neurons can we see with current spike sorting algorithms?
Pedreira, Carlos; Martinez, Juan; Ison, Matias J; Quian Quiroga, Rodrigo
2012-10-15
Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8-10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms. Copyright © 2012 Elsevier B.V. All rights reserved.
An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks
Directory of Open Access Journals (Sweden)
Huan-Yuan Chen
2017-09-01
Full Text Available This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.
An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks
Chen, Huan-Yuan; Chen, Chih-Chang
2017-01-01
This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859
Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm
Piroozfard, Hamed; Wong, Kuan Yew
2015-05-01
The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are considered based on the number of non-dominated solutions and quality of schedules obtained by the algorithm.
International Nuclear Information System (INIS)
Colavita, A.; Capello, G.
1997-01-01
In this paper we present a novel parallel sorting algorithm, which works through a cascade of elementary sorting units and leads to a scalable architecture. The algorithm's complexity is analyzed and compared with a classical parallel algorithm. It comes out that, although it may be less efficient than classical approaches, the proposed algorithm is highly suited for VLSI implementation for its simplicity and scalability. The paper describes the applications of such device to the asynchronous data acquisition for a gamma-ray telescope. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Loring, Burlen; Karimabadi, Homa; Rortershteyn, Vadim
2014-07-01
The surface line integral convolution(LIC) visualization technique produces dense visualization of vector fields on arbitrary surfaces. We present a screen space surface LIC algorithm for use in distributed memory data parallel sort last rendering infrastructures. The motivations for our work are to support analysis of datasets that are too large to fit in the main memory of a single computer and compatibility with prevalent parallel scientific visualization tools such as ParaView and VisIt. By working in screen space using OpenGL we can leverage the computational power of GPUs when they are available and run without them when they are not. We address efficiency and performance issues that arise from the transformation of data from physical to screen space by selecting an alternate screen space domain decomposition. We analyze the algorithm's scaling behavior with and without GPUs on two high performance computing systems using data from turbulent plasma simulations.
A graph-Laplacian-based feature extraction algorithm for neural spike sorting.
Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos
2009-01-01
Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.
Application of a fast sorting algorithm to the assignment of mass spectrometric cross-linking data.
Petrotchenko, Evgeniy V; Borchers, Christoph H
2014-09-01
Cross-linking combined with MS involves enzymatic digestion of cross-linked proteins and identifying cross-linked peptides. Assignment of cross-linked peptide masses requires a search of all possible binary combinations of peptides from the cross-linked proteins' sequences, which becomes impractical with increasing complexity of the protein system and/or if digestion enzyme specificity is relaxed. Here, we describe the application of a fast sorting algorithm to search large sequence databases for cross-linked peptide assignments based on mass. This same algorithm has been used previously for assigning disulfide-bridged peptides (Choi et al., ), but has not previously been applied to cross-linking studies. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Dagum, Leonardo
1989-01-01
The data parallel implementation of a particle simulation for hypersonic rarefied flow described by Dagum associates a single parallel data element with each particle in the simulation. The simulated space is divided into discrete regions called cells containing a variable and constantly changing number of particles. The implementation requires a global sort of the parallel data elements so as to arrange them in an order that allows immediate access to the information associated with cells in the simulation. Described here is a very fast algorithm for performing the necessary ranking of the parallel data elements. The performance of the new algorithm is compared with that of the microcoded instruction for ranking on the Connection Machine.
International Nuclear Information System (INIS)
Genderen, J v; Marghany, M
2014-01-01
The paper focusses on three-dimensional (3-D) coastline deformation using interferometric synthetic aperture radar data(InSAR). Conventional InSAR procedures were implemented on three repeat passes of ENVISAT ASAR data. Furthermore, the three-dimensional sorting reliabilities algorithm (3D-SRA) were implemented with the phase unwrapping technique. Subsequently, the 3D-SRA was used to eliminate the phase decorrelation impact from the interferograms. The study showed that the performance of the InSAR method using the 3D-SRA algorithm, is better than the conventional InSAR procedure. In conclusion, the integration of the 3D-SRA, together with phase unwrapping, can produce accurate 3-D coastline deformation information
Directory of Open Access Journals (Sweden)
Qianwang Deng
2017-01-01
Full Text Available Flexible job-shop scheduling problem (FJSP is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II for multiobjective FJSP (MO-FJSP with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.
Deng, Qianwang; Gong, Guiliang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua
2017-01-01
Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N , in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.
An algorithm for intelligent sorting of CT-related dose parameters
Cook, Tessa S.; Zimmerman, Stefan L.; Steingal, Scott; Boonn, William W.; Kim, Woojin
2011-03-01
Imaging centers nationwide are seeking innovative means to record and monitor CT-related radiation dose in light of multiple instances of patient over-exposure to medical radiation. As a solution, we have developed RADIANCE, an automated pipeline for extraction, archival and reporting of CT-related dose parameters. Estimation of whole-body effective dose from CT dose-length product (DLP)-an indirect estimate of radiation dose-requires anatomy-specific conversion factors that cannot be applied to total DLP, but instead necessitate individual anatomy-based DLPs. A challenge exists because the total DLP reported on a dose sheet often includes multiple separate examinations (e.g., chest CT followed by abdominopelvic CT). Furthermore, the individual reported series DLPs may not be clearly or consistently labeled. For example, Arterial could refer to the arterial phase of the triple liver CT or the arterial phase of a CT angiogram. To address this problem, we have designed an intelligent algorithm to parse dose sheets for multi-series CT examinations and correctly separate the total DLP into its anatomic components. The algorithm uses information from the departmental PACS to determine how many distinct CT examinations were concurrently performed. Then, it matches the number of distinct accession numbers to the series that were acquired, and anatomically matches individual series DLPs to their appropriate CT examinations. This algorithm allows for more accurate dose analytics, but there remain instances where automatic sorting is not feasible. To ultimately improve radiology patient care, we must standardize series names and exam names to unequivocally sort exams by anatomy and correctly estimate whole-body effective dose.
An algorithm for intelligent sorting of CT-related dose parameters.
Cook, Tessa S; Zimmerman, Stefan L; Steingall, Scott R; Boonn, William W; Kim, Woojin
2012-02-01
Imaging centers nationwide are seeking innovative means to record and monitor computed tomography (CT)-related radiation dose in light of multiple instances of patient overexposure to medical radiation. As a solution, we have developed RADIANCE, an automated pipeline for extraction, archival, and reporting of CT-related dose parameters. Estimation of whole-body effective dose from CT dose length product (DLP)--an indirect estimate of radiation dose--requires anatomy-specific conversion factors that cannot be applied to total DLP, but instead necessitate individual anatomy-based DLPs. A challenge exists because the total DLP reported on a dose sheet often includes multiple separate examinations (e.g., chest CT followed by abdominopelvic CT). Furthermore, the individual reported series DLPs may not be clearly or consistently labeled. For example, "arterial" could refer to the arterial phase of the triple liver CT or the arterial phase of a CT angiogram. To address this problem, we have designed an intelligent algorithm to parse dose sheets for multi-series CT examinations and correctly separate the total DLP into its anatomic components. The algorithm uses information from the departmental PACS to determine how many distinct CT examinations were concurrently performed. Then, it matches the number of distinct accession numbers to the series that were acquired and anatomically matches individual series DLPs to their appropriate CT examinations. This algorithm allows for more accurate dose analytics, but there remain instances where automatic sorting is not feasible. To ultimately improve radiology patient care, we must standardize series names and exam names to unequivocally sort exams by anatomy and correctly estimate whole-body effective dose.
A fully automated non-external marker 4D-CT sorting algorithm using a serial cine scanning protocol.
Carnes, Greg; Gaede, Stewart; Yu, Edward; Van Dyk, Jake; Battista, Jerry; Lee, Ting-Yim
2009-04-07
Current 4D-CT methods require external marker data to retrospectively sort image data and generate CT volumes. In this work we develop an automated 4D-CT sorting algorithm that performs without the aid of data collected from an external respiratory surrogate. The sorting algorithm requires an overlapping cine scan protocol. The overlapping protocol provides a spatial link between couch positions. Beginning with a starting scan position, images from the adjacent scan position (which spatial match the starting scan position) are selected by maximizing the normalized cross correlation (NCC) of the images at the overlapping slice position. The process was continued by 'daisy chaining' all couch positions using the selected images until an entire 3D volume was produced. The algorithm produced 16 phase volumes to complete a 4D-CT dataset. Additional 4D-CT datasets were also produced using external marker amplitude and phase angle sorting methods. The image quality of the volumes produced by the different methods was quantified by calculating the mean difference of the sorted overlapping slices from adjacent couch positions. The NCC sorted images showed a significant decrease in the mean difference (p < 0.01) for the five patients.
A lossless one-pass sorting algorithm for symmetric three-dimensional gamma-ray data sets
International Nuclear Information System (INIS)
Brinkman, M.J.; Manatt, D.R.; Becker, J.A.; Henry, E.A.
1992-01-01
An algorithm for three-dimensional sorting and storing of the large data sets expected from the next generation of large gamma-ray detector arrays (i.e., EUROGAM, GAMMASPHERE) is presented. The algorithm allows the storage of realistic data sets on standard mass storage media. A discussion of an efficient implementation of the algorithm is provided with a proposed technique for exploiting its inherently parallel nature. (author). 5 refs., 2 figs
A lossless one-pass sorting algorithm for symmetric three-dimensional gamma-ray data sets
Energy Technology Data Exchange (ETDEWEB)
Brinkman, M J; Manatt, D R; Becker, J A; Henry, E A [Lawrence Livermore National Lab., CA (United States)
1992-08-01
An algorithm for three-dimensional sorting and storing of the large data sets expected from the next generation of large gamma-ray detector arrays (i.e., EUROGAM, GAMMASPHERE) is presented. The algorithm allows the storage of realistic data sets on standard mass storage media. A discussion of an efficient implementation of the algorithm is provided with a proposed technique for exploiting its inherently parallel nature. (author). 5 refs., 2 figs.
An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm
Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En
2015-01-01
A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193
A multiple objective magnet sorting algorithm for the Advanced Light Source insertion devices
International Nuclear Information System (INIS)
Humphries, D.; Goetz, F.; Kownacki, P.; Marks, S.; Schlueter, R.
1995-01-01
Insertion devices for the Advanced Light Source (ALS) incorporate large numbers of permanent magnets which have a variety of magnetization orientation errors. These orientation errors can produce field errors which affect both the spectral brightness of the insertion devices and the storage ring electron beam dynamics. A perturbation study was carried out to quantify the effects of orientation errors acting in a hybrid magnetic structure. The results of this study were used to develop a multiple stage sorting algorithm which minimizes undesirable integrated field errors and essentially eliminates pole excitation errors. When applied to a measured magnet population for an existing insertion device, an order of magnitude reduction in integrated field errors was achieved while maintaining near zero pole excitation errors
A multiple objective magnet sorting algorithm for the ALS insertion devices
International Nuclear Information System (INIS)
Humphries, D.; Goetz, F.; Kownacki, P.; Marks, S.; Schlueter, R.
1994-07-01
Insertion devices for the Advanced Light Source (ALS) incorporate large numbers of permanent magnets which have a variety of magnetization orientation errors. These orientation errors can produce field errors which affect both the spectral brightness of the insertion devices and the storage ring electron beam dynamics. A perturbation study was carried out to quantify the effects of orientation errors acting in a hybrid magnetic structure. The results of this study were used to develop a multiple stage sorting algorithm which minimizes undesirable integrated field errors and essentially eliminates pole excitation errors. When applied to a measured magnet population for an existing insertion device, an order of magnitude reduction in integrated field errors was achieved while maintaining near zero pole excitation errors
Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En
2015-08-13
A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.
Empirical tests of the Gradual Learning Algorithm
Boersma, P.; Hayes, B.
1999-01-01
The Gradual Learning Algorithm (Boersma 1997) is a constraint ranking algorithm for learning Optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion algorithm of Tesar and
Empirical tests of the Gradual Learning Algorithm
Boersma, P.; Hayes, B.
2001-01-01
The Gradual Learning Algorithm (Boersma 1997) is a constraint-ranking algorithm for learning optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion algorithm of Tesar and
Linn, S
One of the more often used measures of multiple injuries is the injury severity score (ISS). Determination of the ISS is based on the abbreviated injury scale (AIS). This paper suggests a new algorithm to sort the AISs for each case and calculate ISS. The program uses unsorted abbreviated injury scale (AIS) levels for each case and rearranges them in descending order. The first three sorted AISs representing the three most severe injuries of a person are then used to calculate injury severity score (ISS). This algorithm should be useful for analyses of clusters of injuries especially when more patients have multiple injuries.
Verification of counting sort and radix sort
C.P.T. de Gouw (Stijn); F.S. de Boer (Frank); J.C. Rot (Jurriaan)
2016-01-01
textabstractSorting is an important algorithmic task used in many applications. Two main aspects of sorting algorithms which have been studied extensively are complexity and correctness. [Foley and Hoare, 1971] published the first formal correctness proof of a sorting algorithm (Quicksort). While
Storage capacity of the Tilinglike Learning Algorithm
International Nuclear Information System (INIS)
Buhot, Arnaud; Gordon, Mirta B.
2001-01-01
The storage capacity of an incremental learning algorithm for the parity machine, the Tilinglike Learning Algorithm, is analytically determined in the limit of a large number of hidden perceptrons. Different learning rules for the simple perceptron are investigated. The usual Gardner-Derrida rule leads to a storage capacity close to the upper bound, which is independent of the learning algorithm considered
Boon, K H; Khalil-Hani, M; Malarvili, M B
2018-01-01
This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity. Copyright © 2017 Elsevier B.V. All rights reserved.
Végh, Ladislav
2016-01-01
The first data structure that first-year undergraduate students learn during the programming and algorithms courses is the one-dimensional array. For novice programmers, it might be hard to understand different algorithms on arrays (e.g. searching, mirroring, sorting algorithms), because the algorithms dynamically change the values of elements. In…
The Dropout Learning Algorithm
Baldi, Pierre; Sadowski, Peter
2014-01-01
Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations. PMID:24771879
Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation
Directory of Open Access Journals (Sweden)
Pandiarajan K.
2014-09-01
Full Text Available This paper presents an effective method of network overload management in power systems. The three competing objectives 1 generation cost 2 transmission line overload and 3 real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO and Differential evolution (DE. Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem
Perbandingan Bubble Sort dengan Insertion Sort pada Bahasa Pemrograman C dan Fortran
Reina, Reina; Gautama, Josef Bernadi
2013-01-01
Sorting is a basic algorithm studied by students of computer science major. Sorting algorithm is the basis of other algorithms such as searching algorithm, pattern matching algorithm. Bubble sort is a popular basic sorting algorithm due to its easiness to be implemented. Besides bubble sort, there is insertion sort. It is lesspopular than bubble sort because it has more difficult algorithm. This paper discusses about process time between insertion sort and bubble sort with two kinds of data. ...
Khoje, Suchitra
2018-02-01
Images of four qualities of mangoes and guavas are evaluated for color and textural features to characterize and classify them, and to model the fruit appearance grading. The paper discusses three approaches to identify most discriminating texture features of both the fruits. In the first approach, fruit's color and texture features are selected using Mahalanobis distance. A total of 20 color features and 40 textural features are extracted for analysis. Using Mahalanobis distance and feature intercorrelation analyses, one best color feature (mean of a* [L*a*b* color space]) and two textural features (energy a*, contrast of H*) are selected as features for Guava while two best color features (R std, H std) and one textural features (energy b*) are selected as features for mangoes with the highest discriminate power. The second approach studies some common wavelet families for searching the best classification model for fruit quality grading. The wavelet features extracted from five basic mother wavelets (db, bior, rbior, Coif, Sym) are explored to characterize fruits texture appearance. In third approach, genetic algorithm is used to select only those color and wavelet texture features that are relevant to the separation of the class, from a large universe of features. The study shows that image color and texture features which were identified using a genetic algorithm can distinguish between various qualities classes of fruits. The experimental results showed that support vector machine classifier is elected for Guava grading with an accuracy of 97.61% and artificial neural network is elected from Mango grading with an accuracy of 95.65%. The proposed method is nondestructive fruit quality assessment method. The experimental results has proven that Genetic algorithm along with wavelet textures feature has potential to discriminate fruit quality. Finally, it can be concluded that discussed method is an accurate, reliable, and objective tool to determine fruit
Cascade Error Projection: A New Learning Algorithm
Duong, T. A.; Stubberud, A. R.; Daud, T.; Thakoor, A. P.
1995-01-01
A new neural network architecture and a hardware implementable learning algorithm is proposed. The algorithm, called cascade error projection (CEP), handles lack of precision and circuit noise better than existing algorithms.
An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm
Directory of Open Access Journals (Sweden)
Ying-Lun Chen
2015-08-01
Full Text Available A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO, and the feature extraction is carried out by the generalized Hebbian algorithm (GHA. To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.
A simple model based magnet sorting algorithm for planar hybrid undulators
International Nuclear Information System (INIS)
Rakowsky, G.
2010-01-01
Various magnet sorting strategies have been used to optimize undulator performance, ranging from intuitive pairing of high- and low-strength magnets, to full 3D FEM simulation with 3-axis Helmholtz coil magnet data. In the extreme, swapping magnets in a full field model to minimize trajectory wander and rms phase error can be time consuming. This paper presents a simpler approach, extending the field error signature concept to obtain trajectory displacement, kick angle and phase error signatures for each component of magnetization error from a Radia model of a short hybrid-PM undulator. We demonstrate that steering errors and phase errors are essentially decoupled and scalable from measured X, Y and Z components of magnetization. Then, for any given sequence of magnets, rms trajectory and phase errors are obtained from simple cumulative sums of the scaled displacements and phase errors. The cost function (a weighted sum of these errors) is then minimized by swapping magnets, using one's favorite optimization algorithm. This approach was applied recently at NSLS to a short in-vacuum undulator, which required no subsequent trajectory or phase shimming. Trajectory and phase signatures are also obtained for some mechanical errors, to guide 'virtual shimming' and specifying mechanical tolerances. Some simple inhomogeneities are modeled to assess their error contributions.
Directory of Open Access Journals (Sweden)
Ziaul Huque
2012-01-01
Full Text Available A Computational Fluid Dynamics (CFD and response surface-based multiobjective design optimization were performed for six different 2D airfoil profiles, and the Pareto optimal front of each airfoil is presented. FLUENT, which is a commercial CFD simulation code, was used to determine the relevant aerodynamic loads. The Lift Coefficient (CL and Drag Coefficient (CD data at a range of 0° to 12° angles of attack (α and at three different Reynolds numbers (Re=68,459, 479, 210, and 958, 422 for all the six airfoils were obtained. Realizable k-ε turbulence model with a second-order upwind solution method was used in the simulations. The standard least square method was used to generate response surface by the statistical code JMP. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II was used to determine the Pareto optimal set based on the response surfaces. Each Pareto optimal solution represents a different compromise between design objectives. This gives the designer a choice to select a design compromise that best suits the requirements from a set of optimal solutions. The Pareto solution set is presented in the form of a Pareto optimal front.
A polynomial-time algorithm to design push plans for sensorless parts sorting
Berg, de M.; Goaoc, X.; van der Stappen, A.F.
2005-01-01
We consider the efficient computation of sequences of push actions that simultaneously orient two different polygons. Our motivation for studying this problem comes from the observation that appropriately oriented parts admit simple sensorless sorting. We study the sorting of two polygonal parts by
Dragas, Jelena; Jackel, David; Hierlemann, Andreas; Franke, Felix
2015-03-01
Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.
Machine learning approaches for the prediction of signal peptides and otherprotein sorting signals
DEFF Research Database (Denmark)
Nielsen, Henrik; Brunak, Søren; von Heijne, Gunnar
1999-01-01
Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently,the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, havemade it possible to achieve...
International Nuclear Information System (INIS)
Yang, Ming-Der; Lin, Min-Der; Lin, Yu-Hao; Tsai, Kang-Ting
2017-01-01
Highlights: • An effective envelope energy performance model (BEM) was developed. • We integrated NSGA-II with the BEM to optimize the green building envelope. • A tradeoff plan of green building design for three conflict objectives was obtained. • The optimal envelope design efficiently reduced the construction cost of green building. - Abstract: To realize the goal of environmental sustainability, improving energy efficiency in buildings is a major priority worldwide. However, the practical design of green building envelopes for energy conservation is a highly complex optimization problem, and architects must make multiobjective decisions. In practice, methods such as multicriteria analyses that entail capitalizing on possibly many (but in nearly any case limited) alternatives are commonly employed. This study investigated the feasibility of applying a multiobjective optimal model on building envelope design (MOPBEM), which involved integrating a building envelope energy performance model with a multiobjective optimizer. The MOPBEM was established to provide a reference for green designs. A nondominated sorting genetic algorithm-II (NSGA-II) was used to achieve a tradeoff design set between three conflicting objectives, namely minimizing the envelope construction cost (ENVCOST), minimizing the envelope energy performance (ENVLOAD), and maximizing the window opening rate (WOPR). A real office building case was designed using the MOPBEM to identify the potential strengths and weaknesses of the proposed MOPBEM. The results showed that a high ENVCOST was expended in simultaneously satisfying the low ENVLOAD and high WOPR. Various designs exhibited obvious cost reductions compared with the original architects' manual design, demonstrating the practicability of the MOPBEM.
Quantum learning algorithms for quantum measurements
Energy Technology Data Exchange (ETDEWEB)
Bisio, Alessandro, E-mail: alessandro.bisio@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); D' Ariano, Giacomo Mauro, E-mail: dariano@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Perinotti, Paolo, E-mail: paolo.perinotti@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Sedlak, Michal, E-mail: michal.sedlak@unipv.it [QUIT Group, Dipartimento di Fisica ' A. Volta' and INFN, via Bassi 6, 27100 Pavia (Italy); Institute of Physics, Slovak Academy of Sciences, Dubravska cesta 9, 845 11 Bratislava (Slovakia)
2011-09-12
We study quantum learning algorithms for quantum measurements. The optimal learning algorithm is derived for arbitrary von Neumann measurements in the case of training with one or two examples. The analysis of the case of three examples reveals that, differently from the learning of unitary gates, the optimal algorithm for learning of quantum measurements cannot be parallelized, and requires quantum memories for the storage of information. -- Highlights: → Optimal learning algorithm for von Neumann measurements. → From 2 copies to 1 copy: the optimal strategy is parallel. → From 3 copies to 1 copy: the optimal strategy must be non-parallel.
Quantum learning algorithms for quantum measurements
International Nuclear Information System (INIS)
Bisio, Alessandro; D'Ariano, Giacomo Mauro; Perinotti, Paolo; Sedlak, Michal
2011-01-01
We study quantum learning algorithms for quantum measurements. The optimal learning algorithm is derived for arbitrary von Neumann measurements in the case of training with one or two examples. The analysis of the case of three examples reveals that, differently from the learning of unitary gates, the optimal algorithm for learning of quantum measurements cannot be parallelized, and requires quantum memories for the storage of information. -- Highlights: → Optimal learning algorithm for von Neumann measurements. → From 2 copies to 1 copy: the optimal strategy is parallel. → From 3 copies to 1 copy: the optimal strategy must be non-parallel.
Learning algorithms and automatic processing of languages
International Nuclear Information System (INIS)
Fluhr, Christian Yves Andre
1977-01-01
This research thesis concerns the field of artificial intelligence. It addresses learning algorithms applied to automatic processing of languages. The author first briefly describes some mechanisms of human intelligence in order to describe how these mechanisms are simulated on a computer. He outlines the specific role of learning in various manifestations of intelligence. Then, based on the Markov's algorithm theory, the author discusses the notion of learning algorithm. Two main types of learning algorithms are then addressed: firstly, an 'algorithm-teacher dialogue' type sanction-based algorithm which aims at learning how to solve grammatical ambiguities in submitted texts; secondly, an algorithm related to a document system which structures semantic data automatically obtained from a set of texts in order to be able to understand by references to any question on the content of these texts
Directory of Open Access Journals (Sweden)
Rubén Iván Bolaños
2015-06-01
Full Text Available This paper considers a multi-objective version of the Multiple Traveling Salesman Problem (MOmTSP. In particular, two objectives are considered: the minimization of the total traveled distance and the balance of the working times of the traveling salesmen. The problem is formulated as an integer multi-objective optimization model. A non-dominated sorting genetic algorithm (NSGA-II is proposed to solve the MOmTSP. The solution scheme allows one to find a set of ordered solutions in Pareto fronts by considering the concept of dominance. Tests on real world instances and instances adapted from the literature show the effectiveness of the proposed algorithm.
Wang, Jiali; Byrne, James; Franquiz, Juan; McGoron, Anthony
2007-08-01
develop and validate a PET sorting algorithm based on the respiratory amplitude to correct for abnormal respiratory cycles. using the 4D NCAT phantom model, 3D PET images were simulated in lung and other structures at different times within a respiratory cycle and noise was added. To validate the amplitude binning algorithm, NCAT phantom was used to simulate one case of five different respiratory periods and another case of five respiratory periods alone with five respiratory amplitudes. Comparison was performed for gated and un-gated images and for the new amplitude binning algorithm with the time binning algorithm by calculating the mean number of counts in the ROI (region of interest). an average of 8.87+/-5.10% improvement was reported for total 16 tumors with different tumor sizes and different T/B (tumor to background) ratios using the new sorting algorithm. As both the T/B ratio and tumor size decreases, image degradation due to respiration increases. The greater benefit for smaller diameter tumor and lower T/B ratio indicates a potential improvement in detecting more problematic tumors.
Yu, Shuzhi; Hao, Fanchang; Leong, Hon Wai
2016-02-01
We consider the problem of sorting signed permutations by reversals, transpositions, transreversals, and block-interchanges. The problem arises in the study of species evolution via large-scale genome rearrangement operations. Recently, Hao et al. gave a 2-approximation scheme called genome sorting by bridges (GSB) for solving this problem. Their result extended and unified the results of (i) He and Chen - a 2-approximation algorithm allowing reversals, transpositions, and block-interchanges (by also allowing transversals) and (ii) Hartman and Sharan - a 1.5-approximation algorithm allowing reversals, transpositions, and transversals (by also allowing block-interchanges). The GSB result is based on introduction of three bridge structures in the breakpoint graph, the L-bridge, T-bridge, and X-bridge that models goodreversal, transposition/transreversal, and block-interchange, respectively. However, the paper by Hao et al. focused on proving the 2-approximation GSB scheme and only mention a straightforward [Formula: see text] algorithm. In this paper, we give an [Formula: see text] algorithm for implementing the GSB scheme. The key idea behind our faster GSB algorithm is to represent cycles in the breakpoint graph by their canonical sequences, which greatly simplifies the search for these bridge structures. We also give some comparison results (running time and computed distances) against the original GSB implementation.
Peningkatan Prestasi Belajar Pendidikan Agama Islam Melalui Penerapan Card Sort Learning
Directory of Open Access Journals (Sweden)
Nur Fadilah
2017-11-01
Full Text Available Appropriate learning methods should be applied in order to maximize the students’ ability during learning activities. The purpose of this study is to determine the improvement of learning achievement of Islamic Religious Education (PAI through the application of card sort learning method. Action study conducted on PAI learning. The material of this learning is to understand the provision of sholat of fourth graders of Gunungsari State Elementary School 2 Kaliori Sub district Rembang District Lesson Year 2015/2016. The indicator of successful learning in this research is 75%. The results showed that the percentage of learning mastery at the pre cycle stage was 10.7%, 67.9% in the first cycle, and in the second cycle reached 92.9%. The average score of students' test results also increased significantly, ie the pre cycle stage was 58.8, the first cycle was 72.4, and in the second cycle reached 78.9. This means, through the implementation of card sort learning methode can improve student learning achievement on PAI learning material understanding the provision of sholat. lAbstrak Metode pembelajaran yang tepat harus diterapkan untuk memaksimalkan kemampuan siswa selama kegiatan pembelajaran. Penelitian ini bertujuan untuk mengetahui peningkatan prestasi belajar Pendidikan Agama Islam (PAI melalui penerapan metode card sort. Studi tindakan (action research dilakukan pada pembelajaran PAI materi mengenai rukun sholat siswa kelas IV Sekolah Dasar Negeri Gunungsari 2 Kecamatan Kaliori Kabupaten Rembang Tahun Pelajaran 2015/2016. Indikator eHasil penelitian menunjukkan bahwa persentase ketuntasan belajar pada tahap pra siklus sebesar 10,7%, pada siklus I sebesar 67,9%, dan pada siklus II mencapai 92,9%. Nilai rata-rata hasil tes siswa juga mengalami peningkatan yang signifikan, yaitu para tahap pra siklus sebesar 58,8, siklus I sebesar 72,4, dan pada siklus II naik menjadi 78,9. Hal ini berarti, melalui penerapan card sort learning dapat
Exploitation of linkage learning in evolutionary algorithms
Chen, Ying-ping
2010-01-01
The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.
Thengumpallil, Sheeba; Germond, Jean-François; Bourhis, Jean; Bochud, François; Moeckli, Raphaël
2016-06-01
To investigate the impact of Toshiba phase- and amplitude-sorting algorithms on the margin strategies for free-breathing lung radiotherapy treatments in the presence of breathing variations. 4D CT of a sphere inside a dynamic thorax phantom was acquired. The 4D CT was reconstructed according to the phase- and amplitude-sorting algorithms. The phantom was moved by reproducing amplitude, frequency, and a mix of amplitude and frequency variations. Artefact analysis was performed for Mid-Ventilation and ITV-based strategies on the images reconstructed by phase- and amplitude-sorting algorithms. The target volume deviation was assessed by comparing the target volume acquired during irregular motion to the volume acquired during regular motion. The amplitude-sorting algorithm shows reduced artefacts for only amplitude variations while the phase-sorting algorithm for only frequency variations. For amplitude and frequency variations, both algorithms perform similarly. Most of the artefacts are blurring and incomplete structures. We found larger artefacts and volume differences for the Mid-Ventilation with respect to the ITV strategy, resulting in a higher relative difference of the surface distortion value which ranges between maximum 14.6% and minimum 4.1%. The amplitude- is superior to the phase-sorting algorithm in the reduction of motion artefacts for amplitude variations while phase-sorting for frequency variations. A proper choice of 4D CT sorting algorithm is important in order to reduce motion artefacts, especially if Mid-Ventilation strategy is used. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Learning Intelligent Genetic Algorithms Using Japanese Nonograms
Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen
2012-01-01
An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…
Regalia, Giulia; Coelli, Stefania; Biffi, Emilia; Ferrigno, Giancarlo; Pedrocchi, Alessandra
2016-01-01
Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.
Learning theory of distributed spectral algorithms
International Nuclear Information System (INIS)
Guo, Zheng-Chu; Lin, Shao-Bo; Zhou, Ding-Xuan
2017-01-01
Spectral algorithms have been widely used and studied in learning theory and inverse problems. This paper is concerned with distributed spectral algorithms, for handling big data, based on a divide-and-conquer approach. We present a learning theory for these distributed kernel-based learning algorithms in a regression framework including nice error bounds and optimal minimax learning rates achieved by means of a novel integral operator approach and a second order decomposition of inverse operators. Our quantitative estimates are given in terms of regularity of the regression function, effective dimension of the reproducing kernel Hilbert space, and qualification of the filter function of the spectral algorithm. They do not need any eigenfunction or noise conditions and are better than the existing results even for the classical family of spectral algorithms. (paper)
Kernel learning algorithms for face recognition
Li, Jun-Bao; Pan, Jeng-Shyang
2013-01-01
Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new
Quantum algorithms and learning theory
Arunachalam, S.
2018-01-01
This thesis studies strengths and weaknesses of quantum computers. In the first part we present three contributions to quantum algorithms. 1) consider a search space of N elements. One of these elements is "marked" and our goal is to find this. We describe a quantum algorithm to solve this problem
The efficiency of the RULES-4 classification learning algorithm in predicting the density of agents
Directory of Open Access Journals (Sweden)
Ziad Salem
2014-12-01
Full Text Available Learning is the act of obtaining new or modifying existing knowledge, behaviours, skills or preferences. The ability to learn is found in humans, other organisms and some machines. Learning is always based on some sort of observations or data such as examples, direct experience or instruction. This paper presents a classification algorithm to learn the density of agents in an arena based on the measurements of six proximity sensors of a combined actuator sensor units (CASUs. Rules are presented that were induced by the learning algorithm that was trained with data-sets based on the CASU’s sensor data streams collected during a number of experiments with “Bristlebots (agents in the arena (environment”. It was found that a set of rules generated by the learning algorithm is able to predict the number of bristlebots in the arena based on the CASU’s sensor readings with satisfying accuracy.
Directory of Open Access Journals (Sweden)
M. Balasubbareddy
2015-12-01
Full Text Available A novel optimization algorithm is proposed to solve single and multi-objective optimization problems with generation fuel cost, emission, and total power losses as objectives. The proposed method is a hybridization of the conventional cuckoo search algorithm and arithmetic crossover operations. Thus, the non-linear, non-convex objective function can be solved under practical constraints. The effectiveness of the proposed algorithm is analyzed for various cases to illustrate the effect of practical constraints on the objectives' optimization. Two and three objective multi-objective optimization problems are formulated and solved using the proposed non-dominated sorting-based hybrid cuckoo search algorithm. The effectiveness of the proposed method in confining the Pareto front solutions in the solution region is analyzed. The results for single and multi-objective optimization problems are physically interpreted on standard test functions as well as the IEEE-30 bus test system with supporting numerical and graphical results and also validated against existing methods.
Comparison of sorting algorithms to increase the range of Hartmann-Shack aberrometry.
Bedggood, Phillip; Metha, Andrew
2010-01-01
Recently many software-based approaches have been suggested for improving the range and accuracy of Hartmann-Shack aberrometry. We compare the performance of four representative algorithms, with a focus on aberrometry for the human eye. Algorithms vary in complexity from the simplistic traditional approach to iterative spline extrapolation based on prior spot measurements. Range is assessed for a variety of aberration types in isolation using computer modeling, and also for complex wavefront shapes using a real adaptive optics system. The effects of common sources of error for ocular wavefront sensing are explored. The results show that the simplest possible iterative algorithm produces comparable range and robustness compared to the more complicated algorithms, while keeping processing time minimal to afford real-time analysis.
Parallelization of TMVA Machine Learning Algorithms
Hajili, Mammad
2017-01-01
This report reflects my work on Parallelization of TMVA Machine Learning Algorithms integrated to ROOT Data Analysis Framework during summer internship at CERN. The report consists of 4 impor- tant part - data set used in training and validation, algorithms that multiprocessing applied on them, parallelization techniques and re- sults of execution time changes due to number of workers.
Top Tagging by Deep Learning Algorithm
Akil, Ali
2015-01-01
In this report I will show the application of a deep learning algorithm on a Monte Carlo simulation sample to test its performance in tagging hadronic decays of boosted top quarks and compare what we get with the results of the application of some other algorithms.
A distributed algorithm for machine learning
Chen, Shihong
2018-04-01
This paper considers a distributed learning problem in which a group of machines in a connected network, each learning its own local dataset, aim to reach a consensus at an optimal model, by exchanging information only with their neighbors but without transmitting data. A distributed algorithm is proposed to solve this problem under appropriate assumptions.
Algorithmic learning in a random world
Vovk, Vladimir; Shafer, Glenn
2005-01-01
A new scientific monograph developing significant new algorithmic foundations in machine learning theory. Researchers and postgraduates in CS, statistics, and A.I. will find the book an authoritative and formal presentation of some of the most promising theoretical developments in machine learning.
A Learning Algorithm for Multimodal Grammar Inference.
D'Ulizia, A; Ferri, F; Grifoni, P
2011-12-01
The high costs of development and maintenance of multimodal grammars in integrating and understanding input in multimodal interfaces lead to the investigation of novel algorithmic solutions in automating grammar generation and in updating processes. Many algorithms for context-free grammar inference have been developed in the natural language processing literature. An extension of these algorithms toward the inference of multimodal grammars is necessary for multimodal input processing. In this paper, we propose a novel grammar inference mechanism that allows us to learn a multimodal grammar from its positive samples of multimodal sentences. The algorithm first generates the multimodal grammar that is able to parse the positive samples of sentences and, afterward, makes use of two learning operators and the minimum description length metrics in improving the grammar description and in avoiding the over-generalization problem. The experimental results highlight the acceptable performances of the algorithm proposed in this paper since it has a very high probability of parsing valid sentences.
Al Rivan, Muhammad Ezar
2017-01-01
Ordering is one of the process done before doing data processing. The sorting algorithm has its own strengths and weaknesses. By taking strengths of each algorithm then combined can be a better algorithm. Quick Sort and Merge Sort are algorithms that divide the data into parts and each part divide again into sub-section until one element. Usually one element join with others and then sorted by. In this experiment data divide into parts that have size not more than threshold. This part then so...
Institute of Scientific and Technical Information of China (English)
Xi JIN; Jie ZHANG; Jin-liang GAO; Wen-yan WU
2008-01-01
Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Aigorithm-Ⅱ (NSGA-Ⅱ) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-Ⅱ into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by introduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated; this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.
Lee, Fook Choon; Rangaiah, Gade Pandu; Ray, Ajay Kumar
2007-10-15
Bulk of the penicillin produced is used as raw material for semi-synthetic penicillin (such as amoxicillin and ampicillin) and semi-synthetic cephalosporins (such as cephalexin and cefadroxil). In the present paper, an industrial penicillin V bioreactor train is optimized for multiple objectives simultaneously. An industrial train, comprising a bank of identical bioreactors, is run semi-continuously in a synchronous fashion. The fermentation taking place in a bioreactor is modeled using a morphologically structured mechanism. For multi-objective optimization for two and three objectives, the elitist non-dominated sorting genetic algorithm (NSGA-II) is chosen. Instead of a single optimum as in the traditional optimization, a wide range of optimal design and operating conditions depicting trade-offs of key performance indicators such as batch cycle time, yield, profit and penicillin concentration, is successfully obtained. The effects of design and operating variables on the optimal solutions are discussed in detail. Copyright 2007 Wiley Periodicals, Inc.
Lin, Ying Chih; Lu, Chin Lung; Chang, Hwan-You; Tang, Chuan Yi
2005-01-01
In the study of genome rearrangement, the block-interchanges have been proposed recently as a new kind of global rearrangement events affecting a genome by swapping two nonintersecting segments of any length. The so-called block-interchange distance problem, which is equivalent to the sorting-by-block-interchange problem, is to find a minimum series of block-interchanges for transforming one chromosome into another. In this paper, we study this problem by considering the circular chromosomes and propose a Omicron(deltan) time algorithm for solving it by making use of permutation groups in algebra, where n is the length of the circular chromosome and delta is the minimum number of block-interchanges required for the transformation, which can be calculated in Omicron(n) time in advance. Moreover, we obtain analogous results by extending our algorithm to linear chromosomes. Finally, we have implemented our algorithm and applied it to the circular genomic sequences of three human vibrio pathogens for predicting their evolutionary relationships. Consequently, our experimental results coincide with the previous ones obtained by others using a different comparative genomics approach, which implies that the block-interchange events seem to play a significant role in the evolution of vibrio species.
Zheng, Ling; Duan, Xuwei; Deng, Zhaoxue; Li, Yinong
2014-03-01
A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.
Learning from nature: Nature-inspired algorithms
DEFF Research Database (Denmark)
Albeanu, Grigore; Madsen, Henrik; Popentiu-Vladicescu, Florin
2016-01-01
.), genetic and evolutionary strategies, artificial immune systems etc. Well-known examples of applications include: aircraft wing design, wind turbine design, bionic car, bullet train, optimal decisions related to traffic, appropriate strategies to survive under a well-adapted immune system etc. Based......During last decade, the nature has inspired researchers to develop new algorithms. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees' algorithm, bat algorithm, firefly algorithm etc...... on collective social behaviour of organisms, researchers have developed optimization strategies taking into account not only the individuals, but also groups and environment. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms...
A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm.
Zhang, Aizhu; Sun, Genyun; Ren, Jinchang; Li, Xiaodong; Wang, Zhenjie; Jia, Xiuping
2018-01-01
Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named , which stores those superior agents after fitness sorting in each iteration. Since the global property of remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA.
Jonathan B. Berk
1998-01-01
In this paper we analyze the theoretical implications of sorting data into groups and then running asset pricing tests within each group. We show that the way this procedure is implemented introduces a severe bias in favor of rejecting the model under consideration. By simply picking enough groups to sort into even the true asset pricing model can be shown to have no explanatory power within each group.
Noel, Camille E; Parikh, Parag J
2011-05-01
Though it is known that irregular breathing can introduce artifacts in commercial 4DCT, this has not been systematically explored. The purpose of this study is to investigate the effect of variations in basic parameters of the breathing wave on 4DCT imaging quality. A four-dimensional motion platform holding an acrylic sphere was scanned while moving in a trajectory modeled from a lung cancer patient. A bellows device was used as a respiratory surrogate, and the images were sorted by a commercial phase-based sorting algorithm. Motion during the first half of the scan was produced at a baseline trajectory with a consistent frequency and amplitude of 15 breaths per minute and 1 cm, peak to peak. The two parameters were then varied mid-scan to new frequency and amplitude values, with frequencies ranging from 7.5 to 22 bpm and amplitudes ranging from 0.5 to 1.5 cm. Image sets representing four respiratory phases were contoured. Each set was analyzed to compare centroid displacement, density homogeneity, and volumetric and geometric distortions of the imaged sphere. Undercoverage of the target ITV and overcoverage of healthy tissue was also evaluated. Changes in amplitude of 25% or more, with or without changes in frequency, consistently caused measurable distortions in shape, position, and density of the imaged sphere. Frequency changes over 50% showed a similar trend. This study suggests that basic breathing statistics can be used to quickly assess the quality of a 4DCT scan prior to image reconstruction. Such information can help give indication of the proper course of action when irregular breathing patterns are observed during CT scanning.
DEFF Research Database (Denmark)
Henglein, Fritz
2009-01-01
What is a sorting function—not a sorting function for a given ordering relation, but a sorting function with nothing given? Formulating four basic properties of sorting algorithms as defining requirements, we arrive at intrinsic notions of sorting and stable sorting: A function is a sorting...... are derivable without compromising data abstraction. Finally we point out that stable sorting functions as default representations of ordering relations have the advantage of permitting linear-time sorting algorithms; inequality tests forfeit this possibility....... function if and only it is an intrinsically parametric permutation function. It is a stable sorting function if and only if it is an intrinsically stable permutation function. We show that ordering relations can be represented isomorphically as inequality tests, comparators and stable sorting functions...
Fifolt, Matthew; Preskitt, Julie; Rucks, Andrew; Corvey, Kathryn; Benton, Elizabeth Cason
Q-sort methodology is an underutilized tool for differentiating among multiple priority measures. The authors describe steps to identify, delimit, and sort potential health measures and use selected priority measures to establish an overall agenda for continuous quality improvement (CQI) activities within learning collaboratives. Through an iterative process, the authors vetted a list of potential child and adolescent health measures. Multiple stakeholders, including payers, direct care providers, and organizational representatives sorted and prioritized measures, using Q-methodology. Q-methodology provided the Alabama Child Health Improvement Alliance (ACHIA) an objective and rigorous approach to system improvement. Selected priority measures were used to design learning collaboratives. An open dialogue among stakeholders about state health priorities spurred greater organizational buy-in for ACHIA and increased its credibility as a statewide provider of learning collaboratives. The integrated processes of Q-sort methodology, learning collaboratives, and CQI offer a practical yet innovative way to identify and prioritize state measures for child and adolescent health and establish a learning agenda for targeted quality improvement activities.
International Nuclear Information System (INIS)
Yang, Yu; Fritzsching, Keith J.; Hong, Mei
2013-01-01
A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra (“good connections”), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra (“bad connections”), and minimizing the number of assigned peaks that have no matching peaks in the other spectra (“edges”). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct
Yang, Yu; Fritzsching, Keith J; Hong, Mei
2013-11-01
A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra ("good connections"), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra ("bad connections"), and minimizing the number of assigned peaks that have no matching peaks in the other spectra ("edges"). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a
Mahmoud, Hosam M
2011-01-01
A cutting-edge look at the emerging distributional theory of sorting Research on distributions associated with sorting algorithms has grown dramatically over the last few decades, spawning many exact and limiting distributions of complexity measures for many sorting algorithms. Yet much of this information has been scattered in disparate and highly specialized sources throughout the literature. In Sorting: A Distribution Theory, leading authority Hosam Mahmoud compiles, consolidates, and clarifies the large volume of available research, providing a much-needed, comprehensive treatment of the
A Sequence of Sorting Strategies.
Duncan, David R.; Litwiller, Bonnie H.
1984-01-01
Describes eight increasingly sophisticated and efficient sorting algorithms including linear insertion, binary insertion, shellsort, bubble exchange, shakersort, quick sort, straight selection, and tree selection. Provides challenges for the reader and the student to program these efficiently. (JM)
A Decomposition Algorithm for Learning Bayesian Network Structures from Data
DEFF Research Database (Denmark)
Zeng, Yifeng; Cordero Hernandez, Jorge
2008-01-01
It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....
Niayifar, A.; Perona, P.
2015-12-01
River impoundment by dams is known to strongly affect the natural flow regime and in turn the river attributes and the related ecosystem biodiversity. Making hydropower sustainable implies to seek for innovative operational policies able to generate dynamic environmental flows while maintaining economic efficiency. For dammed systems, we build the ecological and economical efficiency plot for non-proportional flow redistribution operational rules compared to minimal flow operational. As for the case of small hydropower plants (e.g., see the companion paper by Gorla et al., this session), we use a four parameters Fermi-Dirac statistical distribution to mathematically formulate non-proportional redistribution rules. These rules allocate a fraction of water to the riverine environment depending on current reservoir inflows and storage. Riverine ecological benefits associated to dynamic environmental flows are computed by integrating the Weighted Usable Area (WUA) for fishes with Richter's hydrological indicators. Then, we apply nondominated sorting genetic algorithm II (NSGA-II) to an ensemble of non-proportional and minimal flow redistribution rules in order to generate the Pareto frontier showing the system performances in the ecologic and economic space. This fast and elitist multiobjective optimization method is eventually applied to a case study. It is found that non-proportional dynamic flow releases ensure maximal power production on the one hand, while conciliating ecological sustainability on the other hand. Much of the improvement in the environmental indicator is seen to arise from a better use of the reservoir storage dynamics, which allows to capture, and laminate flood events while recovering part of them for energy production. In conclusion, adopting such new operational policies would unravel a spectrum of globally-efficient performances of the dammed system when compared with those resulting from policies based on constant minimum flow releases.
Pengembangan Algoritma Pengurutan SMS (Scan, Move, And Sort)
Lubis, Denni Aprilsyah
2015-01-01
Sorting has been a profound area for the algorithmic researchers. And many resources are invested to suggest a more working sorting algorithm. For this purpose many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. Efficient sorting is important to optimize the use of other algorithms that require sorted lists to work correctly. sorting has been considered as a fundamental problem in the study of algorithms that due to many reas...
International Nuclear Information System (INIS)
Chen, Fang; Zhou, Jianzhong; Wang, Chao; Li, Chunlong; Lu, Peng
2017-01-01
Wind power is a type of clean and renewable energy, and reasonable utilization of wind power is beneficial to environmental protection and economic development. Therefore, a short-term hydro-thermal-wind economic emission dispatching (SHTW-EED) problem is presented in this paper. The proposed problem aims to distribute the load among hydro, thermal and wind power units to simultaneously minimize economic cost and pollutant emission. To solve the SHTW-EED problem with complex constraints, a modified gravitational search algorithm based on the non-dominated sorting genetic algorithm-III (MGSA-NSGA-III) is proposed. In the proposed MGSA-NSGA-III, a non-dominated sorting approach, reference-point based selection mechanism and chaotic mutation strategy are applied to improve the evolutionary process of the original gravitational search algorithm (GSA) and maintain the distribution diversity of Pareto optimal solutions. Moreover, a parallel computing strategy is introduced to improve the computational efficiency. Finally, the proposed MGSA-NSGA-III is applied to a typical hydro-thermal-wind system to verify its feasibility and effectiveness. The simulation results indicate that the proposed algorithm can obtain low economic cost and small pollutant emission when dealing with the SHTW-EED problem. - Highlights: • A hybrid algorithm is proposed to handle hydro-thermal-wind power dispatching. • Several improvement strategies are applied to the algorithm. • A parallel computing strategy is applied to improve computational efficiency. • Two cases are analyzed to verify the efficiency of the optimize mode.
LazySorted: A Lazily, Partially Sorted Python List
Directory of Open Access Journals (Sweden)
Naftali Harris
2015-06-01
Full Text Available LazySorted is a Python C extension implementing a partially and lazily sorted list data structure. It solves a common problem faced by programmers, in which they need just part of a sorted list, like its middle element (the median, but sort the entire list to get it. LazySorted presents them with the abstraction that they are working with a fully sorted list, while actually only sorting the list partially with quicksort partitions to return the requested sub-elements. This enables programmers to use naive "sort first" algorithms but nonetheless attain linear run-times when possible. LazySorted may serve as a drop-in replacement for the built-in sorted function in most cases, and can sometimes achieve run-times more than 7 times faster.
Perbandingan Bubble Sort dengan Insertion Sort pada Bahasa Pemrograman C dan Fortran
Directory of Open Access Journals (Sweden)
Reina Reina
2013-12-01
Full Text Available Sorting is a basic algorithm studied by students of computer science major. Sorting algorithm is the basis of other algorithms such as searching algorithm, pattern matching algorithm. Bubble sort is a popular basic sorting algorithm due to its easiness to be implemented. Besides bubble sort, there is insertion sort. It is lesspopular than bubble sort because it has more difficult algorithm. This paper discusses about process time between insertion sort and bubble sort with two kinds of data. First is randomized data, and the second is data of descending list. Comparison of process time has been done in two kinds of programming language that is C programming language and FORTRAN programming language. The result shows that bubble sort needs more time than insertion sort does.
TAO-robust backpropagation learning algorithm.
Pernía-Espinoza, Alpha V; Ordieres-Meré, Joaquín B; Martínez-de-Pisón, Francisco J; González-Marcos, Ana
2005-03-01
In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models. We combine the benefits of the non-linear regression model tau-estimates [introduced by Tabatabai, M. A. Argyros, I. K. Robust Estimation and testing for general nonlinear regression models. Applied Mathematics and Computation. 58 (1993) 85-101] with the backpropagation algorithm to produce the TAO-robust learning algorithm, in order to deal with the problems of modelling with outliers. The cost function of this approach has a bounded influence function given by the weighted average of two psi functions, one corresponding to a very robust estimate and the other to a highly efficient estimate. The advantages of the proposed algorithm are studied with an example.
A Learning Algorithm based on High School Teaching Wisdom
Philip, Ninan Sajeeth
2010-01-01
A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate a student and to give them training on the examples for which they repeatedly fail, until, they can correctly answer all types of questions. This incremental learning procedure produces better learning curves by demanding the student to optimally dedicate their learning time on the failed examples. When used in machine learning, the algorithm is found to train a machine...
"Accelerated Perceptron": A Self-Learning Linear Decision Algorithm
Zuev, Yu. A.
2003-01-01
The class of linear decision rules is studied. A new algorithm for weight correction, called an "accelerated perceptron", is proposed. In contrast to classical Rosenblatt's perceptron this algorithm modifies the weight vector at each step. The algorithm may be employed both in learning and in self-learning modes. The theoretical aspects of the behaviour of the algorithm are studied when the algorithm is used for the purpose of increasing the decision reliability by means of weighted voting. I...
A Newton-type neural network learning algorithm
International Nuclear Information System (INIS)
Ivanov, V.V.; Puzynin, I.V.; Purehvdorzh, B.
1993-01-01
First- and second-order learning methods for feed-forward multilayer networks are considered. A Newton-type algorithm is proposed and compared with the common back-propagation algorithm. It is shown that the proposed algorithm provides better learning quality. Some recommendations for their usage are given. 11 refs.; 1 fig.; 1 tab
A rank-based Prediction Algorithm of Learning User's Intention
Shen, Jie; Gao, Ying; Chen, Cang; Gong, HaiPing
Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query "Palace" contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.
Energy Technology Data Exchange (ETDEWEB)
Fluhr, Christian Yves Andre
1977-06-15
This research thesis concerns the field of artificial intelligence. It addresses learning algorithms applied to automatic processing of languages. The author first briefly describes some mechanisms of human intelligence in order to describe how these mechanisms are simulated on a computer. He outlines the specific role of learning in various manifestations of intelligence. Then, based on the Markov's algorithm theory, the author discusses the notion of learning algorithm. Two main types of learning algorithms are then addressed: firstly, an 'algorithm-teacher dialogue' type sanction-based algorithm which aims at learning how to solve grammatical ambiguities in submitted texts; secondly, an algorithm related to a document system which structures semantic data automatically obtained from a set of texts in order to be able to understand by references to any question on the content of these texts.
Online learning algorithm for ensemble of decision rules
Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata
2011-01-01
We describe an online learning algorithm that builds a system of decision rules for a classification problem. Rules are constructed according to the minimum description length principle by a greedy algorithm or using the dynamic programming approach
Cascade Error Projection: An Efficient Hardware Learning Algorithm
Duong, T. A.
1995-01-01
A new learning algorithm termed cascade error projection (CEP) is presented. CEP is an adaption of a constructive architecture from cascade correlation and the dynamical stepsize of A/D conversion from the cascade back propagation algorithm.
Boosting Learning Algorithm for Stock Price Forecasting
Wang, Chengzhang; Bai, Xiaoming
2018-03-01
To tackle complexity and uncertainty of stock market behavior, more studies have introduced machine learning algorithms to forecast stock price. ANN (artificial neural network) is one of the most successful and promising applications. We propose a boosting-ANN model in this paper to predict the stock close price. On the basis of boosting theory, multiple weak predicting machines, i.e. ANNs, are assembled to build a stronger predictor, i.e. boosting-ANN model. New error criteria of the weak studying machine and rules of weights updating are adopted in this study. We select technical factors from financial markets as forecasting input variables. Final results demonstrate the boosting-ANN model works better than other ones for stock price forecasting.
Research on machine learning framework based on random forest algorithm
Ren, Qiong; Cheng, Hui; Han, Hai
2017-03-01
With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.
An algorithm for learning real-time automata
Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.
2007-01-01
We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe
Relevance as a metric for evaluating machine learning algorithms
Kota Gopalakrishna, A.; Ozcelebi, T.; Liotta, A.; Lukkien, J.J.
2013-01-01
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel
Challenges in the Verification of Reinforcement Learning Algorithms
Van Wesel, Perry; Goodloe, Alwyn E.
2017-01-01
Machine learning (ML) is increasingly being applied to a wide array of domains from search engines to autonomous vehicles. These algorithms, however, are notoriously complex and hard to verify. This work looks at the assumptions underlying machine learning algorithms as well as some of the challenges in trying to verify ML algorithms. Furthermore, we focus on the specific challenges of verifying reinforcement learning algorithms. These are highlighted using a specific example. Ultimately, we do not offer a solution to the complex problem of ML verification, but point out possible approaches for verification and interesting research opportunities.
Location-Aware Mobile Learning of Spatial Algorithms
Karavirta, Ville
2013-01-01
Learning an algorithm--a systematic sequence of operations for solving a problem with given input--is often difficult for students due to the abstract nature of the algorithms and the data they process. To help students understand the behavior of algorithms, a subfield in computing education research has focused on algorithm…
Rout, Sachindra K.; Choudhury, Balaji K.; Sahoo, Ranjit K.; Sarangi, Sunil K.
2014-07-01
The modeling and optimization of a Pulse Tube Refrigerator is a complicated task, due to its complexity of geometry and nature. The aim of the present work is to optimize the dimensions of pulse tube and regenerator for an Inertance-Type Pulse Tube Refrigerator (ITPTR) by using Response Surface Methodology (RSM) and Non-Sorted Genetic Algorithm II (NSGA II). The Box-Behnken design of the response surface methodology is used in an experimental matrix, with four factors and two levels. The diameter and length of the pulse tube and regenerator are chosen as the design variables where the rest of the dimensions and operating conditions of the ITPTR are constant. The required output responses are the cold head temperature (Tcold) and compressor input power (Wcomp). Computational fluid dynamics (CFD) have been used to model and solve the ITPTR. The CFD results agreed well with those of the previously published paper. Also using the results from the 1-D simulation, RSM is conducted to analyse the effect of the independent variables on the responses. To check the accuracy of the model, the analysis of variance (ANOVA) method has been used. Based on the proposed mathematical RSM models a multi-objective optimization study, using the Non-sorted genetic algorithm II (NSGA-II) has been performed to optimize the responses.
Gradient descent learning algorithm overview: a general dynamical systems perspective.
Baldi, P
1995-01-01
Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning.
Yu, Jessica S; Pertusi, Dante A; Adeniran, Adebola V; Tyo, Keith E J
2017-03-15
High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required. We have developed CellSort, a support vector machine (SVM) algorithm that identifies optimal sorting gates based on machine learning using positive and negative control populations. CellSort can take advantage of more than two dimensions to enhance the ability to distinguish between populations. We also present a Bayesian approach to predict the number of sorting rounds required to enrich a population from a given library size. This Bayesian approach allowed us to determine strategies for biasing the sorting gates in order to reduce the required number of enrichment rounds. This algorithm should be generally useful for improve sorting outcomes and reducing effort when using FACS. Source code available at http://tyolab.northwestern.edu/tools/ . k-tyo@northwestern.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Automated training for algorithms that learn from genomic data.
Cilingir, Gokcen; Broschat, Shira L
2015-01-01
Supervised machine learning algorithms are used by life scientists for a variety of objectives. Expert-curated public gene and protein databases are major resources for gathering data to train these algorithms. While these data resources are continuously updated, generally, these updates are not incorporated into published machine learning algorithms which thereby can become outdated soon after their introduction. In this paper, we propose a new model of operation for supervised machine learning algorithms that learn from genomic data. By defining these algorithms in a pipeline in which the training data gathering procedure and the learning process are automated, one can create a system that generates a classifier or predictor using information available from public resources. The proposed model is explained using three case studies on SignalP, MemLoci, and ApicoAP in which existing machine learning models are utilized in pipelines. Given that the vast majority of the procedures described for gathering training data can easily be automated, it is possible to transform valuable machine learning algorithms into self-evolving learners that benefit from the ever-changing data available for gene products and to develop new machine learning algorithms that are similarly capable.
SOL: A Library for Scalable Online Learning Algorithms
Wu, Yue; Hoi, Steven C. H.; Liu, Chenghao; Lu, Jing; Sahoo, Doyen; Yu, Nenghai
2016-01-01
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, portability, and extensibility. SOL was implemented in C++, and provided with a collection of easy-to-use command-line tools, python wrappers and library calls for users and develope...
International Nuclear Information System (INIS)
Chen, Lei; Yan, Changqi; Liao, Yi; Song, Feifei; Jia, Zhen
2017-01-01
Highlights: • The optimization ability of NSGA-II is improved. • The design targets can be obvious optimized through optimization methodology. • Multi-objective optimization is implanted into the design of nuclear power plant. - Abstract: The design of nuclear component can be optimized by seeking out the best combination of article operational and structural parameters. Through multi-objective optimization, the optimized scheme can not only meets the design requirements, but also satisfies the safety regulations. In this work, a hybrid non-dominated sorting genetic algorithm is proposed, and its performance is verified by comparing it with its prototype and immune memory clone constraint multi-objective algorithm through four test-functions; the designs of the steam generator and the primary loop of Qinshan I nuclear power plant are optimized by the proposed algorithm. The results show that the algorithm outperforms the other two through overall evaluation; the reactor inlet temperature is an important parameter which influences the distribution of the Pareto optimal front; through optimization, the weight of the steam generator can be reduced by 16.5%, and the primary flow-rate can be reduced by 17.0%, the weight of the primary loop can be reduced by 11.4%, and the volume can be reduced by 9.8%.
Human resource recommendation algorithm based on ensemble learning and Spark
Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie
2017-08-01
Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.
A strategy for quantum algorithm design assisted by machine learning
International Nuclear Information System (INIS)
Bang, Jeongho; Lee, Jinhyoung; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin
2014-01-01
We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum–classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch–Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method. (paper)
A strategy for quantum algorithm design assisted by machine learning
Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin; Lee, Jinhyoung
2014-07-01
We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.
International Nuclear Information System (INIS)
Hawkins, A.P.; Richards, A.W.
1982-01-01
In an ore sorting apparatus, ore particles are bombarded with neutrons in a chamber and sorted by detecting radiation emitted by isotopes of elements, such as gold, forming or contained in the particles, using detectors and selectively controlling fluid jets. The isotopes can be selectively recognised by their radiation characteristics. In an alternative embodiment, shorter life isotopes are formed by neutron bombardment and detection of radiation takes place immediately adjacent the region of bombardment
Balbin, Jessie R.; Fausto, Janette C.; Janabajab, John Michael M.; Malicdem, Daryl James L.; Marcelo, Reginald N.; Santos, Jan Jeffrey Z.
2017-06-01
Mango production is highly vital in the Philippines. It is very essential in the food industry as it is being used in markets and restaurants daily. The quality of mangoes can affect the income of a mango farmer, thus incorrect time of harvesting will result to loss of quality mangoes and income. Scientific farming is much needed nowadays together with new gadgets because wastage of mangoes increase annually due to uncouth quality. This research paper focuses on profiling and sorting of Mangifera Indica using image processing techniques and pattern recognition. The image of a mango is captured on a weekly basis from its early stage. In this study, the researchers monitor the growth and color transition of a mango for profiling purposes. Actual dimensions of the mango are determined through image conversion and determination of pixel and RGB values covered through MATLAB. A program is developed to determine the range of the maximum size of a standard ripe mango. Hue, light, saturation (HSL) correction is used in the filtering process to assure the exactness of RGB values of a mango subject. By pattern recognition technique, the program can determine if a mango is standard and ready to be exported.
Imbalanced learning foundations, algorithms, and applications
He, Haibo
2013-01-01
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles,
Leave-two-out stability of ontology learning algorithm
International Nuclear Information System (INIS)
Wu, Jianzhang; Yu, Xiao; Zhu, Linli; Gao, Wei
2016-01-01
Ontology is a semantic analysis and calculation model, which has been applied to many subjects. Ontology similarity calculation and ontology mapping are employed as machine learning approaches. The purpose of this paper is to study the leave-two-out stability of ontology learning algorithm. Several leave-two-out stabilities are defined in ontology learning setting and the relationship among these stabilities are presented. Furthermore, the results manifested reveal that leave-two-out stability is a sufficient and necessary condition for ontology learning algorithm.
QUEST : Eliminating online supervised learning for efficient classification algorithms
Zwartjes, Ardjan; Havinga, Paul J.M.; Smit, Gerard J.M.; Hurink, Johann L.
2016-01-01
In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting
Neuronal spike sorting based on radial basis function neural networks
Directory of Open Access Journals (Sweden)
Taghavi Kani M
2011-02-01
Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
Teaching learning based optimization algorithm and its engineering applications
Rao, R Venkata
2016-01-01
Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.
Extreme learning machines 2013 algorithms and applications
Toh, Kar-Ann; Romay, Manuel; Mao, Kezhi
2014-01-01
In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability. This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discu...
A parallel ILP algorithm that incorporates incremental batch learning
Nuno Fonseca; Rui Camacho; Fernado Silva
2003-01-01
In this paper we tackle the problems of eciency and scala-bility faced by Inductive Logic Programming (ILP) systems. We proposethe use of parallelism to improve eciency and the use of an incrementalbatch learning to address the scalability problem. We describe a novelparallel algorithm that incorporates into ILP the method of incremen-tal batch learning. The theoretical complexity of the algorithm indicatesthat a linear speedup can be achieved.
Machine Learning in Production Systems Design Using Genetic Algorithms
Abu Qudeiri Jaber; Yamamoto Hidehiko Rizauddin Ramli
2008-01-01
To create a solution for a specific problem in machine learning, the solution is constructed from the data or by use a search method. Genetic algorithms are a model of machine learning that can be used to find nearest optimal solution. While the great advantage of genetic algorithms is the fact that they find a solution through evolution, this is also the biggest disadvantage. Evolution is inductive, in nature life does not evolve towards a good solution but it evolves aw...
International Nuclear Information System (INIS)
Tian, Hao; Yuan, Xiaohui; Ji, Bin; Chen, Zhihuan
2014-01-01
Highlights: • An improved non-dominated sorting gravitational search algorithm (NSGSA-CM) is proposed. • NSGSA-CM is used to solve the problem of short-term multi-objective hydrothermal scheduling. • We enhance the search capability of NSGSA-CM by chaotic mutation. • New strategies are devised to handle various constraints in NSGSA-CM. • We obtain better compromise solutions with less fuel cost and emissions. - Abstract: This paper proposes a non-dominated sorting gravitational search algorithm with chaotic mutation (NSGSA-CM) to solve short-term economic/environmental hydrothermal scheduling (SEEHTS) problem. The SEEHTS problem is formulated as a multi-objective optimization problem with many equality and inequality constraints. By introducing the concept of non-dominated sorting and crowding distance, NSGSA-CM can optimize two objectives of fuel cost and pollutant emission simultaneously and obtain a set of Pareto optimal solutions in one trial. In order to improve the performance of NSGSA-CM, the paper introduces particle memory character and population social information in velocity update process. And a chaotic mutation is adopted to prevent the premature convergence. Furthermore, NSGSA-CM utilizes an elitism strategy which selects better solutions in parent and offspring populations based on their non-domination rank and crowding distance to update new generations. When dealing with the constraints of the SEEHTS, new strategies without penalty factors are proposed. In order to handle the water dynamic balance and system load balance constraints, this paper uses a combined strategy which adjusts the violation averagely to each decision variable at first and adjusts the rest violation randomly later. Meanwhile, a new symmetrical adjustment strategy by modifying the discharges at current and later interval without breaking water dynamic balance is adopted to handle reservoir storage constraints. To test the performance of the proposed NSGSA
Online learning algorithm for ensemble of decision rules
Chikalov, Igor
2011-01-01
We describe an online learning algorithm that builds a system of decision rules for a classification problem. Rules are constructed according to the minimum description length principle by a greedy algorithm or using the dynamic programming approach. © 2011 Springer-Verlag.
Mind the Gaps: Controversies about Algorithms, Learning and Trendy Knowledge
Argenton, Gerald
2017-01-01
This article critically explores the ways by which the Web could become a more learning-oriented medium in the age of, but also in spite of, the newly bred algorithmic cultures. The social dimension of algorithms is reported in literature as being a socio-technological entanglement that has a powerful influence on users' practices and their lived…
Recommending Learning Activities in Social Network Using Data Mining Algorithms
Mahnane, Lamia
2017-01-01
In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). "NSN-AP-CF" processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the…
Machine learning algorithms for datasets popularity prediction
Kancys, Kipras
2016-01-01
This report represents continued study where ML algorithms were used to predict databases popularity. Three topics were covered. First of all, there was a discrepancy between old and new meta-data collection procedures, so a reason for that had to be found. Secondly, different parameters were analysed and dropped to make algorithms perform better. And third, it was decided to move modelling part on Spark.
Assessment of various supervised learning algorithms using different performance metrics
Susheel Kumar, S. M.; Laxkar, Deepak; Adhikari, Sourav; Vijayarajan, V.
2017-11-01
Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.
Convergence analysis of Chauvin's PCA learning algorithm with a constant learning rate
International Nuclear Information System (INIS)
Lv Jiancheng; Yi Zhang
2007-01-01
The convergence of Chauvin's PCA learning algorithm with a constant learning rate is studied in this paper by using a DDT method (deterministic discrete-time system method). Different from the DCT method (deterministic continuous-time system method), the DDT method does not require that the learning rate converges to zero. An invariant set of Chauvin's algorithm with a constant learning rate is obtained so that the non-divergence of this algorithm can be guaranteed. Rigorous mathematic proofs are provided to prove the local convergence of this algorithm
Convergence analysis of Chauvin's PCA learning algorithm with a constant learning rate
Energy Technology Data Exchange (ETDEWEB)
Lv Jiancheng [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China); Yi Zhang [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)]. E-mail: zhangyi@uestc.edu.cn
2007-05-15
The convergence of Chauvin's PCA learning algorithm with a constant learning rate is studied in this paper by using a DDT method (deterministic discrete-time system method). Different from the DCT method (deterministic continuous-time system method), the DDT method does not require that the learning rate converges to zero. An invariant set of Chauvin's algorithm with a constant learning rate is obtained so that the non-divergence of this algorithm can be guaranteed. Rigorous mathematic proofs are provided to prove the local convergence of this algorithm.
Vignesh, S.; Dinesh Babu, P.; Surya, G.; Dinesh, S.; Marimuthu, P.
2018-02-01
The ultimate goal of all production entities is to select the process parameters that would be of maximum strength, minimum wear and friction. The friction and wear are serious problems in most of the industries which are influenced by the working set of parameters, oxidation characteristics and mechanism involved in formation of wear. The experimental input parameters such as sliding distance, applied load, and temperature are utilized in finding out the optimized solution for achieving the desired output responses such as coefficient of friction, wear rate, and volume loss. The optimization is performed with the help of a novel method, Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) based on an evolutionary algorithm. The regression equations obtained using Response Surface Methodology (RSM) are used in determining the optimum process parameters. Further, the results achieved through desirability approach in RSM are compared with that of the optimized solution obtained through NSGA-II. The results conclude that proposed evolutionary technique is much effective and faster than the desirability approach.
Learning motor skills from algorithms to robot experiments
Kober, Jens
2014-01-01
This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters, and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation, and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which wo...
Cognitive Radio Transceivers: RF, Spectrum Sensing, and Learning Algorithms Review
Directory of Open Access Journals (Sweden)
Lise Safatly
2014-01-01
reconfigurable radio frequency (RF parts, enhanced spectrum sensing algorithms, and sophisticated machine learning techniques. In this paper, we present a review of the recent advances in CR transceivers hardware design and algorithms. For the RF part, three types of antennas are presented: UWB antennas, frequency-reconfigurable/tunable antennas, and UWB antennas with reconfigurable band notches. The main challenges faced by the design of the other RF blocks are also discussed. Sophisticated spectrum sensing algorithms that overcome main sensing challenges such as model uncertainty, hardware impairments, and wideband sensing are highlighted. The cognitive engine features are discussed. Moreover, we study unsupervised classification algorithms and a reinforcement learning (RL algorithm that has been proposed to perform decision-making in CR networks.
Evolving Stochastic Learning Algorithm based on Tsallis entropic index
Anastasiadis, A. D.; Magoulas, G. D.
2006-03-01
In this paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neural networks. The new algorithm combines deterministic and stochastic search steps by employing a different adaptive stepsize for each network weight, and applies a form of noise that is characterized by the nonextensive entropic index q, regulated by a weight decay term. The behavior of the learning algorithm can be made more stochastic or deterministic depending on the trade off between the temperature T and the q values. This is achieved by introducing a formula that defines a time-dependent relationship between these two important learning parameters. Our experimental study verifies that there are indeed improvements in the convergence speed of this new evolving stochastic learning algorithm, which makes learning faster than using the original Hybrid Learning Scheme (HLS). In addition, experiments are conducted to explore the influence of the entropic index q and temperature T on the convergence speed and stability of the proposed method.
Optimal quantum sample complexity of learning algorithms
Arunachalam, S.; de Wolf, R.
2017-01-01
In learning theory, the VC dimension of a concept class C is the most common way to measure its "richness." A fundamental result says that the number of examples needed to learn an unknown target concept c 2 C under an unknown distribution D, is tightly determined by the VC dimension d of the
Optimizing learning path selection through memetic algorithms
Acampora, G.; Gaeta, M.; Loia, V.; Ritrovato, P.; Salerno, S.
2008-01-01
e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering
Interactive Algorithms for Unsupervised Machine Learning
2015-06-01
in Neural Information Processing Systems, 2013. 14 [3] Louigi Addario-Berry, Nicolas Broutin, Luc Devroye, and Gábor Lugosi. On combinato- rial...Myung Jin Choi, Vincent Y F Tan , Animashree Anandkumar, and Alan S Willsky. Learn- ing Latent Tree Graphical Models. Journal of Machine Learning
Hagen, Espen; Ness, Torbjørn V; Khosrowshahi, Amir; Sørensen, Christina; Fyhn, Marianne; Hafting, Torkel; Franke, Felix; Einevoll, Gaute T
2015-04-30
New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy. ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.
QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms
Directory of Open Access Journals (Sweden)
Ardjan Zwartjes
2016-10-01
Full Text Available In this work, we introduce QUEST (QUantile Estimation after Supervised Training, an adaptive classification algorithm for Wireless Sensor Networks (WSNs that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.
QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.
Zwartjes, Ardjan; Havinga, Paul J M; Smit, Gerard J M; Hurink, Johann L
2016-10-01
In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.
International Nuclear Information System (INIS)
Chen, Zhihuan; Yuan, Xiaohui; Ji, Bin; Wang, Pengtao; Tian, Hao
2014-01-01
Highlights: • Multi-objective optimization based fractional order controller is designed for HTRS. • NSGAII is improved by iterative chaotic map with infinite collapses (ICMIC) operator. • ISE and ITSE are as chosen as objective functions in tuning parameters of HTRS. • FOPID controller outperforms the PID controller under various running conditions. • Trade-off between speed of reference tracking and damping of oscillation are shown. - Abstract: Fractional-order PID (FOPID) controller is a generalization of traditional PID controller using fractional calculus. Compared to the traditional PID controller, in FOPID controller, the order of derivative portion and integral portion is not integer, which provides more flexibility in achieving control objectives. Design stage of such an FOPID controller consists of determining five parameters, i.e. proportional, integral and derivative gains {Kp, Ki, Kd}, and extra integration and differentiation orders {λ,μ}, which has a large difference comparing with the conventional PID tuning rules, thus a suitable optimization algorithm is essential to the parameters tuning of FOPID controller. This paper focuses on the design of the FOPID controller using chaotic non-dominated sorting genetic algorithm II (NSGAII) for hydraulic turbine regulating system (HTRS). The parameters chosen of the FOPID controller is formulated as a multi-objective optimization problem, in which the objective functions are composed by the integral of the squared error (ISE) and integral of the time multiplied squared error (ITSE). The chaotic NSGAII algorithm, which is an incorporation of chaotic behaviors into NSGAII, is used as the optimizer to search true Pareto-front of the FOPID controller and designers can implement each of them based on objective functions priority. The designed chaotic NSGAII based FOPID controller procedure is applied to a HTRS system. A comparison study between the optimum integer order PID controller and optimum
Trans-algorithmic nature of learning in biological systems.
Shimansky, Yury P
2018-05-02
Learning ability is a vitally important, distinctive property of biological systems, which provides dynamic stability in non-stationary environments. Although several different types of learning have been successfully modeled using a universal computer, in general, learning cannot be described by an algorithm. In other words, algorithmic approach to describing the functioning of biological systems is not sufficient for adequate grasping of what is life. Since biosystems are parts of the physical world, one might hope that adding some physical mechanisms and principles to the concept of algorithm could provide extra possibilities for describing learning in its full generality. However, a straightforward approach to that through the so-called physical hypercomputation so far has not been successful. Here an alternative approach is proposed. Biosystems are described as achieving enumeration of possible physical compositions though random incremental modifications inflicted on them by active operating resources (AORs) in the environment. Biosystems learn through algorithmic regulation of the intensity of the above modifications according to a specific optimality criterion. From the perspective of external observers, biosystems move in the space of different algorithms driven by random modifications imposed by the environmental AORs. A particular algorithm is only a snapshot of that motion, while the motion itself is essentially trans-algorithmic. In this conceptual framework, death of unfit members of a population, for example, is viewed as a trans-algorithmic modification made in the population as a biosystem by environmental AORs. Numerous examples of AOR utilization in biosystems of different complexity, from viruses to multicellular organisms, are provided.
Cache-Aware and Cache-Oblivious Adaptive Sorting
DEFF Research Database (Denmark)
Brodal, Gerth Stølting; Fagerberg, Rolf; Moruz, Gabriel
2005-01-01
Two new adaptive sorting algorithms are introduced which perform an optimal number of comparisons with respect to the number of inversions in the input. The first algorithm is based on a new linear time reduction to (non-adaptive) sorting. The second algorithm is based on a new division protocol...... for the GenericSort algorithm by Estivill-Castro and Wood. From both algorithms we derive I/O-optimal cache-aware and cache-oblivious adaptive sorting algorithms. These are the first I/O-optimal adaptive sorting algorithms....
Some chaotic behaviors in a MCA learning algorithm with a constant learning rate
International Nuclear Information System (INIS)
Lv Jiancheng; Yi Zhang
2007-01-01
Douglas's minor component analysis algorithm with a constant learning rate has both stability and chaotic dynamical behavior under some conditions. The paper explores such dynamical behavior of this algorithm. Certain stability and chaos of this algorithm are derived. Waveform plots, Lyapunov exponents and bifurcation diagrams are presented to illustrate the existence of chaotic behavior
Gradient Learning Algorithms for Ontology Computing
Gao, Wei; Zhu, Linli
2014-01-01
The gradient learning model has been raising great attention in view of its promising perspectives for applications in statistics, data dimensionality reducing, and other specific fields. In this paper, we raise a new gradient learning model for ontology similarity measuring and ontology mapping in multidividing setting. The sample error in this setting is given by virtue of the hypothesis space and the trick of ontology dividing operator. Finally, two experiments presented on plant and humanoid robotics field verify the efficiency of the new computation model for ontology similarity measure and ontology mapping applications in multidividing setting. PMID:25530752
Gradient Learning Algorithms for Ontology Computing
Directory of Open Access Journals (Sweden)
Wei Gao
2014-01-01
Full Text Available The gradient learning model has been raising great attention in view of its promising perspectives for applications in statistics, data dimensionality reducing, and other specific fields. In this paper, we raise a new gradient learning model for ontology similarity measuring and ontology mapping in multidividing setting. The sample error in this setting is given by virtue of the hypothesis space and the trick of ontology dividing operator. Finally, two experiments presented on plant and humanoid robotics field verify the efficiency of the new computation model for ontology similarity measure and ontology mapping applications in multidividing setting.
Machine-Learning Algorithms to Code Public Health Spending Accounts.
Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David
Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.
Fast algorithm selection using learning curves
Rijn, van J.N.; Abdulrahman, S.M.; Brazdil, P.; Vanschoren, J.; Fromont, E.; De Bie, T.; Leeuwen, van M.
2015-01-01
One of the challenges in Machine Learning to find a classifier and parameter settings that work well on a given dataset. Evaluating all possible combinations typically takes too much time, hence many solutions have been proposed that attempt to predict which classifiers are most promising to try. As
Evolutionary Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory
Institute of Scientific and Technical Information of China (English)
Sheng-Zhi Du; Zeng-Qiang Chen; Zhu-Zhi Yuan
2005-01-01
This paper analyzes the sensitivity to noise in BAM (Bidirectional Associative Memory), and then proves the noise immunity of BAM relates not only to the minimum absolute value of net inputs (MAV) but also to the variance of weights associated with synapse connections. In fact, it is a positive monotonically increasing function of the quotient of MAV divided by the variance of weights. Besides, the performance of pseudo-relaxation method depends on learning parameters (λ and ζ), but the relation of them is not linear. So it is hard to find a best combination of λ and ζ which leads to the best BAM performance. And it is obvious that pseudo-relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. In this paper, a novel learning algorithm EPRBAM (evolutionary psendo-relaxation learning algorithm for bidirectional association memory) employing genetic algorithm and pseudo-relaxation method is proposed to get feasible solution of BAM weight matrix. This algorithm uses the quotient as the fitness of each individual and employs pseudo-relaxation method to adjust individual solution when it does not satisfy constraining condition any more after genetic operation. Experimental results show this algorithm improves noise immunity of BAM greatly. At the same time, EPRBAM does not depend on learning parameters and can get global optimal solution.
Algorithm-Dependent Generalization Bounds for Multi-Task Learning.
Liu, Tongliang; Tao, Dacheng; Song, Mingli; Maybank, Stephen J
2017-02-01
Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order O(1/n), where n is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order O(1/T), where T is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.
A globally convergent MC algorithm with an adaptive learning rate.
Peng, Dezhong; Yi, Zhang; Xiang, Yong; Zhang, Haixian
2012-02-01
This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.
MINING ON CAR DATABASE EMPLOYING LEARNING AND CLUSTERING ALGORITHMS
Muhammad Rukunuddin Ghalib; Shivam Vohra; Sunish Vohra; Akash Juneja
2013-01-01
In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the known learning algorithms used are Naïve Bayesian (NB) and SMO (Self-Minimal-Optimisation) .Thus the following two learning algorithms are used on a Car review database and thus a model is hence created which predicts the characteristic of a review comment after getting trained. It was found that model successfully predicted correctly about the review comm...
Learning Search Algorithms: An Educational View
Directory of Open Access Journals (Sweden)
Ales Janota
2014-12-01
Full Text Available Artificial intelligence methods find their practical usage in many applications including maritime industry. The paper concentrates on the methods of uninformed and informed search, potentially usable in solving of complex problems based on the state space representation. The problem of introducing the search algorithms to newcomers has its technical and psychological dimensions. The authors show how it is possible to cope with both of them through design and use of specialized authoring systems. A typical example of searching a path through the maze is used to demonstrate how to test, observe and compare properties of various search strategies. Performance of search methods is evaluated based on the common criteria.
Sinha, Snehal K; Kumar, Mithilesh; Guria, Chandan; Kumar, Anup; Banerjee, Chiranjib
2017-10-01
Algal model based multi-objective optimization using elitist non-dominated sorting genetic algorithm with inheritance was carried out for batch cultivation of Dunaliella tertiolecta using NPK-fertilizer. Optimization problems involving two- and three-objective functions were solved simultaneously. The objective functions are: maximization of algae-biomass and lipid productivity with minimization of cultivation time and cost. Time variant light intensity and temperature including NPK-fertilizer, NaCl and NaHCO 3 loadings are the important decision variables. Algal model involving Monod/Andrews adsorption kinetics and Droop model with internal nutrient cell quota was used for optimization studies. Sets of non-dominated (equally good) Pareto optimal solutions were obtained for the problems studied. It was observed that time variant optimal light intensity and temperature trajectories, including optimum NPK fertilizer, NaCl and NaHCO 3 concentration has significant influence to improve biomass and lipid productivity under minimum cultivation time and cost. Proposed optimization studies may be helpful to implement the control strategy in scale-up operation. Copyright © 2017 Elsevier Ltd. All rights reserved.
Dhingra, Sunil; Bhushan, Gian; Dubey, Kashyap Kumar
2014-03-01
The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (Pmax)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO x , unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, Pmax), performance (BTE) and emission (CO, NO x , HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NO x , HC, smoke, a multiobjective optimization problem is formulated. Nondominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.
Shojaeefard, Mohammad Hassan; Khalkhali, Abolfazl; Faghihian, Hamed; Dahmardeh, Masoud
2018-03-01
Unlike conventional approaches where optimization is performed on a unique component of a specific product, optimum design of a set of components for employing in a product family can cause significant reduction in costs. Increasing commonality and performance of the product platform simultaneously is a multi-objective optimization problem (MOP). Several optimization methods are reported to solve these MOPs. However, what is less discussed is how to find the trade-off points among the obtained non-dominated optimum points. This article investigates the optimal design of a product family using non-dominated sorting genetic algorithm II (NSGA-II) and proposes the employment of technique for order of preference by similarity to ideal solution (TOPSIS) method to find the trade-off points among the obtained non-dominated results while compromising all objective functions together. A case study for a family of suspension systems is presented, considering performance and commonality. The results indicate the effectiveness of the proposed method to obtain the trade-off points with the best possible performance while maximizing the common parts.
Wang, Dengfeng; Cai, Kefang
2018-04-01
This article presents a hybrid method combining a modified non-dominated sorting genetic algorithm (MNSGA-II) with grey relational analysis (GRA) to improve the static-dynamic performance of a body-in-white (BIW). First, an implicit parametric model of the BIW was built using SFE-CONCEPT software, and then the validity of the implicit parametric model was verified by physical testing. Eight shape design variables were defined for BIW beam structures based on the implicit parametric technology. Subsequently, MNSGA-II was used to determine the optimal combination of the design parameters that can improve the bending stiffness, torsion stiffness and low-order natural frequencies of the BIW without considerable increase in the mass. A set of non-dominated solutions was then obtained in the multi-objective optimization design. Finally, the grey entropy theory and GRA were applied to rank all non-dominated solutions from best to worst to determine the best trade-off solution. The comparison between the GRA and the technique for order of preference by similarity to ideal solution (TOPSIS) illustrated the reliability and rationality of GRA. Moreover, the effectiveness of the hybrid method was verified by the optimal results such that the bending stiffness, torsion stiffness, first order bending and first order torsion natural frequency were improved by 5.46%, 9.30%, 7.32% and 5.73%, respectively, with the mass of the BIW increasing by 1.30%.
Randomized Algorithms for Scalable Machine Learning
Kleiner, Ariel Jacob
2012-01-01
Many existing procedures in machine learning and statistics are computationally intractable in the setting of large-scale data. As a result, the advent of rapidly increasing dataset sizes, which should be a boon yielding improved statistical performance, instead severely blunts the usefulness of a variety of existing inferential methods. In this work, we use randomness to ameliorate this lack of scalability by reducing complex, computationally difficult inferential problems to larger sets o...
Directory of Open Access Journals (Sweden)
Muncarno Muncarno
2015-12-01
Full Text Available This research was motivated by the low activities and student learning outcomes on mathematics. The purpose of this research was to increase the activities and student learning outcomes on mathematics by applying the active learning model of card sort game. The method of this research was classroom action research that consist of planning, implementation, observation, and reflection. The instrument of data collection used was observation sheet and test questions. The technique of analysis data used were qualitative analysis and quantitative analysis. The results of this research showed that aplication of active learning model of card sort game on mathematics learning can increase the activities and student learning outcomes. It can be showed that students learning completeness reached 75%, the average activities of students in the first cycle were 59.80% and 78.39% in the second cycle with the increasing of 18.59%. The average student learning outcomes in the first cycle and the second cycle were 69.52 78.70, with an increase of 9.18.
IMPLEMENTATION OF SERIAL AND PARALLEL BUBBLE SORT ON FPGA
Directory of Open Access Journals (Sweden)
Dwi Marhaendro Jati Purnomo
2016-06-01
Full Text Available Sorting is common process in computational world. Its utilization are on many fields from research to industry. There are many sorting algorithm in nowadays. One of the simplest yet powerful is bubble sort. In this study, bubble sort is implemented on FPGA. The implementation was taken on serial and parallel approach. Serial and parallel bubble sort then compared by means of its memory, execution time, and utility which comprises slices and LUTs. The experiments show that serial bubble sort required smaller memory as well as utility compared to parallel bubble sort. Meanwhile, parallel bubble sort performed faster than serial bubble sort
Reinforcement Learning for Online Control of Evolutionary Algorithms
Eiben, A.; Horvath, Mark; Kowalczyk, Wojtek; Schut, Martijn
2007-01-01
The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We
Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study
Directory of Open Access Journals (Sweden)
I. G. Damousis
2012-01-01
Full Text Available We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs, Artificial Neural Networks (ANNs, Fuzzy Expert Systems (FESs, and Support Vector Machines (SVMs. The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns from a well-known benchmark biometrics database, and the validation/testing took place with patterns from the same database which were not included in the training dataset. The comparison of the algorithms reveals that the biometrics fusion system is superior to the original unimodal systems and also other fusion schemes found in the literature.
An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules
Directory of Open Access Journals (Sweden)
Antonio
2012-04-01
Full Text Available Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able to represent the knowledge using this model is proposed as well. On the other hand, potential relations among initial variables imply an exponential growth in the feasible rule search space. Consequently, two filters for detecting relevant potential relations are added to the learning algorithm. These filters allows to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an experimental study to demonstrate the benefits of using fuzzy relational rules.
Implementation of Serial and Parallel Bubble Sort on Fpga
Purnomo, Dwi Marhaendro Jati; Arinaldi, Ahmad; Priyantini, Dwi Teguh; Wibisono, Ari; Febrian, Andreas
2016-01-01
Sorting is common process in computational world. Its utilization are on many fields from research to industry. There are many sorting algorithm in nowadays. One of the simplest yet powerful is bubble sort. In this study, bubble sort is implemented on FPGA. The implementation was taken on serial and parallel approach. Serial and parallel bubble sort then compared by means of its memory, execution time, and utility which comprises slices and LUTs. The experiments show that serial bubble sort r...
Application of radix sorting in high energy physics experiment
International Nuclear Information System (INIS)
Chen Xuan; Gu Minhao; Zhu Kejun
2012-01-01
In the high energy physics experiments, there are always requirements to sort the large scale of experiment data. To meet the demand, this paper introduces one radix sorting algorithms, whose sub-sort is counting sorting and time complex is O (n), based on the characteristic of high energy physics experiment data that is marked by time stamp. This paper gives the description, analysis, implementation and experimental result of the sorting algorithms. (authors)
Algorithm for sorting chromosomal aberrations
DEFF Research Database (Denmark)
Vogel, Ida; Lund, Najaaraq; Rasmussen, Steen
2018-01-01
Prenatal diagnostic methods and screening procedures change rapidly in these years. Years ago only karyotyping was performed prenatally, and we monitored only Down syndrome(1) . Since then the diagnostic possibilities have increased to QF-PCR, FISH, MLPA and chromosomal microarray.......Prenatal diagnostic methods and screening procedures change rapidly in these years. Years ago only karyotyping was performed prenatally, and we monitored only Down syndrome(1) . Since then the diagnostic possibilities have increased to QF-PCR, FISH, MLPA and chromosomal microarray....
Generalized SMO algorithm for SVM-based multitask learning.
Cai, Feng; Cherkassky, Vladimir
2012-06-01
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.
Inverse Problems in Geodynamics Using Machine Learning Algorithms
Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.
2018-01-01
During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.
Component Pin Recognition Using Algorithms Based on Machine Learning
Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang
2018-04-01
The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.
Automated Essay Grading using Machine Learning Algorithm
Ramalingam, V. V.; Pandian, A.; Chetry, Prateek; Nigam, Himanshu
2018-04-01
Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator’s time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.
DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation.
Kalsi, Shruti; Kaur, Harleen; Chang, Victor
2017-12-05
Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.
Denoising of gravitational wave signals via dictionary learning algorithms
Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.
2016-12-01
Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.
Genetic algorithm enhanced by machine learning in dynamic aperture optimization
Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert
2018-05-01
With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.
Dynamics of the evolution of learning algorithms by selection
International Nuclear Information System (INIS)
Neirotti, Juan Pablo; Caticha, Nestor
2003-01-01
We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate populations of programs that implement algorithms used by neural network classifiers to learn a rule in a supervised learning scenario. In contrast to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process. Phenotypic and genotypic entropies, which describe the distribution of fitness and of symbols, respectively, are used to monitor the dynamics. We identify significant functional structures responsible for the improvements in the learning process. In particular, some combinations of variables and operators are useful in assessing performance in rule extraction and can thus implement annealing of the learning schedule. We also find combinations that can signal surprise, measured on a single example, by the difference between predicted and correct classification. When such favorable structures appear, they are disseminated on very short time scales throughout the population. Due to such abruptness they can be thought of as dynamical transitions. But foremost, we find a strict temporal order of such discoveries. Structures that measure performance are never useful before those for measuring surprise. Invasions of the population by such structures in the reverse order were never observed. Asymptotically, the generalization ability approaches Bayesian results
Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis
Directory of Open Access Journals (Sweden)
Charles Frank
2018-03-01
Full Text Available Smoking has been proven to negatively affect health in a multitude of ways. As of 2009, smoking has been considered the leading cause of preventable morbidity and mortality in the United States, continuing to plague the country’s overall health. This study aims to investigate the viability and effectiveness of some machine learning algorithms for predicting the smoking status of patients based on their blood tests and vital readings results. The analysis of this study is divided into two parts: In part 1, we use One-way ANOVA analysis with SAS tool to show the statistically significant difference in blood test readings between smokers and non-smokers. The results show that the difference in INR, which measures the effectiveness of anticoagulants, was significant in favor of non-smokers which further confirms the health risks associated with smoking. In part 2, we use five machine learning algorithms: Naïve Bayes, MLP, Logistic regression classifier, J48 and Decision Table to predict the smoking status of patients. To compare the effectiveness of these algorithms we use: Precision, Recall, F-measure and Accuracy measures. The results show that the Logistic algorithm outperformed the four other algorithms with Precision, Recall, F-Measure, and Accuracy of 83%, 83.4%, 83.2%, 83.44%, respectively.
Comparison of machine learning algorithms for detecting coral reef
Directory of Open Access Journals (Sweden)
Eduardo Tusa
2014-09-01
Full Text Available (Received: 2014/07/31 - Accepted: 2014/09/23This work focuses on developing a fast coral reef detector, which is used for an autonomous underwater vehicle, AUV. A fast detection secures the AUV stabilization respect to an area of reef as fast as possible, and prevents devastating collisions. We use the algorithm of Purser et al. (2009 because of its precision. This detector has two parts: feature extraction that uses Gabor Wavelet filters, and feature classification that uses machine learning based on Neural Networks. Due to the extensive time of the Neural Networks, we exchange for a classification algorithm based on Decision Trees. We use a database of 621 images of coral reef in Belize (110 images for training and 511 images for testing. We implement the bank of Gabor Wavelets filters using C++ and the OpenCV library. We compare the accuracy and running time of 9 machine learning algorithms, whose result was the selection of the Decision Trees algorithm. Our coral detector performs 70ms of running time in comparison to 22s executed by the algorithm of Purser et al. (2009.
Video game for learning and metaphorization of recursive algorithms
Directory of Open Access Journals (Sweden)
Ricardo Inacio Alvares Silva
2013-09-01
Full Text Available The learning of recursive algorithms in computer programming is problematic, because its execution and resolution is not natural to the thinking way people are trained and used to since young. As with other topics in algorithms, we use metaphors to make parallels between the abstract and the concrete to help in understanding the operation of recursive algorithms. However, the classic metaphors employed in this area, such as calculating factorial recursively and Towers of Hanoi game, may just confuse more or be insufficient. In this work, we produced a computer game to assist students in computer courses in learning recursive algorithms. It was designed to have regular video game characteristics, with narrative and classical gameplay elements, commonly found in this kind of product. Aiding to education occurs through metaphorization, or in other words, through experiences provided by game situations that refer to recursive algorithms. To this end, we designed and imbued in the game four valid metaphors related to the theory, and other minor references to the subject.
External parallel sorting with multiprocessor computers
International Nuclear Information System (INIS)
Comanceau, S.I.
1984-01-01
This article describes methods of external sorting in which the entire main computer memory is used for the internal sorting of entries, forming out of them sorted segments of the greatest possible size, and outputting them to external memories. The obtained segments are merged into larger segments until all entries form one ordered segment. The described methods are suitable for sequential files stored on magnetic tape. The needs of the sorting algorithm can be met by using the relatively slow peripheral storage devices (e.g., tapes, disks, drums). The efficiency of the external sorting methods is determined by calculating the total sorting time as a function of the number of entries to be sorted and the number of parallel processors participating in the sorting process
Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
Energy Technology Data Exchange (ETDEWEB)
Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)
2014-08-15
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.
Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
International Nuclear Information System (INIS)
Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka
2014-01-01
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology
Energy Technology Data Exchange (ETDEWEB)
Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)
1993-07-01
A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.
International Nuclear Information System (INIS)
Bornholdt, S.
1993-07-01
A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback
Directory of Open Access Journals (Sweden)
Wenjing Zhao
2018-01-01
Full Text Available SGK (sequential generalization of K-means dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1 The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2 The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3 Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.
Lau, Wilfred W. F.; Yuen, Allan H. K.
2010-01-01
It has been advocated that pedagogical content knowledge as well as subject matter knowledge are important for improving classroom instructions. To develop pedagogical content knowledge, it is argued that understanding of students' mental representations of concepts is deemed necessary. Yet assessing and comparing mental model of each individual…
A Preliminary Study of MSD-First Radix-Sorting Methed
小田, 哲久
1984-01-01
Many kinds of sorting algorithms have been developed from the age of Punched Card System. Nowadays, any sorting algorithm can be called either (1) internal sorting methed or (2) external sorting method. Internal sorting method is used only when the number of records to be sorted (N) is not so large for the internal memory of the computer system. Larger memory space has become available with the aid of semiconductor technology. Therefore, it might be desired to develop a new internal sorting m...
An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance
Grivokostopoulou, Foteini; Perikos, Isidoros; Hatzilygeroudis, Ioannis
2017-01-01
In this paper, first we present an educational system that assists students in learning and tutors in teaching search algorithms, an artificial intelligence topic. Learning is achieved through a wide range of learning activities. Algorithm visualizations demonstrate the operational functionality of algorithms according to the principles of active…
Alignment of Custom Standards by Machine Learning Algorithms
Directory of Open Access Journals (Sweden)
Adela Sirbu
2010-09-01
Full Text Available Building an efficient model for automatic alignment of terminologies would bring a significant improvement to the information retrieval process. We have developed and compared two machine learning based algorithms whose aim is to align 2 custom standards built on a 3 level taxonomy, using kNN and SVM classifiers that work on a vector representation consisting of several similarity measures. The weights utilized by the kNN were optimized with an evolutionary algorithm, while the SVM classifier's hyper-parameters were optimized with a grid search algorithm. The database used for train was semi automatically obtained by using the Coma++ tool. The performance of our aligners is shown by the results obtained on the test set.
Implementation of dictionary pair learning algorithm for image quality improvement
Vimala, C.; Aruna Priya, P.
2018-04-01
This paper proposes an image denoising on dictionary pair learning algorithm. Visual information is transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmissions is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image.
Towards the compression of parton densities through machine learning algorithms
Carrazza, Stefano
2016-01-01
One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy adopted by the PDF4LHC15 recommendation and then, we discuss about a new approach to Monte Carlo PDF compression based on clustering through machine learning algorithms.
Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.
Xu, Dongpo; Xia, Yili; Mandic, Danilo P
2016-02-01
The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks.
Learning-based meta-algorithm for MRI brain extraction.
Shi, Feng; Wang, Li; Gilmore, John H; Lin, Weili; Shen, Dinggang
2011-01-01
Multiple-segmentation-and-fusion method has been widely used for brain extraction, tissue segmentation, and region of interest (ROI) localization. However, such studies are hindered in practice by their computational complexity, mainly coming from the steps of template selection and template-to-subject nonlinear registration. In this study, we address these two issues and propose a novel learning-based meta-algorithm for MRI brain extraction. Specifically, we first use exemplars to represent the entire template library, and assign the most similar exemplar to the test subject. Second, a meta-algorithm combining two existing brain extraction algorithms (BET and BSE) is proposed to conduct multiple extractions directly on test subject. Effective parameter settings for the meta-algorithm are learned from the training data and propagated to subject through exemplars. We further develop a level-set based fusion method to combine multiple candidate extractions together with a closed smooth surface, for obtaining the final result. Experimental results show that, with only a small portion of subjects for training, the proposed method is able to produce more accurate and robust brain extraction results, at Jaccard Index of 0.956 +/- 0.010 on total 340 subjects under 6-fold cross validation, compared to those by the BET and BSE even using their best parameter combinations.
MACHINE LEARNING METHODS IN DIGITAL AGRICULTURE: ALGORITHMS AND CASES
Directory of Open Access Journals (Sweden)
Aleksandr Vasilyevich Koshkarov
2018-05-01
Full Text Available Ensuring food security is a major challenge in many countries. With a growing global population, the issues of improving the efficiency of agriculture have become most relevant. Farmers are looking for new ways to increase yields, and governments of different countries are developing new programs to support agriculture. This contributes to a more active implementation of digital technologies in agriculture, helping farmers to make better decisions, increase yields and take care of the environment. The central point is the collection and analysis of data. In the industry of agriculture, data can be collected from different sources and may contain useful patterns that identify potential problems or opportunities. Data should be analyzed using machine learning algorithms to extract useful insights. Such methods of precision farming allow the farmer to monitor individual parts of the field, optimize the consumption of water and chemicals, and identify problems quickly. Purpose: to make an overview of the machine learning algorithms used for data analysis in agriculture. Methodology: an overview of the relevant literature; a survey of farmers. Results: relevant algorithms of machine learning for the analysis of data in agriculture at various levels were identified: soil analysis (soil assessment, soil classification, soil fertility predictions, weather forecast (simulation of climate change, temperature and precipitation prediction, and analysis of vegetation (weed identification, vegetation classification, plant disease identification, crop forecasting. Practical implications: agriculture, crop production.
Inference algorithms and learning theory for Bayesian sparse factor analysis
International Nuclear Information System (INIS)
Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John
2009-01-01
Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.
Inference algorithms and learning theory for Bayesian sparse factor analysis
Energy Technology Data Exchange (ETDEWEB)
Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)
2009-12-01
Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.
Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model
Hamam, Alwaleed A.
2017-03-13
Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it\\'s time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.
Robust Semi-Supervised Manifold Learning Algorithm for Classification
Directory of Open Access Journals (Sweden)
Mingxia Chen
2018-01-01
Full Text Available In the recent years, manifold learning methods have been widely used in data classification to tackle the curse of dimensionality problem, since they can discover the potential intrinsic low-dimensional structures of the high-dimensional data. Given partially labeled data, the semi-supervised manifold learning algorithms are proposed to predict the labels of the unlabeled points, taking into account label information. However, these semi-supervised manifold learning algorithms are not robust against noisy points, especially when the labeled data contain noise. In this paper, we propose a framework for robust semi-supervised manifold learning (RSSML to address this problem. The noisy levels of the labeled points are firstly predicted, and then a regularization term is constructed to reduce the impact of labeled points containing noise. A new robust semi-supervised optimization model is proposed by adding the regularization term to the traditional semi-supervised optimization model. Numerical experiments are given to show the improvement and efficiency of RSSML on noisy data sets.
Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model
Hamam, Alwaleed A.; Khan, Ayaz H.
2017-01-01
Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it's time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.
An augmented Lagrangian multi-scale dictionary learning algorithm
Directory of Open Access Journals (Sweden)
Ye Meng
2011-01-01
Full Text Available Abstract Learning overcomplete dictionaries for sparse signal representation has become a hot topic fascinated by many researchers in the recent years, while most of the existing approaches have a serious problem that they always lead to local minima. In this article, we present a novel augmented Lagrangian multi-scale dictionary learning algorithm (ALM-DL, which is achieved by first recasting the constrained dictionary learning problem into an AL scheme, and then updating the dictionary after each inner iteration of the scheme during which majorization-minimization technique is employed for solving the inner subproblem. Refining the dictionary from low scale to high makes the proposed method less dependent on the initial dictionary hence avoiding local optima. Numerical tests for synthetic data and denoising applications on real images demonstrate the superior performance of the proposed approach.
Head pose estimation algorithm based on deep learning
Cao, Yuanming; Liu, Yijun
2017-05-01
Head pose estimation has been widely used in the field of artificial intelligence, pattern recognition and intelligent human-computer interaction and so on. Good head pose estimation algorithm should deal with light, noise, identity, shelter and other factors robustly, but so far how to improve the accuracy and robustness of attitude estimation remains a major challenge in the field of computer vision. A method based on deep learning for pose estimation is presented. Deep learning with a strong learning ability, it can extract high-level image features of the input image by through a series of non-linear operation, then classifying the input image using the extracted feature. Such characteristics have greater differences in pose, while they are robust of light, identity, occlusion and other factors. The proposed head pose estimation is evaluated on the CAS-PEAL data set. Experimental results show that this method is effective to improve the accuracy of pose estimation.
Behavioral Modeling for Mental Health using Machine Learning Algorithms.
Srividya, M; Mohanavalli, S; Bhalaji, N
2018-04-03
Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.
Validating module network learning algorithms using simulated data.
Michoel, Tom; Maere, Steven; Bonnet, Eric; Joshi, Anagha; Saeys, Yvan; Van den Bulcke, Tim; Van Leemput, Koenraad; van Remortel, Piet; Kuiper, Martin; Marchal, Kathleen; Van de Peer, Yves
2007-05-03
In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network
A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms
Hayden Wimmer; Loreen Powell
2014-01-01
While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM), a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learning and data mining algorithms, namely neural networks, logistic regression, decision trees, and Baye...
A numeric comparison of variable selection algorithms for supervised learning
International Nuclear Information System (INIS)
Palombo, G.; Narsky, I.
2009-01-01
Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various variable selection algorithms for supervised learning using several datasets such as, for instance, imaging gamma-ray Cherenkov telescope (MAGIC) data found at the UCI repository. We use classifiers and variable selection methods implemented in the statistical package StatPatternRecognition (SPR), a free open-source C++ package developed in the HEP community ( (http://sourceforge.net/projects/statpatrec/)). For each dataset, we select a powerful classifier and estimate its learning accuracy on variable subsets obtained by various selection algorithms. When possible, we also estimate the CPU time needed for the variable subset selection. The results of this analysis are compared with those published previously for these datasets using other statistical packages such as R and Weka. We show that the most accurate, yet slowest, method is a wrapper algorithm known as generalized sequential forward selection ('Add N Remove R') implemented in SPR.
Quantum lower bound for sorting
Shi, Yaoyun
2000-01-01
We prove that \\Omega(n log(n)) comparisons are necessary for any quantum algorithm that sorts n numbers with high success probability and uses only comparisons. If no error is allowed, at least 0.110nlog_2(n) - 0.067n + O(1) comparisons must be made. The previous known lower bound is \\Omega(n).
Data parallel sorting for particle simulation
Dagum, Leonardo
1992-01-01
Sorting on a parallel architecture is a communications intensive event which can incur a high penalty in applications where it is required. In the case of particle simulation, only integer sorting is necessary, and sequential implementations easily attain the minimum performance bound of O (N) for N particles. Parallel implementations, however, have to cope with the parallel sorting problem which, in addition to incurring a heavy communications cost, can make the minimun performance bound difficult to attain. This paper demonstrates how the sorting problem in a particle simulation can be reduced to a merging problem, and describes an efficient data parallel algorithm to solve this merging problem in a particle simulation. The new algorithm is shown to be optimal under conditions usual for particle simulation, and its fieldwise implementation on the Connection Machine is analyzed in detail. The new algorithm is about four times faster than a fieldwise implementation of radix sort on the Connection Machine.
Data Sorting Using Graphics Processing Units
Directory of Open Access Journals (Sweden)
M. J. Mišić
2012-06-01
Full Text Available Graphics processing units (GPUs have been increasingly used for general-purpose computation in recent years. The GPU accelerated applications are found in both scientific and commercial domains. Sorting is considered as one of the very important operations in many applications, so its efficient implementation is essential for the overall application performance. This paper represents an effort to analyze and evaluate the implementations of the representative sorting algorithms on the graphics processing units. Three sorting algorithms (Quicksort, Merge sort, and Radix sort were evaluated on the Compute Unified Device Architecture (CUDA platform that is used to execute applications on NVIDIA graphics processing units. Algorithms were tested and evaluated using an automated test environment with input datasets of different characteristics. Finally, the results of this analysis are briefly discussed.
Fall detection using supervised machine learning algorithms: A comparative study
Zerrouki, Nabil; Harrou, Fouzi; Houacine, Amrane; Sun, Ying
2017-01-01
Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.
Fall detection using supervised machine learning algorithms: A comparative study
Zerrouki, Nabil
2017-01-05
Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.
Machine learning based global particle indentification algorithms at LHCb experiment
Derkach, Denis; Likhomanenko, Tatiana; Rogozhnikov, Aleksei; Ratnikov, Fedor
2017-01-01
One of the most important aspects of data processing at LHC experiments is the particle identification (PID) algorithm. In LHCb, several different sub-detector systems provide PID information: the Ring Imaging CHerenkov (RICH) detector, the hadronic and electromagnetic calorimeters, and the muon chambers. To improve charged particle identification, several neural networks including a deep architecture and gradient boosting have been applied to data. These new approaches provide higher identification efficiencies than existing implementations for all charged particle types. It is also necessary to achieve a flat dependency between efficiencies and spectator variables such as particle momentum, in order to reduce systematic uncertainties during later stages of data analysis. For this purpose, "flat” algorithms that guarantee the flatness property for efficiencies have also been developed. This talk presents this new approach based on machine learning and its performance.
Heuristic framework for parallel sorting computations | Nwanze ...
African Journals Online (AJOL)
Parallel sorting techniques have become of practical interest with the advent of new multiprocessor architectures. The decreasing cost of these processors will probably in the future, make the solutions that are derived thereof to be more appealing. Efficient algorithms for sorting scheme that are encountered in a number of ...
Q-learning-based adjustable fixed-phase quantum Grover search algorithm
International Nuclear Information System (INIS)
Guo Ying; Shi Wensha; Wang Yijun; Hu, Jiankun
2017-01-01
We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one. (author)
A self-learning algorithm for biased molecular dynamics
Tribello, Gareth A.; Ceriotti, Michele; Parrinello, Michele
2010-01-01
A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences. PMID:20876135
Directory of Open Access Journals (Sweden)
Vivek Patel
2012-08-01
Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.
MODIS Science Algorithms and Data Systems Lessons Learned
Wolfe, Robert E.; Ridgway, Bill L.; Patt, Fred S.; Masuoka, Edward J.
2009-01-01
For almost 10 years, standard global products from NASA's Earth Observing System s (EOS) two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors are being used world-wide for earth science research and applications. This paper discusses the lessons learned in developing the science algorithms and the data systems needed to produce these high quality data products for the earth sciences community. Strong science team leadership and communication, an evolvable and scalable data system, and central coordination of QA and validation activities enabled the data system to grow by two orders of magnitude from the initial at-launch system to the current system able to reprocess data from both the Terra and Aqua missions in less than a year. Many of the lessons learned from MODIS are already being applied to follow-on missions.
Prediction of Baseflow Index of Catchments using Machine Learning Algorithms
Yadav, B.; Hatfield, K.
2017-12-01
We present the results of eight machine learning techniques for predicting the baseflow index (BFI) of ungauged basins using a surrogate of catchment scale climate and physiographic data. The tested algorithms include ordinary least squares, ridge regression, least absolute shrinkage and selection operator (lasso), elasticnet, support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Our work seeks to identify the dominant controls of BFI that can be readily obtained from ancillary geospatial databases and remote sensing measurements, such that the developed techniques can be extended to ungauged catchments. More than 800 gauged catchments spanning the continental United States were selected to develop the general methodology. The BFI calculation was based on the baseflow separated from daily streamflow hydrograph using HYSEP filter. The surrogate catchment attributes were compiled from multiple sources including digital elevation model, soil, landuse, climate data, other publicly available ancillary and geospatial data. 80% catchments were used to train the ML algorithms, and the remaining 20% of the catchments were used as an independent test set to measure the generalization performance of fitted models. A k-fold cross-validation using exhaustive grid search was used to fit the hyperparameters of each model. Initial model development was based on 19 independent variables, but after variable selection and feature ranking, we generated revised sparse models of BFI prediction that are based on only six catchment attributes. These key predictive variables selected after the careful evaluation of bias-variance tradeoff include average catchment elevation, slope, fraction of sand, permeability, temperature, and precipitation. The most promising algorithms exceeding an accuracy score (r-square) of 0.7 on test data include support vector machine, gradient boosted regression trees, random forests, and extremely randomized
Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.
Walter, Florian; Röhrbein, Florian; Knoll, Alois
2015-12-01
The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.
From the social learning theory to a social learning algorithm for global optimization
Gong, Yue-Jiao; Zhang, Jun; Li, Yun
2014-01-01
Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization...
Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao
2016-01-01
Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.
Two Algorithms for Learning the Parameters of Stochastic Context-Free Grammars
National Research Council Canada - National Science Library
Heeringa, Brent; Oates, Tim
2001-01-01
.... Most algorithms for learning them require storage and repeated processing of a sentence corpus. The memory and computational demands of such algorithms are illsuited for embedded agents such as a mobile robot...
International Nuclear Information System (INIS)
Abramov, N.A.; Matveev, V.A.; Fedotov, O.P.
1977-01-01
The description is given of the MASKA program, based on the principle of sorting points array at surface due to their belonging to the topologically connected regions with boundaries of locked broken lines. The algorithm is realized on the ES-1010 computer for automatic image processing from the bubble chambers by scanning measuring projector. The methods are considered for constructing the above mentioned regions for all the images according to the base points measured on the semiautomatic measuring table. The MASKA program is written in the ASSEMBLER-2 language and equals 3.5K words of the main memory. The average processing time for 10000 points according to one mask is 1 sec
On Sorting Genomes with DCJ and Indels
Braga, Marília D. V.
A previous work of Braga, Willing and Stoye compared two genomes with unequal content, but without duplications, and presented a new linear time algorithm to compute the genomic distance, considering double cut and join (DCJ) operations, insertions and deletions. Here we derive from this approach an algorithm to sort one genome into another one also using DCJ, insertions and deletions. The optimal sorting scenarios can have different compositions and we compare two types of sorting scenarios: one that maximizes and one that minimizes the number of DCJ operations with respect to the number of insertions and deletions.
Genetic algorithm learning in a New Keynesian macroeconomic setup.
Hommes, Cars; Makarewicz, Tomasz; Massaro, Domenico; Smits, Tom
2017-01-01
In order to understand heterogeneous behavior amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a Genetic Algorithms (GA) model to replicate the results from their LtF experiment. In this GA model, individuals optimize an adaptive, a trend following and an anchor coefficient in a population of general prediction heuristics. We replicate experimental treatments in a New-Keynesian environment with increasing complexity and use Monte Carlo simulations to investigate how well the model explains the experimental data. We find that the evolutionary learning model is able to replicate the three different types of behavior, i.e. convergence to steady state, stable oscillations and dampened oscillations in the treatments using one GA model. Heterogeneous behavior can thus be explained by an adaptive, anchor and trend extrapolating component and the GA model can be used to explain heterogeneous behavior in LtF experiments with different types of complexity.
Sorting and selection in posets
DEFF Research Database (Denmark)
Daskalakis, Constantinos; Karp, Richard M.; Mossel, Elchanan
2011-01-01
from two decades ago by Faigle and Turán. In particular, we present the first algorithm that sorts a width-$w$ poset of size $n$ with query complexity $O(n(w+\\log n))$ and prove that this query complexity is asymptotically optimal. We also describe a variant of Mergesort with query complexity $O......(wn\\log\\frac{n}{w})$ and total complexity $O(w^{2}n\\log\\frac{n}{w})$; an algorithm with the same query complexity was given by Faigle and Turán, but no efficient implementation of that algorithm is known. Both our sorting algorithms can be applied with negligible overhead to the more general problem of reconstructing transitive......Classical problems of sorting and searching assume an underlying linear ordering of the objects being compared. In this paper, we study these problems in the context of partially ordered sets, in which some pairs of objects are incomparable. This generalization is interesting from a combinatorial...
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2013-01-01
Full Text Available Teaching-Learning-based optimization (TLBO is a recently proposed population based algorithm, which simulates the teaching-learning process of the class room. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. In this paper, the effect of elitism on the performance of the TLBO algorithm is investigated while solving unconstrained benchmark problems. The effects of common control parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 76 unconstrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. A statistical test is also performed to investigate the results obtained using different algorithms. The results have proved the effectiveness of the proposed elitist TLBO algorithm.
Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms
Directory of Open Access Journals (Sweden)
Krzysztof Gajowniczek
2017-10-01
Full Text Available Forecasting of electricity demand has become one of the most important areas of research in the electric power industry, as it is a critical component of cost-efficient power system management and planning. In this context, accurate and robust load forecasting is supposed to play a key role in reducing generation costs, and deals with the reliability of the power system. However, due to demand peaks in the power system, forecasts are inaccurate and prone to high numbers of errors. In this paper, our contributions comprise a proposed data-mining scheme for demand modeling through peak detection, as well as the use of this information to feed the forecasting system. For this purpose, we have taken a different approach from that of time series forecasting, representing it as a two-stage pattern recognition problem. We have developed a peak classification model followed by a forecasting model to estimate an aggregated demand volume. We have utilized a set of machine learning algorithms to benefit from both accurate detection of the peaks and precise forecasts, as applied to the Polish power system. The key finding is that the algorithms can detect 96.3% of electricity peaks (load value equal to or above the 99th percentile of the load distribution and deliver accurate forecasts, with mean absolute percentage error (MAPE of 3.10% and resistant mean absolute percentage error (r-MAPE of 2.70% for the 24 h forecasting horizon.
Experiments on Supervised Learning Algorithms for Text Categorization
Namburu, Setu Madhavi; Tu, Haiying; Luo, Jianhui; Pattipati, Krishna R.
2005-01-01
Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.
Overlay improvements using a real time machine learning algorithm
Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank
2014-04-01
While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.
Effective and efficient optics inspection approach using machine learning algorithms
International Nuclear Information System (INIS)
Abdulla, G.; Kegelmeyer, L.; Liao, Z.; Carr, W.
2010-01-01
The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is 'truthed' or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called 'Avatar Tools' is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation.
Parallel integer sorting with medium and fine-scale parallelism
Dagum, Leonardo
1993-01-01
Two new parallel integer sorting algorithms, queue-sort and barrel-sort, are presented and analyzed in detail. These algorithms do not have optimal parallel complexity, yet they show very good performance in practice. Queue-sort designed for fine-scale parallel architectures which allow the queueing of multiple messages to the same destination. Barrel-sort is designed for medium-scale parallel architectures with a high message passing overhead. The performance results from the implementation of queue-sort on a Connection Machine CM-2 and barrel-sort on a 128 processor iPSC/860 are given. The two implementations are found to be comparable in performance but not as good as a fully vectorized bucket sort on the Cray YMP.
An Unsupervised Online Spike-Sorting Framework.
Knieling, Simeon; Sridharan, Kousik S; Belardinelli, Paolo; Naros, Georgios; Weiss, Daniel; Mormann, Florian; Gharabaghi, Alireza
2016-08-01
Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.
Creating Engaging Online Learning Material with the JSAV JavaScript Algorithm Visualization Library
Karavirta, Ville; Shaffer, Clifford A.
2016-01-01
Data Structures and Algorithms are a central part of Computer Science. Due to their abstract and dynamic nature, they are a difficult topic to learn for many students. To alleviate these learning difficulties, instructors have turned to algorithm visualizations (AV) and AV systems. Research has shown that especially engaging AVs can have an impact…
International Nuclear Information System (INIS)
Dong Yun Kim; Poong Hyun Seong; .
1997-01-01
In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)
Stability and chaos of LMSER PCA learning algorithm
International Nuclear Information System (INIS)
Lv Jiancheng; Y, Zhang
2007-01-01
LMSER PCA algorithm is a principal components analysis algorithm. It is used to extract principal components on-line from input data. The algorithm has both stability and chaotic dynamic behavior under some conditions. This paper studies the local stability of the LMSER PCA algorithm via a corresponding deterministic discrete time system. Conditions for local stability are derived. The paper also explores the chaotic behavior of this algorithm. It shows that the LMSER PCA algorithm can produce chaos. Waveform plots, Lyapunov exponents and bifurcation diagrams are presented to illustrate the existence of chaotic behavior of this algorithm
Directory of Open Access Journals (Sweden)
Weilin Nie
2017-01-01
Full Text Available Abstract Convex risk minimization is a commonly used setting in learning theory. In this paper, we firstly give a perturbation analysis for such algorithms, and then we apply this result to differential private learning algorithms. Our analysis needs the objective functions to be strongly convex. This leads to an extension of our previous analysis to the non-differentiable loss functions, when constructing differential private algorithms. Finally, an error analysis is then provided to show the selection for the parameters.
Modeling the Swift Bat Trigger Algorithm with Machine Learning
Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori
2016-01-01
To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift / BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of greater than or equal to 97 percent (less than or equal to 3 percent error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6 percent (10.4 percent error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n (sub 0) approaching 0.48 (sup plus 0.41) (sub minus 0.23) per cubic gigaparsecs per year with power-law indices of n (sub 1) approaching 1.7 (sup plus 0.6) (sub minus 0.5) and n (sub 2) approaching minus 5.9 (sup plus 5.7) (sub minus 0.1) for GRBs above and below a break point of z (redshift) (sub 1) approaching 6.8 (sup plus 2.8) (sub minus 3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.
Efficient generation of image chips for training deep learning algorithms
Han, Sanghui; Fafard, Alex; Kerekes, John; Gartley, Michael; Ientilucci, Emmett; Savakis, Andreas; Law, Charles; Parhan, Jason; Turek, Matt; Fieldhouse, Keith; Rovito, Todd
2017-05-01
Training deep convolutional networks for satellite or aerial image analysis often requires a large amount of training data. For a more robust algorithm, training data need to have variations not only in the background and target, but also radiometric variations in the image such as shadowing, illumination changes, atmospheric conditions, and imaging platforms with different collection geometry. Data augmentation is a commonly used approach to generating additional training data. However, this approach is often insufficient in accounting for real world changes in lighting, location or viewpoint outside of the collection geometry. Alternatively, image simulation can be an efficient way to augment training data that incorporates all these variations, such as changing backgrounds, that may be encountered in real data. The Digital Imaging and Remote Sensing Image Image Generation (DIRSIG) model is a tool that produces synthetic imagery using a suite of physics-based radiation propagation modules. DIRSIG can simulate images taken from different sensors with variation in collection geometry, spectral response, solar elevation and angle, atmospheric models, target, and background. Simulation of Urban Mobility (SUMO) is a multi-modal traffic simulation tool that explicitly models vehicles that move through a given road network. The output of the SUMO model was incorporated into DIRSIG to generate scenes with moving vehicles. The same approach was used when using helicopters as targets, but with slight modifications. Using the combination of DIRSIG and SUMO, we quickly generated many small images, with the target at the center with different backgrounds. The simulations generated images with vehicles and helicopters as targets, and corresponding images without targets. Using parallel computing, 120,000 training images were generated in about an hour. Some preliminary results show an improvement in the deep learning algorithm when real image training data are augmented with
Modeling the Swift BAT Trigger Algorithm with Machine Learning
Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori
2015-01-01
To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online.
Indian Academy of Sciences (India)
polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.
Huang, Yin; Chen, Jianhua; Xiong, Shaojun
2009-07-01
Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.
Self-learning health monitoring algorithm in composite structures
Grassia, Luigi; Iannone, Michele; Califano, America; D'Amore, Alberto
2018-02-01
The paper describes a system that it is able of monitoring the health state of a composite structure in real time. The hardware of the system consists of a wire of strain sensors connected to a control unit. The software of the system elaborates the strain data and in real time is able to detect the presence of an eventual damage of the structures monitored with the strain sensors. The algorithm requires as input only the strains of the monitored structured measured on real time, i.e. those strains coming from the deformations of the composite structure due to the working loads. The health monitoring system does not require any additional device to interrogate the structure as often used in the literature, instead it is based on a self-learning procedure. The strain data acquired when the structure is healthy are used to set up the correlations between the strain in different positions of structure by means of neural network. Once the correlations between the strains in different position have been set up, these correlations act as a fingerprint of the healthy structure. In case of damage the correlation between the strains in the position of the structure near the damage will change due to the change of the stiffness of the structure caused by the damage. The developed software is able to recognize the change of the transfer function between the strains and consequently is able to detect the damage.
Bejnordi, Babak Ehteshami; Veta, Mitko; van Diest, Paul Johannes; Van Ginneken, Bram; Karssemeijer, Nico; Litjens, Geert; van der Laak, Jeroen A.W.M.; Hermsen, Meyke; Manson, Quirine F.; Balkenhol, Maschenka; Geessink, Oscar; Stathonikos, Nikolaos; Van Dijk, Marcory C.R.F.; Bult, Peter; Beca, Francisco; Beck, Andrew H.; Wang, Dayong; Khosla, Aditya; Gargeya, Rishab; Irshad, Humayun; Zhong, Aoxiao; Dou, Qi; Li, Quanzheng; Chen, Hao; Lin, Huang Jing; Heng, Pheng Ann; Haß, Christian; Bruni, Elia; Wong, Quincy; Halici, Ugur; Öner, Mustafa Ümit; Cetin-Atalay, Rengul; Berseth, Matt; Khvatkov, Vitali; Vylegzhanin, Alexei; Kraus, Oren; Shaban, Muhammad; Rajpoot, Nasir; Awan, Ruqayya; Sirinukunwattana, Korsuk; Qaiser, Talha; Tsang, Yee Wah; Tellez, David; Annuscheit, Jonas; Hufnagl, Peter; Valkonen, Mira; Kartasalo, Kimmo; Latonen, Leena; Ruusuvuori, Pekka; Liimatainen, Kaisa
2017-01-01
IMPORTANCE: Application of deep learning algorithms to whole-slide pathology imagescan potentially improve diagnostic accuracy and efficiency. OBJECTIVE: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph
Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System
Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang
2018-03-01
Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.
A Coupled User Clustering Algorithm Based on Mixed Data for Web-Based Learning Systems
Directory of Open Access Journals (Sweden)
Ke Niu
2015-01-01
Full Text Available In traditional Web-based learning systems, due to insufficient learning behaviors analysis and personalized study guides, a few user clustering algorithms are introduced. While analyzing the behaviors with these algorithms, researchers generally focus on continuous data but easily neglect discrete data, each of which is generated from online learning actions. Moreover, there are implicit coupled interactions among the data but are frequently ignored in the introduced algorithms. Therefore, a mass of significant information which can positively affect clustering accuracy is neglected. To solve the above issues, we proposed a coupled user clustering algorithm for Wed-based learning systems by taking into account both discrete and continuous data, as well as intracoupled and intercoupled interactions of the data. The experiment result in this paper demonstrates the outperformance of the proposed algorithm.
Forsström, J
1992-01-01
The ID3 algorithm for inductive learning was tested using preclassified material for patients suspected to have a thyroid illness. Classification followed a rule-based expert system for the diagnosis of thyroid function. Thus, the knowledge to be learned was limited to the rules existing in the knowledge base of that expert system. The learning capability of the ID3 algorithm was tested with an unselected learning material (with some inherent missing data) and with a selected learning material (no missing data). The selected learning material was a subgroup which formed a part of the unselected learning material. When the number of learning cases was increased, the accuracy of the program improved. When the learning material was large enough, an increase in the learning material did not improve the results further. A better learning result was achieved with the selected learning material not including missing data as compared to unselected learning material. With this material we demonstrate a weakness in the ID3 algorithm: it can not find available information from good example cases if we add poor examples to the data.
The solution space of sorting by DCJ.
Braga, Marília D V; Stoye, Jens
2010-09-01
In genome rearrangements, the double cut and join (DCJ) operation, introduced by Yancopoulos et al. in 2005, allows one to represent most rearrangement events that could happen in multichromosomal genomes, such as inversions, translocations, fusions, and fissions. No restriction on the genome structure considering linear and circular chromosomes is imposed. An advantage of this general model is that it leads to considerable algorithmic simplifications compared to other genome rearrangement models. Recently, several works concerning the DCJ operation have been published, and in particular, an algorithm was proposed to find an optimal DCJ sequence for sorting one genome into another one. Here we study the solution space of this problem and give an easy-to-compute formula that corresponds to the exact number of optimal DCJ sorting sequences for a particular subset of instances of the problem. We also give an algorithm to count the number of optimal sorting sequences for any instance of the problem. Another interesting result is the demonstration of the possibility of obtaining one optimal sorting sequence by properly replacing any pair of consecutive operations in another optimal sequence. As a consequence, any optimal sorting sequence can be obtained from one other by applying such replacements successively, but the problem of finding the shortest number of replacements between two sorting sequences is still open.
Behavior Self-Organization in Multi-Agent Learning
National Research Council Canada - National Science Library
Bay, John
1999-01-01
There are four primary results of the first year of the project: It was discovered that clustering algorithms for pre-sorting high-dimensional datasets was not effective in improving subsequent processing by reinforcement learning methods...
New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems
Directory of Open Access Journals (Sweden)
Xiguang Li
2017-01-01
Full Text Available Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA, is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent.
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.
Tian, Yuling; Zhang, Hongxian
2016-01-01
For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.
Developing robust arsenic awareness prediction models using machine learning algorithms.
Singh, Sushant K; Taylor, Robert W; Rahman, Mohammad Mahmudur; Pradhan, Biswajeet
2018-04-01
Arsenic awareness plays a vital role in ensuring the sustainability of arsenic mitigation technologies. Thus far, however, few studies have dealt with the sustainability of such technologies and its associated socioeconomic dimensions. As a result, arsenic awareness prediction has not yet been fully conceptualized. Accordingly, this study evaluated arsenic awareness among arsenic-affected communities in rural India, using a structured questionnaire to record socioeconomic, demographic, and other sociobehavioral factors with an eye to assessing their association with and influence on arsenic awareness. First a logistic regression model was applied and its results compared with those produced by six state-of-the-art machine-learning algorithms (Support Vector Machine [SVM], Kernel-SVM, Decision Tree [DT], k-Nearest Neighbor [k-NN], Naïve Bayes [NB], and Random Forests [RF]) as measured by their accuracy at predicting arsenic awareness. Most (63%) of the surveyed population was found to be arsenic-aware. Significant arsenic awareness predictors were divided into three types: (1) socioeconomic factors: caste, education level, and occupation; (2) water and sanitation behavior factors: number of family members involved in water collection, distance traveled and time spent for water collection, places for defecation, and materials used for handwashing after defecation; and (3) social capital and trust factors: presence of anganwadi and people's trust in other community members, NGOs, and private agencies. Moreover, individuals' having higher social network positively contributed to arsenic awareness in the communities. Results indicated that both the SVM and the RF algorithms outperformed at overall prediction of arsenic awareness-a nonlinear classification problem. Lower-caste, less educated, and unemployed members of the population were found to be the most vulnerable, requiring immediate arsenic mitigation. To this end, local social institutions and NGOs could play a
Directory of Open Access Journals (Sweden)
Aina Mulyana
2010-06-01
Full Text Available This article is based on a report of classroom action research (CAR done among students of class VII C SMPN 1 Cadasari by implementation of Contextual Teaching and Learning (CTL based Card Sort teaching model, and its contribution toward increase of students learning output in civic education Selecting class VII C as model class is based on consideration of low student learning outcomes of civic education. This note was taken from average of grades in report book and percentage of pass in test of last semester compared by other class. So that, Writer tries to conduct CAR by implementing CTL Card Sort teaching model to increase student learning outcomes of civic education. CAR is conducted by implementing action plan in the form of using some varied teaching model in class VII C. While in the other classes namely Class VIIA and Class VIIB as comparator classes, teaching model used is the conventional one although also based on CTL principles. Analysis process is done by analyzing daily test results to know progress of students learning outcomes and employ a Collaborator to observe and analyze excess and lack of researcher during teaching-learning process. The observation result from partner is used as reflection source and consideration to select following action. After four cycles of research and based on reflection with a Collaborator, it is resulted that using CTL based Card Sort teaching model can effectively increase students learning outcomes in subject matter of civic education. The result proves that CTL based Card Sort teaching model compared to other model has some advantages: a relevant for ages of junior high school, b simple and cheap, c prioritizing collaboration, d joyful and not boring, e requiring mutual support, and f encouraging student to be active.
Learning Path Recommendation Based on Modified Variable Length Genetic Algorithm
Dwivedi, Pragya; Kant, Vibhor; Bharadwaj, Kamal K.
2018-01-01
With the rapid advancement of information and communication technologies, e-learning has gained a considerable attention in recent years. Many researchers have attempted to develop various e-learning systems with personalized learning mechanisms for assisting learners so that they can learn more efficiently. In this context, curriculum sequencing…
A controllable sensor management algorithm capable of learning
Osadciw, Lisa A.; Veeramacheneni, Kalyan K.
2005-03-01
Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.
Keivanian, Farshid; Mehrshad, Nasser; Bijari, Abolfazl
2016-01-01
D Flip-Flop as a digital circuit can be used as a timing element in many sophisticated circuits. Therefore the optimum performance with the lowest power consumption and acceptable delay time will be critical issue in electronics circuits. The newly proposed Dual-Edge Triggered Static D Flip-Flop circuit layout is defined as a multi-objective optimization problem. For this, an optimum fuzzy inference system with fuzzy rules is proposed to enhance the performance and convergence of non-dominated sorting Genetic Algorithm-II by adaptive control of the exploration and exploitation parameters. By using proposed Fuzzy NSGA-II algorithm, the more optimum values for MOSFET channel widths and power supply are discovered in search space than ordinary NSGA types. What is more, the design parameters involving NMOS and PMOS channel widths and power supply voltage and the performance parameters including average power consumption and propagation delay time are linked. To do this, the required mathematical backgrounds are presented in this study. The optimum values for the design parameters of MOSFETs channel widths and power supply are discovered. Based on them the power delay product quantity (PDP) is 6.32 PJ at 125 MHz Clock Frequency, L = 0.18 µm, and T = 27 °C.
The Container Problem in Bubble-Sort Graphs
Suzuki, Yasuto; Kaneko, Keiichi
Bubble-sort graphs are variants of Cayley graphs. A bubble-sort graph is suitable as a topology for massively parallel systems because of its simple and regular structure. Therefore, in this study, we focus on n-bubble-sort graphs and propose an algorithm to obtain n-1 disjoint paths between two arbitrary nodes in time bounded by a polynomial in n, the degree of the graph plus one. We estimate the time complexity of the algorithm and the sum of the path lengths after proving the correctness of the algorithm. In addition, we report the results of computer experiments evaluating the average performance of the algorithm.
Indian Academy of Sciences (India)
to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...
Sorting Real Numbers in $O(n\\sqrt{\\log n})$ Time and Linear Space
Han, Yijie
2017-01-01
We present an $O(n\\sqrt{\\log n})$ time and linear space algorithm for sorting real numbers. This breaks the long time illusion that real numbers have to be sorted by comparison sorting and take $\\Omega (n\\log n)$ time to be sorted.
G.T. Post (Thierry); P. van Vliet (Pim); S.D. Lansdorp (Simon)
2009-01-01
textabstractDownside risk, when properly defined and estimated, helps to explain the cross-section of US stock returns. Sorting stocks by a proper estimate of downside market beta leads to a substantially larger cross-sectional spread in average returns than sorting on regular market beta. This
DEFF Research Database (Denmark)
Fink, Hans
2006-01-01
In "Two sorts of Naturalism" John McDowell is sketching his own sort of naturalism in ethics as an alternative to "bald naturalism". In this paper I distinguish materialist, idealist and absolute conceptions of nature and of naturalism in order to provide a framework for a clearer understanding...
Sorting processes with energy-constrained comparisons*
Geissmann, Barbara; Penna, Paolo
2018-05-01
We study very simple sorting algorithms based on a probabilistic comparator model. In this model, errors in comparing two elements are due to (1) the energy or effort put in the comparison and (2) the difference between the compared elements. Such algorithms repeatedly compare and swap pairs of randomly chosen elements, and they correspond to natural Markovian processes. The study of these Markov chains reveals an interesting phenomenon. Namely, in several cases, the algorithm that repeatedly compares only adjacent elements is better than the one making arbitrary comparisons: in the long-run, the former algorithm produces sequences that are "better sorted". The analysis of the underlying Markov chain poses interesting questions as the latter algorithm yields a nonreversible chain, and therefore its stationary distribution seems difficult to calculate explicitly. We nevertheless provide bounds on the stationary distributions and on the mixing time of these processes in several restrictions.
Efficient sorting using registers and caches
DEFF Research Database (Denmark)
Wickremesinghe, Rajiv; Arge, Lars Allan; Chase, Jeffrey S.
2002-01-01
. Inadequate models lead to poor algorithmic choices and an incomplete understanding of algorithm behavior on real machines.A key step toward developing better models is to quantify the performance effects of features not reflected in the models. This paper explores the effect of memory system features...... on sorting performance. We introduce a new cache-conscious sorting algorithm, R-MERGE, which achieves better performance in practice over algorithms that are superior in the theoretical models. R-MERGE is designed to minimize memory stall cycles rather than cache misses by considering features common to many......Modern computer systems have increasingly complex memory systems. Common machine models for algorithm analysis do not reflect many of the features of these systems, e.g., large register sets, lockup-free caches, cache hierarchies, associativity, cache line fetching, and streaming behavior...
Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality.
Braithwaite, Scott R; Giraud-Carrier, Christophe; West, Josh; Barnes, Michael D; Hanson, Carl Lee
2016-05-16
One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data.
Elements of Causal Inference: Foundations and Learning Algorithms
DEFF Research Database (Denmark)
Peters, Jonas Martin; Janzing, Dominik; Schölkopf, Bernhard
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning......A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning...
Automatic spike sorting using tuning information.
Ventura, Valérie
2009-09-01
Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.
Directory of Open Access Journals (Sweden)
REIS, M V. S. de A.
2017-06-01
Full Text Available This paper aims to evaluate the use of machine learning techniques in a database of marine accidents. We analyzed and evaluated the main causes and types of marine accidents in the Northern Fluminense region. For this, machine learning techniques were used. The study showed that the modeling can be done in a satisfactory manner using different configurations of classification algorithms, varying the activation functions and training parameters. The SMO (Sequential Minimal Optimization algorithm showed the best performance result.
An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.
Zhang, Ye; Yu, Tenglong; Wang, Wenwu
2014-01-01
Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.
International Nuclear Information System (INIS)
Park, Gee Yong; Seong, Poong Hyun
1994-01-01
In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the control actions of the plant operator or other controllers. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection
An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint
Directory of Open Access Journals (Sweden)
Ye Zhang
2014-01-01
Full Text Available Two common problems are often encountered in analysis dictionary learning (ADL algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high, as represented by the Analysis K-SVD (AK-SVD algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.
International Nuclear Information System (INIS)
Kim, Dong Yun
1997-02-01
In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller
Comfort, Shaun; Perera, Sujan; Hudson, Zoe; Dorrell, Darren; Meireis, Shawman; Nagarajan, Meenakshi; Ramakrishnan, Cartic; Fine, Jennifer
2018-06-01
There is increasing interest in social digital media (SDM) as a data source for pharmacovigilance activities; however, SDM is considered a low information content data source for safety data. Given that pharmacovigilance itself operates in a high-noise, lower-validity environment without objective 'gold standards' beyond process definitions, the introduction of large volumes of SDM into the pharmacovigilance workflow has the potential to exacerbate issues with limited manual resources to perform adverse event identification and processing. Recent advances in medical informatics have resulted in methods for developing programs which can assist human experts in the detection of valid individual case safety reports (ICSRs) within SDM. In this study, we developed rule-based and machine learning (ML) models for classifying ICSRs from SDM and compared their performance with that of human pharmacovigilance experts. We used a random sampling from a collection of 311,189 SDM posts that mentioned Roche products and brands in combination with common medical and scientific terms sourced from Twitter, Tumblr, Facebook, and a spectrum of news media blogs to develop and evaluate three iterations of an automated ICSR classifier. The ICSR classifier models consisted of sub-components to annotate the relevant ICSR elements and a component to make the final decision on the validity of the ICSR. Agreement with human pharmacovigilance experts was chosen as the preferred performance metric and was evaluated by calculating the Gwet AC1 statistic (gKappa). The best performing model was tested against the Roche global pharmacovigilance expert using a blind dataset and put through a time test of the full 311,189-post dataset. During this effort, the initial strict rule-based approach to ICSR classification resulted in a model with an accuracy of 65% and a gKappa of 46%. Adding an ML-based adverse event annotator improved the accuracy to 74% and gKappa to 60%. This was further improved by
Identification of chaotic systems by neural network with hybrid learning algorithm
International Nuclear Information System (INIS)
Pan, S.-T.; Lai, C.-C.
2008-01-01
Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods
Directory of Open Access Journals (Sweden)
Jie Zhu
2014-02-01
Full Text Available A multi-objective optimization method for the structural design of horizontal-axis wind turbine (HAWT blades is presented. The main goal is to minimize the weight and cost of the blade which uses glass fiber reinforced plastic (GFRP coupled with carbon fiber reinforced plastic (CFRP materials. The number and the location of layers in the spar cap, the width of the spar cap and the position of the shear webs are employed as the design variables, while the strain limit, blade/tower clearance limit and vibration limit are taken into account as the constraint conditions. The optimization of the design of a commercial 1.5 MW HAWT blade is carried out by combining FEM analysis and a multi-objective evolutionary algorithm under ultimate (extreme flap-wise load and edge-wise load conditions. The best solutions are described and the comparison of the obtained results with the original design is performed to prove the efficiency and applicability of the method.
A new supervised learning algorithm for spiking neurons.
Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming
2013-06-01
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.
A QR code identification technology in package auto-sorting system
di, Yi-Juan; Shi, Jian-Ping; Mao, Guo-Yong
2017-07-01
Traditional manual sorting operation is not suitable for the development of Chinese logistics. For better sorting packages, a QR code recognition technology is proposed to identify the QR code label on the packages in package auto-sorting system. The experimental results compared with other algorithms in literatures demonstrate that the proposed method is valid and its performance is superior to other algorithms.
Directory of Open Access Journals (Sweden)
Jiří Fejfar
2012-01-01
Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.
An improved clustering algorithm based on reverse learning in intelligent transportation
Qiu, Guoqing; Kou, Qianqian; Niu, Ting
2017-05-01
With the development of artificial intelligence and data mining technology, big data has gradually entered people's field of vision. In the process of dealing with large data, clustering is an important processing method. By introducing the reverse learning method in the clustering process of PAM clustering algorithm, to further improve the limitations of one-time clustering in unsupervised clustering learning, and increase the diversity of clustering clusters, so as to improve the quality of clustering. The algorithm analysis and experimental results show that the algorithm is feasible.
Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning
Duan, Haibin; Li, Pei; Shi, Yuhui; Zhang, Xiangyin; Sun, Changhao
2015-01-01
This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the…
Bandyopadhyay, Sanghamitra
2007-01-01
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.
Lago Garcia, J.; De Ridder, Fjo; De Schutter, B.H.K.
2018-01-01
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning
Beyond the "c" and the "x": Learning with Algorithms in Massive Open Online Courses (MOOCs)
Knox, Jeremy
2018-01-01
This article examines how algorithms are shaping student learning in massive open online courses (MOOCs). Following the dramatic rise of MOOC platform organisations in 2012, over 4,500 MOOCs have been offered to date, in increasingly diverse languages, and with a growing requirement for fees. However, discussions of "learning" in MOOCs…
Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning
Directory of Open Access Journals (Sweden)
Guangyi Liu
2014-01-01
Full Text Available Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues. Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper. We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are given as a result; after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network. Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.
Directory of Open Access Journals (Sweden)
OMER MAHMOUD
2007-08-01
Full Text Available One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.
Sorting Out Seasonal Allergies
... Close ‹ Back to Healthy Living Sorting Out Seasonal Allergies Sneezing, runny nose, nasal congestion. Symptoms of the ... How do I know if I have seasonal allergies? According to Dr. Georgeson, the best way to ...
DEFF Research Database (Denmark)
Bagger, Jesper; Vejlin, Rune Majlund; Sørensen, Kenneth Lykke
Using a population-wide Danish Matched Employer-Employee panel from 1980-2006, we document a strong trend towards more positive assortative wage sorting. The correlation between worker and firm fixed effects estimated from a log wage regression increases from -0.07 in 1981 to .14 in 2001. The non......Using a population-wide Danish Matched Employer-Employee panel from 1980-2006, we document a strong trend towards more positive assortative wage sorting. The correlation between worker and firm fixed effects estimated from a log wage regression increases from -0.07 in 1981 to .14 in 2001....... The nonstationary wage sorting pattern is not due to compositional changes in the labor market, primarily occurs among high wage workers, and comprises 41 percent of the increase in the standard deviation of log real wages between 1980 and 2006. We show that the wage sorting trend is associated with worker...
Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants
International Nuclear Information System (INIS)
Parlos, A.G.; Atiya, Amir; Chong, K.T.
1991-01-01
A newly developed dynamic gradient descent-based learning algorithm is used to train a recurrent multilayer perceptron network for use in empirical modeling of power plants. The two main advantages of the proposed learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation, instead of one forward and one backward pass of the backpropagation algorithm. The latter advantage results in computational time saving because both passes can be performed simultaneously. The dynamic learning algorithm is used to train a hybrid feedforward/feedback neural network, a recurrent multilayer perceptron, which was previously found to exhibit good interpolation and extrapolation capabilities in modeling nonlinear dynamic systems. One of the drawbacks, however, of the previously reported work has been the long training times associated with accurate empirical models. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm are demonstrated by a case study of a steam power plant. The number of iterations required for accurate empirical modeling has been reduced from tens of thousands to hundreds, thus significantly expediting the learning process
Directory of Open Access Journals (Sweden)
Ho-Young Kim
2017-07-01
Full Text Available Improving the performance of power systems has become a challenging task for system operators in an open access environment. This paper presents an optimization approach for solving the multi-objective scheduling problem using a modified non-dominated sorting genetic algorithm in a hybrid network of meshed alternating current (AC/wind farm grids. This approach considers voltage and power control modes based on multi-terminal voltage source converter high-voltage direct current (MTDC and battery energy storage systems (BESS. To enhance the hybrid network station performance, we implement an optimal process based on the battery energy storage system operational strategy for multi-objective scheduling over a 24 h demand profile. Furthermore, the proposed approach is formulated as a master problem and a set of sub-problems associated with the hybrid network station to improve the overall computational efficiency using Benders’ decomposition. Based on the results of the simulations conducted on modified institute of electrical and electronics engineers (IEEE-14 bus and IEEE-118 bus test systems, we demonstrate and confirm the applicability, effectiveness and validity of the proposed approach.
Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K
2015-01-01
Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (pmachine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273
Smoothsort, an alternative for sorting in situ
Dijkstra, E.W.
1982-01-01
Like heapsort - which inspired it - smoothsort is an algorithm for sorting in situ. It is of order N · log N in the worst case, but of order N in the best case, with a smooth transition between the two. (Hence its name.)
Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm
International Nuclear Information System (INIS)
Rao, R.V.; More, K.C.
2015-01-01
Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. - Highlights: • The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe. • Two examples of heat pipe design and optimization are presented. • The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence
An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning
Directory of Open Access Journals (Sweden)
Xiaohui Yan
2012-01-01
Full Text Available Bacterial Foraging Algorithm (BFO is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.
Directory of Open Access Journals (Sweden)
MATHODIYIL S. MANJULA
2015-12-01
Full Text Available Plant disease resistance genes (R-genes are responsible for initiation of defense mechanism against various phytopathogens. The majority of plant R-genes are members of very large multi-gene families, which encode structurally related proteins containing nucleotide binding site domains (NBS and C-terminal leucine rich repeats (LRR. Other classes possess' an extracellular LRR domain, a transmembrane domain and sometimes, an intracellular serine/threonine kinase domain. R-proteins work in pathogen perception and/or the activation of conserved defense signaling networks. In the present study, sequences representing resistance gene analogues (RGAs of coconut, arecanut, oil palm and date palm were collected from NCBI, sorted based on domains and assembled into a database. The sequences were analyzed in PRINTS database to find out the conserved domains and their motifs present in the RGAs. Based on these domains, we have also developed a tool to predict the domains of palm R-genes using various machine learning algorithms. The model files were selected based on the performance of the best classifier in training and testing. All these information is stored and made available in the online ‘PRGpred' database and prediction tool.
Indian Academy of Sciences (India)
ticians but also forms the foundation of computer science. Two ... with methods of developing algorithms for solving a variety of problems but ... applications of computers in science and engineer- ... numerical calculus are as important. We will ...
Algorithms that Defy the Gravity of Learning Curve
2017-04-28
yield the best perform- ing 1NN ensembles There is no magic to the gravity-defiant algorithms such as aNNE and iNNE which mani- fest that small data...isolation using nearest neighbour en- semble. Proceedings of the 2014 IEEE international conference on data mining, work- shop on incremental
Spectral Regularization Algorithms for Learning Large Incomplete Matrices.
Mazumder, Rahul; Hastie, Trevor; Tibshirani, Robert
2010-03-01
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and very efficient convex algorithm for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm Soft-Impute iteratively replaces the missing elements with those obtained from a soft-thresholded SVD. With warm starts this allows us to efficiently compute an entire regularization path of solutions on a grid of values of the regularization parameter. The computationally intensive part of our algorithm is in computing a low-rank SVD of a dense matrix. Exploiting the problem structure, we show that the task can be performed with a complexity linear in the matrix dimensions. Our semidefinite-programming algorithm is readily scalable to large matrices: for example it can obtain a rank-80 approximation of a 10(6) × 10(6) incomplete matrix with 10(5) observed entries in 2.5 hours, and can fit a rank 40 approximation to the full Netflix training set in 6.6 hours. Our methods show very good performance both in training and test error when compared to other competitive state-of-the art techniques.
Variants of Learning Algorithm Based on Kolmogorov Theorem
Czech Academy of Sciences Publication Activity Database
Neruda, Roman; Štědrý, Arnošt; Drkošová, Jitka
2002-01-01
Roč. 12, č. 6 (2002), s. 587-597 ISSN 1210-0552 R&D Projects: GA AV ČR IAB1030006 Institutional research plan: AV0Z1030915 Keywords : Kolmogorov networks * approximation theory * parallel algorithms Subject RIV: BA - General Mathematics
Learning JavaScript data structures and algorithms
Groner, Loiane
2014-01-01
If you are a JavaScript developer or someone who has basic knowledge of JavaScript, and want to explore its optimum ability, this fast-paced book is definitely for you. Programming logic is the only thing you need to know to start having fun with algorithms.
Schwarz, Daniel; Štourač, Petr; Komenda, Martin; Harazim, Hana; Kosinová, Martina; Gregor, Jakub; Hůlek, Richard; Smékalová, Olga; Křikava, Ivo; Štoudek, Roman; Dušek, Ladislav
2013-07-08
Medical Faculties Network (MEFANET) has established itself as the authority for setting standards for medical educators in the Czech Republic and Slovakia, 2 independent countries with similar languages that once comprised a federation and that still retain the same curricular structure for medical education. One of the basic goals of the network is to advance medical teaching and learning with the use of modern information and communication technologies. We present the education portal AKUTNE.CZ as an important part of the MEFANET's content. Our focus is primarily on simulation-based tools for teaching and learning acute medicine issues. Three fundamental elements of the MEFANET e-publishing system are described: (1) medical disciplines linker, (2) authentication/authorization framework, and (3) multidimensional quality assessment. A new set of tools for technology-enhanced learning have been introduced recently: Sandbox (works in progress), WikiLectures (collaborative content authoring), Moodle-MEFANET (central learning management system), and Serious Games (virtual casuistics and interactive algorithms). The latest development in MEFANET is designed for indexing metadata about simulation-based learning objects, also known as electronic virtual patients or virtual clinical cases. The simulations assume the form of interactive algorithms for teaching and learning acute medicine. An anonymous questionnaire of 10 items was used to explore students' attitudes and interests in using the interactive algorithms as part of their medical or health care studies. Data collection was conducted over 10 days in February 2013. In total, 25 interactive algorithms in the Czech and English languages have been developed and published on the AKUTNE.CZ education portal to allow the users to test and improve their knowledge and skills in the field of acute medicine. In the feedback survey, 62 participants completed the online questionnaire (13.5%) from the total 460 addressed
Empirical Studies On Machine Learning Based Text Classification Algorithms
Shweta C. Dharmadhikari; Maya Ingle; Parag Kulkarni
2011-01-01
Automatic classification of text documents has become an important research issue now days. Properclassification of text documents requires information retrieval, machine learning and Natural languageprocessing (NLP) techniques. Our aim is to focus on important approaches to automatic textclassification based on machine learning techniques viz. supervised, unsupervised and semi supervised.In this paper we present a review of various text classification approaches under machine learningparadig...
A Weighted Block Dictionary Learning Algorithm for Classification
Shi, Zhongrong
2016-01-01
Discriminative dictionary learning, playing a critical role in sparse representation based classification, has led to state-of-the-art classification results. Among the existing discriminative dictionary learning methods, two different approaches, shared dictionary and class-specific dictionary, which associate each dictionary atom to all classes or a single class, have been studied. The shared dictionary is a compact method but with lack of discriminative information; the class-specific dict...
Automatic learning algorithm for the MD-logic artificial pancreas system.
Miller, Shahar; Nimri, Revital; Atlas, Eran; Grunberg, Eli A; Phillip, Moshe
2011-10-01
Applying real-time learning into an artificial pancreas system could effectively track the unpredictable behavior of glucose-insulin dynamics and adjust insulin treatment accordingly. We describe a novel learning algorithm and its performance when integrated into the MD-Logic Artificial Pancreas (MDLAP) system developed by the Diabetes Technology Center, Schneider Children's Medical Center of Israel, Petah Tikva, Israel. The algorithm was designed to establish an initial patient profile using open-loop data (Initial Learning Algorithm component) and then make periodic adjustments during closed-loop operation (Runtime Learning Algorithm component). The MDLAP system, integrated with the learning algorithm, was tested in seven different experiments using the University of Virginia/Padova simulator, comprising adults, adolescents, and children. The experiments included simulations using the open-loop and closed-loop control strategy under nominal and varying insulin sensitivity conditions. The learning algorithm was automatically activated at the end of the open-loop segment and after every day of the closed-loop operation. Metabolic control parameters achieved at selected time points were compared. The percentage of time glucose levels were maintained within 70-180 mg/dL for children and adolescents significantly improved when open-loop was compared with day 6 of closed-loop control (Psignificantly reduced by approximately sevenfold (Psignificant reduction in the Low Blood Glucose Index (P<0.001). The new algorithm was effective in characterizing the patient profiles from open-loop data and in adjusting treatment to provide better glycemic control during closed-loop control in both conditions. These findings warrant corroboratory clinical trials.
Beyond the "c" and the "x": Learning with algorithms in massive open online courses (MOOCs)
Knox, Jeremy
2018-02-01
This article examines how algorithms are shaping student learning in massive open online courses (MOOCs). Following the dramatic rise of MOOC platform organisations in 2012, over 4,500 MOOCs have been offered to date, in increasingly diverse languages, and with a growing requirement for fees. However, discussions of learning in MOOCs remain polarised around the "xMOOC" and "cMOOC" designations. In this narrative, the more recent extended or platform MOOC ("xMOOC") adopts a broadcast pedagogy, assuming a direct transmission of information to its largely passive audience (i.e. a teacher-centred approach), while the slightly older connectivist model ("cMOOC") offers only a simplistic reversal of the hierarchy, posing students as highly motivated, self-directed and collaborative learners (i.e. a learner-centred approach). The online nature of both models generates data (e.g. on how many times a particular resource was viewed, or the ways in which participants communicated with each other) which MOOC providers use for analysis, albeit only after these data have been selectively processed. Central to many learning analytics approaches is the desire to predict students' future behaviour. Educators need to be aware that MOOC learning is not just about teachers and students, but that it also involves algorithms: instructions which perform automated calculations on data. Education is becoming embroiled in an "algorithmic culture" that defines educational roles, forecasts attainment, and influences pedagogy. Established theories of learning appear wholly inadequate in addressing the agential role of algorithms in the educational domain of the MOOC. This article identifies and examines four key areas where algorithms influence the activities of the MOOC: (1) data capture and discrimination; (2) calculated learners; (3) feedback and entanglement; and (4) learning with algorithms. The article concludes with a call for further research in these areas to surface a critical
A fast and accurate online sequential learning algorithm for feedforward networks.
Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N
2006-11-01
In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.
Super-resolution reconstruction of MR image with a novel residual learning network algorithm
Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu
2018-04-01
Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.
The Challenge of Promoting Algorithmic Thinking of Both Sciences- and Humanities-Oriented Learners
Katai, Z.
2015-01-01
The research results we present in this paper reveal that properly calibrated e-learning tools have potential to effectively promote the algorithmic thinking of both science-oriented and humanities-oriented students. After students had watched an illustration (by a folk dance choreography) and an animation of the studied sorting algorithm (bubble…
Laëtitia Pedroso
2010-01-01
The selective or ecological sorting of waste is already second nature to many of us and concerns us all. As the GS Department's new awareness-raising campaign reminds us, everything we do to sort waste contributes to preserving the environment. Placemats printed on recycled paper using vegetable-based ink will soon be distributed in Restaurant No.1. Environmental protection is never far from the headlines, and CERN has a responsibility to ensure that the 3000 tonnes and more of waste it produces every year are correctly and selectively sorted. Materials can be given a second life through recycling and re-use, thereby avoiding pollution from landfill sites and incineration plants and saving on processing costs. The GS Department is launching a new poster campaign designed to raise awareness of the importance of waste sorting and recycling. "After conducting a survey to find out whether members of the personnel were prepared to make an effort to sort a...
Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms
2014-03-27
Acquisition ( SCADA ) System Overview SCADA systems control and monitor processes for water distribution, oil and natural gas pipelines , electrical...the desire for remote control and monitoring of industrial processes. The ability to identify SCADA devices on a mixed traffic network with zero...optimal attribute subset, while maintaining the desired TPR of .99 for SCADA network traffic. The attributes and ML algorithms chosen for
Asymmetric Variate Generation via a Parameterless Dual Neural Learning Algorithm
Directory of Open Access Journals (Sweden)
Simone Fiori
2008-01-01
Full Text Available In a previous work (S. Fiori, 2006, we proposed a random number generator based on a tunable non-linear neural system, whose learning rule is designed on the basis of a cardinal equation from statistics and whose implementation is based on look-up tables (LUTs. The aim of the present manuscript is to improve the above-mentioned random number generation method by changing the learning principle, while retaining the efficient LUT-based implementation. The new method proposed here proves easier to implement and relaxes some previous limitations.
Recent progress in multi-electrode spike sorting methods.
Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier
2016-11-01
In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Liu, Hui; Tian, Hong-qi; Li, Yan-fei
2015-01-01
Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions
A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition
Zhang, Wei; Zhang, Xueying; Sun, Ying
2017-01-01
Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between e...
CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms
Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert
2018-04-01
CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.
International Nuclear Information System (INIS)
Kim, Dong Yun; Seong, Poong Hyun
1996-01-01
In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller
Bisele, Maria; Bencsik, Martin; Lewis, Martin G C; Barnett, Cleveland T
2017-01-01
Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statistical methods, such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), have been adopted since they have the potential to overcome these data handling issues. The aim of the current study was to develop and optimise a specific machine learning algorithm for processing human locomotion data. Twenty participants ran at a self-selected speed across a 15m runway in barefoot and shod conditions. Ground reaction forces (BW) and kinematics were measured at 1000 Hz and 100 Hz, respectively from which joint angles (°), joint moments (N.m.kg-1) and joint powers (W.kg-1) for the hip, knee and ankle joints were calculated in all three anatomical planes. Using PCA and DFA, power spectra of the kinematic and kinetic variables were used as a training database for the development of a machine learning algorithm. All possible combinations of 10 out of 20 participants were explored to find the iteration of individuals that would optimise the machine learning algorithm. The results showed that the algorithm was able to successfully predict whether a participant ran shod or barefoot in 93.5% of cases. To the authors' knowledge, this is the first study to optimise the development of a machine learning algorithm.
An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
Directory of Open Access Journals (Sweden)
Ying Mei
2017-06-01
Full Text Available Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade. The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.
SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid
Directory of Open Access Journals (Sweden)
Hossam Gabbar
2016-03-01
Full Text Available Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC with Fuzzy Model Reference Learning Controller (FMRLC. The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.
Sorting and sustaining cooperation
DEFF Research Database (Denmark)
Vikander, Nick
2013-01-01
This paper looks at cooperation in teams where some people are selfish and others are conditional cooperators, and where lay-offs will occur at a fixed future date. I show that the best way to sustain cooperation prior to the lay-offs is often in a sorting equilibrium, where conditional cooperators...... can identify and then work with one another. Changes to parameters that would seem to make cooperation more attractive, such as an increase in the discount factor or the fraction of conditional cooperators, can reduce equilibrium cooperation if they decrease a selfish player's incentive to sort....
Fink, Hans
2006-01-01
In "Two sorts of Naturalism" John McDowell is sketching his own sort of naturalism in ethics as an alternative to "bald naturalism". In this paper I distinguish materialist, idealist and absolute conceptions of nature and of naturalism in order to provide a framework for a clearer understanding of what McDowell's own naturalism amounts to. I argue that nothing short of an absolute naturalism will do for a number of McDowell's own purposes, but that it is far from obvious that this is his posi...
Indian Academy of Sciences (India)
algorithm design technique called 'divide-and-conquer'. One of ... Turtle graphics, September. 1996. 5. ... whole list named 'PO' is a pointer to the first element of the list; ..... Program for computing matrices X and Y and placing the result in C *).
Indian Academy of Sciences (India)
algorithm that it is implicitly understood that we know how to generate the next natural ..... Explicit comparisons are made in line (1) where maximum and minimum is ... It can be shown that the function T(n) = 3/2n -2 is the solution to the above ...
Extracting quantum dynamics from genetic learning algorithms through principal control analysis
International Nuclear Information System (INIS)
White, J L; Pearson, B J; Bucksbaum, P H
2004-01-01
Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results. (letter to the editor)
Forecasting with Universal Approximators and a Learning Algorithm
DEFF Research Database (Denmark)
Kock, Anders Bredahl
2011-01-01
to the performance of the best single model in the set of models combined from. The use of universal approximators along with a combination scheme for which explicit loss bounds exist should give a solid theoretical foundation to the way the forecasts are performed. The practical performance will be investigated...... combination has a long history in econometrics focus has not been on proving loss bounds for the combination rules applied. We apply the Weighted Average Algorithm (WAA) of Kivinen & Warmuth (1999) for which such loss bounds exist. Specifically, one can bound the worst case performance of the WAA compared...
Forecasting with Universal Approximators and a Learning Algorithm
DEFF Research Database (Denmark)
Kock, Anders Bredahl
bounds for the combination rules applied. We apply the Weighted Average Algorithm (WAA) of Kivinen and Warmuth (1999) for which such loss bounds exist. Specifically, one can bound the worst case performance of the WAA compared to the performance of the best single model in the set of models combined from....... The use of universal approximators along with a combination scheme for which explicit loss bounds exist should give a solid theoretical foundation to the way the forecasts are performed. The practical performance will be investigated by considering various monthly postwar macroeconomic data sets for the G...
Niazmardi, S.; Safari, A.; Homayouni, S.
2017-09-01
Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.
Yuldashev, M. N.; Vlasov, A. I.; Novikov, A. N.
2018-05-01
This paper focuses on the development of an energy-efficient algorithm for classification of states of a wireless sensor network using machine learning methods. The proposed algorithm reduces energy consumption by: 1) elimination of monitoring of parameters that do not affect the state of the sensor network, 2) reduction of communication sessions over the network (the data are transmitted only if their values can affect the state of the sensor network). The studies of the proposed algorithm have shown that at classification accuracy close to 100%, the number of communication sessions can be reduced by 80%.
A method for classification of network traffic based on C5.0 Machine Learning Algorithm
DEFF Research Database (Denmark)
Bujlow, Tomasz; Riaz, M. Tahir; Pedersen, Jens Myrup
2012-01-01
current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown...... and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options...
A new evolutionary algorithm with LQV learning for combinatorial problems optimization
International Nuclear Information System (INIS)
Machado, Marcelo Dornellas; Schirru, Roberto
2000-01-01
Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for combinatorial problems optimization. In this work, a new learning mode, to be used by the population-based incremental learning algorithm, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process known as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors, in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problems. Due to the fact that the reload problem is a combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)
A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks
Directory of Open Access Journals (Sweden)
Sho Fukuda
2014-12-01
Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks
Integration through a Card-Sort Activity
Green, Kris; Ricca, Bernard P.
2015-01-01
Learning to compute integrals via the various techniques of integration (e.g., integration by parts, partial fractions, etc.) is difficult for many students. Here, we look at how students in a college level Calculus II course develop the ability to categorize integrals and the difficulties they encounter using a card sort-resort activity. Analysis…
The Design and Analysis of Efficient Learning Algorithms
1991-01-01
31] describe in detail how this can be done efficiently; see also Duda and Hart [22]. Let a&,..., &d be the resulting solution, and let h0 = Fd=1 af...Measure. Wiley, second edition, 1986. [13] Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K. Warmuth. Occam’s razor. Information...Processing Letters, 24(6):377-380, April 1987. [14] Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K. Warmuth. Learn- ability and the
Prediction of Employee Turnover in Organizations using Machine Learning Algorithms
Rohit Punnoose; Pankaj Ajit
2016-01-01
Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared t...
PACMan to Help Sort Hubble Proposals
Kohler, Susanna
2017-04-01
conceivably do this matching instead?Comparison of PACMans categorization to the manual sorting for HST Cycle 24 proposals. Green: proposals similarly categorized by both. Yellow: proposals whose manual classifications are within the top 60% of sorted PACMan classifications. Red: proposals categorized differently by each. [Strolger et al. 2017]Introducing PACManLed by Louis-Gregory Strolger (STScI), a team of scientists has developed PACMan: the Proposal Auto-Categorizer and Manager. PACMan is whats known as a Naive Bayesian classifier; its essentially a spam-filtering routine that uses word probabilities to sort proposals into multiple scientific categories and identify people to serve on review panels in those same scientific areas.PACMan works by looking at the words in aproposal and comparing them to those in a training set of proposals in this case, the previous years HST proposals, sorted by humans. By using the training set, PACMan learns how to accurately classify proposals.PACMan then looks up each reviewer on the Astrophysical Data System (ADS) and compiles the abstracts from the reviewers past 10 years worth of scientific publications. This text is then evaluated in a similar way to the text of the proposals, determining each reviewers suitability to evaluate a proposal.How Did It Do?Comparison of PACMan sorting to manual sorting, specifically for the HST Cycle 24 proposals that were recategorized by the Science Policies Group (SPG) from what the submitter (PI) selected. Of these swaps, 48% would have been predicted by PACMan. [Strolger et al. 2017]Strolger and collaborators show that with a training set of one previous cycles proposals, PACMan correctly categorizes the next cycles proposals roughly 87% of the time. By increasing the size of the training set to include more past cycles, PACMans accuracy can be improved up to 95% (though the algorithm will have to be retrained any time the proposal categories change).PACMans results were also consistent for
Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.
Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J
2017-12-01
Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction , where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.
Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
Paganini, Michela; The ATLAS collaboration
2017-01-01
The separation of $b$-quark initiated jets from those coming from lighter quark flavors ($b$-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful $b$-tagging algorithms combine information from low-level taggers, exploiting reconstructed track and vertex information, into machine learning classifiers. The potential of modern deep learning techniques is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.
Cheng, Hong
2015-01-01
This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition; describes sparse recovery approaches, robust and efficient sparse represen
Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms
DEFF Research Database (Denmark)
Nielsen, Henrik
2015-01-01
was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular...
Indian Academy of Sciences (India)
will become clear in the next article when we discuss a simple logo like programming language. ... Rod B may be used as an auxiliary store. The problem is to find an algorithm which performs this task. ... No disks are moved from A to Busing C as auxiliary rod. • move _disk (A, C);. (No + l)th disk is moved from A to C directly ...
International Nuclear Information System (INIS)
Roche-Lima, Abiel; Thulasiram, Ruppa K
2012-01-01
Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.
Machine learning algorithms to classify spinal muscular atrophy subtypes.
Srivastava, Tuhin; Darras, Basil T; Wu, Jim S; Rutkove, Seward B
2012-07-24
The development of better biomarkers for disease assessment remains an ongoing effort across the spectrum of neurologic illnesses. One approach for refining biomarkers is based on the concept of machine learning, in which individual, unrelated biomarkers are simultaneously evaluated. In this cross-sectional study, we assess the possibility of using machine learning, incorporating both quantitative muscle ultrasound (QMU) and electrical impedance myography (EIM) data, for classification of muscles affected by spinal muscular atrophy (SMA). Twenty-one normal subjects, 15 subjects with SMA type 2, and 10 subjects with SMA type 3 underwent EIM and QMU measurements of unilateral biceps, wrist extensors, quadriceps, and tibialis anterior. EIM and QMU parameters were then applied in combination using a support vector machine (SVM), a type of machine learning, in an attempt to accurately categorize 165 individual muscles. For all 3 classification problems, normal vs SMA, normal vs SMA 3, and SMA 2 vs SMA 3, use of SVM provided the greatest accuracy in discrimination, surpassing both EIM and QMU individually. For example, the accuracy, as measured by the receiver operating characteristic area under the curve (ROC-AUC) for the SVM discriminating SMA 2 muscles from SMA 3 muscles was 0.928; in comparison, the ROC-AUCs for EIM and QMU parameters alone were only 0.877 (p < 0.05) and 0.627 (p < 0.05), respectively. Combining EIM and QMU data categorizes individual SMA-affected muscles with very high accuracy. Further investigation of this approach for classifying and for following the progression of neuromuscular illness is warranted.
LMS learning algorithms: misconceptions and new results on converence.
Wang, Z Q; Manry, M T; Schiano, J L
2000-01-01
The Widrow-Hoff delta rule is one of the most popular rules used in training neural networks. It was originally proposed for the ADALINE, but has been successfully applied to a few nonlinear neural networks as well. Despite its popularity, there exist a few misconceptions on its convergence properties. In this paper we consider repetitive learning (i.e., a fixed set of samples are used for training) and provide an in-depth analysis in the least mean square (LMS) framework. Our main result is that contrary to common belief, the nonbatch Widrow-Hoff rule does not converge in general. It converges only to a limit cycle.
Algorithms for Learning Preferences for Sets of Objects
Wagstaff, Kiri L.; desJardins, Marie; Eaton, Eric
2010-01-01
A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical
Energy Technology Data Exchange (ETDEWEB)
Aziz, H. M. Abdul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Zhu, Feng [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering; Ukkusuri, Satish V. [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering
2017-10-04
Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO_{2}, NO_{x}, VOC, PM_{10}) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.
A learning algorithm for oscillatory cellular neural networks.
Ho, C Y.; Kurokawa, H
1999-07-01
We present a cellular type oscillatory neural network for temporal segregation of stationary input patterns. The model comprises an array of locally connected neural oscillators with connections limited to a 4-connected neighborhood. The architecture is reminiscent of the well-known cellular neural network that consists of local connection for feature extraction. By means of a novel learning rule and an initialization scheme, global synchronization can be accomplished without incurring any erroneous synchrony among uncorrelated objects. Each oscillator comprises two mutually coupled neurons, and neurons share a piecewise-linear activation function characteristic. The dynamics of traditional oscillatory models is simplified by using only one plastic synapse, and the overall complexity for hardware implementation is reduced. Based on the connectedness of image segments, it is shown that global synchronization and desynchronization can be achieved by means of locally connected synapses, and this opens up a tremendous application potential for the proposed architecture. Furthermore, by using special grouping synapses it is demonstrated that temporal segregation of overlapping gray-level and color segments can also be achieved. Finally, simulation results show that the learning rule proposed circumvents the problem of component mismatches, and hence facilitates a large-scale integration.
Application of Machine Learning Algorithms for the Query Performance Prediction
Directory of Open Access Journals (Sweden)
MILICEVIC, M.
2015-08-01
Full Text Available This paper analyzes the relationship between the system load/throughput and the query response time in a real Online transaction processing (OLTP system environment. Although OLTP systems are characterized by short transactions, which normally entail high availability and consistent short response times, the need for operational reporting may jeopardize these objectives. We suggest a new approach to performance prediction for concurrent database workloads, based on the system state vector which consists of 36 attributes. There is no bias to the importance of certain attributes, but the machine learning methods are used to determine which attributes better describe the behavior of the particular database server and how to model that system. During the learning phase, the system's profile is created using multiple reference queries, which are selected to represent frequent business processes. The possibility of the accurate response time prediction may be a foundation for automated decision-making for database (DB query scheduling. Possible applications of the proposed method include adaptive resource allocation, quality of service (QoS management or real-time dynamic query scheduling (e.g. estimation of the optimal moment for a complex query execution.
Think big: learning contexts, algorithms and data science
Directory of Open Access Journals (Sweden)
Baldassarre Michele
2016-12-01
Full Text Available Due to the increasing growth in available data in recent years, all areas of research and the managements of institutions and organisations, specifically schools and universities, feel the need to give meaning to this availability of data. This article, after a brief reference to the definition of big data, intends to focus attention and reflection on their type to proceed to an extension of their characterisation. One of the hubs to make feasible the use of Big Data in operational contexts is to give a theoretical basis to which to refer. The Data, Information, Knowledge and Wisdom (DIKW model correlates these four aspects, concluding in Data Science, which in many ways could revolutionise the established pattern of scientific investigation. The Learning Analytics applications on online learning platforms can be tools for evaluating the quality of teaching. And that is where some problems arise. It becomes necessary to handle with care the available data. Finally, a criterion for deciding whether it makes sense to think of an analysis based on Big Data can be to think about the interpretability and relevance in relation to both institutional and personal processes.
DEFF Research Database (Denmark)
Yoo, C.; Gernaey, Krist
2008-01-01
importance in the projection (VIP) information of the DPLS method. The power of the gene selection method and the proposed supervised hierarchical clustering method is illustrated on a three microarray data sets of leukemia, breast, and colon cancer. Supervised machine learning algorithms thus enable...
Evaluation of a Didactic Method for the Active Learning of Greedy Algorithms
Esteban-Sánchez, Natalia; Pizarro, Celeste; Velázquez-Iturbide, J. Ángel
2014-01-01
An evaluation of the educational effectiveness of a didactic method for the active learning of greedy algorithms is presented. The didactic method sets students structured-inquiry challenges to be addressed with a specific experimental method, supported by the interactive system GreedEx. This didactic method has been refined over several years of…
Using machine learning algorithms to guide rehabilitation planning for home care clients.
Zhu, Mu; Zhang, Zhanyang; Hirdes, John P; Stolee, Paul
2007-12-20
Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.
A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children
Sutoko, Stephanie; Funane, Tsukasa; Katura, Takusige; Sato, Hiroki; Kiguchi, Masashi; Maki, Atsushi; Monden, Yukifumi; Nagashima, Masako; Yamagata, Takanori; Dan, Ippeita
2017-02-01
In pediatrics studies, the quality of functional near infrared spectroscopy (fNIRS) signals is often reduced by motion artifacts. These artifacts likely mislead brain functionality analysis, causing false discoveries. While noise correction methods and their performance have been investigated, these methods require several parameter assumptions that apparently result in noise overfitting. In contrast, the rejection of noisy signals serves as a preferable method because it maintains the originality of the signal waveform. Here, we describe a semi-learning algorithm to detect and eliminate noisy signals. The algorithm dynamically adjusts noise detection according to the predetermined noise criteria, which are spikes, unusual activation values (averaged amplitude signals within the brain activation period), and high activation variances (among trials). Criteria were sequentially organized in the algorithm and orderly assessed signals based on each criterion. By initially setting an acceptable rejection rate, particular criteria causing excessive data rejections are neglected, whereas others with tolerable rejections practically eliminate noises. fNIRS data measured during the attention response paradigm (oddball task) in children with attention deficit/hyperactivity disorder (ADHD) were utilized to evaluate and optimize the algorithm's performance. This algorithm successfully substituted the visual noise identification done in the previous studies and consistently found significantly lower activation of the right prefrontal and parietal cortices in ADHD patients than in typical developing children. Thus, we conclude that the semi-learning algorithm confers more objective and standardized judgment for noise rejection and presents a promising alternative to visual noise rejection
Thubagere, Anupama J; Li, Wei; Johnson, Robert F; Chen, Zibo; Doroudi, Shayan; Lee, Yae Lim; Izatt, Gregory; Wittman, Sarah; Srinivas, Niranjan; Woods, Damien; Winfree, Erik; Qian, Lulu
2017-09-15
Two critical challenges in the design and synthesis of molecular robots are modularity and algorithm simplicity. We demonstrate three modular building blocks for a DNA robot that performs cargo sorting at the molecular level. A simple algorithm encoding recognition between cargos and their destinations allows for a simple robot design: a single-stranded DNA with one leg and two foot domains for walking, and one arm and one hand domain for picking up and dropping off cargos. The robot explores a two-dimensional testing ground on the surface of DNA origami, picks up multiple cargos of two types that are initially at unordered locations, and delivers them to specified destinations until all molecules are sorted into two distinct piles. The robot is designed to perform a random walk without any energy supply. Exploiting this feature, a single robot can repeatedly sort multiple cargos. Localization on DNA origami allows for distinct cargo-sorting tasks to take place simultaneously in one test tube or for multiple robots to collectively perform the same task. Copyright © 2017, American Association for the Advancement of Science.
Directory of Open Access Journals (Sweden)
C. Sunil Kumar
2014-07-01
Full Text Available Automation of descriptive answers evaluation is the need of the hour because of the huge increase in the number of students enrolling each year in educational institutions and the limited staff available to spare their time for evaluations. In this paper, we use a machine learning workbench called LightSIDE to accomplish auto evaluation and scoring of descriptive answers. We attempted to identify the best supervised machine learning algorithm given a limited training set sample size scenario. We evaluated performances of Bayes, SVM, Logistic Regression, Random forests, Decision stump and Decision trees algorithms. We confirmed SVM as best performing algorithm based on quantitative measurements across accuracy, kappa, training speed and prediction accuracy with supplied test set.
Directory of Open Access Journals (Sweden)
A. A. Salama
2015-03-01
Full Text Available In this paper, we present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salama et al. in [24-66].The purpose of this paper is to utilize a neutrosophic set to analyze social networks data conducted through learning activities.
Efficient Online Learning Algorithms Based on LSTM Neural Networks.
Ergen, Tolga; Kozat, Suleyman Serdar
2017-09-13
We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.
A stochastic learning algorithm for layered neural networks
International Nuclear Information System (INIS)
Bartlett, E.B.; Uhrig, R.E.
1992-01-01
The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given
Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms
Directory of Open Access Journals (Sweden)
Richard Lamb
2015-09-01
Full Text Available Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a “virtual” student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning. Comparison of the STAC-M and the STAC-M with inclusion of the Multiobjective Evolutionary Algorithm shows greater success in solving the Piagetian science-tasks post cognitive retraining with the Multiobjective Evolutionary Algorithm. This illustrates the potential uses of cognitive and neuropsychological computational modeling in educational research. The authors also outline the limitations and assumptions of computational modeling.
Sorting signed permutations by inversions in O(nlogn) time.
Swenson, Krister M; Rajan, Vaibhav; Lin, Yu; Moret, Bernard M E
2010-03-01
The study of genomic inversions (or reversals) has been a mainstay of computational genomics for nearly 20 years. After the initial breakthrough of Hannenhalli and Pevzner, who gave the first polynomial-time algorithm for sorting signed permutations by inversions, improved algorithms have been designed, culminating with an optimal linear-time algorithm for computing the inversion distance and a subquadratic algorithm for providing a shortest sequence of inversions--also known as sorting by inversions. Remaining open was the question of whether sorting by inversions could be done in O(nlogn) time. In this article, we present a qualified answer to this question, by providing two new sorting algorithms, a simple and fast randomized algorithm and a deterministic refinement. The deterministic algorithm runs in time O(nlogn + kn), where k is a data-dependent parameter. We provide the results of extensive experiments showing that both the average and the standard deviation for k are small constants, independent of the size of the permutation. We conclude (but do not prove) that almost all signed permutations can be sorted by inversions in O(nlogn) time.
Gaur, Pallavi; Chaturvedi, Anoop
2017-07-22
The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms. Data were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as 'BLASTN' and 'MEME suite'. The machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.
Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning
Fu, QiMing
2016-01-01
To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2-regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency. PMID:27795704
Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro
1995-02-01
We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.
Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning
Directory of Open Access Journals (Sweden)
Yingfeng Cai
2016-01-01
Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.
Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang
2016-09-22
To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.
An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Donghao Wang
2016-09-01
Full Text Available To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.
Algorithm Building and Learning Programming Languages Using a New Educational Paradigm
Jain, Anshul K.; Singhal, Manik; Gupta, Manu Sheel
2011-08-01
This research paper presents a new concept of using a single tool to associate syntax of various programming languages, algorithms and basic coding techniques. A simple framework has been programmed in Python that helps students learn skills to develop algorithms, and implement them in various programming languages. The tool provides an innovative and a unified graphical user interface for development of multimedia objects, educational games and applications. It also aids collaborative learning amongst students and teachers through an integrated mechanism based on Remote Procedure Calls. The paper also elucidates an innovative method for code generation to enable students to learn the basics of programming languages using drag-n-drop methods for image objects.
International Nuclear Information System (INIS)
Kopecna, Renata; Tomasik, Boris
2016-01-01
We propose a novel method for sorting events of multiparticle production according to the azimuthal anisotropy of their momentum distribution. Although the method is quite general, we advocate its use in analysis of ultra-relativistic heavy-ion collisions where a large number of hadrons is produced. The advantage of our method is that it can automatically sort out samples of events with histograms that indicate similar distributions of hadrons. It takes into account the whole measured histograms with all orders of anisotropy instead of a specific observable (e.g., v 2 , v 3 , q 2 ). It can be used for more exclusive experimental studies of flow anisotropies which are then more easily compared to theoretical calculations. It may also be useful in the construction of mixed-events background for correlation studies as it allows to select events with similar momentum distribution. (orig.)
A Novel and Simple Spike Sorting Implementation.
Petrantonakis, Panagiotis C; Poirazi, Panayiota
2017-04-01
Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.
Efficient Sorting on the Tilera Manycore Architecture
Energy Technology Data Exchange (ETDEWEB)
Morari, Alessandro; Tumeo, Antonino; Villa, Oreste; Secchi, Simone; Valero, Mateo
2012-10-24
e present an efficient implementation of the radix sort algo- rithm for the Tilera TILEPro64 processor. The TILEPro64 is one of the first successful commercial manycore processors. It is com- posed of 64 tiles interconnected through multiple fast Networks- on-chip and features a fully coherent, shared distributed cache. The architecture has a large degree of flexibility, and allows various optimization strategies. We describe how we mapped the algorithm to this architecture. We present an in-depth analysis of the optimizations for each phase of the algorithm with respect to the processor’s sustained performance. We discuss the overall throughput reached by our radix sort implementation (up to 132 MK/s) and show that it provides comparable or better performance-per-watt with respect to state-of-the art implemen- tations on x86 processors and graphic processing units.
Energy Technology Data Exchange (ETDEWEB)
Beer, Neil Reginald; Lee, Abraham; Hatch, Andrew
2017-11-21
A non-contact system for sorting monodisperse water-in-oil emulsion droplets in a microfluidic device based on the droplet's contents and their interaction with an applied electromagnetic field or by identification and sorting.
DEFF Research Database (Denmark)
Mahnke, Martina; Uprichard, Emma
2014-01-01
Imagine sailing across the ocean. The sun is shining, vastness all around you. And suddenly [BOOM] you’ve hit an invisible wall. Welcome to the Truman Show! Ever since Eli Pariser published his thoughts on a potential filter bubble, this movie scenario seems to have become reality, just with slight...... changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall. Building on this assumption, most research is trying to ‘tame the algorithmic tiger’. While this is a valuable and often inspiring approach, we...
A Quality Sorting of Fruit Using a New Automatic Image Processing Method
Amenomori, Michihiro; Yokomizu, Nobuyuki
This paper presents an innovative approach for quality sorting of objects such as apples sorting in an agricultural factory, using an image processing algorithm. The objective of our approach are; firstly to sort the objects by their colors precisely; secondly to detect any irregularity of the colors surrounding the apples efficiently. An experiment has been conducted and the results have been obtained and compared with that has been preformed by human sorting process and by color sensor sorting devices. The results demonstrate that our approach is capable to sort the objects rapidly and the percentage of classification valid rate was 100 %.
Directory of Open Access Journals (Sweden)
Mazyar Seraj
2014-06-01
Full Text Available In recent years, many studies have been carried out on how to engage and support students in e-learning environments. Portable devices such as Personal Digital Assistants (PDAs, Tablet PCs, mobile phones and other mobile equipment have been used as parts of electronic learning environments to facilitate learning and teaching for both lecturers and students. However, there is still a dearth of study investigating the effects of small screen interfaces on mobile-based learning environments. This study aims to address two objectives: (i investigate lecturer and student difficulties encountered in teaching-learning process in traditional face-to-face classroom settings, and (ii to explore lecturer and student perceptions about learning the subject through mobile devices. This paper presents the results of a qualitative study using structured interviews to investigate lecturer and student experiences and perceptions on teaching and learning Dijkstra’s shortest path algorithm via mobile devices. The interview insights were then used as inputs to define user requirements for a mobile learning prototype. The findings show that the lecturers and students raised many issues about interactivity and the flexibility of effective learning applications on small screen devices, especially for a technical subject.
Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
Directory of Open Access Journals (Sweden)
Muneki Yasuda
2018-04-01
Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.
Wahyudin; Riza, L. S.; Putro, B. L.
2018-05-01
E-learning as a learning activity conducted online by the students with the usual tools is favoured by students. The use of computer media in learning provides benefits that are not owned by other learning media that is the ability of computers to interact individually with students. But the weakness of many learning media is to assume that all students have a uniform ability, when in reality this is not the case. The concept of Intelligent Tutorial System (ITS) combined with cyberblog application can overcome the weaknesses in neglecting diversity. An Intelligent Tutorial System-based Cyberblog application (ITS) is a web-based interactive application program that implements artificial intelligence which can be used as a learning and evaluation media in the learning process. The use of ITS-based Cyberblog in learning is one of the alternative learning media that is interesting and able to help students in measuring ability in understanding the material. This research will be associated with the improvement of logical thinking ability (logical thinking) of students, especially in algorithm subjects.
The algorithm for duration acceleration of repetitive projects considering the learning effect
Chen, Hongtao; Wang, Keke; Du, Yang; Wang, Liwan
2018-03-01
Repetitive project optimization problem is common in project scheduling. Repetitive Scheduling Method (RSM) has many irreplaceable advantages in the field of repetitive projects. As the same or similar work is repeated, the proficiency of workers will be correspondingly low to high, and workers will gain experience and improve the efficiency of operations. This is learning effect. Learning effect is one of the important factors affecting the optimization results in repetitive project scheduling. This paper analyzes the influence of the learning effect on the controlling path in RSM from two aspects: one is that the learning effect changes the controlling path, the other is that the learning effect doesn't change the controlling path. This paper proposes corresponding methods to accelerate duration for different types of critical activities and proposes the algorithm for duration acceleration based on the learning effect in RSM. And the paper chooses graphical method to identity activities' types and considers the impacts of the learning effect on duration. The method meets the requirement of duration while ensuring the lowest acceleration cost. A concrete bridge construction project is given to verify the effectiveness of the method. The results of this study will help project managers understand the impacts of the learning effect on repetitive projects, and use the learning effect to optimize project scheduling.
Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?
Karim, Mohammad Ehsanul; Pang, Menglan; Platt, Robert W
2018-03-01
The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.
An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration
Directory of Open Access Journals (Sweden)
Wenping Ma
2014-01-01
Full Text Available We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED. Differential evolution (DE is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images.
An efficient dictionary learning algorithm and its application to 3-D medical image denoising.
Li, Shutao; Fang, Leyuan; Yin, Haitao
2012-02-01
In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a dictionary structuring strategy to cluster the atoms with high coherence together, and then employs a multiple-selection strategy to select several competitive atoms at each iteration. These two strategies can greatly reduce the computation complexity of the MCP and assist it to obtain better sparse solution. On the dictionary updating stage, the alternating optimization that efficiently approximates the singular value decomposition is introduced. Furthermore, in the 3-D medical image denoising application, a joint 3-D operation is proposed for taking the learning capabilities of the presented algorithm to simultaneously capture the correlations within each slice and correlations across the nearby slices, thereby obtaining better denoising results. The experiments on both synthetically generated data and real 3-D medical images demonstrate that the proposed approach has superior performance compared to some well-known methods. © 2011 IEEE
Natural Selection Is a Sorting Process: What Does that Mean?
Price, Rebecca M.
2013-01-01
To learn why natural selection acts only on existing variation, students categorize processes as either creative or sorting. This activity helps students confront the misconception that adaptations evolve because species need them.
A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition
Directory of Open Access Journals (Sweden)
Wei Zhang
2017-01-01
Full Text Available Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between emotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large number of concepts, whereas a typical FCM can handle only relatively simple networks (maps. Different acoustic features, including fundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three experiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison between the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases. Results of the feature combination experiments show that the recognition rates of the combination features are 10%–20% better than those of single features. The proposed FCM learning algorithm generates 5%–20% performance improvement compared with traditional classification networks.
Narula, Sukrit; Shameer, Khader; Salem Omar, Alaa Mabrouk; Dudley, Joel T; Sengupta, Partho P
2016-11-29
Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K-fold cross-validation. Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p 13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e', and strain. Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
Sorting signed permutations by short operations.
Galvão, Gustavo Rodrigues; Lee, Orlando; Dias, Zanoni
2015-01-01
During evolution, global mutations may alter the order and the orientation of the genes in a genome. Such mutations are referred to as rearrangement events, or simply operations. In unichromosomal genomes, the most common operations are reversals, which are responsible for reversing the order and orientation of a sequence of genes, and transpositions, which are responsible for switching the location of two contiguous portions of a genome. The problem of computing the minimum sequence of operations that transforms one genome into another - which is equivalent to the problem of sorting a permutation into the identity permutation - is a well-studied problem that finds application in comparative genomics. There are a number of works concerning this problem in the literature, but they generally do not take into account the length of the operations (i.e. the number of genes affected by the operations). Since it has been observed that short operations are prevalent in the evolution of some species, algorithms that efficiently solve this problem in the special case of short operations are of interest. In this paper, we investigate the problem of sorting a signed permutation by short operations. More precisely, we study four flavors of this problem: (i) the problem of sorting a signed permutation by reversals of length at most 2; (ii) the problem of sorting a signed permutation by reversals of length at most 3; (iii) the problem of sorting a signed permutation by reversals and transpositions of length at most 2; and (iv) the problem of sorting a signed permutation by reversals and transpositions of length at most 3. We present polynomial-time solutions for problems (i) and (iii), a 5-approximation for problem (ii), and a 3-approximation for problem (iv). Moreover, we show that the expected approximation ratio of the 5-approximation algorithm is not greater than 3 for random signed permutations with more than 12 elements. Finally, we present experimental results that show
A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation.
Wang, Hongxun; Zhang, Weifang; Sun, Fuqiang; Zhang, Wei
2017-05-18
The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.
Sampling algorithms for validation of supervised learning models for Ising-like systems
Portman, Nataliya; Tamblyn, Isaac
2017-12-01
In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).
Directory of Open Access Journals (Sweden)
Marc Wieland
2014-03-01
Full Text Available In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS and very high resolution (WorldView-2, Quickbird, Ikonos multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based, have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.
Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).
Energy Technology Data Exchange (ETDEWEB)
Pinar, Ali [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kolda, Tamara G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Carlberg, Kevin Thomas [Wake Forest Univ., Winston-Salem, MA (United States); Ballard, Grey [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mahoney, Michael [Univ. of California, Berkeley, CA (United States)
2018-01-01
Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.
Modified Bat Algorithm Based on Lévy Flight and Opposition Based Learning
Directory of Open Access Journals (Sweden)
Xian Shan
2016-01-01
Full Text Available Bat Algorithm (BA is a swarm intelligence algorithm which has been intensively applied to solve academic and real life optimization problems. However, due to the lack of good balance between exploration and exploitation, BA sometimes fails at finding global optimum and is easily trapped into local optima. In order to overcome the premature problem and improve the local searching ability of Bat Algorithm for optimization problems, we propose an improved BA called OBMLBA. In the proposed algorithm, a modified search equation with more useful information from the search experiences is introduced to generate a candidate solution, and Lévy Flight random walk is incorporated with BA in order to avoid being trapped into local optima. Furthermore, the concept of opposition based learning (OBL is embedded to BA to enhance the diversity and convergence capability. To evaluate the performance of the proposed approach, 16 benchmark functions have been employed. The results obtained by the experiments demonstrate the effectiveness and efficiency of OBMLBA for global optimization problems. Comparisons with some other BA variants and other state-of-the-art algorithms have shown the proposed approach significantly improves the performance of BA. Performances of the proposed algorithm on large scale optimization problems and real world optimization problems are not discussed in the paper, and it will be studied in the future work.
Impedance learning for robotic contact tasks using natural actor-critic algorithm.
Kim, Byungchan; Park, Jooyoung; Park, Shinsuk; Kang, Sungchul
2010-04-01
Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.
An e-Learning environment for algorithmic: toward an active construction of skills
Directory of Open Access Journals (Sweden)
Abdelghani Babori
2016-07-01
Full Text Available Assimilating an algorithmic course is a persistent problem for many undergraduate students. The major problem faced by students is the lack of problem solving ability and flexibility. Therefore, students are generally passive, unmotivated and unable to mobilize all the acquired knowledge (loops, test, variables, etc. to deal with new encountered problems. Our study is structured around building, step by step, problem solving skills among novice learners. Our approach is based on the use of problem based learning in an e-Learning environment. We begin by establishing a cognitive model which represents knowledge elements, grouped into categories of skills, judged necessary to be appropriated. We then propose a problem built on a concrete situation which aims to actively construct a skill category. We conclude by presenting around the proposed problem a pedagogical scenario for the set of learning activities designed to be incorporated in an E-learning platform.
A correctness proof of sorting by means of formal procedures
Fokkinga, M.M.
1987-01-01
We consider a recursive sorting algorithm in which, in each invocation, a new variable and a new procedure (using the variable globally) are defined and the procedure is passed to recursive calls. This algorithm is proved correct with Hoare-style pre- and postassertions. We also discuss the same
Sorting, Searching, and Simulation in the MapReduce Framework
DEFF Research Database (Denmark)
Goodrich, Michael T.; Sitchinava, Nodar; Zhang, Qin
2011-01-01
We study the MapReduce framework from an algorithmic standpoint, providing a generalization of the previous algorithmic models for MapReduce. We present optimal solutions for the fundamental problems of all-prefix-sums, sorting and multi-searching. Additionally, we design optimal simulations...
DEFF Research Database (Denmark)
Nielsen, Henrik
2017-01-01
and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.......Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths...
DEFF Research Database (Denmark)
Brøndal, Jørn
Bogen gennemgår det sorte USAs historie fra 1776 til 2016, idet grundtemaet er spændingsforholdet mellem USAs grundlæggelsesidealer og den racemæssige praksis, et spændingsforhold som Gunnar Myrdal kaldte "det amerikanske dilemma." Bogen, der er opbygget som politisk, social og racemæssig histori......, er opdelt i 13 kapitler og består af fire dele: Første del: Slaveriet; anden del: Jim Crow; tredje del. King-årene; fjerde del: Frem mod Obama....
DEFF Research Database (Denmark)
Merlino, Luca Paolo; Parrotta, Pierpaolo; Pozzoli, Dario
and causing the most productive female workers to seek better jobs in more female-friendly firms in which they can pursue small career advancements. Nonetheless, gender differences in promotion persist and are found to be similar in all firms when we focus on large career advancements. These results provide......In this paper, we investigate the sorting of workers in firms to understand gender gaps in labor market outcomes. Using Danish employer-employee matched data, we fiend strong evidence of glass ceilings in certain firms, especially after motherhood, preventing women from climbing the career ladder...
A Comparison of Card-sorting Analysis Methods
DEFF Research Database (Denmark)
Nawaz, Ather
2012-01-01
This study investigates how the choice of analysis method for card sorting studies affects the suggested information structure for websites. In the card sorting technique, a variety of methods are used to analyse the resulting data. The analysis of card sorting data helps user experience (UX......) designers to discover the patterns in how users make classifications and thus to develop an optimal, user-centred website structure. During analysis, the recurrence of patterns of classification between users influences the resulting website structure. However, the algorithm used in the analysis influences...... the recurrent patterns found and thus has consequences for the resulting website design. This paper draws an attention to the choice of card sorting analysis and techniques and shows how it impacts the results. The research focuses on how the same data for card sorting can lead to different website structures...
Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
Paganini, Michela; The ATLAS collaboration
2017-01-01
The separation of b-quark initiated jets from those coming from lighter quark flavours (b-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful b-tagging algorithms combine information from low-level taggers exploiting reconstructed track and vertex information using a multivariate classifier. The potential of modern Machine Learning techniques such as Recurrent Neural Networks and Deep Learning is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.
Schwaibold, M; Schöchlin, J; Bolz, A
2002-01-01
For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.
Classification of large-sized hyperspectral imagery using fast machine learning algorithms
Xia, Junshi; Yokoya, Naoto; Iwasaki, Akira
2017-07-01
We present a framework of fast machine learning algorithms in the context of large-sized hyperspectral images classification from the theoretical to a practical viewpoint. In particular, we assess the performance of random forest (RF), rotation forest (RoF), and extreme learning machine (ELM) and the ensembles of RF and ELM. These classifiers are applied to two large-sized hyperspectral images and compared to the support vector machines. To give the quantitative analysis, we pay attention to comparing these methods when working with high input dimensions and a limited/sufficient training set. Moreover, other important issues such as the computational cost and robustness against the noise are also discussed.
Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin
2018-02-22
The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.
Development of fuzzy algorithm with learning function for nuclear steam generator level control
International Nuclear Information System (INIS)
Park, Gee Yong; Seong, Poong Hyun
1993-01-01
A fuzzy algorithm with learning function is applied to the steam generator level control of nuclear power plant. This algorithm can make its rule base and membership functions suited for steam generator level control by use of the data obtained from the control actions of a skilled operator or of other controllers (i.e., PID controller). The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0 % - 30 % of full power) and the other to level control at high power level (30 % - 100 % of full power). Response time of steam generator level control at low power range with this rule base is shown to be shorter than that of fuzzy controller with direct inference. (Author)
Shuxin, Li; Zhilong, Zhang; Biao, Li
2018-01-01
Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.
Zhang, Shang; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin
2018-01-01
The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. PMID:29470406
Meyer, Hanna; Kühnlein, Meike; Appelhans, Tim; Nauss, Thomas
2016-03-01
Machine learning (ML) algorithms have successfully been demonstrated to be valuable tools in satellite-based rainfall retrievals which show the practicability of using ML algorithms when faced with high dimensional and complex data. Moreover, recent developments in parallel computing with ML present new possibilities for training and prediction speed and therefore make their usage in real-time systems feasible. This study compares four ML algorithms - random forests (RF), neural networks (NNET), averaged neural networks (AVNNET) and support vector machines (SVM) - for rainfall area detection and rainfall rate assignment using MSG SEVIRI data over Germany. Satellite-based proxies for cloud top height, cloud top temperature, cloud phase and cloud water path serve as predictor variables. The results indicate an overestimation of rainfall area delineation regardless of the ML algorithm (averaged bias = 1.8) but a high probability of detection ranging from 81% (SVM) to 85% (NNET). On a 24-hour basis, the performance of the rainfall rate assignment yielded R2 values between 0.39 (SVM) and 0.44 (AVNNET). Though the differences in the algorithms' performance were rather small, NNET and AVNNET were identified as the most suitable algorithms. On average, they demonstrated the best performance in rainfall area delineation as well as in rainfall rate assignment. NNET's computational speed is an additional advantage in work with large datasets such as in remote sensing based rainfall retrievals. However, since no single algorithm performed considerably better than the others we conclude that further research in providing suitable predictors for rainfall is of greater necessity than an optimization through the choice of the ML algorithm.
Directory of Open Access Journals (Sweden)
Qingyang Zhang
2015-02-01
Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.
Hougardy, Stefan
2016-01-01
Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.
MODELING WORK OF SORTING STATION USING UML
Directory of Open Access Journals (Sweden)
O. V. Gorbova
2014-12-01
Full Text Available Purpose. The purpose of this paper is the construction of methods and models for the graphical representation process of sorting station, using the unified modeling language (UML. Methodology. Methods of graph theory, finite automata and the representation theory of queuing systems were used as the methods of investigation. A graphical representation of the process was implemented with using the Unified Modeling Language UML. The sorting station process representation is implemented as a state diagram and actions through a set of IBM Rational Rose. Graphs can show parallel operation of sorting station, the parallel existence and influence of objects process and the transition from one state to another. The IBM Rational Rose complex allows developing a diagram of work sequence of varying degrees of detailing. Findings. The study has developed a graphical representation method of the process of sorting station of different kind of complexity. All graphical representations are made using the UML. They are represented as a directed graph with the states. It is clear enough in the study of the subject area. Applying the methodology of the representation process, it allows becoming friendly with the work of any automation object very fast, and exploring the process during algorithms construction of sorting stations and other railway facilities. This model is implemented with using the Unified Modeling Language (UML using a combination of IBM Rational Rose. Originality. The representation process of sorting station was developed by means of the Unified Modeling Language (UML use. Methodology of representation process allows creating the directed graphs based on the order of execution of the works chain, objects and performers of these works. The UML allows visualizing, specifying, constructing and documenting, formalizing the representation process of sorting station and developing sequence diagrams of works of varying degrees of detail. Practical
International Nuclear Information System (INIS)
Sánchez-Oro, J.; Duarte, A.; Salcedo-Sanz, S.
2016-01-01
Highlights: • The total energy demand in Spain is estimated with a Variable Neighborhood algorithm. • Socio-economic variables are used, and one year ahead prediction horizon is considered. • Improvement of the prediction with an Extreme Learning Machine network is considered. • Experiments are carried out in real data for the case of Spain. - Abstract: Energy demand prediction is an important problem whose solution is evaluated by policy makers in order to take key decisions affecting the economy of a country. A number of previous approaches to improve the quality of this estimation have been proposed in the last decade, the majority of them applying different machine learning techniques. In this paper, the performance of a robust hybrid approach, composed of a Variable Neighborhood Search algorithm and a new class of neural network called Extreme Learning Machine, is discussed. The Variable Neighborhood Search algorithm is focused on obtaining the most relevant features among the set of initial ones, by including an exponential prediction model. While previous approaches consider that the number of macroeconomic variables used for prediction is a parameter of the algorithm (i.e., it is fixed a priori), the proposed Variable Neighborhood Search method optimizes both: the number of variables and the best ones. After this first step of feature selection, an Extreme Learning Machine network is applied to obtain the final energy demand prediction. Experiments in a real case of energy demand estimation in Spain show the excellent performance of the proposed approach. In particular, the whole method obtains an estimation of the energy demand with an error lower than 2%, even when considering the crisis years, which are a real challenge.
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms
Zhang, James; Vukotic, Ilija; Gardner, Robert
2018-01-01
Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network performance data, gathered by perfSONAR, using two machine learning algorithms: Boosted Decision Trees (BDT...
Wu, Jianning; Wu, Bin
2015-01-01
The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of...
Simulating Visual Learning and Optical Illusions via a Network-Based Genetic Algorithm
Siu, Theodore; Vivar, Miguel; Shinbrot, Troy
We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions
Nishizuka, N.; Sugiura, K.; Kubo, Y.; Den, M.; Watari, S.; Ishii, M.
2017-02-01
We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ˜60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.
Energy Technology Data Exchange (ETDEWEB)
Nishizuka, N.; Kubo, Y.; Den, M.; Watari, S.; Ishii, M. [Applied Electromagnetic Research Institute, National Institute of Information and Communications Technology, 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795 (Japan); Sugiura, K., E-mail: nishizuka.naoto@nict.go.jp [Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology (Japan)
2017-02-01
We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite . We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.
International Nuclear Information System (INIS)
Nishizuka, N.; Kubo, Y.; Den, M.; Watari, S.; Ishii, M.; Sugiura, K.
2017-01-01
We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite . We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.
Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression
Sato, Jo?o R.; Moll, Jorge; Green, Sophie; Deakin, John F.W.; Thomaz, Carlos E.; Zahn, Roland
2015-01-01
Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the hi...
2007-01-01
Not much effort needed, just willpower In order to keep the cost of disposing of waste materials as low as possible, CERN provides two types of recipient at the entrance to each building: a green plastic one for paper/cardboard and a metal one for general refuse. For some time now we have noticed, to our great regret, a growing negligence as far as selective sorting is concerned, with, for example, the green recipients being filled with a mixture of cardboard boxes full of polystyrene or protective wrappers, plastic bottles, empty yogurts pots, etc. …We have been able to ascertain, after careful checking, that this haphazard mixing of waste cannot be attributed to the cleaning staff but rather to members of the personnel who unscrupulously throw away their rubbish in a completely random manner. Non-sorted waste entails heavy costs for CERN. For information, once a non-compliant item is found in a green recipient, the entire contents are sent off for incineration rather than recycling… We are all concerned...
Chen, Fangyue; Chen, Guanrong Ron; He, Guolong; Xu, Xiubin; He, Qinbin
2009-10-01
Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons. Inspired by the concept of DNA sequences in biological systems, a novel learning algorithm named DNA-like learning is developed, which is able to quickly train a network with any prescribed BF. The focus is on performing LSBF and PBF by a single-layer perceptron (SLP) with the new algorithm. Two criteria for LSBF and PBF are proposed, respectively, and a new measure for a BF, named nonlinearly separable degree (NLSD), is introduced. In the sense of this measure, the PBF is the most complex one. The new algorithm has many advantages including, in particular, fast running speed, good robustness, and no need of considering the convergence property. For example, the number of iterations and computations in implementing the basic 2-bit logic operations such as AND, OR, and XOR by using the new algorithm is far smaller than the ones needed by using other existing algorithms such as error-correction (EC) and backpropagation (BP) algorithms. Moreover, the synaptic weights and threshold values derived from UP can be directly used in designing of the template of cellular neural networks (CNNs), which has been considered as a new spatial-temporal sensory computing paradigm.
How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Directory of Open Access Journals (Sweden)
Jenna Burrell
2016-01-01
Full Text Available This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1 opacity as intentional corporate or state secrecy, (2 opacity as technical illiteracy, and (3 an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm.
Directory of Open Access Journals (Sweden)
Juan Pardo
2015-04-01
Full Text Available Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.
Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma
2015-01-01
Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698
AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM
International Nuclear Information System (INIS)
Farrell, Sean A.; Murphy, Tara; Lo, Kitty K.
2015-01-01
In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.
Premaladha, J; Ravichandran, K S
2016-04-01
Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.
A comparison of algorithms for inference and learning in probabilistic graphical models.
Frey, Brendan J; Jojic, Nebojsa
2005-09-01
Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy" belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.
Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.
Koivu, Aki; Korpimäki, Teemu; Kivelä, Petri; Pahikkala, Tapio; Sairanen, Mikko
2018-05-04
Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population. Copyright © 2018 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Jianning Wu
2015-01-01
Full Text Available The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.
Wu, Jianning; Wu, Bin
2015-01-01
The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.
Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma
2015-04-21
Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.
Separation of pulsar signals from noise using supervised machine learning algorithms
Bethapudi, S.; Desai, S.
2018-04-01
We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier (GBC), and XGBoost, for the separation of pulsars from radio frequency interference (RFI) and other sources of noise, using a dataset obtained from the post-processing of a pulsar search pipeline. This dataset was previously used for the cross-validation of the SPINN-based machine learning engine, obtained from the reprocessing of the HTRU-S survey data (Morello et al., 2014). We have used the Synthetic Minority Over-sampling Technique (SMOTE) to deal with high-class imbalance in the dataset. We report a variety of quality scores from all four of these algorithms on both the non-SMOTE and SMOTE datasets. For all the above ML methods, we report high accuracy and G-mean for both the non-SMOTE and SMOTE cases. We study the feature importances using Adaboost, GBC, and XGBoost and also from the minimum Redundancy Maximum Relevance approach to report algorithm-agnostic feature ranking. From these methods, we find that the signal to noise of the folded profile to be the best feature. We find that all the ML algorithms report FPRs about an order of magnitude lower than the corresponding FPRs obtained in Morello et al. (2014), for the same recall value.
Development of a general learning algorithm with applications in nuclear reactor systems
International Nuclear Information System (INIS)
Brittain, C.R.; Otaduy, P.J.; Perez, R.B.
1989-12-01
The objective of this study was development of a generalized learning algorithm that can learn to predict a particular feature of a process by observation of a set of representative input examples. The algorithm uses pattern matching and statistical analysis techniques to find a functional relationship between descriptive attributes of the input examples and the feature to be predicted. The algorithm was tested by applying it to a set of examples consisting of performance descriptions for 277 fuel cycles of Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR). The program learned to predict the critical rod position for the HFIR from core configuration data prior to reactor startup. The functional relationship bases its predictions on initial core reactivity, the number of certain targets placed in the center of the reactor, and the total exposure of the control plates. Twelve characteristic fuel cycle clusters were identified. Nine fuel cycles were diagnosed as having noisy data, and one could not be predicted by the functional relationship. 13 refs., 6 figs
Development of a general learning algorithm with applications in nuclear reactor systems
Energy Technology Data Exchange (ETDEWEB)
Brittain, C.R.; Otaduy, P.J.; Perez, R.B.
1989-12-01
The objective of this study was development of a generalized learning algorithm that can learn to predict a particular feature of a process by observation of a set of representative input examples. The algorithm uses pattern matching and statistical analysis techniques to find a functional relationship between descriptive attributes of the input examples and the feature to be predicted. The algorithm was tested by applying it to a set of examples consisting of performance descriptions for 277 fuel cycles of Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR). The program learned to predict the critical rod position for the HFIR from core configuration data prior to reactor startup. The functional relationship bases its predictions on initial core reactivity, the number of certain targets placed in the center of the reactor, and the total exposure of the control plates. Twelve characteristic fuel cycle clusters were identified. Nine fuel cycles were diagnosed as having noisy data, and one could not be predicted by the functional relationship. 13 refs., 6 figs.
Sequence-based prediction of protein protein interaction using a deep-learning algorithm.
Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng
2017-05-25
Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2014-01-01
Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.
Buddala, Raviteja; Mahapatra, Siba Sankar
2017-11-01
Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having `g' operations is performed on `g' operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching-learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.
Teleoperated robotic sorting system
Roos, Charles E.; Sommer, Edward J.; Parrish, Robert H.; Russell, James R.
2000-01-01
A method and apparatus are disclosed for classifying materials utilizing a computerized touch sensitive screen or other computerized pointing device for operator identification and electronic marking of spatial coordinates of materials to be extracted. An operator positioned at a computerized touch sensitive screen views electronic images of the mixture of materials to be sorted as they are conveyed past a sensor array which transmits sequences of images of the mixture either directly or through a computer to the touch sensitive display screen. The operator manually "touches" objects displayed on the screen to be extracted from the mixture thereby registering the spatial coordinates of the objects within the computer. The computer then tracks the registered objects as they are conveyed and directs automated devices including mechanical means such as air jets, robotic arms, or other mechanical diverters to extract the registered objects.
Colour based sorting station with Matlab simulation
Directory of Open Access Journals (Sweden)
Constantin Victor
2017-01-01
Full Text Available The paper presents the design process and manufacturing elements of a colour-based sorting station. The system is comprised of a gravitational storage, which also contains the colour sensor. Parts are extracted using a linear pneumatic motor and are fed onto an electrically driven conveyor belt. Extraction of the parts is done at 4 points, using two pneumatic motors and a geared DC motor, while the 4th position is at the end of the belt. The mechanical parts of the system are manufactured using 3D printer technology, allowing for easy modification and adaption to the geometry of different parts. The paper shows all of the stages needed to design, optimize, test and implement the proposed solution. System optimization was performed using a graphical Matlab interface which also allows for sorting algorithm optimization.
A STUDENT MODEL AND LEARNING ALGORITHM FOR THE EXPERT TUTORING SYSTEM OF POLISH GRAMMAR
Directory of Open Access Journals (Sweden)
Kostikov Mykola
2014-11-01
Full Text Available When creating computer-assisted language learning software, it is necessary to use the potential of information technology in controlling the learning process fully. Modern intelligent tutoring systems help to make this process adaptive and personalized thanks to modeling the domain and students’ knowledge. The aim of the paper is to investigate possibilities for applying these methods in teaching Polish grammar in Ukraine taking into account its specifics. The article is concerned with the approaches of using student models in modern intelligent tutoring systems in order to provide personalized learning. A structure of the student model and a general working algorithm of the expert tutoring system of Polish grammar have been developed. The modeling of knowing and forgetting particular learning elements within the probabilistic (stochastic model has been studied, as well as the prognostication of future probabilities of students’ knowledge, taking into account their individual forgetting rates. The objective function of instruction quality with allowance for frequency of grammar rules within a certain amount of words being learned and their connections to another rules has been formulated. The problem of generating the next learning step taking into account the need for mastering previous, connected rules has been studied, as well as determining the optimal time period between the lessons depending on the current knowledge level.
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2015-12-01
Full Text Available This paper presents the performance of teaching–learning-based optimization (TLBO algorithm to obtain the optimum set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH. The TLBO algorithm is a recently proposed population-based algorithm, which simulates the teaching–learning process of the classroom. Maximization of thermal efficiency is considered as an objective function for the thermal performance of SFPSAH. The number of glass plates, irradiance, and the Reynolds number are considered as the design parameters and wind velocity, tilt angle, ambient temperature, and emissivity of the plate are considered as the operating parameters to obtain the thermal performance of the SFPSAH using the TLBO algorithm. The computational results have shown that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.
Shahinfar, Saleh; Page, David; Guenther, Jerry; Cabrera, Victor; Fricke, Paul; Weigel, Kent
2014-02-01
When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most
Sun, Wenqing; Zheng, Bin; Qian, Wei
2017-10-01
This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.
Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco; Vaste, Giulia
2011-01-01
LS-Lab provides automatic support to comparison/evaluation of the Learning Object Sequences produced by different Curriculum Sequencing Algorithms. Through this framework a teacher can verify the correspondence between the behaviour of different sequencing algorithms and her pedagogical preferences. In fact the teacher can compare algorithms…
Ling, J.; Templeton, J.
2015-08-01
Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. Feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.
FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks.
Zhang, Zhen; Zhao, Dongbin; Gao, Junwei; Wang, Dongqing; Dai, Yujie
2017-06-01
In this paper, we propose a multiagent reinforcement learning algorithm dealing with fully cooperative tasks. The algorithm is called frequency of the maximum reward Q-learning (FMRQ). FMRQ aims to achieve one of the optimal Nash equilibria so as to optimize the performance index in multiagent systems. The frequency of obtaining the highest global immediate reward instead of immediate reward is used as the reinforcement signal. With FMRQ each agent does not need the observation of the other agents' actions and only shares its state and reward at each step. We validate FMRQ through case studies of repeated games: four cases of two-player two-action and one case of three-player two-action. It is demonstrated that FMRQ can converge to one of the optimal Nash equilibria in these cases. Moreover, comparison experiments on tasks with multiple states and finite steps are conducted. One is box-pushing and the other one is distributed sensor network problem. Experimental results show that the proposed algorithm outperforms others with higher performance.