A New Profile Learning Model for Recommendation System based on Machine Learning Technique

Directory of Open Access Journals (Sweden)

Shereen H. Ali

2016-03-01

Full Text Available Recommender systems (RSs have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent Adaptive Vertical Recommendation (IAVR system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy.

Multidisciplinary Views on Applying Explicit and Implicit Motor Learning in Practice : an International Survey

NARCIS (Netherlands)

Sascha Rasquin; Michel Bleijlevens; Jos Halfens; Mark Wilson; Rich Masters; Anna Beurskens; Melanie Kleynen; Monique Lexis; Susy Braun

2015-01-01

Background A variety of options and techniques for causing implicit and explicit motor learning have been described in the literature. The aim of the current paper was to provide clearer guidance for practitioners on how to apply motor learning in practice by exploring experts’ opinions and

The application of machine learning techniques in the clinical drug therapy.

Science.gov (United States)

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

New Techniques for Deep Learning with Geospatial Data using TensorFlow, Earth Engine, and Google Cloud Platform

Science.gov (United States)

Hancher, M.

2017-12-01

Recent years have seen promising results from many research teams applying deep learning techniques to geospatial data processing. In that same timeframe, TensorFlow has emerged as the most popular framework for deep learning in general, and Google has assembled petabytes of Earth observation data from a wide variety of sources and made them available in analysis-ready form in the cloud through Google Earth Engine. Nevertheless, developing and applying deep learning to geospatial data at scale has been somewhat cumbersome to date. We present a new set of tools and techniques that simplify this process. Our approach combines the strengths of several underlying tools: TensorFlow for its expressive deep learning framework; Earth Engine for data management, preprocessing, postprocessing, and visualization; and other tools in Google Cloud Platform to train TensorFlow models at scale, perform additional custom parallel data processing, and drive the entire process from a single familiar Python development environment. These tools can be used to easily apply standard deep neural networks, convolutional neural networks, and other custom model architectures to a variety of geospatial data structures. We discuss our experiences applying these and related tools to a range of machine learning problems, including classic problems like cloud detection, building detection, land cover classification, as well as more novel problems like illegal fishing detection. Our improved tools will make it easier for geospatial data scientists to apply modern deep learning techniques to their own problems, and will also make it easier for machine learning researchers to advance the state of the art of those techniques.

MACHINE LEARNING TECHNIQUES USED IN BIG DATA

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STEFANIA LOREDANA NITA

2016-07-01

Full Text Available The classical tools used in data analysis are not enough in order to benefit of all advantages of big data. The amount of information is too large for a complete investigation, and the possible connections and relations between data could be missed, because it is difficult or even impossible to verify all assumption over the information. Machine learning is a great solution in order to find concealed correlations or relationships between data, because it runs at scale machine and works very well with large data sets. The more data we have, the more the machine learning algorithm is useful, because it “learns” from the existing data and applies the found rules on new entries. In this paper, we present some machine learning algorithms and techniques used in big data.

Machine Learning Techniques for Stellar Light Curve Classification

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Hinners, Trisha A.; Tat, Kevin; Thorp, Rachel

2018-07-01

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB of Kepler light curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light-curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a long short-term memory recurrent neural network produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (∼2%–4%) and good accuracy (∼75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger data sets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

Techniques to Promote Reflective Practice and Empowered Learning.

Science.gov (United States)

Nguyen-Truong, Connie Kim Yen; Davis, Andra; Spencer, Cassius; Rasmor, Melody; Dekker, Lida

2018-02-01

Health care environments are fraught with fast-paced critical demands and ethical dilemmas requiring decisive nursing actions. Nurse educators must prepare nursing students to practice skills, behaviors, and attitudes needed to meet the challenges of health care demands. Evidence-based, innovative, multimodal techniques with novice and seasoned nurses were incorporated into a baccalaureate (BSN) completion program (RN to-BSN) to deepen learning, complex skill building, reflective practice, teamwork, and compassion toward the experiences of others. Principles of popular education for engaged teaching-learning were applied. Nursing students experience equitable access to content through co-constructing knowledge with four creative techniques. Four creative techniques include poem reading aloud to facilitate connectedness; mindfulness to cultivate self-awareness; string figure activities to demonstrate indigenous knowledge and teamwork; and cartooning difficult subject matter. Nursing school curricula can promote a milieu for developing organizational skills to manage simultaneous priorities, practice reflectively, and develop empathy and the authenticity that effective nursing requires. [J Nurs Educ. 2018;57(2):115-120.]. Copyright 2018, SLACK Incorporated.

Advanced Learning Theories Applied to Leadership Development

Science.gov (United States)

2006-11-01

Center for Army Leadership Technical Report 2006-2 Advanced Learning Theories Applied to Leadership Development Christina Curnow...2006 5a. CONTRACT NUMBER W91QF4-05-F-0026 5b. GRANT NUMBER 4. TITLE AND SUBTITLE Advanced Learning Theories Applied to Leadership Development 5c...ABSTRACT This report describes the development and implementation of an application of advanced learning theories to leadership development. A

Machine learning techniques applied to system characterization and equalization

DEFF Research Database (Denmark)

Zibar, Darko; Thrane, Jakob; Wass, Jesper

2016-01-01

Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.......Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals....

Applied learning-based color tone mapping for face recognition in video surveillance system

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Yew, Chuu Tian; Suandi, Shahrel Azmin

2012-04-01

In this paper, we present an applied learning-based color tone mapping technique for video surveillance system. This technique can be applied onto both color and grayscale surveillance images. The basic idea is to learn the color or intensity statistics from a training dataset of photorealistic images of the candidates appeared in the surveillance images, and remap the color or intensity of the input image so that the color or intensity statistics match those in the training dataset. It is well known that the difference in commercial surveillance cameras models, and signal processing chipsets used by different manufacturers will cause the color and intensity of the images to differ from one another, thus creating additional challenges for face recognition in video surveillance system. Using Multi-Class Support Vector Machines as the classifier on a publicly available video surveillance camera database, namely SCface database, this approach is validated and compared to the results of using holistic approach on grayscale images. The results show that this technique is suitable to improve the color or intensity quality of video surveillance system for face recognition.

Learning-curve estimation techniques for nuclear industry

Energy Technology Data Exchange (ETDEWEB)

Vaurio, J.K.

1983-01-01

Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on acturial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year.

Learning-curve estimation techniques for nuclear industry

International Nuclear Information System (INIS)

Vaurio, J.K.

1983-01-01

Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on acturial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year

Learning curve estimation techniques for nuclear industry

International Nuclear Information System (INIS)

Vaurio, Jussi K.

1983-01-01

Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on actuarial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year

MACHINE LEARNING TECHNIQUES APPLIED TO LIGNOCELLULOSIC ETHANOL IN SIMULTANEOUS HYDROLYSIS AND FERMENTATION

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J. Fischer

Full Text Available Abstract This paper investigates the use of machine learning (ML techniques to study the effect of different process conditions on ethanol production from lignocellulosic sugarcane bagasse biomass using S. cerevisiae in a simultaneous hydrolysis and fermentation (SHF process. The effects of temperature, enzyme concentration, biomass load, inoculum size and time were investigated using artificial neural networks, a C5.0 classification tree and random forest algorithms. The optimization of ethanol production was also evaluated. The results clearly depict that ML techniques can be used to evaluate the SHF (R2 between actual and model predictions higher than 0.90, absolute average deviation lower than 8.1% and RMSE lower than 0.80 and predict optimized conditions which are in close agreement with those found experimentally. Optimal conditions were found to be a temperature of 35 ºC, an SHF time of 36 h, enzymatic load of 99.8%, inoculum size of 29.5 g/L and bagasse concentration of 24.9%. The ethanol concentration and volumetric productivity for these conditions were 12.1 g/L and 0.336 g/L.h, respectively.

Learning mediastinoscopy: the need for education, experience and modern techniques--interdependency of the applied technique and surgeon's training level.

Science.gov (United States)

Walles, Thorsten; Friedel, Godehard; Stegherr, Tobias; Steger, Volker

2013-04-01

Mediastinoscopy represents the gold standard for invasive mediastinal staging. While learning and teaching the surgical technique are challenging due to the limited accessibility of the operation field, both benefited from the implementation of video-assisted techniques. However, it has not been established yet whether video-assisted mediastinoscopy improves the mediastinal staging in itself. Retrospective single-centre cohort analysis of 657 mediastinoscopies performed at a specialized tertiary care thoracic surgery unit from 1994 to 2006. The number of specimens obtained per procedure and per lymph node station (2, 4, 7, 8 for mediastinoscopy and 2-9 for open lymphadenectomy), the number of lymph node stations examined, sensitivity and negative predictive value with a focus on the technique employed (video-assisted vs standard technique) and the surgeon's experience were calculated. Overall sensitivity was 60%, accuracy was 90% and negative predictive value 88%. With the conventional technique, experience alone improved sensitivity from 49 to 57% and it was predominant at the paratracheal right region (from 62 to 82%). But with the video-assisted technique, experienced surgeons rose sensitivity from 57 to 79% in contrast to inexperienced surgeons who lowered sensitivity from 49 to 33%. We found significant differences concerning (i) the total number of specimens taken, (ii) the amount of lymph node stations examined, (iii) the number of specimens taken per lymph node station and (iv) true positive mediastinoscopies. The video-assisted technique can significantly improve the results of mediastinoscopy. A thorough education on the modern video-assisted technique is mandatory for thoracic surgeons until they can fully exhaust its potential.

FísicActiva: Applying Active Learning Strategies to a Large Engineering Lecture

Science.gov (United States)

Auyuanet, Adriana; Modzelewski, Helena; Loureiro, Silvia; Alessandrini, Daniel; Míguez, Marina

2018-01-01

This paper presents and analyses the results obtained by applying Active Learning techniques in overcrowded Physics lectures at the University of the Republic, Uruguay. The course referred to is Physics 1, the first Physics course that all students of the Faculty of Engineering take in their first semester for all the Engineering-related careers.…

Figure analysis: A teaching technique to promote visual literacy and active Learning.

Science.gov (United States)

Wiles, Amy M

2016-07-08

Learning often improves when active learning techniques are used in place of traditional lectures. For many of these techniques, however, students are expected to apply concepts that they have already grasped. A challenge, therefore, is how to incorporate active learning into the classroom of courses with heavy content, such as molecular-based biology courses. An additional challenge is that visual literacy is often overlooked in undergraduate science education. To address both of these challenges, a technique called figure analysis was developed and implemented in three different levels of undergraduate biology courses. Here, students learn content while gaining practice in interpreting visual information by discussing figures with their peers. Student groups also make connections between new and previously learned concepts on their own while in class. The instructor summarizes the material for the class only after students grapple with it in small groups. Students reported a preference for learning by figure analysis over traditional lecture, and female students in particular reported increased confidence in their analytical abilities. There is not a technology requirement for this technique; therefore, it may be utilized both in classrooms and in nontraditional spaces. Additionally, the amount of preparation required is comparable to that of a traditional lecture. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(4):336-344, 2016. © 2016 The International Union of Biochemistry and Molecular Biology.

Machine Learning Techniques in Clinical Vision Sciences.

Science.gov (United States)

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression

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Land Walker H

2011-01-01

Full Text Available Abstract Background When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be difficult to characterize. When the relationship is nonlinear, linear modeling techniques do not capture the nonlinear information content. Statistical learning (SL techniques with kernels are capable of addressing nonlinear problems without making parametric assumptions. However, these techniques do not produce findings relevant for epidemiologic interpretations. A simulated case-control study was used to contrast the information embedding characteristics and separation boundaries produced by a specific SL technique with logistic regression (LR modeling representing a parametric approach. The SL technique was comprised of a kernel mapping in combination with a perceptron neural network. Because the LR model has an important epidemiologic interpretation, the SL method was modified to produce the analogous interpretation and generate odds ratios for comparison. Results The SL approach is capable of generating odds ratios for main effects and risk factor interactions that better capture nonlinear relationships between exposure variables and outcome in comparison with LR. Conclusions The integration of SL methods in epidemiology may improve both the understanding and interpretation of complex exposure/disease relationships.

E-learning systems intelligent techniques for personalization

CERN Document Server

Klašnja-Milićević, Aleksandra; Ivanović, Mirjana; Budimac, Zoran; Jain, Lakhmi C

2017-01-01

This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques. .

Learning curve estimation techniques for the nuclear industry

International Nuclear Information System (INIS)

Vaurio, J.K.

1983-01-01

Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on actuarial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

Science.gov (United States)

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

Science.gov (United States)

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time

Tools and methodologies applied to eLearning

OpenAIRE

Seoane Pardo, Antonio M.; García-Peñalvo, Francisco José

2006-01-01

The aim of this paper is to show how eLearning technologies and methodologies should be useful for teaching and researching Logic. Firstly, a definition and explanation of eLearning and its main modalities will be given. Then, the most important elements and tools of eLearning activities will be shown. Finally, we will give three suggestions to improve learning experience with eLearning applied to Logic. Se muestran diversas tecnologías y metodologías de e-learning útiles en la enseñanza e...

Exploring the Earth Using Deep Learning Techniques

Science.gov (United States)

Larraondo, P. R.; Evans, B. J. K.; Antony, J.

2016-12-01

Research using deep neural networks have significantly matured in recent times, and there is now a surge in interest to apply such methods to Earth systems science and the geosciences. When combined with Big Data, we believe there are opportunities for significantly transforming a number of areas relevant to researchers and policy makers. In particular, by using a combination of data from a range of satellite Earth observations as well as computer simulations from climate models and reanalysis, we can gain new insights into the information that is locked within the data. Global geospatial datasets describe a wide range of physical and chemical parameters, which are mostly available using regular grids covering large spatial and temporal extents. This makes them perfect candidates to apply deep learning methods. So far, these techniques have been successfully applied to image analysis through the use of convolutional neural networks. However, this is only one field of interest, and there is potential for many more use cases to be explored. The deep learning algorithms require fast access to large amounts of data in the form of tensors and make intensive use of CPU in order to train its models. The Australian National Computational Infrastructure (NCI) has recently augmented its Raijin 1.2 PFlop supercomputer with hardware accelerators. Together with NCI's 3000 core high performance OpenStack cloud, these computational systems have direct access to NCI's 10+ PBytes of datasets and associated Big Data software technologies (see http://geonetwork.nci.org.au/ and http://nci.org.au/systems-services/national-facility/nerdip/). To effectively use these computing infrastructures requires that both the data and software are organised in a way that readily supports the deep learning software ecosystem. Deep learning software, such as the open source TensorFlow library, has allowed us to demonstrate the possibility of generating geospatial models by combining information from

CRDM motion analysis using machine learning technique

International Nuclear Information System (INIS)

Nishimura, Takuya; Nakayama, Hiroyuki; Saitoh, Mayumi; Yaguchi, Seiji

2017-01-01

Magnetic jack type Control Rod Drive Mechanism (CRDM) for pressurized water reactor (PWR) plant operates control rods in response to electrical signals from a reactor control system. CRDM operability is evaluated by quantifying armature's response of closed/opened time which means interval time between coil energizing/de-energizing points and armature closed/opened points. MHI has already developed an automatic CRDM motion analysis and applied it to actual plants so far. However, CRDM operational data has wide variation depending on their characteristics such as plant condition, plant, etc. In the existing motion analysis, there is an issue of analysis accuracy for applying a single analysis technique to all plant conditions, plants, etc. In this study, MHI investigated motion analysis using machine learning (Random Forests) which is flexibly accommodated to CRDM operational data with wide variation, and is improved analysis accuracy. (author)

Improving face image extraction by using deep learning technique

Science.gov (United States)

Xue, Zhiyun; Antani, Sameer; Long, L. R.; Demner-Fushman, Dina; Thoma, George R.

2016-03-01

The National Library of Medicine (NLM) has made a collection of over a 1.2 million research articles containing 3.2 million figure images searchable using the Open-iSM multimodal (text+image) search engine. Many images are visible light photographs, some of which are images containing faces ("face images"). Some of these face images are acquired in unconstrained settings, while others are studio photos. To extract the face regions in the images, we first applied one of the most widely-used face detectors, a pre-trained Viola-Jones detector implemented in Matlab and OpenCV. The Viola-Jones detector was trained for unconstrained face image detection, but the results for the NLM database included many false positives, which resulted in a very low precision. To improve this performance, we applied a deep learning technique, which reduced the number of false positives and as a result, the detection precision was improved significantly. (For example, the classification accuracy for identifying whether the face regions output by this Viola- Jones detector are true positives or not in a test set is about 96%.) By combining these two techniques (Viola-Jones and deep learning) we were able to increase the system precision considerably, while avoiding the need to manually construct a large training set by manual delineation of the face regions.

Storytelling: a teaching-learning technique.

Science.gov (United States)

Geanellos, R

1996-03-01

Nurses' stories, arising from the practice world, reconstruct the essence of experience as lived and provide vehicles for learning about nursing. The learning process is forwarded by combining storytelling and reflection. Reflection represents an active, purposive, contemplative and deliberative approach to learning through which learners create meaning from the learning experience. The combination of storytelling and reflection allows the creation of links between the materials at hand and prior and future learning. As a teaching-learning technique storytelling engages learners; organizes information; allows exploration of shared lived experiences without the demands, responsibilities and consequences of practice; facilitates remembering; enhances discussion, problem posing and problem solving; and aids understanding of what it is to nurse and to be a nurse.

Modern machine learning techniques and their applications in cartoon animation research

CERN Document Server

Yu, Jun

2013-01-01

The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations

Teacher Formation in Super Learning Techniques Applied to the Teaching of the Mathematic in the Education Secondary

Directory of Open Access Journals (Sweden)

Avilner Rafael Páez Pereira

2017-11-01

Full Text Available The purpose of the study was to train LB "José Véliz" teacher for the teaching of mathematics through the application of super-learning techniques, based on the Research Participatory Action modality, proposed by López de Ceballos, (2008, following the model of the Lewin cycles of action (1946, quoted by Latorre (2007, based on the theories of humanism, Martínez (2009; multiple intelligence, Armstrong (2006; the Super learning of Sambrano and Stainer, (2003. Within the framework of the Critical - Social paradigm, in the type Qualitative Research, a plan of approach to the group was made, where through brainstorming and informal interviews the main problems were listed, which were hierarchized and then carried out an awareness - raising process. formulation of an overall plan of action. Among the results were 6 training workshops on techniques of breathing, relaxation, aromatherapy, music therapy, positive programming, color in the classroom, song in mathematical algorithms, in which processes of reflection were established on the benefits or obstacles obtained in the application of these in the transformation of the educational reality, elaborating a didactic strategy product of the experiences reached.

m-Learning and holography: Compatible techniques?

Science.gov (United States)

Calvo, Maria L.

2014-07-01

Since the last decades, cell phones have become increasingly popular and are nowadays ubiquitous. New generations of cell phones are now equipped with text messaging, internet, and camera features. They are now making their way into the classroom. This is creating a new teaching and learning technique, the so called m-Learning (or mobile-Learning). Because of the many benefits that cell phones offer, teachers could easily use them as a teaching and learning tool. However, an additional work from the teachers for introducing their students into the m-Learning in the classroom needs to be defined and developed. As an example, optical techniques, based upon interference and diffraction phenomena, such as holography, appear to be convenient topics for m-Learning. They can be approached with simple examples and experiments within the cell phones performances and classroom accessibility. We will present some results carried out at the Faculty of Physical Sciences in UCM to obtain very simple holographic recordings via cell phones. The activities were carried out inside the course on Optical Coherence and Laser, offered to students in the fourth course of the Grade in Physical Sciences. Some open conclusions and proposals will be presented.

A Comprehensive Review and meta-analysis on Applications of Machine Learning Techniques in Intrusion Detection

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Manojit Chattopadhyay

2018-05-01

Full Text Available Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for changing usage pattern by comparing different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.

Multidisciplinary Views on Applying Explicit and Implicit Motor Learning in Practice: An International Survey.

Directory of Open Access Journals (Sweden)

Melanie Kleynen

Full Text Available A variety of options and techniques for causing implicit and explicit motor learning have been described in the literature. The aim of the current paper was to provide clearer guidance for practitioners on how to apply motor learning in practice by exploring experts' opinions and experiences, using the distinction between implicit and explicit motor learning as a conceptual departure point.A survey was designed to collect and aggregate informed opinions and experiences from 40 international respondents who had demonstrable expertise related to motor learning in practice and/or research. The survey was administered through an online survey tool and addressed potential options and learning strategies for applying implicit and explicit motor learning. Responses were analysed in terms of consensus (≥ 70% and trends (≥ 50%. A summary figure was developed to illustrate a taxonomy of the different learning strategies and options indicated by the experts in the survey.Answers of experts were widely distributed. No consensus was found regarding the application of implicit and explicit motor learning. Some trends were identified: Explicit motor learning can be promoted by using instructions and various types of feedback, but when promoting implicit motor learning, instructions and feedback should be restricted. Further, for implicit motor learning, an external focus of attention should be considered, as well as practicing the entire skill. Experts agreed on three factors that influence motor learning choices: the learner's abilities, the type of task, and the stage of motor learning (94.5%; n = 34/36. Most experts agreed with the summary figure (64.7%; n = 22/34.The results provide an overview of possible ways to cause implicit or explicit motor learning, signposting examples from practice and factors that influence day-to-day motor learning decisions.

APPLICABILITY OF COOPERATIVE LEARNING TECHNIQUES IN DIFFERENT CLASSROOM CONTEXTS

Directory of Open Access Journals (Sweden)

Dr. Issy Yuliasri

2017-04-01

Full Text Available This paper is based on the results of pre-test post-test, feedback questionnaire and observation during a community service program entitled ―Training on English Teaching using Cooperative Learning Techniques for Elementary and Junior High School Teachers of Sekolah Alam Arridho Semarang‖. It was an English teaching training program intended to equip the teachers with the knowledge and skills of using the different cooperative learning techniques such as jigsaw, think-pair-share, three-step interview, roundrobin braistorming, three-minute review, numbered heads together, team-pair-solo, circle the sage, dan partners. This program was participated by 8 teachers of different subjects (not only English, but most of them had good mastery of English. The objectives of this program was to improve teachers‘ skills in using the different cooperative learning techniques to vary their teaching, so that students would be more motivated to learn and improve their English skill. Besides, the training also gave the teachers the knowledge and skills to adjust their techniques with the basic competence and learning objectives to be achieved as well as with the teaching materials to be used. This was also done through workshops using cooperative learning techniques, so that the participants had real experiences of using cooperative learning techniques (learning by doing. The participants were also encouraged to explore the applicability of the techniques in their classroom contexts, in different areas of their teaching. This community service program showed very positive results. The pre-test and post-test results showed that before the training program all the participants did not know the nine cooperative techniques to be trained, but after the program they mastered the techniques as shown from the teaching-learning scenarios they developed following the test instructions. In addition, the anonymous questionnaires showed that all the participants

The Degree of Applying E-Learning in English Departments at Al-Balqa Applied University from Instructors' Perspectives

Science.gov (United States)

Alzu'bi, Mohammad Akram Mohammad

2018-01-01

The study aimed at identifying the degree of applying e-learning in Al-Balqa Applied University from instructors' perspectives so the researcher designed a questionnaire of 20 items which is applied on a sample of 48 lecturers. The study showed that the percentage of (64.0%) out of 48 participants apply e-learning in English departments at…

Application of Machine Learning Techniques in Aquaculture

OpenAIRE

Rahman, Akhlaqur; Tasnim, Sumaira

2014-01-01

In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications.

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Science.gov (United States)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Cross-Cultural Service Learning: American and Russian Students Learn Applied Organizational Communication.

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Stevens, Betsy

2001-01-01

Describes how American and Russian students engaged in service learning in their own communities as part of an organizational communication class in which they learned communication principles and applied their skills to assist non-profit organizations. Describes both projects, stumbling blocks, and course outcomes. (SR)

A Learning Evaluation for an Immersive Virtual Laboratory for Technical Training Applied into a Welding Workshop

Science.gov (United States)

Torres, Francisco; Neira Tovar, Leticia A.; del Rio, Marta Sylvia

2017-01-01

This study aims to explore the results of welding virtual training performance, designed using a learning model based on cognitive and usability techniques, applying an immersive concept focused on person attention. Moreover, it also intended to demonstrate that exits a moderating effect of performance improvement when the user experience is taken…

Opportunities to Create Active Learning Techniques in the Classroom

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Camacho, Danielle J.; Legare, Jill M.

2015-01-01

The purpose of this article is to contribute to the growing body of research that focuses on active learning techniques. Active learning techniques require students to consider a given set of information, analyze, process, and prepare to restate what has been learned--all strategies are confirmed to improve higher order thinking skills. Active…

Machine Learning Techniques for Modelling Short Term Land-Use Change

Directory of Open Access Journals (Sweden)

Mileva Samardžić-Petrović

2017-11-01

Full Text Available The representation of land use change (LUC is often achieved by using data-driven methods that include machine learning (ML techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT, Neural Networks (NN, and Support Vector Machines (SVM for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.

Applying Brain-Based Learning Principles to Athletic Training Education

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Craig, Debbie I.

2007-01-01

Objective: To present different concepts and techniques related to the application of brain-based learning principles to Athletic Training clinical education. Background: The body of knowledge concerning how our brains physically learn continues to grow. Brain-based learning principles, developed by numerous authors, offer advice on how to…

Machine learning applied to the prediction of citrus production

OpenAIRE

Díaz Rodríguez, Susana Irene; Mazza, Silvia M.; Fernández-Combarro Álvarez, Elías; Giménez, Laura I.; Gaiad, José E.

2017-01-01

An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analyse...

Active learning techniques for librarians practical examples

CERN Document Server

Walsh, Andrew

2010-01-01

A practical work outlining the theory and practice of using active learning techniques in library settings. It explains the theory of active learning and argues for its importance in our teaching and is illustrated using a large number of examples of techniques that can be easily transferred and used in teaching library and information skills to a range of learners within all library sectors. These practical examples recognise that for most of us involved in teaching library and information skills the one off session is the norm, so we need techniques that allow us to quickly grab and hold our

Applying effective teaching and learning techniques to nephrology education.

Science.gov (United States)

Rondon-Berrios, Helbert; Johnston, James R

2016-10-01

The interest in nephrology as a career has declined over the last several years. Some of the reasons cited for this decline include the complexity of the specialty, poor mentoring and inadequate teaching of nephrology from medical school through residency. The purpose of this article is to introduce the reader to advances in the science of adult learning, illustrate best teaching practices in medical education that can be extrapolated to nephrology and introduce the basic teaching methods that can be used on the wards, in clinics and in the classroom.

The Effect of Group Investigation Learning Model with Brainstroming Technique on Students Learning Outcomes

Directory of Open Access Journals (Sweden)

Astiti Kade kAyu

2018-01-01

Full Text Available This study aims to determine the effect of group investigation (GI learning model with brainstorming technique on student physics learning outcomes (PLO compared to jigsaw learning model with brainstroming technique. The learning outcome in this research are the results of learning in the cognitive domain. The method used in this research is experiment with Randomised Postest Only Control Group Design. Population in this research is all students of class XI IPA SMA Negeri 9 Kupang year lesson 2015/2016. The selected sample are 40 students of class XI IPA 1 as the experimental class and 38 students of class XI IPA 2 as the control class using simple random sampling technique. The instrument used is 13 items description test. The first hypothesis was tested by using two tailed t-test. From that, it is obtained that H0 rejected which means there are differences of students physics learning outcome. The second hypothesis was tested using one tailed t-test. It is obtained that H0 rejected which means the students PLO in experiment class were higher than control class. Based on the results of this study, researchers recommend the use of GI learning models with brainstorming techniques to improve PLO, especially in the cognitive domain.

The Effect of Learning Based on Technology Model and Assessment Technique toward Thermodynamic Learning Achievement

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Makahinda, T.

2018-02-01

The purpose of this research is to find out the effect of learning model based on technology and assessment technique toward thermodynamic achievement by controlling students intelligence. This research is an experimental research. The sample is taken through cluster random sampling with the total respondent of 80 students. The result of the research shows that the result of learning of thermodynamics of students who taught the learning model of environmental utilization is higher than the learning result of student thermodynamics taught by simulation animation, after controlling student intelligence. There is influence of student interaction, and the subject between models of technology-based learning with assessment technique to student learning result of Thermodynamics, after controlling student intelligence. Based on the finding in the lecture then should be used a thermodynamic model of the learning environment with the use of project assessment technique.

Student’s Perceptions on Simulation as Part of Experiential Learning in Approaches, Methods, and Techniques (AMT Course

Directory of Open Access Journals (Sweden)

Marselina Karina Purnomo

2017-03-01

Full Text Available Simulation is a part of Experiential Learning which represents certain real-life events. In this study, simulation is used as a learning activity in Approaches, Methods, and Techniques (AMT course which is one of the courses in English Language Education Study Program (ELESP of Sanata Dharma University. Since simulation represents the real-life events, it encourages students to apply the approaches, methods, and techniques being studied based on the real-life classroom. Several experts state that students are able to involve their personal experiences through simulation which additionally is believed to create a meaningful learning in the class. This study aimed to discover ELESP students’ perceptions toward simulation as a part of Experiential Learning in AMT course. From the findings, it could be inferred that students agreed that simulation in class was important for students’ learning for it formed a meaningful learning in class.  DOI: https://doi.org/10.24071/llt.2017.200104

Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

Directory of Open Access Journals (Sweden)

Sugiarto Indar

2018-01-01

Full Text Available Deep learning (DL has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still sceptical about its true capability: can the intelligence demonstrated by deep learning technique be applied for general tasks? This question motivates the emergence of another research discipline: neuromorphic computing (NC. In NC, researchers try to identify the most fundamental ingredients that construct intelligence behaviour produced by the brain itself. To achieve this, neuromorphic systems are developed to mimic the brain functionality down to cellular level. In this paper, a neuromorphic platform called SpiNNaker is described and evaluated in order to understand its potential use as a platform for a deep learning approach. This paper is a literature review that contains comparative study on algorithms that have been implemented in SpiNNaker.

Learning to Apply Models of Materials While Explaining Their Properties

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Karpin, Tiia; Juuti, Kalle; Lavonen, Jari

2014-01-01

Background: Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose: This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials.…

Beyond Astro 101: A First Report on Applying Interactive Education Techniques to an Astronphysics Class for Majors

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Perrin, Marshall D.; Ghez, A. M.

2009-05-01

Learner-centered interactive instruction methods now have a proven track record in improving learning in "Astro 101" courses for non-majors, but have rarely been applied to higher-level astronomy courses. Can we hope for similar gains in classes aimed at astrophysics majors, or is the subject matter too fundamentally different for those techniques to apply? We present here an initial report on an updated calculus-based Introduction to Astrophysics class at UCLA that suggests such techniques can indeed result in increased learning for major students. We augmented the traditional blackboard-derivation lectures and challenging weekly problem sets by adding online questions on pre-reading assignments (''just-in-time teaching'') and frequent multiple-choice questions in class ("Think-Pair-Share''). We describe our approach, and present examples of the new Think-Pair-Share questions developed for this more sophisticated material. Our informal observations after one term are that with this approach, students are more engaged and alert, and score higher on exams than typical in previous years. This is anecdotal evidence, not hard data yet, and there is clearly a vast amount of work to be done in this area. But our first impressions strongly encourage us that interactive methods should be able improve the astrophysics major just as they have improved Astro 101.

Machine learning techniques to examine large patient databases.

Science.gov (United States)

Meyfroidt, Geert; Güiza, Fabian; Ramon, Jan; Bruynooghe, Maurice

2009-03-01

Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

Study of CT image texture using deep learning techniques

Science.gov (United States)

Dutta, Sandeep; Fan, Jiahua; Chevalier, David

2018-03-01

For CT imaging, reduction of radiation dose while improving or maintaining image quality (IQ) is currently a very active research and development topic. Iterative Reconstruction (IR) approaches have been suggested to be able to offer better IQ to dose ratio compared to the conventional Filtered Back Projection (FBP) reconstruction. However, it has been widely reported that often CT image texture from IR is different compared to that from FBP. Researchers have proposed different figure of metrics to quantitate the texture from different reconstruction methods. But there is still a lack of practical and robust method in the field for texture description. This work applied deep learning method for CT image texture study. Multiple dose scans of a 20cm diameter cylindrical water phantom was performed on Revolution CT scanner (GE Healthcare, Waukesha) and the images were reconstructed with FBP and four different IR reconstruction settings. The training images generated were randomly allotted (80:20) to a training and validation set. An independent test set of 256-512 images/class were collected with the same scan and reconstruction settings. Multiple deep learning (DL) networks with Convolution, RELU activation, max-pooling, fully-connected, global average pooling and softmax activation layers were investigated. Impact of different image patch size for training was investigated. Original pixel data as well as normalized image data were evaluated. DL models were reliably able to classify CT image texture with accuracy up to 99%. Results show that the deep learning techniques suggest that CT IR techniques may help lower the radiation dose compared to FBP.

THE GAME TECHNIQUE NTCHNIQUE STIMULATING LEARNING ACTIVITY OF JUNIOR STUDENTS SPECIALIZING IN ECONOMICS

Directory of Open Access Journals (Sweden)

Juri. S. Ezrokh

2014-01-01

Full Text Available The research is aimed at specifying and developing the modern control system of current academic achievements of junior university students; and the main task is to find the adequate ways for stimulating the junior students’ learning activities, and estimating their individual achievements.Methods: The author applies his own assessment method for estimating and stimulating students’ learning outcomes, based on the rating-point system of gradually obtained points building up a student’s integrated learning outcomes.Results: The research findings prove that implementation of the given method can increase the motivational, multiplicative and controlling components of the learning process.Scientific novelty: The method in question is based on the new original game approach to controlling procedures and stimulation of learning motivation of the economic profile students.Practical significance: The recommended technique can intensify the incentivebased training activities both in and outside a classroom, developing thereby students’ professional and personal qualities.

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

Science.gov (United States)

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

eLearning techniques supporting problem based learning in clinical simulation.

Science.gov (United States)

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2005-08-01

This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

Applying adult learning practices in medical education.

Science.gov (United States)

Reed, Suzanne; Shell, Richard; Kassis, Karyn; Tartaglia, Kimberly; Wallihan, Rebecca; Smith, Keely; Hurtubise, Larry; Martin, Bryan; Ledford, Cynthia; Bradbury, Scott; Bernstein, Henry Hank; Mahan, John D

2014-07-01

The application of the best practices of teaching adults to the education of adults in medical education settings is important in the process of transforming learners to become and remain effective physicians. Medical education at all levels should be designed to equip physicians with the knowledge, clinical skills, and professionalism that are required to deliver quality patient care. The ultimate outcome is the health of the patient and the health status of the society. In the translational science of medical education, improved patient outcomes linked directly to educational events are the ultimate goal and are best defined by rigorous medical education research efforts. To best develop faculty, the same principles of adult education and teaching adults apply. In a systematic review of faculty development initiatives designed to improve teaching effectiveness in medical education, the use of experiential learning, feedback, effective relationships with peers, and diverse educational methods were found to be most important in the success of these programs. In this article, we present 5 examples of applying the best practices in teaching adults and utilizing the emerging understanding of the neurobiology of learning in teaching students, trainees, and practitioners. These include (1) use of standardized patients to develop communication skills, (2) use of online quizzes to assess knowledge and aid self-directed learning, (3) use of practice sessions and video clips to enhance significant learning of teaching skills, (4) use of case-based discussions to develop professionalism concepts and skills, and (5) use of the American Academy of Pediatrics PediaLink as a model for individualized learner-directed online learning. These examples highlight how experiential leaning, providing valuable feedback, opportunities for practice, and stimulation of self-directed learning can be utilized as medical education continues its dynamic transformation in the years ahead

Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

OpenAIRE

Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

2015-01-01

Machine learning techniques can be used to analyse data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful in terms of software bug prediction. In this paper, a comparative performance analysis of different machine learning techniques is explored f or software bug prediction on public available data sets. Results showed most of the mac ...

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

Science.gov (United States)

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

Rethinking Game Based Learning: applying pedagogical standards to educational games

NARCIS (Netherlands)

Schmitz, Birgit; Kelle, Sebastian

2010-01-01

Schmitz, B., & Kelle, S. (2010, 1-6 February). Rethinking Game Based Learning: applying pedagogical standards to educational games. Presentation at JTEL Winter School 2010 on Advanced Learning Technologies, Innsbruck, Austria.

Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

OpenAIRE

Sugiarto Indar; Pasila Felix

2018-01-01

Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still scept...

Learning Programming Technique through Visual Programming Application as Learning Media with Fuzzy Rating

Science.gov (United States)

Buditjahjanto, I. G. P. Asto; Nurlaela, Luthfiyah; Ekohariadi; Riduwan, Mochamad

2017-01-01

Programming technique is one of the subjects at Vocational High School in Indonesia. This subject contains theory and application of programming utilizing Visual Programming. Students experience some difficulties to learn textual learning. Therefore, it is necessary to develop media as a tool to transfer learning materials. The objectives of this…

BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

OpenAIRE

Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

2015-01-01

Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data...

Action Learning in Virtual Higher Education: Applying Leadership Theory

Science.gov (United States)

Curtin, Joseph

2016-01-01

This paper reports the historical foundation of Northeastern University's course, LDR 6100: Developing Your Leadership Capability, a partial literature review of action learning (AL) and virtual action learning (VAL), a course methodology of LDR 6100 requiring students to apply leadership perspectives using VAL as instructed by the author,…

Teaching organization theory for healthcare management: three applied learning methods.

Science.gov (United States)

Olden, Peter C

2006-01-01

Organization theory (OT) provides a way of seeing, describing, analyzing, understanding, and improving organizations based on patterns of organizational design and behavior (Daft 2004). It gives managers models, principles, and methods with which to diagnose and fix organization structure, design, and process problems. Health care organizations (HCOs) face serious problems such as fatal medical errors, harmful treatment delays, misuse of scarce nurses, costly inefficiency, and service failures. Some of health care managers' most critical work involves designing and structuring their organizations so their missions, visions, and goals can be achieved-and in some cases so their organizations can survive. Thus, it is imperative that graduate healthcare management programs develop effective approaches for teaching OT to students who will manage HCOs. Guided by principles of education, three applied teaching/learning activities/assignments were created to teach OT in a graduate healthcare management program. These educationalmethods develop students' competency with OT applied to HCOs. The teaching techniques in this article may be useful to faculty teaching graduate courses in organization theory and related subjects such as leadership, quality, and operation management.

Learning Physics through Project-Based Learning Game Techniques

Science.gov (United States)

Baran, Medine; Maskan, Abdulkadir; Yasar, Seyma

2018-01-01

The aim of the present study, in which Project and game techniques are used together, is to examine the impact of project-based learning games on students' physics achievement. Participants of the study consist of 34 9th grade students (N = 34). The data were collected using achievement tests and a questionnaire. Throughout the applications, the…

Applying reinforcement learning to the weapon assignment problem in air defence

CSIR Research Space (South Africa)

Mouton, H

2011-12-01

Full Text Available . The techniques investigated in this article were two methods from the machine-learning subfield of reinforcement learning (RL), namely a Monte Carlo (MC) control algorithm with exploring starts (MCES), and an off-policy temporal-difference (TD) learning...

IoT Security Techniques Based on Machine Learning

OpenAIRE

Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di

2018-01-01

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine le...

Machine learning techniques in optical communication

DEFF Research Database (Denmark)

Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

2015-01-01

Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework...

Distance Learning

National Research Council Canada - National Science Library

Braddock, Joseph

1997-01-01

A study reviewing the existing Army Distance Learning Plan (ADLP) and current Distance Learning practices, with a focus on the Army's training and educational challenges and the benefits of applying Distance Learning techniques...

Machine learning in Python essential techniques for predictive analysis

CERN Document Server

Bowles, Michael

2015-01-01

Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, d

Machine learning techniques in optical communication

DEFF Research Database (Denmark)

Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

2016-01-01

Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter...

[Digital learning object for diagnostic reasoning in nursing applied to the integumentary system].

Science.gov (United States)

da Costa, Cecília Passos Vaz; Luz, Maria Helena Barros Araújo

2015-12-01

To describe the creation of a digital learning object for diagnostic reasoning in nursing applied to the integumentary system at a public university of Piaui. A methodological study applied to technological production based on the pedagogical framework of problem-based learning. The methodology for creating the learning object observed the stages of analysis, design, development, implementation and evaluation recommended for contextualized instructional design. The revised taxonomy of Bloom was used to list the educational goals. The four modules of the developed learning object were inserted into the educational platform Moodle. The theoretical assumptions allowed the design of an important online resource that promotes effective learning in the scope of nursing education. This study should add value to nursing teaching practices through the use of digital learning objects for teaching diagnostic reasoning applied to skin and skin appendages.

Machine learning and statistical techniques : an application to the prediction of insolvency in Spanish non-life insurance companies

OpenAIRE

Díaz, Zuleyka; Segovia, María Jesús; Fernández, José

2005-01-01

Prediction of insurance companies insolvency has arisen as an important problem in the field of financial research. Most methods applied in the past to tackle this issue are traditional statistical techniques which use financial ratios as explicative variables. However, these variables often do not satisfy statistical assumptions, which complicates the application of the mentioned methods. In this paper, a comparative study of the performance of two non-parametric machine learning techniques ...

Three visual techniques to enhance interprofessional learning.

Science.gov (United States)

Parsell, G; Gibbs, T; Bligh, J

1998-07-01

Many changes in the delivery of healthcare in the UK have highlighted the need for healthcare professionals to learn to work together as teams for the benefit of patients. Whatever the profession or level, whether for postgraduate education and training, continuing professional development, or for undergraduates, learners should have an opportunity to learn about and with, other healthcare practitioners in a stimulating and exciting way. Learning to understand how people think, feel, and react, and the parts they play at work, both as professionals and individuals, can only be achieved through sensitive discussion and exchange of views. Teaching and learning methods must provide opportunities for this to happen. This paper describes three small-group teaching techniques which encourage a high level of learner collaboration and team-working. Learning content is focused on real-life health-care issues and strong visual images are used to stimulate lively discussion and debate. Each description includes the learning objectives of each exercise, basic equipment and resources, and learning outcomes.

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

Directory of Open Access Journals (Sweden)

Laura Cornejo-Bueno

2017-11-01

Full Text Available Wind Power Ramp Events (WPREs are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains. Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

Digital learning object for diagnostic reasoning in nursing applied to the integumentary system

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Cecília Passos Vaz da Costa

Full Text Available Objective: To describe the creation of a digital learning object for diagnostic reasoning in nursing applied to the integumentary system at a public university of Piaui. Method: A methodological study applied to technological production based on the pedagogical framework of problem-based learning. The methodology for creating the learning object observed the stages of analysis, design, development, implementation and evaluation recommended for contextualized instructional design. The revised taxonomy of Bloom was used to list the educational goals. Results: The four modules of the developed learning object were inserted into the educational platform Moodle. The theoretical assumptions allowed the design of an important online resource that promotes effective learning in the scope of nursing education. Conclusion: This study should add value to nursing teaching practices through the use of digital learning objects for teaching diagnostic reasoning applied to skin and skin appendages.

Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

Directory of Open Access Journals (Sweden)

Steren Chabert

2017-01-01

Full Text Available Cerebral aneurysm is a cerebrovascular disorder characterized by a bulging in a weak area in the wall of an artery that supplies blood to the brain. It is relevant to understand the mechanisms leading to the apparition of aneurysms, their growth and, more important, leading to their rupture. The purpose of this study is to study the impact on aneurysm rupture of the combination of different parameters, instead of focusing on only one factor at a time as is frequently found in the literature, using machine learning and feature extraction techniques. This discussion takes relevance in the context of the complex decision that the physicians have to take to decide which therapy to apply, as each intervention bares its own risks, and implies to use a complex ensemble of resources (human resources, OR, etc. in hospitals always under very high work load. This project has been raised in our actual working team, composed of interventional neuroradiologist, radiologic technologist, informatics engineers and biomedical engineers, from Valparaiso public Hospital, Hospital Carlos van Buren, and from Universidad de Valparaíso – Facultad de Ingeniería and Facultad de Medicina. This team has been working together in the last few years, and is now participating in the implementation of an “interdisciplinary platform for innovation in health”, as part of a bigger project leaded by Universidad de Valparaiso (PMI UVA1402. It is relevant to emphasize that this project is made feasible by the existence of this network between physicians and engineers, and by the existence of data already registered in an orderly manner, structured and recorded in digital format. The present proposal arises from the description in nowadays literature that the actual indicators, whether based on morphological description of the aneurysm, or based on characterization of biomechanical factor or others, these indicators were shown not to provide sufficient information in order

Finding the Right Fit: Helping Students Apply Theory to Service-Learning Contexts

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Ricke, Audrey

2018-01-01

Background: Although past studies of service-learning focus on assessing student growth, few studies address how to support students in applying theory to their service-learning experiences. Yet, the task of applying theory is a central component of critical reflections within the social sciences in higher education and often causes anxiety among…

Chemical vapor deposition: A technique for applying protective coatings

Energy Technology Data Exchange (ETDEWEB)

Wallace, T.C. Sr.; Bowman, M.G.

1979-01-01

Chemical vapor deposition is discussed as a technique for applying coatings for materials protection in energy systems. The fundamentals of the process are emphasized in order to establish a basis for understanding the relative advantages and limitations of the technique. Several examples of the successful application of CVD coating are described. 31 refs., and 18 figs.

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

Science.gov (United States)

Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

2016-01-01

Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

Directory of Open Access Journals (Sweden)

Nadia Said

Full Text Available Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

Action learning in virtual higher education: applying leadership theory.

Science.gov (United States)

Curtin, Joseph

2016-05-03

This paper reports the historical foundation of Northeastern University's course, LDR 6100: Developing Your Leadership Capability, a partial literature review of action learning (AL) and virtual action learning (VAL), a course methodology of LDR 6100 requiring students to apply leadership perspectives using VAL as instructed by the author, questionnaire and survey results of students who evaluated the effectiveness of their application of leadership theories using VAL and insights believed to have been gained by the author administering VAL. Findings indicate most students thought applying leadership perspectives using AL was better than considering leadership perspectives not using AL. In addition as implemented in LDR 6100, more students evaluated VAL positively than did those who assessed VAL negatively.

Revitalizing pathology laboratories in a gastrointestinal pathophysiology course using multimedia and team-based learning techniques.

Science.gov (United States)

Carbo, Alexander R; Blanco, Paola G; Graeme-Cooke, Fiona; Misdraji, Joseph; Kappler, Steven; Shaffer, Kitt; Goldsmith, Jeffrey D; Berzin, Tyler; Leffler, Daniel; Najarian, Robert; Sepe, Paul; Kaplan, Jennifer; Pitman, Martha; Goldman, Harvey; Pelletier, Stephen; Hayward, Jane N; Shields, Helen M

2012-05-15

In 2008, we changed the gastrointestinal pathology laboratories in a gastrointestinal pathophysiology course to a more interactive format using modified team-based learning techniques and multimedia presentations. The results were remarkably positive and can be used as a model for pathology laboratory improvement in any organ system. Over a two-year period, engaging and interactive pathology laboratories were designed. The initial restructuring of the laboratories included new case material, Digital Atlas of Video Education Project videos, animations and overlays. Subsequent changes included USMLE board-style quizzes at the beginning of each laboratory, with individual readiness assessment testing and group readiness assessment testing, incorporation of a clinician as a co-teacher and role playing for the student groups. Student responses for pathology laboratory contribution to learning improved significantly compared to baseline. Increased voluntary attendance at pathology laboratories was observed. Spontaneous student comments noted the positive impact of the laboratories on their learning. Pathology laboratory innovations, including modified team-based learning techniques with individual and group self-assessment quizzes, multimedia presentations, and paired teaching by a pathologist and clinical gastroenterologist led to improvement in student perceptions of pathology laboratory contributions to their learning and better pathology faculty evaluations. These changes can be universally applied to other pathology laboratories to improve student satisfaction. Copyright © 2012 Elsevier GmbH. All rights reserved.

Early counterpulse technique applied to vacuum interrupters

International Nuclear Information System (INIS)

Warren, R.W.

1979-11-01

Interruption of dc currents using counterpulse techniques is investigated with vacuum interrupters and a novel approach in which the counterpulse is applied before contact separation. Important increases have been achieved in this way in the maximum interruptible current as well as large reductions in contact erosion. The factors establishing these new limits are presented and ways are discussed to make further improvements

Who is that masked educator? Deconstructing the teaching and learning processes of an innovative humanistic simulation technique.

Science.gov (United States)

McAllister, Margaret; Searl, Kerry Reid; Davis, Susan

2013-12-01

Simulation learning in nursing has long made use of mannequins, standardized actors and role play to allow students opportunity to practice technical body-care skills and interventions. Even though numerous strategies have been developed to mimic or amplify clinical situations, a common problem that is difficult to overcome in even the most well-executed simulation experiences, is that students may realize the setting is artificial and fail to fully engage, remember or apply the learning. Another problem is that students may learn technical competence but remain uncertain about communicating with the person. Since communication capabilities are imperative in human service work, simulation learning that only achieves technical competence in students is not fully effective for the needs of nursing education. Furthermore, while simulation learning is a burgeoning space for innovative practices, it has been criticized for the absence of a basis in theory. It is within this context that an innovative simulation learning experience named "Mask-Ed (KRS simulation)", has been deconstructed and the active learning components examined. Establishing a theoretical basis for creative teaching and learning practices provides an understanding of how, why and when simulation learning has been effective and it may help to distinguish aspects of the experience that could be improved. Three conceptual theoretical fields help explain the power of this simulation technique: Vygotskian sociocultural learning theory, applied theatre and embodiment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Classifying Structures in the ISM with Machine Learning Techniques

Science.gov (United States)

Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

2011-01-01

The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

Contemporary machine learning: techniques for practitioners in the physical sciences

Science.gov (United States)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Use of the Learning together technique associated to the theory of significative learning

Directory of Open Access Journals (Sweden)

Ester López Donoso

2008-09-01

Full Text Available This article deals with an experimental research, regarding a qualitative and quantitative design, applied to a group of students of General Physics course during the first semester of the university career of Engineering. Historically, students of this course present learning difficulties that directly affect their performance, conceptualization and permanence in the university. The present methodology integrates the collaborative learning, denominated Learning Together", with the theory of significant learning to avoid the above-written difficulties. Results of this research show that the proposed methodology works properly, especially to improve the conceptualization.

Applying Deep Learning in Medical Images: The Case of Bone Age Estimation.

Science.gov (United States)

Lee, Jang Hyung; Kim, Kwang Gi

2018-01-01

A diagnostic need often arises to estimate bone age from X-ray images of the hand of a subject during the growth period. Together with measured physical height, such information may be used as indicators for the height growth prognosis of the subject. We present a way to apply the deep learning technique to medical image analysis using hand bone age estimation as an example. Age estimation was formulated as a regression problem with hand X-ray images as input and estimated age as output. A set of hand X-ray images was used to form a training set with which a regression model was trained. An image preprocessing procedure is described which reduces image variations across data instances that are unrelated to age-wise variation. The use of Caffe, a deep learning tool is demonstrated. A rather simple deep learning network was adopted and trained for tutorial purpose. A test set distinct from the training set was formed to assess the validity of the approach. The measured mean absolute difference value was 18.9 months, and the concordance correlation coefficient was 0.78. It is shown that the proposed deep learning-based neural network can be used to estimate a subject's age from hand X-ray images, which eliminates the need for tedious atlas look-ups in clinical environments and should improve the time and cost efficiency of the estimation process.

Designing the Electronic Classroom: Applying Learning Theory and Ergonomic Design Principles.

Science.gov (United States)

Emmons, Mark; Wilkinson, Frances C.

2001-01-01

Applies learning theory and ergonomic principles to the design of effective learning environments for library instruction. Discusses features of electronic classroom ergonomics, including the ergonomics of physical space, environmental factors, and workstations; and includes classroom layouts. (Author/LRW)

Applying Social Cognitive Theory to Academic Advising to Assess Student Learning Outcomes

Science.gov (United States)

Erlich, Richard J.; Russ-Eft, Darlene

2011-01-01

Review of social cognitive theory constructs of self-efficacy and self-regulated learning is applied to academic advising for the purposes of assessing student learning. A brief overview of the history of student learning outcomes in higher education is followed by an explanation of self-efficacy and self-regulated learning constructs and how they…

Early counterpulse technique applied to vacuum interrupters

International Nuclear Information System (INIS)

Warren, R.W.

1979-01-01

Interruption of dc currents using counterpulse techniques is investigated with vacuum interrupters and a novel approach in which the counterpulse is applied before contact separation. Important increases have been achieved in this way in the maximum interruptible current and large reductions in contact erosion. The factors establishing these new limits are presented and ways are discussed to make further improvements to the maximum interruptible current

Encouraging Interaction by Applying Cooperative Learning

Directory of Open Access Journals (Sweden)

González Sonia Helena

2001-08-01

Full Text Available A project was conducted in order to improve oral interaction in English by applying cooperative learning to students of seventh grade. These students have lower levels of oral production and attend Marco Fidel Suárez public school. So, I decided to choose topics related to real life and to plan a series of activities of sensitization to create stable work groups and to increase oral interaction. According to the analysis and results, I can say that cooperative work and the oral activities help the students increase oral production, express better and use a foreign language with more security. In spite of the results, I consider that cooperative learning needs more time so that it can be successful. Students must have the will to cooperate. Only when students have that good will and can work together is the potential of acquisition of knowledge maximized.

Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning

Science.gov (United States)

Prabakaran, S.; Mitra, Shilpa

2018-04-01

Data mining is the field containing procedures for finding designs or patterns in a huge dataset, it includes strategies at the convergence of machine learning and database framework. It can be applied to various fields like future healthcare, market basket analysis, education, manufacturing engineering, crime investigation etc. Among these, crime investigation is an interesting application to process crime characteristics to help the society for a better living. This paper survey various data mining techniques used in this domain. This study may be helpful in designing new strategies for crime prediction and analysis.

Computational optimization techniques applied to microgrids planning

DEFF Research Database (Denmark)

Gamarra, Carlos; Guerrero, Josep M.

2015-01-01

Microgrids are expected to become part of the next electric power system evolution, not only in rural and remote areas but also in urban communities. Since microgrids are expected to coexist with traditional power grids (such as district heating does with traditional heating systems......), their planning process must be addressed to economic feasibility, as a long-term stability guarantee. Planning a microgrid is a complex process due to existing alternatives, goals, constraints and uncertainties. Usually planning goals conflict each other and, as a consequence, different optimization problems...... appear along the planning process. In this context, technical literature about optimization techniques applied to microgrid planning have been reviewed and the guidelines for innovative planning methodologies focused on economic feasibility can be defined. Finally, some trending techniques and new...

A Comparative Analysis of Machine Learning Techniques for Credit Scoring

OpenAIRE

Nwulu, Nnamdi; Oroja, Shola; İlkan, Mustafa

2012-01-01

Abstract Credit Scoring has become an oft researched topic in light of the increasing volatility of the global economy and the recent world financial crisis. Amidst the many methods used for credit scoring, machine learning techniques are becoming increasingly popular due to their efficient and accurate nature and relative simplicity. Furthermore machine learning techniques minimize the risk of human bias and error and maximize speed as they are able to perform computation...

Applied Drama and the Higher Education Learning Spaces: A Reflective Analysis

Science.gov (United States)

Moyo, Cletus

2015-01-01

This paper explores Applied Drama as a teaching approach in Higher Education learning spaces. The exploration takes a reflective analysis approach by first examining the impact that Applied Drama has had on my career as a Lecturer/Educator/Teacher working in Higher Education environments. My engagement with Applied Drama practice and theory is…

Modelling tick abundance using machine learning techniques and satellite imagery

DEFF Research Database (Denmark)

Kjær, Lene Jung; Korslund, L.; Kjelland, V.

satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

The Effect of using Teams Games Tournaments (TGT Technique for Learning Mathematics in Bangladesh

Directory of Open Access Journals (Sweden)

Abdus Salam

2015-07-01

Full Text Available Games-based learning has captured the interest of educationalists and industrialists who seek to reveal the characteristics of computer games as they are perceived by some to be a potentially effective approach for teaching and learning. Despite this interest in using games-based learning, there is a dearth of studies context of gaming and education in third world countries. This study investigated the effects of game playing on performance and attitudes of students towards mathematics of Grade VIII. The study was undergone by implementing TGT technique for the experimental group and typical lecture-based approach for the control group. A same achievement test was employed as in both pretest and post test, an inventory of attitudes towards mathematics were applied for the pretest and post test on TGT experimental and control group, an attitude scale on computer games was employed for the TGT experimental group, a semi-structured interview for teacher and an FGD guideline for students were applied to serving the purpose of research objectives. After three-weeks of intervention, it had been found out that TGT experimental group students had achieved a significant learning outcome than lecture based control group students. Attitude towards mathematics were differed to a certain positive extent on TGT experimental group. On the basis of findings of this study, some recommendations were made to overcome the barriers of integrating web-based game playing in a classroom.

Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

Science.gov (United States)

Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

2014-01-01

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Science.gov (United States)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Cervical spine mobilisation forces applied by physiotherapy students.

Science.gov (United States)

Snodgrass, Suzanne J; Rivett, Darren A; Robertson, Val J; Stojanovski, Elizabeth

2010-06-01

Postero-anterior (PA) mobilisation is commonly used in cervical spine treatment and included in physiotherapy curricula. The manual forces that students apply while learning cervical mobilisation are not known. Quantifying these forces informs the development of strategies for learning to apply cervical mobilisation effectively and safely. This study describes the mechanical properties of cervical PA mobilisation techniques applied by students, and investigates factors associated with force application. Physiotherapy students (n=120) mobilised one of 32 asymptomatic subjects. Students applied Grades I to IV central and unilateral PA mobilisation to C2 and C7 of one asymptomatic subject. Manual forces were measured in three directions using an instrumented treatment table. Spinal stiffness of mobilised subjects was measured at C2 and C7 using a device that applied a standard oscillating force while measuring this force and its concurrent displacement. Analysis of variance was used to determine differences between techniques and grades, intraclass correlation coefficients (ICC) were used to calculate the inter- and intrastudent repeatability of forces, and linear regression was used to determine the associations between applied forces and characteristics of students and mobilised subjects. Mobilisation forces increased from Grades I to IV (highest mean peak force, Grade IV C7 central PA technique: 63.7N). Interstudent reliability was poor [ICC(2,1)=0.23, 95% confidence interval (CI) 0.14 to 0.43], but intrastudent repeatability of forces was somewhat better (0.83, 95% CI 0.81 to 0.86). Higher applied force was associated with greater C7 stiffness, increased frequency of thumb pain, male gender of the student or mobilised subject, and a student being earlier in their learning process. Lower forces were associated with greater C2 stiffness. This study describes the cervical mobilisation forces applied by students, and the characteristics of the student and mobilised

Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

Directory of Open Access Journals (Sweden)

Wei-Chien Wang

2018-05-01

Full Text Available Deep learning has become the most popular research subject in the fields of artificial intelligence (AI and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA, support vector machines (SVM, k-nearest neighbors algorithm (K-NN, and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW, with machine-learning classifiers, and histogram of oriented gradients (HOG/pyramid histogram of oriented gradients (PHOG with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10−4, and this accuracy is far higher than those achieved by BOW with SVM (80.50% and PHOG with SVM (53.0%.

A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

Science.gov (United States)

Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Huang, Te-Chia; Chuang, Wei-Yao

2013-06-01

Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis. The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks. The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619. The

Applying of USB interface technique in nuclear spectrum acquisition system

International Nuclear Information System (INIS)

Zhou Jianbin; Huang Jinhua

2004-01-01

This paper introduces applying of USB technique and constructing nuclear spectrum acquisition system via PC's USB interface. The authors choose the USB component USB100 module and the W77E58μc to do the key work. It's easy to apply USB interface technique, when USB100 module is used. USB100 module can be treated as a common I/O component for the μc controller, and can be treated as a communication interface (COM) when connected to PC' USB interface. It's easy to modify the PC's program for the new system with USB100 module. The authors can smoothly change from ISA, RS232 bus to USB bus. (authors)

APPLYING PBL AND ZUVIO TO ENHANCE ENGLISH LEARNING MOTIVATION

Directory of Open Access Journals (Sweden)

BOR-TYNG WANG

2016-06-01

Full Text Available To inspire college students’ English learning motivation, this study proposed to combine Project-Based Learning (PBL with ZUVIO online teaching platform. The traditional teaching methods focus on teachers’ direct instruction in class, which mean that students only receive knowledge from teachers instead of formulating the answers on their own. This also decreases interaction in the classroom and prevents students from collaborating with other peers. However, implementing PBL and ZUVIO would allow students to apply knowledge in the social context and work with their classmates. In this study, two freshman English classes in a private university in central Taiwan were chosen as the sample. The students in both classes were low-level students (CEF A2 level. One class (N = 39 was chosen as the experimental group which had to complete the PBL tasks assigned by the teacher and use peer assessment function in ZUVIO for one academic year. The other class (N = 43 was chosen as the control group which was given the traditional teaching instructions. The results showed that the experimental group performed better on the midterm exam compared to the control group during both semesters (p = 0.001. Additionally, the results of the questionnaire showed that students’ motivation to learn English increased when using PBL and ZUVIO as teaching methods. To cite this document: Bor-Tyng Wang, "Applying PBL and ZUVIO to enhance English learning motivation", International Journal of Cyber Society and Education, Vol. 9, No. 1, pp. 1-16, 2016.

Machine learning techniques for gait biometric recognition using the ground reaction force

CERN Document Server

Mason, James Eric; Woungang, Isaac

2016-01-01

This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of ...

Adaptive Landmark-Based Navigation System Using Learning Techniques

DEFF Research Database (Denmark)

Zeidan, Bassel; Dasgupta, Sakyasingha; Wörgötter, Florentin

2014-01-01

The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. In...... hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.......The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal....... Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex...

Applied predictive analytics principles and techniques for the professional data analyst

CERN Document Server

Abbott, Dean

2014-01-01

Learn the art and science of predictive analytics - techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive mode

Teachers and Learners’ Perceptions of Applying Translation as a Method, Strategy, or Technique in an Iranian EFL Setting

Directory of Open Access Journals (Sweden)

Fatemeh Mollaei

2017-04-01

Full Text Available It has been found that translation is an efficient means to teach/learn grammar, syntax, and lexis of a foreign language. Meanwhile, translation is good for beginners who do not still enjoy the critical level of proficiency in their target language for expression.  This study was conducted to examine the teachers and learners’ perceptions of employing translation in the foreign language classroom; i.e., the effects, merits, demerits, limitations, as well as its use as a method, strategy or technique. Both quantitative and qualitative methods were used to collect and analyze the data from graduate and undergraduate learners (n=56 and teachers (n=44, male and female, who responded to two questionnaires. Additionally, only the teachers were interviewed to gain richer insight into their perceptions and attitudes. According to the results of independent samples t-test, there was no significant difference between teachers and learners’ attitude to applying translation as a method, strategy, or technique in learning a foreign language.  Based on the interview results, some teachers believed that employing translation in the foreign language context was helpful but not constantly. They claimed that translation was only effective in teaching vocabulary and grammar apart from leaners’ proficiency level as it can clarify meaning. But some other teachers noted that mother tongue would interfere with learning foreign language; they considered translation as a time-consuming activity through which students cannot capture the exact meaning.

Simulation-based optimization parametric optimization techniques and reinforcement learning

CERN Document Server

Gosavi, Abhijit

2003-01-01

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

Study on the effect of smart learning applied at a radiationtherapy subject on self directed learning, self learning efficacy, learning satisfaction of college students

Energy Technology Data Exchange (ETDEWEB)

Shin, Jae Goo; Park, Soo Jin [Daegu Health College, Daegu (Korea, Republic of); Kim, Yon Min [Dept. of Radiotechnology, Wonkwang Health Science University, Iksan (Korea, Republic of)

2016-12-15

The purpose of this was to study and analyze smart learning the self directed learning, self efficacy, learning satisfaction about department of radiology in a college. For this study total students 102 in 3 classes were surveyed at the end of semester. The research data was analyzed using SPSS also self directed learning ,self learning efficacy, learning satisfaction analyzed t-test, ANOVA and Pearson's correlation coefficient results were followings. First, Men is more higher than women in a self learning efficacy, self directed learning, learning satisfaction. Second, in a learning satisfaction smart learning ever heard in a first time group more satisfaction. Third, during the smart learning classes a students appeared a positive response. As a results, learning satisfaction will increase a learning when learners need a ability of self control planning and learning motivation by themselves in voluntarily and actively. Suggest to change a paradigm in a radiology classes so we have to improve a teaching skills this solution recommend is two way communication. In conclusion, smart learning applied for classes of college is meaningful as a new teaching, which can be change gradually learning satisfaction by teaching methods.

Study on the effect of smart learning applied at a radiationtherapy subject on self directed learning, self learning efficacy, learning satisfaction of college students

International Nuclear Information System (INIS)

Shin, Jae Goo; Park, Soo Jin; Kim, Yon Min

2016-01-01

The purpose of this was to study and analyze smart learning the self directed learning, self efficacy, learning satisfaction about department of radiology in a college. For this study total students 102 in 3 classes were surveyed at the end of semester. The research data was analyzed using SPSS also self directed learning ,self learning efficacy, learning satisfaction analyzed t-test, ANOVA and Pearson's correlation coefficient results were followings. First, Men is more higher than women in a self learning efficacy, self directed learning, learning satisfaction. Second, in a learning satisfaction smart learning ever heard in a first time group more satisfaction. Third, during the smart learning classes a students appeared a positive response. As a results, learning satisfaction will increase a learning when learners need a ability of self control planning and learning motivation by themselves in voluntarily and actively. Suggest to change a paradigm in a radiology classes so we have to improve a teaching skills this solution recommend is two way communication. In conclusion, smart learning applied for classes of college is meaningful as a new teaching, which can be change gradually learning satisfaction by teaching methods

Applying Authentic Data Analysis in Learning Earth Atmosphere

Science.gov (United States)

Johan, H.; Suhandi, A.; Samsudin, A.; Wulan, A. R.

2017-09-01

The aim of this research was to develop earth science learning material especially earth atmosphere supported by science research with authentic data analysis to enhance reasoning through. Various earth and space science phenomenon require reasoning. This research used experimental research with one group pre test-post test design. 23 pre-service physics teacher participated in this research. Essay test was conducted to get data about reason ability. Essay test was analyzed quantitatively. Observation sheet was used to capture phenomena during learning process. The results showed that student’s reasoning ability improved from unidentified and no reasoning to evidence based reasoning and inductive/deductive rule-based reasoning. Authentic data was considered using Grid Analysis Display System (GrADS). Visualization from GrADS facilitated students to correlate the concepts and bring out real condition of nature in classroom activity. It also helped student to reason the phenomena related to earth and space science concept. It can be concluded that applying authentic data analysis in learning process can help to enhance students reasoning. This study is expected to help lecture to bring out result of geoscience research in learning process and facilitate student understand concepts.

Memorization techniques: Using mnemonics to learn fifth grade science terms

Science.gov (United States)

Garcia, Juan O.

The purpose of this study was to determine whether mnemonic instruction could assist students in learning fifth-grade science terminology more effectively than traditional-study methods of recall currently in practice The task was to examine if fifth-grade students were able to learn a mnemonic and then use it to understand science vocabulary; subsequently, to determine if students were able to remember the science terms after a period of time. The problem is that in general, elementary school students are not being successful in science achievement at the fifth grade level. In view of this problem, if science performance is increased at the elementary level, then it is likely that students will be successful when tested at the 8th and 10th grade in science with the Texas Assessment of Knowledge and Skills (TAKS) in the future. Two research questions were posited: (1) Is there a difference in recall achievement when a mnemonic such as method of loci, pegword method, or keyword method is used in learning fifth-grade science vocabulary as compared to the traditional-study method? (2) If using a mnemonic in learning fifth-grade science vocabulary was effective on recall achievement, would this achievement be maintained over a span of time? The need for this study was to assist students in learning science terms and concepts for state accountability purposes. The first assumption was that memorization techniques are not commonly applied in fifth-grade science classes in elementary schools. A second assumption was that mnemonic devices could be used successfully in learning science terms and increase long term retention. The first limitation was that the study was conducted on one campus in one school district in South Texas which limited the generalization of the study. The second limitation was that it included random assigned intact groups as opposed to random student assignment to fifth-grade classroom groups.

TEACHING AND LEARNING METHODOLOGIES SUPPORTED BY ICT APPLIED IN COMPUTER SCIENCE

Directory of Open Access Journals (Sweden)

Jose CAPACHO

2016-04-01

Full Text Available The main objective of this paper is to show a set of new methodologies applied in the teaching of Computer Science using ICT. The methodologies are framed in the conceptual basis of the following sciences: Psychology, Education and Computer Science. The theoretical framework of the research is supported by Behavioral Theory, Gestalt Theory. Genetic-Cognitive Psychology Theory and Dialectics Psychology. Based on the theoretical framework the following methodologies were developed: Game Theory, Constructivist Approach, Personalized Teaching, Problem Solving, Cooperative Collaborative learning, Learning projects using ICT. These methodologies were applied to the teaching learning process during the Algorithms and Complexity – A&C course, which belongs to the area of ​​Computer Science. The course develops the concepts of Computers, Complexity and Intractability, Recurrence Equations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Shortest Path Problem and Graph Theory. The main value of the research is the theoretical support of the methodologies and their application supported by ICT using learning objects. The course aforementioned was built on the Blackboard platform evaluating the operation of methodologies. The results of the evaluation are presented for each of them, showing the learning outcomes achieved by students, which verifies that methodologies are functional.

Computer Game-based Learning: Applied Game Development Made Simpler

NARCIS (Netherlands)

Nyamsuren, Enkhbold

2018-01-01

The RAGE project (Realising an Applied Gaming Ecosystem, http://rageproject.eu/) is an ongoing initiative that aims to offer an ecosystem to support serious games’ development and use. Its two main objectives are to provide technologies for computer game-based pedagogy and learning and to establish

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

Science.gov (United States)

Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

2017-06-14

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

Comparative exploration of learning styles and teaching techniques between Thai and Vietnamese EFL students and instructors

Directory of Open Access Journals (Sweden)

Supalak Nakhornsri

2016-09-01

Full Text Available Learning styles have been a particular focus of a number of researchers over the past decades. Findings from various studies researching into how students learn highlight significant relationships between learners’ styles of learning and their language learning processes and achievement. This research focuses on a comparative analysis of the preferences of English learning styles and teaching techniques perceived by students from Thailand and Vietnam, and the teaching styles and techniques practiced by their instructors. The purposes were 1 to investigate the learning styles and teaching techniques students from both countries preferred, 2 to investigate the compatibility of the teaching styles and techniques practiced by instructors and those preferred by the students, 3 to specify the learning styles and teaching techniques students with high level of English proficiency preferred, and 4 to investigate the similarities of Thai and Vietnamese students’ preferences for learning styles and teaching techniques. The sample consisted of two main groups: 1 undergraduate students from King Mongkut’s University of Technology North Bangkok (KMUTNB, Thailand and Thai Nguyen University (TNU, Vietnam and 2 English instructors from both institutions. The instruments employed comprised the Students’ Preferred English Learning Style and Teaching Technique Questionnaire and the Teachers’ Practiced English Teaching Style and Technique Questionnaire. The collected data were analyzed using arithmetic means and standard deviation. The findings can contribute to the curriculum development and assist teachers to teach outside their comfort level to match the students’ preferred learning styles. In addition, the findings could better promote the courses provided for students. By understanding the learning style make-up of the students enrolled in the courses, faculty can adjust their modes of content delivery to match student preferences and maximize

The training and learning process of transseptal puncture using a modified technique.

Science.gov (United States)

Yao, Yan; Ding, Ligang; Chen, Wensheng; Guo, Jun; Bao, Jingru; Shi, Rui; Huang, Wen; Zhang, Shu; Wong, Tom

2013-12-01

As the transseptal (TS) puncture has become an integral part of many types of cardiac interventional procedures, its technique that was initial reported for measurement of left atrial pressure in 1950s, continue to evolve. Our laboratory adopted a modified technique which uses only coronary sinus catheter as the landmark to accomplishing TS punctures under fluoroscopy. The aim of this study is prospectively to evaluate the training and learning process for TS puncture guided by this modified technique. Guided by the training protocol, TS puncture was performed in 120 consecutive patients by three trainees without previous personal experience in TS catheterization and one experienced trainer as a controller. We analysed the following parameters: one puncture success rate, total procedure time, fluoroscopic time, and radiation dose. The learning curve was analysed using curve-fitting methodology. The first attempt at TS crossing was successful in 74 (82%), a second attempt was successful in 11 (12%), and 5 patients failed to puncture the interatrial septal finally. The average starting process time was 4.1 ± 0.8 min, and the estimated mean learning plateau was 1.2 ± 0.2 min. The estimated mean learning rate for process time was 25 ± 3 cases. Important aspects of learning curve can be estimated by fitting inverse curves for TS puncture. The study demonstrated that this technique was a simple, safe, economic, and effective approach for learning of TS puncture. Base on the statistical analysis, approximately 29 TS punctures will be needed for trainee to pass the steepest area of learning curve.

Machine Learning Method Applied in Readout System of Superheated Droplet Detector

Science.gov (United States)

Liu, Yi; Sullivan, Clair Julia; d'Errico, Francesco

2017-07-01

Direct readability is one advantage of superheated droplet detectors in neutron dosimetry. Utilizing such a distinct characteristic, an imaging readout system analyzes image of the detector for neutron dose readout. To improve the accuracy and precision of algorithms in the imaging readout system, machine learning algorithms were developed. Deep learning neural network and support vector machine algorithms are applied and compared with generally used Hough transform and curvature analysis methods. The machine learning methods showed a much higher accuracy and better precision in recognizing circular gas bubbles.

Diagonal ordering operation technique applied to Morse oscillator

Energy Technology Data Exchange (ETDEWEB)

Popov, Dušan, E-mail: dusan_popov@yahoo.co.uk [Politehnica University Timisoara, Department of Physical Foundations of Engineering, Bd. V. Parvan No. 2, 300223 Timisoara (Romania); Dong, Shi-Hai [CIDETEC, Instituto Politecnico Nacional, Unidad Profesional Adolfo Lopez Mateos, Mexico D.F. 07700 (Mexico); Popov, Miodrag [Politehnica University Timisoara, Department of Steel Structures and Building Mechanics, Traian Lalescu Street, No. 2/A, 300223 Timisoara (Romania)

2015-11-15

We generalize the technique called as the integration within a normally ordered product (IWOP) of operators referring to the creation and annihilation operators of the harmonic oscillator coherent states to a new operatorial approach, i.e. the diagonal ordering operation technique (DOOT) about the calculations connected with the normally ordered product of generalized creation and annihilation operators that generate the generalized hypergeometric coherent states. We apply this technique to the coherent states of the Morse oscillator including the mixed (thermal) state case and get the well-known results achieved by other methods in the corresponding coherent state representation. Also, in the last section we construct the coherent states for the continuous dynamics of the Morse oscillator by using two new methods: the discrete–continuous limit, respectively by solving a finite difference equation. Finally, we construct the coherent states corresponding to the whole Morse spectrum (discrete plus continuous) and demonstrate their properties according the Klauder’s prescriptions.

Applied potential tomography. A new noninvasive technique for measuring gastric emptying

International Nuclear Information System (INIS)

Avill, R.; Mangnall, Y.F.; Bird, N.C.; Brown, B.H.; Barber, D.C.; Seagar, A.D.; Johnson, A.G.; Read, N.W.

1987-01-01

Applied potential tomography is a new, noninvasive technique that yields sequential images of the resistivity of gastric contents after subjects have ingested a liquid or semisolid meal. This study validates the technique as a means of measuring gastric emptying. Experiments in vitro showed an excellent correlation between measurements of resistivity and either the square of the radius of a glass rod or the volume of water in a spherical balloon when both were placed in an oval tank containing saline. Altering the lateral position of the rod in the tank did not alter the values obtained. Images of abdominal resistivity were also directly correlated with the volume of air in a gastric balloon. Profiles of gastric emptying of liquid meals obtained using applied potential tomography were very similar to those obtained using scintigraphy or dye dilution techniques, provided that acid secretion was inhibited by cimetidine. Profiles of emptying of a mashed potato meal using applied potential tomography were also very similar to those obtained by scintigraphy. Measurements of the emptying of a liquid meal from the stomach were reproducible if acid secretion was inhibited by cimetidine. Thus, applied potential tomography is an accurate and reproducible method of measuring gastric emptying of liquids and particulate food. It is inexpensive, well tolerated, easy to use, and ideally suited for multiple studies in patients, even those who are pregnant

Applied potential tomography. A new noninvasive technique for measuring gastric emptying

Energy Technology Data Exchange (ETDEWEB)

Avill, R.; Mangnall, Y.F.; Bird, N.C.; Brown, B.H.; Barber, D.C.; Seagar, A.D.; Johnson, A.G.; Read, N.W.

1987-04-01

Applied potential tomography is a new, noninvasive technique that yields sequential images of the resistivity of gastric contents after subjects have ingested a liquid or semisolid meal. This study validates the technique as a means of measuring gastric emptying. Experiments in vitro showed an excellent correlation between measurements of resistivity and either the square of the radius of a glass rod or the volume of water in a spherical balloon when both were placed in an oval tank containing saline. Altering the lateral position of the rod in the tank did not alter the values obtained. Images of abdominal resistivity were also directly correlated with the volume of air in a gastric balloon. Profiles of gastric emptying of liquid meals obtained using applied potential tomography were very similar to those obtained using scintigraphy or dye dilution techniques, provided that acid secretion was inhibited by cimetidine. Profiles of emptying of a mashed potato meal using applied potential tomography were also very similar to those obtained by scintigraphy. Measurements of the emptying of a liquid meal from the stomach were reproducible if acid secretion was inhibited by cimetidine. Thus, applied potential tomography is an accurate and reproducible method of measuring gastric emptying of liquids and particulate food. It is inexpensive, well tolerated, easy to use, and ideally suited for multiple studies in patients, even those who are pregnant.

Microscale and nanoscale strain mapping techniques applied to creep of rocks

Science.gov (United States)

Quintanilla-Terminel, Alejandra; Zimmerman, Mark E.; Evans, Brian; Kohlstedt, David L.

2017-07-01

Usually several deformation mechanisms interact to accommodate plastic deformation. Quantifying the contribution of each to the total strain is necessary to bridge the gaps from observations of microstructures, to geomechanical descriptions, to extrapolating from laboratory data to field observations. Here, we describe the experimental and computational techniques involved in microscale strain mapping (MSSM), which allows strain produced during high-pressure, high-temperature deformation experiments to be tracked with high resolution. MSSM relies on the analysis of the relative displacement of initially regularly spaced markers after deformation. We present two lithography techniques used to pattern rock substrates at different scales: photolithography and electron-beam lithography. Further, we discuss the challenges of applying the MSSM technique to samples used in high-temperature and high-pressure experiments. We applied the MSSM technique to a study of strain partitioning during creep of Carrara marble and grain boundary sliding in San Carlos olivine, synthetic forsterite, and Solnhofen limestone at a confining pressure, Pc, of 300 MPa and homologous temperatures, T/Tm, of 0.3 to 0.6. The MSSM technique works very well up to temperatures of 700 °C. The experimental developments described here show promising results for higher-temperature applications.

Applying machine learning to predict patient-specific current CD4 ...

African Journals Online (AJOL)

Apple apple

This work shows the application of machine learning to predict current CD4 cell count of an HIV- .... Pre-processing ... remaining data elements of the PR and RT datasets. ... technique based on the structure of the human brain's neuron.

Applications of Deep Learning and Reinforcement Learning to Biological Data.

Science.gov (United States)

Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

2018-06-01

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Competitive debate classroom as a cooperative learning technique for the human resources subject

Directory of Open Access Journals (Sweden)

Guillermo A. SANCHEZ PRIETO

2018-01-01

Full Text Available The paper shows an academic debate model as a cooperative learning technique for teaching human resources at University. The general objective of this paper is to conclude if academic debate can be included in the category of cooperative learning. The Specific objective it is presenting a model to implement this technique. Thus the first part of the paper shows the concept of cooperative learning and its main characteristics. The second part presents the debate model believed to be labelled as cooperative learning. Last part concludes with the characteristics of the model that match different aspects or not of the cooperative learning.

The learning theories’ knowledge applied in the performance of distance tutor

Directory of Open Access Journals (Sweden)

Fernanda Abreu de Moraes Figueiredo

2016-07-01

Full Text Available Abstract: This study aimed to identify the most influential theory of learning related to the practice of mentoring from behaviorism, cognitivism, humanism, the sociocultural theory and connectivism, and apply the most appropriate theories to solve common problems in distance education. For this purpose, we used the literature method. It was noted that each of the theories end up being influential to the role of tutor. Therefore, the learning tends to be richer in the ratio and effective to apply different theories together. However, that support better substantiating tutor's role is humanism, the sociocultural theory and connectivism. It was noticed that the problems often experienced by students in distance education are due to failures tutor interaction and affection, implying to resolve them closer tutor with the student to have more responsibility in the exchange of information, meeting deadlines and clarity in the disclosure notes assessments. Knowledge are mainly from humanism and sociocultural theory that end up not only reasons for existence of the tutor as serving to improve the development of the quality of tutor-student interaction. Keywords: learning theories; distance learning (DL; tutor distance.

The experience of applying academic service learning within the ...

African Journals Online (AJOL)

The experience of applying academic service learning within the discipline of speech pathology and audiology at a South African university. ... The argument put forward is that this type of pedagogy would appear to be applicable across a broad range of disciplines and represents one strategy for assisting higher education ...

Dielectric spectroscopy technique applied to study the behaviour of irradiated polymer

International Nuclear Information System (INIS)

Saoud, R.; Soualmia, A.; Guerbi, C.A.; Benrekaa, N.

2006-01-01

Relaxation spectroscopy provides an excellent method for the study of motional processes in materials and has been widely applied to macromolecules and polymers. The technique is potentially of most interest when applied to irradiated systems. Application to the study of the structure beam-irradiated Teflon is thus an outstanding opportunity for the dielectric relaxation technique, particularly as this material exhibits clamping problems when subjected to dynamic mechanical relaxation studies. A very wide frequency range is necessary to resolve dipolar effects. In this paper, we discuss some significant results about the behavior and the modification of the structure of Teflon submitted to weak energy radiations

Machine learning techniques for persuasion dectection in conversation

OpenAIRE

Ortiz, Pedro.

2010-01-01

Approved for public release; distribution is unlimited We determined that it is possible to automatically detect persuasion in conversations using three traditional machine learning techniques, naive bayes, maximum entropy, and support vector machine. These results are the first of their kind and serve as a baseline for all future work in this field. The three techniques consistently outperformed the baseline F-score, but not at a level that would be useful for real world applications. The...

An empirical study on the performance of spectral manifold learning techniques

DEFF Research Database (Denmark)

Mysling, Peter; Hauberg, Søren; Pedersen, Kim Steenstrup

2011-01-01

In recent years, there has been a surge of interest in spectral manifold learning techniques. Despite the interest, only little work has focused on the empirical behavior of these techniques. We construct synthetic data of variable complexity and observe the performance of the techniques as they ...

Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

Science.gov (United States)

Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

The impact of applying product-modelling techniques in configurator projects

DEFF Research Database (Denmark)

Hvam, Lars; Kristjansdottir, Katrin; Shafiee, Sara

2018-01-01

This paper aims to increase understanding of the impact of using product-modelling techniques to structure and formalise knowledge in configurator projects. Companies that provide customised products increasingly apply configurators in support of sales and design activities, reaping benefits...... that include shorter lead times, improved quality of specifications and products, and lower overall product costs. The design and implementation of configurators are a challenging task that calls for scientifically based modelling techniques to support the formal representation of configurator knowledge. Even...... the phenomenon model and information model are considered visually, (2) non-UML-based modelling techniques, in which only the phenomenon model is considered and (3) non-formal modelling techniques. This study analyses the impact to companies from increased availability of product knowledge and improved control...

E-Learning System Using Segmentation-Based MR Technique for Learning Circuit Construction

Science.gov (United States)

Takemura, Atsushi

2016-01-01

This paper proposes a novel e-Learning system using the mixed reality (MR) technique for technical experiments involving the construction of electronic circuits. The proposed system comprises experimenters' mobile computers and a remote analysis system. When constructing circuits, each learner uses a mobile computer to transmit image data from the…

A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets

Science.gov (United States)

Voß, Lydia; Schatten, Carlotta; Mazziotti, Claudia; Schmidt-Thieme, Lars

2015-01-01

Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL),…

FísicActiva: applying active learning strategies to a large engineering lecture

Science.gov (United States)

Auyuanet, Adriana; Modzelewski, Helena; Loureiro, Silvia; Alessandrini, Daniel; Míguez, Marina

2018-01-01

This paper presents and analyses the results obtained by applying Active Learning techniques in overcrowded Physics lectures at the University of the Republic, Uruguay. The course referred to is Physics 1, the first Physics course that all students of the Faculty of Engineering take in their first semester for all the Engineering-related careers. Qualitative and quantitative data corresponding to three semesters are shown and discussed, indicating that the students that attended these lectures outperformed the students that followed the course in the traditional way: the pass rates increased, whereas the failure rates decreased. The students highly valued this methodology, in particular, the interactive and relaxed dynamics, highlighting the concern of professors to answer questions by means of new questions so as to promote reasoning. The results obtained point to a work path that deserves to be deepened and extended to other Engineering courses.

Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

Directory of Open Access Journals (Sweden)

H Kimura

2009-04-01

Full Text Available In climate research, pressure patterns are often very important. When a climatologists need to know the days of a specific pressure pattern, for example "low pressure in Western areas of Japan and high pressure in Eastern areas of Japan (Japanese winter-type weather," they have to visually check a huge number of surface weather charts. To overcome this problem, we propose an automatic classification system using a support vector machine (SVM, which is a machine-learning method. We attempted to classify pressure patterns into two classes: "winter type" and "non-winter type". For both training datasets and test datasets, we used the JRA-25 dataset from 1981 to 2000. An experimental evaluation showed that our method obtained a greater than 0.8 F-measure. We noted that variations in results were based on differences in training datasets.

Personal recommender systems for learners in lifelong learning: requirements, techniques and model

NARCIS (Netherlands)

Drachsler, Hendrik; Hummel, Hans; Koper, Rob

2007-01-01

Drachsler, H., Hummel, H. G. K., & Koper, R. (2008). Personal recommender systems for learners in lifelong learning: requirements, techniques and model. International Journal of Learning Technology, 3(4), 404-423.

Mobile Robot Navigation Based on Q-Learning Technique

Directory of Open Access Journals (Sweden)

Lazhar Khriji

2011-03-01

Full Text Available This paper shows how Q-learning approach can be used in a successful way to deal with the problem of mobile robot navigation. In real situations where a large number of obstacles are involved, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. In this paper, we propose a navigation approach for mobile robot, in which the prior knowledge is used within Q-learning. We address the issue of individual behavior design using fuzzy logic. The strategy of behaviors based navigation reduces the complexity of the navigation problem by dividing them in small actions easier for design and implementation. The Q-Learning algorithm is applied to coordinate between these behaviors, which make a great reduction in learning convergence times. Simulation and experimental results confirm the convergence to the desired results in terms of saved time and computational resources.

Computer-aided auscultation learning system for nursing technique instruction.

Science.gov (United States)

Hou, Chun-Ju; Chen, Yen-Ting; Hu, Ling-Chen; Chuang, Chih-Chieh; Chiu, Yu-Hsien; Tsai, Ming-Shih

2008-01-01

Pulmonary auscultation is a physical assessment skill learned by nursing students for examining the respiratory system. Generally, a sound simulator equipped mannequin is used to group teach auscultation techniques via classroom demonstration. However, nursing students cannot readily duplicate this learning environment for self-study. The advancement of electronic and digital signal processing technologies facilitates simulating this learning environment. This study aims to develop a computer-aided auscultation learning system for assisting teachers and nursing students in auscultation teaching and learning. This system provides teachers with signal recording and processing of lung sounds and immediate playback of lung sounds for students. A graphical user interface allows teachers to control the measuring device, draw lung sound waveforms, highlight lung sound segments of interest, and include descriptive text. Effects on learning lung sound auscultation were evaluated for verifying the feasibility of the system. Fifteen nursing students voluntarily participated in the repeated experiment. The results of a paired t test showed that auscultative abilities of the students were significantly improved by using the computer-aided auscultation learning system.

Determination of palladium in biological samples applying nuclear analytical techniques

International Nuclear Information System (INIS)

Cavalcante, Cassio Q.; Sato, Ivone M.; Salvador, Vera L. R.; Saiki, Mitiko

2008-01-01

This study presents Pd determinations in bovine tissue samples containing palladium prepared in the laboratory, and CCQM-P63 automotive catalyst materials of the Proficiency Test, using instrumental thermal and epithermal neutron activation analysis and energy dispersive X-ray fluorescence techniques. Solvent extraction and solid phase extraction procedures were also applied to separate Pd from interfering elements before the irradiation in the nuclear reactor. The results obtained by different techniques were compared against each other to examine sensitivity, precision and accuracy. (author)

Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

Science.gov (United States)

Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

2017-07-01

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

An Examination of Digital Game-Based Situated Learning Applied to Chinese Language Poetry Education

Science.gov (United States)

Chen, Hong-Ren; Lin, You-Shiuan

2016-01-01

By gradually placing more importance on game-based education and changing learning motivation by applying game-playing characteristics, students' learning experiences can be enhanced and a better learning effect can be achieved. When teaching the content of Chinese poetry in Taiwanese junior high schools, most teachers only explain the meaning of…

Enhanced Quality Control in Pharmaceutical Applications by Combining Raman Spectroscopy and Machine Learning Techniques

Science.gov (United States)

Martinez, J. C.; Guzmán-Sepúlveda, J. R.; Bolañoz Evia, G. R.; Córdova, T.; Guzmán-Cabrera, R.

2018-06-01

In this work, we applied machine learning techniques to Raman spectra for the characterization and classification of manufactured pharmaceutical products. Our measurements were taken with commercial equipment, for accurate assessment of variations with respect to one calibrated control sample. Unlike the typical use of Raman spectroscopy in pharmaceutical applications, in our approach the principal components of the Raman spectrum are used concurrently as attributes in machine learning algorithms. This permits an efficient comparison and classification of the spectra measured from the samples under study. This also allows for accurate quality control as all relevant spectral components are considered simultaneously. We demonstrate our approach with respect to the specific case of acetaminophen, which is one of the most widely used analgesics in the market. In the experiments, commercial samples from thirteen different laboratories were analyzed and compared against a control sample. The raw data were analyzed based on an arithmetic difference between the nominal active substance and the measured values in each commercial sample. The principal component analysis was applied to the data for quantitative verification (i.e., without considering the actual concentration of the active substance) of the difference in the calibrated sample. Our results show that by following this approach adulterations in pharmaceutical compositions can be clearly identified and accurately quantified.

Supervised Learning Applied to Air Traffic Trajectory Classification

Science.gov (United States)

Bosson, Christabelle; Nikoleris, Tasos

2018-01-01

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Journaling; an active learning technique.

Science.gov (United States)

Blake, Tim K

2005-01-01

Journaling is a method frequently discussed in nursing literature and educational literature as an active learning technique that is meant to enhance reflective practice. Reflective practice is a means of self-examination that involves looking back over what has happened in practice in an effort to improve, or encourage professional growth. Some of the benefits of reflective practice include discovering meaning, making connections between experiences and the classroom, instilling values of the profession, gaining the perspective of others, reflection on professional roles, and development of critical thinking. A review of theory and research is discussed, as well as suggestions for implementation of journaling into coursework.

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.

Science.gov (United States)

Sakr, Sherif; Elshawi, Radwa; Ahmed, Amjad M; Qureshi, Waqas T; Brawner, Clinton A; Keteyian, Steven J; Blaha, Michael J; Al-Mallah, Mouaz H

2017-12-19

Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness

An Aural Learning Project: Assimilating Jazz Education Methods for Traditional Applied Pedagogy

Science.gov (United States)

Gamso, Nancy M.

2011-01-01

The Aural Learning Project (ALP) was developed to incorporate jazz method components into the author's classical practice and her applied woodwind lesson curriculum. The primary objective was to place a more focused pedagogical emphasis on listening and hearing than is traditionally used in the classical applied curriculum. The components of the…

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

Science.gov (United States)

Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

2018-05-24

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

Effect of active learning techniques on students' choice of approach ...

African Journals Online (AJOL)

The purpose of this article is to report on empirical work, related to a techniques module, undertaken with the dental students of the University of the Western Cape, South Africa. I will relate how a range of different active learning techniques (tutorials; question papers and mock tests) assisted students to adopt a deep ...

Machine Learning

CERN Multimedia

CERN. Geneva

2017-01-01

Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

Enhancing Engineering Students’ Learning in an Environmental Microbiology Course

Directory of Open Access Journals (Sweden)

Zhi Zhou

2012-08-01

Full Text Available While environmental engineering students have gained some knowledge of biogeochemical cycles and sewage treatment, most of them haven’t learned microbiology previously and usually have difficulty in learning environmental microbiology because microbiology deals with invisible living microorganisms instead of visible built environment. Many teaching techniques can be used to enhance students’ learning in microbiology courses, such as lectures, animations, videos, small-group discussions, and active learning techniques. All of these techniques have been applied in the engineering class, but the results indicate that these techniques are often inadequate for students. Learning difficulties have to be identified to enhance students’ learning.

Optimizing physicians' instruction of PACS through e-learning: cognitive load theory applied.

Science.gov (United States)

Devolder, P; Pynoo, B; Voet, T; Adang, L; Vercruysse, J; Duyck, P

2009-03-01

This article outlines the strategy used by our hospital to maximize the knowledge transfer to referring physicians on using a picture archiving and communication system (PACS). We developed an e-learning platform underpinned by the cognitive load theory (CLT) so that in depth knowledge of PACS' abilities becomes attainable regardless of the user's prior experience with computers. The application of the techniques proposed by CLT optimizes the learning of the new actions necessary to obtain and manipulate radiological images. The application of cognitive load reducing techniques is explained with several examples. We discuss the need to safeguard the physicians' main mental processes to keep the patient's interests in focus. A holistic adoption of CLT techniques both in teaching and in configuration of information systems could be adopted to attain this goal. An overview of the advantages of this instruction method is given both on the individual and organizational level.

Instructional Television: Visual Production Techniques and Learning Comprehension.

Science.gov (United States)

Silbergleid, Michael Ian

The purpose of this study was to determine if increasing levels of complexity in visual production techniques would increase the viewer's learning comprehension and the degree of likeness expressed for a college level instructional television program. A total of 119 mass communications students at the University of Alabama participated in the…

Application and evaluation of a combination of socratice and learning through discussion techniques.

Science.gov (United States)

van Aswegen, E J; Brink, H I; Steyn, P J

2001-11-01

This article has its genesis in the inquirer's interest in the need for internalizing critical thinking, creative thinking and reflective skills in adult learners. As part of a broader study the inquirer used a combination of two techniques over a period of nine months, namely: Socratic discussion/questioning and Learning Through Discussion Technique. The inquirer within this inquiry elected mainly qualitative methods, because they were seen as more adaptable to dealing with multiple realities and more sensitive and adaptable to the many shaping influences and value patterns that may be encountered (Lincoln & Guba, 1989). Purposive sampling was used and sample size (n = 10) was determined by the willingness of potential participants to enlist in the chosen techniques. Feedback from participants was obtained: (1) verbally after each discussion session, and (2) in written format after completion of the course content. The final/summative evaluation was obtained through a semi-structured questionnaire. This was deemed necessary, in that the participants were already studying for the end of the year examination. For the purpose of this condensed report the inquirer reflected only on the feedback obtained with the help of the questionnaire. The empirical study showed that in spite of various adaptation problems experienced, eight (8) of the ten (10) participants felt positive toward the applied techniques.

E-learning Materials Development: Applying and Implementing Software Reuse Principles and Granularity Levels in the Small

OpenAIRE

Nabil Arman

2010-01-01

E-learning materials development is typically acknowledged as an expensive, complicated, and lengthy process, often producing materials that are of low quality and difficult to adaptand maintain. It has always been a challenge to identify proper e-learning materials that can be reused at a reasonable cost and effort. In this paper, software engineering reuse principlesare applied to e-learning materials development process. These principles are then applied and implemented in a prototype that...

A mixed methods evaluation of team-based learning for applied pathophysiology in undergraduate nursing education.

Science.gov (United States)

Branney, Jonathan; Priego-Hernández, Jacqueline

2018-02-01

It is important for nurses to have a thorough understanding of the biosciences such as pathophysiology that underpin nursing care. These courses include content that can be difficult to learn. Team-based learning is emerging as a strategy for enhancing learning in nurse education due to the promotion of individual learning as well as learning in teams. In this study we sought to evaluate the use of team-based learning in the teaching of applied pathophysiology to undergraduate student nurses. A mixed methods observational study. In a year two, undergraduate nursing applied pathophysiology module circulatory shock was taught using Team-based Learning while all remaining topics were taught using traditional lectures. After the Team-based Learning intervention the students were invited to complete the Team-based Learning Student Assessment Instrument, which measures accountability, preference and satisfaction with Team-based Learning. Students were also invited to focus group discussions to gain a more thorough understanding of their experience with Team-based Learning. Exam scores for answers to questions based on Team-based Learning-taught material were compared with those from lecture-taught material. Of the 197 students enrolled on the module, 167 (85% response rate) returned the instrument, the results from which indicated a favourable experience with Team-based Learning. Most students reported higher accountability (93%) and satisfaction (92%) with Team-based Learning. Lectures that promoted active learning were viewed as an important feature of the university experience which may explain the 76% exhibiting a preference for Team-based Learning. Most students wanted to make a meaningful contribution so as not to let down their team and they saw a clear relevance between the Team-based Learning activities and their own experiences of teamwork in clinical practice. Exam scores on the question related to Team-based Learning-taught material were comparable to those

Application of Learning Engineering Techniques Thinking Aloud Pair Problem Solving in Learning Mathematics Students Class VII SMPN 15 Padang

Science.gov (United States)

Widuri, S. Y. S.; Almash, L.; Zuzano, F.

2018-04-01

The students activity and responsible in studying mathematic is still lack. It gives an effect for the bad result in studying mathematic. There is one of learning technic to increase students activity in the classroom and the result of studying mathematic with applying a learning technic. It is “Thinking Aloud Pair Problem Solving (TAPPS)”. The purpose of this research is to recognize the developing of students activity in mathematic subject during applying that technic “TAPPS” in seven grade at SMPN 15 Padang and compare the students proportion in learning mathematic with TAPPS between learning process without it in seven grade at SMPN 15 Padang. Students activity for indicators 1, 2, 3, 4, 5, 6 at each meeting is likely to increase and students activity for indicator 7 at each meeting is likely to decrease. The finding of this research is χ 2 = 9,42 and the value of p is 0,0005 < p < 0,005. Therefore p < 0,05 has means H 0 was rejected and H 1 was accepted. Thus, it was concluded that the activities and result in studying mathematic increased after applying learning technic the TAPPS.

Statistical and Machine-Learning Data Mining Techniques for Better Predictive Modeling and Analysis of Big Data

CERN Document Server

Ratner, Bruce

2011-01-01

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has

Applying the Science of Learning: Evidence-Based Principles for the Design of Multimedia Instruction

Science.gov (United States)

Mayer, Richard E.

2008-01-01

During the last 100 years, a major accomplishment of psychology has been the development of a science of learning aimed at understanding how people learn. In attempting to apply the science of learning, a central challenge of psychology and education is the development of a science of instruction aimed at understanding how to present material in…

Some Consequences of Learning Theory Applied to Division of Fractions

Science.gov (United States)

Bidwell, James K.

1971-01-01

Reviews the learning theories of Robert Gagne and David Ausubel, and applies these theories to the three most common approaches to teaching division of fractions: common denominator, complex fraction, and inverse operation methods. Such analysis indicates the inverse approach should be most effective for meaningful teaching, as is verified by…

Lessons learned applying CASE methods/tools to Ada software development projects

Science.gov (United States)

Blumberg, Maurice H.; Randall, Richard L.

1993-01-01

This paper describes the lessons learned from introducing CASE methods/tools into organizations and applying them to actual Ada software development projects. This paper will be useful to any organization planning to introduce a software engineering environment (SEE) or evolving an existing one. It contains management level lessons learned, as well as lessons learned in using specific SEE tools/methods. The experiences presented are from Alpha Test projects established under the STARS (Software Technology for Adaptable and Reliable Systems) project. They reflect the front end efforts by those projects to understand the tools/methods, initial experiences in their introduction and use, and later experiences in the use of specific tools/methods and the introduction of new ones.

Swarm Intelligence: New Techniques for Adaptive Systems to Provide Learning Support

Science.gov (United States)

Wong, Lung-Hsiang; Looi, Chee-Kit

2012-01-01

The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…

Evaluation of the Use of Two Teaching Techniques in Engineering

Directory of Open Access Journals (Sweden)

Jose Antonio Alvarez Salas

2014-06-01

Full Text Available This paper presents an analysis of the practical implementation of two teaching techniques so-called Problem-Based Learning and Cooperative Learning. These techniques were applied to some courses in the Department of Mechanical and Electrical Engineering and evaluated through assessment rubrics. In a sample of students and teachers, the assessment rubrics were applied to numerically evaluate the proportion of each course, in which the teacher uses traditional teaching versus teaching for meaningful learning. The results of the presented analysis allow to verify the use of these teaching techniques by professors of the Department of Mechanical and Electrical Engineering. This activity was developed as a part of the work established by the Institutional Development Plan of the Faculty of Engineering, which includes the strategic objective of developing an innovative educational model in the following ten years.

Data mining practical machine learning tools and techniques

CERN Document Server

Witten, Ian H

2005-01-01

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same

Applying DEA Technique to Library Evaluation in Academic Research Libraries.

Science.gov (United States)

Shim, Wonsik

2003-01-01

This study applied an analytical technique called Data Envelopment Analysis (DEA) to calculate the relative technical efficiency of 95 academic research libraries, all members of the Association of Research Libraries. DEA, with the proper model of library inputs and outputs, can reveal best practices in the peer groups, as well as the technical…

A review of supervised machine learning applied to ageing research.

Science.gov (United States)

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Machine learning applied to the prediction of citrus production

International Nuclear Information System (INIS)

Díaz, I.; Mazza, S.M.; Combarro, E.F.; Giménez, L.I.; Gaiad, J.E.

2017-01-01

An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

Machine learning applied to the prediction of citrus production

Directory of Open Access Journals (Sweden)

Irene Díaz

2017-07-01

Full Text Available An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i studies the effectiveness of machine learning techniques for predicting orchards production; and (ii variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees’ age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8 and relative mean absolute error (~0.1. These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

Machine learning applied to the prediction of citrus production

Energy Technology Data Exchange (ETDEWEB)

Díaz, I.; Mazza, S.M.; Combarro, E.F.; Giménez, L.I.; Gaiad, J.E.

2017-07-01

An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

Considerations for Task Analysis Methods and Rapid E-Learning Development Techniques

Directory of Open Access Journals (Sweden)

Dr. Ismail Ipek

2014-02-01

Full Text Available The purpose of this paper is to provide basic dimensions for rapid training development in e-learning courses in education and business. Principally, it starts with defining task analysis and how to select tasks for analysis and task analysis methods for instructional design. To do this, first, learning and instructional technologies as visions of the future were discussed. Second, the importance of task analysis methods in rapid e-learning was considered, with learning technologies as asynchronous and synchronous e-learning development. Finally, rapid instructional design concepts and e-learning design strategies were defined and clarified with examples, that is, all steps for effective task analysis and rapid training development techniques based on learning and instructional design approaches were discussed, such as m-learning and other delivery systems. As a result, the concept of task analysis, rapid e-learning development strategies and the essentials of online course design were discussed, alongside learner interface design features for learners and designers.

A Learning Method for Neural Networks Based on a Pseudoinverse Technique

Directory of Open Access Journals (Sweden)

Chinmoy Pal

1996-01-01

Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.

Super Resolution and Interference Suppression Technique applied to SHARAD Radar Data

Science.gov (United States)

Raguso, M. C.; Mastrogiuseppe, M.; Seu, R.; Piazzo, L.

2017-12-01

We will present a super resolution and interference suppression technique applied to the data acquired by the SHAllow RADar (SHARAD) on board the NASA's 2005 Mars Reconnaissance Orbiter (MRO) mission, currently operating around Mars [1]. The algorithms allow to improve the range resolution roughly by a factor of 3 and the Signal to Noise Ratio (SNR) by a several decibels. Range compression algorithms usually adopt conventional Fourier transform techniques, which are limited in the resolution by the transmitted signal bandwidth, analogous to the Rayleigh's criterion in optics. In this work, we investigate a super resolution method based on autoregressive models and linear prediction techniques [2]. Starting from the estimation of the linear prediction coefficients from the spectral data, the algorithm performs the radar bandwidth extrapolation (BWE), thereby improving the range resolution of the pulse-compressed coherent radar data. Moreover, the EMIs (ElectroMagnetic Interferences) are detected and the spectra is interpolated in order to reconstruct an interference free spectrum, thereby improving the SNR. The algorithm can be applied to the single complex look image after synthetic aperture processing (SAR). We apply the proposed algorithm to simulated as well as to real radar data. We will demonstrate the effective enhancement on vertical resolution with respect to the classical spectral estimator. We will show that the imaging of the subsurface layered structures observed in radargrams is improved, allowing additional insights for the scientific community in the interpretation of the SHARAD radar data, which will help to further our understanding of the formation and evolution of known geological features on Mars. References: [1] Seu et al. 2007, Science, 2007, 317, 1715-1718 [2] K.M. Cuomo, "A Bandwidth Extrapolation Technique for Improved Range Resolution of Coherent Radar Data", Project Report CJP-60, Revision 1, MIT Lincoln Laboratory (4 Dec. 1992).

Greedy Deep Dictionary Learning

OpenAIRE

Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

2016-01-01

In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

Surface analytical techniques applied to minerals processing

International Nuclear Information System (INIS)

Smart, R.St.C.

1991-01-01

An understanding of the chemical and physical forms of the chemically altered layers on the surfaces of base metal sulphides, particularly in the form of hydroxides, oxyhydroxides and oxides, and the changes that occur in them during minerals processing lies at the core of a complete description of flotation chemistry. This paper reviews the application of a variety of surface-sensitive techniques and methodologies applied to the study of surface layers on single minerals, mixed minerals, synthetic ores and real ores. Evidence from combined XPS/SAM/SEM studies have provided images and analyses of three forms of oxide, oxyhydroxide and hydroxide products on the surfaces of single sulphide minerals, mineral mixtures and complex sulphide ores. 4 refs., 2 tabs., 4 figs

The correlated k-distribution technique as applied to the AVHRR channels

Science.gov (United States)

Kratz, David P.

1995-01-01

Correlated k-distributions have been created to account for the molecular absorption found in the spectral ranges of the five Advanced Very High Resolution Radiometer (AVHRR) satellite channels. The production of the k-distributions was based upon an exponential-sum fitting of transmissions (ESFT) technique which was applied to reference line-by-line absorptance calculations. To account for the overlap of spectral features from different molecular species, the present routines made use of the multiplication transmissivity property which allows for considerable flexibility, especially when altering relative mixing ratios of the various molecular species. To determine the accuracy of the correlated k-distribution technique as compared to the line-by-line procedure, atmospheric flux and heating rate calculations were run for a wide variety of atmospheric conditions. For the atmospheric conditions taken into consideration, the correlated k-distribution technique has yielded results within about 0.5% for both the cases where the satellite spectral response functions were applied and where they were not. The correlated k-distribution's principal advantages is that it can be incorporated directly into multiple scattering routines that consider scattering as well as absorption by clouds and aerosol particles.

An experimental result of estimating an application volume by machine learning techniques.

Science.gov (United States)

Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

2015-01-01

In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

Application and evaluation of a combination of socratice and learning through discussion techniques

Directory of Open Access Journals (Sweden)

EJ van Aswegen

2001-09-01

Full Text Available This article has its genesis in the inquirer’s interest in the need for internalizing critical thinking, creative thinking and reflective skills in adult learners. As part of a broader study the inquirer used a combination of two techniques over a period of nine months, namely: Socratic discussion/questioning and Learning Through Discussion Technique. The inquirer within this inquiry elected mainly qualitative methods, because they were seen as more adaptable to dealing with multiple realities and more sensitive and adaptable to the many shaping influences and value patterns that may be encountered (Lincoln & Guba, 1989. Purposive sampling was used and sample size (n =10 was determined by the willingness of potential participants to enlist in the chosen techniques. Feedback from participants was obtained: (1 verbally after each discussion session, and (2 in written format after completion of the course content. The final/ summative evaluation was obtained through a semi-structured questionnaire. This was deemed necessary, in that the participants were already studying for the end of the year examination. For the purpose of this condensed report the inquirer reflected only on the feedback obtained with the help of the questionnaire. The empirical study showed that in spite of various adaptation problems experienced, eight (8 of the ten (10 participants felt positive toward the applied techniques.

Action Research to Improve the Learning Space for Diagnostic Techniques

Directory of Open Access Journals (Sweden)

Ellen Ariel

2015-08-01

Full Text Available The module described and evaluated here was created in response to perceived learning difficulties in diagnostic test design and interpretation for students in third-year Clinical Microbiology. Previously, the activities in lectures and laboratory classes in the module fell into the lower cognitive operations of “knowledge” and “understanding.” The new approach was to exchange part of the traditional activities with elements of interactive learning, where students had the opportunity to engage in deep learning using a variety of learning styles. The effectiveness of the new curriculum was assessed by means of on-course student assessment throughout the module, a final exam, an anonymous questionnaire on student evaluation of the different activities and a focus group of volunteers. Although the new curriculum enabled a major part of the student cohort to achieve higher pass grades (p < 0.001, it did not meet the requirements of the weaker students, and the proportion of the students failing the module remained at 34%. The action research applied here provided a number of valuable suggestions from students on how to improve future curricula from their perspective. Most importantly, an interactive online program that facilitated flexibility in the learning space for the different reagents and their interaction in diagnostic tests was proposed. The methods applied to improve and assess a curriculum refresh by involving students as partners in the process, as well as the outcomes, are discussed.

Action Research to Improve the Learning Space for Diagnostic Techniques.

Science.gov (United States)

Ariel, Ellen; Owens, Leigh

2015-12-01

The module described and evaluated here was created in response to perceived learning difficulties in diagnostic test design and interpretation for students in third-year Clinical Microbiology. Previously, the activities in lectures and laboratory classes in the module fell into the lower cognitive operations of "knowledge" and "understanding." The new approach was to exchange part of the traditional activities with elements of interactive learning, where students had the opportunity to engage in deep learning using a variety of learning styles. The effectiveness of the new curriculum was assessed by means of on-course student assessment throughout the module, a final exam, an anonymous questionnaire on student evaluation of the different activities and a focus group of volunteers. Although the new curriculum enabled a major part of the student cohort to achieve higher pass grades (p < 0.001), it did not meet the requirements of the weaker students, and the proportion of the students failing the module remained at 34%. The action research applied here provided a number of valuable suggestions from students on how to improve future curricula from their perspective. Most importantly, an interactive online program that facilitated flexibility in the learning space for the different reagents and their interaction in diagnostic tests was proposed. The methods applied to improve and assess a curriculum refresh by involving students as partners in the process, as well as the outcomes, are discussed. Journal of Microbiology & Biology Education.

MLnet report: training in Europe on machine learning

OpenAIRE

Ellebrecht, Mario; Morik, Katharina

1999-01-01

Machine learning techniques offer opportunities for a variety of applications and the theory of machine learning investigates problems that are of interest for other fields of computer science (e.g., complexity theory, logic programming, pattern recognition). However, the impacts of machine learning can only be recognized by those who know the techniques and are able to apply them. Hence, teaching machine learning is necessary before this field can diversify computer science. In order ...

Does the modality principle for multimedia learning apply to science classrooms?

NARCIS (Netherlands)

Harskamp, Egbert G.; Mayer, Richard E.; Suhre, Cor

2007-01-01

This study demonstrated that the modality principle applies to multimedia learning of regular science lessons in school settings. In the first field experiment, 27 Dutch secondary school students (age 16-17) received a self-paced, web-based multimedia lesson in biology. Students who received lessons

Chemically intuited, large-scale screening of MOFs by machine learning techniques

Science.gov (United States)

Borboudakis, Giorgos; Stergiannakos, Taxiarchis; Frysali, Maria; Klontzas, Emmanuel; Tsamardinos, Ioannis; Froudakis, George E.

2017-10-01

A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

Technique applied in electrical power distribution for Satellite Launch Vehicle

Directory of Open Access Journals (Sweden)

João Maurício Rosário

2010-09-01

Full Text Available The Satellite Launch Vehicle electrical network, which is currently being developed in Brazil, is sub-divided for analysis in the following parts: Service Electrical Network, Controlling Electrical Network, Safety Electrical Network and Telemetry Electrical Network. During the pre-launching and launching phases, these electrical networks are associated electrically and mechanically to the structure of the vehicle. In order to succeed in the integration of these electrical networks it is necessary to employ techniques of electrical power distribution, which are proper to Launch Vehicle systems. This work presents the most important techniques to be considered in the characterization of the electrical power supply applied to Launch Vehicle systems. Such techniques are primarily designed to allow the electrical networks, when submitted to the single-phase fault to ground, to be able of keeping the power supply to the loads.

[Technique and value of direct MR arthrography applying articular distraction].

Science.gov (United States)

Becce, Fabio; Wettstein, Michael; Guntern, Daniel; Mouhsine, Elyazid; Palhais, Nuno; Theumann, Nicolas

2010-02-24

Direct MR arthrography has a better diagnostic accuracy than MR imaging alone. However, contrast material is not always homogeneously distributed in the articular space. Lesions of cartilage surfaces or intra-articular soft tissues can thus be misdiagnosed. Concomitant application of axial traction during MR arthrography leads to articular distraction. This enables better distribution of contrast material in the joint and better delineation of intra-articular structures. Therefore, this technique improves detection of cartilage lesions. Moreover, the axial stress applied on articular structures may reveal lesions invisible on MR images without traction. Based on our clinical experience, we believe that this relatively unknown technique is promising and should be further developed.

Optimization technique applied to interpretation of experimental data and research of constitutive laws

International Nuclear Information System (INIS)

Grossette, J.C.

1982-01-01

The feasibility of identification technique applied to one dimensional numerical analysis of the split-Hopkinson pressure bar experiment is proven. A general 1-D elastic-plastic-viscoplastic computer program was written down so as to give an adequate solution for elastic-plastic-viscoplastic response of a pressure bar subjected to a general Heaviside step loading function in time which is applied over one end of the bar. Special emphasis is placed on the response of the specimen during the first microseconds where no equilibrium conditions can be stated. During this transient phase discontinuity conditions related to wave propagation are encountered and must be carefully taken into account. Having derived an adequate numerical model, then Pontryagin identification technique has been applied in such a way that the unknowns are physical parameters. The solutions depend mainly on the selection of a class of proper eigen objective functionals (cost functions) which may be combined so as to obtain a convenient numerical objective function. A number of significant questions arising in the choice of parameter adjustment algorithms are discussed. In particular, this technique leads to a two point boundary value problem which has been solved using an iterative gradient like technique usually referred to as a double operator gradient method. This method combines the classical Fletcher-Powell technique and a partial quadratic technique with an automatic parameter step size selection. This method is much more efficient than usual ones. Numerical experimentation with simulated data was performed to test the accuracy and stability of the identification algorithm and to determine the most adequate type and quantity of data for estimation purposes

Simulation-based learning: Just like the real thing.

Science.gov (United States)

Lateef, Fatimah

2010-10-01

Simulation is a technique for practice and learning that can be applied to many different disciplines and trainees. It is a technique (not a technology) to replace and amplify real experiences with guided ones, often "immersive" in nature, that evoke or replicate substantial aspects of the real world in a fully interactive fashion. Simulation-based learning can be the way to develop health professionals' knowledge, skills, and attitudes, whilst protecting patients from unnecessary risks. Simulation-based medical education can be a platform which provides a valuable tool in learning to mitigate ethical tensions and resolve practical dilemmas. Simulation-based training techniques, tools, and strategies can be applied in designing structured learning experiences, as well as be used as a measurement tool linked to targeted teamwork competencies and learning objectives. It has been widely applied in fields such aviation and the military. In medicine, simulation offers good scope for training of interdisciplinary medical teams. The realistic scenarios and equipment allows for retraining and practice till one can master the procedure or skill. An increasing number of health care institutions and medical schools are now turning to simulation-based learning. Teamwork training conducted in the simulated environment may offer an additive benefit to the traditional didactic instruction, enhance performance, and possibly also help reduce errors.

An Investigation of Employees' Use of E-Learning Systems: Applying the Technology Acceptance Model

Science.gov (United States)

Lee, Yi-Hsuan; Hsieh, Yi-Chuan; Chen, Yen-Hsun

2013-01-01

The purpose of this study is to apply the technology acceptance model to examine the employees' attitudes and acceptance of electronic learning (e-learning) systems in organisations. This study examines four factors (organisational support, computer self-efficacy, prior experience and task equivocality) that are believed to influence employees'…

Promoting Cooperative Learning in the Classroom: Comparing Explicit and Implicit Training Techniques

Directory of Open Access Journals (Sweden)

Anne Elliott

2003-07-01

Full Text Available In this study, we investigated whether providing 4th and 5th-grade students with explicit instruction in prerequisite cooperative-learning skills and techniques would enhance their academic performance and promote in them positive attitudes towards cooperative learning. Overall, students who received explicit training outperformed their peers on both the unit project and test and presented more favourable attitudes towards cooperative learning. The findings of this study support the use of explicitly instructing students about the components of cooperative learning prior to engaging in collaborative activities. Implications for teacher-education are discussed.

Applying squeezing technique to clay-rocks: lessons learned from ten years experiments at Mont Terri

International Nuclear Information System (INIS)

Fernandez, A. M.; Melon, A.; Sanchez-Ledesma, D.M.; Tournassat, C.; Gaucher, E.; Astudillo, J.; Vinsot, A.

2012-01-01

Document available in extended abstract form only. Argillaceous formations of low permeability are considered in several countries as potential host rocks for the disposal of high level radioactive wastes (HLRW). In order to determine their suitability for waste disposal, evaluations of the hydro-geochemistry and transport mechanisms from such geologic formations to the biosphere must be undertaken. The migration of radionuclides through the geosphere will occur predominantly in the aqueous phase, and hence the pore water chemistry plays an important role in determining ion diffusion characteristics in argillaceous formations. Consequently, a great effort has been made to characterise the pore water chemistry in clay-rocks formations. In the last 10 years various techniques were developed for determining pore water composition of clay-rocks including both direct and indirect methods: 1) In situ pore water sampling (water and gas) from sealed boreholes (Pearson et al., 2003; Vinsot et al. 2008); 2) Laboratory pore water sampling from unaltered core samples by the squeezing technique at high pressures (Fernandez et al., 2009); and 3) Characterization of the water chemistry by geochemical modelling (Gaucher et al. 2009). Pore water chemistry in clay-rocks and extraction techniques were documented and reviewed in different studies (Sacchi et al., 2001). Recovering pristine pore water from low permeable and low water content systems is very difficult and sometimes impossible. Besides, uncertainties are associated to each method used for the pore water characterization. In this paper, a review about the high pressure squeezing technique applied to indurate clay-rocks was performed. For this purpose, the experimental work on Opalinus Clay at the Mont Terri Research Laboratory during the last ten years was evaluated. A complete discussion was made about different issues such as: a) why is necessary to obtain the pore water by squeezing in the context of radioactive waste

Generating a Spanish Affective Dictionary with Supervised Learning Techniques

Science.gov (United States)

Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

2016-01-01

Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

Science.gov (United States)

Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

2017-12-01

In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

Evaluation of irradiation damage effect by applying electric properties based techniques

International Nuclear Information System (INIS)

Acosta, B.; Sevini, F.

2004-01-01

The most important effect of the degradation by radiation is the decrease in the ductility of the pressure vessel of the reactor (RPV) ferritic steels. The main way to determine the mechanical behaviour of the RPV steels is tensile and impact tests, from which the ductile to brittle transition temperature (DBTT) and its increase due to neutron irradiation can be calculated. These tests are destructive and regularly applied to surveillance specimens to assess the integrity of RPV. The possibility of applying validated non-destructive ageing monitoring techniques would however facilitate the surveillance of the materials that form the reactor vessel. The JRC-IE has developed two devices, focused on the measurement of the electrical properties to assess non-destructively the embrittlement state of materials. The first technique, called Seebeck and Thomson Effects on Aged Material (STEAM), is based on the measurement of the Seebeck coefficient, characteristic of the material and related to the microstructural changes induced by irradiation embrittlement. With the same aim the second technique, named Resistivity Effects on Aged Material (REAM), measures instead the resistivity of the material. The purpose of this research is to correlate the results of the impact tests, STEAM and REAM measurements with the change in the mechanical properties due to neutron irradiation. These results will make possible the improvement of such techniques based on the measurement of material electrical properties for their application to the irradiation embrittlement assessment

Using the IGCRA (individual, group, classroom reflective action technique to enhance teaching and learning in large accountancy classes

Directory of Open Access Journals (Sweden)

Cristina Poyatos

2011-02-01

Full Text Available First year accounting has generally been perceived as one of the more challenging first year business courses for university students. Various Classroom Assessment Techniques (CATs have been proposed to attempt to enrich and enhance student learning, with these studies generally positioning students as learners alone. This paper uses an educational case study approach and examines the implementation of the IGCRA (individual, group, classroom reflective action technique, a Classroom Assessment Technique, on first year accounting students’ learning performance. Building on theoretical frameworks in the areas of cognitive learning, social development, and dialogical learning, the technique uses reports to promote reflection on both learning and teaching. IGCRA was found to promote feedback on the effectiveness of student, as well as teacher satisfaction. Moreover, the results indicated formative feedback can assist to improve the learning and learning environment for a large group of first year accounting students. Clear guidelines for its implementation are provided in the paper.

Sentiment Analysis in Geo Social Streams by using Machine Learning Techniques

OpenAIRE

Twanabasu, Bikesh

2018-01-01

Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2017-2018 Massive amounts of sentiment rich data are generated on social media in the form of Tweets, status updates, blog post, reviews, etc. Different people and organizations are using these user generated content for decision making. Symbolic techniques or Knowledge base approaches and Machine learning techniques are two main techniques used for analysis sentiment...

Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning

Science.gov (United States)

Feng, Mingyu; Beck, Joseph E.; Heffernan, Neil T.

2009-01-01

A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by…

Applying field mapping refractive beam shapers to improve holographic techniques

Science.gov (United States)

Laskin, Alexander; Williams, Gavin; McWilliam, Richard; Laskin, Vadim

2012-03-01

Performance of various holographic techniques can be essentially improved by homogenizing the intensity profile of the laser beam with using beam shaping optics, for example, the achromatic field mapping refractive beam shapers like πShaper. The operational principle of these devices presumes transformation of laser beam intensity from Gaussian to flattop one with high flatness of output wavefront, saving of beam consistency, providing collimated output beam of low divergence, high transmittance, extended depth of field, negligible residual wave aberration, and achromatic design provides capability to work with several laser sources with different wavelengths simultaneously. Applying of these beam shapers brings serious benefits to the Spatial Light Modulator based techniques like Computer Generated Holography or Dot-Matrix mastering of security holograms since uniform illumination of an SLM allows simplifying mathematical calculations and increasing predictability and reliability of the imaging results. Another example is multicolour Denisyuk holography when the achromatic πShaper provides uniform illumination of a field at various wavelengths simultaneously. This paper will describe some design basics of the field mapping refractive beam shapers and optical layouts of their applying in holographic systems. Examples of real implementations and experimental results will be presented as well.

A Delphi Study on Technology Enhanced Learning (TEL) Applied on Computer Science (CS) Skills

Science.gov (United States)

Porta, Marcela; Mas-Machuca, Marta; Martinez-Costa, Carme; Maillet, Katherine

2012-01-01

Technology Enhanced Learning (TEL) is a new pedagogical domain aiming to study the usage of information and communication technologies to support teaching and learning. The following study investigated how this domain is used to increase technical skills in Computer Science (CS). A Delphi method was applied, using three-rounds of online survey…

Problems and advantages of applying the e-learning model to the teaching of English

OpenAIRE

Shaparenko, А.; Golikova, А.

2013-01-01

In this article we mention some potential and noted problems and advantages of applying the e-learning model to the teaching of English. In the area of foreign language teaching a lot has been done, but there are constant attempts for new solutions. Another option for e-learning is a hybrid course.

Hypnosis and Language Learning.

Science.gov (United States)

Hammerman, Myrna Lynn

A thorough investiqation is attempted of efforts to apply hypnosis and suggestive learning techniques to education in general and specifically to second language learning. Hypnosis is discussed in terms of its dangers, its definition, and its application. Included in this discussion is a comparison of auto- and hetero-hypnosis, an overview of the…

Deep Learning Techniques for Top-Quark Reconstruction

CERN Document Server

Naderi, Kiarash

2017-01-01

Top quarks are unique probes of the standard model (SM) predictions and have the potential to be a window for physics beyond the SM (BSM). Top quarks decay to a $Wb$ pair, and the $W$ can decay in leptons or jets. In a top pair event, assigning jets to their correct source is a challenge. In this study, I studied different methods for improving top reconstruction. The main motivation was to use Deep Learning Techniques in order to enhance the precision of top reconstruction.

Imbalanced Learning for Functional State Assessment

Science.gov (United States)

Li, Feng; McKenzie, Frederick; Li, Jiang; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

2011-01-01

This paper presents results of several imbalanced learning techniques applied to operator functional state assessment where the data is highly imbalanced, i.e., some function states (majority classes) have much more training samples than other states (minority classes). Conventional machine learning techniques usually tend to classify all data samples into majority classes and perform poorly for minority classes. In this study, we implemented five imbalanced learning techniques, including random undersampling, random over-sampling, synthetic minority over-sampling technique (SMOTE), borderline-SMOTE and adaptive synthetic sampling (ADASYN) to solve this problem. Experimental results on a benchmark driving lest dataset show thai accuracies for minority classes could be improved dramatically with a cost of slight performance degradations for majority classes,

Just-in-Time techniques as applied to hazardous materials management

OpenAIRE

Spicer, John S.

1996-01-01

Approved for public release; distribution is unlimited This study investigates the feasibility of integrating JIT techniques in the context of hazardous materials management. This study provides a description of JIT, a description of environmental compliance issues and the outgrowth of related HAZMAT policies, and a broad perspective on strategies for applying JIT to HAZMAT management. http://archive.org/details/justintimetechn00spic Lieutenant Commander, United States Navy

Students' perceptions of effective learning experiences in dental school: a qualitative study using a critical incident technique.

Science.gov (United States)

Victoroff, Kristin Zakariasen; Hogan, Sarah

2006-02-01

Students' views of their educational experience can be an important source of information for curriculum assessment. Although quantitative methods, particularly surveys, are frequently used to gather such data, fewer studies have employed qualitative methods to examine students' dental education experiences. The purpose of this study is to explore characteristics of effective learning experiences in dental school using a qualitative method. All third-year (seventy) and fourth-year (seventy) dental students enrolled in one midwestern dental school were invited to participate. Fifty-three dental students (thirty-five male and eighteen female; thirty-two third-year and twenty-one fourth-year) were interviewed using a critical incident interview technique. Each student was asked to describe a specific, particularly effective learning incident that he or she had experienced in dental school and a specific, particularly ineffective learning incident, for comparison. Each interview was audiotaped. Students were assured that only the interviewer and one additional researcher would have access to the tapes. Data analysis resulted in identification of key themes in the data describing characteristics of effective learning experiences. The following characteristics of effective learning experiences were identified: 1) instructor characteristics (personal qualities, "checking-in" with students, and an interactive style); 2) characteristics of the learning process (focus on the "big picture," modeling and demonstrations, opportunities to apply new knowledge, high-quality feedback, focus, specificity and relevance, and peer interactions); and 3) learning environment (culture of the learning environment, technology). Common themes emerged across a wide variety of learning incidents. Although additional research is needed, the characteristics of effective learning experiences identified in this study may have implications for individual course design and for the dental school

Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques

CSIR Research Space (South Africa)

Ngxande, Mkhuseli

2017-11-01

Full Text Available This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features...

Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.

Science.gov (United States)

Vallmuur, Kirsten; Marucci-Wellman, Helen R; Taylor, Jennifer A; Lehto, Mark; Corns, Helen L; Smith, Gordon S

2016-04-01

Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

LEARNING SEMANTICS-ENHANCED LANGUAGE MODELS APPLIED TO UNSUEPRVISED WSD

Energy Technology Data Exchange (ETDEWEB)

VERSPOOR, KARIN [Los Alamos National Laboratory; LIN, SHOU-DE [Los Alamos National Laboratory

2007-01-29

An N-gram language model aims at capturing statistical syntactic word order information from corpora. Although the concept of language models has been applied extensively to handle a variety of NLP problems with reasonable success, the standard model does not incorporate semantic information, and consequently limits its applicability to semantic problems such as word sense disambiguation. We propose a framework that integrates semantic information into the language model schema, allowing a system to exploit both syntactic and semantic information to address NLP problems. Furthermore, acknowledging the limited availability of semantically annotated data, we discuss how the proposed model can be learned without annotated training examples. Finally, we report on a case study showing how the semantics-enhanced language model can be applied to unsupervised word sense disambiguation with promising results.

Three-dimensional integrated CAE system applying computer graphic technique

International Nuclear Information System (INIS)

Kato, Toshisada; Tanaka, Kazuo; Akitomo, Norio; Obata, Tokayasu.

1991-01-01

A three-dimensional CAE system for nuclear power plant design is presented. This system utilizes high-speed computer graphic techniques for the plant design review, and an integrated engineering database for handling the large amount of nuclear power plant engineering data in a unified data format. Applying this system makes it possible to construct a nuclear power plant using only computer data from the basic design phase to the manufacturing phase, and it increases the productivity and reliability of the nuclear power plants. (author)

The colloquial approach: An active learning technique

Science.gov (United States)

Arce, Pedro

1994-09-01

This paper addresses the very important problem of the effectiveness of teaching methodologies in fundamental engineering courses such as transport phenomena. An active learning strategy, termed the colloquial approach, is proposed in order to increase student involvement in the learning process. This methodology is a considerable departure from traditional methods that use solo lecturing. It is based on guided discussions, and it promotes student understanding of new concepts by directing the student to construct new ideas by building upon the current knowledge and by focusing on key cases that capture the essential aspects of new concepts. The colloquial approach motivates the student to participate in discussions, to develop detailed notes, and to design (or construct) his or her own explanation for a given problem. This paper discusses the main features of the colloquial approach within the framework of other current and previous techniques. Problem-solving strategies and the need for new textbooks and for future investigations based on the colloquial approach are also outlined.

Applying recursive numerical integration techniques for solving high dimensional integrals

International Nuclear Information System (INIS)

Ammon, Andreas; Genz, Alan; Hartung, Tobias; Jansen, Karl; Volmer, Julia; Leoevey, Hernan

2016-11-01

The error scaling for Markov-Chain Monte Carlo techniques (MCMC) with N samples behaves like 1/√(N). This scaling makes it often very time intensive to reduce the error of computed observables, in particular for applications in lattice QCD. It is therefore highly desirable to have alternative methods at hand which show an improved error scaling. One candidate for such an alternative integration technique is the method of recursive numerical integration (RNI). The basic idea of this method is to use an efficient low-dimensional quadrature rule (usually of Gaussian type) and apply it iteratively to integrate over high-dimensional observables and Boltzmann weights. We present the application of such an algorithm to the topological rotor and the anharmonic oscillator and compare the error scaling to MCMC results. In particular, we demonstrate that the RNI technique shows an error scaling in the number of integration points m that is at least exponential.

Applying recursive numerical integration techniques for solving high dimensional integrals

Energy Technology Data Exchange (ETDEWEB)

Ammon, Andreas [IVU Traffic Technologies AG, Berlin (Germany); Genz, Alan [Washington State Univ., Pullman, WA (United States). Dept. of Mathematics; Hartung, Tobias [King' s College, London (United Kingdom). Dept. of Mathematics; Jansen, Karl; Volmer, Julia [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Leoevey, Hernan [Humboldt Univ. Berlin (Germany). Inst. fuer Mathematik

2016-11-15

The error scaling for Markov-Chain Monte Carlo techniques (MCMC) with N samples behaves like 1/√(N). This scaling makes it often very time intensive to reduce the error of computed observables, in particular for applications in lattice QCD. It is therefore highly desirable to have alternative methods at hand which show an improved error scaling. One candidate for such an alternative integration technique is the method of recursive numerical integration (RNI). The basic idea of this method is to use an efficient low-dimensional quadrature rule (usually of Gaussian type) and apply it iteratively to integrate over high-dimensional observables and Boltzmann weights. We present the application of such an algorithm to the topological rotor and the anharmonic oscillator and compare the error scaling to MCMC results. In particular, we demonstrate that the RNI technique shows an error scaling in the number of integration points m that is at least exponential.

Machine learning modelling for predicting soil liquefaction susceptibility

Directory of Open Access Journals (Sweden)

P. Samui

2011-01-01

Full Text Available This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN based on multi-layer perceptions (MLP that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N₁₆₀] and cyclic stress ratio (CSR. Further, an attempt has been made to simplify the models, requiring only the two parameters [(N₁₆₀ and peck ground acceleration (a_max/g], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Applying Web-Based Co-Regulated Learning to Develop Students' Learning and Involvement in a Blended Computing Course

Science.gov (United States)

Tsai, Chia-Wen

2015-01-01

This research investigated, via quasi-experiments, the effects of web-based co-regulated learning (CRL) on developing students' computing skills. Two classes of 68 undergraduates in a one-semester course titled "Applied Information Technology: Data Processing" were chosen for this research. The first class (CRL group, n = 38) received…

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

Directory of Open Access Journals (Sweden)

Bin Xu

2017-01-01

Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

Science.gov (United States)

Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

Simulation-based learning: Just like the real thing

Directory of Open Access Journals (Sweden)

Lateef Fatimah

2010-01-01

Full Text Available Simulation is a technique for practice and learning that can be applied to many different disciplines and trainees. It is a technique (not a technology to replace and amplify real experiences with guided ones, often "immersive" in nature, that evoke or replicate substantial aspects of the real world in a fully interactive fashion. Simulation-based learning can be the way to develop health professionals′ knowledge, skills, and attitudes, whilst protecting patients from unnecessary risks. Simulation-based medical education can be a platform which provides a valuable tool in learning to mitigate ethical tensions and resolve practical dilemmas. Simulation-based training techniques, tools, and strategies can be applied in designing structured learning experiences, as well as be used as a measurement tool linked to targeted teamwork competencies and learning objectives. It has been widely applied in fields such aviation and the military. In medicine, simulation offers good scope for training of interdisciplinary medical teams. The realistic scenarios and equipment allows for retraining and practice till one can master the procedure or skill. An increasing number of health care institutions and medical schools are now turning to simulation-based learning. Teamwork training conducted in the simulated environment may offer an additive benefit to the traditional didactic instruction, enhance performance, and possibly also help reduce errors.

Development of technique to apply induction heating stress improvement to recirculation inlet nozzle

International Nuclear Information System (INIS)

Chiba, Kunihiko; Nihei, Kenichi; Ootaka, Minoru

2009-01-01

Stress corrosion cracking (SCC) have been found in the primary loop recirculation (PLR) systems of boiling water reactors (BWR). Residual stress in welding heat-affected zone is one of the factors of SCC, and the residual stress improvement is one of the most effective methods to prevent SCC. Induction heating stress improvement (IHSI) is one of the techniques to improve reduce residual stress. However, it is difficult to apply IHSI to the place such as the recirculation inlet nozzle where the flow stagnates. In this present study, the technique to apply IHSI to the recirculation inlet nozzle was developed using water jet which blowed into the crevice between the nozzle safe end and the thermal sleeve. (author)

Stimulating Deep Learning Using Active Learning Techniques

Science.gov (United States)

Yew, Tee Meng; Dawood, Fauziah K. P.; a/p S. Narayansany, Kannaki; a/p Palaniappa Manickam, M. Kamala; Jen, Leong Siok; Hoay, Kuan Chin

2016-01-01

When students and teachers behave in ways that reinforce learning as a spectator sport, the result can often be a classroom and overall learning environment that is mostly limited to transmission of information and rote learning rather than deep approaches towards meaningful construction and application of knowledge. A group of college instructors…

Applying Cognitive Linguistics to Instructed L2 Learning: The English Modals

Science.gov (United States)

Tyler, Andrea; Mueller, Charles M.; Ho, Vu

2010-01-01

This paper reports the results of a quasi-experimental effects-of-instruction study examining the efficacy of applying a Cognitive Linguistic (CL) approach to L2 learning of the semantics of English modals. In spite of their frequency in typical input, modal verbs present L2 learners with difficulties, party due to their inherent…

Using the 5E Learning Cycle with Metacognitive Technique to Enhance Students’ Mathematical Critical Thinking Skills

Directory of Open Access Journals (Sweden)

Runisah Runisah

2017-02-01

Full Text Available This study aims to describe enhancement and achievement of mathematical critical thinking skills of students who received the 5E Learning Cycle with Metacognitive technique, the 5E Learning Cycle, and conventional learning. This study use experimental method with pretest-posttest control group design. Population are junior high school students in Indramayu city, Indonesia. Sample are three classes of eighth grade students from high level school and three classes from medium level school. The study reveal that in terms of overall, mathematical critical thinking skills enhancement and achievement of students who received the 5E Learning Cycle with Metacognitive technique is better than students who received the 5E Learning Cycle and conventional learning. Mathematical critical thinking skills of students who received the 5E Learning Cycle is better than students who received conventional learning. There is no interaction effect between learning model and school level toward enhancement and achievement of students’ mathematical critical thinking skills.

Machine learning with R cookbook

CERN Document Server

Chiu, Yu-Wei

2015-01-01

If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

Science.gov (United States)

Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

2017-09-01

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Airflow measurement techniques applied to radon mitigation problems

International Nuclear Information System (INIS)

Harrje, D.T.; Gadsby, K.J.

1989-01-01

During the past decade a multitude of diagnostic procedures associated with the evaluation of air infiltration and air leakage sites have been developed. The spirit of international cooperation and exchange of ideas within the AIC-AIVC conferences has greatly facilitated the adoption and use of these measurement techniques in the countries participating in Annex V. But wide application of such diagnostic methods are not limited to air infiltration alone. The subject of this paper concerns the ways to evaluate and improve radon reduction in buildings using diagnostic methods directly related to developments familiar to the AIVC. Radon problems are certainly not unique to the United States, and the methods described here have to a degree been applied by researchers of other countries faced with similar problems. The radon problem involves more than a harmful pollutant of the living spaces of our buildings -- it also involves energy to operate radon removal equipment and the loss of interior conditioned air as a direct result. The techniques used for air infiltration evaluation will be shown to be very useful in dealing with the radon mitigation challenge. 10 refs., 7 figs., 1 tab

Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

Directory of Open Access Journals (Sweden)

Shuibo Hu

2018-03-01

Full Text Available The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs, in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD high performance liquid chromatography (HPLC database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS, artificial neural networks (ANN, support vector machine (SVM and random forests (RF, and feature selection techniques, including genetic algorithm (GA, successive projection algorithm (SPA and recursive feature elimination based on support vector machine (SVM-RFE, for inferring PSCs from remote sensing data. Results showed that: (1 SVM-RFE worked better in selecting sensitive features; (2 RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3 machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4 sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5 the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing.

Database 'catalogue of techniques applied to materials and products of nuclear engineering'

International Nuclear Information System (INIS)

Lebedeva, E.E.; Golovanov, V.N.; Podkopayeva, I.A.; Temnoyeva, T.A.

2002-01-01

The database 'Catalogue of techniques applied to materials and products of nuclear engineering' (IS MERI) was developed to provide informational support for SSC RF RIAR and other enterprises in scientific investigations. This database contains information on the techniques used at RF Minatom enterprises for reactor material properties investigation. The main purpose of this system consists in the assessment of the current status of the reactor material science experimental base for the further planning of experimental activities and methodical support improvement. (author)

Some Cognitive Variables in Meaningful Learning of the Physics Concepts of Work and Energy: A Study of Ausubelian Learning Model.

Science.gov (United States)

Talisayon, Vivien Millan

This study is an empirical investigation of Ausubel's paradigm of meaningful learning, applied specifically to the learning of high school physics students. In the first phase of the study path analysis and multiple regression techniques were used to describe the Ausubelian learning variables: available relevant ideas in learner's cognitive…

Machine learning techniques for razor triggers

CERN Document Server

Kolosova, Marina

2015-01-01

My project was focused on the development of a neural network which can predict if an event passes or not a razor trigger. Using synthetic data containing jets and missing transverse energy we built and trained a razor network by supervised learning. We accomplished a ∼ 91% agreement between the output of the neural network and the target while the other 10% was due to the noise of the neural network. We could apply such networks during the L1 trigger using neuromorhic hardware. Neuromorphic chips are electronic systems that function in a way similar to an actual brain, they are faster than GPUs or CPUs, but they can only be used with spiking neural networks.

Applying multimedia design principles enhances learning in medical education.

Science.gov (United States)

Issa, Nabil; Schuller, Mary; Santacaterina, Susan; Shapiro, Michael; Wang, Edward; Mayer, Richard E; DaRosa, Debra A

2011-08-01

The Association of American Medical Colleges' Institute for Improving Medical Education's report entitled 'Effective Use of Educational Technology' called on researchers to study the effectiveness of multimedia design principles. These principles were empirically shown to result in superior learning when used with college students in laboratory studies, but have not been studied with undergraduate medical students as participants. A pre-test/post-test control group design was used, in which the traditional-learning group received a lecture on shock using traditionally designed slides and the modified-design group received the same lecture using slides modified in accord with Mayer's principles of multimedia design. Participants included Year 3 medical students at a private, midwestern medical school progressing through their surgery clerkship during the academic year 2009-2010. The medical school divides students into four groups; each group attends the surgery clerkship during one of the four quarters of the academic year. Students in the second and third quarters served as the modified-design group (n=91) and students in the fourth-quarter clerkship served as the traditional-design group (n=39). Both student cohorts had similar levels of pre-lecture knowledge. Both groups showed significant improvements in retention (paffect transfer of learning. Further research on applying the principles of multimedia design to medical education is needed to verify the impact it has on the long-term learning of medical students, as well as its impact on other forms of multimedia instructional programmes used in the education of medical students. © Blackwell Publishing Ltd 2011.

Applying Augmented Reality to a Mobile-Assisted Learning System for Martial Arts Using Kinect Motion Capture

Science.gov (United States)

Hsu, Wen-Chun; Shih, Ju-Ling

2016-01-01

In this study, to learn the routine of Tantui, a branch of martial arts was taken as an object of research. Fitts' stages of motor learning and augmented reality (AR) were applied to a 3D mobile-assisted learning system for martial arts, which was characterized by free viewing angles. With the new system, learners could rotate the viewing angle of…

Does Applied STEM Course Taking Link to STEM Outcomes for High School Students With Learning Disabilities?

Science.gov (United States)

Gottfried, Michael A; Sublett, Cameron

Over the most recent two decades, federal policy has urged high schools to embed applied science, technology, engineering, and mathematics (STEM) courses into the curriculum to reinforce concepts learned in traditional math and science classes as well as to motivate students' interests and long-term pursuits in STEM areas. While prior research has examined whether these courses link to STEM persistence for the general student population, no work has examined the role of these courses for students with learning disabilities (LDs). This is a critical lapse, as these courses have been supported as being one path by which STEM material can become more accessible for students with diverse learning needs. Hence, this descriptive study examines the landscape of applied STEM course taking for students with LDs. The findings suggest students with LDs are less likely to take applied STEM courses in high school compared to the general population. Additionally, while the general population does benefit from taking these courses, there is a unique association between applied STEM course taking and advanced math and science course taking or math achievement for students with LDs. Hence, there is no evidence that applied STEM course taking is related to any closure of the STEM achievement gap for students with LDs.

To what degree does the missing-data technique influence the estimated growth in learning strategies over time? A tutorial example of sensitivity analysis for longitudinal data.

Science.gov (United States)

Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

2017-01-01

Longitudinal data is almost always burdened with missing data. However, in educational and psychological research, there is a large discrepancy between methodological suggestions and research practice. The former suggests applying sensitivity analysis in order to the robustness of the results in terms of varying assumptions regarding the mechanism generating the missing data. However, in research practice, participants with missing data are usually discarded by relying on listwise deletion. To help bridge the gap between methodological recommendations and applied research in the educational and psychological domain, this study provides a tutorial example of sensitivity analysis for latent growth analysis. The example data concern students' changes in learning strategies during higher education. One cohort of students in a Belgian university college was asked to complete the Inventory of Learning Styles-Short Version, in three measurement waves. A substantial number of students did not participate on each occasion. Change over time in student learning strategies was assessed using eight missing data techniques, which assume different mechanisms for missingness. The results indicated that, for some learning strategy subscales, growth estimates differed between the models. Guidelines in terms of reporting the results from sensitivity analysis are synthesised and applied to the results from the tutorial example.

Predicting breast screening attendance using machine learning techniques.

Science.gov (United States)

Baskaran, Vikraman; Guergachi, Aziz; Bali, Rajeev K; Naguib, Raouf N G

2011-03-01

Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale.

Improving the quality of learning in science through optimization of lesson study for learning community

Science.gov (United States)

Setyaningsih, S.

2018-03-01

Lesson Study for Learning Community is one of lecturer profession building system through collaborative and continuous learning study based on the principles of openness, collegiality, and mutual learning to build learning community in order to form professional learning community. To achieve the above, we need a strategy and learning method with specific subscription technique. This paper provides a description of how the quality of learning in the field of science can be improved by implementing strategies and methods accordingly, namely by applying lesson study for learning community optimally. Initially this research was focused on the study of instructional techniques. Learning method used is learning model Contextual teaching and Learning (CTL) and model of Problem Based Learning (PBL). The results showed that there was a significant increase in competence, attitudes, and psychomotor in the four study programs that were modelled. Therefore, it can be concluded that the implementation of learning strategies in Lesson study for Learning Community is needed to be used to improve the competence, attitude and psychomotor of science students.

Self-Regulated Learning Strategies Applied to Undergraduate, Graduate and Specialization Students from Civil Engineering

Directory of Open Access Journals (Sweden)

Jose Carlos Redaelli

2013-03-01

Full Text Available The current demand for civil engineering work requires new skills and knowledge and calls for new and effective learning methods. This paper shows self-regulated learning strategies applied to undergraduate, graduate and specialization students from Civil Engineering in a Brazilian University. A Scale of Evaluation of Learning Strategies was administered with a view to identifying students´ cognitive, metacognitive and dysfunctional learning strategies.

Using Machine Learning Techniques in the Analysis of Oceanographic Data

Science.gov (United States)

Falcinelli, K. E.; Abuomar, S.

2017-12-01

Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction

Directory of Open Access Journals (Sweden)

Ruijian Zhang

2017-12-01

Full Text Available Water quality assessment and prediction is a more and more important issue. Traditional ways either take lots of time or they can only do assessments. In this research, by applying machine learning algorithm to a long period time of water attributes’ data; we can generate a decision tree so that it can predict the future day’s water quality in an easy and efficient way. The idea is to combine the traditional ways and the computer algorithms together. Using machine learning algorithms, the assessment of water quality will be far more efficient, and by generating the decision tree, the prediction will be quite accurate. The drawback of the machine learning modeling is that the execution takes quite long time, especially when we employ a better accuracy but more time-consuming algorithm in clustering. Therefore, we applied the high performance computing (HPC System to deal with this problem. Up to now, the pilot experiments have achieved very promising preliminary results. The visualized water quality assessment and prediction obtained from this project would be published in an interactive website so that the public and the environmental managers could use the information for their decision making.

Optimization of the Kinetic Activation-Relaxation Technique, an off-lattice and self-learning kinetic Monte-Carlo method

International Nuclear Information System (INIS)

Joly, Jean-François; Béland, Laurent Karim; Brommer, Peter; Mousseau, Normand; El-Mellouhi, Fedwa

2012-01-01

We present two major optimizations for the kinetic Activation-Relaxation Technique (k-ART), an off-lattice self-learning kinetic Monte Carlo (KMC) algorithm with on-the-fly event search THAT has been successfully applied to study a number of semiconducting and metallic systems. K-ART is parallelized in a non-trivial way: A master process uses several worker processes to perform independent event searches for possible events, while all bookkeeping and the actual simulation is performed by the master process. Depending on the complexity of the system studied, the parallelization scales well for tens to more than one hundred processes. For dealing with large systems, we present a near order 1 implementation. Techniques such as Verlet lists, cell decomposition and partial force calculations are implemented, and the CPU time per time step scales sublinearly with the number of particles, providing an efficient use of computational resources.

Use of Jigsaw Technique to Teach the Unit "Science within Time" in Secondary 7th Grade Social Sciences Course and Students' Views on This Technique

Science.gov (United States)

Yapici, Hakki

2016-01-01

The aim of this study is to apply the jigsaw technique in Social Sciences teaching and to unroll the effects of this technique on learning. The unit "Science within Time" in the secondary 7th grade Social Sciences text book was chosen for the research. It is aimed to compare the jigsaw technique with the traditional teaching method in…

Column-Oriented Storage Techniques for MapReduce

OpenAIRE

Floratou, Avrilia; Patel, Jignesh; Shekita, Eugene; Tata, Sandeep

2011-01-01

Users of MapReduce often run into performance problems when they scale up their workloads. Many of the problems they encounter can be overcome by applying techniques learned from over three decades of research on parallel DBMSs. However, translating these techniques to a MapReduce implementation such as Hadoop presents unique challenges that can lead to new design choices. This paper describes how column-oriented storage techniques can be incorporated in Hadoop in a way that preserves its pop...

Applying Brainstorming Techniques to EFL Classroom

OpenAIRE

Toshiya, Oishi; 湘北短期大学; aPart-time Lecturer at Shohoku College

2015-01-01

This paper focuses on brainstorming techniques for English language learners. From the author's teaching experiences at Shohoku College during the academic year 2014-2015, the importance of brainstorming techniques was made evident. The author explored three elements of brainstorming techniques for writing using literaturereviews: lack of awareness, connecting to prior knowledge, and creativity. The literature reviews showed the advantage of using brainstorming techniques in an English compos...

Strategies and techniques of communication and public relations applied to non-profit sector

Directory of Open Access Journals (Sweden)

Ioana – Julieta Josan

2010-05-01

Full Text Available The aim of this paper is to summarize the strategies and techniques of communication and public relations applied to non-profit sector.The approach of the paper is to identify the most appropriate strategies and techniques that non-profit sector can use to accomplish its objectives, to highlight specific differences between the strategies and techniques of the profit and non-profit sectors and to identify potential communication and public relations actions in order to increase visibility among target audience, create brand awareness and to change into positive brand sentiment the target perception about the non-profit sector.

Toward accelerating landslide mapping with interactive machine learning techniques

Science.gov (United States)

Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

2013-04-01

Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also

Learning to discover: machine learning in high-energy physics

CERN Multimedia

CERN. Geneva

2014-01-01

In this talk we will survey some of the latest developments in machine learning research through the optics of potential applications in high-energy physics. We will then describe three ongoing projects in detail. The main subject of the talk is the data challenge we are organizing with ATLAS on optimizing the discovery significance for the Higgs to tau-tau channel. Second, we describe our collaboration with the LHCb experiment on designing and optimizing fast multi-variate techniques that can be implemented as online classifiers in triggers. Finally, we will sketch a relatively young project with the ILC (Calice) group in which we are attempting to apply deep learning techniques for inference on imaging calorimeter data.

Temporal difference learning for the game Tic-Tac-Toe 3D : applying structure to neural networks

NARCIS (Netherlands)

van de Steeg, M.; Drugan, M.M.; Wiering, M.

2015-01-01

When reinforcement learning is applied to large state spaces, such as those occurring in playing board games, the use of a good function approximator to learn to approximate the value function is very important. In previous research, multi-layer perceptrons have often been quite successfully used as

Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction

OpenAIRE

Ruijian Zhang; Deren Li

2017-01-01

Water quality assessment and prediction is a more and more important issue. Traditional ways either take lots of time or they can only do assessments. In this research, by applying machine learning algorithm to a long period time of water attributes’ data; we can generate a decision tree so that it can predict the future day’s water quality in an easy and efficient way. The idea is to combine the traditional ways and the computer algorithms together. Using machine learning algorithms, the ass...

Applying the Flipped Learning Model to an English-Medium Nursing Course.

Science.gov (United States)

Choi, Heeseung; Kim, Jeongeun; Bang, Kyung Sook; Park, Yeon Hwan; Lee, Nam Ju; Kim, Chanhee

2015-12-01

An emerging trend in Asian higher education is English-medium instruction (EMI), which uses English as the primary instructional language. EMI prepares domestic students for international leadership; however, students report difficulty in learning, and educators have raised questions concerning the effectiveness of EMI. The flipped learning model (FLM), in which lecture and homework activities for a course are reversed, was applied to an English-medium course offered by a college of nursing in Korea. The aims of this study were to: 1) revise an existing English-medium nursing course using the FLM; 2) explore students' learning experiences and their acceptance of the FLM; and 3) identify key factors in the success of FLM. We used a descriptive, cross-sectional, mixed-methods design and the participants were students at one nursing school in Korea. A series of course development meetings with faculties from the nursing school and the center for teaching and learning were used to develop the course format and content. We conducted course evaluations using the Flipped Course Evaluation Questionnaire with open-ended questions and focus group interviews. Students (N=75) in a 15-week nursing course responded to a survey after completing the course. Among them, seven students participated in one of two focus groups. Overall, students accepted and favored the flipped learning strategy, and indicated that the method enhanced lecture content and their understanding of it. Factors associated with effective instruction included structured monitoring systems and motivational environments. The FLM requires sufficient preparation to facilitate student motivation and maximize learning outcomes.

Perspective for applying traditional and inovative teaching and learning methods to nurses continuing education

OpenAIRE

Bendinskaitė, Irmina

2015-01-01

Bendinskaitė I. Perspective for applying traditional and innovative teaching and learning methods to nurse’s continuing education, magister thesis / supervisor Assoc. Prof. O. Riklikienė; Departament of Nursing and Care, Faculty of Nursing, Lithuanian University of Health Sciences. – Kaunas, 2015, – p. 92 The purpose of this study was to investigate traditional and innovative teaching and learning methods perspective to nurse’s continuing education. Material and methods. In a period fro...

HyDR-MI : A hybrid algorithm to reduce dimensionality in multiple instance learning

NARCIS (Netherlands)

Zafra, A.; Pechenizkiy, M.; Ventura, S.

2013-01-01

Feature selection techniques have been successfully applied in many applications for making supervised learning more effective and efficient. These techniques have been widely used and studied in traditional supervised learning settings, where each instance is expected to have a label. In multiple

Position Paper: Applying Machine Learning to Software Analysis to Achieve Trusted, Repeatable Scientific Computing

Energy Technology Data Exchange (ETDEWEB)

Prowell, Stacy J [ORNL; Symons, Christopher T [ORNL

2015-01-01

Producing trusted results from high-performance codes is essential for policy and has significant economic impact. We propose combining rigorous analytical methods with machine learning techniques to achieve the goal of repeatable, trustworthy scientific computing.

Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

KAUST Repository

AlQuerm, Ismail A.

2018-02-21

There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for

The Next Era: Deep Learning in Pharmaceutical Research.

Science.gov (United States)

Ekins, Sean

2016-11-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Applying Cognitive Psychology Based Instructional Design Principles in Mathematics Teaching and Learning: Introduction

Science.gov (United States)

Verschaffel, Lieven; Van Dooren, W.; Star, J.

2017-01-01

This special issue comprises contributions that address the breadth of current lines of recent research from cognitive psychology that appear promising for positively impacting students' learning of mathematics. More specifically, we included contributions (a) that refer to cognitive psychology based principles and techniques, such as explanatory…

Bullying in Virtual Learning Communities.

Science.gov (United States)

Nikiforos, Stefanos; Tzanavaris, Spyros; Kermanidis, Katia Lida

2017-01-01

Bullying through the internet has been investigated and analyzed mainly in the field of social media. In this paper, it is attempted to analyze bullying in the Virtual Learning Communities using Natural Language Processing (NLP) techniques, mainly in the context of sociocultural learning theories. Therefore four case studies took place. We aim to apply NLP techniques to speech analysis on communication data of online communities. Emphasis is given on qualitative data, taking into account the subjectivity of the collaborative activity. Finally, this is the first time such type of analysis is attempted on Greek data.

Can active learning principles be applied to the bioscience assessments of nursing students? A review of the literature.

Science.gov (United States)

Bakon, Shannon; Craft, Judy; Christensen, Martin; Wirihana, Lisa

2016-02-01

To explore if active learning principles be applied to nursing bioscience assessments and will this influence student perception of confidence in applying theory to practice? A review of the literature utilising searches of various databases including CINAHL, PUBMED, Google Scholar and Mosby's Journal Index. The literature search identified research from twenty-six original articles, two electronic books, one published book and one conference proceedings paper. Bioscience has been identified as an area that nurses struggle to learn in tertiary institutions and then apply to clinical practice. A number of problems have been identified and explored that may contribute to this poor understanding and retention. University academics need to be knowledgeable of innovative teaching and assessing modalities that focus on enhancing student learning and address the integration issues associated with the theory practice gap. Increased bioscience education is associated with improved patient outcomes therefore by addressing this "bioscience problem" and improving the integration of bioscience in clinical practice there will subsequently be an improvement in health care outcomes. From the literature several themes were identified. First there are many problems with teaching nursing students bioscience education. These include class sizes, motivation, concentration, delivery mode, lecturer perspectives, student's previous knowledge, anxiety, and a lack of confidence. Among these influences the type of assessment employed by the educator has not been explored or identified as a contributor to student learning specifically in nursing bioscience instruction. Second that educating could be achieved more effectively if active learning principles were applied and the needs and expectations of the student were met. Lastly, assessment influences student retention and the student experience and as such assessment should be congruent with the subject content, align with the learning

Machine learning and next-generation asteroid surveys

Science.gov (United States)

Nugent, Carrie R.; Dailey, John; Cutri, Roc M.; Masci, Frank J.; Mainzer, Amy K.

2017-10-01

Next-generation surveys such as NEOCam (Mainzer et al., 2016) will sift through tens of millions of point source detections daily to detect and discover asteroids. This requires new, more efficient techniques to distinguish between solar system objects, background stars and galaxies, and artifacts such as cosmic rays, scattered light and diffraction spikes.Supervised machine learning is a set of algorithms that allows computers to classify data on a training set, and then apply that classification to make predictions on new datasets. It has been employed by a broad range of fields, including computer vision, medical diagnoses, economics, and natural language processing. It has also been applied to astronomical datasets, including transient identification in the Palomar Transient Factory pipeline (Masci et al., 2016), and in the Pan-STARRS1 difference imaging (D. E. Wright et al., 2015).As part of the NEOCam extended phase A work we apply machine learning techniques to the problem of asteroid detection. Asteroid detection is an ideal application of supervised learning, as there is a wealth of metrics associated with each extracted source, and suitable training sets are easily created. Using the vetted NEOWISE dataset (E. L. Wright et al., 2010, Mainzer et al., 2011) as a proof-of-concept of this technique, we applied the python package sklearn. We report on reliability, feature set selection, and the suitability of various algorithms.

Applying Squeezing Technique to Clayrocks: Lessons Learned from Experiments at Mont Terri Rock Laboratory

International Nuclear Information System (INIS)

Fernandez, A. M.; Sanchez-Ledesma, D. M.; Tournassat, C.; Melon, A.; Gaucher, E.; Astudillo, E.; Vinsot, A.

2013-01-01

Knowledge of the pore water chemistry in clay rock formations plays an important role in determining radionuclide migration in the context of nuclear waste disposal. Among the different in situ and ex-situ techniques for pore water sampling in clay sediments and soils, squeezing technique dates back 115 years. Although different studies have been performed about the reliability and representativeness of squeezed pore waters, more of them were achieved on high porosity, high water content and unconsolidated clay sediments. A very few of them tackled the analysis of squeezed pore water from low-porosity, low water content and highly consolidated clay rocks. In this work, a specially designed and fabricated one-dimensional compression cell two directional fluid flow was used to extract and analyse the pore water composition of Opalinus Clay core samples from Mont Terri (Switzerland). The reproducibility of the technique is good and no ionic ultrafiltration, chemical fractionation or anion exclusion was found in the range of pressures analysed: 70-200 MPa. Pore waters extracted in this range of pressures do not decrease in concentration, which would indicate a dilution of water by mixing of the free pore water and the outer layers of double layer water (Donnan water). A threshold (safety) squeezing pressure of 175 MPa was established for avoiding membrane effects (ion filtering, anion exclusion, etc.) from clay particles induced by increasing pressures. Besides, the pore waters extracted at these pressures are representative of the Opalinus Clay formation from a direct comparison against in situ collected borehole waters. (Author)

Applying Squeezing Technique to Clayrocks: Lessons Learned from Experiments at Mont Terri Rock Laboratory

Energy Technology Data Exchange (ETDEWEB)

Fernandez, A. M.; Sanchez-Ledesma, D. M.; Tournassat, C.; Melon, A.; Gaucher, E.; Astudillo, E.; Vinsot, A.

2013-07-01

Knowledge of the pore water chemistry in clay rock formations plays an important role in determining radionuclide migration in the context of nuclear waste disposal. Among the different in situ and ex-situ techniques for pore water sampling in clay sediments and soils, squeezing technique dates back 115 years. Although different studies have been performed about the reliability and representativeness of squeezed pore waters, more of them were achieved on high porosity, high water content and unconsolidated clay sediments. A very few of them tackled the analysis of squeezed pore water from low-porosity, low water content and highly consolidated clay rocks. In this work, a specially designed and fabricated one-dimensional compression cell two directional fluid flow was used to extract and analyse the pore water composition of Opalinus Clay core samples from Mont Terri (Switzerland). The reproducibility of the technique is good and no ionic ultrafiltration, chemical fractionation or anion exclusion was found in the range of pressures analysed: 70-200 MPa. Pore waters extracted in this range of pressures do not decrease in concentration, which would indicate a dilution of water by mixing of the free pore water and the outer layers of double layer water (Donnan water). A threshold (safety) squeezing pressure of 175 MPa was established for avoiding membrane effects (ion filtering, anion exclusion, etc.) from clay particles induced by increasing pressures. Besides, the pore waters extracted at these pressures are representative of the Opalinus Clay formation from a direct comparison against in situ collected borehole waters. (Author)

Improvement technique of sensitized HAZ by GTAW cladding applied to a BWR power plant

International Nuclear Information System (INIS)

Tujimura, Hiroshi; Tamai, Yasumasa; Furukawa, Hideyasu; Kurosawa, Kouichi; Chiba, Isao; Nomura, Keiichi.

1995-01-01

A SCC(Stress Corrosion Cracking)-resistant technique, in which the sleeve installed by expansion is melted by GTAW process without filler metal with outside water cooling, was developed. The technique was applied to ICM (In-Core Monitor) housings of a BWR power plant in 1993. The ICM housings of which materials are type 304 Stainless Steels are sensitized with high tensile residual stresses by welding to the RPV (Reactor Pressure Vessel). As the result, ICM housings have potential of SCC initiation. Therefore, the improvement technique resistant to SCC was needed. The technique can improve chemical composition of the housing inside and residual stresses of the housing outside at the same time. Sensitization of the housing inner surface area is eliminated by replacing low-carbon with proper-ferrite microstructure clad. High tensile residual stresses of housing outside surface area is improved into compressive side. Compressive stresses of outside surface are induced by thermal stresses which are caused by inside cladding with outside water cooling. The clad is required to be low-carbon metal with proper ferrite and not to have the new sensitized HAZ (Heat Affected Zone) on the surface by cladding. The effect of the technique was qualified by SCC test, chemical composition check, ferrite content measurement and residual stresses measurement etc. All equipment for remote application were developed and qualified, too. The technique was successfully applied to a BWR plant after sufficient training

Applying AI techniques to improve alarm display effectiveness

International Nuclear Information System (INIS)

Gross, J.M.; Birrer, S.A.; Crosberg, D.R.

1987-01-01

The Alarm Filtering System (AFS) addresses the problem of information overload in a control room during abnormal operations. Since operators can miss vital information during these periods, systems which emphasize important messages are beneficial. AFS uses the artificial intelligence (AI) technique of object-oriented programming to filter and dynamically prioritize alarm messages. When an alarm's status changes, AFS determines the relative importance of that change according to the current process state. AFS bases that relative importance on relationships the newly changed alarm has with other activated alarms. Evaluations of a alarm importance take place without regard to the activation sequence of alarm signals. The United States Department of Energy has applied for a patent on the approach used in this software. The approach was originally developed by EG and G Idaho for a nuclear reactor control room

A comparison of machine learning techniques for predicting downstream acid mine drainage

CSIR Research Space (South Africa)

van Zyl, TL

2014-07-01

Full Text Available windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear...

Generalized query-based active learning to identify differentially methylated regions in DNA.

Science.gov (United States)

Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

2013-01-01

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

Towards ABET accreditation for a SWE program: alternative student assessment techniques

International Nuclear Information System (INIS)

Alghamdi, A.; Nasir, M.; Alnafjan, K.

2011-01-01

This paper describes assessment techniques utilized for assessing undergraduate students studying in a software engineering program. The purpose behind this work is to get the program accredited by the Accreditation Board of Engineering and Technology (ABET). Therefore, a number of applied direct and indirect assessment techniques are described. These techniques are implemented towards the end of the semester to assess the extent to which the student and course outcomes are satisfied. Consequently, results are obtained and analyzed and various learning issues are eventually identified. Finally, the paper provides suggestions for improvement in course delivery as well as learning mechanism. (author)

CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

Energy Technology Data Exchange (ETDEWEB)

Saz Parkinson, P. M. [Department of Physics, The University of Hong Kong, Pokfulam Road, Hong Kong (China); Xu, H.; Yu, P. L. H. [Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong (China); Salvetti, D.; Marelli, M. [INAF—Istituto di Astrofisica Spaziale e Fisica Cosmica Milano, via E. Bassini 15, I-20133, Milano (Italy); Falcone, A. D. [Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA 16802 (United States)

2016-03-20

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.

CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

International Nuclear Information System (INIS)

Saz Parkinson, P. M.; Xu, H.; Yu, P. L. H.; Salvetti, D.; Marelli, M.; Falcone, A. D.

2016-01-01

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

Science.gov (United States)

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species

Validation and qualification of surface-applied fibre optic strain sensors using application-independent optical techniques

International Nuclear Information System (INIS)

Schukar, Vivien G; Kadoke, Daniel; Kusche, Nadine; Münzenberger, Sven; Gründer, Klaus-Peter; Habel, Wolfgang R

2012-01-01

Surface-applied fibre optic strain sensors were investigated using a unique validation facility equipped with application-independent optical reference systems. First, different adhesives for the sensor's application were analysed regarding their material properties. Measurements resulting from conventional measurement techniques, such as thermo-mechanical analysis and dynamic mechanical analysis, were compared with measurements resulting from digital image correlation, which has the advantage of being a non-contact technique. Second, fibre optic strain sensors were applied to test specimens with the selected adhesives. Their strain-transfer mechanism was analysed in comparison with conventional strain gauges. Relative movements between the applied sensor and the test specimen were visualized easily using optical reference methods, digital image correlation and electronic speckle pattern interferometry. Conventional strain gauges showed limited opportunities for an objective strain-transfer analysis because they are also affected by application conditions. (paper)

Meta-learning from an experiment database

OpenAIRE

Driessens, Kurt; Vanwinckelen, Gitte; Blockeel, Hendrik

2012-01-01

In this short paper, we present a student project run as part of the Machine Learning and Inductive Inference course at KU Leuven during the 2010-2011 academic year. The goal of the project was to analyze a Machine Learning Experiment database, using standard SQL queries and data mining tools with the goals of (1) giving the students some practice with applying the machine learning techniques on a real problem, (2) teaching them something about the properties of machine learning algorithms...

Learning-based computing techniques in geoid modeling for precise height transformation

Science.gov (United States)

Erol, B.; Erol, S.

2013-03-01

Precise determination of local geoid is of particular importance for establishing height control in geodetic GNSS applications, since the classical leveling technique is too laborious. A geoid model can be accurately obtained employing properly distributed benchmarks having GNSS and leveling observations using an appropriate computing algorithm. Besides the classical multivariable polynomial regression equations (MPRE), this study attempts an evaluation of learning based computing algorithms: artificial neural networks (ANNs), adaptive network-based fuzzy inference system (ANFIS) and especially the wavelet neural networks (WNNs) approach in geoid surface approximation. These algorithms were developed parallel to advances in computer technologies and recently have been used for solving complex nonlinear problems of many applications. However, they are rather new in dealing with precise modeling problem of the Earth gravity field. In the scope of the study, these methods were applied to Istanbul GPS Triangulation Network data. The performances of the methods were assessed considering the validation results of the geoid models at the observation points. In conclusion the ANFIS and WNN revealed higher prediction accuracies compared to ANN and MPRE methods. Beside the prediction capabilities, these methods were also compared and discussed from the practical point of view in conclusions.

A Case Study of Universal Design for Learning Applied in the College Classroom

Science.gov (United States)

Leichliter, Marie E.

2010-01-01

As the landscape of education and the demographics of the postsecondary classroom continue to evolve, so too must the teaching practices at our nation's institutions of higher education. This study follows an instructor who has evolved to incorporate Universal Design for Learning (UDL) techniques into her classroom, even though prior to…

Analytical techniques applied to study cultural heritage objects

Energy Technology Data Exchange (ETDEWEB)

Rizzutto, M.A.; Curado, J.F.; Bernardes, S.; Campos, P.H.O.V.; Kajiya, E.A.M.; Silva, T.F.; Rodrigues, C.L.; Moro, M.; Tabacniks, M.; Added, N., E-mail: rizzutto@if.usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Instituto de Fisica

2015-07-01

The scientific study of artistic and cultural heritage objects have been routinely performed in Europe and the United States for decades. In Brazil this research area is growing, mainly through the use of physical and chemical characterization methods. Since 2003 the Group of Applied Physics with Particle Accelerators of the Physics Institute of the University of Sao Paulo (GFAA-IF) has been working with various methodologies for material characterization and analysis of cultural objects. Initially using ion beam analysis performed with Particle Induced X-Ray Emission (PIXE), Rutherford Backscattering (RBS) and recently Ion Beam Induced Luminescence (IBIL), for the determination of the elements and chemical compounds in the surface layers. These techniques are widely used in the Laboratory of Materials Analysis with Ion Beams (LAMFI-USP). Recently, the GFAA expanded the studies to other possibilities of analysis enabled by imaging techniques that coupled with elemental and compositional characterization provide a better understanding on the materials and techniques used in the creative process in the manufacture of objects. The imaging analysis, mainly used to examine and document artistic and cultural heritage objects, are performed through images with visible light, infrared reflectography (IR), fluorescence with ultraviolet radiation (UV), tangential light and digital radiography. Expanding more the possibilities of analysis, new capabilities were added using portable equipment such as Energy Dispersive X-Ray Fluorescence (ED-XRF) and Raman Spectroscopy that can be used for analysis 'in situ' at the museums. The results of these analyzes are providing valuable information on the manufacturing process and have provided new information on objects of different University of Sao Paulo museums. Improving the arsenal of cultural heritage analysis it was recently constructed an 3D robotic stage for the precise positioning of samples in the external beam setup

Analytical techniques applied to study cultural heritage objects

International Nuclear Information System (INIS)

Rizzutto, M.A.; Curado, J.F.; Bernardes, S.; Campos, P.H.O.V.; Kajiya, E.A.M.; Silva, T.F.; Rodrigues, C.L.; Moro, M.; Tabacniks, M.; Added, N.

2015-01-01

The scientific study of artistic and cultural heritage objects have been routinely performed in Europe and the United States for decades. In Brazil this research area is growing, mainly through the use of physical and chemical characterization methods. Since 2003 the Group of Applied Physics with Particle Accelerators of the Physics Institute of the University of Sao Paulo (GFAA-IF) has been working with various methodologies for material characterization and analysis of cultural objects. Initially using ion beam analysis performed with Particle Induced X-Ray Emission (PIXE), Rutherford Backscattering (RBS) and recently Ion Beam Induced Luminescence (IBIL), for the determination of the elements and chemical compounds in the surface layers. These techniques are widely used in the Laboratory of Materials Analysis with Ion Beams (LAMFI-USP). Recently, the GFAA expanded the studies to other possibilities of analysis enabled by imaging techniques that coupled with elemental and compositional characterization provide a better understanding on the materials and techniques used in the creative process in the manufacture of objects. The imaging analysis, mainly used to examine and document artistic and cultural heritage objects, are performed through images with visible light, infrared reflectography (IR), fluorescence with ultraviolet radiation (UV), tangential light and digital radiography. Expanding more the possibilities of analysis, new capabilities were added using portable equipment such as Energy Dispersive X-Ray Fluorescence (ED-XRF) and Raman Spectroscopy that can be used for analysis 'in situ' at the museums. The results of these analyzes are providing valuable information on the manufacturing process and have provided new information on objects of different University of Sao Paulo museums. Improving the arsenal of cultural heritage analysis it was recently constructed an 3D robotic stage for the precise positioning of samples in the external beam setup

Evaluation Models for E-Learning Platform in Riyadh City Universities (RCU with Applied of Geographical Information System (GIS

Directory of Open Access Journals (Sweden)

Abdulaziz I. Alharrah

2014-12-01

Full Text Available E-learning that integrates digital knowledge content, network and information technology has become an emerging learning method. As the e-learning platform approach is becoming an important tool to allow the flexibility and quality requested by such a kind of learning process. There is a new kind of problem faced by organizations consisting in the selection of the most suitable e-learning platform. This paper proposes evaluation model for E-Learning platform in Riyadh City universities (RCU with Applied Geographic Information System (GIS. The E-Learning platform solution selection is a multiple criteria decision-making problem that needs to be addressed objectively taking into consideration the relative weights of the criteria for any organization. We formulate the quoted multi criteria problem as a decision hierarchy to be solved using GIS. AGIS-based evaluation index system and web-based evaluating platform were established. In this paper we will show the general evaluation strategy and some obtained results using our model to evaluate some existing commercial platforms.The results of evaluation model are outlined as follows: Total weights of the proposed framework in management feature is 20.25/25, in collaborative feature is 9.2/10, in adaption learning path is 6.8/10 and in interactive learning object is 5/5. The total weights of all features are 41.25/50. In this study an evaluation model was applied on Riyadh City universities like KSU, IMAMU, NAUSS, YU and FU. Then, the results were compared with each other. The total weighs of KSU was 41. While the total weights of FU, IMAMU, YU and NAUSS was 40, 37, 36 and 32, respectively. Evaluation process shows that the proposed framework satisfied the objectives with applied GIS.

Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Directory of Open Access Journals (Sweden)

Manoja Kumar Behera

2018-06-01

Full Text Available Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC maximum power point tracking (MPPT technique that is based on proportional integral (PI controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN, ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO techniques and their performance are compared with existing models like back propagation (BP forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network

A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning

Directory of Open Access Journals (Sweden)

Bruno A. Angélico

2013-12-01

Full Text Available This work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO, Genetic Algorithm (GA and an Ant Colony Optimization (ACO are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.

COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning.

Science.gov (United States)

Gratch, Jonathan; DeJong, Gerald

In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge.…

Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

Directory of Open Access Journals (Sweden)

Jonas eKaplan

2015-03-01

Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

Exploring Graduate Students' Perspectives towards Using Gamification Techniques in Online Learning

Directory of Open Access Journals (Sweden)

Daniah ALABBASI

2017-07-01

Full Text Available Teachers and educational institutions are attempting to find an appropriate strategy to motivate as well as engage students in the learning process. Institutions are encouraging the use of gamification in education for the purpose of improving the intrinsic motivation as well as engagement. However, the students’ perspective of the issue is under-investigated. The purpose of this research study was to explore graduate students’ perspectives toward the use of gamification techniques in online learning. The study used exploratory research and survey as the data collection tool. Forty-seven graduate students (n = 47 enrolled in an instructional technology program studied in a learning management system that supports gamification (TalentLMS. The average total percentages were calculated for each survey section to compose the final perspective of the included students. The results showed a positive perception toward the use of gamification tools in online learning among graduate students. Students require effort-demanding, challenging, sophisticated learning systems that increase competency, enhance recall memory, concentration, attentiveness, commitment, and social interaction. Limitations of the study are identified, which highlights the need for further research on the subject matter.

The implementation of portfolio assessment by the educators on the mathematics learning process in senior high school

Science.gov (United States)

Lestariani, Ida; Sujadi, Imam; Pramudya, Ikrar

2018-05-01

Portfolio assessment can shows the development of the ability of learners in a period through the work so that can be seen progress monitored learning of each learner. The purpose of research to describe and know the implementation of portfolio assessment on the mathematics learning process with the Senior High school math teacher class X as the subject because of the importance of applying the assessment for the progress of learning outcomes of learners. This research includes descriptive qualitative research type. Techniques of data collecting is done by observation method, interview and documentation. Data collection then validated using triangulation technique that is observation technique, interview and documentation. Data analysis technique is done by data reduction, data presentation and conclusion. The results showed that the steps taken by teachers in applying portfolio assessment obtained focused on learning outcomes. Student learning outcomes include homework and daily tests. Based on the results of research can be concluded that the implementation of portfolio assessment is the form of learning results are scored. Teachers have not yet implemented other portfolio assessment techniques such as student work.

76 FR 45334 - Innovative Techniques for Delivering ITS Learning; Request for Information

Science.gov (United States)

2011-07-28

... adult learners? 5. Are you aware of any ITS training applications that work on a mobile phone or smart... DEPARTMENT OF TRANSPORTATION Research and Innovative Technology Administration Innovative Techniques for Delivering ITS Learning; Request for Information AGENCY: Research and Innovative Technology...

ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

Directory of Open Access Journals (Sweden)

VIMALA C.

2015-05-01

Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

Learning outcomes and effective communication techniques for hazard recognition learning programmes in the transportation thrust area.

CSIR Research Space (South Africa)

Krige, PD

2001-12-01

Full Text Available on South African mines ............................................ 32 4.3 People development and training techniques associated with confidence, attitudes and leadership............................................ 34 Page 4 4.4 Recommended learning... to rules and procedures, safety commitment of management, supervision style, organising for safety, equipment design and maintenance. Only the last two are engineering issues. The trend is clear. Improvements in engineering design have significantly...

Learner Open Modeling in Adaptive Mobile Learning System for Supporting Student to Learn English

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Van Cong Pham

2011-10-01

Full Text Available This paper represents a personalized context-aware mobile learning architecture for supporting student to learn English as foreign language in order to prepare for TOEFL test. We consider how to apply open learner modeling techniques to adapt contents for different learners based on context, which includes location, amount of time to learn, the manner as well as learner's knowledge in learning progress. Through negotiation with system, the editable learner model will be updated to support adaptive engine to select adaptive contents meeting learner's demands. Empirical testing results for students who used application prototype indicate that interaction user modeling is helpful in supporting learner to learn adaptive materials.

The Delphi Technique in Educational Research

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Ravonne A. Green

2014-04-01

Full Text Available The Delphi Technique has been useful in educational settings in forming guidelines, standards, and in predicting trends. Judd lists these major uses of the Delphi Technique in higher education: (a cost-effectiveness, (b cost–benefit analysis, (c curriculum and campus planning, and (d university-wide educational goals and objectives. The thorough Delphi researcher seeks to reconcile the Delphi consensus with current literature, institutional research, and the campus environment. This triangle forms a sound base for responsible research practice. This book gives an overview of the Delphi Technique and the primary uses of this technique in research. This article on the Delphi Technique will give the researcher an invaluable resource for learning about the Delphi Technique and for applying this method in educational research projects.

Applying Agnotology-Based Learning in a Mooc to Counter Climate Misconceptions

Science.gov (United States)

Cook, J.

2014-12-01

A key challenge facing educators and climate communicators is the wide array of misconceptions about climate science, often fostered by misinformation. A number of myths interfere with a sound understanding of the science, with key myths moderating public support for mitigation policies. An effective way to reduce the influence of misinformation is through agnotology-based learning. Agnotology is the study of ignorance while agnotology-based learning teaches students through the direct addressing of myths and misconceptions. This approach of "refutational teaching" is being applied in a MOOC (Massive Online Open Course) currently being developed by Skeptical Science and The University of Queensland, in collaboration with universities in Canada, USA and the UK. The MOOC will examine the science of climate change denial. Why is the issue so controversial given there is an overwhelming consensus on human-caused global warming? How do climate myths distort the science? What can scientists and laypeople do in response to misinformation? The MOOC will be released on the EdX platform in early 2015. I will summarise the research underpinning agnotology-based learning and present the approach taken in the MOOC to be released in early 2015

Applying Metrological Techniques to Satellite Fundamental Climate Data Records

Science.gov (United States)

Woolliams, Emma R.; Mittaz, Jonathan PD; Merchant, Christopher J.; Hunt, Samuel E.; Harris, Peter M.

2018-02-01

Quantifying long-term environmental variability, including climatic trends, requires decadal-scale time series of observations. The reliability of such trend analysis depends on the long-term stability of the data record, and understanding the sources of uncertainty in historic, current and future sensors. We give a brief overview on how metrological techniques can be applied to historical satellite data sets. In particular we discuss the implications of error correlation at different spatial and temporal scales and the forms of such correlation and consider how uncertainty is propagated with partial correlation. We give a form of the Law of Propagation of Uncertainties that considers the propagation of uncertainties associated with common errors to give the covariance associated with Earth observations in different spectral channels.

Modelling the effects of the sterile insect technique applied to Eldana saccharina Walker in sugarcane

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L Potgieter

2012-12-01

Full Text Available A mathematical model is formulated for the population dynamics of an Eldana saccharina Walker infestation of sugarcane under the influence of partially sterile released insects. The model describes the population growth of and interaction between normal and sterile E.saccharina moths in a temporally variable, but spatially homogeneous environment. The model consists of a deterministic system of difference equations subject to strictly positive initial data. The primary objective of this model is to determine suitable parameters in terms of which the above population growth and interaction may be quantified and according to which E.saccharina infestation levels and the associated sugarcane damage may be measured. Although many models have been formulated in the past describing the sterile insect technique, few of these models describe the technique for Lepidopteran species with more than one life stage and where F1-sterility is relevant. In addition, none of these models consider the technique when fully sterile females and partially sterile males are being released. The model formulated is also the first to describe the technique applied specifically to E.saccharina, and to consider the economic viability of applying the technique to this species. Pertinent decision support is provided to farm managers in terms of the best timing for releases, release ratios and release frequencies.

Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

Science.gov (United States)

Wang, Ding; Liu, Derong

2018-06-01

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

An Interactive Learning Environment for Teaching the Imperative and Object-Oriented Programming Techniques in Various Learning Contexts

Science.gov (United States)

Xinogalos, Stelios

The acquisition of problem-solving and programming skills in the era of knowledge society seems to be particularly important. Due to the intrinsic difficulty of acquiring such skills various educational tools have been developed. Unfortunately, most of these tools are not utilized. In this paper we present the programming microworlds Karel and objectKarel that support the procedural-imperative and Object-Oriented Programming (OOP) techniques and can be used for supporting the teaching and learning of programming in various learning contexts and audiences. The paper focuses on presenting the pedagogical features that are common to both environments and mainly on presenting the potential uses of these environments.

Applying Economics Using Interactive Learning Modules

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Goma, Ophelia D.

2010-01-01

This article describes the use of web-based, interactive learning modules in the principles of economics course. The learning modules introduce students to important, historical economic events while providing real-world application of the economic theory presented in class. Each module is designed to supplement and complement the economic theory…

Research on Mobile Learning Activities Applying Tablets

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Kurilovas, Eugenijus; Juskeviciene, Anita; Bireniene, Virginija

2015-01-01

The paper aims to present current research on mobile learning activities in Lithuania while implementing flagship EU-funded CCL project on application of tablet computers in education. In the paper, the quality of modern mobile learning activities based on learning personalisation, problem solving, collaboration, and flipped class methods is…

Creating adaptive environment for e-learning courses

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Bozidar Radenkovic

2009-06-01

Full Text Available In this paper we provide an approach to creating adaptive environment for e-learning courses. In the context of e-education, successful adaptation has to be performed upon learners’ characteristics. Currently, modeling and discovering users’ needs, goals, knowledge preferences and motivations is one of the most challenging tasks in e-learning systems that deal with large volumes of information. Primary goal of the research is to perform personalizing of distance education system, according to students’ learning styles. Main steps and requirements in applying business intelligence techniques in process of personalization are identified. In addition, we propose generic model and architecture of an adaptive e-learning system by describing the structure of an adaptive course and exemplify correlations among e-learning course content and different learning styles. Moreover, research that dealt with application of data mining technique in a real e-learning system was carried out. We performed adaptation of our e-learning courses using the results from the research.

Satellite SAR interferometric techniques applied to emergency mapping

Science.gov (United States)

Stefanova Vassileva, Magdalena; Riccardi, Paolo; Lecci, Daniele; Giulio Tonolo, Fabio; Boccardo Boccardo, Piero; Chiesa, Giuliana; Angeluccetti, Irene

2017-04-01

This paper aim to investigate the capabilities of the currently available SAR interferometric algorithms in the field of emergency mapping. Several tests have been performed exploiting the Copernicus Sentinel-1 data using the COTS software ENVI/SARscape 5.3. Emergency Mapping can be defined as "creation of maps, geo-information products and spatial analyses dedicated to providing situational awareness emergency management and immediate crisis information for response by means of extraction of reference (pre-event) and crisis (post-event) geographic information/data from satellite or aerial imagery". The conventional differential SAR interferometric technique (DInSAR) and the two currently available multi-temporal SAR interferometric approaches, i.e. Permanent Scatterer Interferometry (PSI) and Small BAseline Subset (SBAS), have been applied to provide crisis information useful for the emergency management activities. Depending on the considered Emergency Management phase, it may be distinguished between rapid mapping, i.e. fast provision of geospatial data regarding the area affected for the immediate emergency response, and monitoring mapping, i.e. detection of phenomena for risk prevention and mitigation activities. In order to evaluate the potential and limitations of the aforementioned SAR interferometric approaches for the specific rapid and monitoring mapping application, five main factors have been taken into account: crisis information extracted, input data required, processing time and expected accuracy. The results highlight that DInSAR has the capacity to delineate areas affected by large and sudden deformations and fulfills most of the immediate response requirements. The main limiting factor of interferometry is the availability of suitable SAR acquisition immediately after the event (e.g. Sentinel-1 mission characterized by 6-day revisiting time may not always satisfy the immediate emergency request). PSI and SBAS techniques are suitable to produce

Learning Theory Applied to the Biology Classroom.

Science.gov (United States)

Novak, Joseph D.

1980-01-01

The material presented in this article is intended to help students learn how to learn. The seven key concepts of David Ausubel's assimilation theory for cognitive learning are discussed with reference to the classroom. Concept mapping is suggested as a tool for demonstrating how the seven key concepts function. (SA)

Archaeometry: nuclear and conventional techniques applied to the archaeological research

International Nuclear Information System (INIS)

Esparza L, R.; Cardenas G, E.

2005-01-01

The book that now is presented is formed by twelve articles that approach from different perspective topics as the archaeological prospecting, the analysis of the pre hispanic and colonial ceramic, the obsidian and the mural painting, besides dating and questions about the data ordaining. Following the chronological order in which the exploration techniques and laboratory studies are required, there are presented in the first place the texts about the systematic and detailed study of the archaeological sites, later we pass to relative topics to the application of diverse nuclear techniques as PIXE, RBS, XRD, NAA, SEM, Moessbauer spectroscopy and other conventional techniques. The multidisciplinary is an aspect that highlights in this work, that which owes to the great specialization of the work that is presented even in the archaeological studies including in the open ground of the topography, mapping, excavation and, of course, in the laboratory tests. Most of the articles are the result of several years of investigation and it has been consigned in the responsibility of each article. The texts here gathered emphasize the technical aspects of each investigation, the modern compute systems applied to the prospecting and the archaeological mapping, the chemical and physical analysis of organic materials, of metal artifacts, of diverse rocks used in the pre hispanic epoch, of mural and ceramic paintings, characteristics that justly underline the potential of the collective works. (Author)

Applying the ISO 9126 Model to the Evaluation of an E-learning System in Iran

OpenAIRE

Hossein Pedram; Davood Karimzadegan Moghaddam; Zhaleh Asheghi

2012-01-01

One of the models presented in e-learning quality system field is ISO 9126 model, which applied in this research to evaluate e-learning system of Amirkabir University. This model system for evaluation, the six main variables provided that each of these variables by several other indicators was measured. Thus, the model parameters as ISO 9126 and turned the questionnaire survey among samples (120 experts and students of Amirkabir University) and the distribution were completed. Based on the re...

Distance learning in the Applied Sciences of Oncology

Energy Technology Data Exchange (ETDEWEB)

Barton, Michael B., E-mail: Michael.Barton@swsahs.nsw.gov.a [CCORE and the South Western Clinical School, Liverpool Hospital, University of NSW (Australia); Thode, Richard J [CCORE and the South Western Clinical School, Liverpool Hospital, University of NSW (Australia)

2010-04-15

Background: The major impediment to the expansion of oncology services is a shortage of personnel. Purpose: To develop a distance learning course for radiation oncology trainees. Materials: Under the sponsorship of the Asia Pacific Regional Cooperative Agreement administered by the International Atomic Energy Agency (IAEA), a CD ROM-based Applied Sciences of Oncology (ASOC) distance learning course of 71 modules was created. The course covers communications, critical appraisal, functional anatomy, molecular biology, pathology. The materials include interactive text and illustrations that require students to answer questions before they can progress. The course aims to supplement existing oncology curricula and does not provide a qualification. It aims to assist students in acquiring their own profession's qualification. The course was piloted in seven countries in Asia, Africa and Latin America during 2004. After feedback from the pilot course, a further nine modules were added to cover imaging physics (three modules), informed consent, burnout and coping with death and dying, Economic analysis and cancer care, Nutrition, cachexia and fatigue, radiation-induced second cancers and mathematical tools and background for radiation oncology. The course was widely distributed and can be downloaded from (http://www.iaea.org/Publications/Training/Aso/register.html). ASOC has been downloaded over 1100 times in the first year after it was posted. There is a huge demand for educational materials but the interactive approach is labour-intensive and expensive to compile. The course must be maintained to remain relevant.

Distance learning in the Applied Sciences of Oncology

International Nuclear Information System (INIS)

Barton, Michael B.; Thode, Richard J.

2010-01-01

Background: The major impediment to the expansion of oncology services is a shortage of personnel. Purpose: To develop a distance learning course for radiation oncology trainees. Materials: Under the sponsorship of the Asia Pacific Regional Cooperative Agreement administered by the International Atomic Energy Agency (IAEA), a CD ROM-based Applied Sciences of Oncology (ASOC) distance learning course of 71 modules was created. The course covers communications, critical appraisal, functional anatomy, molecular biology, pathology. The materials include interactive text and illustrations that require students to answer questions before they can progress. The course aims to supplement existing oncology curricula and does not provide a qualification. It aims to assist students in acquiring their own profession's qualification. The course was piloted in seven countries in Asia, Africa and Latin America during 2004. After feedback from the pilot course, a further nine modules were added to cover imaging physics (three modules), informed consent, burnout and coping with death and dying, Economic analysis and cancer care, Nutrition, cachexia and fatigue, radiation-induced second cancers and mathematical tools and background for radiation oncology. The course was widely distributed and can be downloaded from (http://www.iaea.org/Publications/Training/Aso/register.html). ASOC has been downloaded over 1100 times in the first year after it was posted. There is a huge demand for educational materials but the interactive approach is labour-intensive and expensive to compile. The course must be maintained to remain relevant.

English Language Teachers' Perceptions on Knowing and Applying Contemporary Language Teaching Techniques

Science.gov (United States)

Sucuoglu, Esen

2017-01-01

The aim of this study is to determine the perceptions of English language teachers teaching at a preparatory school in relation to their knowing and applying contemporary language teaching techniques in their lessons. An investigation was conducted of 21 English language teachers at a preparatory school in North Cyprus. The SPSS statistical…

[Learning experience of acupuncture technique from professor ZHANG Jin].

Science.gov (United States)

Xue, Hongsheng; Zhang, Jin

2017-08-12

As a famous acupuncturist in the world, professor ZHANG Jin believes the key of acupuncture technique is the use of force, and the understanding of the "concentrating the force into needle body" is essential to understand the essence of acupuncture technique. With deep study of Huangdi Neijing ( The Inner Canon of Huangdi ) and Zhenjiu Dacheng ( Compendium of Acupuncture and Moxibustion ), the author further learned professor ZHANG Jin 's theory and operation specification of "concentrating force into needle body, so the force arriving before and together with needle". The whole-body force should be subtly focused on the tip of needle, and gentle force at tip of needle could get significant reinforcing and reducing effect. In addition, proper timing at tip of needle could start reinforcing and reducing effect, lead qi to disease location, and achieve superior clinical efficacy.

Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

Science.gov (United States)

Mohammed, Husam Jasim; Kasim, Maznah Mat; Shaharanee, Izwan Nizal Mohd

2017-11-01

This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.

Improving skill development: an exploratory study comparing a philosophical and an applied ethical analysis technique

Science.gov (United States)

Al-Saggaf, Yeslam; Burmeister, Oliver K.

2012-09-01

This exploratory study compares and contrasts two types of critical thinking techniques; one is a philosophical and the other an applied ethical analysis technique. The two techniques analyse an ethically challenging situation involving ICT that a recent media article raised to demonstrate their ability to develop the ethical analysis skills of ICT students and professionals. In particular the skill development focused on includes: being able to recognise ethical challenges and formulate coherent responses; distancing oneself from subjective judgements; developing ethical literacy; identifying stakeholders; and communicating ethical decisions made, to name a few.

Evaluation of Economic Merger Control Techniques Applied to the European Electricity Sector

International Nuclear Information System (INIS)

Vandezande, Leen; Meeus, Leonardo; Delvaux, Bram; Van Calster, Geert; Belmans, Ronnie

2006-01-01

With European electricity markets not yet functioning on a competitive basis and consolidation increasing, the European Commission has said it intends to more intensively apply competition law in the electricity sector. Yet economic techniques and theories used in EC merger control fail to take sufficiently into account some specific features of electricity markets. The authors offer suggestions to enhance their reliability and applicability in the electricity sector. (author)

Applying traditional signal processing techniques to social media exploitation for situational understanding

Science.gov (United States)

Abdelzaher, Tarek; Roy, Heather; Wang, Shiguang; Giridhar, Prasanna; Al Amin, Md. Tanvir; Bowman, Elizabeth K.; Kolodny, Michael A.

2016-05-01

Signal processing techniques such as filtering, detection, estimation and frequency domain analysis have long been applied to extract information from noisy sensor data. This paper describes the exploitation of these signal processing techniques to extract information from social networks, such as Twitter and Instagram. Specifically, we view social networks as noisy sensors that report events in the physical world. We then present a data processing stack for detection, localization, tracking, and veracity analysis of reported events using social network data. We show using a controlled experiment that the behavior of social sources as information relays varies dramatically depending on context. In benign contexts, there is general agreement on events, whereas in conflict scenarios, a significant amount of collective filtering is introduced by conflicted groups, creating a large data distortion. We describe signal processing techniques that mitigate such distortion, resulting in meaningful approximations of actual ground truth, given noisy reported observations. Finally, we briefly present an implementation of the aforementioned social network data processing stack in a sensor network analysis toolkit, called Apollo. Experiences with Apollo show that our techniques are successful at identifying and tracking credible events in the physical world.

Applied methods and techniques for mechatronic systems modelling, identification and control

CERN Document Server

Zhu, Quanmin; Cheng, Lei; Wang, Yongji; Zhao, Dongya

2014-01-01

Applied Methods and Techniques for Mechatronic Systems brings together the relevant studies in mechatronic systems with the latest research from interdisciplinary theoretical studies, computational algorithm development and exemplary applications. Readers can easily tailor the techniques in this book to accommodate their ad hoc applications. The clear structure of each paper, background - motivation - quantitative development (equations) - case studies/illustration/tutorial (curve, table, etc.) is also helpful. It is mainly aimed at graduate students, professors and academic researchers in related fields, but it will also be helpful to engineers and scientists from industry. Lei Liu is a lecturer at Huazhong University of Science and Technology (HUST), China; Quanmin Zhu is a professor at University of the West of England, UK; Lei Cheng is an associate professor at Wuhan University of Science and Technology, China; Yongji Wang is a professor at HUST; Dongya Zhao is an associate professor at China University o...

The Development of Gamified Learning Activities to Increase Student Engagement in Learning

Science.gov (United States)

Poondej, Chanut; Lerdpornkulrat, Thanita

2016-01-01

In the literature, the potential efficacy of the gamification of education has been demonstrated. The aim of this study was to explore the influence of applying gamification techniques to increase student engagement in learning. The quasi-experimental nonequivalent-control group design was used with 577 undergraduate students from six classes. The…

ENHANCING STUDENTS‟ MOTIVATION AND ACHIEVEMENT IN LEARNING GRAMMAR THROUGH CONTEXTUAL TEACHING AND LEARNING THROUGH RELATING, EXPERIENCING, APPLYING, COOPERATING AND TRANSFERRING (REACT STRATEGY

Directory of Open Access Journals (Sweden)

Mashlihatul Umami Umami

2017-04-01

Full Text Available This research addresses the issue of whether Contextual Teaching and Learning (CTL through REACT (Relating, Experiencing, Applying, Cooperating and Transferring strategy is able to enhance motivation and achievement of English Department students‘ in learning grammar. The researcher uses a classroom action research in which it was held for about two cycles. The instruments of collecting the data are observation, rubric, questionaire and test. The researcher analyzes the data using three steps, i.e. students‘ motivation to learn are analyzed by the sheet of observation, each of individuals is also analyzed by fulfilling the questionnaire of self assessment, the progress of students‘ motivation and achievement are all monitored by rubric assessment tool, seven components of REACT strategy in learning is also recorded by the sheets of observation and the statistical analysis using t-test measures the improvement occurred. In addition, the researcher prepares field note and questionnaire to monitor the process of learning. Based on the results of qualitative-quantitative analysis, it can be found that the use of CTL approach especially using project based and cooperative learning improves the students‘ motivation and achievement in learning grammar.

Implementation of Collaborative Learning during the Applied Pharmaceutical Calculations Laboratory at the School of Pharmacy from the Universidad de Costa Rica

Directory of Open Access Journals (Sweden)

Juan José Mora Román

2014-05-01

Full Text Available The School of Pharmacy is currently facing a problem due to little or no communication among students of the same class or same academic level. Collaborative learning is a methodological strategy that goes beyond just working in groups. Small groups are formed and, after receiving instructions from the professor, group members exchange knowledge and work on an assignment until every person in the group has understood and completed the task, thus learning through collaboration. The main elements of this learning technique include: intentional design through the use of activities prepared by the teacher, collaboration through the active commitment of all the members of the work team, and significant learning through the increase of individual and collective in-depth knowledge on a given topic. Due to the foregoing, this learning experience was conducted during three sessions of the Applied Pharmaceutical Calculations Laboratory (FA-2023 during the second semester of 2012.  During these sessions, students were paired and assigned specific tasks that had to be completed before, during and after each lab session. In order to determine the result of the strategy used, the grades obtained by all the groups (24 students in quizzes and reports during those sessions were compared against the grades obtained in both items during the sessions where no collaborative learning approach was used. In addition, a survey in the form of a questionnaire was used to know the students’ opinion regarding this methodological strategy. Data was examined using a sociodemographic analysis for age and gender, and a descriptive analysis with frequency distribution for the rest of the items in the questionnaire. Results obtained show an enriching experience from the perspective of both the professor and the students. Consequently, the implementation of this strategy is necessary and advisable for the education processes of all learning levels in Costa Rica.

Learning by Teaching: Implementation of a Multimedia Project in Astro 101

Science.gov (United States)

Perrodin, D.; Lommen, A.

2011-09-01

Astro 101 students have deep-seated pre-conceptions regarding such topics as the cause of moon phases or the seasons. Beyond exploring the topics in a learner-centered fashion, the "learning by teaching" philosophy enables students to truly master concepts. In order to make students teach the cause of moon phases, we created a multimedia project where groups of students taught other students and filmed the session. They were to produce a 10-minute final movie highlighting their teaching techniques and showing students in the process of learning the concepts. This "experiment" turned out to be a great success for a few reasons. First, students gained experience explaining conceptually-challenging topics, making them learn the material better. Additionally, they learned to apply learner-centered techniques, most likely learning to teach for the first time. Finally, this project provided the students a connection between the classroom and the rest of the college, making them responsible for applying and sharing their knowledge with their peers.

Applying the principles of augmented learning to photonics laboratory work

Science.gov (United States)

Fischer, U. H. P.; Haupt, Matthias; Reinboth, Christian; Just, Jens-Uwe

2007-06-01

Most modern communication systems are based on opto-electrical methods, wavelength division multiplex (WDM) being the most widespread. Likewise, the use of polymeric fibres (POF) as an optical transmission medium is expanding rapidly. Therefore, enabling students to understand how WDM and/or POF systems are designed and maintained is an important task of universities and vocational schools that offer education in photonics. In the current academic setting, theory is mostly being taught in the classroom, while students gain practical knowledge by performing lab experiments utilizing specialized teaching systems. In an ideal setting, students should perform such experiments with a high degree of autonomy. By applying the principles of augmented learning to photonics training, contemporary lab work can be brought closer to these ideal conditions. This paper introduces "OPTOTEACH", a new teaching system for photonics lab work, designed by Harz University and successfully released on the German market by HarzOptics. OPTOTEACH is the first POF-WDM teaching system, specifically designed to cover a multitude of lab experiments in the field of optical communication technology. It is illustrated, how this lab system is supplemented by a newly developed optical teaching software - "OPTOSOFT" - and how the combination of system and software creates a unique augmented learning environment. The paper details, how the didactic concept for the software was conceptualised and introduces the latest beta version. OPTOSOFT is specifically designed not only as an attachment to OPTOTEACH, it also allows students to rehearse various aspects of theoretical optics and experience a fully interactive and feature-rich self-learning environment. The paper further details the first experiences educators at Harz University have made working with the lab system as well as the teaching software. So far, the augmented learning concept was received mostly positive, although there is some potential

Machine Learning or Information Retrieval Techniques for Bug Triaging: Which is better?

Directory of Open Access Journals (Sweden)

Anjali Goyal

2017-07-01

Full Text Available Bugs are the inevitable part of a software system. Nowadays, large software development projects even release beta versions of their products to gather bug reports from users. The collected bug reports are then worked upon by various developers in order to resolve the defects and make the final software product more reliable. The high frequency of incoming bugs makes the bug handling a difficult and time consuming task. Bug assignment is an integral part of bug triaging that aims at the process of assigning a suitable developer for the reported bug who corrects the source code in order to resolve the bug. There are various semi and fully automated techniques to ease the task of bug assignment. This paper presents the current state of the art of various techniques used for bug report assignment. Through exhaustive research, the authors have observed that machine learning and information retrieval based bug assignment approaches are most popular in literature. A deeper investigation has shown that the trend of techniques is taking a shift from machine learning based approaches towards information retrieval based approaches. Therefore, the focus of this work is to find the reason behind the observed drift and thus a comparative analysis is conducted on the bug reports of the Mozilla, Eclipse, Gnome and Open Office projects in the Bugzilla repository. The results of the study show that the information retrieval based technique yields better efficiency in recommending the developers for bug reports.

Challenges of Using Learning Analytics Techniques to Support Mobile Learning

Science.gov (United States)

Arrigo, Marco; Fulantelli, Giovanni; Taibi, Davide

2015-01-01

Evaluation of Mobile Learning remains an open research issue, especially as regards the activities that take place outside the classroom. In this context, Learning Analytics can provide answers, and offer the appropriate tools to enhance Mobile Learning experiences. In this poster we introduce a task-interaction framework, using learning analytics…

Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

Science.gov (United States)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

2018-03-01

Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p breast cancer detected in the next subsequent mammography screening.

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

Science.gov (United States)

Kassahun, Yohannes; Yu, Bingbin; Tibebu, Abraham Temesgen; Stoyanov, Danail; Giannarou, Stamatia; Metzen, Jan Hendrik; Vander Poorten, Emmanuel

2016-04-01

Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical

Applied research on air pollution using nuclear-related analytical techniques

International Nuclear Information System (INIS)

1994-01-01

A co-ordinated research programme (CRP) on applied research on air pollution using nuclear-related techniques is a global CRP which will run from 1992-1996, and will build upon the experience gained by the Agency from the laboratory support that it has been providing for several years to BAPMoN - the Background Air Pollution Monitoring Network programme organized under the auspices of the World Meterological Organization. The purpose of this CRP is to promote the use of nuclear analytical techniques in air pollution studies, e.g. NAA, XFR, and PIXE for the analysis of toxic and other trace elements in suspended particulate matter (including air filter samples), rainwater and fog-water samples, and in biological indicators of air pollution (e.g. lichens and mosses). The main purposes of the core programme are i) to support the use of nuclear and nuclear-related analytical techniques for practically-oriented research and monitoring studies on air pollution ii) to identify major sources of air pollution affecting each of the participating countries with particular reference to toxic heavy metals, and iii) to obtain comparative data on pollution levels in areas of high pollution (e.g. a city centre or a populated area downwind of a large pollution source) and low pollution (e.g. rural areas). This document reports the discussions held during the first Research Co-ordination Meeting (RCM) for the CRP which took place at the IAEA Headquarters in Vienna. Refs, figs and tabs

Renormalization techniques applied to the study of density of states in disordered systems

International Nuclear Information System (INIS)

Ramirez Ibanez, J.

1985-01-01

A general scheme for real space renormalization of formal scattering theory is presented and applied to the calculation of density of states (DOS) in some finite width systems. This technique is extended in a self-consistent way, to the treatment of disordered and partially ordered chains. Numerical results of moments and DOS are presented in comparison with previous calculations. In addition, a self-consistent theory for the magnetic order problem in a Hubbard chain is derived and a parametric transition is observed. Properties of localization of the electronic states in disordered chains are studied through various decimation averaging techniques and using numerical simulations. (author) [pt

Group Guidance Services with Self-Regulation Technique to Improve Student Learning Motivation in Junior High School (JHS)

Science.gov (United States)

Pranoto, Hadi; Atieka, Nurul; Wihardjo, Sihadi Darmo; Wibowo, Agus; Nurlaila, Siti; Sudarmaji

2016-01-01

This study aims at: determining students motivation before being given a group guidance with self-regulation technique, determining students' motivation after being given a group counseling with self-regulation technique, generating a model of group counseling with self-regulation technique to improve motivation of learning, determining the…

Using spreadsheets to develop applied skills in a business math course: Student feedback and perceived learning

Directory of Open Access Journals (Sweden)

Thomas Mays

2015-10-01

Full Text Available This paper describes the redesign of a business math course and its delivery in both face-to-face and online formats. Central to the redesigned course was the addition of applied spreadsheet exercises that served as both learning and summative assessment tools. Several other learning activities and assignments were integrated in the course to address diverse student learning styles and levels of math anxiety. Students were invited to complete a survey that asked them to rank course activities and assignments based on how well they helped the student learn course material. Open-ended items were also included in the survey. In the online course sections, students reported higher perceived learning from the use the spreadsheet-based application assignments, while face-to-face students preferred demonstrations. Qualitative remarks from the online students included numerous comments about the positive learning impact of the business application spreadsheet-based assignments, as well as the link between these assignments and what students considered the “real world.”

Capturing information need by learning user context

OpenAIRE

Goker, A.S.

1999-01-01

Learning techniques can be applied to help information retrieval systems adapt to users' specific needs. They can be used to learn from user searches to improve subsequent searches. This paper describes the approach taken to learn about particular users' contexts in order to improve document ranking produced by a probabilistic information retrieval system. The approach is based on the argument that there is a pattern in user queries in that they tend to remain within a particular context over...

Applying findings from a systematic review of workplace-based e-learning: implications for health information professionals.

Science.gov (United States)

Booth, Andrew; Carroll, Christopher; Papaioannou, Diana; Sutton, Anthea; Wong, Ruth

2009-03-01

To systematically review the UK published literature on e-learning in the health workplace and to apply the findings to one of the most prolific UK e-learning initiatives in the health sector--the National Library for Health Facilitated Online Learning Interactive Opportunity (FOLIO) Programme. Sensitive searches were conducted across ASSIA, Australian Education Index, British Education Index, cinahl, CSA Abstracts, Dissertation Abstracts, Emerald, ERIC, IBSS, Index to Theses, LISA, MEDLINE, PSYCINFO and Social Science Citation Index. Additional citations were identified from reference lists of included studies and of relevant reviews; citation tracking and contact with experts. Twenty-nine studies met the inclusion criteria and were coded and analysed using thematic analysis as described by Miles & Huberman (Qualitative Data Analysis: A Sourcebook of New Methods. Newbury Park, CA: Sage, 1984). Five broad themes were identified from the 29 included studies: (i) peer communication; (ii) flexibility; (iii) support; (iv) knowledge validation; and (v) course presentation and design. These broad themes were supported by a total of eleven sub-themes. Components from the FOLIO Programme were analysed and existing and proposed developments were mapped against each sub-theme. This provides a valuable framework for ongoing course development. Librarians involved in delivering and supporting e-learning can benefit from applying the findings from the systematic review to existing programmes, exemplified by the FOLIO Programme. The resultant framework can also be used in developing new e-learning programmes.

Lessons Learned from Developing SAWA: A Situation Awareness Assistant

National Research Council Canada - National Science Library

Matheus, Christopher J; Kokar, Mieczyslaw M; Letkowski, Jerzy J; Call, Catherine; Baclawski, Kenneth; Hinman, Michael; Salerno, John; Boulware, Douglas

2005-01-01

.... During the process of its development several lessons were learned about advantages and limitations of certain approaches, techniques and technologies as they are applied to situation awareness...

Putting Business Students in the Shoes of an Executive: An Applied Learning Approach to Developing Decision Making Skills

Directory of Open Access Journals (Sweden)

Jeanny Liu, PhD

2011-08-01

Full Text Available Students often struggle with how to translate textbook concepts into real-world applications that allow them to personally experience the importance of these concepts. This is an ongoing challenge within all disciplines in higher education. To address this, faculty design their courses using methods beyond traditional classroom lectures to facilitate and reinforce student learning. The authors believe that students who are given hands-on problem-solving opportunities are more likely to retain such knowledge and apply it outside the classroom, in the workplace, volunteer activities, and other personal pursuits. In an attempt to engage students and provide them with meaningful opportunities to apply course concepts, the authors have initiated a number of experiential learning methods in the classroom. Since fall of 2008, elements of problem-based learning were integrated in the authors’ business courses. Specifically, real-world consulting projects were introduced into their classrooms. This paper focuses on the authors’ experiences implementing problem-based learning processes and practical project assignments that actively engage students in the learning process. The experiences and the feedback gathered from students and executives who participated in the “realworld” project are reported in this paper.

Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation

Science.gov (United States)

Qiu, Yuchen; Lu, Xianglan; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Li, Shibo; Liu, Hong; Zheng, Bin

2016-03-01

Automated high throughput scanning microscopy is a fast developing screening technology used in cytogenetic laboratories for the diagnosis of leukemia or other genetic diseases. However, one of the major challenges of using this new technology is how to efficiently detect the analyzable metaphase chromosomes during the scanning process. The purpose of this investigation is to develop a computer aided detection (CAD) scheme based on deep learning technology, which can identify the metaphase chromosomes with high accuracy. The CAD scheme includes an eight layer neural network. The first six layers compose of an automatic feature extraction module, which has an architecture of three convolution-max-pooling layer pairs. The 1st, 2nd and 3rd pair contains 30, 20, 20 feature maps, respectively. The seventh and eighth layers compose of a multiple layer perception (MLP) based classifier, which is used to identify the analyzable metaphase chromosomes. The performance of new CAD scheme was assessed by receiver operation characteristic (ROC) method. A number of 150 regions of interest (ROIs) were selected to test the performance of our new CAD scheme. Each ROI contains either interphase cell or metaphase chromosomes. The results indicate that new scheme is able to achieve an area under the ROC curve (AUC) of 0.886+/-0.043. This investigation demonstrates that applying a deep learning technique may enable to significantly improve the accuracy of the metaphase chromosome detection using a scanning microscopic imaging technology in the future.

MULTIVARIATE TECHNIQUES APPLIED TO EVALUATION OF LIGNOCELLULOSIC RESIDUES FOR BIOENERGY PRODUCTION

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Thiago de Paula Protásio

2013-12-01

Full Text Available http://dx.doi.org/10.5902/1980509812361The evaluation of lignocellulosic wastes for bioenergy production demands to consider several characteristicsand properties that may be correlated. This fact demands the use of various multivariate analysis techniquesthat allow the evaluation of relevant energetic factors. This work aimed to apply cluster analysis and principalcomponents analyses for the selection and evaluation of lignocellulosic wastes for bioenergy production.8 types of residual biomass were used, whose the elemental components (C, H, O, N, S content, lignin, totalextractives and ashes contents, basic density and higher and lower heating values were determined. Bothmultivariate techniques applied for evaluation and selection of lignocellulosic wastes were efficient andsimilarities were observed between the biomass groups formed by them. Through the interpretation of thefirst principal component obtained, it was possible to create a global development index for the evaluationof the viability of energetic uses of biomass. The interpretation of the second principal component alloweda contrast between nitrogen and sulfur contents with oxygen content.

Evaluation of teaching and learning strategies

Directory of Open Access Journals (Sweden)

SK Lechner

2001-08-01

Full Text Available With the growing awareness of the importance of teaching and learning in universities and the need to move towards evidence-based teaching, it behooves the professions to re-examine their educational research methodology. While the what, how and why of student learning have become more explicit, the professions still struggle to find valid methods of evaluating the explosion of new innovation in teaching/learning strategies. This paper discusses the problems inherent in applying traditional experimental design techniques to advances in educational practice.

Development of adaptive control applied to chaotic systems

Science.gov (United States)

Rhode, Martin Andreas

1997-12-01

Continuous-time derivative control and adaptive map-based recursive feedback control techniques are used to control chaos in a variety of systems and in situations that are of practical interest. The theoretical part of the research includes the review of fundamental concept of control theory in the context of its applications to deterministic chaotic systems, the development of a new adaptive algorithm to identify the linear system properties necessary for control, and the extension of the recursive proportional feedback control technique, RPF, to high dimensional systems. Chaos control was applied to models of a thermal pulsed combustor, electro-chemical dissolution and the hyperchaotic Rossler system. Important implications for combustion engineering were suggested by successful control of the model of the thermal pulsed combustor. The system was automatically tracked while maintaining control into regions of parameter and state space where no stable attractors exist. In a simulation of the electrochemical dissolution system, application of derivative control to stabilize a steady state, and adaptive RPF to stabilize a period one orbit, was demonstrated. The high dimensional adaptive control algorithm was applied in a simulation using the Rossler hyperchaotic system, where a period-two orbit with two unstable directions was stabilized and tracked over a wide range of a system parameter. In the experimental part, the electrochemical system was studied in parameter space, by scanning the applied potential and the frequency of the rotating copper disk. The automated control algorithm is demonstrated to be effective when applied to stabilize a period-one orbit in the experiment. We show the necessity of small random perturbations applied to the system in order to both learn the dynamics and control the system at the same time. The simultaneous learning and control capability is shown to be an important part of the active feedback control.

Approximate multi-state reliability expressions using a new machine learning technique

International Nuclear Information System (INIS)

Rocco S, Claudio M.; Muselli, Marco

2005-01-01

The machine-learning-based methodology, previously proposed by the authors for approximating binary reliability expressions, is now extended to develop a new algorithm, based on the procedure of Hamming Clustering, which is capable to deal with multi-state systems and any success criterion. The proposed technique is presented in details and verified on literature cases: experiment results show that the new algorithm yields excellent predictions

Peningkatan hasil belajar mahasiswa melalui metode quantum learning dengan teknik Mind mapping

Directory of Open Access Journals (Sweden)

Andi Mariani Ramlan

2017-08-01

Full Text Available The purpose of this study is to improve the student learning outcomes in the course of Complex Analysis by applying Quantum Learning method with Mind Mapping technique. This research is conducted to give innovation method and technique of lecturer to reach the purpose and result of learning as expected. The research runs from September to December 2013 at the University of Sembilanbelas November Kolaka, Outheast Sulawesi.  The subject of the reserach is the B grade students of class VII 2011 with a total of 34 students. This research is included in Classroom Action Research (CAR. The researchers designed the study in several cycles each cycle with stages: 1 Planning, 2 Implementation; 3 Observation and Evaluation, and 4 Reflection. The students responded is positively to learning by using Quantum Learning method with Mind Mapping technique.  The students' learning achievement is 3,29 from the ideal value of 4,00; and 88,3% Student get A Or B. Then, it is concluded that Quantum Learning method with mind mapping technique can improve student learning outcomes in Complex Analysis program.

Contextual Teaching and Learning for Practitioners

Directory of Open Access Journals (Sweden)

Clemente Charles Hudson

2008-08-01

Full Text Available Contextual Teaching and Learning (CTL is defined as a way to introduce content using a variety of activelearning techniques designed to help students connect what they already know to what they are expected to learn, and to construct new knowledge from the analysis and synthesis of this learning process. A theoretical basis for CTL is outlined, with a focus on Connection, Constructivist, and Active Learning theories. A summary of brain activity during the learning process illustrates the physiological changes and connections that occur during educational activities. Three types of learning scenarios (project-based, goal-based, and inquiry-oriented are presented to illustrate how CTL can be applied by practitioners.

Learning and Model-checking Networks of I/O Automata

DEFF Research Database (Denmark)

Mao, Hua; Jaeger, Manfred

2012-01-01

We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating nite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can...... learn generic models for network entities in the form of automata templates. As is characteristic for SRL techniques, the abstraction level aorded by learning generic templates enables one to apply the learned model to new domains. A main benet of learning models based on nite automata lies in the fact...

Learning banknote fitness for sorting

NARCIS (Netherlands)

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

SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

Directory of Open Access Journals (Sweden)

T. Hamsapriya

2011-12-01

Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

[Applying Game-Based Learning in Nursing Education: Empathy Board Game Learning].

Science.gov (United States)

Lu, Chueh-Fen; Wu, Shu-Mei; Shu, Ying-Mei; Yeh, Mei-Yu

2018-02-01

Attending lectures and reading are two common approaches to acquiring knowledge, while repetitive practice is a common approach to acquiring skills. Nurturing proper attitudes in students is one of the greatest challenges for educators. Health professionals must incorporate empathy into their practice. Creative teaching strategies may offer a feasible approach to enhancing empathy-related competence. The present article focuses on analyzing current, empathy-related curriculums in nursing education in Taiwan, exploring the concepts of empathy and game-based learning, presenting the development of an empathy board game as a teaching aid, and, finally, evaluating the developed education application. Based on the learner-centered principle, this aid was designed with peer learning, allowing learners to influence the learning process, to simulate the various roles of clients, and to develop diverse interpersonal dialogues. The continuous learning loops were formed using the gamification mechanism and transformation, enabling students to connect and practice the three elements of empathy ability: emotion, cognition and expression. Via the game elements of competition, interaction, storytelling, real-time responses, concretizing feedback, integrated peer learning, and equality between teachers and students, students who play patient roles are able to perceive different levels of comfort, which encourages the development of insight into the meaning of empathy. Thereby, the goals of the empathy lesson is achievable within a creative game-based learning environment.

Fostering students’ thinking skill and social attitude through STAD cooperative learning technique on tenth grade students of chemistry class

Science.gov (United States)

Kriswintari, D.; Yuanita, L.; Widodo, W.

2018-04-01

The aim of this study was to develop chemistry learning package using Student Teams Achievement Division (STAD) cooperative learning technique to foster students’ thinking skills and social attitudes. The chemistry learning package consisting of lesson plan, handout, students’ worksheet, thinking skill test, and observation sheet of social attitude was developed using the Dick and Carey model. Research subject of this study was chemistry learning package using STAD which was tried out on tenth grade students of SMA Trimurti Surabaya. The tryout was conducted using the one-group pre-test post-test design. Data was collected through observation, test, and questionnaire. The obtained data were analyzed using descriptive qualitative analysis. The findings of this study revealed that the developed chemistry learning package using STAD cooperative learning technique was categorized valid, practice and effective to be implemented in the classroom to foster students’ thinking skill and social attitude.

Machine learning applied to crime prediction

OpenAIRE

Vaquero Barnadas, Miquel

2016-01-01

Machine Learning is a cornerstone when it comes to artificial intelligence and big data analysis. It provides powerful algorithms that are capable of recognizing patterns, classifying data, and, basically, learn by themselves to perform a specific task. This field has incredibly grown in popularity these days, however, it still remains unknown for the majority of people, and even for most professionals. This project intends to provide an understandable explanation of what is it, what types ar...

A service based adaptive U-learning system using UX.

Science.gov (United States)

Jeong, Hwa-Young; Yi, Gangman

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

Arrangement and Applying of Movement Patterns in the Cerebellum Based on Semi-supervised Learning.

Science.gov (United States)

Solouki, Saeed; Pooyan, Mohammad

2016-06-01

Biological control systems have long been studied as a possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. Therefore, highly regular structure of the cerebellum has been in the core of attention in theoretical and computational modeling. However, most of these models reflect some special features of the cerebellum without regarding the whole motor command computational process. In this paper, we try to make a logical relation between the most significant models of the cerebellum and introduce a new learning strategy to arrange the movement patterns: cerebellar modular arrangement and applying of movement patterns based on semi-supervised learning (CMAPS). We assume here the cerebellum like a big archive of patterns that has an efficient organization to classify and recall them. The main idea is to achieve an optimal use of memory locations by more than just a supervised learning and classification algorithm. Surely, more experimental and physiological researches are needed to confirm our hypothesis.

3rd International Conference on Computer Science, Applied Mathematics and Applications

CERN Document Server

Nguyen, Ngoc; Do, Tien

2015-01-01

This volume contains the extended versions of papers presented at the 3rd International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2015) held on 11-13 May, 2015 in Metz, France. The book contains 5 parts: 1. Mathematical programming and optimization: theory, methods and software, Operational research and decision making, Machine learning, data security, and bioinformatics, Knowledge information system, Software engineering. All chapters in the book discuss theoretical and algorithmic as well as practical issues connected with computation methods & optimization methods for knowledge engineering and machine learning techniques.  

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Directory of Open Access Journals (Sweden)

Evanthia E. Tripoliti

Full Text Available Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years. About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented. Keywords: Heart failure, Diagnosis, Prediction, Severity estimation, Classification, Data mining

Applying Student Team Achievement Divisions (STAD) Model on Material of Basic Programme Branch Control Structure to Increase Activity and Student Result

Science.gov (United States)

Akhrian Syahidi, Aulia; Asyikin, Arifin Noor; Asy’ari

2018-04-01

Based on my experience of teaching the material of branch control structure, it is found that the condition of the students is less active causing the low activity of the students on the attitude assessment during the learning process on the material of the branch control structure i.e. 2 students 6.45% percentage of good activity and 29 students percentage 93.55% enough and less activity. Then from the low activity resulted in low student learning outcomes based on a daily re-examination of branch control material, only 8 students 26% percentage reached KKM and 23 students 74% percent did not reach KKM. The purpose of this research is to increase the activity and learning outcomes of students of class X TKJ B SMK Muhammadiyah 1 Banjarmasin after applying STAD type cooperative learning model on the material of branch control structure. The research method used is Classroom Action Research. The study was conducted two cycles with six meetings. The subjects of this study were students of class X TKJ B with a total of 31 students consisting of 23 men and 8 women. The object of this study is the activity and student learning outcomes. Data collection techniques used are test and observation techniques. Data analysis technique used is a percentage and mean. The results of this study indicate that: an increase in activity and learning outcomes of students on the basic programming learning material branch control structure after applying STAD type cooperative learning model.

Aircraft operability methods applied to space launch vehicles

Science.gov (United States)

Young, Douglas

1997-01-01

The commercial space launch market requirement for low vehicle operations costs necessitates the application of methods and technologies developed and proven for complex aircraft systems. The ``building in'' of reliability and maintainability, which is applied extensively in the aircraft industry, has yet to be applied to the maximum extent possible on launch vehicles. Use of vehicle system and structural health monitoring, automated ground systems and diagnostic design methods derived from aircraft applications support the goal of achieving low cost launch vehicle operations. Transforming these operability techniques to space applications where diagnostic effectiveness has significantly different metrics is critical to the success of future launch systems. These concepts will be discussed with reference to broad launch vehicle applicability. Lessons learned and techniques used in the adaptation of these methods will be outlined drawing from recent aircraft programs and implementation on phase 1 of the X-33/RLV technology development program.

Cognitive Heterogeneous Reconfigurable Optical Networks (CHRON): Enabling Technologies and Techniques

DEFF Research Database (Denmark)

Tafur Monroy, Idelfonso; Zibar, Darko; Guerrero Gonzalez, Neil

2011-01-01

We present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network. We introduce and discuss in particular the technologies and techniques that will enable a cognitive optical...... network to observe, act, learn and optimizes its performance, taking into account its high degree of heterogeneity with respect to quality of service, transmission and switching techniques....

Investigation of the shear bond strength to dentin of universal adhesives applied with two different techniques

Directory of Open Access Journals (Sweden)

Elif Yaşa

2017-09-01

Full Text Available Objective: The aim of this study was to evaluate the shear bond strength of universal adhesives applied with self-etch and etch&rinse techniques to dentin. Materials and Method: Fourty-eight sound extracted human third molars were used in this study. Occlusal enamel was removed in order to expose the dentinal surface, and the surface was flattened. Specimens were randomly divided into four groups and were sectioned vestibulo-lingually using a diamond disc. The universal adhesives: All Bond Universal (Group 1a and 1b, Gluma Bond Universal (Group 2a and 2b and Single Bond Universal (Group 3a and 3b were applied onto the tooth specimens either with self-etch technique (a or with etch&rinse technique (b according to the manufacturers’ instructions. Clearfil SE Bond (Group 4a; self-etch and Optibond FL (Group 4b; etch&rinse were used as control groups. Then the specimens were restored with a nanohybrid composite resin (Filtek Z550. After thermocycling, shear bond strength test was performed with a universal test machine at a crosshead speed of 0.5 mm/min. Fracture analysis was done under a stereomicroscope (×40 magnification. Data were analyzed using two-way ANOVA and post-hoc Tukey tests. Results: Statistical analysis showed significant differences in shear bond strength values between the universal adhesives (p<0.05. Significantly higher bond strength values were observed in self-etch groups (a in comparison to etch&rinse groups (b (p<0.05. Among all groups, Single Bond Universal showed the greatest shear bond strength values, whereas All Bond Universal showed the lowest shear bond strength values with both application techniques. Conclusion: Dentin bonding strengths of universal adhesives applied with different techniques may vary depending on the adhesive material. For the universal bonding agents tested in this study, the etch&rinse technique negatively affected the bond strength to dentin.

IMPROVING THE STUDENTS‘ READING COMPREHENSION THROUGH KNOW-WANT-LEARN (KWL TECHNIQUE TO TEACH ANALYTICAL EXPOSITION ( Class Action Research

Directory of Open Access Journals (Sweden)

Meike Imelda Wachyu

2017-12-01

Full Text Available This study is aimed at finding out the impacts of the use of Know-Want-Learn technique in improving the reading comprehension to teach analytical exposition among eleventh grade students of SMA N 2 Indramayu in the academic year of 2017/2018. The study was action research in two research cycles. In the study, the researcher collaborated with the English teachers and the students. The data of this study were qualitative in nature supported by quantitative data. Qualitative data were obtained from the results of classroom observation and collaborators‘ discussion. Quantitative data were obtained from pre-test and post test results. The instruments for collecting the data were observation guides, interview guides, and the pre-test and posttest. The data were in the form of field notes, interview transcripts, and the scores of the students‘ pre-test and posttest. The results of the two cycles show that the use of Know-WantLearn technique is effective to improve the students‘ reading comprehension. It is supported by the qualitative data which show that (1 Know-Want-Learn technique can help the teacher to scaffold the students‘ comprehension of the text by focusing on the steps before, during, and after reading; (2 Know-Want-Learn technique can help the students to preview the text, assess what they have learned after reading, and motivate their interest in reading; (3 The kind of activities given such as preeteaching vocabulary, using skimming and scanning, using fix-up strategies, and guessing meaning can help the students to read the text efficiently.

The digital geometric phase technique applied to the deformation evaluation of MEMS devices

International Nuclear Information System (INIS)

Liu, Z W; Xie, H M; Gu, C Z; Meng, Y G

2009-01-01

Quantitative evaluation of the structure deformation of microfabricated electromechanical systems is of importance for the design and functional control of microsystems. In this investigation, a novel digital geometric phase technique was developed to meet the deformation evaluation requirement of microelectromechanical systems (MEMS). The technique is performed on the basis of regular artificial lattices, instead of a natural atom lattice. The regular artificial lattices with a pitch ranging from micrometer to nanometer will be directly fabricated on the measured surface of MEMS devices by using a focused ion beam (FIB). Phase information can be obtained from the Bragg filtered images after fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) of the scanning electronic microscope (SEM) images. Then the in-plane displacement field and the local strain field related to the phase information will be evaluated. The obtained results show that the technique can be well applied to deformation measurement with nanometer sensitivity and stiction force estimation of a MEMS device

[Motor capacities involved in the psychomotor skills of the cardiopulmonary resuscitation technique: recommendations for the teaching-learning process].

Science.gov (United States)

Miyadahira, A M

2001-12-01

It is a bibliographic study about the identification of the motor capacities involved in the psychomotor skills of the cardiopulmonary resuscitation (CPR) which aims to obtain subsidies to the planning of the teaching-learning process of this skill. It was found that: the motor capacities involved in the psychomotor skill of the CPR technique are predominantly cognitive and motor, involving 9 perceptive-motor capacities and 8 physical proficiency capacities. The CPR technique is a psychomotor skill classified as open, done in series and categorized as a thin and global skill and the teaching-learning process of the CPR technique has an elevated degree of complexity.

Collaborative DFA learning applied to Grid administration

NARCIS (Netherlands)

Mulder, W.; Jacobs, C.J.H.; van Someren, M.; van Erp, M.; Stehouwer, H.; van Zaanen, M.

2009-01-01

This paper proposes a distributed learning mechanism that learns patterns from distributed datasets. The complex and dynamic settings of grid environments requires supporting systems to be of a more sophisticated level. Contemporary tools lack the ability to relate and infer events. We developed an

Markerless gating for lung cancer radiotherapy based on machine learning techniques

International Nuclear Information System (INIS)

Lin Tong; Li Ruijiang; Tang Xiaoli; Jiang, Steve B; Dy, Jennifer G

2009-01-01

In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks-ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.

Semi-supervised learning of hyperspectral image segmentation applied to vine tomatoes and table grapes

Directory of Open Access Journals (Sweden)

Jeroen van Roy

2018-03-01

Full Text Available Nowadays, quality inspection of fruit and vegetables is typically accomplished through visual inspection. Automation of this inspection is desirable to make it more objective. For this, hyperspectral imaging has been identified as a promising technique. When the field of view includes multiple objects, hypercubes should be segmented to assign individual pixels to different objects. Unsupervised and supervised methods have been proposed. While the latter are labour intensive as they require masking of the training images, the former are too computationally intensive for in-line use and may provide different results for different hypercubes. Therefore, a semi-supervised method is proposed to train a computationally efficient segmentation algorithm with minimal human interaction. As a first step, an unsupervised classification model is used to cluster spectra in similar groups. In the second step, a pixel selection algorithm applied to the output of the unsupervised classification is used to build a supervised model which is fast enough for in-line use. To evaluate this approach, it is applied to hypercubes of vine tomatoes and table grapes. After first derivative spectral preprocessing to remove intensity variation due to curvature and gloss effects, the unsupervised models segmented 86.11% of the vine tomato images correctly. Considering overall accuracy, sensitivity, specificity and time needed to segment one hypercube, partial least squares discriminant analysis (PLS-DA was found to be the best choice for in-line use, when using one training image. By adding a second image, the segmentation results improved considerably, yielding an overall accuracy of 96.95% for segmentation of vine tomatoes and 98.52% for segmentation of table grapes, demonstrating the added value of the learning phase in the algorithm.

[Molecular techniques applied in species identification of Toxocara].

Science.gov (United States)

Fogt, Renata

2006-01-01

Toxocarosis is still an important and actual problem in human medicine. It can manifest as visceral (VLM), ocular (OLM) or covert (CT) larva migrans syndroms. Complicated life cycle of Toxocara, lack of easy and practical methods of species differentiation of the adult nematode and embarrassing in recognition of the infection in definitive hosts create difficulties in fighting with the infection. Although studies on human toxocarosis have been continued for over 50 years there is no conclusive answer, which of species--T. canis or T. cati constitutes a greater risk of transmission of the nematode to man. Neither blood serological examinations nor microscopic observations of the morphological features of the nematode give the satisfied answer on the question. Since the 90-ths molecular methods were developed for species identification and became useful tools being widely applied in parasitological diagnosis. This paper cover the survey of methods of DNA analyses used for identification of Toxocara species. The review may be helpful for researchers focused on Toxocara and toxocarosis as well as on detection of new species. The following techniques are described: PCR (Polymerase Chain Reaction), RFLP (Restriction Fragment Length Polymorphism), RAPD (Random Amplified Polymorphic DNA) and SSCP (Single Strand Conformation Polymorphism).

Software Engineering and eLearning: The MuSofT Project - www.musoft.org

Directory of Open Access Journals (Sweden)

Ernst-Erich Doberkat

2005-12-01

Full Text Available eLearning supports the education in certain disciplines. Here, we report about novel eLearning concepts, techniques, and tools to support education in Software Engineering, a subdiscipline of computer science. We call this "Software Engineering eLearning". On the other side, software support is a substantial prerequisite for eLearning in any discipline. Thus, Software Engineering techniques have to be applied to develop and maintain those software systems. We call this "eLearning Software Engineering". Both aspects have been investigated in a large joint, BMBF-funded research project, termed MuSofT (Multimedia in Software Engineering. The main results are summarized in this paper.

SOCAP: Lessons learned in applying SIPE-2 to the military operations crisis action planning domain

Science.gov (United States)

Desimone, Roberto

1992-01-01

This report describes work funded under the DARPA Planning and Scheduling Initiative that led to the development of SOCAP (System for Operations Crisis Action Planning). In particular, it describes lessons learned in applying SIPE-2, the underlying AI planning technology within SOCAP, to the domain of military operations deliberate and crisis action planning. SOCAP was demonstrated at the U.S. Central Command and at the Pentagon in early 1992. A more detailed report about the lessons learned is currently being prepared. This report was presented during one of the panel discussions on 'The Relevance of Scheduling to AI Planning Systems.'

Classification of Phishing Email Using Random Forest Machine Learning Technique

Directory of Open Access Journals (Sweden)

Andronicus A. Akinyelu

2014-01-01

Full Text Available Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN and false positive (FP rates.

MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques

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Cerqueira Fabio R

2012-10-01

Full Text Available Abstract Background The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. Results Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. Conclusion Our approach not only enhances the computational performance, and

Applications of NLP Techniques to Computer-Assisted Authoring of Test Items for Elementary Chinese

Science.gov (United States)

Liu, Chao-Lin; Lin, Jen-Hsiang; Wang, Yu-Chun

2010-01-01

The authors report an implemented environment for computer-assisted authoring of test items and provide a brief discussion about the applications of NLP techniques for computer assisted language learning. Test items can serve as a tool for language learners to examine their competence in the target language. The authors apply techniques for…

Strategies Applied by Teachers as Classroom Managers to Strengthen Learning

Directory of Open Access Journals (Sweden)

Franco Javier Jáuregui Contreras

2017-05-01

Full Text Available The present research was executed within the quantitative paradigm, under the feasible project modality, with the objective of designing strategies aimed at teachers to strengthen learning in the students of the Arnoldo Gabaldon educational unit, located in Delicias, Rafael Urdaneta municipality State Táchira. The methodology used responds to the characteristics of field descriptive research, not experimental. The study population consisted of 72 teachers. To obtain the information, a 20-item contentive instrument was applied, which measured the factors that influence the application of managerial strategies to strengthen student learning. This instrument was validated and a reliability index of 0.81 was obtained. In the analysis of results, the single and absolute frequencies of each reagent were determined. This resulted in a series of constantly occurring situations that led to the following conclusions: managerial strategies are employed by teachers on average, as well as in organization and planning, there is no commitment in relation to it, although there It was possible to detect that teachers comply with the planned activities on a regular basis. In addition, in the case of management and control, it is denoted how forcefully both processes are fulfilled, even in the majority, and both are assumed as managerial strategies. Therefore, the implementation of strategies is recommended.

Development of Innovation by Constructivist Theory with using Cooperative Learning Technique STAD of Mathayomsuksa 3 Students at Anuban Mahasarakham School

Directory of Open Access Journals (Sweden)

Apinya Phonpinyo

2017-03-01

Full Text Available The purposes of the this research: 1. were study the problems and needed science activities learning 2. to improve students activities 3. study the activities; 3.1 to improve the learning of course to pass the standard in 70 percentage 3.2 to improve basic science process skills to pass in 70 percentage 3.3 to study on attitude in science. the Target group was mathayomsuksa 3 students in the class 1 of Anuban Mahasarakham school by using purposive sampling technique that totally were 32 persons. The research instruments were an interview of teacher, the questionnaires of students who were managed in science learning activities and learning management based, the evaluation of learning achievement that had 4 choices were totally 30 items are have discrimination levels from 0.20 - 0.64 and all reliability levels were 0.74, the test of science process skills on basic level that had 4 choices with 30 items had discrimination levels from 0.28 - 0.83 and all reliability levels were 0.73. The evaluation of attitude to science course had 5-scale levels scale 5 levels, 20-item and difficulty levels from 0.20 - 0.71. The reliability levels were 0.69. The statistics used was percentage, mean and standard division. The research found as follows; 1. Study of the problems and needed science activities learning was found that concerning learning activities management focused on description, note by student non-action with learning activities, it non-evaluating science process skills and attitude in science. The knowledge of most student on science was lower. The motivated students students in learning activities in science were at high level ( = 3.81 2. Learning activities management was developed by 5 stages as follow; 1 introduction stage, 2 review old idea stage, 3 improvement and change concept stage, 4 applying a new idea stage, 5 conclusion stage and appropriately learning activities plan was at high level ( = 4.30 3. the Effects of learning activities

Comparison of two different techniques of cooperative learning approach: Undergraduates' conceptual understanding in the context of hormone biochemistry.

Science.gov (United States)

Mutlu, Ayfer

2018-03-01

The purpose of the research was to compare the effects of two different techniques of the cooperative learning approach, namely Team-Game Tournament and Jigsaw, on undergraduates' conceptual understanding in a Hormone Biochemistry course. Undergraduates were randomly assigned to Group 1 (N = 23) and Group 2 (N = 29). Instructions were accomplished using Team-Game Tournament in Group 1 and Jigsaw in Group 2. Before the instructions, all groups were informed about cooperative learning and techniques, their responsibilities in the learning process and accessing of resources. Instructions were conducted under the guidance of the researcher for nine weeks and the Hormone Concept Test developed by the researcher was used before and after the instructions for data collection. According to the results, while both techniques improved students' understanding, Jigsaw was more effective than Team-Game Tournament. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(2):114-120, 2018. © 2017 The International Union of Biochemistry and Molecular Biology.

Applying Consumer and Homemaking Skills to Jobs and Careers. Secondary Learning Guide 13. Project Connect. Linking Self-Family-Work.

Science.gov (United States)

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on applying consumer and homemaking skills to jobs and careers is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative…

Testing protects against proactive interference in face-name learning.

Science.gov (United States)

Weinstein, Yana; McDermott, Kathleen B; Szpunar, Karl K

2011-06-01

Learning face-name pairings at a social function becomes increasingly more difficult the more individuals one meets. This phenomenon is attributable to proactive interference--the negative influence of prior learning on subsequent learning. Recent evidence suggests that taking a memory test can alleviate proactive interference in verbal list learning paradigms. We apply this technique to face-name pair learning. Participants studied four lists of 12 face-name pairings and either attempted to name the 12 faces just studied after every list or did not. Recall attempts after every list improved learning of the fourth list by over 100%. Moreover, no reduction in learning of face-name pairings occurred from list 1 to list 4 for participants who attempted to name studied faces between lists. These results suggest that testing oneself on the names of a group of new acquaintances before moving on to the next group is an effective mnemonic technique for social functions.

Vibration monitoring/diagnostic techniques, as applied to reactor coolant pumps

International Nuclear Information System (INIS)

Sculthorpe, B.R.; Johnson, K.M.

1986-01-01

With the increased awareness of reactor coolant pump (RCP) cracked shafts, brought about by the catastrophic shaft failure at Crystal River number3, Florida Power and Light Company, in conjunction with Bently Nevada Corporation, undertook a test program at St. Lucie Nuclear Unit number2, to confirm the integrity of all four RCP pump shafts. Reactor coolant pumps play a major roll in the operation of nuclear-powered generation facilities. The time required to disassemble and physically inspect a single RCP shaft would be lengthy, monetarily costly to the utility and its customers, and cause possible unnecessary man-rem exposure to plant personnel. When properly applied, vibration instrumentation can increase unit availability/reliability, as well as provide enhanced diagnostic capability. This paper reviews monitoring benefits and diagnostic techniques applicable to RCPs/motor drives

Machine Learning for Medical Imaging.

Science.gov (United States)

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

Integrative Teaching Techniques and Improvement of German Speaking Learning Skills

Science.gov (United States)

Litualy, Samuel Jusuf

2016-01-01

This research ist a Quasi-Experimental research which only applied to one group without comparison group. It aims to prove whether the implementation of integrative teaching technique has influenced the speaking skill of the students in German Education Study Program of FKIP, Pattimura University. The research was held in the German Education…

Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

Science.gov (United States)

Jaspers, Arne; Beéck, Tim Op De; Brink, Michel S; Frencken, Wouter G P; Staes, Filip; Davis, Jesse J; Helsen, Werner F

2017-12-28

Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Training data were collected from 38 professional soccer players over two seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using two machine learning techniques, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (LASSO), and one naive baseline method. The predictions were based on a large set of external load indicators. Using each technique, one group model involving all players and one individual model for each player was constructed. These models' performance on predicting the reported RPE values for future training sessions was compared to the naive baseline's performance. Both the ANN and LASSO models outperformed the baseline. Additionally, the LASSO model made more accurate predictions for the RPE than the ANN model. Furthermore, decelerations were identified as important external load indicators. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Machine learning techniques may have added value in predicting the RPE for future sessions to optimize training design and evaluation. Additionally, these techniques may be used in conjunction with expert knowledge to select key external load indicators for load monitoring.

Volcanic Monitoring Techniques Applied to Controlled Fragmentation Experiments

Science.gov (United States)

Kueppers, U.; Alatorre-Ibarguengoitia, M. A.; Hort, M. K.; Kremers, S.; Meier, K.; Scharff, L.; Scheu, B.; Taddeucci, J.; Dingwell, D. B.

2010-12-01

Volcanic eruptions are an inevitable natural threat. The range of eruptive styles is large and short term fluctuations of explosivity or vent position pose a large risk that is not necessarily confined to the immediate vicinity of a volcano. Explosive eruptions rather may also affect aviation, infrastructure and climate, regionally as well as globally. Multiparameter monitoring networks are deployed on many active volcanoes to record signs of magmatic processes and help elucidate the secrets of volcanic phenomena. However, our mechanistic understanding of many processes hiding in recorded signals is still poor. As a direct consequence, a solid interpretation of the state of a volcano is still a challenge. In an attempt to bridge this gap, we combined volcanic monitoring and experimental volcanology. We performed 15 well-monitored, field-based, experiments and fragmented natural rock samples from Colima volcano (Mexico) by rapid decompression. We used cylindrical samples of 60 mm height and 25 mm and 60 mm diameter, respectively, and 25 and 35 vol.% open porosity. The applied pressure range was from 4 to 18 MPa. Using different experimental set-ups, the pressurised volume above the samples ranged from 60 - 170 cm3. The experiments were performed at ambient conditions and at controlled sample porosity and size, confinement geometry, and applied pressure. The experiments have been thoroughly monitored with 1) Doppler Radar (DR), 2) high-speed and high-definition cameras, 3) acoustic and infrasound sensors, 4) pressure transducers, and 5) electrically conducting wires. Our aim was to check for common results achieved by the different approaches and, if so, calibrate state-of-the-art monitoring tools. We present how the velocity of the ejected pyroclasts was measured by and evaluated for the different approaches and how it was affected by the experimental conditions and sample characteristics. We show that all deployed instruments successfully measured the pyroclast

Removal of benzaldehyde from a water/ethanol mixture by applying scavenging techniques

DEFF Research Database (Denmark)

Mitic, Aleksandar; Skov, Thomas; Gernaey, Krist V.

2017-01-01

A presence of carbonyl compounds is very common in the food industry. The nature of such compounds is to be reactive and thus many products involve aldehydes/ketones in their synthetic routes. By contrast, the high reactivity of carbonyl compounds could also lead to formation of undesired compounds......, such as genotoxic impurities. It can therefore be important to remove carbonyl compounds by implementing suitable removal techniques, with the aim of protecting final product quality. This work is focused on benzaldehyde as a model component, studying its removal from a water/ethanol mixture by applying different...

Wire-mesh and ultrasound techniques applied for the characterization of gas-liquid slug flow

Energy Technology Data Exchange (ETDEWEB)

Ofuchi, Cesar Y.; Sieczkowski, Wytila Chagas; Neves Junior, Flavio; Arruda, Lucia V.R.; Morales, Rigoberto E.M.; Amaral, Carlos E.F.; Silva, Marco J. da [Federal University of Technology of Parana, Curitiba, PR (Brazil)], e-mails: ofuchi@utfpr.edu.br, wytila@utfpr.edu.br, neves@utfpr.edu.br, lvrarruda@utfpr.edu.br, rmorales@utfpr.edu.br, camaral@utfpr.edu.br, mdasilva@utfpr.edu.br

2010-07-01

Gas-liquid two-phase flows are found in a broad range of industrial applications, such as chemical, petrochemical and nuclear industries and quite often determine the efficiency and safety of process and plants. Several experimental techniques have been proposed and applied to measure and quantify two-phase flows so far. In this experimental study the wire-mesh sensor and an ultrasound technique are used and comparatively evaluated to study two-phase slug flows in horizontal pipes. The wire-mesh is an imaging technique and thus appropriated for scientific studies while ultrasound-based technique is robust and non-intrusive and hence well suited for industrial applications. Based on the measured raw data it is possible to extract some specific slug flow parameters of interest such as mean void fraction and characteristic frequency. The experiments were performed in the Thermal Sciences Laboratory (LACIT) at UTFPR, Brazil, in which an experimental two-phase flow loop is available. The experimental flow loop comprises a horizontal acrylic pipe of 26 mm diameter and 9 m length. Water and air were used to produce the two phase flow under controlled conditions. The results show good agreement between the techniques. (author)

Machine Learning for Security

CERN Multimedia

CERN. Geneva

2015-01-01

Applied statistics, aka ‘Machine Learning’, offers a wealth of techniques for answering security questions. It’s a much hyped topic in the big data world, with many companies now providing machine learning as a service. This talk will demystify these techniques, explain the math, and demonstrate their application to security problems. The presentation will include how-to’s on classifying malware, looking into encrypted tunnels, and finding botnets in DNS data. About the speaker Josiah is a security researcher with HP TippingPoint DVLabs Research Group. He has over 15 years of professional software development experience. Josiah used to do AI, with work focused on graph theory, search, and deductive inference on large knowledge bases. As rules only get you so far, he moved from AI to using machine learning techniques identifying failure modes in email traffic. There followed digressions into clustered data storage and later integrated control systems. Current ...

Machine learning a Bayesian and optimization perspective

CERN Document Server

Theodoridis, Sergios

2015-01-01

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as shor...

A Service Based Adaptive U-Learning System Using UX

Directory of Open Access Journals (Sweden)

Hwa-Young Jeong

2014-01-01

Full Text Available In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users’ tailored materials according to their learning style. That is, we analyzed the user’s data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

Big data - modelling of midges in Europa using machine learning techniques and satellite imagery

DEFF Research Database (Denmark)

Cuellar, Ana Carolina; Kjær, Lene Jung; Skovgaard, Henrik

2017-01-01

coordinates of each trap, start and end dates of trapping. We used 120 environmental predictor variables together with Random Forest machine learning algorithms to predict the overall species distribution (probability of occurrence) and monthly abundance in Europe. We generated maps for every month...... and the Obsoletus group, although abundance was generally higher for a longer period of time for C. imicula than for the Obsoletus group. Using machine learning techniques, we were able to model the spatial distribution in Europe for C. imicola and the Obsoletus group in terms of abundance and suitability...

Introducing E-Learning in a Norwegian Service Company with Participatory Design and Evolutionary Prototyping Techniques

OpenAIRE

Mørch , Anders I.; Engen , Bård Ketil; Hansen Åsand , Hege-René; Brynhildsen , Camilla; Tødenes , Ida

2004-01-01

Over a 2-year period, we have participated in the introduction of e-learning in a Norwegian service company, a gas station division of an oil company. This company has an advanced computer network infrastructure for communication and information sharing, but the primary task of the employees is serving customers. We identify some challenges to introducing e-learning in this kind of environment. A primary emphasis has been on using participatory design techniques during the planning stages and...

A framework for detection of malicious software in Android handheld systems using machine learning techniques

OpenAIRE

Torregrosa García, Blas

2015-01-01

The present study aims at designing and developing new approaches to detect malicious applications in Android-based devices. More precisely, MaLDroide (Machine Learning-based Detector for Android malware), a framework for detection of Android malware based on machine learning techniques, is introduced here. It is devised to identify malicious applications. Este trabajo tiene como objetivo el diseño y el desarrollo de nuevas formas de detección de aplicaciones maliciosas en los dispositivos...

The influence of inquiry learning model on additives theme with ethnoscience content to cultural awareness of students

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Sudarmin, S.; Selia, E.; Taufiq, M.

2018-03-01

The purpose of this research is to determine the influence of inquiry learning model on additives theme with ethnoscience content to cultural awareness of students and how the students’ responses to learning. The method applied in this research is a quasi-experimental with non-equivalent control group design. The sampling technique applied in this research is the technique of random sampling. The samples were eight grade students of one of junior high schools in Semarang. The results of this research were (1) thestudents’ cultural awareness of the experiment class is better than the control class (2) inquiry learning model with ethnoscience content strongly influencing the cultural awareness of students by 78% and (3) students gave positive responses to inquiry learning model with ethnoscience content. The conclusions of this research are inquiry-learning model with ethnoscience content has positive influence on students’ cultural awareness.

Case study: how to apply data mining techniques in a healthcare data warehouse.

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Silver, M; Sakata, T; Su, H C; Herman, C; Dolins, S B; O'Shea, M J

2001-01-01

Healthcare provider organizations are faced with a rising number of financial pressures. Both administrators and physicians need help analyzing large numbers of clinical and financial data when making decisions. To assist them, Rush-Presbyterian-St. Luke's Medical Center and Hitachi America, Ltd. (HAL), Inc., have partnered to build an enterprise data warehouse and perform a series of case study analyses. This article focuses on one analysis, which was performed by a team of physicians and computer science researchers, using a commercially available on-line analytical processing (OLAP) tool in conjunction with proprietary data mining techniques developed by HAL researchers. The initial objective of the analysis was to discover how to use data mining techniques to make business decisions that can influence cost, revenue, and operational efficiency while maintaining a high level of care. Another objective was to understand how to apply these techniques appropriately and to find a repeatable method for analyzing data and finding business insights. The process used to identify opportunities and effect changes is described.

Mic it! microphones, microphone techniques, and their impact on the final mix

CERN Document Server

Corbett, Ian

2014-01-01

Capture great sound in the first place, and spend less time ""fixing it in the mix"" with Ian Corbett's Mic It! Microphones, Microphone Techniques, and Their Impact on the Final Mix. With his expert guidance, you'll quickly understand essential audio concepts as they relate to microphones and mic techniques, and learn how to apply them to your recording situation. Whether you only ever buy one microphone, are equipping a studio on a budget, or have a vast selection of great mics to use, you'll learn to better use whatever tools you have. Mic It! gives you the background to design and discover

Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets.

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Soria Morillo, Luis M; Alvarez-Garcia, Juan A; Gonzalez-Abril, Luis; Ortega Ramírez, Juan A

2016-07-15

In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.

Career Goal-Based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

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Ma, Xueying; Ye, Lu

2018-01-01

This article describes how e-learning recommender systems nowadays have applied different kinds of techniques to recommend personalized learning content for users based on their preference, goals, interests and background information. However, the cold-start problem which exists in traditional recommendation algorithms are still left over in…

Human semi-supervised learning.

Science.gov (United States)

Gibson, Bryan R; Rogers, Timothy T; Zhu, Xiaojin

2013-01-01

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. Copyright © 2013 Cognitive Science Society, Inc.

Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

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Ivana Šemanjski

2015-12-01

Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

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Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Li, Yuanyao; Catani, Filippo; Pourghasemi, Hamid Reza

2018-03-01

Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was taken as a case study for landslide susceptibility assessment in order to develop effective risk prevention and mitigation strategies. To begin, 202 landslides were identified, including 95 colluvial landslides and 107 rockfalls. Twelve landslide causal factor maps were prepared initially, and the relationship between these factors and each landslide type was analyzed using the information value model. Later, the unimportant factors were selected and eliminated using the information gain ratio technique. The landslide locations were randomly divided into two groups: 70% for training and 30% for verifying. Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment results of the two landslide types, were classified into five levels. The performance of the LSMs was evaluated using the receiver operating characteristics curve and Friedman test. Results show that the elimination of noise-generating factors and the separated modeling of each landslide type have significantly increased the prediction accuracy. The machine learning models outperformed the multivariate statistical model and SVM model was found ideal for the case study area.

Improving Student Learning Outcomes Marketing Strategy Lesson By Applying SFAE Learning Model

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Winda Nur Rohmawati

2017-11-01

Full Text Available Research objectives for improving student learning outcomes on the subjects of marketing strategy through the implementation of model learning SFAE. This type of research this is a class action research using a qualitative approach which consists of two cycles with the subject Marketing X grade SMK YPI Darussalam 2 Cerme Gresik Regency. This research consists of four stages: (1 the Planning Act, (2 the implementation of the action, (3 observations (observation, and (4 Reflection. The result of the research shows that cognitive and affective learning outcomes of students have increased significantly.

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

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Hua KL

2015-08-01

Full Text Available Kai-Lung Hua,1 Che-Hao Hsu,1 Shintami Chusnul Hidayati,1 Wen-Huang Cheng,2 Yu-Jen Chen3 1Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 2Research Center for Information Technology Innovation, Academia Sinica, 3Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan Abstract: Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. Keywords: nodule classification, deep learning, deep belief network, convolutional neural network

EFFECTS OF COOPERATIVE LEARNING METHOD ON THE DEVELOPMENT OF LISTENING COMPREHENSION AND LISTENING SKILLS

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Abdülkadir

2017-04-01

Full Text Available In this study, the effect of the learning together technique, which is one of the cooperative learning methods, on the development of the listening comprehension and listening skills of the secondary school eighth grade students was investigated. Regarding the purpose of the research, experimental and control groups consisting of 75 students from, Yakutiye district Şair Nef'i Secondary School and Palandöken District, Alparslan Secondary School of Erzurum province were selected. Socio-economic statuses and success rates were taken into consideration when selecting the experimental and control groups. 'Listening-Comprehension Achievement Test' was applied to measure the listening skills of the experimental and control groups. In terms of pre-test scores, it was determined that the listening skills of the experiment and control group were similar. The selected experimental groups were taught by the learning together technique of cooperative learning method for seven weeks and the control group was taught in the traditional way. As a result of the research, the 'Listening-Comprehension Achievement Test', which was applied as the pre-test to the experimental and control groups, was applied again as the final test. When the findings obtained from the research were examined, it was determined that the students in the experimental group were more successful than the students in the control group in terms of post - test achievement scores. When the results of the study are examined, it can be said that the learning together technique, which is one of the cooperative learning methods, is more effective than the traditional learning method in improving the listening comprehension and the listening skills of the eighth grade students in Turkish class.

Mathematical Model and Artificial Intelligent Techniques Applied to a Milk Industry through DSM

Science.gov (United States)

Babu, P. Ravi; Divya, V. P. Sree

2011-08-01

The resources for electrical energy are depleting and hence the gap between the supply and the demand is continuously increasing. Under such circumstances, the option left is optimal utilization of available energy resources. The main objective of this chapter is to discuss about the Peak load management and overcome the problems associated with it in processing industries such as Milk industry with the help of DSM techniques. The chapter presents a generalized mathematical model for minimizing the total operating cost of the industry subject to the constraints. The work presented in this chapter also deals with the results of application of Neural Network, Fuzzy Logic and Demand Side Management (DSM) techniques applied to a medium scale milk industrial consumer in India to achieve the improvement in load factor, reduction in Maximum Demand (MD) and also the consumer gets saving in the energy bill.

Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation

Science.gov (United States)

Hindriks, Koen V.; Tykhonov, Dmytro

In automated negotiation, information gained about an opponent's preference profile by means of learning techniques may significantly improve an agent's negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent's preference profile and discuss our findings.

Multiple-Choice Testing Using Immediate Feedback--Assessment Technique (IF AT®) Forms: Second-Chance Guessing vs. Second-Chance Learning?

Science.gov (United States)

Merrel, Jeremy D.; Cirillo, Pier F.; Schwartz, Pauline M.; Webb, Jeffrey A.

2015-01-01

Multiple choice testing is a common but often ineffective method for evaluating learning. A newer approach, however, using Immediate Feedback Assessment Technique (IF AT®, Epstein Educational Enterprise, Inc.) forms, offers several advantages. In particular, a student learns immediately if his or her answer is correct and, in the case of an…

Classification of breast tumour using electrical impedance and machine learning techniques.

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Al Amin, Abdullah; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

2014-06-01

When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy.

Classification of breast tumour using electrical impedance and machine learning techniques

International Nuclear Information System (INIS)

Amin, Abdullah Al; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

2014-01-01

When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy. (paper)

Departing from PowerPoint default mode: Applying Mayer's multimedia principles for enhanced learning of parasitology.

Science.gov (United States)

Nagmoti, Jyoti Mahantesh

2017-01-01

PowerPoint (PPT™) presentation has become an integral part of day-to-day teaching in medicine. Most often, PPT™ is used in its default mode which in fact, is known to cause boredom and ineffective learning. Research has shown improved short-term memory by applying multimedia principles for designing and delivering lectures. However, such evidence in medical education is scarce. Therefore, we attempted to evaluate the effect of multimedia principles on enhanced learning of parasitology. Second-year medical students received a series of lectures, half of the lectures used traditionally designed PPT™ and the rest used slides designed by Mayer's multimedia principles. Students answered pre and post-tests at the end of each lecture (test-I) and an essay test after six months (test-II) which assessed their short and long term knowledge retention respectively. Students' feedback on quality and content of lectures were collected. Statistically significant difference was found between post test scores of traditional and modified lectures (P = 0.019) indicating, improved short-term memory after modified lectures. Similarly, students scored better in test II on the contents learnt through modified lectures indicating, enhanced comprehension and improved long-term memory (P learning through multimedia designed PPT™ and suggested for their continued use. It is time to depart from default PPT™ and adopt multimedia principles to enhance comprehension and improve short and long term knowledge retention. Further, medical educators may be trained and encouraged to apply multimedia principles for designing and delivering effective lectures.

Learning to Learn.

Science.gov (United States)

Weiss, Helen; Weiss, Martin

1988-01-01

The article reviews theories of learning (e.g., stimulus-response, trial and error, operant conditioning, cognitive), considers the role of motivation, and summarizes nine research-supported rules of effective learning. Suggestions are applied to teaching learning strategies to learning-disabled students. (DB)

Evaluation of undergraduate clinical learning experiences in the subject of pediatric dentistry using critical incident technique.

Science.gov (United States)

Vyawahare, S; Banda, N R; Choubey, S; Parvekar, P; Barodiya, A; Dutta, S

2013-01-01

In pediatric dentistry, the experiences of dental students may help dental educators better prepare graduates to treat the children. Research suggests that student's perceptions should be considered in any discussion of their education, but there has been no systematic examination of India's undergraduate dental students learning experiences. This qualitative investigation aimed to gather and analyze information about experiences in pediatric dentistry from the students' viewpoint using critical incident technique (CIT). The sample group for this investigation came from all 240 3rd and 4th year dental students from all the four dental colleges in Indore. Using CIT, participants were asked to describe at least one positive and one negative experience in detail. They described 308 positive and 359 negative experiences related to the pediatric dentistry clinic. Analysis of the data resulted in the identification of four key factors related to their experiences: 1) The instructor; 2) the patient; 3) the learning process; and 4) the learning environment. The CIT is a useful data collection and analysis technique that provides rich, useful data and has many potential uses in dental education.

The effect of learning models and emotional intelligence toward students learning outcomes on reaction rate

Science.gov (United States)

Sutiani, Ani; Silitonga, Mei Y.

2017-08-01

This research focused on the effect of learning models and emotional intelligence in students' chemistry learning outcomes on reaction rate teaching topic. In order to achieve the objectives of the research, with 2x2 factorial research design was used. There were two factors tested, namely: the learning models (factor A), and emotional intelligence (factor B) factors. Then, two learning models were used; problem-based learning/PBL (A1), and project-based learning/PjBL (A2). While, the emotional intelligence was divided into higher and lower types. The number of population was six classes containing 243 grade X students of SMAN 10 Medan, Indonesia. There were 15 students of each class were chosen as the sample of the research by applying purposive sampling technique. The data were analyzed by applying two-ways analysis of variance (2X2) at the level of significant α = 0.05. Based on hypothesis testing, there was the interaction between learning models and emotional intelligence in students' chemistry learning outcomes. Then, the finding of the research showed that students' learning outcomes in reaction rate taught by using PBL with higher emotional intelligence is higher than those who were taught by using PjBL. There was no significant effect between students with lower emotional intelligence taught by using both PBL and PjBL in reaction rate topic. Based on the finding, the students with lower emotional intelligence were quite hard to get in touch with other students in group discussion.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

Science.gov (United States)

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Applying Learning Analytics to Explore the Effects of Motivation on Online Students' Reading Behavioral Patterns

Science.gov (United States)

Sun, Jerry Chih-Yuan; Lin, Che-Tsun; Chou, Chien

2018-01-01

This study aims to apply a sequential analysis to explore the effect of learning motivation on online reading behavioral patterns. The study's participants consisted of 160 graduate students who were classified into three group types: low reading duration with low motivation, low reading duration with high motivation, and high reading duration…

Applying a learning design methodology in the flipped classroom approach – empowering teachers to reflect and design for learning

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Evangelia Triantafyllou

2016-05-01

Full Text Available One of the recent developments in teaching that heavily relies on current technology is the “flipped classroom” approach. In a flipped classroom the traditional lecture and homework sessions are inverted. Students are provided with online material in order to gain necessary knowledge before class, while class time is devoted to clarifications and application of this knowledge. The hypothesis is that there could be deep and creative discussions when teacher and students physically meet. This paper discusses how the learning design methodology can be applied to represent, share and guide educators through flipped classroom designs. In order to discuss the opportunities arising by this approach, the different components of the Learning Design – Conceptual Map (LD-CM are presented and examined in the context of the flipped classroom. It is shown that viewing the flipped classroom through the lens of learning design can promote the use of theories and methods to evaluate its effect on the achievement of learning objectives, and that it may draw attention to the employment of methods to gather learner responses. Moreover, a learning design approach can enforce the detailed description of activities, tools and resources used in specific flipped classroom models, and it can make educators more aware of the decisions that have to be taken and people who have to be involved when designing a flipped classroom. By using the LD-CM, this paper also draws attention to the importance of characteristics and values of different stakeholders (i.e. institutions, educators, learners, and external agents, which influence the design and success of flipped classrooms. Moreover, it looks at the teaching cycle from a flipped instruction model perspective and adjusts it to cater for the reflection loops educators are involved when designing, implementing and re-designing a flipped classroom. Finally, it highlights the effect of learning design on the guidance

Estimation of Alpine Skier Posture Using Machine Learning Techniques

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Bojan Nemec

2014-10-01

Full Text Available High precision Global Navigation Satellite System (GNSS measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier’s neck. A key issue is how to estimate other more relevant parameters of the skier’s body, like the center of mass (COM and ski trajectories. Previously, these parameters were estimated by modeling the skier’s body with an inverted-pendulum model that oversimplified the skier’s body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier’s body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing.

Using machine learning techniques to differentiate acute coronary syndrome

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Sougand Setareh

2015-02-01

Full Text Available Backgroud: Acute coronary syndrome (ACS is an unstable and dynamic process that includes unstable angina, ST elevation myocardial infarction, and non-ST elevation myocardial infarction. Despite recent technological advances in early diognosis of ACS, differentiating between different types of coronary diseases in the early hours of admission is controversial. The present study was aimed to accurately differentiate between various coronary events, using machine learning techniques. Such methods, as a subset of artificial intelligence, include algorithms that allow computers to learn and play a major role in treatment decisions. Methods: 1902 patients diagnosed with ACS and admitted to hospital were selected according to Euro Heart Survey on ACS. Patients were classified based on decision tree J48. Bagging aggregation algorithms was implemented to increase the efficiency of algorithm. Results: The performance of classifiers was estimated and compared based on their accuracy computed from confusion matrix. The accuracy rates of decision tree and bagging algorithm were calculated to be 91.74% and 92.53%, respectively. Conclusion: The proposed methods used in this study proved to have the ability to identify various ACS. In addition, using matrix of confusion, an acceptable number of subjects with acute coronary syndrome were identified in each class.

Learning L2 German vocabulary through reading: the effect of three enhancement techniques compared

NARCIS (Netherlands)

Peters, E.; Hulstijn, J.H.; Sercu, L.; Lutjeharms, M.

2009-01-01

This study investigated three techniques designed to increase the chances that second language (L2) readers look up and learn unfamiliar words during and after reading an L2 text. Participants in the study, 137 college students in Belgium (L1 = Dutch, L2 = German), were randomly assigned to one of

Applying Data Mining Techniques to Improve Information Security in the Cloud: A Single Cache System Approach

OpenAIRE

Amany AlShawi

2016-01-01

Presently, the popularity of cloud computing is gradually increasing day by day. The purpose of this research was to enhance the security of the cloud using techniques such as data mining with specific reference to the single cache system. From the findings of the research, it was observed that the security in the cloud could be enhanced with the single cache system. For future purposes, an Apriori algorithm can be applied to the single cache system. This can be applied by all cloud providers...

Non destructive assay techniques applied to nuclear materials

International Nuclear Information System (INIS)

Gavron, A.

2001-01-01

Nondestructive assay is a suite of techniques that has matured and become precise, easily implementable, and remotely usable. These techniques provide elaborate safeguards of nuclear material by providing the necessary information for materials accounting. NDA techniques are ubiquitous, reliable, essentially tamper proof, and simple to use. They make the world a safer place to live in, and they make nuclear energy viable. (author)

Effect of the reinforcement bar arrangement on the efficiency of electrochemical chloride removal technique applied to reinforced concrete structures

International Nuclear Information System (INIS)

Garces, P.; Sanchez de Rojas, M.J.; Climent, M.A.

2006-01-01

This paper reports on the research done to find out the effect that different bar arrangements may have on the efficiency of the electrochemical chloride removal (ECR) technique when applied to a reinforced concrete structural member. Five different types of bar arrangements were considered, corresponding to typical structural members such as columns (with single and double bar reinforcing), slabs, beams and footings. ECR was applied in several steps. We observe that the extraction efficiency depends on the reinforcing bar arrangement. A uniform layer set-up favours chloride extraction. Electrochemical techniques were also used to estimate the reinforcing bar corrosion states, as well as measure the corrosion potential, and instant corrosion rate based on the polarization resistance technique. After ECR treatment, a reduction in the corrosion levels is observed falling short of the depassivation threshold

The Usage of E-Learning Model To Optimize Learning System In Higher Education by Using Dick and Carey Design Approach

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Anak Agung Gde Satia Utama

2016-04-01

Full Text Available Nowadays many universities in the world apply technology enhanced learning in order to help learning activities. Due to the potentials technology enhanced learning offers, recent education using it and universities in particular are trying to apply it. One of the subjects of this research is The Accounting Department of Airlangga University in Surabaya. The idea of this research is to investigate the students about how they know deeply about e-learning system and learning objectives as a first step to conduct e-learning model. After the model completed, the next step is to prepare database learning. Entity Relationship Diagram (ERD can help to explain the model. The purpose of this research was done by using Dick and Carey Design Model. There are nine steps to conduct e-learning model. All steps can be categorized into three steps research: first is the introduction or empirical study, the next step is the design and the last is the feedback after the implementation. The methodology used in this research is using Qualitative Exploratory, by using questionnaire and interviews as data collection techniques. The analysis of the data shows organization requires information about e-learning content, user as a learning subject and information technology infrastructures. E-learning model as one of the alternative learning can help users to optimized learning.

Teaching laryngeal endoscopy skills to speech and language therapists: applying learning theory to optimize practical skills mastery.

Science.gov (United States)

Robinson, H Fiona; Dennick, Reg

2015-06-01

This review was carried out to highlight relevant learning theory and its application to the teaching of endoscopic skills to speech and language therapists (SLTs). This article explains the most relevant models from Constructivist, Experiential and Humanistic Learning Theory, a combination that has been described as Constructive Experience, and describes the relevance and the benefits of applying educational frameworks in course design. This approach has been formally used to design and deliver practical skills teaching in medicine. SLTs carry out endoscopic evaluation of the larynx (EEL) to provide information for evaluation and rehabilitation of voice and swallowing disorders. These are essential procedures in ear, nose and throat, voice and swallowing specialist centres. Training in endoscopy skills for SLTs working in the ear, nose and throat specialist centres in the United Kingdom has traditionally been provided external to the local clinic environment as 1 or 2-day courses. In one survey in the United Kingdom, 79% of SLTs reported that they did not acquire the depth of skill required to carry out EEL autonomously after attending such courses. Course development to teach practical skills should be underpinned by educational theory. One EEL course in the United Kingdom is described, wherein sessions are interactive and experiential, promoting deep learning, constructive feedback and reflection, enriched by the completion of logs and portfolios. From course evaluations, all the learners met the learning objectives, developing and applying skills to become confident endoscopists in autonomous clinical practice.