WorldWideScience

Sample records for learning methods results

  1. Project Oriented Immersion Learning: Method and Results

    DEFF Research Database (Denmark)

    Icaza, José I.; Heredia, Yolanda; Borch, Ole M.

    2005-01-01

    A pedagogical approach called “project oriented immersion learning” is presented and tested on a graduate online course. The approach combines the Project Oriented Learning method with immersion learning in a virtual enterprise. Students assumed the role of authors hired by a fictitious publishing...... house that develops digital products including e-books, tutorials, web sites and so on. The students defined the problem that their product was to solve; choose the type of product and the content; and built the product following a strict project methodology. A wiki server was used as a platform to hold...

  2. Learning phacoemulsification. Results of different teaching methods.

    Directory of Open Access Journals (Sweden)

    Hennig Albrecht

    2004-01-01

    Full Text Available We report the learning curves of three eye surgeons converting from sutureless extracapsular cataract extraction to phacoemulsification using different teaching methods. Posterior capsule rupture (PCR as a per-operative complication and visual outcome of the first 100 operations were analysed. The PCR rate was 4% and 15% in supervised and unsupervised surgery respectively. Likewise, an uncorrected visual acuity of > or = 6/18 on the first postoperative day was seen in 62 (62% of patients and in 22 (22% in supervised and unsupervised surgery respectively.

  3. Effect of Chemistry Triangle Oriented Learning Media on Cooperative, Individual and Conventional Method on Chemistry Learning Result

    Science.gov (United States)

    Latisma D, L.; Kurniawan, W.; Seprima, S.; Nirbayani, E. S.; Ellizar, E.; Hardeli, H.

    2018-04-01

    The purpose of this study was to see which method are well used with the Chemistry Triangle-oriented learning media. This quasi experimental research involves first grade of senior high school students in six schools namely each two SMA N in Solok city, in Pasaman and two SMKN in Pariaman. The sampling technique was done by Cluster Random Sampling. Data were collected by test and analyzed by one-way anova and Kruskall Wallish test. The results showed that the high school students in Solok learning taught by cooperative method is better than the results of student learning taught by conventional and Individual methods, both for students who have high initial ability and low-ability. Research in SMK showed that the overall student learning outcomes taught by conventional method is better than the student learning outcomes taught by cooperative and individual methods. Student learning outcomes that have high initial ability taught by individual method is better than student learning outcomes that are taught by cooperative method and for students who have low initial ability, there is no difference in student learning outcomes taught by cooperative, individual and conventional methods. Learning in high school in Pasaman showed no significant difference in learning outcomes of the three methods undertaken.

  4. Learning Method and Its Influence on Nutrition Study Results Throwing the Ball

    Science.gov (United States)

    Samsudin; Nugraha, Bayu

    2015-01-01

    This study aimed to know the difference between playing and learning methods of exploratory learning methods to learning outcomes throwing the ball. In addition, this study also aimed to determine the effect of nutritional status of these two learning methods mentioned above. This research was conducted at SDN Cipinang Besar Selatan 16 Pagi East…

  5. Results of a study assessing teaching methods of faculty after measuring student learning style preference.

    Science.gov (United States)

    Stirling, Bridget V

    2017-08-01

    Learning style preference impacts how well groups of students respond to their curricula. Faculty have many choices in the methods for delivering nursing content, as well as assessing students. The purpose was to develop knowledge around how faculty delivered curricula content, and then considering these findings in the context of the students learning style preference. Following an in-service on teaching and learning styles, faculty completed surveys on their methods of teaching and the proportion of time teaching, using each learning style (visual, aural, read/write and kinesthetic). This study took place at the College of Nursing a large all-female university in Saudi Arabia. 24 female nursing faculty volunteered to participate in the project. A cross-sectional design was used. Faculty reported teaching using mostly methods that were kinesthetic and visual, although lecture was also popular (aural). Students preferred kinesthetic and aural learning methods. Read/write was the least preferred by students and the least used method of teaching by faculty. Faculty used visual methods about one third of the time, although they were not preferred by the students. Students' preferred learning style (kinesthetic) was the method most used by faculty. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Improving the accuracy of myocardial perfusion scintigraphy results by machine learning method

    International Nuclear Information System (INIS)

    Groselj, C.; Kukar, M.

    2002-01-01

    Full text: Machine learning (ML) as rapidly growing artificial intelligence subfield has already proven in last decade to be a useful tool in many fields of decision making, also in some fields of medicine. Its decision accuracy usually exceeds the human one. To assess applicability of ML in interpretation the results of stress myocardial perfusion scintigraphy for CAD diagnosis. The 327 patient's data of planar stress myocardial perfusion scintigraphy were reevaluated in usual way. Comparing them with the results of coronary angiography the sensitivity, specificity and accuracy for the investigation was computed. The data were digitized and the decision procedure repeated by ML program 'Naive Bayesian classifier'. As the ML is able to simultaneously manipulate of whatever number of data, all reachable disease connected data (regarding history, habitus, risk factors, stress results) were added. The sensitivity, specificity and accuracy for scintigraphy were expressed in this way. The results of both decision procedures were compared. With ML method 19 patients more out of 327 (5.8 %) were correctly diagnosed by stress myocardial perfusion scintigraphy. ML could be an important tool for decision making in myocardial perfusion scintigraphy. (author)

  7. Challenges of E-learning in medicine: methods and results of a systematical exploration

    Directory of Open Access Journals (Sweden)

    Spreckelsen, Cord

    2006-11-01

    Full Text Available E-learning in medicine traditionally concentrates on case oriented or problem oriented learning scenarios, the development of multimedia courseware or the implementation of simulators. This paper aims at a systematic exploration of actual and new challenges for E-learning in the medical domain. The exploration is based on the analysis of the scientific discourse in the field of Medical Education. The analysis starts from text based sources: the concept hierarchy of the Medical Subject Headings, the profiles of the relevant scientific associations, and the scientific program of scientific conferences or annual meetings. These sources are subjected to conceptual analysis, supported by network visualization tools and supplemented by network theoretic indices (Betweeness Centrality. As a result, the main concerns of the Medical Education community and their modifications during the last six years can be identified. The analysis discovers new challenges, which result from central issues of Medical Education, namely from e.g. curricular and faculty development or the sustainable integration of postgraduate education and continuing medial education. The main challenges are: 1 the implementation of integrative conceptions of the application of learning management systems (LMS and 2 the necessity of combining aspects of organizational development, knowledge management and learning management within the scope of a comprehensive learning life cycle management.

  8. Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

    Science.gov (United States)

    Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

    2015-04-01

    With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  9. Does It Matter? Analyzing the Results of Three Different Learning Delivery Methods

    Science.gov (United States)

    Chernish, William N.; DeFranco, Agnes L.; Lindner, James R.; Dooley, Kim E.

    2005-01-01

    The increasing diversity of learners and their preferences coupled with increasing usage of the computer and Internet prompted the need for testing and verifying the ways that knowledge can be delivered and learned effectively. This research addresses these concerns by comparing the results of a college course, hospitality human resource…

  10. Alpha test results for a Housing First eLearning strategy: the value of multiple qualitative methods for intervention design.

    Science.gov (United States)

    Ahonen, Emily Q; Watson, Dennis P; Adams, Erin L; McGuire, Alan

    2017-01-01

    Detailed descriptions of implementation strategies are lacking, and there is a corresponding dearth of information regarding methods employed in implementation strategy development. This paper describes methods and findings related to the alpha testing of eLearning modules developed as part of the Housing First Technical Assistance and Training (HFTAT) program's development. Alpha testing is an approach for improving the quality of a product prior to beta (i.e., real world) testing with potential applications for intervention development. Ten participants in two cities tested the modules. We collected data through (1) a structured log where participants were asked to record their experiences as they worked through the modules; (2) a brief online questionnaire delivered at the end of each module; and (3) focus groups. The alpha test provided useful data related to the acceptability and feasibility of eLearning as an implementation strategy, as well as identifying a number of technical issues and bugs. Each of the qualitative methods used provided unique and valuable information. In particular, logs were the most useful for identifying technical issues, and focus groups provided high quality data regarding how the intervention could best be used as an implementation strategy. Alpha testing was a valuable step in intervention development, providing us an understanding of issues that would have been more difficult to address at a later stage of the study. As a result, we were able to improve the modules prior to pilot testing of the entire HFTAT. Researchers wishing to alpha test interventions prior to piloting should balance the unique benefits of different data collection approaches with the need to minimize burdens for themselves and participants.

  11. Self-directed learning can outperform direct instruction in the course of a modern German medical curriculum - results of a mixed methods trial.

    Science.gov (United States)

    Peine, Arne; Kabino, Klaus; Spreckelsen, Cord

    2016-06-03

    Modernised medical curricula in Germany (so called "reformed study programs") rely increasingly on alternative self-instructed learning forms such as e-learning and curriculum-guided self-study. However, there is a lack of evidence that these methods can outperform conventional teaching methods such as lectures and seminars. This study was conducted in order to compare extant traditional teaching methods with new instruction forms in terms of learning effect and student satisfaction. In a randomised trial, 244 students of medicine in their third academic year were assigned to one of four study branches representing self-instructed learning forms (e-learning and curriculum-based self-study) and instructed learning forms (lectures and seminars). All groups participated in their respective learning module with standardised materials and instructions. Learning effect was measured with pre-test and post-test multiple-choice questionnaires. Student satisfaction and learning style were examined via self-assessment. Of 244 initial participants, 223 completed the respective module and were included in the study. In the pre-test, the groups showed relatively homogenous scores. All students showed notable improvements compared with the pre-test results. Participants in the non-self-instructed learning groups reached scores of 14.71 (seminar) and 14.37 (lecture), while the groups of self-instructed learners reached higher scores with 17.23 (e-learning) and 15.81 (self-study). All groups improved significantly (p learning group, whose self-assessment improved by 2.36. The study shows that students in modern study curricula learn better through modern self-instructed methods than through conventional methods. These methods should be used more, as they also show good levels of student acceptance and higher scores in personal self-assessment of knowledge.

  12. Highlighting the complexities of a groundwater pilot study during an avian influenza outbreak: Methods, lessons learned, and select contaminant results

    Science.gov (United States)

    Hubbard, Laura E.; Kolpin, Dana W.; Fields, Chad L.; Hladik, Michelle L.; Iwanowicz, Luke

    2017-01-01

    The highly pathogenic avian influenza (H5N2) outbreak in the Midwestern United States (US) in 2015 was historic due to the number of birds and poultry operations impacted and the corresponding economic loss to the poultry industry and was the largest animal health emergency in US history. The U.S. Geological Survey (USGS), with the assistance of several state and federal agencies, aided the response to the outbreak by developing a study to determine the extent of virus transport in the environment. The study goals were to: develop the appropriate sampling methods and protocols for measuring avian influenza virus (AIV) in groundwater, provide the first baseline data on AIV and outbreak- and poultry-related contaminant occurrence and movement into groundwater, and document climatological factors that may have affected both survival and transport of AIV to groundwater during the months of the 2015 outbreak. While site selection was expedient, there were often delays in sample response times due to both relationship building between agencies, groups, and producers and logistical time constraints. This study's design and sampling process highlights the unpredictable nature of disease outbreaks and the corresponding difficulty in environmental sampling of such events. The lessons learned, including field protocols and approaches, can be used to improve future research on AIV in the environment.

  13. Highlighting the complexities of a groundwater pilot study during an avian influenza outbreak: Methods, lessons learned, and select contaminant results.

    Science.gov (United States)

    Hubbard, Laura E; Kolpin, Dana W; Fields, Chad L; Hladik, Michelle L; Iwanowicz, Luke R

    2017-10-01

    The highly pathogenic avian influenza (H5N2) outbreak in the Midwestern United States (US) in 2015 was historic due to the number of birds and poultry operations impacted and the corresponding economic loss to the poultry industry and was the largest animal health emergency in US history. The U.S. Geological Survey (USGS), with the assistance of several state and federal agencies, aided the response to the outbreak by developing a study to determine the extent of virus transport in the environment. The study goals were to: develop the appropriate sampling methods and protocols for measuring avian influenza virus (AIV) in groundwater, provide the first baseline data on AIV and outbreak- and poultry-related contaminant occurrence and movement into groundwater, and document climatological factors that may have affected both survival and transport of AIV to groundwater during the months of the 2015 outbreak. While site selection was expedient, there were often delays in sample response times due to both relationship building between agencies, groups, and producers and logistical time constraints. This study's design and sampling process highlights the unpredictable nature of disease outbreaks and the corresponding difficulty in environmental sampling of such events. The lessons learned, including field protocols and approaches, can be used to improve future research on AIV in the environment. Published by Elsevier Inc.

  14. Kennedy Space Center's NASA/Contractor Team-Centered Total Quality Management Seminar: Results, methods, and lessons learned

    Science.gov (United States)

    Kinlaw, Dennis C.; Eads, Jeannette

    1992-01-01

    It is apparent to everyone associated with the Nation's aeronautics and space programs that the challenge of continuous improvement can be reasonably addressed only if NASA and its contractors act together in a fully integrated and cooperative manner that transcends the traditional boundaries of proprietary interest. It is, however, one thing to assent to the need for such integration and cooperation; it is quite another thing to undertake the hard tasks of turning such a need into action. Whatever else total quality management is, it is fundamentally a team-centered and team-driven process of continuous improvement. The introduction of total quality management at KSC, therefore, has given the Center a special opportunity to translate the need for closer integration and cooperation among all its organizations into specific initiatives. One such initiative that NASA and its contractors have undertaken at KSC is a NASA/Contractor team-centered Total Quality Management Seminar. It is this seminar which is the subject of this paper. The specific purposes of this paper are to describe the following: Background, development, and evolution of Kennedy Space Center's Total Quality Management Seminar; Special characteristics of the seminar; Content of the seminar; Meaning and utility of a team-centered design for TQM training; Results of the seminar; Use that one KSC contractor, EG&G Florida, Inc. has made of the seminar in its Total Quality Management initiative; and Lessons learned.

  15. Cooperative Learning as a Democratic Learning Method

    Science.gov (United States)

    Erbil, Deniz Gökçe; Kocabas, Ayfer

    2018-01-01

    In this study, the effects of applying the cooperative learning method on the students' attitude toward democracy in an elementary 3rd-grade life studies course was examined. Over the course of 8 weeks, the cooperative learning method was applied with an experimental group, and traditional methods of teaching life studies in 2009, which was still…

  16. Machine learning methods for planning

    CERN Document Server

    Minton, Steven

    1993-01-01

    Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

  17. Active Learning Methods

    Science.gov (United States)

    Zayapragassarazan, Z.; Kumar, Santosh

    2012-01-01

    Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

  18. Qualitative methods in workplace learning

    OpenAIRE

    Fabritius, Hannele

    2015-01-01

    Methods of learning in the workplace will be introduced. The methods are connect to competence development and to the process of conducting development discussions in a dialogical way. The tools developed and applied are a fourfold table, a cycle of work identity, a plan of personal development targets, a learning meeting and a learning map. The methods introduced will aim to better learning at work.

  19. Active teaching methods, studying responses and learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain

    2010-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed....

  20. Geometrical methods in learning theory

    International Nuclear Information System (INIS)

    Burdet, G.; Combe, Ph.; Nencka, H.

    2001-01-01

    The methods of information theory provide natural approaches to learning algorithms in the case of stochastic formal neural networks. Most of the classical techniques are based on some extremization principle. A geometrical interpretation of the associated algorithms provides a powerful tool for understanding the learning process and its stability and offers a framework for discussing possible new learning rules. An illustration is given using sequential and parallel learning in the Boltzmann machine

  1. A Scale Development for Teacher Competencies on Cooperative Learning Method

    Science.gov (United States)

    Kocabas, Ayfer; Erbil, Deniz Gokce

    2017-01-01

    Cooperative learning method is a learning method studied both in Turkey and in the world for long years as an active learning method. Although cooperative learning method takes place in training programs, it cannot be implemented completely in the direction of its principles. The results of the researches point out that teachers have problems with…

  2. Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

    OpenAIRE

    Dwi Nur Rachmah

    2017-01-01

    Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas La...

  3. Activating teaching methods, studying responses and learning

    OpenAIRE

    Christensen, Hans Peter; Vigild, Martin E.; Thomsen, Erik; Szabo, Peter; Horsewell, Andy

    2009-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed. Peer Reviewed

  4. Reflexive Learning through Visual Methods

    DEFF Research Database (Denmark)

    Frølunde, Lisbeth

    2014-01-01

    What. This chapter concerns how visual methods and visual materials can support visually oriented, collaborative, and creative learning processes in education. The focus is on facilitation (guiding, teaching) with visual methods in learning processes that are designerly or involve design. Visual...... methods are exemplified through two university classroom cases about collaborative idea generation processes. The visual methods and materials in the cases are photo elicitation using photo cards, and modeling with LEGO Serious Play sets. Why. The goal is to encourage the reader, whether student...... or professional, to facilitate with visual methods in a critical, reflective, and experimental way. The chapter offers recommendations for facilitating with visual methods to support playful, emergent designerly processes. The chapter also has a critical, situated perspective. Where. This chapter offers case...

  5. Decomposition methods for unsupervised learning

    DEFF Research Database (Denmark)

    Mørup, Morten

    2008-01-01

    This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...

  6. Pragmatics of Contemporary Teaching and Learning Methods

    Directory of Open Access Journals (Sweden)

    Ryszard Józef Panfil

    2013-09-01

    Full Text Available The dynamics of the environment in which educational institutions operate have a significant influence on the basic activity of these institutions, i.e. the process of educating, and particularly teaching and learning methods used during that process: traditional teaching, tutoring, mentoring and coaching. The identity of an educational institution and the appeal of its services depend on how flexible, diverse and adaptable is the educational process it offers as a core element of its services. Such a process is determined by how its pragmatism is displayed in the operational relativism of methods, their applicability, as well as practical dimension of achieved results and values. Based on the above premises, this publication offers a pragmatic-systemic identification of contemporary teaching and learning methods, while taking into account the differences between them and the scope of their compatibility. Secondly, using the case of sport coaches’ education, the author exemplifies the pragmatic theory of perception of contemporary teaching and learning methods.

  7. Learning styles: The learning methods of air traffic control students

    Science.gov (United States)

    Jackson, Dontae L.

    In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

  8. Suggestology as an Effective Language Learning Method.

    Science.gov (United States)

    MaCoy, Katherine W.

    The methods used and the results obtained by means of the accelerated language learning techniques developed by Georgi Lozanov, Director of the Institute of Suggestology in Bulgaria, are discussed. The following topics are included: (1) discussion of hypermnesia, "super memory," and the reasons foreign languages were chosen for purposes…

  9. Personalized search result diversification via structured learning

    NARCIS (Netherlands)

    Liang, S.; Ren, Z.; de Rijke, M.

    2014-01-01

    This paper is concerned with the problem of personalized diversification of search results, with the goal of enhancing the performance of both plain diversification and plain personalization algorithms. In previous work, the problem has mainly been tackled by means of unsupervised learning. To

  10. Statistical learning methods: Basics, control and performance

    Energy Technology Data Exchange (ETDEWEB)

    Zimmermann, J. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de

    2006-04-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms.

  11. Statistical learning methods: Basics, control and performance

    International Nuclear Information System (INIS)

    Zimmermann, J.

    2006-01-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms

  12. Trojan Horse Method: Recent Results

    International Nuclear Information System (INIS)

    Pizzone, R. G.; Spitaleri, C.

    2008-01-01

    Owing the presence of the Coulomb barrier at astrophysically relevant kinetic energies, it is very difficult, or sometimes impossible to measure astrophysical reaction rates in laboratory. This is why different indirect techniques are being used along with direct measurements. The THM is unique indirect technique allowing one measure astrophysical rearrangement reactions down to astrophysical relevant energies. The basic principle and a review of the main application of the Trojan Horse Method are presented. The applications aiming at the extraction of the bare S b (E) astrophysical factor and electron screening potentials U e for several two body processes are discussed

  13. Do students’ styles of learning affect how they adapt to learning methods and to the learning environment?

    OpenAIRE

    Topal, Kenan; Sarıkaya, Özlem; Basturk, Ramazan; Buke, Akile

    2015-01-01

    Objectives: The process of development and evaluation of undergraduate medical education programs should include analysis of learners’ characteristics, needs, and perceptions about learning methods. This study aims to evaluate medical students’ perceptions about problem-based learning methods and to compare these results with their individual learning styles.Materials and Methods: The survey was conducted at Marmara University Medical School where problem-based learning was implemented in the...

  14. Color image definition evaluation method based on deep learning method

    Science.gov (United States)

    Liu, Di; Li, YingChun

    2018-01-01

    In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

  15. Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

    Directory of Open Access Journals (Sweden)

    Dwi Nur Rachmah

    2017-12-01

    Full Text Available Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas Lambung Mangkurat. The method used in the study is the experimental method using one group pre-test and post-test design. The results of the measurements before and after the use of jigsaw learning method were compared using paired samples t-test. The results showed that there is a difference in students’ self-efficacy and motivation to learn before and after subjected to the treatments; therefore, it can be said that jigsaw learning method had significant effects on self-efficacy and motivation to learn. The application of jigsaw learning model in a classroom with large number of students was the discussion of this study.

  16. Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

    Science.gov (United States)

    Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

    2017-01-01

    Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

  17. EFFECTS OF COOPERATIVE LEARNING MODEL TYPE STAD JUST-IN TIME BASED ON THE RESULTS OF LEARNING TEACHING PHYSICS COURSE IN PHYSICS SCHOOL IN PHYSICS PROGRAM FACULTY UNIMED

    Directory of Open Access Journals (Sweden)

    Teguh Febri Sudarma

    2013-06-01

    Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students

  18. COOPERATIVE LEARNING IN DISTANCE LEARNING: A MIXED METHODS STUDY

    Directory of Open Access Journals (Sweden)

    Lori Kupczynski

    2012-07-01

    Full Text Available Distance learning has facilitated innovative means to include Cooperative Learning (CL in virtual settings. This study, conducted at a Hispanic-Serving Institution, compared the effectiveness of online CL strategies in discussion forums with traditional online forums. Quantitative and qualitative data were collected from 56 graduate student participants. Quantitative results revealed no significant difference on student success between CL and Traditional formats. The qualitative data revealed that students in the cooperative learning groups found more learning benefits than the Traditional group. The study will benefit instructors and students in distance learning to improve teaching and learning practices in a virtual classroom.

  19. New e-learning method using databases

    Directory of Open Access Journals (Sweden)

    Andreea IONESCU

    2012-10-01

    Full Text Available The objective of this paper is to present a new e-learning method that use databases. The solution could pe implemented for any typeof e-learning system in any domain. The article will purpose a solution to improve the learning process for virtual classes.

  20. Statistical Learning Theory: Models, Concepts, and Results

    OpenAIRE

    von Luxburg, Ulrike; Schoelkopf, Bernhard

    2008-01-01

    Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We target at a broad audience, not necessarily machine learning researchers. This paper can serve as a starting point for people who want to get an overview on the field before diving into technical details.

  1. The Effect of Animation in Multimedia Computer-Based Learning and Learning Style to the Learning Results

    Directory of Open Access Journals (Sweden)

    Muhammad RUSLI

    2017-10-01

    Full Text Available The effectiveness of a learning depends on four main elements, they are content, desired learning outcome, instructional method and the delivery media. The integration of those four elements can be manifested into a learning modul which is called multimedia learning or learning by using multimedia. In learning context by using computer-based multimedia, there are two main things that need to be noticed so that the learning process can run effectively: how the content is presented, and what the learner’s chosen way in accepting and processing the information into a meaningful knowledge. First it is related with the way to visualize the content and how people learn. The second one is related with the learning style of the learner. This research aims to investigate the effect of the type of visualization—static vs animated—on a multimedia computer-based learning, and learning styles—visual vs verbal, towards the students’ capability in applying the concepts, procedures, principles of Java programming. Visualization type act as independent variables, and learning styles of the students act as a moderator variable. Moreover, the instructional strategies followed the Component Display Theory of Merril, and the format of presentation of multimedia followed the Seven Principles of Multimedia Learning of Mayer and Moreno. Learning with the multimedia computer-based learning has been done in the classroom. The subject of this research was the student of STMIK-STIKOM Bali in odd semester 2016-2017 which followed the course of Java programming. The Design experiments used multivariate analysis of variance, MANOVA 2 x 2, with a large sample of 138 students in 4 classes. Based on the results of the analysis, it can be concluded that the animation in multimedia interactive learning gave a positive effect in improving students’ learning outcomes, particularly in the applying the concepts, procedures, and principles of Java programming. The

  2. Learning Science, Learning about Science, Doing Science: Different Goals Demand Different Learning Methods

    Science.gov (United States)

    Hodson, Derek

    2014-01-01

    This opinion piece paper urges teachers and teacher educators to draw careful distinctions among four basic learning goals: learning science, learning about science, doing science and learning to address socio-scientific issues. In elaboration, the author urges that careful attention is paid to the selection of teaching/learning methods that…

  3. Methods for control over learning individual trajectory

    Science.gov (United States)

    Mitsel, A. A.; Cherniaeva, N. V.

    2015-09-01

    The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.

  4. The Guided Autobiography Method: A Learning Experience

    Science.gov (United States)

    Thornton, James E.

    2008-01-01

    This article discusses the proposition that learning is an unexplored feature of the guided autobiography method and its developmental exchange. Learning, conceptualized and explored as the embedded and embodied processes, is essential in narrative activities of the guided autobiography method leading to psychosocial development and growth in…

  5. Deep Learning and Bayesian Methods

    Directory of Open Access Journals (Sweden)

    Prosper Harrison B.

    2017-01-01

    Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.

  6. Deep Learning and Bayesian Methods

    OpenAIRE

    Prosper Harrison B.

    2017-01-01

    A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such meth...

  7. The Effect of Cooperative Learning Model and Kolb Learning Styles on Learning Result of the Basics of Politics

    Science.gov (United States)

    Sugiharto

    2015-01-01

    The aims of this research were to determine the effect of cooperative learning model and learning styles on learning result. This quasi-experimental study employed a 2x2 treatment by level, involved independent variables, i.e. cooperative learning model and learning styles, and learning result as the dependent variable. Findings signify that: (1)…

  8. Teaching learning methods of an entrepreneurship curriculum

    Directory of Open Access Journals (Sweden)

    KERAMAT ESMI

    2015-10-01

    Full Text Available Introduction: One of the most significant elements of entrepreneurship curriculum design is teaching-learning methods, which plays a key role in studies and researches related to such a curriculum. It is the teaching method, and systematic, organized and logical ways of providing lessons that should be consistent with entrepreneurship goals and contents, and should also be developed according to the learners’ needs. Therefore, the current study aimed to introduce appropriate, modern, and effective methods of teaching entrepreneurship and their validation Methods: This is a mixed method research of a sequential exploratory kind conducted through two stages: a developing teaching methods of entrepreneurship curriculum, and b validating developed framework. Data were collected through “triangulation” (study of documents, investigating theoretical basics and the literature, and semi-structured interviews with key experts. Since the literature on this topic is very rich, and views of the key experts are vast, directed and summative content analysis was used. In the second stage, qualitative credibility of research findings was obtained using qualitative validation criteria (credibility, confirmability, and transferability, and applying various techniques. Moreover, in order to make sure that the qualitative part is reliable, reliability test was used. Moreover, quantitative validation of the developed framework was conducted utilizing exploratory and confirmatory factor analysis methods and Cronbach’s alpha. The data were gathered through distributing a three-aspect questionnaire (direct presentation teaching methods, interactive, and practical-operational aspects with 29 items among 90 curriculum scholars. Target population was selected by means of purposive sampling and representative sample. Results: Results obtained from exploratory factor analysis showed that a three factor structure is an appropriate method for describing elements of

  9. Kernel methods for deep learning

    OpenAIRE

    Cho, Youngmin

    2012-01-01

    We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector mach...

  10. Deep kernel learning method for SAR image target recognition

    Science.gov (United States)

    Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

    2017-10-01

    With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

  11. Teaching learning methods of an entrepreneurship curriculum.

    Science.gov (United States)

    Esmi, Keramat; Marzoughi, Rahmatallah; Torkzadeh, Jafar

    2015-10-01

    One of the most significant elements of entrepreneurship curriculum design is teaching-learning methods, which plays a key role in studies and researches related to such a curriculum. It is the teaching method, and systematic, organized and logical ways of providing lessons that should be consistent with entrepreneurship goals and contents, and should also be developed according to the learners' needs. Therefore, the current study aimed to introduce appropriate, modern, and effective methods of teaching entrepreneurship and their validation. This is a mixed method research of a sequential exploratory kind conducted through two stages: a) developing teaching methods of entrepreneurship curriculum, and b) validating developed framework. Data were collected through "triangulation" (study of documents, investigating theoretical basics and the literature, and semi-structured interviews with key experts). Since the literature on this topic is very rich, and views of the key experts are vast, directed and summative content analysis was used. In the second stage, qualitative credibility of research findings was obtained using qualitative validation criteria (credibility, confirmability, and transferability), and applying various techniques. Moreover, in order to make sure that the qualitative part is reliable, reliability test was used. Moreover, quantitative validation of the developed framework was conducted utilizing exploratory and confirmatory factor analysis methods and Cronbach's alpha. The data were gathered through distributing a three-aspect questionnaire (direct presentation teaching methods, interactive, and practical-operational aspects) with 29 items among 90 curriculum scholars. Target population was selected by means of purposive sampling and representative sample. Results obtained from exploratory factor analysis showed that a three factor structure is an appropriate method for describing elements of teaching-learning methods of entrepreneurship curriculum

  12. Subsampled Hessian Newton Methods for Supervised Learning.

    Science.gov (United States)

    Wang, Chien-Chih; Huang, Chun-Heng; Lin, Chih-Jen

    2015-08-01

    Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification.

  13. Are students' impressions of improved learning through active learning methods reflected by improved test scores?

    Science.gov (United States)

    Everly, Marcee C

    2013-02-01

    To report the transformation from lecture to more active learning methods in a maternity nursing course and to evaluate whether student perception of improved learning through active-learning methods is supported by improved test scores. The process of transforming a course into an active-learning model of teaching is described. A voluntary mid-semester survey for student acceptance of the new teaching method was conducted. Course examination results, from both a standardized exam and a cumulative final exam, among students who received lecture in the classroom and students who had active learning activities in the classroom were compared. Active learning activities were very acceptable to students. The majority of students reported learning more from having active-learning activities in the classroom rather than lecture-only and this belief was supported by improved test scores. Students who had active learning activities in the classroom scored significantly higher on a standardized assessment test than students who received lecture only. The findings support the use of student reflection to evaluate the effectiveness of active-learning methods and help validate the use of student reflection of improved learning in other research projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

  14. Machine learning methods for metabolic pathway prediction

    Directory of Open Access Journals (Sweden)

    Karp Peter D

    2010-01-01

    Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

  15. Machine learning methods for metabolic pathway prediction

    Science.gov (United States)

    2010-01-01

    Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

  16. "Mastery Learning" Como Metodo Psicoeducativo para Ninos con Problemas Especificos de Aprendizaje. ("Mastery Learning" as a Psychoeducational Method for Children with Specific Learning Problems.)

    Science.gov (United States)

    Coya, Liliam de Barbosa; Perez-Coffie, Jorge

    1982-01-01

    "Mastery Learning" was compared with the "conventional" method of teaching reading skills to Puerto Rican children with specific learning disabilities. The "Mastery Learning" group showed significant gains in the cognitive and affective domains. Results suggested Mastery Learning is a more effective method of teaching…

  17. Effect of Methods of Learning and Self Regulated Learning toward Outcomes of Learning Social Studies

    Science.gov (United States)

    Tjalla, Awaluddin; Sofiah, Evi

    2015-01-01

    This research aims to reveal the influence of learning methods and self-regulated learning on students learning scores for Social Studies object. The research was done in Islamic Junior High School (MTs Manba'ul Ulum), Batuceper City Tangerang using quasi-experimental method. The research employed simple random technique to 28 students. Data were…

  18. Active learning methods for interactive image retrieval.

    Science.gov (United States)

    Gosselin, Philippe Henri; Cord, Matthieu

    2008-07-01

    Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

  19. e-Learning Business Research Methods

    Science.gov (United States)

    Cowie, Jonathan

    2004-01-01

    This paper outlines the development of a generic Business Research Methods course from a simple name in a box to a full e-Learning web based module. It highlights particular issues surrounding the nature of the discipline and the integration of a large number of cross faculty subject specific research methods courses into a single generic module.…

  20. Characterizing Reinforcement Learning Methods through Parameterized Learning Problems

    Science.gov (United States)

    2011-06-03

    extraneous. The agent could potentially adapt these representational aspects by applying methods from feature selection ( Kolter and Ng, 2009; Petrik et al...611–616. AAAI Press. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature selection in least-squares temporal difference learning. In A. P

  1. Tracking by Machine Learning Methods

    CERN Document Server

    Jofrehei, Arash

    2015-01-01

    Current track reconstructing methods start with two points and then for each layer loop through all possible hits to find proper hits to add to that track. Another idea would be to use this large number of already reconstructed events and/or simulated data and train a machine on this data to find tracks given hit pixels. Training time could be long but real time tracking is really fast Simulation might not be as realistic as real data but tacking has been done for that with 100 percent efficiency while by using real data we would probably be limited to current efficiency.

  2. Learning in Non-Stationary Environments Methods and Applications

    CERN Document Server

    Lughofer, Edwin

    2012-01-01

    Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences.   Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dyna...

  3. A cross-benchmark comparison of 87 learning to rank methods

    NARCIS (Netherlands)

    Tax, N.; Bockting, S.; Hiemstra, D.

    2015-01-01

    Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered

  4. [Which learning methods are expected for ultrasound training? Blended learning on trial].

    Science.gov (United States)

    Röhrig, S; Hempel, D; Stenger, T; Armbruster, W; Seibel, A; Walcher, F; Breitkreutz, R

    2014-10-01

    Current teaching methods in graduate and postgraduate training often include frontal presentations. Especially in ultrasound education not only knowledge but also sensomotory and visual skills need to be taught. This requires new learning methods. This study examined which types of teaching methods are preferred by participants in ultrasound training courses before, during and after the course by analyzing a blended learning concept. It also investigated how much time trainees are willing to spend on such activities. A survey was conducted at the end of a certified ultrasound training course. Participants were asked to complete a questionnaire based on a visual analogue scale (VAS) in which three categories were defined: category (1) vote for acceptance with a two thirds majority (VAS 67-100%), category (2) simple acceptance (50-67%) and category (3) rejection (learning program with interactive elements, short presentations (less than 20 min), incorporating interaction with the audience, hands-on sessions in small groups, an alternation between presentations and hands-on-sessions, live demonstrations and quizzes. For post-course learning, interactive and media-assisted approaches were preferred, such as e-learning, films of the presentations and the possibility to stay in contact with instructors in order to discuss the results. Participants also voted for maintaining a logbook for documentation of results. The results of this study indicate the need for interactive learning concepts and blended learning activities. Directors of ultrasound courses may consider these aspects and are encouraged to develop sustainable learning pathways.

  5. The WOMBAT Attack Attribution Method: Some Results

    Science.gov (United States)

    Dacier, Marc; Pham, Van-Hau; Thonnard, Olivier

    In this paper, we present a new attack attribution method that has been developed within the WOMBAT project. We illustrate the method with some real-world results obtained when applying it to almost two years of attack traces collected by low interaction honeypots. This analytical method aims at identifying large scale attack phenomena composed of IP sources that are linked to the same root cause. All malicious sources involved in a same phenomenon constitute what we call a Misbehaving Cloud (MC). The paper offers an overview of the various steps the method goes through to identify these clouds, providing pointers to external references for more detailed information. Four instances of misbehaving clouds are then described in some more depth to demonstrate the meaningfulness of the concept.

  6. Learning Method, Facilities And Infrastructure, And Learning Resources In Basic Networking For Vocational School

    OpenAIRE

    Pamungkas, Bian Dwi

    2017-01-01

    This study aims to examine the contribution of learning methods on learning output, the contribution of facilities and infrastructure on output learning, the contribution of learning resources on learning output, and the contribution of learning methods, the facilities and infrastructure, and learning resources on learning output. The research design is descriptive causative, using a goal-oriented assessment approach in which the assessment focuses on assessing the achievement of a goal. The ...

  7. Enriching behavioral ecology with reinforcement learning methods.

    Science.gov (United States)

    Frankenhuis, Willem E; Panchanathan, Karthik; Barto, Andrew G

    2018-02-13

    This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  8. Early Language Learning: Complexity and Mixed Methods

    Science.gov (United States)

    Enever, Janet, Ed.; Lindgren, Eva, Ed.

    2017-01-01

    This is the first collection of research studies to explore the potential for mixed methods to shed light on foreign or second language learning by young learners in instructed contexts. It brings together recent studies undertaken in Cameroon, China, Croatia, Ethiopia, France, Germany, Italy, Kenya, Mexico, Slovenia, Spain, Sweden, Tanzania and…

  9. Keystone Method: A Learning Paradigm in Mathematics

    Science.gov (United States)

    Siadat, M. Vali; Musial, Paul M.; Sagher, Yoram

    2008-01-01

    This study reports the effects of an integrated instructional program (the Keystone Method) on the students' performance in mathematics and reading, and tracks students' persistence and retention. The subject of the study was a large group of students in remedial mathematics classes at the college, willing to learn but lacking basic educational…

  10. Students' Ideas on Cooperative Learning Method

    Science.gov (United States)

    Yoruk, Abdulkadir

    2016-01-01

    Aim of this study is to investigate students' ideas on cooperative learning method. For that purpose students who are studying at elementary science education program are distributed into two groups through an experimental design. Factors threaten the internal validity are either eliminated or reduced to minimum value. Data analysis is done…

  11. Think Pair Share (TPS as Method to Improve Student’s Learning Motivation and Learning Achievement

    Directory of Open Access Journals (Sweden)

    Hetika Hetika

    2018-03-01

    Full Text Available This research aims to find out the application of Think Pair Share (TPS learning method in improving learning motivation and learning achievement in the subject of Introduction to Accounting I of the Accounting Study Program students of Politeknik Harapan Bersama. The Method of data collection in this study used observation method, test method, and documentation method. The research instruments used observation sheet, questionnaire and test question. This research used Class Action Research Design which is an action implementation oriented research, with the aim of improving quality or problem solving in a group by carefully and observing the success rate due to the action. The method of analysis used descriptive qualitative and quantitative analysis method. The results showed that the application of Think Pair Share Learning (TPS Method can improve the Learning Motivation and Achievement. Before the implementation of the action, the obtained score is 67% then in the first cycle increases to 72%, and in the second cycle increasws to 80%. In addition, based on questionnaires distributed to students, it also increases the score of Accounting Learning Motivation where the score in the first cycle of 76% increases to 79%. In addition, in the first cycle, the score of pre test and post test of the students has increased from 68.86 to 76.71 while in the second cycle the score of pre test and post test of students has increased from 79.86 to 84.86.

  12. The method of global learning in teaching foreign languages

    Directory of Open Access Journals (Sweden)

    Tatjana Dragovič

    2001-12-01

    Full Text Available The authors describe the method of global learning of foreign languages, which is based on the principles of neurolinguistic programming (NLP. According to this theory, the educator should use the method of the so-called periphery learning, where students learn relaxation techniques and at the same time they »incidentally « or subconsciously learn a foreign language. The method of global learning imitates successful strategies of learning in early childhood and therefore creates a relaxed attitude towards learning. Global learning is also compared with standard methods.

  13. The effects of autonomous learning on cognitive load and learning results

    NARCIS (Netherlands)

    Gorissen, Chantal; Kester, Liesbeth; Brand-Gruwel, Saskia; Martens, Rob

    2012-01-01

    Gorissen, C. J. J., Kester, L., Brand-Gruwel, S., & Martens, R. L. (2011, August). The Effects of Autonomous Learning on Cognitive Load and Learning Results. Presentation at the EARLI conference. Exeter, UK.

  14. TEACHING METHODS IN MBA AND LIFELONG LEARNING PROGRAMMES FOR MANAGERS

    Directory of Open Access Journals (Sweden)

    Jarošová, Eva

    2017-09-01

    Full Text Available Teaching methods in MBA and Lifelong Learning Programmes (LLP for managers should be topically relevant in terms of content as well as the teaching methods used. In terms of the content, the integral part of MBA and Lifelong Learning Programmes for managers should be the development of participants’ leadership competencies and their understanding of current leadership concepts. The teaching methods in educational programmes for managers as adult learners should correspond to the strategy of learner-centred teaching that focuses on the participants’ learning process and their active involvement in class. The focus on the participants’ learning process also raises questions about whether the programme’s participants perceive the teaching methods used as useful and relevant for their development as leaders. The paper presents the results of the analysis of the responses to these questions in a sample of 54 Czech participants in the MBA programme and of lifelong learning programmes at the University of Economics, Prague. The data was acquired based on written or electronically submitted questionnaires. The data was analysed in relation to the usefulness of the teaching methods for understanding the concepts of leadership, leadership skills development as well as respondents’ personal growth. The results show that the respondents most valued the methods that enabled them to get feedback, activated them throughout the programme and got them involved in discussions with others in class. Implications for managerial education practices are discussed.

  15. Machine learning improves the accuracy of myocardial perfusion scintigraphy results

    International Nuclear Information System (INIS)

    Groselj, C.; Kukar, M.

    2002-01-01

    Objective: Machine learning (ML) an artificial intelligence method has in last decade proved to be an useful tool in many fields of decision making, also in some fields of medicine. By reports, its decision accuracy usually exceeds the human one. Aim: To assess applicability of ML in interpretation of the stress myocardial perfusion scintigraphy results in coronary artery disease diagnostic process. Patients and methods: The 327 patient's data of planar stress myocardial perfusion scintigraphy were reevaluated in usual way. Comparing them with the results of coronary angiography the sensitivity, specificity and accuracy of the investigation were computed. The data were digitized and the decision procedure repeated by ML program 'Naive Bayesian classifier'. As the ML is able to simultaneously manipulate with whatever number of data, all reachable disease connected data (regarding history, habitus, risk factors, stress results) were added. The sensitivity, specificity and accuracy of scintigraphy were expressed in this way. The results of both decision procedures were compared. Conclusion: Using ML method, 19 more patients out of 327 (5.8%) were correctly diagnosed by stress myocardial perfusion scintigraphy. In this way ML could be an important tool for myocardial perfusion scintigraphy decision making

  16. FLIPPED CLASSROOM LEARNING METHOD TO IMPROVE CARING AND LEARNING OUTCOME IN FIRST YEAR NURSING STUDENT

    Directory of Open Access Journals (Sweden)

    Ni Putu Wulan Purnama Sari

    2017-08-01

    Full Text Available Background and Purpose: Caring is the essence of nursing profession. Stimulation of caring attitude should start early. Effective teaching methods needed to foster caring attitude and improve learning achievement. This study aimed to explain the effect of applying flipped classroom learning method for improving caring attitude and learning achievement of new student nurses at nursing institutions in Surabaya. Method: This is a pre-experimental study using the one group pretest posttest and posttest only design. Population was all new student nurses on nursing institutions in Surabaya. Inclusion criteria: female, 18-21 years old, majoring in nursing on their own volition and being first choice during students selection process, status were active in the even semester of 2015/2016 academic year. Sample size was 67 selected by total sampling. Variables: 1 independent: application of flipped classroom learning method; 2 dependent: caring attitude, learning achievement. Instruments: teaching plan, assignment descriptions, presence list, assignment assessment rubrics, study materials, questionnaires of caring attitude. Data analysis: paired and one sample t test. Ethical clearance was available. Results: Most respondents were 20 years old (44.8%, graduated from high school in Surabaya (38.8%, living with parents (68.7% in their homes (64.2%. All data were normally distributed. Flipped classroom learning method could improve caring attitude by 4.13%. Flipped classroom learning method was proved to be effective for improving caring attitude (p=0.021 and learning achievement (p=0.000. Conclusion and Recommendation: Flipped classroom was effective for improving caring attitude and learning achievement of new student nurse. It is recommended to use mix-method and larger sample for further study.

  17. Characterizing Engineering Learners' Preferences for Active and Passive Learning Methods

    Science.gov (United States)

    Magana, Alejandra J.; Vieira, Camilo; Boutin, Mireille

    2018-01-01

    This paper studies electrical engineering learners' preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called "slectures," and some others. The results suggest that…

  18. Parallelization of the ROOT Machine Learning Methods

    CERN Document Server

    Vakilipourtakalou, Pourya

    2016-01-01

    Today computation is an inseparable part of scientific research. Specially in Particle Physics when there is a classification problem like discrimination of Signals from Backgrounds originating from the collisions of particles. On the other hand, Monte Carlo simulations can be used in order to generate a known data set of Signals and Backgrounds based on theoretical physics. The aim of Machine Learning is to train some algorithms on known data set and then apply these trained algorithms to the unknown data sets. However, the most common framework for data analysis in Particle Physics is ROOT. In order to use Machine Learning methods, a Toolkit for Multivariate Data Analysis (TMVA) has been added to ROOT. The major consideration in this report is the parallelization of some TMVA methods, specially Cross-Validation and BDT.

  19. SMALL GROUP LEARNING METHODS AND THEIR EFFECT ON LEARNERS’ RELATIONSHIPS

    Directory of Open Access Journals (Sweden)

    Radka Borůvková

    2016-04-01

    cooperative learning methods help to integrate isolated learners into the class? The research was realized as a pre-test-post-test design for the sample of 207 learners of first, second and third grades of lower secondary school. Standardized sociometric questionnaire B-3 was used to determine the number of isolated learners before and after using the CL methods. Consequently, using the Wilcoxon statistic test of significance, the hypothesis was verified that the number of isolates after the CL methods is statistically significantly lower than that of the case before using the non-traditional teaching/learning method. Accordingly, the research results justify implementation CL methods into education.

  20. Conduct disorders as a result of specific learning disorders

    OpenAIRE

    VOKROJOVÁ, Nela

    2012-01-01

    This thesis focuses on relationship between specific learning disorders and conduct disorders in puberty. The theoretical part explains the basic terms apearing in the thesis such as specific learning disorders, conduct disorders, puberty and prevention of conduct disorder formation. It presents Czech and foreign research which have already been done in this and related areas. The empirical part uses a quantitative method to measure anxiety and occurrence of conduct disorders in second grade ...

  1. Machine Learning Methods for Production Cases Analysis

    Science.gov (United States)

    Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

    2018-03-01

    Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

  2. Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results

    Science.gov (United States)

    Simon, Donald L.; Borguet, Sebastien; Leonard, Olivier; Zhang, Xiaodong (Frank)

    2013-01-01

    Recent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a gas path diagnostic benchmark problem has been created and made publicly available. This software tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been constructed based on feedback provided by the aircraft EHM community. It provides a standard benchmark problem enabling users to develop, evaluate and compare diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind-test-case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed.

  3. Decentralized indirect methods for learning automata games.

    Science.gov (United States)

    Tilak, Omkar; Martin, Ryan; Mukhopadhyay, Snehasis

    2011-10-01

    We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper.

  4. Implementing Collaborative Learning Methods in the Political Science Classroom

    Science.gov (United States)

    Wolfe, Angela

    2012-01-01

    Collaborative learning is one, among other, active learning methods, widely acclaimed in higher education. Consequently, instructors in fields that lack pedagogical training often implement new learning methods such as collaborative learning on the basis of trial and error. Moreover, even though the benefits in academic circles are broadly touted,…

  5. THE EFFECTS OF COOPERATIVE LEARNING MODEL GROUP INVESTIGATION AND MOTIVATION TOWARD PHYSICS LEARNING RESULTS MAN TANJUNGBALAI

    Directory of Open Access Journals (Sweden)

    Amalia Febri Aristi

    2014-12-01

    Full Text Available This study aimed to determine: (1 Is there a difference in student's learning outcomes with the application of learning models Investigation Group and Direct Instruction teaching model. (2 Is there a difference in students' motivation with the application of learning models Investigation Group and Direct Instruction teaching model, (3 Is there an interaction between learning models Investigation Group and Direct Instruction to improve students' motivation in learning outcomes Physics. This research is a quasi experimental. The study population was a student of class XII Tanjung Balai MAN. Random sample selection is done by randomizing the class. The instrument used consisted of: (1 achievement test (2 students' motivation questionnaire. The tests are used to obtain the data is shaped essay. The data in this study were analyzed using ANOVA analysis of two paths. The results showed that: (1 there were differences in learning outcomes between students who used the physics model of Group Investigation learning compared with students who used the Direct Instruction teaching model. (2 There was a difference in student's learning outcomes that had a low learning motivation and high motivation to learn both in the classroom and in the classroom Investigation Group Direct Instruction. (3 There was interaction between learning models Instruction Direct Group Investigation and motivation to learn in improving learning outcomes Physics.

  6. German precursor study: methods and results

    International Nuclear Information System (INIS)

    Hoertner, H.; Frey, W.; von Linden, J.; Reichart, G.

    1985-01-01

    This study has been prepared by the GRS by contract of the Federal Minister of Interior. The purpose of the study is to show how the application of system-analytic tools and especially of probabilistic methods on the Licensee Event Reports (LERs) and on other operating experience can support a deeper understanding of the safety-related importance of the events reported in reactor operation, the identification of possible weak points, and further conclusions to be drawn from the events. Additionally, the study aimed at a comparison of its results for the severe core damage frequency with those of the German Risk Study as far as this is possible and useful. The German Precursor Study is a plant-specific study. The reference plant is Biblis NPP with its very similar Units A and B, whereby the latter was also the reference plant for the German Risk Study

  7. Mechanics of Nanostructures: Methods and Results

    Science.gov (United States)

    Ruoff, Rod

    2003-03-01

    We continue to develop and use new tools to measure the mechanics and electromechanics of nanostructures. Here we discuss: (a) methods for making nanoclamps and the resulting: nanoclamp geometry, chemical composition and type of chemical bonding, and nanoclamp strength (effectiveness as a nanoclamp for the mechanics measurements to be made); (b) mechanics of carbon nanocoils. We have received carbon nanocoils from colleagues in Japan [1], measured their spring constants, and have observed extensions exceeding 100% relative to the unloaded length, using our scanning electron microscope nanomanipulator tool; (c) several new devices that are essentially MEMS-based, that allow for improved measurements of the mechanics of psuedo-1D and planar nanostructures. [1] Zhang M., Nakayama Y., Pan L., Japanese J. Appl. Phys. 39, L1242-L1244 (2000).

  8. The Effect Of Islamic Education Learning Pai And Learning Results To Students Religious Behavior Of Stisip Widyapuri Mandiri Sukabumi Student

    Directory of Open Access Journals (Sweden)

    U. Abdullah Mumin

    2017-06-01

    Full Text Available The purpose of this study is to measure the level of the effect of Islamic Education learning and learning result on religious behaviour in STISIP Widyapuri Mandiri Sukabumi. The method used in this research is quantitative analysis based on inferential statistical model. The data collection is done by using observation techniques interviews and questionnaires. The researcher analize the data by using logic analysis for qualitative and statistical analysis for quantitative data by using descriptive statistics regression and correlation. Based on the hypothesis test simultaneously PAI learning and learning result have a positive and significant effect on students religious behaviour. Partially only PAI learning alone has a positive and significant impact on religious behavior.

  9. Comparisons and Analyses of Gifted Students' Characteristics and Learning Methods

    Science.gov (United States)

    Lu, Jiamei; Li, Daqi; Stevens, Carla; Ye, Renmin

    2017-01-01

    Using PISA 2009, an international education database, this study compares gifted and talented (GT) students in three groups with normal (non-GT) students by examining student characteristics, reading, schooling, learning methods, and use of strategies for understanding and memorizing. Results indicate that the GT and non-GT gender distributions…

  10. The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods

    NARCIS (Netherlands)

    Verpoorten, Dominique; Poumay, M; Leclercq, D

    2006-01-01

    Please, cite this publication as: Verpoorten, D., Poumay, M., & Leclercq, D. (2006). The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence

  11. Statistical learning methods in high-energy and astrophysics analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zimmermann, J. [Forschungszentrum Juelich GmbH, Zentrallabor fuer Elektronik, 52425 Juelich (Germany) and Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de; Kiesling, C. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)

    2004-11-21

    We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application.

  12. Statistical learning methods in high-energy and astrophysics analysis

    International Nuclear Information System (INIS)

    Zimmermann, J.; Kiesling, C.

    2004-01-01

    We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application

  13. Multiband discrete ordinates method: formalism and results

    International Nuclear Information System (INIS)

    Luneville, L.

    1998-06-01

    The multigroup discrete ordinates method is a classical way to solve transport equation (Boltzmann) for neutral particles. Self-shielding effects are not correctly treated due to large variations of cross sections in a group (in the resonance range). To treat the resonance domain, the multiband method is introduced. The main idea is to divide the cross section domain into bands. We obtain the multiband parameters using the moment method; the code CALENDF provides probability tables for these parameters. We present our implementation in an existing discrete ordinates code: SN1D. We study deep penetration benchmarks and show the improvement of the method in the treatment of self-shielding effects. (author)

  14. RELATIONSHIP OF INTEREST, LEARNING MOTIVATION AND ATTITUDE WITH RESULTS LEARNING CLASS VIII SMP STATE 13 MAKASSAR

    Directory of Open Access Journals (Sweden)

    Putri Athirah Azis

    2016-12-01

    Full Text Available The study aims at examining (1 the correlation of learning interest towards learning result of grade students, (2 the correlation of learning motivation towards learning result of grade students, (3 the correlation of students attitude towards learning result, (4 the correlationof interest, learning motivation, and attitude collaboratively towards learning result. The study is an ex post facto. The population of the study was grade VIII at SMPN 13 Makassar. Samples were 105 students taken by employing random sampling technique. Data were collected through questionnaire and documentation. Data were analyzed using regression test. The result of study reveal that (1 there is significant correlation (p<0,01 of interest towards learning result of grade VIII students at SMPN 13 Makassar. Co-efficient correlation (r is 0,718 and its effectiveness contribution (R2 is 51,5%, (2 there is significant correlation (p<0,01 of motivation towards learning result of grade VIII students at SMPN 13 Makassar. Co-efficient correlation (r is 0,775 and its effectiveness contribution (R2 is 60,1%, (3 there is significant correlation (p<0,01 of attitude towards learning result of grade VIII students at SMPN 13 Makassar. Co-efficient correlation (r is 0,737 and its effectiveness contribution (R2 is 54,4%, (4 there is significant correlation (p<0,01 of interest, motivation and attitude collaboratively towards learning result of grade VIII students at SMPN 13 Makassar. Co-efficient correlation (r is 0,861 and its effectiveness contribution (R2 is 74,1%,

  15. The Contribution of Vocational Students' Learning Discipline, Motivation and Learning Results

    Science.gov (United States)

    Yussi; Syaad; Purnomo

    2017-01-01

    A good vocational high school prepares students for developing capability of working independently, demonstrating professional attitude at work, and being productive which that require good learning results for the realization thereof. the learning results serve as the yardstick of students' success. The purpose of this article is to find out the…

  16. Student Achievement in Basic College Mathematics: Its Relationship to Learning Style and Learning Method

    Science.gov (United States)

    Gunthorpe, Sydney

    2006-01-01

    From the assumption that matching a student's learning style with the learning method best suited for the student, it follows that developing courses that correlate learning method with learning style would be more successful for students. Albuquerque Technical Vocational Institute (TVI) in New Mexico has attempted to provide students with more…

  17. Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

    Science.gov (United States)

    Soroush, Masoud; Weinberger, Charles B.

    2010-01-01

    This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

  18. The Method of High School English Word Learning

    Institute of Scientific and Technical Information of China (English)

    吴博涵

    2016-01-01

    Most Chinese students are not interested in English learning, especially English words. In this paper, I focus on English vocabulary learning, for example, the study of high school students English word learning method, and also introduce several ways to make vocabulary memory becomes more effective. The purpose is to make high school students grasp more English word learning skills.

  19. The Keyimage Method of Learning Sound-Symbol Correspondences: A Case Study of Learning Written Khmer

    Directory of Open Access Journals (Sweden)

    Elizabeth Lavolette

    2009-01-01

    Full Text Available I documented my strategies for learning sound-symbol correspondences during a Khmer course. I used a mnemonic strategy that I call the keyimage method. In this method, a character evokes an image (the keyimage, which evokes the corresponding sound. For example, the keyimage for the character 2 could be a swan with its head tucked in. This evokes the sound "kaw" that a swan makes, which sounds similar to the Khmer sound corresponding to 2. The method has some similarities to the keyword method. Considering the results of keyword studies, I hypothesize that the keyimage method is more effective than rote learning and that peer-generated keyimages are more effective than researcher- or teacher-generated keyimages, which are more effective than learner-generated ones. In Dr. Andrew Cohen's plenary presentation at the Hawaii TESOL 2007 conference, he mentioned that more case studies are needed on learning strategies (LSs. One reason to study LSs is that what learners do with input to produce output is unclear, and knowing what strategies learners use may help us understand that process (Dornyei, 2005, p. 170. Hopefully, we can use that knowledge to improve language learning, perhaps by teaching learners to use the strategies that we find. With that in mind, I have examined the LSs that I used in studying Khmer as a foreign language, focusing on learning the syllabic alphabet.

  20. Implementation and Results of a Learning Assistant Program

    Science.gov (United States)

    Bogue, Thomas B.; Seeley, L.; Vokos, S.

    2006-12-01

    The Physics Department at Seattle Pacific University has recently completed a three-year CCLI grant to integrate Tutorials in Introductory Physics , Activity Based Physics , and Real Time Physics into our one-year introductory curriculum. One of the difficulties encountered in doing this at a small undergraduate university was the need for additional instructors. This need is met through the use of undergraduate learning assistants. The development of recruitment and implementation methods will be discussed, along with the advantages to physics education, and the challenges encountered. We will also discuss several strategies we have identified as critical to a successful learning assistant program.

  1. Learning to merge search results for efficient Distributed Information Retrieval

    NARCIS (Netherlands)

    Tjin-Kam-Jet, Kien; Hiemstra, Djoerd

    2010-01-01

    Merging search results from different servers is a major problem in Distributed Information Retrieval. We used Regression-SVM and Ranking-SVM which would learn a function that merges results based on information that is readily available: i.e. the ranks, titles, summaries and URLs contained in the

  2. Method to predict process signals to learn for SVM

    International Nuclear Information System (INIS)

    Minowa, Hirotsugu; Gofuku, Akio

    2013-01-01

    Study of diagnostic system using machine learning to reduce the incidents of the plant is in advance because an accident causes large damage about human, economic and social loss. There is a problem that 2 performances between a classification performance and generalization performance on the machine diagnostic machine is exclusive. However, multi agent diagnostic system makes it possible to use a diagnostic machine specialized either performance by multi diagnostic machines can be used. We propose method to select optimized variables to improve classification performance. The method can also be used for other supervised learning machine but Support Vector Machine. This paper reports that our method and result of evaluation experiment applied our method to output 40% of Monju. (author)

  3. A Fast Optimization Method for General Binary Code Learning.

    Science.gov (United States)

    Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng

    2016-09-22

    Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

  4. Do Facilitated Online Dual Credit Classes Result in Deep Learning?

    Science.gov (United States)

    Stark Education Partnership, 2015

    2015-01-01

    This study, with funding from the Jennings Foundation, sought to answer the following broad research question: Do facilitated online dual credit courses result in deep learning? The answer to this question is key to addressing barriers many students face in bridging from high school to college. This report includes a descriptive case study that…

  5. Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

    Science.gov (United States)

    da Costa Tavares, Ofelia Cizela; Suyoto; Pranowo

    2018-02-01

    In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process) and SAW (simple additive Weighting) method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

  6. Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

    Directory of Open Access Journals (Sweden)

    da Costa Tavares Ofelia Cizela

    2018-01-01

    Full Text Available In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process and SAW (simple additive Weighting method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

  7. Modeling learning and memory using verbal learning tests: results from ACTIVE.

    Science.gov (United States)

    Gross, Alden L; Rebok, George W; Brandt, Jason; Tommet, Doug; Marsiske, Michael; Jones, Richard N

    2013-03-01

    To investigate the influence of memory training on initial recall and learning. The Advanced Cognitive Training for Independent and Vital Elderly study of community-dwelling adults older than age 65 (n = 1,401). We decomposed trial-level recall in the Auditory Verbal Learning Test (AVLT) and Hopkins Verbal Learning Test (HVLT) into initial recall and learning across trials using latent growth models. Trial-level increases in words recalled in the AVLT and HVLT at each follow-up visit followed an approximately logarithmic shape. Over the 5-year study period, memory training was associated with slower decline in Trial 1 AVLT recall (Cohen's d = 0.35, p = .03) and steep pre- and posttraining acceleration in learning (d = 1.56, p learning, d = 3.10, p memory-trained group had a higher level of recall than the control group through the end of the 5-year study period despite faster decline in learning. This study contributes to the understanding of the mechanisms by which training benefits memory and expands current knowledge by reporting long-term changes in initial recall and learning, as measured from growth models and by characterization of the impact of memory training on these components. Results reveal that memory training delays the worsening of memory span and boosts learning.

  8. Teaching and learning methods in IVET

    DEFF Research Database (Denmark)

    Aarkrog, Vibe

    The cases deals about learner centered learning in a commercial program and a technical program.......The cases deals about learner centered learning in a commercial program and a technical program....

  9. Effects of two different types of physics learning on the results of CLASS test

    Directory of Open Access Journals (Sweden)

    Mirko Marušić1

    2012-02-01

    Full Text Available During a one-semester-long research project with high school students, we deployed and gauged efficiency of two different reform teaching methods: reading, presenting, and questioning (RPQ and experimenting and discussion (ED. In this paper we report on changes in students’ attitudes and beliefs about physics and learning physics. We used the Colorado Learning Attitudes about Science Survey (CLASS v3 to assess the relative effectiveness of the two methods. The data show that both methods improved student attitudes and beliefs but to different extents. The RPQ group (91 students achieved an overall improvement of +5.8% in attitudes and beliefs, while the ED group (85 students attained an improvement of +25.6%. These results suggest that both methods may have a substantial potential for improving students’ attitudes and beliefs about physics and physics learning, with the ED method being more promising than the RPQ. method

  10. Sunspot drawings handwritten character recognition method based on deep learning

    Science.gov (United States)

    Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

    2016-05-01

    High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

  11. A diagram retrieval method with multi-label learning

    Science.gov (United States)

    Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

    2015-01-01

    In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

  12. RESULTS OF THE QUESTIONNAIRE: ANALYSIS METHODS

    CERN Multimedia

    Staff Association

    2014-01-01

    Five-yearly review of employment conditions   Article S V 1.02 of our Staff Rules states that the CERN “Council shall periodically review and determine the financial and social conditions of the members of the personnel. These periodic reviews shall consist of a five-yearly general review of financial and social conditions;” […] “following methods […] specified in § I of Annex A 1”. Then, turning to the relevant part in Annex A 1, we read that “The purpose of the five-yearly review is to ensure that the financial and social conditions offered by the Organization allow it to recruit and retain the staff members required for the execution of its mission from all its Member States. […] these staff members must be of the highest competence and integrity.” And for the menu of such a review we have: “The five-yearly review must include basic salaries and may include any other financial or soc...

  13. Data Mining and Machine Learning Methods for Dementia Research.

    Science.gov (United States)

    Li, Rui

    2018-01-01

    Patient data in clinical research often includes large amounts of structured information, such as neuroimaging data, neuropsychological test results, and demographic variables. Given the various sources of information, we can develop computerized methods that can be a great help to clinicians to discover hidden patterns in the data. The computerized methods often employ data mining and machine learning algorithms, lending themselves as the computer-aided diagnosis (CAD) tool that assists clinicians in making diagnostic decisions. In this chapter, we review state-of-the-art methods used in dementia research, and briefly introduce some recently proposed algorithms subsequently.

  14. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.

    Science.gov (United States)

    Luo, Gang

    2016-01-01

    Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. For the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.

  15. [Adverse events management. Methods and results of a development project].

    Science.gov (United States)

    Rabøl, Louise Isager; Jensen, Elisabeth Brøgger; Hellebek, Annemarie H; Pedersen, Beth Lilja

    2006-11-27

    This article describes the methods and results of a project in the Copenhagen Hospital Corporation (H:S) on preventing adverse events. The aim of the project was to raise awareness about patients' safety, test a reporting system for adverse events, develop and test methods of analysis of events and propagate ideas about how to prevent adverse events. H:S developed an action plan and a reporting system for adverse events, founded an organization and developed an educational program on theories and methods of learning from adverse events for both leaders and employees. During the three-year period from 1 January 2002 to 31 December 2004, the H:S staff reported 6011 adverse events. In the same period, the organization completed 92 root cause analyses. More than half of these dealt with events that had been optional to report, the other half events that had been mandatory to report. The number of reports and the front-line staff's attitude towards reporting shows that the H:S succeeded in founding a safety culture. Future work should be centred on developing and testing methods that will prevent adverse events from happening. The objective is to suggest and complete preventive initiatives which will help increase patient safety.

  16. Two different hematocrit detection methods: Different methods, different results?

    Directory of Open Access Journals (Sweden)

    Schuepbach Reto A

    2010-03-01

    Full Text Available Abstract Background Less is known about the influence of hematocrit detection methodology on transfusion triggers. Therefore, the aim of the present study was to compare two different hematocrit-assessing methods. In a total of 50 critically ill patients hematocrit was analyzed using (1 blood gas analyzer (ABLflex 800 and (2 the central laboratory method (ADVIA® 2120 and compared. Findings Bland-Altman analysis for repeated measurements showed a good correlation with a bias of +1.39% and 2 SD of ± 3.12%. The 24%-hematocrit-group showed a correlation of r2 = 0.87. With a kappa of 0.56, 22.7% of the cases would have been transfused differently. In the-28%-hematocrit group with a similar correlation (r2 = 0.8 and a kappa of 0.58, 21% of the cases would have been transfused differently. Conclusions Despite a good agreement between the two methods used to determine hematocrit in clinical routine, the calculated difference of 1.4% might substantially influence transfusion triggers depending on the employed method.

  17. Nurse practitioner preferences for distance education methods related to learning style, course content, and achievement.

    Science.gov (United States)

    Andrusyszyn, M A; Cragg, C E; Humbert, J

    2001-04-01

    The relationships among multiple distance delivery methods, preferred learning style, content, and achievement was sought for primary care nurse practitioner students. A researcher-designed questionnaire was completed by 86 (71%) participants, while 6 engaged in follow-up interviews. The results of the study included: participants preferred learning by "considering the big picture"; "setting own learning plans"; and "focusing on concrete examples." Several positive associations were found: learning on own with learning by reading, and setting own learning plans; small group with learning through discussion; large group with learning new things through hearing and with having learning plans set by others. The most preferred method was print-based material and the least preferred method was audio tape. The most suited method for content included video teleconferencing for counseling, political action, and transcultural issues; and video tape for physical assessment. Convenience, self-direction, and timing of learning were more important than delivery method or learning style. Preferred order of learning was reading, discussing, observing, doing, and reflecting. Recommended considerations when designing distance courses include a mix of delivery methods, specific content, outcomes, learner characteristics, and state of technology.

  18. The Learners’ Attitudes towards Using Different Learning Methods in E-Learning Portal Environment

    Directory of Open Access Journals (Sweden)

    Issham Ismail

    2011-09-01

    Full Text Available This study investigates the learners’ preference of academic, collaborative and social interaction towards interaction methods in e-learning portal. Academic interaction consists of interaction between learners and online learning resources such as online reading, online explanation, online examination and also online question answering. Collaborative interaction occurs when learners interact among themselves using online group discussion. Social interaction happens when learners and instructors participate in the session either via online text chatting or voice chatting. The study employed qualitative methodology where data were collected through questionnaire that was administered to 933 distance education students from Bachelor of Management, Bachelor of Science, Bachelor of Social Science and Bachelor of Art. The survey responses were tabulated in a 5-point Likert scale and analyzed using the Statistical Package for Social Science (SPSS Version 12.0 based on frequency and percentage distribution. The result of the study suggest that among three types of interaction, most of the student prefer academic interaction for their learning supports in e-learning portal compared to collaborative and social interaction. They wish to interact with learning content rather than interact with people. They prefer to read and learn from the resources rather than sharing knowledge among themselves and instructors via collaborative and social interaction.

  19. Deep learning versus traditional machine learning methods for aggregated energy demand prediction

    NARCIS (Netherlands)

    Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

    2018-01-01

    In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

  20. Preparing Students for Flipped or Team-Based Learning Methods

    Science.gov (United States)

    Balan, Peter; Clark, Michele; Restall, Gregory

    2015-01-01

    Purpose: Teaching methods such as Flipped Learning and Team-Based Learning require students to pre-learn course materials before a teaching session, because classroom exercises rely on students using self-gained knowledge. This is the reverse to "traditional" teaching when course materials are presented during a lecture, and students are…

  1. Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

    Directory of Open Access Journals (Sweden)

    Vladimir S. Kublanov

    2017-01-01

    Full Text Available The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.

  2. Realization of Chinese word segmentation based on deep learning method

    Science.gov (United States)

    Wang, Xuefei; Wang, Mingjiang; Zhang, Qiquan

    2017-08-01

    In recent years, with the rapid development of deep learning, it has been widely used in the field of natural language processing. In this paper, I use the method of deep learning to achieve Chinese word segmentation, with large-scale corpus, eliminating the need to construct additional manual characteristics. In the process of Chinese word segmentation, the first step is to deal with the corpus, use word2vec to get word embedding of the corpus, each character is 50. After the word is embedded, the word embedding feature is fed to the bidirectional LSTM, add a linear layer to the hidden layer of the output, and then add a CRF to get the model implemented in this paper. Experimental results show that the method used in the 2014 People's Daily corpus to achieve a satisfactory accuracy.

  3. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. A Swarm-Based Learning Method Inspired by Social Insects

    Science.gov (United States)

    He, Xiaoxian; Zhu, Yunlong; Hu, Kunyuan; Niu, Ben

    Inspired by cooperative transport behaviors of ants, on the basis of Q-learning, a new learning method, Neighbor-Information-Reference (NIR) learning method, is present in the paper. This is a swarm-based learning method, in which principles of swarm intelligence are strictly complied with. In NIR learning, the i-interval neighbor's information, namely its discounted reward, is referenced when an individual selects the next state, so that it can make the best decision in a computable local neighborhood. In application, different policies of NIR learning are recommended by controlling the parameters according to time-relativity of concrete tasks. NIR learning can remarkably improve individual efficiency, and make swarm more "intelligent".

  5. Arts-based Methods and Organizational Learning

    DEFF Research Database (Denmark)

    This thematic volume explores the relationship between the arts and learning in various educational contexts and across cultures, but with a focus on higher education and organizational learning. Arts-based interventions are at the heart of this volume, which addresses how they are conceived, des...

  6. Reasons and Methods to Learn the Management

    Science.gov (United States)

    Li, Hongxin; Ding, Mengchun

    2010-01-01

    Reasons for learning the management include (1) perfecting the knowledge structure, (2) the management is the base of all organizations, (3) one person may be the manager or the managed person, (4) the management is absolutely not simple knowledge, and (5) the learning of the theoretical knowledge of the management can not be replaced by the…

  7. Microgenetic Learning Analytics Methods: Workshop Report

    Science.gov (United States)

    Aghababyan, Ani; Martin, Taylor; Janisiewicz, Philip; Close, Kevin

    2016-01-01

    Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 2014. Specifically, this paper…

  8. Finding protein sites using machine learning methods

    Directory of Open Access Journals (Sweden)

    Jaime Leonardo Bobadilla Molina

    2003-07-01

    Full Text Available The increasing amount of protein three-dimensional (3D structures determined by x-ray and NMR technologies as well as structures predicted by computational methods results in the need for automated methods to provide inital annotations. We have developed a new method for recognizing sites in three-dimensional protein structures. Our method is based on a previosly reported algorithm for creating descriptions of protein microenviroments using physical and chemical properties at multiple levels of detail. The recognition method takes three inputs: 1. A set of control nonsites that share some structural or functional role. 2. A set of control nonsites that lack this role. 3. A single query site. A support vector machine classifier is built using feature vectors where each component represents a property in a given volume. Validation against an independent test set shows that this recognition approach has high sensitivity and specificity. We also describe the results of scanning four calcium binding proteins (with the calcium removed using a three dimensional grid of probe points at 1.25 angstrom spacing. The system finds the sites in the proteins giving points at or near the blinding sites. Our results show that property based descriptions along with support vector machines can be used for recognizing protein sites in unannotated structures.

  9. RULE-BASE METHOD FOR ANALYSIS OF QUALITY E-LEARNING IN HIGHER EDUCATION

    Directory of Open Access Journals (Sweden)

    darsih darsih darsih

    2016-04-01

    Full Text Available ABSTRACT Assessing the quality of e-learning courses to measure the success of e-learning systems in online learning is essential. The system can be used to improve education. The study analyzes the quality of e-learning course on the web site www.kulon.undip.ac.id used a questionnaire with questions based on the variables of ISO 9126. Penilaiann Likert scale was used with a web app. Rule-base reasoning method is used to subject the quality of e-learningyang assessed. A case study conducted in four e-learning courses with 133 sample / respondents as users of the e-learning course. From the obtained results of research conducted both for the value of e-learning from each subject tested. In addition, each e-learning courses have different advantages depending on certain variables. Keywords : E-Learning, Rule-Base, Questionnaire, Likert, Measuring.

  10. Women with learning disabilities and access to cervical screening: retrospective cohort study using case control methods

    Science.gov (United States)

    Reynolds, Fiona; Stanistreet, Debbi; Elton, Peter

    2008-01-01

    Background Several studies in the UK have suggested that women with learning disabilities may be less likely to receive cervical screening tests and a previous local study in had found that GPs considered screening unnecessary for women with learning disabilities. This study set out to ascertain whether women with learning disabilities are more likely to be ceased from a cervical screening programme than women without; and to examine the reasons given for ceasing women with learning disabilities. It was carried out in Bury, Heywood-and-Middleton and Rochdale. Methods Carried out using retrospective cohort study methods, women with learning disabilities were identified by Read code; and their cervical screening records were compared with the Call-and-Recall records of women without learning disabilities in order to examine their screening histories. Analysis was carried out using case-control methods – 1:2 (women with learning disabilities: women without learning disabilities), calculating odds ratios. Results 267 women's records were compared with the records of 534 women without learning disabilities. Women with learning disabilities had an odds ratio (OR) of 0.48 (Confidence Interval (CI) 0.38 – 0.58; X2: 72.227; p.value learning disabilities. Conclusion The reasons given for ceasing and/or not screening suggest that merely being coded as having a learning disability is not the sole reason for these actions. There are training needs among smear takers regarding appropriate reasons not to screen and providing screening for women with learning disabilities. PMID:18218106

  11. A Doctoral Seminar in Qualitative Research Methods: Lessons Learned

    Directory of Open Access Journals (Sweden)

    Suzanne Franco

    2016-09-01

    Full Text Available New qualitative research methods continue to emerge in response to factors such as renewed interest in mixed methods, better understanding of the importance of a researcher’s philosophical stance, as well as the increased use of technology in data collection and analysis, to name a few. As a result, those facilitating research methods courses must revisit content and instructional strategies in order to prepare well-informed researchers. Approaches range from paradigm to pragmatic emphasis. This descriptive case study of a doctoral seminar for novice qualitative researchers describes the intricacies of the syllabus of a pragmatic approach in a constructivist/social constructionist learning environment. The purpose was to document the delivery and faculty/student interactions and reactions. Noteworthy were the contradictions and frustrations in the delivery as well as in student experiences. In the end, student input led to seminal learning experiences. The confirmation of the effectiveness of a constructivist/social constructivist learning environment is applicable to higher education pedagogy in general.

  12. Question presentation methods for paired-associate learning

    NARCIS (Netherlands)

    Engel, F.L.; Geerings, M.P.W.

    1988-01-01

    Four different methods of question presentation, in interactive computeraided learning of Dutch-English word pairs are evaluated experimentally. These methods are: 1) the 'open-question method', 2) the 'multiple-choice method', 3) the 'sequential method' and 4) the 'true/ false method'. When

  13. Efficient model learning methods for actor-critic control.

    Science.gov (United States)

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

    2012-06-01

    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

  14. Computer game-based and traditional learning method: a comparison regarding students’ knowledge retention

    Directory of Open Access Journals (Sweden)

    Rondon Silmara

    2013-02-01

    Full Text Available Abstract Background Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Methods Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method, short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Results Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. Conclusions The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention.

  15. The Implementation of Discovery Learning Method to Increase Learning Outcomes and Motivation of Student in Senior High School

    Directory of Open Access Journals (Sweden)

    Nanda Saridewi

    2017-11-01

    Full Text Available Based on data from the observation of high school students grade XI that daily low student test scores due to a lack of role of students in the learning process. This classroom action research aims to improve learning outcomes and student motivation through discovery learning method in colloidal material. This study uses the approach developed by Lewin consisting of planning, action, observation, and reflection. Data collection techniques used the questionnaires and ability tests end. Based on the research that results for students received a positive influence on learning by discovery learning model by increasing the average value of 74 students from the first cycle to 90.3 in the second cycle and increased student motivation in the form of two statements based competence (KD categories (sometimes on the first cycle and the first statement KD category in the second cycle. Thus the results of this study can be used to improve learning outcomes and student motivation

  16. Ensemble Machine Learning Methods and Applications

    CERN Document Server

    Ma, Yunqian

    2012-01-01

    It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...

  17. An online supervised learning method based on gradient descent for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Yang, Jing; Zhong, Shuiming

    2017-09-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Application of machine learning methods for traffic signs recognition

    Science.gov (United States)

    Filatov, D. V.; Ignatev, K. V.; Deviatkin, A. V.; Serykh, E. V.

    2018-02-01

    This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition; the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential image processing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks’ learning process.

  19. Adaptive e-learning methods and IMS Learning Design. An integrated approach

    NARCIS (Netherlands)

    Burgos, Daniel; Specht, Marcus

    2006-01-01

    Please, cite this publication as: Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies (pp.

  20. LMS learning algorithms: misconceptions and new results on converence.

    Science.gov (United States)

    Wang, Z Q; Manry, M T; Schiano, J L

    2000-01-01

    The Widrow-Hoff delta rule is one of the most popular rules used in training neural networks. It was originally proposed for the ADALINE, but has been successfully applied to a few nonlinear neural networks as well. Despite its popularity, there exist a few misconceptions on its convergence properties. In this paper we consider repetitive learning (i.e., a fixed set of samples are used for training) and provide an in-depth analysis in the least mean square (LMS) framework. Our main result is that contrary to common belief, the nonbatch Widrow-Hoff rule does not converge in general. It converges only to a limit cycle.

  1. An Innovative Teaching Method To Promote Active Learning: Team-Based Learning

    Science.gov (United States)

    Balasubramanian, R.

    2007-12-01

    Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.

  2. Different protein-protein interface patterns predicted by different machine learning methods.

    Science.gov (United States)

    Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

    2017-11-22

    Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

  3. A Review on Different Virtual Learning Methods in Pharmacy Education

    Directory of Open Access Journals (Sweden)

    Amin Noori

    2015-10-01

    Full Text Available Virtual learning is a type of electronic learning system based on the web. It models traditional in- person learning by providing virtual access to classes, tests, homework, feedbacks and etc. Students and teachers can interact through chat rooms or other virtual environments. Web 2.0 services are usually used for this method. Internet audio-visual tools, multimedia systems, a disco CD-ROMs, videotapes, animation, video conferencing, and interactive phones can all be used to deliver data to the students. E-learning can occur in or out of the classroom. It is time saving with lower costs compared to traditional methods. It can be self-paced, it is suitable for distance learning and it is flexible. It is a great learning style for continuing education and students can independently solve their problems but it has its disadvantages too. Thereby, blended learning (combination of conventional and virtual education is being used worldwide and has improved knowledge, skills and confidence of pharmacy students.The aim of this study is to review, discuss and introduce different methods of virtual learning for pharmacy students.Google scholar, Pubmed and Scupus databases were searched for topics related to virtual, electronic and blended learning and different styles like computer simulators, virtual practice environment technology, virtual mentor, virtual patient, 3D simulators, etc. are discussed in this article.Our review on different studies on these areas shows that the students are highly satisfied withvirtual and blended types of learning.

  4. A Comparison between the Effect of Cooperative Learning Teaching Method and Lecture Teaching Method on Students' Learning and Satisfaction Level

    Science.gov (United States)

    Mohammadjani, Farzad; Tonkaboni, Forouzan

    2015-01-01

    The aim of the present research is to investigate a comparison between the effect of cooperative learning teaching method and lecture teaching method on students' learning and satisfaction level. The research population consisted of all the fourth grade elementary school students of educational district 4 in Shiraz. The statistical population…

  5. Learning How to Write an Academic Text: The Effect of Instructional Method and Reflection on Text Quality

    Science.gov (United States)

    van der Loo, Janneke; Krahmer, Emiel; van Amelsvoort, Marije

    2016-01-01

    In this paper we present preliminary results on a study on the effect of instructional method (observational learning and learning by doing) and reflection (yes or no) on academic text quality and self-efficacy beliefs. 56 undergraduate students were assigned to either an observational learning or learning-by-doing condition, with or without…

  6. Unsupervised process monitoring and fault diagnosis with machine learning methods

    CERN Document Server

    Aldrich, Chris

    2013-01-01

    This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

  7. Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides

    Directory of Open Access Journals (Sweden)

    Stanislawski Jerzy

    2013-01-01

    Full Text Available Abstract Background Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. Results We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%. The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile to 0.5 CPU-hours (simplified 3D profile to seconds (machine learning. Conclusions We showed that the simplified profile generation method does not introduce an error with regard to the original method, while

  8. Application of machine learning methods in bioinformatics

    Science.gov (United States)

    Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

    2018-05-01

    Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

  9. Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

    Science.gov (United States)

    Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

    2017-12-04

    Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further

  10. IMPROVING LEARNING PROCESS AND STUDENT RESULTS LEARNING TO TUNE-UPMOTORCYCLE USING DEMONSTRATION METHODOF CLASS XI SMA N 1 PLAYEN YEAR STUDY2013/2014

    Directory of Open Access Journals (Sweden)

    Haryono Haryono

    2014-12-01

    Full Text Available This research is to improve the learning process and results in learning a tune-up motorcycle  using the demonstrationmethod of class XI SMA 1 Playen.              This research is a classroom action research (PTK, using the demonstration method.Subyek this study were students of class XI SMA Negeri 1 Playen.Theimplementationofthisstudyusing3cycles,there is a (planning, implementation (actuating, observation (observing, and reflection (reflecting. Collecting data in this study are observations of student learning process and student learning outcomes test data pre-test, postesI, II, III and documentation as a support to the two data. Further observation data based on the observation of student learning just learning the positive process of learning student and test data were analyzed for comparison. Indicators of success in this classroom action research that student learning increases towards positive along with the use of methods of demonstration, is to see an increase from the pre-cycle to end the first cycle, the first cycle to the second cycle and the secondcyclebycycle III.             From the results of this study concluded that the method could improve the demonstration of positive student learning, from the first cycle of 30%, 50% second cycle and third cycle of 80%. Learning is also more effective with students indicated more quickly adapt as a positive activity, especially in terms of increased student asked, noting the test and work on the problems. Demonstration method can improve the learning outcomes  students of class XI SMA 1 Playen as evidenced by an increase in the average yield final test first cycle of 64.09; second cycle of 77.82 and 78.86 for the third cycle. So it proved with the increasing positive student learning canalso improve student learning outcomes.

  11. Early results of experiments with responsive open learning environments

    OpenAIRE

    Friedrich, M.; Wolpers, M.; Shen, R.; Ullrich, C.; Klamma, R.; Renzel, D.; Richert, A.; Heiden, B. von der

    2011-01-01

    Responsive open learning environments (ROLEs) are the next generation of personal learning environments (PLEs). While PLEs rely on the simple aggregation of existing content and services mainly using Web 2.0 technologies, ROLEs are transforming lifelong learning by introducing a new infrastructure on a global scale while dealing with existing learning management systems, institutions, and technologies. The requirements engineering process in highly populated test-beds is as important as the t...

  12. Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

    Directory of Open Access Journals (Sweden)

    Mingjie Tan

    2015-02-01

    Full Text Available The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN, Decision Tree (DT and Bayesian Networks (BNs. A large sample of 62375 students was utilized in the procedures of model training and testing. The results of each model were presented in confusion matrix, and analyzed by calculating the rates of accuracy, precision, recall, and F-measure. The results suggested all of the three machine learning methods were effective in student dropout prediction, and DT presented a better performance. Finally, some suggestions were made for considerable future research.

  13. An Efficient Ensemble Learning Method for Gene Microarray Classification

    Directory of Open Access Journals (Sweden)

    Alireza Osareh

    2013-01-01

    Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  14. A novel method for assessing elbow pain resulting from epicondylitis

    Science.gov (United States)

    Polkinghorn, Bradley S.

    2002-01-01

    Abstract Objective To describe a novel orthopedic test (Polk's test) which can assist the clinician in differentiating between me- dial and lateral epicondylitis, 2 of the most common causes of elbow pain. This test has not been previously described in the literature. Clinical Features The testing procedure described in this paper is easy to learn, simple to perform and may provide the clinician with a quick and effective method of differentiating between lateral and medial epicondylitis. The test also helps to elucidate normal activities of daily living that the patient may unknowingly be performing on a repetitive basis that are hindering recovery. The results of this simple test allow the clinician to make immediate lifestyle recommendations to the patient that should improve and hasten the response to subsequent treatment. It may be used in conjunction with other orthopedic testing procedures, as it correlates well with other clinical tests for assessing epicondylitis. Conclusion The use of Polk's Test may help the clinician to diagnostically differentiate between lateral and medial epicondylitis, as well as supply information relative to choosing proper instructions for the patient to follow as part of their treatment program. Further research, performed in an academic setting, should prove helpful in more thoroughly evaluating the merits of this test. In the meantime, clinical experience over the years suggests that the practicing physician should find a great deal of clinical utility in utilizing this simple, yet effective, diagnostic procedure. PMID:19674572

  15. Non-linguistic learning in aphasia: Effects of training method and stimulus characteristics

    Science.gov (United States)

    Vallila-Rohter, Sofia; Kiran, Swathi

    2013-01-01

    Purpose The purpose of the current study was to explore non-linguistic learning ability in patients with aphasia, examining the impact of stimulus typicality and feedback on success with learning. Method Eighteen patients with aphasia and eight healthy controls participated in this study. All participants completed four computerized, non-linguistic category-learning tasks. We probed learning ability under two methods of instruction: feedback-based (FB) and paired-associate (PA). We also examined the impact of task complexity on learning ability, comparing two stimulus conditions: typical (Typ) and atypical (Atyp). Performance was compared between groups and across conditions. Results Results demonstrated that healthy controls were able to successfully learn categories under all conditions. For our patients with aphasia, two patterns of performance arose. One subgroup of patients was able to maintain learning across task manipulations and conditions. The other subgroup of patients demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions. Conclusions Results support the hypothesis that impairments of general learning are present in aphasia. Some patients demonstrated the ability to extract category information under complex training conditions, while others learned only under conditions that were simplified and emphasized salient category features. Overall, the typical training condition facilitated learning for all participants. Findings have implications for therapy, which are discussed. PMID:23695914

  16. Understanding the Causal Path between Action, Learning, and Solutions: Maximizing the Power of Action Learning to Achieve Great Results

    Science.gov (United States)

    Leonard, H. Skipton

    2015-01-01

    Clients and practitioners alike are often confused about the ultimate purpose of action learning (AL). Because of the title of the method, many believe the primary goal of AL is to generate learning. This article clarifies the relationship between action, learning, and solutions. It also provides historical evidence to support the conclusion that…

  17. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    Energy Technology Data Exchange (ETDEWEB)

    Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

    2012-03-15

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  18. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    International Nuclear Information System (INIS)

    Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van’t

    2012-01-01

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  19. An Analytical framework of social learning facilitated by participatory methods

    NARCIS (Netherlands)

    Scholz, G.; Dewulf, A.; Pahl-Wostl, C.

    2014-01-01

    Social learning among different stakeholders is often a goal in problem solving contexts such as environmental management. Participatory methods (e.g., group model-building and role playing games) are frequently assumed to stimulate social learning. Yet understanding if and why this assumption is

  20. A reward optimization method based on action subrewards in hierarchical reinforcement learning.

    Science.gov (United States)

    Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming

    2014-01-01

    Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.

  1. Blended learning – integrating E-learning with traditional learning methods in teaching basic medical science

    OpenAIRE

    J.G. Bagi; N.K. Hashilkar

    2014-01-01

    Background: Blended learning includes an integration of face to face classroom learning with technology enhanced online material. It provides the convenience, speed and cost effectiveness of e-learning with the personal touch of traditional learning. Objective: The objective of the present study was to assess the effectiveness of a combination of e-learning module and traditional teaching (Blended learning) as compared to traditional teaching alone to teach acid base homeostasis to Phase I MB...

  2. Learning Methods for Radial Basis Functions Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Kudová, Petra

    2005-01-01

    Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005

  3. Machine Learning Methods to Predict Diabetes Complications.

    Science.gov (United States)

    Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

    2018-03-01

    One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

  4. MACHINE LEARNING METHODS IN DIGITAL AGRICULTURE: ALGORITHMS AND CASES

    Directory of Open Access Journals (Sweden)

    Aleksandr Vasilyevich Koshkarov

    2018-05-01

    Full Text Available Ensuring food security is a major challenge in many countries. With a growing global population, the issues of improving the efficiency of agriculture have become most relevant. Farmers are looking for new ways to increase yields, and governments of different countries are developing new programs to support agriculture. This contributes to a more active implementation of digital technologies in agriculture, helping farmers to make better decisions, increase yields and take care of the environment. The central point is the collection and analysis of data. In the industry of agriculture, data can be collected from different sources and may contain useful patterns that identify potential problems or opportunities. Data should be analyzed using machine learning algorithms to extract useful insights. Such methods of precision farming allow the farmer to monitor individual parts of the field, optimize the consumption of water and chemicals, and identify problems quickly. Purpose: to make an overview of the machine learning algorithms used for data analysis in agriculture. Methodology: an overview of the relevant literature; a survey of farmers. Results: relevant algorithms of machine learning for the analysis of data in agriculture at various levels were identified: soil analysis (soil assessment, soil classification, soil fertility predictions, weather forecast (simulation of climate change, temperature and precipitation prediction, and analysis of vegetation (weed identification, vegetation classification, plant disease identification, crop forecasting. Practical implications: agriculture, crop production.

  5. Simultaneous anatomical sketching as learning by doing method of teaching human anatomy.

    Science.gov (United States)

    Noorafshan, Ali; Hoseini, Leila; Amini, Mitra; Dehghani, Mohammad-Reza; Kojuri, Javad; Bazrafkan, Leila

    2014-01-01

    Learning by lecture is a passive experience. Many innovative techniques have been presented to stimulate students to assume a more active attitude toward learning. In this study, simultaneous sketch drawing, as an interactive learning technique was applied to teach anatomy to the medical students. We reconstructed a fun interactive model of teaching anatomy as simultaneous anatomic sketching. To test the model's instruction effectiveness, we conducted a quasi- experimental study and then the students were asked to write their learning experiences in their portfolio, also their view was evaluated by a questionnaire. The results of portfolio evaluation revealed that students believed that this method leads to deep learning and understanding anatomical subjects better. Evaluation of the students' views on this teaching approach was showed that, more than 80% of the students were agreed or completely agreed with this statement that leaning anatomy concepts are easier and the class is less boring with this method. More than 60% of the students were agreed or completely agreed to sketch anatomical figures with professor simultaneously. They also found the sketching make anatomy more attractive and it reduced the time for learning anatomy. These number of students were agree or completely agree that the method help them learning anatomical concept in anatomy laboratory. More than 80% of the students found the simultaneous sketching is a good method for learning anatomy overall. Sketch drawing, as an interactive learning technique, is an attractive for students to learn anatomy.

  6. Estimating building energy consumption using extreme learning machine method

    International Nuclear Information System (INIS)

    Naji, Sareh; Keivani, Afram; Shamshirband, Shahaboddin; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Mansor, Zulkefli; Lee, Malrey

    2016-01-01

    The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN. - Highlights: • Buildings consume huge amounts of energy for operation. • Envelope materials and insulation influence building energy consumption. • Extreme learning machine is used to estimate energy usage of a sample building. • The key effective factors in this study are insulation thickness and K-value.

  7. Application of blended learning in teaching statistical methods

    Directory of Open Access Journals (Sweden)

    Barbara Dębska

    2012-12-01

    Full Text Available The paper presents the application of a hybrid method (blended learning - linking traditional education with on-line education to teach selected problems of mathematical statistics. This includes the teaching of the application of mathematical statistics to evaluate laboratory experimental results. An on-line statistics course was developed to form an integral part of the module ‘methods of statistical evaluation of experimental results’. The course complies with the principles outlined in the Polish National Framework of Qualifications with respect to the scope of knowledge, skills and competencies that students should have acquired at course completion. The paper presents the structure of the course and the educational content provided through multimedia lessons made accessible on the Moodle platform. Following courses which used the traditional method of teaching and courses which used the hybrid method of teaching, students test results were compared and discussed to evaluate the effectiveness of the hybrid method of teaching when compared to the effectiveness of the traditional method of teaching.

  8. Interest in Currency Trading Learning – Preferred Methods and Motivational Factors

    Directory of Open Access Journals (Sweden)

    Pintar Rok

    2016-02-01

    Full Text Available Background and purpose: This paper analyzes the interest of potential users for learning in the field of currency trading or foreign exchange (forex, FX. The purpose of our article is a to present currency trading, b to present different options, methods and learning approaches to educating in forex, c to present the research results discovering the interest of potential users for learning in the field of currency trading.

  9. Comparative Analysis of Kernel Methods for Statistical Shape Learning

    National Research Council Canada - National Science Library

    Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen

    2006-01-01

    .... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...

  10. Implementing Adaptive Educational Methods with IMS Learning Design

    NARCIS (Netherlands)

    Specht, Marcus; Burgos, Daniel

    2006-01-01

    Please, cite this publication as: Specht, M. & Burgos, D. (2006). Implementing Adaptive Educational Methods with IMS Learning Design. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

  11. The review and results of different methods for facial recognition

    Science.gov (United States)

    Le, Yifan

    2017-09-01

    In recent years, facial recognition draws much attention due to its wide potential applications. As a unique technology in Biometric Identification, facial recognition represents a significant improvement since it could be operated without cooperation of people under detection. Hence, facial recognition will be taken into defense system, medical detection, human behavior understanding, etc. Several theories and methods have been established to make progress in facial recognition: (1) A novel two-stage facial landmark localization method is proposed which has more accurate facial localization effect under specific database; (2) A statistical face frontalization method is proposed which outperforms state-of-the-art methods for face landmark localization; (3) It proposes a general facial landmark detection algorithm to handle images with severe occlusion and images with large head poses; (4) There are three methods proposed on Face Alignment including shape augmented regression method, pose-indexed based multi-view method and a learning based method via regressing local binary features. The aim of this paper is to analyze previous work of different aspects in facial recognition, focusing on concrete method and performance under various databases. In addition, some improvement measures and suggestions in potential applications will be put forward.

  12. Implementation of Active Learning Method in Unit Operations II Subject

    OpenAIRE

    Ma'mun, Sholeh

    2018-01-01

    ABSTRACT: Active Learning Method which requires students to take an active role in the process of learning in the classroom has been applied in Department of Chemical Engineering, Faculty of Industrial Technology, Islamic University of Indonesia for Unit Operations II subject in the Even Semester of Academic Year 2015/2016. The purpose of implementation of the learning method is to assist students in achieving competencies associated with the Unit Operations II subject and to help in creating...

  13. Studying depression using imaging and machine learning methods

    Directory of Open Access Journals (Sweden)

    Meenal J. Patel

    2016-01-01

    Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.

  14. Studying depression using imaging and machine learning methods.

    Science.gov (United States)

    Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

    2016-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

  15. Deep Learning Methods for Improved Decoding of Linear Codes

    Science.gov (United States)

    Nachmani, Eliya; Marciano, Elad; Lugosch, Loren; Gross, Warren J.; Burshtein, David; Be'ery, Yair

    2018-02-01

    The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.

  16. Statistical learning modeling method for space debris photometric measurement

    Science.gov (United States)

    Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen

    2016-03-01

    Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

  17. A Learning-Based Steganalytic Method against LSB Matching Steganography

    Directory of Open Access Journals (Sweden)

    Z. Xia

    2011-04-01

    Full Text Available This paper considers the detection of spatial domain least significant bit (LSB matching steganography in gray images. Natural images hold some inherent properties, such as histogram, dependence between neighboring pixels, and dependence among pixels that are not adjacent to each other. These properties are likely to be disturbed by LSB matching. Firstly, histogram will become smoother after LSB matching. Secondly, the two kinds of dependence will be weakened by the message embedding. Accordingly, three features, which are respectively based on image histogram, neighborhood degree histogram and run-length histogram, are extracted at first. Then, support vector machine is utilized to learn and discriminate the difference of features between cover and stego images. Experimental results prove that the proposed method possesses reliable detection ability and outperforms the two previous state-of-the-art methods. Further more, the conclusions are drawn by analyzing the individual performance of three features and their fused feature.

  18. Research into Learning Resulting from Quality School Library Media Service.

    Science.gov (United States)

    Marchant, Maurice P.; And Others

    1984-01-01

    This annotated bibliography of 20 research reports identifies what has been determined about the effects of library media services on learning and suggests methodologies available for similar studies. Organization is according to area of learning affected--academic achievement; language, reading, and library skills; mathematics; science; social…

  19. Methods of Efficient Study Habits and Physics Learning

    Science.gov (United States)

    Zettili, Nouredine

    2010-02-01

    We want to discuss the methods of efficient study habits and how they can be used by students to help them improve learning physics. In particular, we deal with the most efficient techniques needed to help students improve their study skills. We focus on topics such as the skills of how to develop long term memory, how to improve concentration power, how to take class notes, how to prepare for and take exams, how to study scientific subjects such as physics. We argue that the students who conscientiously use the methods of efficient study habits achieve higher results than those students who do not; moreover, a student equipped with the proper study skills will spend much less time to learn a subject than a student who has no good study habits. The underlying issue here is not the quantity of time allocated to the study efforts by the students, but the efficiency and quality of actions so that the student can function at peak efficiency. These ideas were developed as part of Project IMPACTSEED (IMproving Physics And Chemistry Teaching in SEcondary Education), an outreach grant funded by the Alabama Commission on Higher Education. This project is motivated by a major pressing local need: A large number of high school physics teachers teach out of field. )

  20. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun

    2013-10-01

    When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

  1. The Effect of Family and School Cultural Environment Through Self Efficacy on Student Learning Result

    Directory of Open Access Journals (Sweden)

    Ely Rizky Amaliyah

    2017-11-01

    Full Text Available This study aims to explain the relationship between environmental variables out g a, school culture, self-efficacy and student learning outcomes Administrative Program Program at SMK. This research includes quantitative research type with the explanatory descriptive method. The sampling technique was proportionate stratified random sampling, the study sample consisted of 114 students. Data analysis in this research using path analysis. Results research shows that there is a positive and significant influence of family environment on self-efficacy, there is the positive and significant influence of school culture on self-efficacy, there is a direct positive and significant influence between the environment to the family on the results of learning. While the school culture The air does not directly influence the learning outcomes, but the air of self-efficacy ng driving direct effect on learning outcomes, and the family environment is not aired directly influence the outcome through self-efficacy jar arts students, and school culture has an indirect effect on learning outcomes through students' self-efficacy.

  2. The experimental field work as practical learning method

    Directory of Open Access Journals (Sweden)

    Nicolás Fernández Losa

    2014-11-01

    Full Text Available This paper describes a teaching experience about experimental field work as practical learning method implemented in the subject of Organizational Behaviour. With this teaching experience we pretend to change the practical training, as well as in its evaluation process, in order to favour the development of transversal skills of students. For this purpose, the use of a practice plan, tackled through an experimental field work and carried out with the collaboration of a business organization within a work team (as organic unity of learning, arises as an alternative to the traditional method of practical teachings and allows the approach of business reality into the classroom, as well as actively promote the use of transversal skills. In particular, we develop the experience in three phases. Initially, the students, after forming a working group and define a field work project, should get the collaboration of a nearby business organization in which to obtain data on one or more functional areas of organizational behaviour. Subsequently, students carry out the field work with the realization of the scheduled visits and elaboration of a memory to establish a diagnosis of the strategy followed by the company in these functional areas in order to propose and justify alternative actions that improve existing ones. Finally, teachers assess the different field work memories and their public presentations according to evaluation rubrics, which try to objectify and unify to the maximum the evaluation criteria and serve to guide the learning process of students. The results of implementation of this teaching experience, measured through a Likert questionnaire, are very satisfactory for students.

  3. Active teaching methods, studying responses and learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching....

  4. Inductive learning of thyroid functional states using the ID3 algorithm. The effect of poor examples on the learning result.

    Science.gov (United States)

    Forsström, J

    1992-01-01

    The ID3 algorithm for inductive learning was tested using preclassified material for patients suspected to have a thyroid illness. Classification followed a rule-based expert system for the diagnosis of thyroid function. Thus, the knowledge to be learned was limited to the rules existing in the knowledge base of that expert system. The learning capability of the ID3 algorithm was tested with an unselected learning material (with some inherent missing data) and with a selected learning material (no missing data). The selected learning material was a subgroup which formed a part of the unselected learning material. When the number of learning cases was increased, the accuracy of the program improved. When the learning material was large enough, an increase in the learning material did not improve the results further. A better learning result was achieved with the selected learning material not including missing data as compared to unselected learning material. With this material we demonstrate a weakness in the ID3 algorithm: it can not find available information from good example cases if we add poor examples to the data.

  5. Improve Business Results by Learning from Experience in Proactive Reviews

    DEFF Research Database (Denmark)

    Kolbæk, Ditte

    2018-01-01

    This article aims to provide a theoretically based and proven educational design for lessons learned. Called a Proactive Review, this educational design is exemplified in a case study of a global information technology company classified as big business, where Proactive Reviews were developed...... and implemented in over 40 countries. This article explores how employees who solve a task together can learn from the experience and share this learning with relevant colleagues to improve work practices, services, and/or products. This article describes the format of Proactive Reviews, suggestions for starting...

  6. Developing a Blended Learning-Based Method for Problem-Solving in Capability Learning

    Science.gov (United States)

    Dwiyogo, Wasis D.

    2018-01-01

    The main objectives of the study were to develop and investigate the implementation of blended learning based method for problem-solving. Three experts were involved in the study and all three had stated that the model was ready to be applied in the classroom. The implementation of the blended learning-based design for problem-solving was…

  7. Statistical and Machine Learning forecasting methods: Concerns and ways forward.

    Science.gov (United States)

    Makridakis, Spyros; Spiliotis, Evangelos; Assimakopoulos, Vassilios

    2018-01-01

    Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.

  8. Statistical and Machine Learning forecasting methods: Concerns and ways forward

    Science.gov (United States)

    Makridakis, Spyros; Assimakopoulos, Vassilios

    2018-01-01

    Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. PMID:29584784

  9. From field results to organizational improvement: Learning from ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2016-06-03

    Jun 3, 2016 ... ... in their staff and recruiting individuals with new skills sets. ... of their partners, to become more strategic at supporting positive social change. ... existing culture of learning of Canadian civil society engaged in international ...

  10. A Simple Deep Learning Method for Neuronal Spike Sorting

    Science.gov (United States)

    Yang, Kai; Wu, Haifeng; Zeng, Yu

    2017-10-01

    Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

  11. Deep learning methods for protein torsion angle prediction.

    Science.gov (United States)

    Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

    2017-09-18

    Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

  12. Exploring an experiential learning project through Kolb's Learning Theory using a qualitative research method

    Science.gov (United States)

    Yuk Chan, Cecilia Ka

    2012-08-01

    Experiential learning pedagogy is taking a lead in the development of graduate attributes and educational aims as these are of prime importance for society. This paper shows a community service experiential project conducted in China. The project enabled students to serve the affected community in a post-earthquake area by applying their knowledge and skills. This paper documented the students' learning process from their project goals, pre-trip preparations, work progress, obstacles encountered to the final results and reflections. Using the data gathered from a focus group interview approach, the four components of Kolb's learning cycle, the concrete experience, reflection observation, abstract conceptualisation and active experimentation, have been shown to transform and internalise student's learning experience, achieving a variety of learning outcomes. The author will also explore how this community service type of experiential learning in the engineering discipline allowed students to experience deep learning and develop their graduate attributes.

  13. In silico machine learning methods in drug development.

    Science.gov (United States)

    Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati

    2014-01-01

    Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.

  14. Science Learning Cycle Method to Enhance the Conceptual Understanding and the Learning Independence on Physics Learning

    Science.gov (United States)

    Sulisworo, Dwi; Sutadi, Novitasari

    2017-01-01

    There have been many studies related to the implementation of cooperative learning. However, there are still many problems in school related to the learning outcomes on science lesson, especially in physics. The aim of this study is to observe the application of science learning cycle (SLC) model on improving scientific literacy for secondary…

  15. A deep learning-based multi-model ensemble method for cancer prediction.

    Science.gov (United States)

    Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

    2018-01-01

    Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  17. Experts in Teams – An experiential learning method

    DEFF Research Database (Denmark)

    Johansen, Steffen Kjær

    2017-01-01

    T becomes a learning method rather than a teaching method. Besides discussing the pedagogical characteristics of EiT, the study also gives a general introduction to EiT as it was taught at SDU fall 2016 as well as a brief review of the basic theory behind experiential learning. As such this study serves...... courses. Most of the practical courses are group work along the lines of project based learning. EiT is in a way both. It is a practical course in as much as our students get hands-on experience with interdisciplinary team work and innovation processes. EiT is a theoretical course in as much as our...... both as an introduction to e.g. new teachers of EiT but also as a starting point for a clarification of the features that makes EiT an experiential learning endeavor....

  18. Research on demand-oriented Business English learning method

    Directory of Open Access Journals (Sweden)

    Zhou Yuan

    2016-01-01

    Full Text Available Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher vocational students to learn Business English Visual-audio-oral Course, master Business English knowledge, and improve communicative competence of Business English.

  19. Research on demand-oriented Business English learning method

    OpenAIRE

    Zhou Yuan

    2016-01-01

    Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher voca...

  20. Future Competencies and Learning Methods in Engineering Education

    DEFF Research Database (Denmark)

    Kolmos, Anette

    2002-01-01

    What are the competencies for tommorow´s enginnering education and the implications of these regarding the choice of teaching content and learning methods? The paper analyses two trends: the traditional and the techo-science approach. These two trends are based on technological innovation...... and change processes and impact on educational content and methods....

  1. Empowering and Engaging Students in Learning Research Methods

    Science.gov (United States)

    Liu, Shuang; Breit, Rhonda

    2013-01-01

    The capacity to conduct research is essential for university graduates to survive and thrive in their future career. However, research methods courses have often been considered by students as "abstract", "uninteresting", and "hard". Thus, motivating students to engage in the process of learning research methods has become a crucial challenge for…

  2. Geocoding location expressions in Twitter messages: A preference learning method

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2014-12-01

    Full Text Available Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors.

  3. Employing Machine-Learning Methods to Study Young Stellar Objects

    Science.gov (United States)

    Moore, Nicholas

    2018-01-01

    Vast amounts of data exist in the astronomical data archives, and yet a large number of sources remain unclassified. We developed a multi-wavelength pipeline to classify infrared sources. The pipeline uses supervised machine learning methods to classify objects into the appropriate categories. The program is fed data that is already classified to train it, and is then applied to unknown catalogues. The primary use for such a pipeline is the rapid classification and cataloging of data that would take a much longer time to classify otherwise. While our primary goal is to study young stellar objects (YSOs), the applications extend beyond the scope of this project. We present preliminary results from our analysis and discuss future applications.

  4. Radiochemical methods. Analytical chemistry by open learning

    Energy Technology Data Exchange (ETDEWEB)

    Geary, W.J.; James, A.M. (ed.)

    1986-01-01

    This book presents the analytical uses of radioactive isotopes within the context of radiochemistry as a whole. It is designed for scientists with relatively little background knowledge of the subject. Thus the initial emphasis is on developing the basic concepts of radioactive decay, particularly as they affect the potential usage of radioisotopes. Discussion of the properties of various types of radiation, and of factors such as half-life, is related to practical considerations such as counting and preparation methods, and handling/disposal problems. Practical aspects are then considered in more detail, and the various radioanalytical methods are outlined with particular reference to their applicability. The approach is 'user friendly' and the use of self assessment questions allows the reader to test his/her understanding of individual sections easily. For those who wish to develop their knowledge further, a reading list is provided.

  5. Housing Value Forecasting Based on Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Jingyi Mu

    2014-01-01

    Full Text Available In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM, least squares support vector machine (LSSVM, and partial least squares (PLS methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.

  6. Uncertain Photometric Redshifts with Deep Learning Methods

    Science.gov (United States)

    D'Isanto, A.

    2017-06-01

    The need for accurate photometric redshifts estimation is a topic that has fundamental importance in Astronomy, due to the necessity of efficiently obtaining redshift information without the need of spectroscopic analysis. We propose a method for determining accurate multi-modal photo-z probability density functions (PDFs) using Mixture Density Networks (MDN) and Deep Convolutional Networks (DCN). A comparison with a Random Forest (RF) is performed.

  7. Monte Carlo methods for preference learning

    DEFF Research Database (Denmark)

    Viappiani, P.

    2012-01-01

    Utility elicitation is an important component of many applications, such as decision support systems and recommender systems. Such systems query the users about their preferences and give recommendations based on the system’s belief about the utility function. Critical to these applications is th...... is the acquisition of prior distribution about the utility parameters and the possibility of real time Bayesian inference. In this paper we consider Monte Carlo methods for these problems....

  8. Machine Learning-Empowered Biometric Methods for Biomedicine Applications

    Directory of Open Access Journals (Sweden)

    Qingxue Zhang

    2017-07-01

    Full Text Available Nowadays, pervasive computing technologies are paving a promising way for advanced smart health applications. However, a key impediment faced by wide deployment of these assistive smart devices, is the increasing privacy and security issue, such as how to protect access to sensitive patient data in the health record. Focusing on this challenge, biometrics are attracting intense attention in terms of effective user identification to enable confidential health applications. In this paper, we take special interest in two bio-potential-based biometric modalities, electrocardiogram (ECG and electroencephalogram (EEG, considering that they are both unique to individuals, and more reliable than token (identity card and knowledge-based (username/password methods. After extracting effective features in multiple domains from ECG/EEG signals, several advanced machine learning algorithms are introduced to perform the user identification task, including Neural Network, K-nearest Neighbor, Bagging, Random Forest and AdaBoost. Experimental results on two public ECG and EEG datasets show that ECG is a more robust biometric modality compared to EEG, leveraging a higher signal to noise ratio and also more distinguishable morphological patterns. Among different machine learning classifiers, the random forest greatly outperforms the others and owns an identification rate as high as 98%. This study is expected to demonstrate that properly selected biometric empowered by an effective machine learner owns a great potential, to enable confidential biomedicine applications in the era of smart digital health.

  9. Choosing Learning Methods Suitable for Teaching and Learning in Computer Science

    Science.gov (United States)

    Taylor, Estelle; Breed, Marnus; Hauman, Ilette; Homann, Armando

    2013-01-01

    Our aim is to determine which teaching methods students in Computer Science and Information Systems prefer. There are in total 5 different paradigms (behaviorism, cognitivism, constructivism, design-based and humanism) with 32 models between them. Each model is unique and states different learning methods. Recommendations are made on methods that…

  10. Teaching Theory in Occupational Therapy Using a Cooperative Learning: A Mixed-Methods Study.

    Science.gov (United States)

    Howe, Tsu-Hsin; Sheu, Ching-Fan; Hinojosa, Jim

    2018-01-01

    Cooperative learning provides an important vehicle for active learning, as knowledge is socially constructed through interaction with others. This study investigated the effect of cooperative learning on occupational therapy (OT) theory knowledge attainment in professional-level OT students in a classroom environment. Using a pre- and post-test group design, 24 first-year, entry-level OT students participated while taking a theory course in their second semester of the program. Cooperative learning methods were implemented via in-class group assignments. The students were asked to complete two questionnaires regarding their attitudes toward group environments and their perception toward group learning before and after the semester. MANCOVA was used to examine changes in attitudes and perceived learning among groups. Students' summary sheets for each in-class assignment and course evaluations were collected for content analysis. Results indicated significant changes in students' attitude toward working in small groups regardless of their prior group experience.

  11. "Learn Young, Learn Fair", a Stress Management Program for Fifth and Sixth Graders: Longitudinal Results from an Experimental Study

    Science.gov (United States)

    Kraag, Gerda; Van Breukelen, Gerard J. P.; Kok, Gerjo; Hosman, Clemens

    2009-01-01

    Background: This study examined the effects of a universal stress management program (Learn Young, Learn Fair) on stress, coping, anxiety and depression in fifth and sixth grade children. Methods: Fifty-two schools (1467 children) participated in a clustered randomized controlled trial. Data was collected in the fall of 2002, the spring of 2003,…

  12. Implementation of Simulation Based-Concept Attainment Method to Increase Interest Learning of Engineering Mechanics Topic

    Science.gov (United States)

    Sultan, A. Z.; Hamzah, N.; Rusdi, M.

    2018-01-01

    The implementation of concept attainment method based on simulation was used to increase student’s interest in the subjects Engineering of Mechanics in second semester of academic year 2016/2017 in Manufacturing Engineering Program, Department of Mechanical PNUP. The result of the implementation of this learning method shows that there is an increase in the students’ learning interest towards the lecture material which is summarized in the form of interactive simulation CDs and teaching materials in the form of printed books and electronic books. From the implementation of achievement method of this simulation based concept, it is noted that the increase of student participation in the presentation and discussion as well as the deposit of individual assignment of significant student. With the implementation of this method of learning the average student participation reached 89%, which before the application of this learning method only reaches an average of 76%. And also with previous learning method, for exam achievement of A-grade under 5% and D-grade above 8%. After the implementation of the new learning method (simulation based-concept attainment method) the achievement of Agrade has reached more than 30% and D-grade below 1%.

  13. Women with learning disabilities and access to cervical screening: retrospective cohort study using case control methods

    Directory of Open Access Journals (Sweden)

    Stanistreet Debbi

    2008-01-01

    Full Text Available Abstract Background Several studies in the UK have suggested that women with learning disabilities may be less likely to receive cervical screening tests and a previous local study in had found that GPs considered screening unnecessary for women with learning disabilities. This study set out to ascertain whether women with learning disabilities are more likely to be ceased from a cervical screening programme than women without; and to examine the reasons given for ceasing women with learning disabilities. It was carried out in Bury, Heywood-and-Middleton and Rochdale. Methods Carried out using retrospective cohort study methods, women with learning disabilities were identified by Read code; and their cervical screening records were compared with the Call-and-Recall records of women without learning disabilities in order to examine their screening histories. Analysis was carried out using case-control methods – 1:2 (women with learning disabilities: women without learning disabilities, calculating odds ratios. Results 267 women's records were compared with the records of 534 women without learning disabilities. Women with learning disabilities had an odds ratio (OR of 0.48 (Confidence Interval (CI 0.38 – 0.58; X2: 72.227; p.value X2: 24.236; p.value X2: 286.341; p.value Conclusion The reasons given for ceasing and/or not screening suggest that merely being coded as having a learning disability is not the sole reason for these actions. There are training needs among smear takers regarding appropriate reasons not to screen and providing screening for women with learning disabilities.

  14. Learn Better by Doing Study: Fourth-Year Results

    Science.gov (United States)

    Moye, Johnny J.; Dugger, William E., Jr.; Starkweather, Kendall N.

    2017-01-01

    The purpose of the "Learn Better by Doing Study" was to determine the extent to which U.S. public elementary, middle, and high school students were doing hands-on activities in their science, technology, engineering, and mathematics (STEM) classrooms. The International Technology and Engineering Educators Association's (ITEEA's)…

  15. Machine Learning Methods for Attack Detection in the Smart Grid.

    Science.gov (United States)

    Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

    2016-08-01

    Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

  16. A Comparative Analysis of Reinforcement Learning Methods

    Science.gov (United States)

    1991-10-01

    Technology. Support for this research was provided in part by the Mazda Corporation, in part by the University Research Initiative under Office of Naval...results in an update rule (e.g. [Goldberg 89]Goldberg85), genetic algorithms which disregards all history accumulated in the current will not be addressed

  17. An improved machine learning protocol for the identification of correct Sequest search results

    Directory of Open Access Journals (Sweden)

    Lu Hui

    2010-12-01

    Full Text Available Abstract Background Mass spectrometry has become a standard method by which the proteomic profile of cell or tissue samples is characterized. To fully take advantage of tandem mass spectrometry (MS/MS techniques in large scale protein characterization studies robust and consistent data analysis procedures are crucial. In this work we present a machine learning based protocol for the identification of correct peptide-spectrum matches from Sequest database search results, improving on previously published protocols. Results The developed model improves on published machine learning classification procedures by 6% as measured by the area under the ROC curve. Further, we show how the developed model can be presented as an interpretable tree of additive rules, thereby effectively removing the 'black-box' notion often associated with machine learning classifiers, allowing for comparison with expert rule-of-thumb. Finally, a method for extending the developed peptide identification protocol to give probabilistic estimates of the presence of a given protein is proposed and tested. Conclusions We demonstrate the construction of a high accuracy classification model for Sequest search results from MS/MS spectra obtained by using the MALDI ionization. The developed model performs well in identifying correct peptide-spectrum matches and is easily extendable to the protein identification problem. The relative ease with which additional experimental parameters can be incorporated into the classification framework, to give additional discriminatory power, allows for future tailoring of the model to take advantage of information from specific instrument set-ups.

  18. The estimation of the measurement results with using statistical methods

    International Nuclear Information System (INIS)

    Ukrmetrteststandard, 4, Metrologichna Str., 03680, Kyiv (Ukraine))" data-affiliation=" (State Enterprise Ukrmetrteststandard, 4, Metrologichna Str., 03680, Kyiv (Ukraine))" >Velychko, O; UkrNDIspirtbioprod, 3, Babushkina Lane, 03190, Kyiv (Ukraine))" data-affiliation=" (State Scientific Institution UkrNDIspirtbioprod, 3, Babushkina Lane, 03190, Kyiv (Ukraine))" >Gordiyenko, T

    2015-01-01

    The row of international standards and guides describe various statistical methods that apply for a management, control and improvement of processes with the purpose of realization of analysis of the technical measurement results. The analysis of international standards and guides on statistical methods estimation of the measurement results recommendations for those applications in laboratories is described. For realization of analysis of standards and guides the cause-and-effect Ishikawa diagrams concerting to application of statistical methods for estimation of the measurement results are constructed

  19. The estimation of the measurement results with using statistical methods

    Science.gov (United States)

    Velychko, O.; Gordiyenko, T.

    2015-02-01

    The row of international standards and guides describe various statistical methods that apply for a management, control and improvement of processes with the purpose of realization of analysis of the technical measurement results. The analysis of international standards and guides on statistical methods estimation of the measurement results recommendations for those applications in laboratories is described. For realization of analysis of standards and guides the cause-and-effect Ishikawa diagrams concerting to application of statistical methods for estimation of the measurement results are constructed.

  20. Learning Unknown Structure in CRFs via Adaptive Gradient Projection Method

    Directory of Open Access Journals (Sweden)

    Wei Xue

    2016-08-01

    Full Text Available We study the problem of fitting probabilistic graphical models to the given data when the structure is not known. More specifically, we focus on learning unknown structure in conditional random fields, especially learning both the structure and parameters of a conditional random field model simultaneously. To do this, we first formulate the learning problem as a convex minimization problem by adding an l_2-regularization to the node parameters and a group l_1-regularization to the edge parameters, and then a gradient-based projection method is proposed to solve it which combines an adaptive stepsize selection strategy with a nonmonotone line search. Extensive simulation experiments are presented to show the performance of our approach in solving unknown structure learning problems.

  1. 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

  2. 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.

  3. Extremely Randomized Machine Learning Methods for Compound Activity Prediction

    Directory of Open Access Journals (Sweden)

    Wojciech M. Czarnecki

    2015-11-01

    Full Text Available Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  4. Robust Control Methods for On-Line Statistical Learning

    Directory of Open Access Journals (Sweden)

    Capobianco Enrico

    2001-01-01

    Full Text Available The issue of controlling that data processing in an experiment results not affected by the presence of outliers is relevant for statistical control and learning studies. Learning schemes should thus be tested for their capacity of handling outliers in the observed training set so to achieve reliable estimates with respect to the crucial bias and variance aspects. We describe possible ways of endowing neural networks with statistically robust properties by defining feasible error criteria. It is convenient to cast neural nets in state space representations and apply both Kalman filter and stochastic approximation procedures in order to suggest statistically robustified solutions for on-line learning.

  5. New Learning Methods for Marine Oil Spill Response Training

    Directory of Open Access Journals (Sweden)

    Justiina Halonen

    2017-06-01

    Full Text Available In Finland the Regional Fire and Rescue Services (RFRS are responsible for near shore oil spill response and shoreline cleanup operations. In addition, they assist in other types of maritime incidents, such as search and rescue operations and fire-fighting on board. These statutory assignments require the RFRS to have capability to act both on land and at sea. As maritime incidents occur infrequently, little routine has been established. In order to improve their performance in maritime operations, the RFRS are participating in a new oil spill training programme to be launched by South-Eastern Finland University of Applied Sciences. This training programme aims to utilize new educational methods; e-learning and simulator based training. In addition to fully exploiting the existing navigational bridge simulator, radio communication simulator and crisis management simulator, an entirely new simulator is developed. This simulator is designed to model the oil recovery process; recovery method, rate and volume in various conditions with different oil types. New simulator enables creation of a comprehensive training programme covering training tasks from a distress call to the completion of an oil spill response operation. Structure of the training programme, as well as the training objectives, are based on the findings from competence and education surveys conducted in spring 2016. In these results, a need for vessel maneuvering and navigation exercises together with actual response measures training were emphasized. Also additional training for maritime radio communication, GMDSS-emergency protocols and collaboration with maritime authorities were seemed important. This paper describes new approach to the maritime operations training designed for rescue authorities, a way of learning by doing, without mobilising the vessels at sea.

  6. DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

    Science.gov (United States)

    Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

    2015-12-01

    Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

  7. The Effect of WhatsApp Messenger As Mobile Learning Integrated with Group Investigation Method of Learning Achievement

    Directory of Open Access Journals (Sweden)

    Hendrik Pratama

    2017-12-01

    Full Text Available The purpose of this research was determined the effect of application WhatsApp Messenger in the Group Investigation (GI method on learning achievement. The methods used experimental research with control group pretest-postest design. The sampling procedure used the purposive sampling technique that consists of 17 students as a control group and 17 students as an experimental group. The sample in this research is students in Electrical Engineering Education Study Program. The experimental group used the GI method that integrated with WhatsApp Messenger. The control group used lecture method without social media integration. The collecting data used observation, documentation, interview, questionnaire, and test. The researcher used a t-test for compared the control group and the experimental group’s learning outcomes at an alpha level of 0,05. The results showed differences between the experiment group and the control group. The study result of the experimental higher than the control groups. This learning was designed with start, grouping, planning, presenting, organizing, investigating, evaluating, ending’s stage. Integration of WhatsApp with group investigation method could cause the positive communication between student and lecturer. Discussion in this learning was well done, the student’s knowledge could appear in a group and the information could spread evenly and quickly.

  8. EQUITY SHARES EQUATING THE RESULTS OF FCFF AND FCFE METHODS

    Directory of Open Access Journals (Sweden)

    Bartłomiej Cegłowski

    2012-06-01

    Full Text Available The aim of the article is to present the method of establishing equity shares in weight average cost of capital (WACC, in which the value of loan capital results from the fixed assumptions accepted in the financial plan (for example a schedule of loan repayment and own equity is evaluated by means of a discount method. The described method causes that, regardless of whether cash flows are calculated as FCFF or FCFE, the result of the company valuation will be identical.

  9. Computerization of Hungarian reforestation manual with machine learning methods

    Science.gov (United States)

    Czimber, Kornél; Gálos, Borbála; Mátyás, Csaba; Bidló, András; Gribovszki, Zoltán

    2017-04-01

    Hungarian forests are highly sensitive to the changing climate, especially to the available precipitation amount. Over the past two decades several drought damages were observed for tree species which are in the lower xeric limit of their distribution. From year to year these affected forest stands become more difficult to reforest with the same native species because these are not able to adapt to the increasing probability of droughts. The climate related parameter set of the Hungarian forest stand database needs updates. Air humidity that was formerly used to define the forest climate zones is not measured anymore and its value based on climate model outputs is highly uncertain. The aim was to develop a novel computerized and objective method to describe the species-specific climate conditions that is essential for survival, growth and optimal production of the forest ecosystems. The method is expected to project the species spatial distribution until 2100 on the basis of regional climate model simulations. Until now, Hungarian forest managers have been using a carefully edited spreadsheet for reforestation purposes. Applying binding regulations this spreadsheet prescribes the stand-forming and admixed tree species and their expected growth rate for each forest site types. We are going to present a new machine learning based method to replace the former spreadsheet. We took into great consideration of various methods, such as maximum likelihood, Bayesian networks, Fuzzy logic. The method calculates distributions, setups classification, which can be validated and modified by experts if necessary. Projected climate change conditions makes necessary to include into this system an additional climate zone that does not exist in our region now, as well as new options for potential tree species. In addition to or instead of the existing ones, the influence of further limiting parameters (climatic extremes, soil water retention) are also investigated. Results will be

  10. An Activity-based Approach to the Learning and Teaching of Research Methods: Measuring Student Engagement and Learning

    Directory of Open Access Journals (Sweden)

    Eimear Fallon

    2013-05-01

    Full Text Available This paper discusses a research project carried out with 82 final and third year undergraduate students, learning Research Methods prior to undertaking an undergraduate thesis during the academic years 2010 and 2011. The research had two separate, linked objectives, (a to develop a Research Methods module that embraces an activity-based approach to learning in a group environment, (b to improve engagement by all students. The Research Methods module was previously taught through a traditional lecture-based format. Anecdotally, it was felt that student engagement was poor and learning was limited. It was believed that successful completion of the development of this Module would equip students with a deeply-learned battery of research skills to take into their further academic and professional careers. Student learning was achieved through completion of a series of activities based on different research methods. In order to encourage student engagement, a wide variety of activities were used. These activities included workshops, brainstorming, mind-mapping, presentations, written submissions, peer critiquing, lecture/seminar, and ‘speed dating’ with more senior students and self reflection. Student engagement was measured through a survey based on a U.S. National Survey of Student Engagement (2000. A questionnaire was devised to establish whether, and to what degree, students were engaged in the material that they were learning, while they were learning it. The results of the questionnaire were very encouraging with between 63% and 96% of students answering positively to a range of questions concerning engagement. In terms of the two objectives set, these were satisfactorily met. The module was successfully developed and continues to be delivered, based upon this new and significant level of student engagement.

  11. New method of scoliosis assessment: preliminary results using computerized photogrammetry.

    Science.gov (United States)

    Aroeira, Rozilene Maria Cota; Leal, Jefferson Soares; de Melo Pertence, Antônio Eustáquio

    2011-09-01

    A new method for nonradiographic evaluation of scoliosis was independently compared with the Cobb radiographic method, for the quantification of scoliotic curvature. To develop a protocol for computerized photogrammetry, as a nonradiographic method, for the quantification of scoliosis, and to mathematically relate this proposed method with the Cobb radiographic method. Repeated exposure to radiation of children can be harmful to their health. Nevertheless, no nonradiographic method until now proposed has gained popularity as a routine method for evaluation, mainly due to a low correspondence to the Cobb radiographic method. Patients undergoing standing posteroanterior full-length spine radiographs, who were willing to participate in this study, were submitted to dorsal digital photography in the orthostatic position with special surface markers over the spinous process, specifically the vertebrae C7 to L5. The radiographic and photographic images were sent separately for independent analysis to two examiners, trained in quantification of scoliosis for the types of images received. The scoliosis curvature angles obtained through computerized photogrammetry (the new method) were compared to those obtained through the Cobb radiographic method. Sixteen individuals were evaluated (14 female and 2 male). All presented idiopathic scoliosis, and were between 21.4 ± 6.1 years of age; 52.9 ± 5.8 kg in weight; 1.63 ± 0.05 m in height, with a body mass index of 19.8 ± 0.2. There was no statistically significant difference between the scoliosis angle measurements obtained in the comparative analysis of both methods, and a mathematical relationship was formulated between both methods. The preliminary results presented demonstrate equivalence between the two methods. More studies are needed to firmly assess the potential of this new method as a coadjuvant tool in the routine following of scoliosis treatment.

  12. Identification of alternative method of teaching and learning the ...

    African Journals Online (AJOL)

    This study examines alternative method of teaching and learning of the concept of diffusion. An improvised U-shape glass tube called ionic mobility tube was used to observed and measure the rate of movement of divalent metal ions in an aqueous medium in the absence of an electric current. The study revealed that the ...

  13. Kernel Methods for Machine Learning with Life Science Applications

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie

    Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...

  14. Research on Language Learning Strategies: Methods, Findings, and Instructional Issues.

    Science.gov (United States)

    Oxford, Rebecca; Crookall, David

    1989-01-01

    Surveys research on formal and informal second-language learning strategies, covering the effectiveness of research methods involving making lists, interviews and thinking aloud, note-taking, diaries, surveys, and training. Suggestions for future and improved research are presented. (131 references) (CB)

  15. Second-Order Learning Methods for a Multilayer Perceptron

    International Nuclear Information System (INIS)

    Ivanov, V.V.; Purehvdorzh, B.; Puzynin, I.V.

    1994-01-01

    First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs

  16. Educational integrating projects as a method of interactive learning

    Directory of Open Access Journals (Sweden)

    Иван Николаевич Куринин

    2013-12-01

    Full Text Available The article describes a method of interactive learning based on educational integrating projects. Some examples of content of such projects for the disciplines related to the study of information and Internet technologies and their application in management are presented.

  17. Learning by Designing Interview Methods in Special Education

    DEFF Research Database (Denmark)

    Jönsson, Lise Høgh

    2017-01-01

    , and people with learning disabilities worked together to develop five new visual and digital methods for interviewing in special education. Thereby not only enhancing the students’ competences, knowledge and proficiency in innovation and research, but also proposing a new teaching paradigm for university...

  18. Arabic Supervised Learning Method Using N-Gram

    Science.gov (United States)

    Sanan, Majed; Rammal, Mahmoud; Zreik, Khaldoun

    2008-01-01

    Purpose: Recently, classification of Arabic documents is a real problem for juridical centers. In this case, some of the Lebanese official journal documents are classified, and the center has to classify new documents based on these documents. This paper aims to study and explain the useful application of supervised learning method on Arabic texts…

  19. A Simulator to Enhance Teaching and Learning of Mining Methods ...

    African Journals Online (AJOL)

    Audio visual education that incorporates devices and materials which involve sight, sound, or both has become a sine qua non in recent times in the teaching and learning process. An automated physical model of mining methods aided with video instructions was designed and constructed by harnessing locally available ...

  20. IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

    Directory of Open Access Journals (Sweden)

    A. S. Potapov

    2014-09-01

    Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

  1. A COMPARISON OF STUDY RESULTS OF BUSINESS ENGLISH STUDENTS IN E-LEARNING AND FACE-TO-FACE COURSES

    Directory of Open Access Journals (Sweden)

    Petr Kučera

    2012-09-01

    Full Text Available The paper deals with the comparison of results of students in thelessons of Business English e-learning course with face-to-faceteaching at the Faculty of Economics and Management of the CULSin Prague. E-learning as a method of instruction refers to learningusing technology, such as the Internet, CD-ROMs and portabledevices. A current trend in university teaching is a particular focus one-learning method of studies enhancing the quality and effectivenessof studies and self-studies. In the paper we have analysed the currentstate in the area of English for Specific Purposes (ESP e-learningresearch, pointed out the results of a pilot ESP e-learning course intesting a control and an experimental group of students and resultsof questionnaires with views of students on e-learning. The paperfocuses on the experimental verification of e-learning influenceon the results of both groups of students. Online study materialsupports an interactive form of the teaching by means of multimediaapplication. It could be used not only for full-time students but alsofor distance students and centers of lifelong learning.

  2. Studying depression using imaging and machine learning methods

    OpenAIRE

    Patel, Meenal J.; Khalaf, Alexander; Aizenstein, Howard J.

    2015-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presen...

  3. E-learning as new method of medical education.

    Science.gov (United States)

    Masic, Izet

    2008-01-01

    NONE DECLARED Distance learning refers to use of technologies based on health care delivered on distance and covers areas such as electronic health, tele-health (e-health), telematics, telemedicine, tele-education, etc. For the need of e-health, telemedicine, tele-education and distance learning there are various technologies and communication systems from standard telephone lines to the system of transmission digitalized signals with modem, optical fiber, satellite links, wireless technologies, etc. Tele-education represents health education on distance, using Information Communication Technologies (ICT), as well as continuous education of a health system beneficiaries and use of electronic libraries, data bases or electronic data with data bases of knowledge. Distance learning (E-learning) as a part of tele-education has gained popularity in the past decade; however, its use is highly variable among medical schools and appears to be more common in basic medical science courses than in clinical education. Distance learning does not preclude traditional learning processes; frequently it is used in conjunction with in-person classroom or professional training procedures and practices. Tele-education has mostly been used in biomedical education as a blended learning method, which combines tele-education technology with traditional instructor-led training, where, for example, a lecture or demonstration is supplemented by an online tutorial. Distance learning is used for self-education, tests, services and for examinations in medicine i.e. in terms of self-education and individual examination services. The possibility of working in the exercise mode with image files and questions is an attractive way of self education. Automated tracking and reporting of learners' activities lessen faculty administrative burden. Moreover, e-learning can be designed to include outcomes assessment to determine whether learning has occurred. This review article evaluates the current

  4. Persuasive technology in teaching acute pain assessment in nursing: Results in learning based on pre and post-testing.

    Science.gov (United States)

    Alvarez, Ana Graziela; Dal Sasso, Grace T Marcon; Iyengar, M Sriram

    2017-03-01

    Thousands of patients seek health services every day with complaints of pain. However, adequate pain assessment is still flawed, a fact that is partly related to gaps in professional learning on this topic. Innovative strategies such as the use of a virtual learning object mediated by persuasive technology in the learning of undergraduate nursing students can help to fill these gaps and to provide different ways of learning to learn. To evaluate the results in learning among undergraduate nursing students about assessment of acute pain in adults and newborns, before and after an online educational intervention. This is a quasi-experimental, non-equivalent study using pre-and post-testing. Federal University of Santa Catarina, Brazil. 75 undergraduate nursing students. Our study was conducted in three steps (pre-test, education intervention, post-test). Data were collected from November 2013 to February 2014. The educational intervention was performed using online access to virtual learning object about acute pain assessment, which students accessed on their mobile devices. A significant difference was seen in student learning (ptechnology and method applied. The use of persuasive technology such as small mobile devices as mediators of online educational interventions broadens learning spaces in an innovative, flexible, motivational, and promising manner. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Machine-learning methods in the classification of water bodies

    Directory of Open Access Journals (Sweden)

    Sołtysiak Marek

    2016-06-01

    Full Text Available Amphibian species have been considered as useful ecological indicators. They are used as indicators of environmental contamination, ecosystem health and habitat quality., Amphibian species are sensitive to changes in the aquatic environment and therefore, may form the basis for the classification of water bodies. Water bodies in which there are a large number of amphibian species are especially valuable even if they are located in urban areas. The automation of the classification process allows for a faster evaluation of the presence of amphibian species in the water bodies. Three machine-learning methods (artificial neural networks, decision trees and the k-nearest neighbours algorithm have been used to classify water bodies in Chorzów – one of 19 cities in the Upper Silesia Agglomeration. In this case, classification is a supervised data mining method consisting of several stages such as building the model, the testing phase and the prediction. Seven natural and anthropogenic features of water bodies (e.g. the type of water body, aquatic plants, the purpose of the water body (destination, position of the water body in relation to any possible buildings, condition of the water body, the degree of littering, the shore type and fishing activities have been taken into account in the classification. The data set used in this study involved information about 71 different water bodies and 9 amphibian species living in them. The results showed that the best average classification accuracy was obtained with the multilayer perceptron neural network.

  6. Recent Advances in Conotoxin Classification by Using Machine Learning Methods.

    Science.gov (United States)

    Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao

    2017-06-25

    Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

  7. Frank Gilbreth and health care delivery method study driven learning.

    Science.gov (United States)

    Towill, Denis R

    2009-01-01

    The purpose of this article is to look at method study, as devised by the Gilbreths at the beginning of the twentieth century, which found early application in hospital quality assurance and surgical "best practice". It has since become a core activity in all modern methods, as applied to healthcare delivery improvement programmes. The article traces the origin of what is now currently and variously called "business process re-engineering", "business process improvement" and "lean healthcare" etc., by different management gurus back to the century-old pioneering work of Frank Gilbreth. The outcome is a consistent framework involving "width", "length" and "depth" dimensions within which healthcare delivery systems can be analysed, designed and successfully implemented to achieve better and more consistent performance. Healthcare method (saving time plus saving motion) study is best practised as co-joint action learning activity "owned" by all "players" involved in the re-engineering process. However, although process mapping is a key step forward, in itself it is no guarantee of effective re-engineering. It is not even the beginning of the end of the change challenge, although it should be the end of the beginning. What is needed is innovative exploitation of method study within a healthcare organisational learning culture accelerated via the Gilbreth Knowledge Flywheel. It is shown that effective healthcare delivery pipeline improvement is anchored into a team approach involving all "players" in the system especially physicians. A comprehensive process study, constructive dialogue, proper and highly professional re-engineering plus managed implementation are essential components. Experience suggests "learning" is thereby achieved via "natural groups" actively involved in healthcare processes. The article provides a proven method for exploiting Gilbreths' outputs and their many successors in enabling more productive evidence-based healthcare delivery as summarised

  8. Convergence results for a class of abstract continuous descent methods

    Directory of Open Access Journals (Sweden)

    Sergiu Aizicovici

    2004-03-01

    Full Text Available We study continuous descent methods for the minimization of Lipschitzian functions defined on a general Banach space. We establish convergence theorems for those methods which are generated by approximate solutions to evolution equations governed by regular vector fields. Since the complement of the set of regular vector fields is $sigma$-porous, we conclude that our results apply to most vector fields in the sense of Baire's categories.

  9. Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method.

    Science.gov (United States)

    Wu, Lin; Wang, Yang; Pan, Shirui

    2017-12-01

    It is now well established that sparse representation models are working effectively for many visual recognition tasks, and have pushed forward the success of dictionary learning therein. Recent studies over dictionary learning focus on learning discriminative atoms instead of purely reconstructive ones. However, the existence of intraclass diversities (i.e., data objects within the same category but exhibit large visual dissimilarities), and interclass similarities (i.e., data objects from distinct classes but share much visual similarities), makes it challenging to learn effective recognition models. To this end, a large number of labeled data objects are required to learn models which can effectively characterize these subtle differences. However, labeled data objects are always limited to access, committing it difficult to learn a monolithic dictionary that can be discriminative enough. To address the above limitations, in this paper, we propose a weakly-supervised dictionary learning method to automatically learn a discriminative dictionary by fully exploiting visual attribute correlations rather than label priors. In particular, the intrinsic attribute correlations are deployed as a critical cue to guide the process of object categorization, and then a set of subdictionaries are jointly learned with respect to each category. The resulting dictionary is highly discriminative and leads to intraclass diversity aware sparse representations. Extensive experiments on image classification and object recognition are conducted to show the effectiveness of our approach.

  10. Non-Gaussian Methods for Causal Structure Learning.

    Science.gov (United States)

    Shimizu, Shohei

    2018-05-22

    Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

  11. Knowledge Transfer in Health Care Through Digitally Collecting Learning Experiences - Results of Witra Care.

    Science.gov (United States)

    Behrends, Marianne; Kupka, Thomas; Schmeer, Regina; Meyenburg-Altwarg, Iris; Marschollek, Michael

    2016-01-01

    The goal of the project Witra Care was to investigate how far the use of mobile technology is suitable to collect experience-based knowledge of nurses. Nine new employees and seven experienced nurses received for six weeks a mobile phone or a tablet pc with a mobile application that allowed them to collect learning object as pictures, videos, audio files or notes. In Witra Care the nurses created 303 learning objects. They have found the collecting of learning experiences was helpful for their learning processes. The learning objects demonstrate various aspects of daily routines in nursing. The results of Witra Care show that the documentation of learning experiences with mobile devices helps to gather information about the practical knowledge in the daily work of nurses, identifies individual learning needs of the employees and supports them in their personal learning processes.

  12. A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

    Science.gov (United States)

    Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

    This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

  13. Visual Display of Scientific Studies, Methods, and Results

    Science.gov (United States)

    Saltus, R. W.; Fedi, M.

    2015-12-01

    The need for efficient and effective communication of scientific ideas becomes more urgent each year.A growing number of societal and economic issues are tied to matters of science - e.g., climate change, natural resource availability, and public health. Societal and political debate should be grounded in a general understanding of scientific work in relevant fields. It is difficult for many participants in these debates to access science directly because the formal method for scientific documentation and dissemination is the journal paper, generally written for a highly technical and specialized audience. Journal papers are very effective and important for documentation of scientific results and are essential to the requirements of science to produce citable and repeatable results. However, journal papers are not effective at providing a quick and intuitive summary useful for public debate. Just as quantitative data are generally best viewed in graphic form, we propose that scientific studies also can benefit from visual summary and display. We explore the use of existing methods for diagramming logical connections and dependencies, such as Venn diagrams, mind maps, flow charts, etc., for rapidly and intuitively communicating the methods and results of scientific studies. We also discuss a method, specifically tailored to summarizing scientific papers that we introduced last year at AGU. Our method diagrams the relative importance and connections between data, methods/models, results/ideas, and implications/importance using a single-page format with connected elements in these four categories. Within each category (e.g., data) the spatial location of individual elements (e.g., seismic, topographic, gravity) indicates relative novelty (e.g., are these new data?) and importance (e.g., how critical are these data to the results of the paper?). The goal is to find ways to rapidly and intuitively share both the results and the process of science, both for communication

  14. Cross-organism learning method to discover new gene functionalities.

    Science.gov (United States)

    Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro

    2016-04-01

    Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones

  15. Life cycle analysis of electricity systems: Methods and results

    International Nuclear Information System (INIS)

    Friedrich, R.; Marheineke, T.

    1996-01-01

    The two methods for full energy chain analysis, process analysis and input/output analysis, are discussed. A combination of these two methods provides the most accurate results. Such a hybrid analysis of the full energy chains of six different power plants is presented and discussed. The results of such analyses depend on time, site and technique of each process step and, therefore have no general validity. For renewable energy systems the emissions form the generation of a back-up system should be added. (author). 7 figs, 1 fig

  16. Aggregative Learning Method and Its Application for Communication Quality Evaluation

    Science.gov (United States)

    Akhmetov, Dauren F.; Kotaki, Minoru

    2007-12-01

    In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

  17. Learning and retention of quantum concepts with different teaching methods

    Science.gov (United States)

    Deslauriers, Louis; Wieman, Carl

    2011-06-01

    We measured mastery and retention of conceptual understanding of quantum mechanics in a modern physics course. This was studied for two equivalent cohorts of students taught with different pedagogical approaches using the Quantum Mechanics Conceptual Survey. We measured the impact of pedagogical approach both on the original conceptual learning and on long-term retention. The cohort of students who had a very highly rated traditional lecturer scored 19% lower than the equivalent cohort that was taught using interactive engagement methods. However, the amount of retention was very high for both cohorts, showing only a few percent decrease in scores when retested 6 and 18 months after completion of the course and with no exposure to the material in the interim period. This high level of retention is in striking contrast to the retention measured for more factual learning from university courses and argues for the value of emphasizing conceptual learning.

  18. Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

    Science.gov (United States)

    Tian, Yuling; Zhang, Hongxian

    2016-01-01

    For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

  19. Advanced Steel Microstructural Classification by Deep Learning Methods.

    Science.gov (United States)

    Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

    2018-02-01

    The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

  20. Case study teaching method improves student performance and perceptions of learning gains.

    Science.gov (United States)

    Bonney, Kevin M

    2015-05-01

    Following years of widespread use in business and medical education, the case study teaching method is becoming an increasingly common teaching strategy in science education. However, the current body of research provides limited evidence that the use of published case studies effectively promotes the fulfillment of specific learning objectives integral to many biology courses. This study tested the hypothesis that case studies are more effective than classroom discussions and textbook reading at promoting learning of key biological concepts, development of written and oral communication skills, and comprehension of the relevance of biological concepts to everyday life. This study also tested the hypothesis that case studies produced by the instructor of a course are more effective at promoting learning than those produced by unaffiliated instructors. Additionally, performance on quantitative learning assessments and student perceptions of learning gains were analyzed to determine whether reported perceptions of learning gains accurately reflect academic performance. The results reported here suggest that case studies, regardless of the source, are significantly more effective than other methods of content delivery at increasing performance on examination questions related to chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. This finding was positively correlated to increased student perceptions of learning gains associated with oral and written communication skills and the ability to recognize connections between biological concepts and other aspects of life. Based on these findings, case studies should be considered as a preferred method for teaching about a variety of concepts in science courses.

  1. Case Study Teaching Method Improves Student Performance and Perceptions of Learning Gains

    Directory of Open Access Journals (Sweden)

    Kevin M. Bonney

    2015-02-01

    Full Text Available Following years of widespread use in business and medical education, the case study teaching method is becoming an increasingly common teaching strategy in science education. However, the current body of research provides limited evidence that the use of published case studies effectively promotes the fulfillment of specific learning objectives integral to many biology courses. This study tested the hypothesis that case studies are more effective than classroom discussions and textbook reading at promoting learning of key biological concepts, development of written and oral communication skills, and comprehension of the relevance of biological concepts to everyday life. This study also tested the hypothesis that case studies produced by the instructor of a course are more effective at promoting learning than those produced by unaffiliated instructors. Additionally, performance on quantitative learning assessments and student perceptions of learning gains were analyzed to determine whether reported perceptions of learning gains accurately reflect academic performance. The results reported here suggest that case studies, regardless of the source, are significantly more effective than other methods of content delivery at increasing performance on examination questions related to chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. This finding was positively correlated to increased student perceptions of learning gains associated with oral and written communication skills and the ability to recognize connections between biological concepts and other aspects of life. Based on these findings, case studies should be considered as a preferred method for teaching about a variety of concepts in science courses.

  2. A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

    Science.gov (United States)

    Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

    2018-06-12

    Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

  3. Guidelines for Reporting Quantitative Methods and Results in Primary Research

    Science.gov (United States)

    Norris, John M.; Plonsky, Luke; Ross, Steven J.; Schoonen, Rob

    2015-01-01

    Adequate reporting of quantitative research about language learning involves careful consideration of the logic, rationale, and actions underlying both study designs and the ways in which data are analyzed. These guidelines, commissioned and vetted by the board of directors of "Language Learning," outline the basic expectations for…

  4. Understanding the effects of time on collaborative learning processes in problem based learning: a mixed methods study.

    Science.gov (United States)

    Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

    2014-10-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.

  5. Machine learning methods without tears: a primer for ecologists.

    Science.gov (United States)

    Olden, Julian D; Lawler, Joshua J; Poff, N LeRoy

    2008-06-01

    Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.

  6. Comparison of the effect of lecture and blended teaching methods on students’ learning and satisfaction

    Science.gov (United States)

    SADEGHI, ROYA; SEDAGHAT, MOHAMMAD MEHDI; SHA AHMADI, FARAMARZ

    2014-01-01

    Introduction: Blended learning, a new approach in educational planning, is defined as an applying more than one method, strategy, technique or media in education. Todays, due to the development of infrastructure of Internet networks and the access of most of the students, the Internet can be utilized along with traditional and conventional methods of training. The aim of this study was to compare the students’ learning and satisfaction in combination of lecture and e-learning with conventional lecture methods. Methods: This quasi-experimental study is conducted among the sophomore students of Public Health School, Tehran University of Medical Science in 2012-2013. Four classes of the school are randomly selected and are divided into two groups. Education in two classes (45 students) was in the form of lecture method and in the other two classes (48 students) was blended method with e-Learning and lecture methods. The students’ knowledge about tuberculosis in two groups was collected and measured by using pre and post-test. This step has been done by sending self-reported electronic questionnaires to the students' email addresses through Google Document software. At the end of educational programs, students' satisfaction and comments about two methods were also collected by questionnaires. Statistical tests such as descriptive methods, paired t-test, independent t-test and ANOVA were done through the SPSS 14 software, and p≤0.05 was considered as significant difference. Results: The mean scores of the lecture and blended groups were 13.18±1.37 and 13.35±1.36, respectively; the difference between the pre-test scores of the two groups was not statistically significant (p=0.535). Knowledge scores increased in both groups after training, and the mean and standard deviation of knowledge scores of the lectures and combined groups were 16.51±0.69 and 16.18±1.06, respectively. The difference between the post-test scores of the two groups was not statistically

  7. Knowledge Reuse Method to Improve the Learning of Interference-Preventive Allocation Policies in Multi-Car Elevators

    Science.gov (United States)

    Valdivielso Chian, Alex; Miyamoto, Toshiyuki

    In this letter, we introduce a knowledge reuse method to improve the performance of a learning algorithm developed to prevent interference in multi-car elevators. This method enables the algorithm to use its previously acquired experience in new learning processes. The simulation results confirm the improvement achieved in the algorithm's performance.

  8. The Effect of Using Cooperative Learning Method on Tenth Grade Students' Learning Achievement and Attitude towards Biology

    Science.gov (United States)

    Rabgay, Tshewang

    2018-01-01

    The study investigated the effect of using cooperative learning method on tenth grade students' learning achievement in biology and their attitude towards the subject in a Higher Secondary School in Bhutan. The study used a mixed method approach. The quantitative component included an experimental design where cooperative learning was the…

  9. Introduction of active learning method in learning physiology by MBBS students.

    Science.gov (United States)

    Gilkar, Suhail Ahmad; Lone, Shabiruddin; Lone, Riyaz Ahmad

    2016-01-01

    Active learning has received considerable attention over the past several years, often presented or perceived as a radical change from traditional instruction methods. Current research on learning indicates that using a variety of teaching strategies in the classroom increases student participation and learning. To introduce active learning methodology, i.e., "jigsaw technique" in undergraduate medical education and assess the student and faculty response to it. This study was carried out in the Department of Physiology in a Medical College of North India. A topic was chosen and taught using one of the active learning methods (ALMs), i.e., jigsaw technique. An instrument (questionnaire) was developed in English through an extensive review of literature and was properly validated. The students were asked to give their response on a five-point Likert scale. The feedback was kept anonymous. Faculty also provided their feedback in a separately provided feedback proforma. The data were collected, compiled, and analyzed. Of 150 students of MBBS-first year batch 2014, 142 participated in this study along with 14 faculty members of the Physiology Department. The majority of the students (>90%) did welcome the introduction of ALM and strongly recommended the use of such methods in teaching many more topics in future. 100% faculty members were of the opinion that many more topics shall be taken up using ALMs. This study establishes the fact that both the medical students and faculty want a change from the traditional way of passive, teacher-centric learning, to the more active teaching-learning techniques.

  10. A Pharmacy Preregistration Course Using Online Teaching and Learning Methods

    Science.gov (United States)

    McDowell, Jenny; Marriott, Jennifer L.; Calandra, Angela; Duncan, Gregory

    2009-01-01

    Objective To design and evaluate a preregistration course utilizing asynchronous online learning as the primary distance education delivery method. Design Online course components including tutorials, quizzes, and moderated small-group asynchronous case-based discussions were implemented. Online delivery was supplemented with self-directed and face-to-face learning. Assessment Pharmacy graduates who had completed the course in 2004 and 2005 were surveyed. The majority felt they had benefited from all components of the course, and that online delivery provided benefits including increased peer support, shared learning, and immediate feedback on performance. A majority of the first cohort reported that the workload associated with asynchronous online discussions was too great. The course was altered in 2005 to reduce the online component. Participant satisfaction improved, and most felt that the balance of online to face-to-face delivery was appropriate. Conclusion A new pharmacy preregistration course was successfully implemented. Online teaching and learning was well accepted and appeared to deliver benefits over traditional distance education methods once workload issues were addressed. PMID:19777092

  11. Effectiveness of Various Innovative Learning Methods in Health Science Classrooms: A Meta-Analysis

    Science.gov (United States)

    Kalaian, Sema A.; Kasim, Rafa M.

    2017-01-01

    This study reports the results of a meta-analysis of the available literature on the effectiveness of various forms of innovative small-group learning methods on student achievement in undergraduate college health science classrooms. The results of the analysis revealed that most of the primary studies supported the effectiveness of the…

  12. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.

    2012-01-01

    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  13. A Fuzzy Logic Based Method for Analysing Test Results

    Directory of Open Access Journals (Sweden)

    Le Xuan Vinh

    2017-11-01

    Full Text Available Network operators must perform many tasks to ensure smooth operation of the network, such as planning, monitoring, etc. Among those tasks, regular testing of network performance, network errors and troubleshooting is very important. Meaningful test results will allow the operators to evaluate network performanceof any shortcomings and to better plan for network upgrade. Due to the diverse and mainly unquantifiable nature of network testing results, there is a needs to develop a method for systematically and rigorously analysing these results. In this paper, we present STAM (System Test-result Analysis Method which employs a bottom-up hierarchical processing approach using Fuzzy logic. STAM is capable of combining all test results into a quantitative description of the network performance in terms of network stability, the significance of various network erros, performance of each function blocks within the network. The validity of this method has been successfully demonstrated in assisting the testing of a VoIP system at the Research Instiute of Post and Telecoms in Vietnam. The paper is organized as follows. The first section gives an overview of fuzzy logic theory the concepts of which will be used in the development of STAM. The next section describes STAM. The last section, demonstrating STAM’s capability, presents a success story in which STAM is successfully applied.

  14. Evaluating rehabilitation methods - some practical results from Rum Jungle

    International Nuclear Information System (INIS)

    Ryan, P.

    1987-01-01

    Research and analysis of the following aspects of rehabilitation have been conducted at the Rum Jungle mine site over the past three years: drainage structure stability; rock batter stability; soil fauna; tree growth in compacted soils; rehabilitation costs. The results show that, for future rehabilitation projects adopting refined methods, attention to final construction detail and biospheric influences is most important. The mine site offers a unique opportunity to evaluate the success of a variety of rehabilitation methods to the benefit of the industry in Australia overseas. It is intended that practical, economic, research will continue for some considerable time

  15. A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction.

    Science.gov (United States)

    Lu, Hongyang; Wei, Jingbo; Liu, Qiegen; Wang, Yuhao; Deng, Xiaohua

    2016-01-01

    Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

  16. A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

    Directory of Open Access Journals (Sweden)

    Hongyang Lu

    2016-01-01

    Full Text Available Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV approach and adaptive dictionary learning (DL. In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

  17. Introduction of e-learning in dental radiology reveals significantly improved results in final examination.

    Science.gov (United States)

    Meckfessel, Sandra; Stühmer, Constantin; Bormann, Kai-Hendrik; Kupka, Thomas; Behrends, Marianne; Matthies, Herbert; Vaske, Bernhard; Stiesch, Meike; Gellrich, Nils-Claudius; Rücker, Martin

    2011-01-01

    Because a traditionally instructed dental radiology lecture course is very time-consuming and labour-intensive, online courseware, including an interactive-learning module, was implemented to support the lectures. The purpose of this study was to evaluate the perceptions of students who have worked with web-based courseware as well as the effect on their results in final examinations. Users (n(3+4)=138) had access to the e-program from any networked computer at any time. Two groups (n(3)=71, n(4)=67) had to pass a final exam after using the e-course. Results were compared with two groups (n(1)=42, n(2)=48) who had studied the same content by attending traditional lectures. In addition a survey of the students was statistically evaluated. Most of the respondents reported a positive attitude towards e-learning and would have appreciated more access to computer-assisted instruction. Two years after initiating the e-course the failure rate in the final examination dropped significantly, from 40% to less than 2%. The very positive response to the e-program and improved test scores demonstrated the effectiveness of our e-course as a learning aid. Interactive modules in step with clinical practice provided learning that is not achieved by traditional teaching methods alone. To what extent staff savings are possible is part of a further study. Copyright © 2010 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

  18. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

    Science.gov (United States)

    Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

    2017-12-01

    Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

  19. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  20. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  1. A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

    Directory of Open Access Journals (Sweden)

    Zekić-Sušac Marijana

    2014-09-01

    Full Text Available Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

  2. Comparing three experiential learning methods and their effect on medical students' attitudes to learning communication skills.

    Science.gov (United States)

    Koponen, Jonna; Pyörälä, Eeva; Isotalus, Pekka

    2012-01-01

    Despite numerous studies exploring medical students' attitudes to communication skills learning (CSL), there are apparently no studies comparing different experiential learning methods and their influence on students' attitudes. We compared medical students' attitudes to learning communication skills before and after a communication course in the data as a whole, by gender and when divided into three groups using different methods. Second-year medical students (n = 129) were randomly assigned to three groups. In group A (n = 42) the theatre in education method, in group B (n = 44) simulated patients and in group C (n = 43) role-play were used. The data were gathered before and after the course using Communication Skills Attitude Scale. Students' positive attitudes to learning communication skills (PAS; positive attitude scale) increased significantly and their negative attitudes (NAS; negative attitude scale) decreased significantly between the beginning and end of the course. Female students had more positive attitudes than the male students. There were no significant differences in the three groups in the mean scores for PAS or NAS measured before or after the course. The use of experiential methods and integrating communication skills training with visits to health centres may help medical students to appreciate the importance of CSL.

  3. A novel time series link prediction method: Learning automata approach

    Science.gov (United States)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2017-09-01

    Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

  4. Effects of Problem-Based Learning Model versus Expository Model and Motivation to Achieve for Student's Physic Learning Result of Senior High School at Class XI

    Science.gov (United States)

    Prayekti

    2016-01-01

    "Problem-based learning" (PBL) is one of an innovative learning model which can provide an active learning to student, include the motivation to achieve showed by student when the learning is in progress. This research is aimed to know: (1) differences of physic learning result for student group which taught by PBL versus expository…

  5. Can learning style predict student satisfaction with different instruction methods and academic achievement in medical education?

    Science.gov (United States)

    Gurpinar, Erol; Alimoglu, Mustafa Kemal; Mamakli, Sumer; Aktekin, Mehmet

    2010-12-01

    The curriculum of our medical school has a hybrid structure including both traditional training (lectures) and problem-based learning (PBL) applications. The purpose of this study was to determine the learning styles of our medical students and investigate the relation of learning styles with each of satisfaction with different instruction methods and academic achievement in them. This study was carried out with the participation of 170 first-year medical students (the participation rate was 91.4%). The researchers prepared sociodemographic and satisfaction questionnaires to determine the characteristics of the participants and their satisfaction levels with traditional training and PBL. The Kolb learning styles inventory was used to explore the learning styles of the study group. The participants completed all forms at the end of the first year of medical education. Indicators of academic achievement were scores of five theoretical block exams and five PBL exams performed throughout the academic year of 2008-2009. The majority of the participants took part in the "diverging" (n = 84, 47.7%) and "assimilating" (n = 73, 41.5%) groups. Numbers of students in the "converging" and "accommodating" groups were 11 (6.3%) and 8 (4.5%), respectively. In all learning style groups, PBL satisfaction scores were significantly higher than those of traditional training. Exam scores for "PBL and traditional training" did not differ among the four learning styles. In logistic regression analysis, learning style (assimilating) predicted student satisfaction with traditional training and success in theoretical block exams. Nothing predicted PBL satisfaction and success. This is the first study conducted among medical students evaluating the relation of learning style with student satisfaction and academic achievement. More research with larger groups is needed to generalize our results. Some learning styles may relate to satisfaction with and achievement in some instruction methods.

  6. Housing Value Forecasting Based on Machine Learning Methods

    OpenAIRE

    Mu, Jingyi; Wu, Fang; Zhang, Aihua

    2014-01-01

    In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing...

  7. Learning with Generalization Capability by Kernel Methods of Bounded Complexity

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Sanguineti, M.

    2005-01-01

    Roč. 21, č. 3 (2005), s. 350-367 ISSN 0885-064X R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : supervised learning * generalization * model complexity * kernel methods * minimization of regularized empirical errors * upper bounds on rates of approximate optimization Subject RIV: BA - General Mathematics Impact factor: 1.186, year: 2005

  8. Perceptions of Teaching Methods for Preclinical Oral Surgery: A Comparison with Learning Styles

    Science.gov (United States)

    Omar, Esam

    2017-01-01

    Purpose: Dental extraction is a routine part of clinical dental practice. For this reason, understanding the way how students’ extraction knowledge and skills development are important. Problem Statement and Objectives: To date, there is no accredited statement about the most effective method for the teaching of exodontia to dental students. Students have different abilities and preferences regarding how they learn and process information. This is defined as learning style. In this study, the effectiveness of active learning in the teaching of preclinical oral surgery was examined. The personality type of the groups involved in this study was determined, and the possible effect of personality type on learning style was investigated. Method: This study was undertaken over five years from 2011 to 2015. The sample consisted of 115 students and eight staff members. Questionnaires were submitted by 68 students and all eight staff members involved. Three measures were used in the study: The Index of Learning Styles (Felder and Soloman, 1991), the Myers-Briggs Type Indicator (MBTI), and the styles of learning typology (Grasha and Hruska-Riechmann). Results and Discussion: Findings indicated that demonstration and minimal clinical exposure give students personal validation. Frequent feedback on their work is strongly indicated to build the cognitive, psychomotor, and interpersonal skills needed from preclinical oral surgery courses. Conclusion: Small group cooperative active learning in the form of demonstration and minimal clinical exposure that gives frequent feedback and students’ personal validation on their work is strongly indicated to build the skills needed for preclinical oral surgery courses. PMID:28357004

  9. Comparison of the effect of lecture and blended teaching methods on students' learning and satisfaction.

    Science.gov (United States)

    Sadeghi, Roya; Sedaghat, Mohammad Mehdi; Sha Ahmadi, Faramarz

    2014-10-01

    ' satisfaction in blended learning method was higher than lecture method. The results revealed that the blended method is effective in increasing the students' learning rate. E-learning can be used to teach some courses and might be considered as economic aspects. Since in universities of medical sciences in the country, the majority of students have access to the Internet and email address, using e-learning could be used as a supplement to traditional teaching methods or sometimes as educational alternative method because this method of teaching increases the students' knowledge, satisfaction and attention.

  10. A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

    Science.gov (United States)

    Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

    2017-05-01

    This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

  11. Deep Learning Methods for Underwater Target Feature Extraction and Recognition

    Directory of Open Access Journals (Sweden)

    Gang Hu

    2018-01-01

    Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

  12. Multiple predictor smoothing methods for sensitivity analysis: Example results

    International Nuclear Information System (INIS)

    Storlie, Curtis B.; Helton, Jon C.

    2008-01-01

    The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. In this, the second and concluding part of the presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

  13. Quantifying the measurement uncertainty of results from environmental analytical methods.

    Science.gov (United States)

    Moser, J; Wegscheider, W; Sperka-Gottlieb, C

    2001-07-01

    The Eurachem-CITAC Guide Quantifying Uncertainty in Analytical Measurement was put into practice in a public laboratory devoted to environmental analytical measurements. In doing so due regard was given to the provisions of ISO 17025 and an attempt was made to base the entire estimation of measurement uncertainty on available data from the literature or from previously performed validation studies. Most environmental analytical procedures laid down in national or international standards are the result of cooperative efforts and put into effect as part of a compromise between all parties involved, public and private, that also encompasses environmental standards and statutory limits. Central to many procedures is the focus on the measurement of environmental effects rather than on individual chemical species. In this situation it is particularly important to understand the measurement process well enough to produce a realistic uncertainty statement. Environmental analytical methods will be examined as far as necessary, but reference will also be made to analytical methods in general and to physical measurement methods where appropriate. This paper describes ways and means of quantifying uncertainty for frequently practised methods of environmental analysis. It will be shown that operationally defined measurands are no obstacle to the estimation process as described in the Eurachem/CITAC Guide if it is accepted that the dominating component of uncertainty comes from the actual practice of the method as a reproducibility standard deviation.

  14. Effectiveness of various innovative learning methods in health science classrooms: a meta-analysis.

    Science.gov (United States)

    Kalaian, Sema A; Kasim, Rafa M

    2017-12-01

    This study reports the results of a meta-analysis of the available literature on the effectiveness of various forms of innovative small-group learning methods on student achievement in undergraduate college health science classrooms. The results of the analysis revealed that most of the primary studies supported the effectiveness of the small-group learning methods in improving students' academic achievement with an overall weighted average effect-size of 0.59 in standard deviation units favoring small-group learning methods. The subgroup analysis showed that the various forms of innovative and reform-based small-group learning interventions appeared to be significantly more effective for students in higher levels of college classes (sophomore, junior, and senior levels), students in other countries (non-U.S.) worldwide, students in groups of four or less, and students who choose their own group. The random-effects meta-regression results revealed that the effect sizes were influenced significantly by the instructional duration of the primary studies. This means that studies with longer hours of instruction yielded higher effect sizes and on average every 1 h increase in instruction, the predicted increase in effect size was 0.009 standard deviation units, which is considered as a small effect. These results may help health science and nursing educators by providing guidance in identifying the conditions under which various forms of innovative small-group learning pedagogies are collectively more effective than the traditional lecture-based teaching instruction.

  15. Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

    Science.gov (United States)

    Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

    2016-06-14

    In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

  16. Influence Cooperative Learning Method and Personality Type to Ability to Write The Scientific Article (Experiment Study on SMAN 2 Students Ciamis Learning Indonesian Subject

    Directory of Open Access Journals (Sweden)

    Supriatna Supriatna

    2017-10-01

    Full Text Available The purpose of this research was to know the influence of cooperative learning method (Jigsaw and TPS and personality type (extrovert and introvert toward students’ ability in scientific writing at the SMA Negeri 2 Ciamis class XII. The research used experimental method with 2 x 2 factorial design. The population was the students of class XII which consisted of 150. The sample was 57 students. The results showed that: (1 The ability to write scientific articles of students learning by cooperative learning method jigsaw model (= 65,88 is higher than students who learn by cooperative technique method of TPS (= 59,88, (2 Ability writing scientific articles of students whose extroverted personality (= 65.69 is higher than introverted students (= 60.06; (3 there is interaction between cooperative learning method and personality type to score of writing ability of scientific article (4 ability to write scientific article of extrovert student and studying with technique of Jigsaw (= 77,75 higher than extrovert student learning with cooperative learning method model of TPS (= 53,63 to score of writing ability of scientific article, (5 ability to write introverted student's scientific article and get treatment of cooperative learning method of jigsaw model (= 54,00 lower than introverted student learning TPS technique = 66,13, (6 the ability to write extroverted students' scientific articles studied with jigsaw techniques, and introverted students who studied Jigsaw techniques (= 77.75 were higher than those with introverted personality types studied by the Jigsaw technique (= 54.00 , (7 Ability to write scientific articles of students learning by cooperative techniques of TPS technique and have extrovert personality type ( = 53.63 lower than introverted students learning TPS techniques (= 66.13.

  17. Kernel methods for interpretable machine learning of order parameters

    Science.gov (United States)

    Ponte, Pedro; Melko, Roger G.

    2017-11-01

    Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines (SVMs), which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.

  18. Machine Learning and Data Mining Methods in Diabetes Research.

    Science.gov (United States)

    Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

    2017-01-01

    The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

  19. Characteristics and Consequences of Adult Learning Methods and Strategies. Practical Evaluation Reports, Volume 2, Number 1

    Science.gov (United States)

    Trivette, Carol M.; Dunst, Carl J.; Hamby, Deborah W.; O'Herin, Chainey E.

    2009-01-01

    The effectiveness of four adult learning methods (accelerated learning, coaching, guided design, and just-in-time training) constituted the focus of this research synthesis. Findings reported in "How People Learn" (Bransford et al., 2000) were used to operationally define six adult learning method characteristics, and to code and analyze…

  20. Introducing the Collaborative E-Learning Design Method (CoED)

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Buus, Lillian; Nyvang, Tom

    2015-01-01

    In this chapter, a specific learning design method is introduced and explained, namely the Collaborative E-learning Design method (CoED), which has been developed through various projects in “e-Learning Lab – Centre for User Driven Innovation, Learning and Design” (Nyvang & Georgsen, 2007). We br...

  1. Actively Teaching Research Methods with a Process Oriented Guided Inquiry Learning Approach

    Science.gov (United States)

    Mullins, Mary H.

    2017-01-01

    Active learning approaches have shown to improve student learning outcomes and improve the experience of students in the classroom. This article compares a Process Oriented Guided Inquiry Learning style approach to a more traditional teaching method in an undergraduate research methods course. Moving from a more traditional learning environment to…

  2. Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

    Science.gov (United States)

    Huang, Xin; Wang, Huaning; Xu, Long; Liu, Jinfu; Li, Rong; Dai, Xinghua

    2018-03-01

    Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

  3. Predicting Solar Activity Using Machine-Learning Methods

    Science.gov (United States)

    Bobra, M.

    2017-12-01

    Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

  4. Measuring the surgical 'learning curve': methods, variables and competency.

    Science.gov (United States)

    Khan, Nuzhath; Abboudi, Hamid; Khan, Mohammed Shamim; Dasgupta, Prokar; Ahmed, Kamran

    2014-03-01

    To describe how learning curves are measured and what procedural variables are used to establish a 'learning curve' (LC). To assess whether LCs are a valuable measure of competency. A review of the surgical literature pertaining to LCs was conducted using the Medline and OVID databases. Variables should be fully defined and when possible, patient-specific variables should be used. Trainee's prior experience and level of supervision should be quantified; the case mix and complexity should ideally be constant. Logistic regression may be used to control for confounding variables. Ideally, a learning plateau should reach a predefined/expert-derived competency level, which should be fully defined. When the group splitting method is used, smaller cohorts should be used in order to narrow the range of the LC. Simulation technology and competence-based objective assessments may be used in training and assessment in LC studies. Measuring the surgical LC has potential benefits for patient safety and surgical education. However, standardisation in the methods and variables used to measure LCs is required. Confounding variables, such as participant's prior experience, case mix, difficulty of procedures and level of supervision, should be controlled. Competency and expert performance should be fully defined. © 2013 The Authors. BJU International © 2013 BJU International.

  5. Processing method and results of meteor shower radar observations

    International Nuclear Information System (INIS)

    Belkovich, O.I.; Suleimanov, N.I.; Tokhtasjev, V.S.

    1987-01-01

    Studies of meteor showers permit the solving of some principal problems of meteor astronomy: to obtain the structure of a stream in cross section and along its orbits; to retrace the evolution of particle orbits of the stream taking into account gravitational and nongravitational forces and to discover the orbital elements of its parent body; to find out the total mass of solid particles ejected from the parent body taking into account physical and chemical evolution of meteor bodies; and to use meteor streams as natural probes for investigation of the average characteristics of the meteor complex in the solar system. A simple and effective method of determining the flux density and mass exponent parameter was worked out. This method and its results are discussed

  6. Method of vacuum correlation functions: Results and prospects

    International Nuclear Information System (INIS)

    Badalian, A. M.; Simonov, Yu. A.; Shevchenko, V. I.

    2006-01-01

    Basic results obtained within the QCD method of vacuum correlation functions over the past 20 years in the context of investigations into strong-interaction physics at the Institute of Theoretical and Experimental Physics (ITEP, Moscow) are formulated Emphasis is placed primarily on the prospects of the general theory developed within QCD by employing both nonperturbative and perturbative methods. On the basis of ab initio arguments, it is shown that the lowest two field correlation functions play a dominant role in QCD dynamics. A quantitative theory of confinement and deconfinement, as well as of the spectra of light and heavy quarkonia, glueballs, and hybrids, is given in terms of these two correlation functions. Perturbation theory in a nonperturbative vacuum (background perturbation theory) plays a significant role, not possessing drawbacks of conventional perturbation theory and leading to the infrared freezing of the coupling constant α s

  7. Application of NUREG-1150 methods and results to accident management

    International Nuclear Information System (INIS)

    Dingman, S.; Sype, T.; Camp, A.; Maloney, K.

    1991-01-01

    The use of NUREG-1150 and similar probabilistic risk assessments in the Nuclear Regulatory Commission (NRC) and industry risk management programs is discussed. Risk management is more comprehensive than the commonly used term accident management. Accident management includes strategies to prevent vessel breach, mitigate radionuclide releases from the reactor coolant system, and mitigate radionuclide releases to the environment. Risk management also addresses prevention of accident initiators, prevention of core damage, and implementation of effective emergency response procedures. The methods and results produced in NUREG-1150 provide a framework within which current risk management strategies can be evaluated, and future risk management programs can be developed and assessed. Examples of the use of the NUREG-1150 framework for identifying and evaluating risk management options are presented. All phases of risk management are discussed, with particular attention given to the early phases of accidents. Plans and methods for evaluating accident management strategies that have been identified in the NRC accident management program are discussed

  8. Application of NUREG-1150 methods and results to accident management

    International Nuclear Information System (INIS)

    Dingman, S.; Sype, T.; Camp, A.; Maloney, K.

    1990-01-01

    The use of NUREG-1150 and similar Probabilistic Risk Assessments in NRC and industry risk management programs is discussed. ''Risk management'' is more comprehensive than the commonly used term ''accident management.'' Accident management includes strategies to prevent vessel breach, mitigate radionuclide releases from the reactor coolant system, and mitigate radionuclide releases to the environment. Risk management also addresses prevention of accident initiators, prevention of core damage, and implementation of effective emergency response procedures. The methods and results produced in NUREG-1150 provide a framework within which current risk management strategies can be evaluated, and future risk management programs can be developed and assessed. Examples of the use of the NUREG-1150 framework for identifying and evaluating risk management options are presented. All phases of risk management are discussed, with particular attention given to the early phases of accidents. Plans and methods for evaluating accident management strategies that have been identified in the NRC accident management program are discussed. 2 refs., 3 figs

  9. Application of unsupervised learning methods in high energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Koevesarki, Peter; Nuncio Quiroz, Adriana Elizabeth; Brock, Ian C. [Physikalisches Institut, Universitaet Bonn, Bonn (Germany)

    2011-07-01

    High energy physics is a home for a variety of multivariate techniques, mainly due to the fundamentally probabilistic behaviour of nature. These methods generally require training based on some theory, in order to discriminate a known signal from a background. Nevertheless, new physics can show itself in ways that previously no one thought about, and in these cases conventional methods give little or no help. A possible way to discriminate between known processes (like vector bosons or top-quark production) or look for new physics is using unsupervised machine learning to extract the features of the data. A technique was developed, based on the combination of neural networks and the method of principal curves, to find a parametrisation of the non-linear correlations of the data. The feasibility of the method is shown on ATLAS data.

  10. PALEOEARTHQUAKES IN THE PRIBAIKALIE: METHODS AND RESULTS OF DATING

    Directory of Open Access Journals (Sweden)

    Oleg P. Smekalin

    2010-01-01

    Full Text Available In the Pribaikalie and adjacent territories, seismogeological studies have been underway for almost a half of the century and resulted in discovery of more than 70 dislocations of seismic or presumably seismic origin. With commencement of paleoseismic studies, dating of paleo-earthquakes was focused on as an indicator useful for long-term prediction of strong earthquakes. V.P. Solonenko [Solonenko, 1977] distinguished five methods for dating paleoseismogenic deformations, i.e. geological, engineering geological, historico-archeological, dendrochronological and radiocarbon methods. However, ages of the majority of seismic deformations, which were subject to studies at the initial stage of development of seismogeology in Siberia, were defined by methods of relative or correlation age determination.Since the 1980s, studies of seismogenic deformation in the Pribaikalie have been widely conducted with trenching. Mass sampling, followed with radiocarbon analyses and definition of absolute ages of paleo-earthquakes, provided new data on seismic regimes of the territory and rates of and recent displacements along active faults, and enhanced validity of methods of relative dating, in particular morphometry. Capacities of the morphometry method has significantly increased with introduction of laser techniques in surveys and digital processing of 3D relief models.Comprehensive seismogeological studies conducted in the Pribaikalie revealed 43 paleo-events within 16 seismogenic structures. Absolute ages of 18 paleo-events were defined by the radiocarbon age determination method. Judging by their ages, a number of dislocations were related with historical earthquakes which occurred in the 18th and 19th centuries, yet any reliable data on epicenters of such events are not available. The absolute and relative dating methods allowed us to identify sections in some paleoseismogenic structures by differences in ages of activation and thus provided new data for

  11. Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

    Science.gov (United States)

    Burlina, Philippe; Billings, Seth; Joshi, Neil; Albayda, Jemima

    2017-01-01

    To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

  12. Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

    Directory of Open Access Journals (Sweden)

    Philippe Burlina

    Full Text Available To evaluate the use of ultrasound coupled with machine learning (ML and deep learning (DL techniques for automated or semi-automated classification of myositis.Eighty subjects comprised of 19 with inclusion body myositis (IBM, 14 with polymyositis (PM, 14 with dermatomyositis (DM, and 33 normal (N subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally were acquired. We considered three problems of classification including (A normal vs. affected (DM, PM, IBM; (B normal vs. IBM patients; and (C IBM vs. other types of myositis (DM or PM. We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification.The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A, 86.6% ± 2.4% for (B and 74.8% ± 3.9% for (C, while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A, 84.3% ± 2.3% for (B and 68.9% ± 2.5% for (C.This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

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

    Directory of Open Access Journals (Sweden)

    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.

  14. A novel Bayesian learning method for information aggregation in modular neural networks

    DEFF Research Database (Denmark)

    Wang, Pan; Xu, Lida; Zhou, Shang-Ming

    2010-01-01

    Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight...... benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling....

  15. Model of Distant Learning Educational Methods for the Students with Disabilities

    Science.gov (United States)

    Naumova, Tatyana A.; Vytovtova, Nadezhda I.; Mitiukov, Nicholas W.; Zulfugarzade, Teymur E.

    2017-01-01

    The present paper represents the results of the studies done at the Udmurt State University with assistance of the Russian Humanitarian Scientific Fund (project 14-16-18004). In the course of studies e-learning educational methods for the students with special educational needs were developed, approved and implemented in educational process.…

  16. Trends and Lessons Learned in Interdisciplinary and Non-Business Case Method Application.

    Science.gov (United States)

    Anyansi-Archibong, Chi; Czuchry, Andrew J.; House, Claudia S.; Cicirello, Tony

    2000-01-01

    Presents results of a survey designed to test the level of development and application of cases in non-business courses such as sciences, mathematics, engineering, health, and technology. Findings support the growing popularity of the case method of teaching and learning outside the business domain. Suggests a framework for establishing win-win…

  17. Improved method for SNR prediction in machine-learning-based test

    NARCIS (Netherlands)

    Sheng, Xiaoqin; Kerkhoff, Hans G.

    2010-01-01

    This paper applies an improved method for testing the signal-to-noise ratio (SNR) of Analogue-to-Digital Converters (ADC). In previous work, a noisy and nonlinear pulse signal is exploited as the input stimulus to obtain the signature results of ADC. By applying a machine-learning-based approach,

  18. Learning Financial Accounting in a Tertiary Institution of a Developing Country. An Investigation into Instructional Methods

    Science.gov (United States)

    Abeysekera, Indra

    2011-01-01

    This study examines three instructional methods (traditional, interactive, and group case-based study), and student opinions on their preference for learning financial accounting in large classes at a metropolitan university in Sri Lanka. It analyses the results of a survey questionnaire of students, using quantitative techniques to determine the…

  19. Procrastination and Motivation of Undergraduates with Learning Disabilities: A Mixed-Methods Inquiry

    Science.gov (United States)

    Klassen, Robert M.; Krawchuk, Lindsey L.; Lynch, Shane L.; Rajani, Sukaina

    2008-01-01

    The purpose of this mixed-methods article was to report two studies exploring the relationships between academic procrastination and motivation in 208 undergraduates with (n = 101) and without (n = 107) learning disabilities (LD). In Study 1, the results from self-report surveys found that individuals with LD reported significantly higher levels…

  20. Preferred Methods of Learning for Nursing Students in an On-Line Degree Program.

    Science.gov (United States)

    Hampton, Debra; Pearce, Patricia F; Moser, Debra K

    Investigators have demonstrated that on-line courses result in effective learning outcomes, but limited information has been published related to preferred teaching strategies. Delivery of on-line courses requires various teaching methods to facilitate interaction between students, content, and technology. The purposes of this study were to understand student teaching/learning preferences in on-line courses to include (a) differences in preferred teaching/learning methods for on-line nursing students across generations and (b) which teaching strategies students found to be most engaging and effective. Participants were recruited from 2 accredited, private school nursing programs (N=944) that admit students from across the United States and deliver courses on-line. Participants provided implied consent, and 217 (23%) students completed the on-line survey. Thirty-two percent of the students were from the Baby Boomer generation (1946-1964), 48% from Generation X (1965-1980), and 20% from the Millennial Generation (born after 1980). The preferred teaching/learning methods for students were videos or narrated PowerPoint presentations, followed by synchronous Adobe Connect educations sessions, assigned journal article reading, and e-mail dialog with the instructor. The top 2 methods identified by participants as the most energizing/engaging and most effective for learning were videos or narrated PowerPoint presentations and case studies. The teaching/learning method least preferred by participants and that was the least energizing/engaging was group collaborative projects with other students; the method that was the least effective for learning was wikis. Baby Boomers and Generation X participants had a significantly greater preference for discussion board (PBaby Boomer and Generation X students and rated on-line games as significantly more energizing/engaging and more effective for learning (PBaby Boomer and Generation X students. In conclusion, the results of this

  1. Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method

    Directory of Open Access Journals (Sweden)

    Yuhan Jia

    2017-01-01

    Full Text Available Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN and long short-term memory (LSTM to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.

  2. Performance of various mathematical methods for calculation of radioimmunoassay results

    International Nuclear Information System (INIS)

    Sandel, P.; Vogt, W.

    1977-01-01

    Interpolation and regression methods are available for computer aided determination of radioimmunological end results. We compared the performance of eight algorithms (weighted and unweighted linear logit-log regression, quadratic logit-log regression, Rodbards logistic model in the weighted and unweighted form, smoothing spline interpolation with a large and small smoothing factor and polygonal interpolation) on the basis of three radioimmunoassays with different reference curve characteristics (digoxin, estriol, human chorionic somatomammotropin = HCS). Great store was set by the accuracy of the approximation at the intermediate points on the curve, ie. those points that lie midway between two standard concentrations. These concentrations were obtained by weighing and inserted as unknown samples. In the case of digoxin and estriol the polygonal interpolation provided the best results while the weighted logit-log regression proved superior in the case of HCS. (orig.) [de

  3. Influence of Discussion Rating in Cooperative Learning Type Numbered Head Together on Learning Results Students VII MTSN Model Padang

    Science.gov (United States)

    Sasmita, E.; Edriati, S.; Yunita, A.

    2018-04-01

    Related to the math score of the first semester in class at seventh grade of MTSN Model Padang which much the score that low (less than KKM). It because of the students who feel less involved in learning process because the teacher don't do assessment the discussions. The solution of the problem is discussion assessment in Cooperative Learning Model type Numbered Head Together. This study aims to determine whether the discussion assessment in NHT effect on student learning outcomes of class VII MTsN Model Padang. The instrument used in this study is discussion assessment and final tests. The data analysis technique used is the simple linear regression analysis. Hypothesis test results Fcount greater than the value of Ftable then the hypothesis in this study received. So it concluded that the assessment of the discussion in NHT effect on student learning outcomes of class VII MTsN Model Padang.

  4. Process Skill Assessment Instrument: Innovation to measure student’s learning result holistically

    Science.gov (United States)

    Azizah, K. N.; Ibrahim, M.; Widodo, W.

    2018-01-01

    Science process skills (SPS) are very important skills for students. However, the fact that SPS is not being main concern in the primary school learning is undeniable. This research aimed to develop a valid, practical, and effective assessment instrument to measure student’s SPS. Assessment instruments comprise of worksheet and test. This development research used one group pre-test post-test design. Data were obtained with validation, observation, and test method to investigate validity, practicality, and the effectivenss of the instruments. Results showed that the validity of assessment instruments is very valid, the reliability is categorized as reliable, student SPS activities have a high percentage, and there is significant improvement on student’s SPS score. It can be concluded that assessment instruments of SPS are valid, practical, and effective to be used to measure student’s SPS result.

  5. Physics-Based Scientific Learning Module to Improve Students Motivation and Results

    Directory of Open Access Journals (Sweden)

    Soni Nugroho Yuliono

    2018-02-01

    Full Text Available Teaching materials that available in the school to learn physics especially scientific-based is limited and become one of the obstacles to achieving the learning objectives on electromagnetic waves maerial. The research aims is to gain scientific Physics-based learning modules for high school grade XII students who have met the eligibility criteria, determine the effectiveness of using scientific-based learning modules Physics to improve motivation and learning outcomes from students of grade XII High School. The development of this research on Physics module using 4D development procedure which consist of the steps of define, design, development, and dissemination. Definition phase consists of the teacher and student’s needs analysis process, material analysis, as well as the formulation of the learning module. The design phase of physics learning modules according to the stage of scientific learning are integrated into the module. The development phase consists of the development process of the modules from the design results, validating the feasibility, module revision, limited testing, and the use of scientifically-based learning modules Physics in grade XII IPA 1 Batik 2 Surakarta senior high school. The deployment phase is the deployment process module to another Senior High School in Surakarta. Data Analysis for the study is quantitative descriptive analysis based on the score criteria and analysis of increasing student motivation through N-gain. Conclusion obtained are ; 1 Physics-based scientific learning modules that developed meet the eligibility criteria on aspects of content and presentation, language, the chart, and aspects of learning. The module is declared worthy of the ideals validation results with the percentage of 85.16%, 83.66% by students and teachers in the response phase of the deployment of 85.93%, which is included in the category of "very good"; 2 Physics-based scietific learning modules with material scientific

  6. Report of the Results of an IMS LEarning Design Expert Workshop

    NARCIS (Netherlands)

    Neumann, Susanne; Klebl, Michael; Griffiths, David; Hernández-Leo, Davinia; De la Fuente-Valentin, Luis; Hummel, Hans; Brouns, Francis; Derntl, Michael; Oberhuemer, Petra

    2009-01-01

    Neumann, S., Klebl, M., Griffiths, D., Hernández-Leo, D., de la Fuente Valentín, L., Hummel, H., Brouns, F., Derntl, M., & Oberhuemer, P. (2010). Report of the Results of an IMS Learning Design Expert Workshop. International Journal Of Emerging Technologies In Learning (IJET), 5(1), pp.

  7. Participant Comfort with and Application of Inquiry-Based Learning: Results from 4-H Volunteer Training

    Science.gov (United States)

    Haugen, Heidi; Stevenson, Anne; Meyer, Rebecca L.

    2016-01-01

    This article explores how a one-time training designed to support learning transfer affected 4-H volunteers' comfort levels with the training content and how comfort levels, in turn, affected the volunteers' application of tools and techniques learned during the training. Results of a follow-up survey suggest that the training participants…

  8. Lending, Learning, Leading: Developing Results-Based Leaders in Opportunity Finance

    Science.gov (United States)

    Annie E. Casey Foundation, 2015

    2015-01-01

    This report tells the story of the CDFI Leadership Learning Network, a Casey Foundation initiative to equip leaders of community development finance institutions with the tools of results-based leadership (RBL). The Foundation shares lessons learned from the network, core RBL concepts and profiles of CDFI leaders as they apply RBL skills and tools…

  9. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

    Science.gov (United States)

    Cuperlovic-Culf, Miroslava

    2018-01-11

    Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

  10. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

    Science.gov (United States)

    Cuperlovic-Culf, Miroslava

    2018-01-01

    Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

  11. NASA Lunabotics Mining Competition for Universities: Results and Lessons Learned

    Science.gov (United States)

    Mueller, Robert P.; Murphy, Gloria A.

    2011-01-01

    Space Mining for resources such as water ice, and regolith, which contain many elements in the form of metals, minerals, volatiles and other compounds, is a necessary step in Space Resource Utilization. One of the primary goals is to extract propellants from the regolith such as oxygen and hydrogen which could then be used for in-space transportation. In addition, the space mining system can be used for various construction tasks that can benefit human and robotic exploration as well as scientific investigations based on the exposed topography. The National Aeronautics & Space Administration (NASA) Lunabotics Mining Competition is a university-level competition designed to engage and retain students in science, technology, engineering and mathematics (STEM). NASA will directly benefit from the competition by encouraging the development of innovative lunar excavation concepts from universities which may result in clever ideas and solutions which could be applied to an actual lunar excavation device or payload. The challenge is for students to design and build a remote controlled or autonomous excavator, called a lunabot, that can collect and deposit a minimum of 10 kilograms of lunar simulant within 15 minutes. The complexities of the challenge include the abrasive characteristics of the lunar simulant, the weight and size limitations of the lunabot, and the ability to control the lunabot from a remote control center. This paper will present the results of the first and second annual Lunabotics Mining Competitions held in May 2010 and May 2011. In 2010, 22 United States (US) universities competed, and in May 2011 the competition was opened to international participation, with 46 Universities expected to attend. There are 12 international teams and 34 US teams. This combined total directly inspired an estimated 544 university students. More students and the public were engaged via internet broadcasting and social networking media. The various designs will be

  12. Apache Leap Tuff INTRAVAL experiments - results and lessons learned

    International Nuclear Information System (INIS)

    Rasmussen, T.C.; Rhodes, S.C.; Guzman, A.; Neuman, S.P.

    1996-03-01

    Data from laboratory and field experiments in unsaturated fractured rock are summarized and interpreted for the purpose of evaluating conceptual and numerical models of fluid, heat and solute transport. The experiments were conducted at four scales, in small cores (2.5-cm long by 6-cm across), a large core (12-cm long by 10-cm across), a small block containing a single fracture (20 x 21 x 93 cm), and at field scales in boreholes (30-m long by 10-cm across) at three scales (1/2-, 1- and 3-meters). The smallest scale in the laboratory provided isothermal hydraulic and thermal properties of unfractured rock. Nonisothermal heat, fluid and solute transport experiments were conducted using the large core. Isothermal gas and liquid flow experiments were conducted in the fractured block. Field-scale experiments using air were used to obtain in situ permeability estimates as a function of the measurement scale. Interpretation of experimental results provides guidance for resolving uncertainties related to radionuclide migration from high level waste repositories in unsaturated fractured rock

  13. Apache Leap Tuff INTRAVAL experiments - results and lessons learned

    Energy Technology Data Exchange (ETDEWEB)

    Rasmussen, T.C. [Univ. of Georgia, Athens, GA (United States); Rhodes, S.C.; Guzman, A.; Neuman, S.P. [Univ. of Arizona, Tucson, AZ (United States). Dept. of Hydrology and Water Resources

    1996-03-01

    Data from laboratory and field experiments in unsaturated fractured rock are summarized and interpreted for the purpose of evaluating conceptual and numerical models of fluid, heat and solute transport. The experiments were conducted at four scales, in small cores (2.5-cm long by 6-cm across), a large core (12-cm long by 10-cm across), a small block containing a single fracture (20 x 21 x 93 cm), and at field scales in boreholes (30-m long by 10-cm across) at three scales (1/2-, 1- and 3-meters). The smallest scale in the laboratory provided isothermal hydraulic and thermal properties of unfractured rock. Nonisothermal heat, fluid and solute transport experiments were conducted using the large core. Isothermal gas and liquid flow experiments were conducted in the fractured block. Field-scale experiments using air were used to obtain in situ permeability estimates as a function of the measurement scale. Interpretation of experimental results provides guidance for resolving uncertainties related to radionuclide migration from high level waste repositories in unsaturated fractured rock.

  14. Learning neuroendoscopy with an exoscope system (video telescopic operating monitor): Early clinical results.

    Science.gov (United States)

    Parihar, Vijay; Yadav, Y R; Kher, Yatin; Ratre, Shailendra; Sethi, Ashish; Sharma, Dhananjaya

    2016-01-01

    Steep learning curve is found initially in pure endoscopic procedures. Video telescopic operating monitor (VITOM) is an advance in rigid-lens telescope systems provides an alternative method for learning basics of neuroendoscopy with the help of the familiar principle of microneurosurgery. The aim was to evaluate the clinical utility of VITOM as a learning tool for neuroendoscopy. Video telescopic operating monitor was used 39 cranial and spinal procedures and its utility as a tool for minimally invasive neurosurgery and neuroendoscopy for initial learning curve was studied. Video telescopic operating monitor was used in 25 cranial and 14 spinal procedures. Image quality is comparable to endoscope and microscope. Surgeons comfort improved with VITOM. Frequent repositioning of scope holder and lack of stereopsis is initial limiting factor was compensated for with repeated procedures. Video telescopic operating monitor is found useful to reduce initial learning curve of neuroendoscopy.

  15. IP-MLI: An Independency of Learning Materials from Platforms in a Mobile Learning using Intelligent Method

    Directory of Open Access Journals (Sweden)

    Mohammed Abdallh Otair

    2006-06-01

    Full Text Available Attempting to deliver a monolithic mobile learning system is too inflexible in view of the heterogeneous mixture of hardware and services available and the desirability of facility blended approaches to learning delivery, and how to build learning materials to run on all platforms[1]. This paper proposes a framework of mobile learning system using an intelligent method (IP-MLI . A fuzzy matching method is used to find suitable learning material design. It will provide a best matching for each specific platform type for each learner. The main contribution of the proposed method is to use software layer to insulate learning materials from device-specific features. Consequently, many versions of learning materials can be designed to work on many platform types.

  16. Lessons learned: advantages and disadvantages of mixed method research

    DEFF Research Database (Denmark)

    Malina, Mary A.; Nørreklit, Hanne; Selto, Frank H.

    2011-01-01

    on the use and usefulness of a specialized balanced scorecard; and third, to encourage researchers to actually use multiple methods and sources of data to address the very many accounting phenomena that are not fully understood. Design/methodology/approach – This paper is an opinion piece based...... on the authors' experience conducting a series of longitudinal mixed method studies. Findings – The authors suggest that in many studies, using a mixed method approach provides the best opportunity for addressing research questions. Originality/value – This paper provides encouragement to those who may wish......Purpose – The purpose of this paper is first, to discuss the theoretical assumptions, qualities, problems and myopia of the dominating quantitative and qualitative approaches; second, to describe the methodological lessons that the authors learned while conducting a series of longitudinal studies...

  17. Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides.

    Science.gov (United States)

    Stanislawski, Jerzy; Kotulska, Malgorzata; Unold, Olgierd

    2013-01-17

    Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning). We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset

  18. Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods.

    Science.gov (United States)

    Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

    2018-01-01

    Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.

  19. Best practices for learning physiology: combining classroom and online methods.

    Science.gov (United States)

    Anderson, Lisa C; Krichbaum, Kathleen E

    2017-09-01

    Physiology is a requisite course for many professional allied health programs and is a foundational science for learning pathophysiology, health assessment, and pharmacology. Given the demand for online learning in the health sciences, it is important to evaluate the efficacy of online and in-class teaching methods, especially as they are combined to form hybrid courses. The purpose of this study was to compare two hybrid physiology sections in which one section was offered mostly in-class (85% in-class), and the other section was offered mostly online (85% online). The two sections in 2 yr ( year 1 and year 2 ) were compared in terms of knowledge of physiology measured in exam scores and pretest-posttest improvement, and in measures of student satisfaction with teaching. In year 1 , there were some differences on individual exam scores between the two sections, but no significant differences in mean exam scores or in pretest-posttest improvements. However, in terms of student satisfaction, the mostly in-class students in year 1 rated the instructor significantly higher than did the mostly online students. Comparisons between in-class and online students in the year 2 cohort yielded data that showed that mean exam scores were not statistically different, but pre-post changes were significantly greater in the mostly online section; student satisfaction among mostly online students also improved significantly. Education researchers must investigate effective combinations of in-class and online methods for student learning outcomes, while maintaining the flexibility and convenience that online methods provide. Copyright © 2017 the American Physiological Society.

  20. Evaluation Methods on Usability of M-Learning Environments

    Directory of Open Access Journals (Sweden)

    Teresa Magal-Royo

    2007-10-01

    Full Text Available Nowadays there are different evaluation methods focused in the assessment of the usability of telematic methods. The assessment of 3rd generation web environments evaluates the effectiveness and usability of application with regard to the user needs. Wireless usability and, specifically in mobile phones, is concentrated in the validation of the features and tools management using conventional interactive environments. There is not a specific and suitable criterion to evaluate created environments and m-learning platforms, where the restricted and sequential representation is a fundamental aspect to be considered.The present paper exposes the importance of the conventional usability methods to verify both: the employed contents in wireless formats, and the possible interfaces from the conception phases, to the validations of the platform with such characteristics.The development of usability adapted inspection could be complemented with the Remote’s techniques of usability testing, which are being carried out these days in the mobile devices field and which pointed out the need to apply common criteria in the validation of non-located learning scenarios.

  1. A Preliminary Survey of the Preferred Learning Methods for Interpretation Students

    Science.gov (United States)

    Heinz, Michael

    2013-01-01

    There are many different methods that individuals use to learn languages like reading books or writing essays. Not all methods are equally successful for second language learners but nor do all successful learners of a second language show identical preferences for learning methods. Additionally, at the highest level of language learning various…

  2. Comparisons of likelihood and machine learning methods of individual classification

    Science.gov (United States)

    Guinand, B.; Topchy, A.; Page, K.S.; Burnham-Curtis, M. K.; Punch, W.F.; Scribner, K.T.

    2002-01-01

    Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high FST), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of

  3. The role of problem solving method on the improvement of mathematical learning

    Directory of Open Access Journals (Sweden)

    Saeed Mokhtari-Hassanabad

    2012-10-01

    Full Text Available In history of education, problem solving is one of the important educational goals and teachers or parents have intended that their students have capacity of problem solving. In present research, it is tried that study the problem solving method for mathematical learning. This research is implemented via quasi-experimental method on 49 boy students at high school. The results of Leven test and T-test indicated that problem solving method has more effective on the improvement of mathematical learning than traditional instruction method. Therefore it seems that teachers of mathematics must apply the problem solving method in educational systems till students became self-efficiency in mathematical problem solving.

  4. Advanced methods in NDE using machine learning approaches

    Science.gov (United States)

    Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

    2018-04-01

    Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

  5. Methodics of computing the results of monitoring the exploratory gallery

    Directory of Open Access Journals (Sweden)

    Krúpa Víazoslav

    2000-09-01

    Full Text Available At building site of motorway tunnel Višòové-Dubná skala , the priority is given to driving of exploration galley that secures in detail: geologic, engineering geology, hydrogeology and geotechnics research. This research is based on gathering information for a supposed use of the full profile driving machine that would drive the motorway tunnel. From a part of the exploration gallery which is driven by the TBM method, a fulfilling information is gathered about the parameters of the driving process , those are gathered by a computer monitoring system. The system is mounted on a driving machine. This monitoring system is based on the industrial computer PC 104. It records 4 basic values of the driving process: the electromotor performance of the driving machine Voest-Alpine ATB 35HA, the speed of driving advance, the rotation speed of the disintegrating head TBM and the total head pressure. The pressure force is evaluated from the pressure in the hydraulic cylinders of the machine. Out of these values, the strength of rock mass, the angle of inner friction, etc. are mathematically calculated. These values characterize rock mass properties as their changes. To define the effectivity of the driving process, the value of specific energy and the working ability of driving head is used. The article defines the methodics of computing the gathered monitoring information, that is prepared for the driving machine Voest – Alpine ATB 35H at the Institute of Geotechnics SAS. It describes the input forms (protocols of the developed method created by an EXCEL program and shows selected samples of the graphical elaboration of the first monitoring results obtained from exploratory gallery driving process in the Višòové – Dubná skala motorway tunnel.

  6. Effects of Learning Style and Training Method on Computer Attitude and Performance in World Wide Web Page Design Training.

    Science.gov (United States)

    Chou, Huey-Wen; Wang, Yu-Fang

    1999-01-01

    Compares the effects of two training methods on computer attitude and performance in a World Wide Web page design program in a field experiment with high school students in Taiwan. Discusses individual differences, Kolb's Experiential Learning Theory and Learning Style Inventory, Computer Attitude Scale, and results of statistical analyses.…

  7. Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads

    International Nuclear Information System (INIS)

    Sharma Mradul; Koul Maharaj Krishna; Mitra Abhas; Nayak Jitadeepa; Bose Smarajit

    2014-01-01

    A detailed case study of γ-hadron segregation for a ground based atmospheric Cherenkov telescope is presented. We have evaluated and compared various supervised machine learning methods such as the Random Forest method, Artificial Neural Network, Linear Discriminant method, Naive Bayes Classifiers, Support Vector Machines as well as the conventional dynamic supercut method by simulating triggering events with the Monte Carlo method and applied the results to a Cherenkov telescope. It is demonstrated that the Random Forest method is the most sensitive machine learning method for γ-hadron segregation. (research papers)

  8. Learning physics: A comparative analysis between instructional design methods

    Science.gov (United States)

    Mathew, Easow

    The purpose of this research was to determine if there were differences in academic performance between students who participated in traditional versus collaborative problem-based learning (PBL) instructional design approaches to physics curricula. This study utilized a quantitative quasi-experimental design methodology to determine the significance of differences in pre- and posttest introductory physics exam performance between students who participated in traditional (i.e., control group) versus collaborative problem solving (PBL) instructional design (i.e., experimental group) approaches to physics curricula over a college semester in 2008. There were 42 student participants (N = 42) enrolled in an introductory physics course at the research site in the Spring 2008 semester who agreed to participate in this study after reading and signing informed consent documents. A total of 22 participants were assigned to the experimental group (n = 22) who participated in a PBL based teaching methodology along with traditional lecture methods. The other 20 students were assigned to the control group (n = 20) who participated in the traditional lecture teaching methodology. Both the courses were taught by experienced professors who have qualifications at the doctoral level. The results indicated statistically significant differences (p traditional (i.e., lower physics posttest scores and lower differences between pre- and posttest scores) versus collaborative (i.e., higher physics posttest scores, and higher differences between pre- and posttest scores) instructional design approaches to physics curricula. Despite some slight differences in control group and experimental group demographic characteristics (gender, ethnicity, and age) there were statistically significant (p = .04) differences between female average academic improvement which was much higher than male average academic improvement (˜63%) in the control group which may indicate that traditional teaching methods

  9. The impact of cooperative learning on student engagement: Results from an intervention

    DEFF Research Database (Denmark)

    Herrmann, Kim Jesper

    2013-01-01

    With an increasing awareness that many undergraduates are passive during teaching sessions, calls for instructional methods that allow students to become actively engaged have increased. Cooperative learning has long been popular at the primary and secondary level and, within recent years, higher...... were measured before and after the intervention to assess the impact on 140 students’ engagement levels. In addition, open-ended comments were analysed, revealing what faculty adopting cooperative learning principles in higher education should be especially aware of....

  10. Study on a pattern classification method of soil quality based on simplified learning sample dataset

    Science.gov (United States)

    Zhang, Jiahua; Liu, S.; Hu, Y.; Tian, Y.

    2011-01-01

    Based on the massive soil information in current soil quality grade evaluation, this paper constructed an intelligent classification approach of soil quality grade depending on classical sampling techniques and disordered multiclassification Logistic regression model. As a case study to determine the learning sample capacity under certain confidence level and estimation accuracy, and use c-means algorithm to automatically extract the simplified learning sample dataset from the cultivated soil quality grade evaluation database for the study area, Long chuan county in Guangdong province, a disordered Logistic classifier model was then built and the calculation analysis steps of soil quality grade intelligent classification were given. The result indicated that the soil quality grade can be effectively learned and predicted by the extracted simplified dataset through this method, which changed the traditional method for soil quality grade evaluation. ?? 2011 IEEE.

  11. Research progress in machine learning methods for gene-gene interaction detection.

    Science.gov (United States)

    Peng, Zhe-Ye; Tang, Zi-Jun; Xie, Min-Zhu

    2018-03-20

    Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

  12. Learning Evaluation: blending quality improvement and implementation research methods to study healthcare innovations.

    Science.gov (United States)

    Balasubramanian, Bijal A; Cohen, Deborah J; Davis, Melinda M; Gunn, Rose; Dickinson, L Miriam; Miller, William L; Crabtree, Benjamin F; Stange, Kurt C

    2015-03-10

    In healthcare change interventions, on-the-ground learning about the implementation process is often lost because of a primary focus on outcome improvements. This paper describes the Learning Evaluation, a methodological approach that blends quality improvement and implementation research methods to study healthcare innovations. Learning Evaluation is an approach to multi-organization assessment. Qualitative and quantitative data are collected to conduct real-time assessment of implementation processes while also assessing changes in context, facilitating quality improvement using run charts and audit and feedback, and generating transportable lessons. Five principles are the foundation of this approach: (1) gather data to describe changes made by healthcare organizations and how changes are implemented; (2) collect process and outcome data relevant to healthcare organizations and to the research team; (3) assess multi-level contextual factors that affect implementation, process, outcome, and transportability; (4) assist healthcare organizations in using data for continuous quality improvement; and (5) operationalize common measurement strategies to generate transportable results. Learning Evaluation principles are applied across organizations by the following: (1) establishing a detailed understanding of the baseline implementation plan; (2) identifying target populations and tracking relevant process measures; (3) collecting and analyzing real-time quantitative and qualitative data on important contextual factors; (4) synthesizing data and emerging findings and sharing with stakeholders on an ongoing basis; and (5) harmonizing and fostering learning from process and outcome data. Application to a multi-site program focused on primary care and behavioral health integration shows the feasibility and utility of Learning Evaluation for generating real-time insights into evolving implementation processes. Learning Evaluation generates systematic and rigorous cross

  13. Radioimmunological determination of plasma progesterone. Methods - Results - Indications

    International Nuclear Information System (INIS)

    Gonon-Estrangin, Chantal.

    1978-10-01

    The aim of this work is to describe the radioimmunological determination of plasma progesterone carried out at the hormonology Laboratory of the Grenoble University Hospital Centre (Professor E. Chambaz), to compare our results with those of the literature and to present the main clinical indications of this analysis. The measurement method has proved reproducible, specific (the steroid purification stage is unnecessary) and sensitive (detection: 10 picograms of progesterone per tube). In seven normally menstruating women our results agree with published values: (in nanograms per millilitre: ng/ml) 0.07 ng/ml to 0.9 ng/ml in the follicular phase, from the start of menstruation until ovulation, then rapid increase at ovulation with a maximum in the middle of the luteal phase (our values for this maximum range from 7.9 ng/ml to 21.7 ng/ml) and gradual drop in progesterone secretion until the next menstrual period. In gynecology the radioimmunoassay of plasma progesterone is valuable for diagnostic and therapeutic purposes: - to diagnosis the absence of corpus luteum, - to judge the effectiveness of an ovulation induction treatment [fr

  14. Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

    Science.gov (United States)

    Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

    2017-12-01

    To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

  15. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' < or = 18 mag), with 4500 K < or = Teff < or = 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of approx.0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  16. Perceived ambiguity as a barrier to intentions to learn genome sequencing results.

    Science.gov (United States)

    Taber, Jennifer M; Klein, William M P; Ferrer, Rebecca A; Han, Paul K J; Lewis, Katie L; Biesecker, Leslie G; Biesecker, Barbara B

    2015-10-01

    Many variants that could be returned from genome sequencing may be perceived as ambiguous-lacking reliability, credibility, or adequacy. Little is known about how perceived ambiguity influences thoughts about sequencing results. Participants (n = 494) in an NIH genome sequencing study completed a baseline survey before sequencing results were available. We examined how perceived ambiguity regarding sequencing results and individual differences in medical ambiguity aversion and tolerance for uncertainty were associated with cognitions and intentions concerning sequencing results. Perceiving sequencing results as more ambiguous was associated with less favorable cognitions about results and lower intentions to learn and share results. Among participants low in tolerance for uncertainty or optimism, greater perceived ambiguity was associated with lower intentions to learn results for non-medically actionable diseases; medical ambiguity aversion did not moderate any associations. Results are consistent with the phenomenon of "ambiguity aversion" and may influence whether people learn and communicate genomic information.

  17. Lesion insertion in the projection domain: Methods and initial results

    International Nuclear Information System (INIS)

    Chen, Baiyu; Leng, Shuai; Yu, Lifeng; Yu, Zhicong; Ma, Chi; McCollough, Cynthia

    2015-01-01

    Purpose: To perform task-based image quality assessment in CT, it is desirable to have a large number of realistic patient images with known diagnostic truth. One effective way of achieving this objective is to create hybrid images that combine patient images with inserted lesions. Because conventional hybrid images generated in the image domain fails to reflect the impact of scan and reconstruction parameters on lesion appearance, this study explored a projection-domain approach. Methods: Lesions were segmented from patient images and forward projected to acquire lesion projections. The forward-projection geometry was designed according to a commercial CT scanner and accommodated both axial and helical modes with various focal spot movement patterns. The energy employed by the commercial CT scanner for beam hardening correction was measured and used for the forward projection. The lesion projections were inserted into patient projections decoded from commercial CT projection data. The combined projections were formatted to match those of commercial CT raw data, loaded onto a commercial CT scanner, and reconstructed to create the hybrid images. Two validations were performed. First, to validate the accuracy of the forward-projection geometry, images were reconstructed from the forward projections of a virtual ACR phantom and compared to physically acquired ACR phantom images in terms of CT number accuracy and high-contrast resolution. Second, to validate the realism of the lesion in hybrid images, liver lesions were segmented from patient images and inserted back into the same patients, each at a new location specified by a radiologist. The inserted lesions were compared to the original lesions and visually assessed for realism by two experienced radiologists in a blinded fashion. Results: For the validation of the forward-projection geometry, the images reconstructed from the forward projections of the virtual ACR phantom were consistent with the images physically

  18. Lesion insertion in the projection domain: Methods and initial results

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Baiyu; Leng, Shuai; Yu, Lifeng; Yu, Zhicong; Ma, Chi; McCollough, Cynthia, E-mail: mccollough.cynthia@mayo.edu [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States)

    2015-12-15

    Purpose: To perform task-based image quality assessment in CT, it is desirable to have a large number of realistic patient images with known diagnostic truth. One effective way of achieving this objective is to create hybrid images that combine patient images with inserted lesions. Because conventional hybrid images generated in the image domain fails to reflect the impact of scan and reconstruction parameters on lesion appearance, this study explored a projection-domain approach. Methods: Lesions were segmented from patient images and forward projected to acquire lesion projections. The forward-projection geometry was designed according to a commercial CT scanner and accommodated both axial and helical modes with various focal spot movement patterns. The energy employed by the commercial CT scanner for beam hardening correction was measured and used for the forward projection. The lesion projections were inserted into patient projections decoded from commercial CT projection data. The combined projections were formatted to match those of commercial CT raw data, loaded onto a commercial CT scanner, and reconstructed to create the hybrid images. Two validations were performed. First, to validate the accuracy of the forward-projection geometry, images were reconstructed from the forward projections of a virtual ACR phantom and compared to physically acquired ACR phantom images in terms of CT number accuracy and high-contrast resolution. Second, to validate the realism of the lesion in hybrid images, liver lesions were segmented from patient images and inserted back into the same patients, each at a new location specified by a radiologist. The inserted lesions were compared to the original lesions and visually assessed for realism by two experienced radiologists in a blinded fashion. Results: For the validation of the forward-projection geometry, the images reconstructed from the forward projections of the virtual ACR phantom were consistent with the images physically

  19. A comparison between students' attitudes and their performance regarding the factors influencing learning and exam results

    Directory of Open Access Journals (Sweden)

    A Ebrahimi

    2009-03-01

    Full Text Available Background: Various educational approaches to learning have been proposed. It is, of course, of vital importance to study those that have resulted in effective educational achievements.Purpose: To assess the student's attitudes and performance in order to evaluate educational approaches and identify the variables leading to students gaining good marks.Methods: The subjects of the study were medical students at pathophysiology  phase. A multiple-choice questionnaire was prepared, the reliability and validity of which were confirmed. A paired-sample T­ test was used to compare and analyze  the student's  Basic Sciences Comprehensive  Exam scores in each discipline and their average score during the basic sciences course.Results: With respect to attitudes, the students highly valued the importance of teacher's methodology and mutual  respect, as an influential  factor in Learning. Furthermore, the majority  of the students relied heavily  on their textbooks  as the main source of information  and preferred to study at home, rather than any other places. Most of the students prefer to study in the morning. They believed that mid-term exams, quizzes, and active class participation do not have much effect on learning. However, it was of high importance to them to attend practical claSSfS (labs. With respect to performance, most of the students used lecture notes as the main references, and considered their home as a good place to study and  preferred to study in the morning,  but have  participated  in theoretical  and  practical classes regularly.Conclusion: Most successful students attributed their success to active class participation,  takingmid-term exams, quizzes, and using library. Having analyzed the data, we recommend the authorities to provide more methodology  workshops for teachers,  sufficient  number of textbooks,  expanding  or increasing the number of the reading rooms with essential equipment

  20. Machine learning methods for clinical forms analysis in mental health.

    Science.gov (United States)

    Strauss, John; Peguero, Arturo Martinez; Hirst, Graeme

    2013-01-01

    In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.

  1. Relabeling exchange method (REM) for learning in neural networks

    Science.gov (United States)

    Wu, Wen; Mammone, Richard J.

    1994-02-01

    The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.

  2. Comparison effectiveness of cooperative learning type STAD with cooperative learning type TPS in terms of mathematical method of Junior High School students

    Science.gov (United States)

    Wahyuni, A.

    2018-05-01

    This research is aimed to find out whether the model of cooperative learning type Student Team Achievement Division (STAD) is more effective than cooperative learning type Think-Pair-Share in SMP Negeri 7 Yogyakarta. This research was a quasi-experimental research, using two experimental groups. The population of research was all students of 7thclass in SMP Negeri 7 Yogyakarta that consists of 5 Classes. From the population were taken 2 classes randomly which used as sample. The instrument to collect data was a description test. Measurement of instrument validity use content validity and construct validity, while measuring instrument reliability use Cronbach Alpha formula. To investigate the effectiveness of cooperative learning type STAD and cooperative learning type TPS on the aspect of student’s mathematical method, the datas were analyzed by one sample test. Comparing the effectiveness of cooperative learning type STAD and TPS in terms of mathematical communication skills by using t-test. Normality test was not conducted because the sample of research more than 30 students, while homogeneity tested by using Kolmogorov Smirnov test. The analysis was performed at 5% confidence level.The results show as follows : 1) The model of cooperative learning type STAD and TPS are effective in terms of mathematical method of junior high school students. 2). STAD type cooperative learning model is more effective than TPS type cooperative learning model in terms of mathematical methods of junior high school students.

  3. What Predicts Use of Learning-Centered, Interactive Engagement Methods?

    Science.gov (United States)

    Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael

    2014-01-01

    What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using interactive…

  4. Incorporating Meaningful Gamification in a Blended Learning Research Methods Class: Examining Student Learning, Engagement, and Affective Outcomes

    Science.gov (United States)

    Tan, Meng; Hew, Khe Foon

    2016-01-01

    In this study, we investigated how the use of meaningful gamification affects student learning, engagement, and affective outcomes in a short, 3-day blended learning research methods class using a combination of experimental and qualitative research methods. Twenty-two postgraduates were randomly split into two groups taught by the same…

  5. PYRAMID METHOD OF DISTANCE LEARNING IN HIGER EDUCATION

    Directory of Open Access Journals (Sweden)

    Дмитрий Васильевич Сенашенко

    2017-12-01

    Full Text Available The article deals with modern methods of distance learning in the corporate sector. On the specifics of the application of the described methods is their classification and be subject to review their specific differences based on the features and applications of these techniques given the characteristics of the organization of teaching in higher education, a conclusion about their preferred sides, which can be used in distance education. Later in the article, taking into account the above factors, it is proposed an innovative method of formation of educational programs. In view of the similarity of the rendered appearance of the pyramids, this technique proposed name “pyramid”. Offered by the authors, this technique is best synthesis of the best features of the previously described in the article for the online teaching methods. In the future, we are given a detailed description and conducted a preliminary analysis of the applicability of this technique to the training process in the Russian Federation. The analysis describes the eight alleged authors of distance education problems of high school that this method can help to solve.

  6. Effectiveness of creative and productive instructional method towards students' learning achievement in steel structure course

    Science.gov (United States)

    Sugiyanto, Pribadi, Supriyanto, Bambang

    2017-09-01

    The purpose of this study was to investigate the effectiveness of Creative & Productive instructional method compared with conventional method. This research was a quasi-experimental study involving all Civil Engineering students at Universitas Negeri Malang who were taking a course of Steel Structure. The students were randomly assigned to two different treatment groups, 30 students in experimental group and 37 students in the control group. It was assumed that these groups were equal in all relevant aspects; they differed only in the treatment administered. We used the t-test to test the hypothesis. The results of this research suggest that: (l) the use of Creative & Productive instructional method can significantly improve students' learning achievement, (2) the use of Creative & Productive instructional method can significantly improve students' retention, (3) students' motivation has a significant effect on their learning achievement, and (4) students' motivation has a significant effect on their retention.

  7. Learning Specific Content in Technology Education: Learning Study as a Collaborative Method in Swedish Preschool Class Using Hands-On Material

    Science.gov (United States)

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study…

  8. 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.

  9. Applying cheerful disco learning for improving of motivation and learning result of PKn in grade viii c students junior high school 1 Kebumen in second semester 2013/2014 academic years

    Directory of Open Access Journals (Sweden)

    Siti Makmuroh

    2017-06-01

    Full Text Available The aim of this classroom action research is to improve students’ learning motivation, learning result of PKn on Basic Competence of Describing Indonesian Government System and the Roles of the State Institutions as the Sovereignty Executive and the characters of Grade VIII C Students of Junior High School 1 of Kebumen in Second Semester of Academic Year 2013/2014 by applying CHEERFUL DISCO learning method. The research is a classroom action research conducted in two cycles; each cycle of which includes planning, conducting, observation, and reflection. The result of the research shows that the learning method was able to improve the students’ learning motivation in learning activities from 62.37% in pre cycle to 73.74% in the first cycle, then from 78.91% in the second cycle, improved the PKn learning achievement in mastering concept of the ability to describe Indonesian Government System and the Roles of the State Institutions as the Sovereignty Executive, which can be seen that the students’ achievement test result is improving in average from 78.18 with 54.55% of mastery learning in pre cycle to 83.23 with 72.73% of mastery learning in the first cycle, then it was improved to 86.59 in average with 81.82% of mastery learning in the second cycle.

  10. Comparison of microstickies measurement methods. Part II, Results and discussion

    Science.gov (United States)

    Mahendra R. Doshi; Angeles Blanco; Carlos Negro; Concepcion Monte; Gilles M. Dorris; Carlos C. Castro; Axel Hamann; R. Daniel Haynes; Carl Houtman; Karen Scallon; Hans-Joachim Putz; Hans Johansson; R. A. Venditti; K. Copeland; H.-M. Chang

    2003-01-01

    In part I of the article we discussed sample preparation procedure and described various methods used for the measurement of microstickies. Some of the important features of different methods are highlighted in Table 1. Temperatures used in the measurement methods vary from room temperature in some cases, 45 °C to 65 °C in other cases. Sample size ranges from as low as...

  11. Predicting protein complexes using a supervised learning method combined with local structural information.

    Science.gov (United States)

    Dong, Yadong; Sun, Yongqi; Qin, Chao

    2018-01-01

    The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

  12. Internet-based versus traditional teaching and learning methods.

    Science.gov (United States)

    Guarino, Salvatore; Leopardi, Eleonora; Sorrenti, Salvatore; De Antoni, Enrico; Catania, Antonio; Alagaratnam, Swethan

    2014-10-01

    The rapid and dramatic incursion of the Internet and social networks in everyday life has revolutionised the methods of exchanging data. Web 2.0 represents the evolution of the Internet as we know it. Internet users are no longer passive receivers, and actively participate in the delivery of information. Medical education cannot evade this process. Increasingly, students are using tablets and smartphones to instantly retrieve medical information on the web or are exchanging materials on their Facebook pages. Medical educators cannot ignore this continuing revolution, and therefore the traditional academic schedules and didactic schemes should be questioned. Analysing opinions collected from medical students regarding old and new teaching methods and tools has become mandatory, with a view towards renovating the process of medical education. A cross-sectional online survey was created with Google® docs and administrated to all students of our medical school. Students were asked to express their opinion on their favourite teaching methods, learning tools, Internet websites and Internet delivery devices. Data analysis was performed using spss. The online survey was completed by 368 students. Although textbooks remain a cornerstone for training, students also identified Internet websites, multimedia non-online material, such as the Encyclopaedia on CD-ROM, and other non-online computer resources as being useful. The Internet represented an important aid to support students' learning needs, but textbooks are still their resource of choice. Among the websites noted, Google and Wikipedia significantly surpassed the peer-reviewed medical databases, and access to the Internet was primarily through personal computers in preference to other Internet access devices, such as mobile phones and tablet computers. Increasingly, students are using tablets and smartphones to instantly retrieve medical information. © 2014 John Wiley & Sons Ltd.

  13. The Keyword Method of Foreign Vocabulary Learning: An Investigation of Its Generalizability. Working Paper No. 270.

    Science.gov (United States)

    Pressley, Michael; And Others

    In five experiments, college-age students of differing foreign language-learning abilities were asked to learn Latin word translations to determine the effectiveness of the keyword method of foreign language vocabulary learning. The Latin words were the types for which it has been argued that the keyword method effects would be maximized (the…

  14. The evaluation of reflective learning from the nursing student's point of view: A mixed method approach.

    Science.gov (United States)

    Fernández-Peña, Rosario; Fuentes-Pumarola, Concepció; Malagón-Aguilera, M Carme; Bonmatí-Tomàs, Anna; Bosch-Farré, Cristina; Ballester-Ferrando, David

    2016-09-01

    Adapting university programmes to European Higher Education Area criteria has required substantial changes in curricula and teaching methodologies. Reflective learning (RL) has attracted growing interest and occupies an important place in the scientific literature on theoretical and methodological aspects of university instruction. However, fewer studies have focused on evaluating the RL methodology from the point of view of nursing students. To assess nursing students' perceptions of the usefulness and challenges of RL methodology. Mixed method design, using a cross-sectional questionnaire and focus group discussion. The research was conducted via self-reported reflective learning questionnaire complemented by focus group discussion. Students provided a positive overall evaluation of RL, highlighting the method's capacity to help them better understand themselves, engage in self-reflection about the learning process, optimize their strengths and discover additional training needs, along with searching for continuous improvement. Nonetheless, RL does not help them as much to plan their learning or identify areas of weakness or needed improvement in knowledge, skills and attitudes. Among the difficulties or challenges, students reported low motivation and lack of familiarity with this type of learning, along with concerns about the privacy of their reflective journals and about the grading criteria. In general, students evaluated RL positively. The results suggest areas of needed improvement related to unfamiliarity with the methodology, ethical aspects of developing a reflective journal and the need for clear evaluation criteria. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

    Science.gov (United States)

    Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

    2017-01-01

    Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

  16. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Qi Huang

    2017-06-01

    Full Text Available Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC, by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC. We compared PAC performance with incremental support vector classifier (ISVC and non-adapting SVC (NSVC in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05 and ISVC (13.38% ± 2.62%, p = 0.001, and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle.

  17. Resource costing for multinational neurologic clinical trials: methods and results.

    Science.gov (United States)

    Schulman, K; Burke, J; Drummond, M; Davies, L; Carlsson, P; Gruger, J; Harris, A; Lucioni, C; Gisbert, R; Llana, T; Tom, E; Bloom, B; Willke, R; Glick, H

    1998-11-01

    We present the results of a multinational resource costing study for a prospective economic evaluation of a new medical technology for treatment of subarachnoid hemorrhage within a clinical trial. The study describes a framework for the collection and analysis of international resource cost data that can contribute to a consistent and accurate intercountry estimation of cost. Of the 15 countries that participated in the clinical trial, we collected cost information in the following seven: Australia, France, Germany, the UK, Italy, Spain, and Sweden. The collection of cost data in these countries was structured through the use of worksheets to provide accurate and efficient cost reporting. We converted total average costs to average variable costs and then aggregated the data to develop study unit costs. When unit costs were unavailable, we developed an index table, based on a market-basket approach, to estimate unit costs. To estimate the cost of a given procedure, the market-basket estimation process required that cost information be available for at least one country. When cost information was unavailable in all countries for a given procedure, we estimated costs using a method based on physician-work and practice-expense resource-based relative value units. Finally, we converted study unit costs to a common currency using purchasing power parity measures. Through this costing exercise we developed a set of unit costs for patient services and per diem hospital services. We conclude by discussing the implications of our costing exercise and suggest guidelines to facilitate more effective multinational costing exercises.

  18. Learning the scientific method using GloFish.

    Science.gov (United States)

    Vick, Brianna M; Pollak, Adrianna; Welsh, Cynthia; Liang, Jennifer O

    2012-12-01

    Here we describe projects that used GloFish, brightly colored, fluorescent, transgenic zebrafish, in experiments that enabled students to carry out all steps in the scientific method. In the first project, students in an undergraduate genetics laboratory course successfully tested hypotheses about the relationships between GloFish phenotypes and genotypes using PCR, fluorescence microscopy, and test crosses. In the second and third projects, students doing independent research carried out hypothesis-driven experiments that also developed new GloFish projects for future genetics laboratory students. Brianna Vick, an undergraduate student, identified causes of the different shades of color found in orange GloFish. Adrianna Pollak, as part of a high school science fair project, characterized the fluorescence emission patterns of all of the commercially available colors of GloFish (red, orange, yellow, green, blue, and purple). The genetics laboratory students carrying out the first project found that learning new techniques and applying their knowledge of genetics were valuable. However, assessments of their learning suggest that this project was not challenging to many of the students. Thus, the independent projects will be valuable as bases to widen the scope and range of difficulty of experiments available to future genetics laboratory students.

  19. E-learning support for Economic-mathematical methods

    Directory of Open Access Journals (Sweden)

    Pavel Kolman

    2009-01-01

    Full Text Available Article is describing process of creating and using of e-learning program for graphical solution of li­near programming problems that is used in the Economic mathematical methods course on Faculty of Business and Economics, MZLU. The program was created within FRVŠ 788/2008 grant and is intended for practicing of graphical solution of LP problems and allows better understanding of the li­near programming problems. In the article is on one hand described the way, how does the program work, it means how were the algorithms implemented, and on the other hand there is described way of use of that program. The program is constructed for working with integer and rational numbers. At the end of the article are shown basic statistics of programs use of students in the present form and the part-time form of study. It is mainly the number of programs downloads and comparison to another programs and students opinion on the e-learning support.

  20. Promoting Self-regulated Learning of Brazilian Preservice Student Teachers: Results of an Intervention Program

    Directory of Open Access Journals (Sweden)

    Danielle Ribeiro Ganda

    2018-02-01

    Full Text Available Self-regulation is the process by which individuals monitor, control, and reflect on their learning. Self-regulated students have motivational, metacognitive, affective, and behavioral characteristics that enhance their learning. As the importance of self-regulated learning is well acknowledged by research nowadays, the aim of this study is to examine the effectiveness of an innovative course designed to promote self-regulated learning among Brazilian preservice student teachers. The innovative approach was developed in the format of a program of intervention based heavily on self-reflection. The content involved student exposure to self-reflexive activities, lectures on the self-regulated learning framework, and theoretical tasks aimed at fostering self-regulation of students in a double perspective: as a student and as a future teacher. The efficacy of the approach was tested by comparison with both the results of students who had taken a course with theoretical content only and those who had not taken any course at all. The sample consisted of 109 students in 4 different freshman classes in a Teacher Education Program in a Brazilian public university in an inner city in the state of São Paulo. The research was conducted using a quasi-experimental design with three stages: pretest, intervention, and posttest. The classes were randomly assigned to experimental and control conditions as follows: an experimental group involving intervention, an experimental group exposed to theory, and two control groups not taking the course. Before and after the intervention program, all the participants responded to the Learning and Study Strategies Inventory and the Self-efficacy for Self-regulated Learning scales. Overall, the results showed that the intervention program format had a positive impact in enhancing student self-regulation. Moreover, students in both the experimental groups reported both higher gains in self-efficacy for self-regulated learning

  1. Implementation of a virtual learning from discrepancy meeting: a method to improve attendance and facilitate shared learning from radiological error

    International Nuclear Information System (INIS)

    Carlton Jones, A.L.; Roddie, M.E.

    2016-01-01

    Aim: To assess the effect on radiologist participation in learning from discrepancy meetings (LDMs) in a multisite radiology department by establishing virtual LDMs using OsiriX (Pixmeo). Materials and methods: Sets of anonymised discrepancy cases were added to an OsiriX database available for viewing on iMacs in all radiology reporting rooms. Radiologists were given a 3-week period to review the cases and send their feedback to the LDM convenor. Group learning points and consensus feedback were added to each case before it was moved to a permanent digital LDM library. Participation was recorded and compared with that from the previous 4 years of conventional LDMs. Radiologist feedback comparing the two types of LDM was collected using an anonymous online questionnaire. Results: Numbers of radiologists attending increased significantly from a mean of 12±2.9 for the conventional LDM to 32.7±7 for the virtual LDM (p<0.0001) and the percentage of radiologists achieving the UK standard of participation in at least 50% of LDMs annually rose from an average of 18% to 68%. The number of cases submitted per meeting rose significantly from an average of 11.1±3 for conventional LDMs to 15.9±5.9 for virtual LDMs (p<0.0097). Analysis of 35 returned questionnaires showed that radiologists welcomed being able to review cases at a time and place of their choosing and at their own pace. Conclusion: Introduction of virtual LDMs in a multisite radiology department improved radiologist participation in shared learning from radiological discrepancy and increased the number of submitted cases. - Highlights: • Learning from error is an important way to improve patient safety. • Consultant attendance at learning from discrepancy meetings (LDMs) was persistently poor in a large, multisite Trust. • Introduction of a ‘virtual’ LDM improved consultant participation and increased the number of cases submitted.

  2. THE POTENTIAL AND LIMITATIONS OF VISUALISATION AS A METHOD IN LEARNING SOCIAL SCIENCES AND HUMANITIES

    Directory of Open Access Journals (Sweden)

    Tatyana T. Sidelnikova

    2016-06-01

    Full Text Available Introduction: the paper is concerned with potential and barriers of application of visualisation as a method in learning social sciences and humanities. Using and employing visual aids becomes the most important resource in modern pedagogical theory and learning process due to the improvement of traditional pedagogical tools and new interpretation of well-known methods. Materials and Methods: the methods of observation, analysis of test results, results of examination session, data of questionnaires were used during the elaboration of the paper. Results: a good visual aid in teaching political science is the smiley as a simplified graphical representation expressing the emotions of a speaker or a writer. Observation, survey and results of examinations indicate that the above visual solutions not only improve students’ knowledge of subjects, but also improve the intellectual activity, contribute to the formation of the methodical approach to learning, associative thinking and creativity. Discussion and Conclusion: visualisation is a sign presentation of the content, functions, structures, stages of a process, a phenomenon through schematisation and associative and illustrative arrays. At the same time it is a way of transforming knowledge into real visual product with the author’s personal touch. Initially, students learn to reflect by drawing the essence of rather abstract concepts such as “parity”, “power” “freedom” etc. Assignments of higher levels involve the use of associative arrays, free images. By doing this, students do not just paint, but on their own initiative work with colours, seek to schematise information, sometimes dressing comments in lyrics.

  3. Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Zhiling Guo

    2016-03-01

    Full Text Available In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost and convolutional neural networks (CNN. To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.

  4. Analyzing the Learning Path of US Shale Players by Using the Learning Curve Method

    Directory of Open Access Journals (Sweden)

    Jong-Hyun Kim

    2017-12-01

    Full Text Available The US shale exploration and production (E&P industry has grown since 2007 due to the development of new techniques such as hydraulic fracturing and horizontal drilling. As a result, the share of shale gas in the US natural gas production is almost 50%, and the share of tight oil in the US crude oil production is almost 52%. Even though oil and gas prices decreased sharply in 2014, the production amounts of shale gas and tight oil increased between 2014 and 2015. We show that many players in the US shale E&P industry succeeded in decreasing their production costs to maintain their business activity and production. However, most of the companies in the US petroleum E&P industry incurred losses in 2015 and 2016. Furthermore, crude oil and natural gas prices could not rebound to their 2015 price levels. Therefore, many companies in the US petroleum E&P industry need to increase their productivity to overcome the low commodity prices situation. Hence, to test the change in their productivity and analyze their ability to survive in the petroleum industry, this study calculates the learning rate using the US shale E&P players’ annual report data from 2008 to 2016. The result of the calculation is that the long-term learning rate is 1.87% and the short-term learning rate is 3.16%. This indicates a change in the technological development trend.

  5. The Effect of Learning Method and Confidence Level on the Ability of Interpreting Religious Poem

    Directory of Open Access Journals (Sweden)

    Kinayati Djojosuroto

    2017-11-01

    Full Text Available This research aims to determine the effect of the learning method (expository and authentic and the level of confidence in the ability of religious poetry interpretation of the students of the third semester, majoring in the Indonesian Language and Literature Education of Universitas Negeri Manado. The method used is the quasi-experimental method with 2 x 2 factorial designs. The measurement of Y variable (ability to interpret the religious poetry uses the writing test and the level of confidence uses a questionnaire. Data analysis technique in this study is analysis of variance (ANOVA followed by two lanes and Tuckey test to look at the interaction of the group. Before the test, the hypothesis is that analysis requirements normality data test using Liliefors test and homogeneity test data using Bartlett test. The results show differences in the ability to explain the religious poetry among students who study with the expository method and the students who study with the authentic method. That is, overall, the expository method is better than the authentic method to improve the ability of the students. To improve the ability of the students to interpret the religious poetry, it is better to use the authentic method for the group that has a lower level of confidence. There is the influence of the interaction between learning method (expository and authentic and the level of confidence in the ability of religious poetry interpretation. Based on these results, it can be concluded that: First, lecturers can determine what materials and method that can be used to enhance the ability to interpret religious poetry when the level of confidence of the students has been known. Second, expository teaching methods and authentic teaching method for group of students with different level of confidence will give you different result on the ability of that group of students to interpret religious poetry as well. Third, the increase of the ability to interpret

  6. BEBP: An Poisoning Method Against Machine Learning Based IDSs

    OpenAIRE

    Li, Pan; Liu, Qiang; Zhao, Wentao; Wang, Dongxu; Wang, Siqi

    2018-01-01

    In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical way. Hence, some attacks that comprise the data for training or testing classifiers significantly challenge the detecting capability of machine learning-based IDSs. Poisoning attack, which is one of the most recognized security threats towards machine learning...

  7. A machine learning approach for efficient uncertainty quantification using multiscale methods

    Science.gov (United States)

    Chan, Shing; Elsheikh, Ahmed H.

    2018-02-01

    Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.

  8. A comparative study on effect of e-learning and instructor-led methods on nurses' documentation competency.

    Science.gov (United States)

    Abbaszadeh, Abbas; Sabeghi, Hakimeh; Borhani, Fariba; Heydari, Abbas

    2011-01-01

    Accurate recording of the nursing care indicates the care performance and its quality, so that, any failure in documentation can be a reason for inadequate patient care. Therefore, improving nurses' skills in this field using effective educational methods is of high importance. Since traditional teaching methods are not suitable for communities with rapid knowledge expansion and constant changes, e-learning methods can be a viable alternative. To show the importance of e-learning methods on nurses' care reporting skills, this study was performed to compare the e-learning methods with the traditional instructor-led methods. This was a quasi-experimental study aimed to compare the effect of two teaching methods (e-learning and lecture) on nursing documentation and examine the differences in acquiring competency on documentation between nurses who participated in the e-learning (n = 30) and nurses in a lecture group (n = 31). The results of the present study indicated that statistically there was no significant difference between the two groups. The findings also revealed that statistically there was no significant correlation between the two groups toward demographic variables. However, we believe that due to benefits of e-learning against traditional instructor-led method, and according to their equal effect on nurses' documentation competency, it can be a qualified substitute for traditional instructor-led method. E-learning as a student-centered method as well as lecture method equally promote competency of the nurses on documentation. Therefore, e-learning can be used to facilitate the implementation of nursing educational programs.

  9. A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization.

    Science.gov (United States)

    Rao, Jinmeng; Qiao, Yanjun; Ren, Fu; Wang, Junxing; Du, Qingyun

    2017-08-24

    The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device's built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

  10. A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

    Directory of Open Access Journals (Sweden)

    Jinmeng Rao

    2017-08-01

    Full Text Available The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

  11. A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

    Science.gov (United States)

    Rao, Jinmeng; Qiao, Yanjun; Ren, Fu; Wang, Junxing; Du, Qingyun

    2017-01-01

    The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction. PMID:28837096

  12. The Impact of Cooperative Learning on Student Engagement: Results from an Intervention

    Science.gov (United States)

    Herrmann, Kim J.

    2013-01-01

    With an increasing awareness that many undergraduates are passive during teaching sessions, calls for instructional methods that allow students to become actively engaged have increased. Cooperative learning has long been popular at the primary and secondary level and, within recent years, higher education. However, empirical evidence of the…

  13. Effects of Two Different Types of Physics Learning on the Results of CLASS Test

    Science.gov (United States)

    Marusic, Mirko; Slisko, Josip

    2012-01-01

    During a one-semester-long research project with high school students, we deployed and gauged efficiency of two different reform teaching methods: reading, presenting, and questioning (RPQ) and experimenting and discussion (ED). In this paper we report on changes in students' attitudes and beliefs about physics and learning physics. We used the…

  14. Results and current trends of nuclear methods used in agriculture

    International Nuclear Information System (INIS)

    Horacek, P.

    1983-01-01

    The significance is evaluated of nuclear methods for agricultural research. The number of breeds induced by radiation mutations is increasing. The main importance of radiation mutation breeding consists in obtaining sources of the desired genetic properties for further hybridization. Radiostimulation is conducted with the aim of increasing yields. The irradiation of foods has not substantially increased worldwide. Very important is the irradiation of excrements and sludges which after such inactivation of pathogenic microorganisms may be used as humus-forming manure or as feed additives. In some countries the method is successfully being used of sexual sterilization for eradication of insect pests. The application of labelled compounds in the nutrition, physiology and protection of plants, farm animals and in food hygiene makes it possible to acquire new and accurate knowledge very quickly. Radioimmunoassay is a highly promising method in this respect. Labelling compounds with the stable 15 N isotope is used for the research of nitrogen metabolism. (M.D.)

  15. A Comparison of Reading Response Methods to Increase Student Learning

    Directory of Open Access Journals (Sweden)

    Cheryl J. Davis

    2016-01-01

    Full Text Available It is common in college courses to test students on the required readings for that course. With a rise in online education it is often the case that students are required to provide evidence of reading the material. However, there is little empirical research stating the best written means to assess that students read the materials. This study experimentally compared the effect of assigned reading summaries or study questions on student test performance. The results revealed that study questions produced higher quiz scores and higher preparation for the quiz, based on student feedback. Limitations of the study included a small sample size and extraneous activities that may have affected general knowledge on a topic. Results suggest that study questions focusing students on critical information in the required readings improve student learning.

  16. Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods

    Science.gov (United States)

    Nissim, Nir; Shahar, Yuval; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

    2018-01-01

    using the labels provided by seven labelers. We also compared the performance of the passive and active learning models when using the consensus label. Results The AL methods produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p = 0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275 to 0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers’ different models during the training phase, compared to the variance of the induced models’ AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation using our two new AL methods (0.02 and 0.019, respectively). The SVM-Margin AL method resulted in an Inter-labeler standard deviation (0.029) that was higher by almost 50% than that of our two AL methods. The difference in the inter-labeler standard deviation between the passive learning method and the SVM-Margin learning method was significant (p = 0.042). The difference between the SVM-Margin and Exploitation method was insignificant (p = 0.29), as was the difference between the Combination_XA and Exploitation methods (p = 0.67). Finally, using the consensus label led to a learning curve that had a higher mean intra-labeler variance, but resulted eventually in an AUC that was at least as high as the AUC achieved using the gold standard label and that was always higher than the expected mean AUC of a randomly selected labeler, regardless of the choice of learning method (including a passive learning method). Using a paired

  17. Results from the Application of Uncertainty Methods in the CSNI Uncertainty Methods Study (UMS)

    International Nuclear Information System (INIS)

    Glaeser, H.

    2008-01-01

    Within licensing procedures there is the incentive to replace the conservative requirements for code application by a - best estimate - concept supplemented by an uncertainty analysis to account for predictive uncertainties of code results. Methods have been developed to quantify these uncertainties. The Uncertainty Methods Study (UMS) Group, following a mandate from CSNI, has compared five methods for calculating the uncertainty in the predictions of advanced -best estimate- thermal-hydraulic codes. Most of the methods identify and combine input uncertainties. The major differences between the predictions of the methods came from the choice of uncertain parameters and the quantification of the input uncertainties, i.e. the wideness of the uncertainty ranges. Therefore, suitable experimental and analytical information has to be selected to specify these uncertainty ranges or distributions. After the closure of the Uncertainty Method Study (UMS) and after the report was issued comparison calculations of experiment LSTF-SB-CL-18 were performed by University of Pisa using different versions of the RELAP 5 code. It turned out that the version used by two of the participants calculated a 170 K higher peak clad temperature compared with other versions using the same input deck. This may contribute to the differences of the upper limit of the uncertainty ranges.

  18. A program wide framework for evaluating data driven teaching and learning - earth analytics approaches, results and lessons learned

    Science.gov (United States)

    Wasser, L. A.; Gold, A. U.

    2017-12-01

    There is a deluge of earth systems data available to address cutting edge science problems yet specific skills are required to work with these data. The Earth analytics education program, a core component of Earth Lab at the University of Colorado - Boulder - is building a data intensive program that provides training in realms including 1) interdisciplinary communication and collaboration 2) earth science domain knowledge including geospatial science and remote sensing and 3) reproducible, open science workflows ("earth analytics"). The earth analytics program includes an undergraduate internship, undergraduate and graduate level courses and a professional certificate / degree program. All programs share the goals of preparing a STEM workforce for successful earth analytics driven careers. We are developing an program-wide evaluation framework that assesses the effectiveness of data intensive instruction combined with domain science learning to better understand and improve data-intensive teaching approaches using blends of online, in situ, asynchronous and synchronous learning. We are using targeted online search engine optimization (SEO) to increase visibility and in turn program reach. Finally our design targets longitudinal program impacts on participant career tracts over time.. Here we present results from evaluation of both an interdisciplinary undergrad / graduate level earth analytics course and and undergraduate internship. Early results suggest that a blended approach to learning and teaching that includes both synchronous in-person teaching and active classroom hands-on learning combined with asynchronous learning in the form of online materials lead to student success. Further we will present our model for longitudinal tracking of participant's career focus overtime to better understand long-term program impacts. We also demonstrate the impact of SEO optimization on online content reach and program visibility.

  19. Application of NDE methods to green ceramics: initial results

    International Nuclear Information System (INIS)

    Kupperman, D.S.; Karplus, H.B.; Poeppel, R.B.; Ellingson, W.A.; Berger, H.; Robbins, C.; Fuller, E.

    1984-03-01

    This paper describes a preliminary investigation to assess the effectiveness of microradiography, ultrasonic methods, nuclear magnetic resonance, and neutron radiography for the nondestructive evaluation of green (unfired), ceramics. Objective is to obtain useful information on defects, cracking, delaminations, agglomerates, inclusions, regions of high porosity, and anisotropy

  20. Model films of cellulose. I. Method development and initial results

    NARCIS (Netherlands)

    Gunnars, S.; Wågberg, L.; Cohen Stuart, M.A.

    2002-01-01

    This report presents a new method for the preparation of thin cellulose films. NMMO (N- methylmorpholine- N-oxide) was used to dissolve cellulose and addition of DMSO (dimethyl sulfoxide) was used to control viscosity of the cellulose solution. A thin layer of the cellulose solution is spin- coated

  1. Missing data imputation using statistical and machine learning methods in a real breast cancer problem.

    Science.gov (United States)

    Jerez, José M; Molina, Ignacio; García-Laencina, Pedro J; Alba, Emilio; Ribelles, Nuria; Martín, Miguel; Franco, Leonardo

    2010-10-01

    Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. This work evaluates the performance of several statistical and machine learning imputation methods that were used to predict recurrence in patients in an extensive real breast cancer data set. Imputation methods based on statistical techniques, e.g., mean, hot-deck and multiple imputation, and machine learning techniques, e.g., multi-layer perceptron (MLP), self-organisation maps (SOM) and k-nearest neighbour (KNN), were applied to data collected through the "El Álamo-I" project, and the results were then compared to those obtained from the listwise deletion (LD) imputation method. The database includes demographic, therapeutic and recurrence-survival information from 3679 women with operable invasive breast cancer diagnosed in 32 different hospitals belonging to the Spanish Breast Cancer Research Group (GEICAM). The accuracies of predictions on early cancer relapse were measured using artificial neural networks (ANNs), in which different ANNs were estimated using the data sets with imputed missing values. The imputation methods based on machine learning algorithms outperformed imputation statistical methods in the prediction of patient outcome. Friedman's test revealed a significant difference (p=0.0091) in the observed area under the ROC curve (AUC) values, and the pairwise comparison test showed that the AUCs for MLP, KNN and SOM were significantly higher (p=0.0053, p=0.0048 and p=0.0071, respectively) than the AUC from the LD-based prognosis model. The methods based on machine learning techniques were the most suited for the imputation of missing values and led to a significant enhancement of prognosis accuracy compared to imputation methods based on statistical procedures. Copyright © 2010 Elsevier B.V. All rights reserved.

  2. A deep learning method for lincRNA detection using auto-encoder algorithm.

    Science.gov (United States)

    Yu, Ning; Yu, Zeng; Pan, Yi

    2017-12-06

    RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly

  3. Identifying different methods for creating knowledge from lessons learned in project oriented organizations

    Directory of Open Access Journals (Sweden)

    Ahmad Norang

    2016-01-01

    Full Text Available Nowadays, the increase in competition has increased the relative importance of innovation for most firms and many managers believe a good innovation must be knowledge oriented. This paper has tried to determine different methods for creating knowledge in project oriented organizations. The study designs a questionnaire in Likert scale and distributes it among 32 experts who were well informed about different methods of knowledge creation and lessons learned. Cronbach alphas for all components of the survey were well above the desirable level. The study has detected 11 methods for knowledge creation and lessons learned. In terms of preliminary assessment, business transactions has received the highest impact while knowledge team has received the highest effect in terms of necessary assessment. The results of this survey have indicated that although there are several methods for detecting knowledge within organizations, in most cases, it is not easy to gain value added knowledge within an organization, quickly. The people who participated in our survey have indicated that organizational commitment, brainstorming, Delphi and storytelling also have played important role for creation of knowledge. The results have also shown that brainstorming, knowledge brokers, map knowledge and work experience were easier to use for knowledge creation and lessons learned compared with other forms of knowledge creation.

  4. Analysis of deep learning methods for blind protein contact prediction in CASP12.

    Science.gov (United States)

    Wang, Sheng; Sun, Siqi; Xu, Jinbo

    2018-03-01

    Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median family size of around 58 effective sequences, our server obtained an average top L/5 long- and medium-range contact accuracy of 47% and 44%, respectively (L = length). A complete implementation has an average accuracy of 59% and 57%, respectively. Our deep learning method formulates contact prediction as a pixel-level image labeling problem and simultaneously predicts all residue pairs of a protein using a combination of two deep residual neural networks, taking as input the residue conservation information, predicted secondary structure and solvent accessibility, contact potential, and coevolution information. Our approach differs from existing methods mainly in (1) formulating contact prediction as a pixel-level image labeling problem instead of an image-level classification problem; (2) simultaneously predicting all contacts of an individual protein to make effective use of contact occurrence patterns; and (3) integrating both one-dimensional and two-dimensional deep convolutional neural networks to effectively learn complex sequence-structure relationship including high-order residue correlation. This paper discusses the RaptorX-Contact pipeline, both contact prediction and contact-based folding results, and finally the strength and weakness of our method. © 2017 Wiley Periodicals, Inc.

  5. Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

    Science.gov (United States)

    Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.

    2014-01-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…

  6. Project-Based Learning Using Discussion and Lesson-Learned Methods via Social Media Model for Enhancing Problem Solving Skills

    Science.gov (United States)

    Jewpanich, Chaiwat; Piriyasurawong, Pallop

    2015-01-01

    This research aims to 1) develop the project-based learning using discussion and lesson-learned methods via social media model (PBL-DLL SoMe Model) used for enhancing problem solving skills of undergraduate in education student, and 2) evaluate the PBL-DLL SoMe Model used for enhancing problem solving skills of undergraduate in education student.…

  7. Wide Binaries in TGAS: Search Method and First Results

    Science.gov (United States)

    Andrews, Jeff J.; Chanamé, Julio; Agüeros, Marcel A.

    2018-04-01

    Half of all stars reside in binary systems, many of which have orbital separations in excess of 1000 AU. Such binaries are typically identified in astrometric catalogs by matching the proper motions vectors of close stellar pairs. We present a fully Bayesian method that properly takes into account positions, proper motions, parallaxes, and their correlated uncertainties to identify widely separated stellar binaries. After applying our method to the >2 × 106 stars in the Tycho-Gaia astrometric solution from Gaia DR1, we identify over 6000 candidate wide binaries. For those pairs with separations less than 40,000 AU, we determine the contamination rate to be ~5%. This sample has an orbital separation (a) distribution that is roughly flat in log space for separations less than ~5000 AU and follows a power law of a -1.6 at larger separations.

  8. Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

    Science.gov (United States)

    Nissim, Nir; Shahar, Yuval; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

    2017-09-01

    labelers. We also compared the performance of the passive and active learning models when using the consensus label. The AL methods: produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p=0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275-0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers' different models during the training phase, compared to the variance of the induced models' AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation using our two new AL methods (0.02 and 0.019, respectively). The SVM-Margin AL method resulted in an Inter-labeler standard deviation (0.029) that was higher by almost 50% than that of our two AL methods The difference in the inter-labeler standard deviation between the passive learning method and the SVM-Margin learning method was significant (p=0.042). The difference between the SVM-Margin and Exploitation method was insignificant (p=0.29), as was the difference between the Combination_XA and Exploitation methods (p=0.67). Finally, using the consensus label led to a learning curve that had a higher mean intra-labeler variance, but resulted eventually in an AUC that was at least as high as the AUC achieved using the gold standard label and that was always higher than the expected mean AUC of a randomly selected labeler, regardless of the choice of learning method (including a passive learning method). Using a paired t-test, the difference between the intra-labeler AUC

  9. Viscous wing theory development. Volume 1: Analysis, method and results

    Science.gov (United States)

    Chow, R. R.; Melnik, R. E.; Marconi, F.; Steinhoff, J.

    1986-01-01

    Viscous transonic flows at large Reynolds numbers over 3-D wings were analyzed using a zonal viscid-inviscid interaction approach. A new numerical AFZ scheme was developed in conjunction with the finite volume formulation for the solution of the inviscid full-potential equation. A special far-field asymptotic boundary condition was developed and a second-order artificial viscosity included for an improved inviscid solution methodology. The integral method was used for the laminar/turbulent boundary layer and 3-D viscous wake calculation. The interaction calculation included the coupling conditions of the source flux due to the wing surface boundary layer, the flux jump due to the viscous wake, and the wake curvature effect. A method was also devised incorporating the 2-D trailing edge strong interaction solution for the normal pressure correction near the trailing edge region. A fully automated computer program was developed to perform the proposed method with one scalar version to be used on an IBM-3081 and two vectorized versions on Cray-1 and Cyber-205 computers.

  10. Algorithms for monitoring warfarin use: Results from Delphi Method.

    Science.gov (United States)

    Kano, Eunice Kazue; Borges, Jessica Bassani; Scomparini, Erika Burim; Curi, Ana Paula; Ribeiro, Eliane

    2017-10-01

    Warfarin stands as the most prescribed oral anticoagulant. New oral anticoagulants have been approved recently; however, their use is limited and the reversibility techniques of the anticoagulation effect are little known. Thus, our study's purpose was to develop algorithms for therapeutic monitoring of patients taking warfarin based on the opinion of physicians who prescribe this medicine in their clinical practice. The development of the algorithm was performed in two stages, namely: (i) literature review and (ii) algorithm evaluation by physicians using a Delphi Method. Based on the articles analyzed, two algorithms were developed: "Recommendations for the use of warfarin in anticoagulation therapy" and "Recommendations for the use of warfarin in anticoagulation therapy: dose adjustment and bleeding control." Later, these algorithms were analyzed by 19 medical doctors that responded to the invitation and agreed to participate in the study. Of these, 16 responded to the first round, 11 to the second and eight to the third round. A 70% consensus or higher was reached for most issues and at least 50% for six questions. We were able to develop algorithms to monitor the use of warfarin by physicians using a Delphi Method. The proposed method is inexpensive and involves the participation of specialists, and it has proved adequate for the intended purpose. Further studies are needed to validate these algorithms, enabling them to be used in clinical practice.

  11. Adapting research-based curricula at Seattle Pacific University: Results on student learning

    Science.gov (United States)

    Close, Eleanor; Vokos, Stamatis; Lindberg, John; Seeley, Lane

    2004-05-01

    Seattle Pacific University is the recent recipient of a NSF CCLI grant to improve student learning in introductory physics and calculus courses. This talk will outline the goals of this collaborative project and present some initial results on student performance. Results from research-based assessments will be presented as well as specific examples of successes and challenges from mechanics and electricity and magnetism.

  12. Machine Learning Methods for Prediction of CDK-Inhibitors

    Science.gov (United States)

    Ramana, Jayashree; Gupta, Dinesh

    2010-01-01

    Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://bioinfo.icgeb.res.in/cdkipred. PMID:20967128

  13. Digital Badges for STEM Learning in Secondary Contexts: A Mixed Methods Study

    Science.gov (United States)

    Elkordy, Angela

    The deficit in STEM skills is a matter of concern for national economies and a major focus for educational policy makers. The development of Information and Communications Technologies (ICT) has resulted in a rapidly changing workforce of global scale. In addition, ICT have fostered the growth of digital and mobile technologies which have been the learning context, formal and informal, for a generation of youth. The purpose of this study was to design an intervention based upon a competency-based, digitally-mediated, learning intervention: digital badges for learning STEM habits of mind and practices. Designed purposefully, digital badge learning trajectories and criteria can be flexible tools for scaffolding, measuring, and communicating the acquisition of knowledge, skills, or competencies. One of the most often discussed attributes of digital badges, is the ability of badges to motivate learners. However, the research base to support this claim is in its infancy; there is little empirical evidence. A skills-based digital badge intervention was designed to demonstrate mastery learning in key, age-appropriate, STEM competencies aligned with Next Generation Science Standards (NGSS) and other educational standards. A mixed methods approach was used to study the impact of a digital badge intervention in the sample middle and high school population. Among the findings were statistically significant measures which substantiate that in this student population, the digital badges increased perceived competence and motivated learners to persist at task.

  14. Learning a specific content in technology education : Learning Study as collaborative method in Swedish preschool class using hands-on material 

    OpenAIRE

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study concerns strong constructions and framed structures. This article describes how this learning study was conducted and discusses reflections made du...

  15. Evaluation of three different methods of distance learning for postgraduate diagnostic imaging education: A pilot study.

    Science.gov (United States)

    Poirier, Jean-Nicolas; Cooley, Jeffrey R; Wessely, Michelle; Guebert, Gary M; Petrocco-Napuli, Kristina

    2014-10-01

    Objective : The purpose of this study was to evaluate the perceived effectiveness and learning potential of 3 Web-based educational methods in a postgraduate radiology setting. Methods : Three chiropractic radiology faculty from diverse geographic locations led mini-courses using asynchronous discussion boards, synchronous Web conferencing, and asynchronous voice-over case presentations formatted for Web viewing. At the conclusion of each course, participants filled out a 14-question survey (using a 5-point Likert scale) designed to evaluate the effectiveness of each method in achieving specified course objectives and goals and their satisfaction when considering the learning potential of each method. The mean, standard deviation, and percentage agreements were tabulated. Results : Twenty, 15, and 10 participants completed the discussion board, Web conferencing, and case presentation surveys, respectively. All educational methods demonstrated a high level of agreement regarding the course objective (total mean rating >4.1). The case presentations had the highest overall rating for achieving the course goals; however, all but one method still had total mean ratings >4.0 and overall agreement levels of 70%-100%. The strongest potential for interactive learning was found with Web conferencing and discussion boards, while case presentations rated very low in this regard. Conclusions : The perceived effectiveness in achieving the course objective and goals was high for each method. Residency-based distance education may be a beneficial adjunct to current methods of training, allowing for international collaboration. When considering all aspects tested, there does not appear to be a clear advantage to any one method. Utilizing various methods may be most appropriate.

  16. Comparison of Chemistry Learning Outcomes with Inquiry Learning Model and Learning Cycle 5E in Material Solubility and Solubility Multiplication Results

    Directory of Open Access Journals (Sweden)

    Nur Indah Firdausi

    2015-04-01

    Full Text Available Perbandingan Hasil Belajar Kimia dengan Model Pembelajaran Inquiry dan Learning Cycle 5E pada Materi Kelarutan dan Hasil Kali Kelarutan   Abstract: This research is aimed to compare the effectiveness between inquiry and LC 5E in solubility equilibria and the solubility product for students with different prior knowledge. The effectiveness of both learning models is measured from students learning outcome. This quasi experimental research uses factorial2x2 with posttest only design. Research samples are chosen using cluster random sampling. They are two classes of XI IPA SMAN 1 Kepanjen in the 2012/2013 academic year which consist of 31 students in each class. Cognitive learning outcome is measured by test items consist of four objective items and nine subjective items. Technique of data analysis in this research is two way ANOVA. Research results show that: (1 cognitive learning outcome and higher cognitive learning outcome of students in inquiry class is higher than students in LC 5E class; (2 cognitive learning outcome and higher cognitive learning outcome of students who have upper prior knowledge is higher than students who have lower prior knowledge in both inquiry and LC 5E. Key Words: learning outcome, inquiry, learning cycle 5E, solubility equilibria and the solubility product   Abstrak: Penelitian ini bertujuan membandingkan keefektifan model inquiry dan LC 5E pada materi kelarutan dan hasil kali kelarutan untuk siswa dengan kemampuan awal berbeda. Keefektifan model pembelajaran dilihat dari hasil belajar kognitif siswa. Penelitian ini menggunakan rancangan eksperimen semu dengan desain faktorial 2x2. Subjek penelitian dipilih secara cluster random sampling yaitu dua kelas XI IPA SMAN 1 Kepanjen dengan jumlah masing-masing kelas sebanyak 31 siswa. Instrumen perlakuan yang digunakan adalah silabus dan RPP sedangkan instrumen pengukuran berupa soal tes terdiri dari empat soal objektif dan sembilan soal subjektif. Teknik analisis data

  17. Comprehensive, Mixed-Methods Assessment of a Blended Learning Model for Geospatial Literacy Instruction

    Science.gov (United States)

    Brodeur, J. J.; Maclachlan, J. C.; Bagg, J.; Chiappetta-Swanson, C.; Vine, M. M.; Vajoczki, S.

    2013-12-01

    Geospatial literacy -- the ability to conceptualize, capture, analyze and communicate spatial phenomena -- represents an important competency for 21st Century learners in a period of 'Geospatial Revolution'. Though relevant to in-course learning, these skills are often taught externally, placing time and resource pressures on the service providers - commonly libraries - that are relied upon to provide instruction. The emergence of online and blended modes of instruction has presented a potential means of increasing the cost-effectiveness of such activities, by simultaneously reducing instructional costs, expanding the audience for these resources, and addressing student preferences for asynchronous learning and '24-7' access. During 2011 and 2012, McMaster University Library coordinated the development, implementation and assessment of blended learning modules for geospatial literacy instruction in first-year undergraduate Social Science courses. In this paper, we present the results of a comprehensive mixed-methods approach to assess the efficacy of implementing blended learning modules to replace traditional (face-to-face), library-led, first-year undergraduate geospatial literacy instruction. Focus groups, personal interviews and an online survey were used to assess modules across dimensions of: student use, satisfaction and accessibility requirements (via Universal Instructional Design [UID] principles); instructor and teaching staff perception of pedagogical efficacy and instructional effectiveness; and, administrator cost-benefit assessment of development and implementation. Results showed that both instructors and students identified significant value in using the online modules in a blended-learning setting. Reaffirming assumptions of students' '24/7' learning preferences, over 80% of students reported using the modules on a repeat basis. Students were more likely to use the modules to better understand course content than simply to increase their grade in

  18. Machine learning for medical ultrasound: status, methods, and future opportunities.

    Science.gov (United States)

    Brattain, Laura J; Telfer, Brian A; Dhyani, Manish; Grajo, Joseph R; Samir, Anthony E

    2018-04-01

    Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.

  19. Further results for crack-edge mappings by ray methods

    International Nuclear Information System (INIS)

    Norris, A.N.; Achenbach, J.D.; Ahlberg, L.; Tittman, B.R.

    1984-01-01

    This chapter discusses further extensions of the local edge mapping method to the pulse-echo case and to configurations of water-immersed specimens and transducers. Crack edges are mapped by the use of arrival times of edge-diffracted signals. Topics considered include local edge mapping in a homogeneous medium, local edge mapping algorithms, local edge mapping through an interface, and edge mapping through an interface using synthetic data. Local edge mapping is iterative, with two or three iterations required for convergence

  20. Method of fabricating nested shells and resulting product

    Science.gov (United States)

    Henderson, Timothy M.; Kool, Lawrence B.

    1982-01-01

    A multiple shell structure and a method of manufacturing such structure wherein a hollow glass microsphere is surface treated in an organosilane solution so as to render the shell outer surface hydrophobic. The surface treated glass shell is then suspended in the oil phase of an oil-aqueous phase dispersion. The oil phase includes an organic film-forming monomer, a polymerization initiator and a blowing agent. A polymeric film forms at each phase boundary of the dispersion and is then expanded in a blowing operation so as to form an outer homogeneously integral monocellular substantially spherical thermoplastic shell encapsulating an inner glass shell of lesser diameter.

  1. Bedside Teaching: Is it Effective Methods in Clinical Nursing Students Learning?

    Directory of Open Access Journals (Sweden)

    Fatikhu Yatuni Asmara

    2017-01-01

    Full Text Available Introduction: Clinical learning is the centre of medical students education. Students not only learn about practical skills but also communication with patient and other health care givers which both competencies are useful for students when they come into working world (Spencer, 2003. There are variations of methods applied in clinical learning process; one of them is bedside teaching. The aim of this study was to observe the bedside teaching process which is held in group of students, teacher, and patient. Another aim was to know responses of students, teacher, and patients to the bedside teaching process. Method: The method which was applied in this study is observation in which bedside teaching process was observed related to the roles and function of each component of bedside teaching: students, teacher, and patient in each phase: preparation, process, and evaluation. Then it was continued by interview to know the responses of students, teacher, and patient related to bedside teaching process. Result: The result showed that both students and teacher felt that bedside teaching is an effective method since it helped students to achieve their competences in clinical setting and develop their communication skill. Furthermore teacher stated that bedside teaching facilitated her to be a good role model for students. As well as students and teacher, patient got advantage from the bedside teaching process that she got information related to her case; however the time to discuss was limited. During the observation, each component of bedside teaching did their roles and function, such as: during the preparation teacher asked inform consent from patient, and patient gave inform consent as well while students prepared the material. Discussions: Suggestion for next research is conducting a deeper study about perception of students, teacher, and patient about bedside teaching process and the strategies to develop it to be better method. Keywords: bedside

  2. Teaching-learning: stereoscopic 3D versus Traditional methods in Mexico City.

    Science.gov (United States)

    Mendoza Oropeza, Laura; Ortiz Sánchez, Ricardo; Ojeda Villagómez, Raúl

    2015-01-01

    In the UNAM Faculty of Odontology, we use a stereoscopic 3D teaching method that has grown more common in the last year, which makes it important to know whether students can learn better with this strategy. The objective of the study is to know, if the 4th year students of the bachelor's degree in dentistry learn more effectively with the use of stereoscopic 3D than the traditional method in Orthodontics. first, we selected the course topics, to be used for both methods; the traditional method using projection of slides and for the stereoscopic third dimension, with the use of videos in digital stereo projection (seen through "passive" polarized 3D glasses). The main topic was supernumerary teeth, including and diverted from their guide eruption. Afterwards we performed an exam on students, containing 24 items, validated by expert judgment in Orthodontics teaching. The results of the data were compared between the two educational methods for determined effectiveness using the model before and after measurement with the statistical package SPSS 20 version. The results presented for the 9 groups of undergraduates in dentistry, were collected with a total of 218 students for 3D and traditional methods, we found in a traditional method a mean 4.91, SD 1.4752 in the pretest and X=6.96, SD 1.26622, St Error 0.12318 for the posttest. The 3D method had a mean 5.21, SD 1.996779 St Error 0.193036 for the pretest X= 7.82, SD =0.963963, St Error 0.09319 posttest; the analysis of Variance between groups F= 5.60 Prob > 0.0000 and Bartlett's test for equal variances 21.0640 Prob > chi2 = 0.007. These results show that the student's learning in 3D means a significant improvement as compared to the traditional teaching method and having a strong association between the two methods. The findings suggest that the stereoscopic 3D method lead to improved student learning compared to traditional teaching.

  3. Survey compare team based learning and lecture teaching method, on learning-teaching process nursing student\\'s, in Surgical and Internal Diseases course

    Directory of Open Access Journals (Sweden)

    AA Vaezi

    2015-12-01

    Full Text Available Introduction: The effect of teaching methods on learning process of students will help teachers to improve the quality of teaching by selecting an appropriate method. This study aimed to compare the team- based learning and lecture teaching method on learning-teaching process of nursing students in surgical and internal diseases courses. Method: This quasi-experimental study was carried on the nursing students in the School of Nursing and Midwifery in Yazd and Meybod cities. Studied sample was all of the students in the sixth term in the Faculty of Nursing in Yazd (48 persons and the Faculty of Nursing in Meybod (28 persons. The rate of students' learning through lecture was measured using MCQ tests and teaching based on team-based learning (TBL method was run using MCQ tests (IRAT, GRAT, Appeals and Task group. Therefore, in order to examine the students' satisfaction about the TBL method, a 5-point Likert scale (translated questionnaire (1=completely disagree, 2= disagree, 3=not effective, 4=agree, and 5=completely agree consisted of 22 items was utilized. The reliability and validity of this translated questionnaire was measured. The collected data were analyzed through SPSS 17.0 using descriptive and analytical statistic. Result: The results showed that the mean scores in team-based learning were meaningful in individual assessment (17±84 and assessment group (17.2±1.17. The mean of overall scores in TBL method (17.84±0.98% was higher compared with the lecture teaching method (16±2.31. Most of the students believed that TBL method has improved their interpersonal and group interaction skills (100%. Among them, 97.7% of students mentioned that this method (TBL helped them to understand the course content better. The lowest levels of the satisfaction have related to the continuous learning during lifelong (51.2%. Conclusion: The results of the present study showed that the TBL method led to improving the communication skills, understanding

  4. EFFECTS OF COOPERATIVE LEARNING MODEL TYPE NUMBERED HEADS TOGETHER USING SIMULATION MEDIA PHET AND ACTIVITIES TOWARD STUDENT RESULTS

    Directory of Open Access Journals (Sweden)

    Fitri Mawaddah Lubis

    2015-12-01

    Full Text Available This study aimed to analyze the differences in learning outcomes of students taught by cooperative learning model NHT using simulation PhET and conventional learning, analyzing the differences in learning outcomes of students who have high activity and low activity, as well as the  interaction between learning model with the level of student activity in  influencing the outcome students learn physics. This research is a quasi experimental. The population in this study were students of class X SMK Tritech Informatika Medan. The tests were used to obtain the data is in the form of multiple choice. Test requirements have been carried out in the form of normality and homogeneity, which showed that the normal data and homogeneous. The data were analyzed using Anova analysis of two paths. The results showed that: The physics learning outcomes of students who use cooperative learning model NHT using PhET simulations media is better than students who use conventional learning models. The physics learning outcomes of students who have high learning activities is better than students who have Low learning activities. There is an interaction between cooperative learning model NHT PhET simulations using the media and the level of learning activity in influencing student learning outcomes. Average increase learning outcomes in the control class is greater than the experimental class.

  5. Feasibility of Machine Learning Methods for Separating Wood and Leaf Points from Terrestrial Laser Scanning Data

    Science.gov (United States)

    Wang, D.; Hollaus, M.; Pfeifer, N.

    2017-09-01

    Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Na¨ıve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an Erytrophleum fordii and a Betula pendula (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

  6. FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD AND LEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA

    Directory of Open Access Journals (Sweden)

    D. Wang

    2017-09-01

    Full Text Available Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM, Na¨ıve Bayes (NB, Random Forest (RF, and Gaussian Mixture Model (GMM, for separating wood and leaf points from terrestrial laser scanning (TLS data. Two trees, an Erytrophleum fordii and a Betula pendula (silver birch are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

  7. L2 Vocabulary Acquisition in Children: Effects of Learning Method and Cognate Status

    Science.gov (United States)

    Tonzar, Claudio; Lotto, Lorella; Job, Remo

    2009-01-01

    In this study we investigated the effects of two learning methods (picture- or word-mediated learning) and of word status (cognates vs. noncognates) on the vocabulary acquisition of two foreign languages: English and German. We examined children from fourth and eighth grades in a school setting. After a learning phase during which L2 words were…

  8. Spatial Visualization Learning in Engineering: Traditional Methods vs. a Web-Based Tool

    Science.gov (United States)

    Pedrosa, Carlos Melgosa; Barbero, Basilio Ramos; Miguel, Arturo Román

    2014-01-01

    This study compares an interactive learning manager for graphic engineering to develop spatial vision (ILMAGE_SV) to traditional methods. ILMAGE_SV is an asynchronous web-based learning tool that allows the manipulation of objects with a 3D viewer, self-evaluation, and continuous assessment. In addition, student learning may be monitored, which…

  9. Application of a Novel Collaboration Engineering Method for Learning Design: A Case Study

    Science.gov (United States)

    Cheng, Xusen; Li, Yuanyuan; Sun, Jianshan; Huang, Jianqing

    2016-01-01

    Collaborative case studies and computer-supported collaborative learning (CSCL) play an important role in the modern education environment. A number of researchers have given significant attention to learning design in order to improve the satisfaction of collaborative learning. Although collaboration engineering (CE) is a mature method widely…

  10. Strategic Management: An Evaluation of the Use of Three Learning Methods.

    Science.gov (United States)

    Jennings, David

    2002-01-01

    A study of 46 management students compared three methods for learning strategic management: cases, simulation, and action learning through consulting projects. Simulation was superior to action learning on all outcomes and equal or superior to cases on two. Simulation gave students a central role in management and greater control of the learning…

  11. Using Problem Based Learning Methods from Engineering Education in Company Based Development

    DEFF Research Database (Denmark)

    Kofoed, Lise B.; Jørgensen, Frances

    2007-01-01

    This paper discusses how Problem-Based Learning (PBL) methods were used to support a Danish company in its efforts to become more of a 'learning organisation', characterized by sharing of knowledge and experiences. One of the central barriers to organisational learning in this company involved...

  12. Investigating Learning with an Interactive Tutorial: A Mixed-Methods Strategy

    Science.gov (United States)

    de Villiers, M. R.; Becker, Daphne

    2017-01-01

    From the perspective of parallel mixed-methods research, this paper describes interactivity research that employed usability-testing technology to analyse cognitive learning processes; personal learning styles and times; and errors-and-recovery of learners using an interactive e-learning tutorial called "Relations." "Relations"…

  13. The Accident Sequence Precursor program: Methods improvements and current results

    International Nuclear Information System (INIS)

    Minarick, J.W.; Manning, F.M.; Harris, J.D.

    1987-01-01

    Changes in the US NRC Accident Sequence Precursor program methods since the initial program evaluations of 1969-81 operational events are described, along with insights from the review of 1984-85 events. For 1984-85, the number of significant precursors was consistent with the number observed in 1980-81, dominant sequences associated with significant events were reasonably consistent with PRA estimates for BWRs, but lacked the contribution due to small-break LOCAs previously observed and predicted in PWRs, and the frequency of initiating events and non-recoverable system failures exhibited some reduction compared to 1980-81. Operational events which provide information concerning additional PRA modeling needs are also described

  14. Use of results from microscopic methods in optical model calculations

    International Nuclear Information System (INIS)

    Lagrange, C.

    1985-11-01

    A concept of vectorization for coupled-channel programs based upon conventional methods is first presented. This has been implanted in our program for its use on the CRAY-1 computer. In a second part we investigate the capabilities of a semi-microscopic optical model involving fewer adjustable parameters than phenomenological ones. The two main ingredients of our calculations are, for spherical or well-deformed nuclei, the microscopic optical-model calculations of Jeukenne, Lejeune and Mahaux and nuclear densities from Hartree-Fock-Bogoliubov calculations using the density-dependent force D1. For transitional nuclei deformation-dependent nuclear structure wave functions are employed to weigh the scattering potentials for different shapes and channels [fr

  15. Lesion insertion in the projection domain: Methods and initial results.

    Science.gov (United States)

    Chen, Baiyu; Leng, Shuai; Yu, Lifeng; Yu, Zhicong; Ma, Chi; McCollough, Cynthia

    2015-12-01

    phantom in terms of Hounsfield unit and high-contrast resolution. For the validation of the lesion realism, lesions of various types were successfully inserted, including well circumscribed and invasive lesions, homogeneous and heterogeneous lesions, high-contrast and low-contrast lesions, isolated and vessel-attached lesions, and small and large lesions. The two experienced radiologists who reviewed the original and inserted lesions could not identify the lesions that were inserted. The same lesion, when inserted into the projection domain and reconstructed with different parameters, demonstrated a parameter-dependent appearance. A framework has been developed for projection-domain insertion of lesions into commercial CT images, which can be potentially expanded to all geometries of CT scanners. Compared to conventional image-domain methods, the authors' method reflected the impact of scan and reconstruction parameters on lesion appearance. Compared to prior projection-domain methods, the authors' method has the potential to achieve higher anatomical complexity by employing clinical patient projections and real patient lesions.

  16. Assessment of South African uranium resources: methods and results

    International Nuclear Information System (INIS)

    Camisani-Calzolari, F.A.G.M.; De Klerk, W.J.; Van der Merwe, P.J.

    1985-01-01

    This paper deals primarily with the methods used by the Atomic Energy Corporation of South Africa, in arriving at the assessment of the South African uranium resources. The Resource Evaluation Group is responsible for this task, which is carried out on a continuous basis. The evaluation is done on a property-by-property basis and relies upon data submitted to the Nuclear Development Corporation of South Africa by the various companies involved in uranium mining and prospecting in South Africa. Resources are classified into Reasonably Assured (RAR), Estimated Additional (EAR) and Speculative (SR) categories as defined by the NEA/IAEA Steering Group on Uranium Resources. Each category is divided into three categories, viz, resources exploitable at less than $80/kg uranium, at $80-130/kg uranium and at $130-260/kg uranium. Resources are reported in quantities of uranium metal that could be recovered after mining and metallurgical losses have been taken into consideration. Resources in the RAR and EAR categories exploitable at costs of less than $130/kg uranium are now estimated at 460 000 t uranium which represents some 14 per cent of WOCA's (World Outside the Centrally Planned Economies Area) resources. The evaluation of a uranium venture is carried out in various steps, of which the most important, in order of implementation, are: geological interpretation, assessment of in situ resources using techniques varying from manual contouring of values, geostatistics, feasibility studies and estimation of recoverable resources. Because the choice of an evaluation method is, to some extent, dictated by statistical consderations, frequency distribution curves of the uranium grade variable are illustrated and discussed for characteristic deposits

  17. UV spectroscopy applied to stratospheric chemistry, methods and results

    Energy Technology Data Exchange (ETDEWEB)

    Karlsen, K.

    1996-03-01

    The publication from the Norwegian Institute for Air Research (NILU) deals with an investigation done on stratospheric chemistry by UV spectroscopy. The scientific goals are briefly discussed, and it gives the results from the measuring and analysing techniques used in the investigation. 6 refs., 11 figs.

  18. Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

    Science.gov (United States)

    Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric

    2016-01-01

    This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…

  19. A case method for Sales and Operations Planning: a learning experience from Germany

    Directory of Open Access Journals (Sweden)

    Luiz Felipe Scavarda

    Full Text Available Abstract Adequate preparation, learning, and training is required for Sales and Operations Planning (S&OP to aid organizations in achieving the full expected benefits from its implementation. This paper presents a case method for S&OP and the learning experience of its application at the University of Münster (Germany. The “constructive alignment principle” was applied with a “team teaching” approach, involving an executive from the case company. Students improved their knowledge on S&OP and their analytical skills by understanding the conceptual S&OP building blocks and by learning how to deal with them to provide a solution for a case based on a real-life situation. The learning results were evaluated positively during the discipline’s student evaluation of teaching (SET. The applied case method enhanced the student’s motivation and engagement (e.g., higher preparation effort and class attendance, which were considered higher than in other disciplines with the traditional lecture-based education.

  20. A literature review about usability evaluation methods for e-learning platforms.

    Science.gov (United States)

    Freire, Luciana Lopes; Arezes, Pedro Miguel; Campos, José Creissac

    2012-01-01

    The usability analysis of information systems has been the target of several research studies over the past thirty years. These studies have highlighted a great diversity of points of view, including researchers from different scientific areas such as Ergonomics, Computer Science, Design and Education. Within the domain of information ergonomics, the study of tools and methods used for usability evaluation dedicated to E-learning presents evidence that there is a continuous and dynamic evolution of E-learning systems, in many different contexts -academics and corporative. These systems, also known as LMS (Learning Management Systems), can be classified according to their educational goals and their technological features. However, in these systems the usability issues are related with the relationship/interactions between user and system in the user's context. This review is a synthesis of research project about Information Ergonomics and embraces three dimensions, namely the methods, models and frameworks that have been applied to evaluate LMS. The study also includes the main usability criteria and heuristics used. The obtained results show a notorious change in the paradigms of usability, with which it will be possible to discuss about the studies carried out by different researchers that were focused on usability ergonomic principles aimed at E-learning.

  1. Comparing Efficacy of Implementing Two Teaching Methods Contract Learning and Traditional Instruction on Clinical Skills of Nursing Students in Psychiatric Wards of Hospitals of Tehran

    Directory of Open Access Journals (Sweden)

    Jamileh Mohtashami

    2009-06-01

    Full Text Available Background and purpose: A learning contract is defined as a written agreement between teacher and student which makes explicit what a learner will do to achieve specified learning outcomes.Learning contracts have been used as a teaching and learning strategy for both undergraduate and graduate nursing students in many countries.Methods : This research is a quasi-experimental study that compares effect of two different teaching methods , Contract learning and traditional on clinical skills for a group of nursing students who were in fourth year of study in a pre-registration bachelor of nursing degree program in Tehran . A learning contract was implemented as a learning tool in the students clinical placement in psychiatric nursing .Data were connected from questionnaires , interviews and clinical evaluation papers with students .Results : The results showed that students agreed that there was an increase in students autonomy and motivation in learning with the use of learning contract . It also increased the sharing between students and clinical instructors.Conclusion : According to the findings of this study , contract learning is considered beneficial to students learning and has the potential to be used in clinical learning .Key words : NURSING STUDENTS, LEARNING CONTRACTS , TRADITIONAL METHOD , MOTIVATION , AUTONOMY, PSYCHIATRIC WARDS .

  2. Results of the implementation of a learning system with incidents in an radiotherapy department

    International Nuclear Information System (INIS)

    Radicchi, Lucas Augusto; Vilela, Ellen Pedroso Severino; Faustino, Fabio de Lima C.; Rodrigues, Fernanda Arantes C.; Gomes, Franciele N.; Souza, Guilherme Vicente de; Silva, Rose Marta S.; Toledo, Jose Carlos de

    2016-01-01

    An incident learning system (ILS) is an important tool for improving aspects of patient and staff safety. In radiation oncology, ILS has been implemented both at the institutional level as at the national level, allowing to share lessons learned from incidents that have already occurred. The objective of this study is to present the preliminary results of the ILS implemented in a radiation oncology department. In total, 128 incidents were reviewed by a multidisciplinary committee, and the professional groups that registered more were medical physicists, radiation oncologists and radiation therapists. In addition, incidents have occurred and have been detected mainly in the treatment step. The incident learning system proved to be an important process improvement tool, according to the results shown,the improvement actions proposed and the perception of the people involved. (author)

  3. Approximation Methods for Inference and Learning in Belief Networks: Progress and Future Directions

    National Research Council Canada - National Science Library

    Pazzan, Michael

    1997-01-01

    .... In this research project, we have investigated methods and implemented algorithms for efficiently making certain classes of inference in belief networks, and for automatically learning certain...

  4. Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion

    National Research Council Canada - National Science Library

    Willsky, Alan

    2004-01-01

    .... Our research blends methods from several fields-statistics and probability, signal and image processing, mathematical physics, scientific computing, statistical learning theory, and differential...

  5. Creep in rock salt with temperature. Testing methods and results

    International Nuclear Information System (INIS)

    Charpentier, J.P.; Berest, P.

    1985-01-01

    The growing interest shown in the delayed behaviour of rocks at elevated temperature has led the Solid Mechanics Laboratory to develop specific equipment designed for creep tests. The design and dimensioning of these units offer the possibility of investigating a wide range of materials. The article describes the test facilities used (uni-axial and tri-axial creep units) and presents the experimental results obtained on samples of Bresse salt [fr

  6. TMI-2 core debris analytical methods and results

    International Nuclear Information System (INIS)

    Akers, D.W.; Cook, B.A.

    1984-01-01

    A series of six grab samples was taken from the debris bed of the TMI-2 core in early September 1983. Five of these samples were sent to the Idaho National Engineering Laboratory for analysis. Presented is the analysis strategy for the samples and some of the data obtained from the early stages of examination of the samples (i.e., particle size-analysis, gamma spectrometry results, and fissile/fertile material analysis)

  7. Computer game-based and traditional learning method: a comparison regarding students' knowledge retention.

    Science.gov (United States)

    Rondon, Silmara; Sassi, Fernanda Chiarion; Furquim de Andrade, Claudia Regina

    2013-02-25

    Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students' prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students' performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students' short and long-term knowledge retention.

  8. Studies of LMFBR: method of analysis and some results

    International Nuclear Information System (INIS)

    Ishiguro, Y.; Dias, A.F.; Nascimento, J.A. do.

    1983-01-01

    Some results of recent studies of LMFBR characteristics are summarized. A two-dimensional model of the LMFBR is taken from a publication and used as the base model for the analysis. Axial structures are added to the base model and a three-dimensional (Δ - Z) calculation has been done. Two dimensional (Δ and RZ) calculations are compared with the three-dimensional and published results. The eigenvalue, flux and power distributions, breeding characteristics, control rod worth, sodium-void and Doppler reactivities are analysed. Calculations are done by CITATION using six-group cross sections collapsed regionwise by EXPANDA in one-dimensional geometries from the 70-group JFS library. Burnup calculations of a simplified thorium-cycle LMFBR have also been done in the RZ geometry. Principal results of the studies are: (1) the JFS library appears adequate for predicting overall characteristics of an LMFBR, (2) the sodium void reactivity is negative within - 25 cm from the outer boundary of the core, (3) the halflife of Pa-233 must be considered explicitly in burnup analyses, and (4) two-dimensional (RZ and Δ) calculations can be used iteratively to analyze three-dimensional reactor systems. (Author) [pt

  9. Method of Automatic Ontology Mapping through Machine Learning and Logic Mining

    Institute of Scientific and Technical Information of China (English)

    王英林

    2004-01-01

    Ontology mapping is the bottleneck of handling conflicts among heterogeneous ontologies and of implementing reconfiguration or interoperability of legacy systems. We proposed an ontology mapping method by using machine learning, type constraints and logic mining techniques. This method is able to find concept correspondences through instances and the result is optimized by using an error function; it is able to find attribute correspondence between two equivalent concepts and the mapping accuracy is enhanced by combining together instances learning, type constraints and the logic relations that are imbedded in instances; moreover, it solves the most common kind of categorization conflicts. We then proposed a merging algorithm to generate the shared ontology and proposed a reconfigurable architecture for interoperation based on multi agents. The legacy systems are encapsulated as information agents to participate in the integration system. Finally we give a simplified case study.

  10. Application of machine-learning methods to solid-state chemistry: ferromagnetism in transition metal alloys

    International Nuclear Information System (INIS)

    Landrum, G.A.Gregory A.; Genin, Hugh

    2003-01-01

    Machine-learning methods are a collection of techniques for building predictive models from experimental data. The algorithms are problem-independent: the chemistry and physics of the problem being studied are contained in the descriptors used to represent the known data. The application of a variety of machine-learning methods to the prediction of ferromagnetism in ordered and disordered transition metal alloys is presented. Applying a decision tree algorithm to build a predictive model for ordered phases results in a model that is 100% accurate. The same algorithm achieves 99% accuracy when trained on a data set containing both ordered and disordered phases. Details of the descriptor sets for both applications are also presented

  11. MATLAB-aided teaching and learning in optics and photonics using the methods of computational photonics

    Science.gov (United States)

    Lin, Zhili; Li, Xiaoyan; Zhu, Daqing; Pu, Jixiong

    2017-08-01

    Due to the nature of light fields of laser waves and pulses as vector quantities with complex spatial distribution and temporal dependence, the optics and photonics courses have always been difficult to teach and learn without the support of graphical visualization, numerical simulations and hands-on experiments. One of the state-of-the-art method of computational photonics, the finite-difference time-domain(FDTD) method, is applied with MATLAB simulations to model typical teaching cases in optics and photonics courses. The obtained results with graphical visualization in the form of animated pictures allow students to more deeply understand the dynamic process of light interaction with classical optical structures. The discussed teaching methodology is aimed to enhance the teaching effectiveness of optics and photonics courses and arousing the students' learning interest.

  12. Sparse feature learning for instrument identification: Effects of sampling and pooling methods.

    Science.gov (United States)

    Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

    2016-05-01

    Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.

  13. Methods and results of radiotherapy in case of medulloblastoma

    International Nuclear Information System (INIS)

    Bamberg, M.; Sauerwein, W.; Scherer, E.

    1982-01-01

    The prognosis of the medulloblastoma with its marked tendency towards early formation of metastases by way of liquor circulation can be decisively improved by post-surgical homogenous irradiation. A successful radiotherapy is only possible by means of new irradiation methods which have been developed for high-voltage units during recent years and which require great experience and skill on the part of the radiotherapeutist. At the Radiological Centre of Essen, 26 patients with medulloblastoma have been submitted to such a specially developed post-surgical radiotherapy since 1974. After a follow-up period of at most seven years, 16 patients have survived (two of them with recurrences) and 10 patients died because of a local recurrence. In dependence on the patient's state of health after surgery and before irradiation, the neurologic state and physical condition of these patients seem favorable after unique post-operative radiotherapy. New therapeutic possibilities are provided by radiosensitizing substances. The actually most effective radiosensitizer Misonidazol, however, could not respond hitherto to clinical expectances. (orig.) [de

  14. Application of NDE methods to green ceramics: initial results

    International Nuclear Information System (INIS)

    Kupperman, D.S.; Karplus, H.B.; Poeppel, R.B.; Ellingson, W.A.; Berger, H.; Robbins, C.; Fuller, E.

    1983-01-01

    The effectiveness of microradiography, ultrasonic methods, unclear magnetic resonance, and neutron radiography was assessed for the nondestructive evaluation of green (unfired) ceramics. The application of microradiography to ceramics is reviewed, and preliminary experiments with a commercial microradiography unit are described. Conventional ultrasonic techniques are difficult to apply to flaw detection green ceramics because of the high attenuation, fragility, and couplant-absorbing properties of these materials. However, velocity, attenuation, and spectral data were obtained with pressure-coupled transducers and provided useful informaion related to density variations and the presence of agglomerates. Nuclear magnetic resonance (NMR) imaging techniques and neutron radiography were considered for detection of anomalies in the distribution of porosity. With NMR, areas of high porosity might be detected after the samples are doped with water. In the case of neutron radiography, although imaging the binder distribution throughout the sample may not be feasible because of the low overall concentration of binder, regions of high binder concentration (thus high porosity) should be detectable

  15. Learners with learning difficulties in mathematics : attitudes, curriculum and methods of teaching mathematics

    OpenAIRE

    2012-01-01

    D.Ed. The aim of this theses is to find out whether there is any relationship between learners' attitudes and learning difficulties in mathematics: To investigate whether learning difficulties in mathematics are associated with learners' gender. To establish the nature of teachers' perceptions of the learning problem areas in the mathematics curriculum. To find out about the teachers' views on the methods of teaching mathematics, resources, learning of mathematics, extra curricular activit...

  16. Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.

    Science.gov (United States)

    Gehrmann, Sebastian; Dernoncourt, Franck; Li, Yeran; Carlson, Eric T; Wu, Joy T; Welt, Jonathan; Foote, John; Moseley, Edward T; Grant, David W; Tyler, Patrick D; Celi, Leo A

    2018-01-01

    In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.

  17. ANALYSIS OF THE DEPENDENCE OF THE E-LEARNING USAGE ON THE STUDY RESULTS

    Directory of Open Access Journals (Sweden)

    KUNCOVÁ, M.

    2015-03-01

    Full Text Available The main aim of this paper is to compare the study results of the selected subjects of the full time and combined forms of study at the study programme Economics and Management. This programme is offered at the College of Polytechnics Jihlava and covers two fields of study - Travel and Tourism, Finance and Management. The comparison is aimed at the results of the period before the start of the e-learning (2008 for full time students and 2010 for combined form with the year 2012 (after the e-learning implementation. The results from eight biggest subjects are tested via Chi-square test of independence. It should answer the question if the e-learning has had an impact on the study results and if it is possible to find dependence between results of two different types of study, two different years, two different study branches and two different subjects. The comparison has shown the differences of combined/full time students but we have not proved the influence of the e-learning on the evaluation.

  18. New test methods for BIPV. Results from IP performance

    International Nuclear Information System (INIS)

    Jol, J.C.; Van Kampen, B.J.M.; De Boer, B.J.; Reil, F.; Geyer, D.

    2009-11-01

    Within the Performance project new test procedures for PV building products and the building performance as a whole when PV is applied in buildings have been drafted. It has resulted in a first draft of new test procedures for PV building products and proposals for tests for novel BIPV technology like thin film. The test proposed are a module breakage test for BIPV products, a fire safety test for BIPV products and a dynamic load test for BIPV products. Furthermore first proposals of how flexible PV modules could be tested in an appropriate way to ensure long time quality and safety of these new products are presented.

  19. Assessing learning outcomes in middle-division classical mechanics: The Colorado Classical Mechanics and Math Methods Instrument

    Science.gov (United States)

    Caballero, Marcos D.; Doughty, Leanne; Turnbull, Anna M.; Pepper, Rachel E.; Pollock, Steven J.

    2017-06-01

    Reliable and validated assessments of introductory physics have been instrumental in driving curricular and pedagogical reforms that lead to improved student learning. As part of an effort to systematically improve our sophomore-level classical mechanics and math methods course (CM 1) at CU Boulder, we have developed a tool to assess student learning of CM 1 concepts in the upper division. The Colorado Classical Mechanics and Math Methods Instrument (CCMI) builds on faculty consensus learning goals and systematic observations of student difficulties. The result is a 9-question open-ended post test that probes student learning in the first half of a two-semester classical mechanics and math methods sequence. In this paper, we describe the design and development of this instrument, its validation, and measurements made in classes at CU Boulder and elsewhere.

  20. Assessing learning outcomes in middle-division classical mechanics: The Colorado Classical Mechanics and Math Methods Instrument

    Directory of Open Access Journals (Sweden)

    Marcos D. Caballero

    2017-04-01

    Full Text Available Reliable and validated assessments of introductory physics have been instrumental in driving curricular and pedagogical reforms that lead to improved student learning. As part of an effort to systematically improve our sophomore-level classical mechanics and math methods course (CM 1 at CU Boulder, we have developed a tool to assess student learning of CM 1 concepts in the upper division. The Colorado Classical Mechanics and Math Methods Instrument (CCMI builds on faculty consensus learning goals and systematic observations of student difficulties. The result is a 9-question open-ended post test that probes student learning in the first half of a two-semester classical mechanics and math methods sequence. In this paper, we describe the design and development of this instrument, its validation, and measurements made in classes at CU Boulder and elsewhere.

  1. Diamagnetic measurements on ISX-B: method and results

    International Nuclear Information System (INIS)

    Neilson, G.H.

    1983-10-01

    A diamagnetic loop is used on the ISX-B tokamak to measure the change in toroidal magnetic flux, sigma phi, caused by finite plasma current and perpendicular pressure. From this measurement, the perpendicular poloidal beta β/sub I perpendicular to/ is determined. The principal difficulty encountered is in identifying and making corrections for various noise components which appear in the measured flux. These result from coupling between the measuring loops and the toroidal and poloidal field windings, both directly and through currents induced in the vacuum vessel and coils themselves. An analysis of these couplings is made and techniques for correcting them developed. Results from the diamagnetic measurement, employing some of these correction techniques, are presented and compared with other data. The obtained values of β/sub I perpendicular to/ agree with those obtained from the equilibrium magnetic analysis (β/sub IΔ/) in ohmically heated plasmas, indicating no anisotropy. However, with 0.3 to 2.0 MW of tangential neutral beam injection, β/sub IΔ/ is consistently greater than β/sub I pependicular to/ and qualitatively consistent with the formation of an anisotropic ion velocity distribution and with toroidal rotation. Quantitatively, the difference between β/sub IΔ/ and β/sub I perpendicular to/ is more than can be accounted for on the basis of the usual classical fast ion calculations and spectroscopic rotation measurements

  2. [Integrated intensive treatment of tinnitus: method and initial results].

    Science.gov (United States)

    Mazurek, B; Georgiewa, P; Seydel, C; Haupt, H; Scherer, H; Klapp, B F; Reisshauer, A

    2005-07-01

    In recent years, no major advances have been made in understanding the mechanisms underlying the development of tinnitus. Hence, the present therapeutic strategies aim at decoupling the subconscious from the perception of tinnitus. Mindful of the lessons drawn from existing tinnitus retraining and desensitisation therapies, a new integrated day hospital strategy of treatment lasting 7-14 days has been developed at the Charité Hospital and is presented in the present paper. The strategy for treating tinnitus in the proximity of patient domicile is designed for patients who feel disturbed in their world of perception and their efficiency due to tinnitus and give evidence of mental and physical strain. In view of the etiologically non-uniform and multiple events connected with tinnitus, consideration was also given to the fact that somatic and psychosocial factors are equally involved. Therefore, therapy should aim at diagnosing and therapeutically influencing those psychosocial factors that reduce the hearing impression to such an extent that the affected persons suffer from strain. The first results of therapy-dependent changes of 46 patients suffering from chronic tinnitus are presented. The data were evaluated before and after 7 days of treatment and 6 months after the end of treatment. Immediately after the treatment, the scores of both the tinnitus questionnaire (Goebel and Hiller) and the subscales improved significantly. These results were maintained during the 6-month post-treatment period and even improved.

  3. A comparison of the cooperative learning and traditional learning methods in theory classes on nursing students' communication skill with patients at clinical settings.

    Science.gov (United States)

    Baghcheghi, Nayereh; Koohestani, Hamid Reza; Rezaei, Koresh

    2011-11-01

    The purpose of this study was to compare the effect of traditional learning and cooperative learning methods on nursing students' communication skill with patients. This was an experimental study in which 34 nursing students in their 2nd semester of program participated. They were divided randomly into two groups, a control group who were taught their medical/surgical nursing course by traditional learning method and an experimental group, who were taught the same material using cooperative learning method. Before and after the teaching intervention, the students' communication skills with patients at clinical settings were examined. The results showed that no significant difference between the two groups in students' communication skills scores before the teaching intervention, but did show a significant difference between the two groups in the interaction skills and problem follow up sub-scales scores after the teaching intervention. This study provides evidence that cooperative learning is an effective method for improving and increasing communication skills of nursing students especially in interactive skills and follow up the problems sub-scale, thereby it is recommended to increase nursing students' participation in arguments by applying active teaching methods which can provide the opportunity for increased communication skills. Copyright © 2011 Elsevier Ltd. All rights reserved.

  4. Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory.

    Science.gov (United States)

    Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas

    2014-07-01

    Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Assessing Internet energy intensity: A review of methods and results

    Energy Technology Data Exchange (ETDEWEB)

    Coroama, Vlad C., E-mail: vcoroama@gmail.com [Instituto Superior Técnico, Universidade Técnica de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Hilty, Lorenz M. [Department of Informatics, University of Zurich, Binzmühlestrasse 14, 8050 Zurich (Switzerland); Empa, Swiss Federal Laboratories for Materials Science and Technology, Lerchenfeldstr. 5, 9014 St. Gallen (Switzerland); Centre for Sustainable Communications, KTH Royal Institute of Technology, Lindstedtsvägen 5, 100 44 Stockholm (Sweden)

    2014-02-15

    Assessing the average energy intensity of Internet transmissions is a complex task that has been a controversial subject of discussion. Estimates published over the last decade diverge by up to four orders of magnitude — from 0.0064 kilowatt-hours per gigabyte (kWh/GB) to 136 kWh/GB. This article presents a review of the methodological approaches used so far in such assessments: i) top–down analyses based on estimates of the overall Internet energy consumption and the overall Internet traffic, whereby average energy intensity is calculated by dividing energy by traffic for a given period of time, ii) model-based approaches that model all components needed to sustain an amount of Internet traffic, and iii) bottom–up approaches based on case studies and generalization of the results. Our analysis of the existing studies shows that the large spread of results is mainly caused by two factors: a) the year of reference of the analysis, which has significant influence due to efficiency gains in electronic equipment, and b) whether end devices such as personal computers or servers are included within the system boundary or not. For an overall assessment of the energy needed to perform a specific task involving the Internet, it is necessary to account for the types of end devices needed for the task, while the energy needed for data transmission can be added based on a generic estimate of Internet energy intensity for a given year. Separating the Internet as a data transmission system from the end devices leads to more accurate models and to results that are more informative for decision makers, because end devices and the networking equipment of the Internet usually belong to different spheres of control. -- Highlights: • Assessments of the energy intensity of the Internet differ by a factor of 20,000. • We review top–down, model-based, and bottom–up estimates from literature. • Main divergence factors are the year studied and the inclusion of end devices

  6. Assessing Internet energy intensity: A review of methods and results

    International Nuclear Information System (INIS)

    Coroama, Vlad C.; Hilty, Lorenz M.

    2014-01-01

    Assessing the average energy intensity of Internet transmissions is a complex task that has been a controversial subject of discussion. Estimates published over the last decade diverge by up to four orders of magnitude — from 0.0064 kilowatt-hours per gigabyte (kWh/GB) to 136 kWh/GB. This article presents a review of the methodological approaches used so far in such assessments: i) top–down analyses based on estimates of the overall Internet energy consumption and the overall Internet traffic, whereby average energy intensity is calculated by dividing energy by traffic for a given period of time, ii) model-based approaches that model all components needed to sustain an amount of Internet traffic, and iii) bottom–up approaches based on case studies and generalization of the results. Our analysis of the existing studies shows that the large spread of results is mainly caused by two factors: a) the year of reference of the analysis, which has significant influence due to efficiency gains in electronic equipment, and b) whether end devices such as personal computers or servers are included within the system boundary or not. For an overall assessment of the energy needed to perform a specific task involving the Internet, it is necessary to account for the types of end devices needed for the task, while the energy needed for data transmission can be added based on a generic estimate of Internet energy intensity for a given year. Separating the Internet as a data transmission system from the end devices leads to more accurate models and to results that are more informative for decision makers, because end devices and the networking equipment of the Internet usually belong to different spheres of control. -- Highlights: • Assessments of the energy intensity of the Internet differ by a factor of 20,000. • We review top–down, model-based, and bottom–up estimates from literature. • Main divergence factors are the year studied and the inclusion of end devices

  7. A lifelong learning hyper-heuristic method for bin packing.

    Science.gov (United States)

    Sim, Kevin; Hart, Emma; Paechter, Ben

    2015-01-01

    We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.

  8. 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.

  9. Modeling Music Emotion Judgments Using Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Naresh N. Vempala

    2018-01-01

    Full Text Available Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.

  10. Ilmenite exploration on the Senegal continental shelf. Methods and results

    International Nuclear Information System (INIS)

    Horn, R.; Le Lann, F.; Scolari, G.; Tixeront, M.

    1975-01-01

    From the results of a study based on geomorphology, geophysics and sedimentology, it has been possible to point out, South of Dakar, the existence of a fossil lagoon (peat dated 8400 years BP) partly isolated from the open sea by a littoral sand bar at -25m and strongly eroded. To the North of Dakar, the unconsolidated sediments, with a thickness over 40m, are thinning out seawards and from North of Dakar, the unconsolidated sediments, with a thickness over 40m, are thinning out seawards and from North to South. This pattern reflect the action of the longshore current which overstates the drainage effect to the Cayar canyon. The distribution of ilmenites in the sediments is studied in terms of a possible exploitation however, the grades are too low in the present economic conditions [fr

  11. Matching Learning Style to Instructional Method: Effects on Comprehension

    Science.gov (United States)

    Rogowsky, Beth A.; Calhoun, Barbara M.; Tallal, Paula

    2015-01-01

    While it is hypothesized that providing instruction based on individuals' preferred learning styles improves learning (i.e., reading for visual learners and listening for auditory learners, also referred to as the "meshing hypothesis"), after a critical review of the literature Pashler, McDaniel, Rohrer, and Bjork (2008) concluded that…

  12. Newton Methods for Large Scale Problems in Machine Learning

    Science.gov (United States)

    Hansen, Samantha Leigh

    2014-01-01

    The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

  13. Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method

    Directory of Open Access Journals (Sweden)

    Muneki Yasuda

    2018-04-01

    Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

  14. Democratic population decisions result in robust policy-gradient learning: a parametric study with GPU simulations.

    Directory of Open Access Journals (Sweden)

    Paul Richmond

    2011-05-01

    Full Text Available High performance computing on the Graphics Processing Unit (GPU is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism, achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic" for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated.

  15. Multitask Learning-Based Security Event Forecast Methods for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hui He

    2016-01-01

    Full Text Available Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wireless sensor networks to various security attacks, a region similarity multitask-based security event forecast method for wireless sensor networks is proposed. This method performs topology partitioning on a large-scale sensor network and calculates the similarity degree among regional subnetworks. The trend of unknown network security events can be predicted through multitask learning of the occurrence and transmission characteristics of known network security events. Second, in case of lacking regional data, the quantitative trend of unknown regional network security events can be calculated. This study introduces a sensor network security event forecast method named Prediction Network Security Incomplete Unmarked Data (PNSIUD method to forecast missing attack data in the target region according to the known partial data in similar regions. Experimental results indicate that for an unknown security event forecast the forecast accuracy and effects of the similarity forecast algorithm are better than those of single-task learning method. At the same time, the forecast accuracy of the PNSIUD method is better than that of the traditional support vector machine method.

  16. THE FIRST RESULTS OF AN IMPLEMENTATION OF THE ACTIVE LEARNING PARADIGM IN UNIVERSITY PHYSICS

    OpenAIRE

    Sliško, Josip; Medina Hernández, Rebeca

    2006-01-01

    Putting students in the center of the educational process and using the results of educational research are basic characteristics of an important movement whose objective is learning improvement in many university courses. For mechanics courses there is experimental evidence that pedagogy with “active students” gives better results than pedagogy with “active professor and passive students”. In this article we present the first results of an implementation of a pedagogy which promotes active s...

  17. Exploring Diversity of Learning Outcomes in E-Learning Courses: Results of a Qualitative Study in a French Multinational Company

    Science.gov (United States)

    Baudoin, Emmanuel

    2010-01-01

    The implementation of e-learning by companies in France is ongoing. One of their issues is to improve the learning experience of their employees. From our point of view, this implies that they must better understand the learning experience of the employees. This paper suggests a qualitative approach to learning in order to identify the diversity…

  18. Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods

    Science.gov (United States)

    Cubuk, E. D.; Schoenholz, S. S.; Rieser, J. M.; Malone, B. D.; Rottler, J.; Durian, D. J.; Kaxiras, E.; Liu, A. J.

    2015-03-01

    We use machine-learning methods on local structure to identify flow defects—or particles susceptible to rearrangement—in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.

  19. WebMail versus WebApp: Comparing Problem-Based Learning Methods in a Business Research Methods Course

    Science.gov (United States)

    Williams van Rooij, Shahron

    2007-01-01

    This study examined the impact of two Problem-Based Learning (PBL) approaches on knowledge transfer, problem-solving self-efficacy, and perceived learning gains among four intact classes of adult learners engaged in a group project in an online undergraduate business research methods course. With two of the classes using a text-only PBL workbook…

  20. Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis.

    Science.gov (United States)

    Isupova, Olga; Kuzin, Danil; Mihaylova, Lyudmila

    2017-09-27

    Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators' load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximization approach and variational Bayes inference are proposed. Theoretical derivations of the posterior estimates of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localization procedure, elegantly embedded in the topic modeling framework. It is shown that the developed learning algorithms can achieve 95% success rate. The proposed framework can be applied to a number of areas, including transportation systems, security, and surveillance.

  1. Improved machine learning method for analysis of gas phase chemistry of peptides

    Directory of Open Access Journals (Sweden)

    Ahn Natalie

    2008-12-01

    Full Text Available Abstract Background Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra. Results We employed a robust data mining strategy using new feature annotation functions of MAE software, which revealed under-prediction of the frequency of occurrence in fragmentation of the second peptide bond. We applied methods of exploratory data analysis to pre-process the information in the MS/MS spectra, including data normalization and attribute selection, to reduce the attributes to a smaller, less correlated set for machine learning studies. We then compared our rule building machine learning program, DataSqueezer, with commonly used association rules and decision tree algorithms. All used machine learning algorithms produced similar results that were consistent with expected properties for a second gas phase mechanism at the second peptide bond. Conclusion The results provide compelling evidence that we have identified underlying chemical properties in the data that suggest the existence of an additional gas phase mechanism for the second peptide bond. Thus, the methods described in this study provide a valuable approach for analyses of this kind in the future.

  2. Anytime, Anywhere Learning Supported by Smart Phones: Experiences and Results from the MUSIS Project

    Science.gov (United States)

    Milrad, Marcelo; Spikol, Daniel

    2007-01-01

    In this paper we report the results of our on-going activities regarding the use of smart phones and mobile services in university classrooms. The purpose of these trials was to explore and identify which content and services could be delivered to the smart phones in order to support learning and communication in the context of university studies.…

  3. Results for Learning Report 2014-15: Basic Education at Risk

    Science.gov (United States)

    Bernard, Jean-Marc; Amelewonou, Kokou; Bonnet, Gabrielle; Rubiano-Matulevich, Eliana; Soman, Kouassi; Sonnenberg, Krystyna

    2014-01-01

    The 2014/2015 Results for Learning Report: Basic Education at Risk examines the progress achieved by Global Partnership for Education (GPE) partner developing countries over the period 2008-2012. Universal primary education has never been so close, yet there are still 58 million children of primary school age who do not go to school around the…

  4. Knowledge of Results after Good Trials Enhances Learning in Older Adults

    Science.gov (United States)

    Chiviacowsky, Suzete; Wulf, Gabriele; Wally, Raquel; Borges, Thiago

    2009-01-01

    In recent years, some researchers have examined motor learning in older adults. Some of these studies have specifically looked at the effectiveness of different manipulations of extrinsic feedback, or knowledge of results (KR). Given that many motor tasks may already be more challenging for older adults compared to younger adults, making KR more…

  5. Cognitive Learning Strategy as a Partial Effect on Major Field Test in Business Results

    Science.gov (United States)

    Strang, Kenneth David

    2014-01-01

    An experiment was developed to determine if cognitive learning strategies improved standardized university business exam results. Previous studies revealed that factors such as prior ability, age, gender, and culture predicted a student's Major Field Test in Business (MFTB) score better than course content. The experiment control consisted of…

  6. Climate Action Gaming Experiment: Methods and Example Results

    Directory of Open Access Journals (Sweden)

    Clifford Singer

    2015-09-01

    Full Text Available An exercise has been prepared and executed to simulate international interactions on policies related to greenhouse gases and global albedo management. Simulation participants are each assigned one of six regions that together contain all of the countries in the world. Participants make quinquennial policy decisions on greenhouse gas emissions, recapture of CO2 from the atmosphere, and/or modification of the global albedo. Costs of climate change and of implementing policy decisions impact each region’s gross domestic product. Participants are tasked with maximizing economic benefits to their region while nearly stabilizing atmospheric CO2 concentrations by the end of the simulation in Julian year 2195. Results are shown where regions most adversely affected by effects of greenhouse gas emissions resort to increases in the earth’s albedo to reduce net solar insolation. These actions induce temperate region countries to reduce net greenhouse gas emissions. An example outcome is a trajectory to the year 2195 of atmospheric greenhouse emissions and concentrations, sea level, and global average temperature.

  7. 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.

  8. COSMIC EVOLUTION OF DUST IN GALAXIES: METHODS AND PRELIMINARY RESULTS

    International Nuclear Information System (INIS)

    Bekki, Kenji

    2015-01-01

    We investigate the redshift (z) evolution of dust mass and abundance, their dependences on initial conditions of galaxy formation, and physical correlations between dust, gas, and stellar contents at different z based on our original chemodynamical simulations of galaxy formation with dust growth and destruction. In this preliminary investigation, we first determine the reasonable ranges of the most important two parameters for dust evolution, i.e., the timescales of dust growth and destruction, by comparing the observed and simulated dust mass and abundances and molecular hydrogen (H 2 ) content of the Galaxy. We then investigate the z-evolution of dust-to-gas ratios (D), H 2 gas fraction (f H 2 ), and gas-phase chemical abundances (e.g., A O = 12 + log (O/H)) in the simulated disk and dwarf galaxies. The principal results are as follows. Both D and f H 2 can rapidly increase during the early dissipative formation of galactic disks (z ∼ 2-3), and the z-evolution of these depends on initial mass densities, spin parameters, and masses of galaxies. The observed A O -D relation can be qualitatively reproduced, but the simulated dispersion of D at a given A O is smaller. The simulated galaxies with larger total dust masses show larger H 2 and stellar masses and higher f H 2 . Disk galaxies show negative radial gradients of D and the gradients are steeper for more massive galaxies. The observed evolution of dust masses and dust-to-stellar-mass ratios between z = 0 and 0.4 cannot be reproduced so well by the simulated disks. Very extended dusty gaseous halos can be formed during hierarchical buildup of disk galaxies. Dust-to-metal ratios (i.e., dust-depletion levels) are different within a single galaxy and between different galaxies at different z

  9. COSMIC EVOLUTION OF DUST IN GALAXIES: METHODS AND PRELIMINARY RESULTS

    Energy Technology Data Exchange (ETDEWEB)

    Bekki, Kenji [ICRAR, M468, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009 (Australia)

    2015-02-01

    We investigate the redshift (z) evolution of dust mass and abundance, their dependences on initial conditions of galaxy formation, and physical correlations between dust, gas, and stellar contents at different z based on our original chemodynamical simulations of galaxy formation with dust growth and destruction. In this preliminary investigation, we first determine the reasonable ranges of the most important two parameters for dust evolution, i.e., the timescales of dust growth and destruction, by comparing the observed and simulated dust mass and abundances and molecular hydrogen (H{sub 2}) content of the Galaxy. We then investigate the z-evolution of dust-to-gas ratios (D), H{sub 2} gas fraction (f{sub H{sub 2}}), and gas-phase chemical abundances (e.g., A {sub O} = 12 + log (O/H)) in the simulated disk and dwarf galaxies. The principal results are as follows. Both D and f{sub H{sub 2}} can rapidly increase during the early dissipative formation of galactic disks (z ∼ 2-3), and the z-evolution of these depends on initial mass densities, spin parameters, and masses of galaxies. The observed A {sub O}-D relation can be qualitatively reproduced, but the simulated dispersion of D at a given A {sub O} is smaller. The simulated galaxies with larger total dust masses show larger H{sub 2} and stellar masses and higher f{sub H{sub 2}}. Disk galaxies show negative radial gradients of D and the gradients are steeper for more massive galaxies. The observed evolution of dust masses and dust-to-stellar-mass ratios between z = 0 and 0.4 cannot be reproduced so well by the simulated disks. Very extended dusty gaseous halos can be formed during hierarchical buildup of disk galaxies. Dust-to-metal ratios (i.e., dust-depletion levels) are different within a single galaxy and between different galaxies at different z.

  10. Effective Teaching Methods--Project-based Learning in Physics

    Science.gov (United States)

    Holubova, Renata

    2008-01-01

    The paper presents results of the research of new effective teaching methods in physics and science. It is found out that it is necessary to educate pre-service teachers in approaches stressing the importance of the own activity of students, in competences how to create an interdisciplinary project. Project-based physics teaching and learning…

  11. Lesson learned - CGID based on the Method 1 and Method 2 for digital equipment

    International Nuclear Information System (INIS)

    Hwang, Wonil; Sohn, Kwang Young; Cho, Chang Hwan; Kim, Sung Jong

    2015-01-01

    The acceptance methods associated with commercial-grade dedication are the following: 1) Special tests and inspection (Method 1) 2) Commercial-grade surveys (Method 2) 3) Source verification (Method 3) 4) An acceptable item and supplier performance record (Method 4) Special tests and inspections, often referred to as Method 1, are performed by the dedicating entity after the item is received to verify selected critical characteristics. Conducting a commercial-grade survey of a supplier is often referred to as Method 2. Supplier audits to verify compliance with a nuclear QA program do not meet the intent of a commercial-grade survey. Source verification, often referred to as Method 3, entails verification of critical characteristics during manufacture and testing of the item being procured. The performance history (good or bad) of the item and supplier is a consideration when determining the use of the other acceptance methods and the rigor with which they are used on a case-by-case basis. Some digital equipment system has the delivery reference and its operating history for Nuclear Power Plant as far as surveyed. However it was found that there is difficulty in collecting this of supporting data sheet, so that supplier usually decide to conduct the CGID based on the Method-1 and Method-2 based on the initial qualification likely. It is conceived that the Method-4 might be a better approach for CGID(Commercial Grade Item Dedication) even if there are some difficulties in data package for justifying CGID from the vendor and operating organization. This paper present the lesson learned from the consulting for Method-1 and 2 for digital equipment dedication. Considering all the information above, there are a couple of issues to remind in order to perform the CGID for Method-2. In doing commercial grade survey based on Method 2, quality personnel as well as technical engineer shall be involved for integral dedication. Other than this, the review of critical

  12. Defining Learning Space in a Serious Game in Terms of Operative and Resultant Actions

    Science.gov (United States)

    Martin, Michael W.; Shen, Yuzhong

    2012-01-01

    This paper explores the distinction between operative and resultant actions in games, and proposes that the learning space created by a serious game is a function of these actions. Further, it suggests a possible relationship between these actions and the forms of cognitive load imposed upon the game player. Association of specific types of cognitive load with respective forms of actions in game mechanics also presents some heuristics for integrating learning content into serious games. Research indicates that different balances of these types of actions are more suitable for novice or experienced learners. By examining these relationships, we can develop a few basic principles of game design which have an increased potential to promote positive learning outcomes.

  13. Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

    Science.gov (United States)

    Bosworth, John

    2006-01-01

    A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

  14. Primary exploration of the application of case based learning method in clinical probation teaching of the integrated curriculum of hematology

    Institute of Scientific and Technical Information of China (English)

    Zi-zhen XU; Ye-fei WANG; Yan WANG; Shu CHENG; Yi-qun HU; Lei DING

    2015-01-01

    Objective To explore the application and the effect of the case based learning(CBL)method in clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.Methods The CBL method was applied to the experimental group,and the traditional approach for the control group.After the lecture,a questionnaire survey was conducted to evaluate the teaching effect in the two groups.Results The CBL method efficiently increased the students’interest in learning and autonomous learning ability,enhanced their ability to solve clinical problems with basic theoretic knowledge and cultivated their clinical thinking ability.Conclusion The CBL method can improve the quality of clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.

  15. A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

    Science.gov (United States)

    Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

    2013-01-01

    Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

  16. Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting

    International Nuclear Information System (INIS)

    Tang, Pingzhou; Chen, Di; Hou, Yushuo

    2016-01-01

    As the world’s energy problem becomes more severe day by day, photovoltaic power generation has opened a new door for us with no doubt. It will provide an effective solution for this severe energy problem and meet human’s needs for energy if we can apply photovoltaic power generation in real life, Similar to wind power generation, photovoltaic power generation is uncertain. Therefore, the forecast of photovoltaic power generation is very crucial. In this paper, entropy method and extreme learning machine (ELM) method were combined to forecast a short-term photovoltaic power generation. First, entropy method is used to process initial data, train the network through the data after unification, and then forecast electricity generation. Finally, the data results obtained through the entropy method with ELM were compared with that generated through generalized regression neural network (GRNN) and radial basis function neural network (RBF) method. We found that entropy method combining with ELM method possesses higher accuracy and the calculation is faster.

  17. Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

    OpenAIRE

    Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

    2016-01-01

    Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal, Ginsburg, & Schau, 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof, Ceroni, Jeong, & Moghaddam, 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to...

  18. Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

    Science.gov (United States)

    Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

    2018-01-01

    Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. A measurement fusion method for nonlinear system identification using a cooperative learning algorithm.

    Science.gov (United States)

    Xia, Youshen; Kamel, Mohamed S

    2007-06-01

    Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.

  20. Change Of Learning Environment Using Game Production ­Theory, Methods And Practice

    DEFF Research Database (Denmark)

    Reng, Lars; Kofoed, Lise; Schoenau-Fog, Henrik

    2018-01-01

    will focus on cases in which development of games did change the learning environments into production units where students or employees were producing games as part of the learning process. The cases indicate that the motivation as well as the learning curve became very high. The pedagogical theories......Game Based Learning has proven to have many possibilities for supporting better learning outcomes, when using educational or commercial games in the classroom. However, there is also a great potential in using game development as a motivator in other kinds of learning scenarios. This study...... and methods are based on Problem Based Learning (PBL), but are developed further by combining PBL with a production-oriented/design based approach. We illustrate the potential of using game production as a learning environment with investigation of three game productions. We can conclude that using game...