WorldWideScience

Sample records for learning technique called

  1. Detection and Classification of Baleen Whale Foraging Calls Combining Pattern Recognition and Machine Learning Techniques

    Science.gov (United States)

    2016-12-01

    for a call is also a time- consuming task. None of these chores bothers the analyst when applying the selection algorithm. With help from the...1365- 2907.2007.00106.x. Castellote, M., C. W. Clark, and M. O. Lammers, 2012: Acoustic and behavioural changes by fin whales (Balaenoptera physalus...separation of blue whale call types on a southern California feeding ground. Animal Behaviour , 74, 881–894, doi:10.1016/j.anbehav.2007.01.022. Rocha, R

  2. Do market participants learn from conference calls?

    NARCIS (Netherlands)

    Roelofsen, E.; Verbeeten, F.; Mertens, G.

    2014-01-01

    We examine whether market participants learn from the information that is disseminated during the Q-and-A section of conference calls. Specifically, we investigate whether stock prices react to information on intangible assets provided during conference calls, and whether conference calls

  3. Computer Assisted Language Learning (CALL) Software: Evaluation ...

    African Journals Online (AJOL)

    Evaluating the nature and extent of the influence of Computer Assisted Language Learning (CALL) on the quality of language learning is highly problematic. This is owing to the number and complexity of interacting variables involved in setting the items for teaching and learning languages. This paper identified and ...

  4. 76 Computer Assisted Language Learning (CALL) Software ...

    African Journals Online (AJOL)

    Ike Odimegwu

    combination with other factors which may enhance or ameliorate the ... form of computer-based learning which carries two important features: .... To take some commonplace examples, a ... photographs, and even full-motion video clips.

  5. Professionals calling in lifelong learning centers

    Directory of Open Access Journals (Sweden)

    Victor Manuel Monteiro Seco

    2013-06-01

    Full Text Available Purpose: This study aims to understand how the way people see their work and the authentizotic character of their organizational climate contribute to the building of a Great Place to Work. Design/methodology/approach: This paper presents the results of a quantitative investigation that correlate the perceptions of organizational climate and the work orientations of professionals with different occupations on Portuguese lifelong education centers. Findings: The study indicates that all the core elements of an authentizotic organization contribute to explain what people potentially expect from their companies:  adequate  material  conditions  plus  a  meaningful contribution. Practical implications: The study has implications in the future for National Qualification Agency directors, education politicians and human resource managers who are responsible for providing good expectations within a healthy context of talent retention. Originality/value: The novel contribution of this paper is the finding that employee’s work orientations and authentizotic climate are related to each other in a Lifelong learning Center in the public education sector.

  6. Sustainability in CALL Learning Environments: A Systemic Functional Grammar Approach

    Science.gov (United States)

    McDonald, Peter

    2014-01-01

    This research aims to define a sustainable resource in Computer-Assisted Language Learning (CALL). In order for a CALL resource to be sustainable it must work within existing educational curricula. This feature is a necessary prerequisite of sustainability because, despite the potential for educational change that digitalization has offered since…

  7. Studying Language Learning Opportunities Afforded by a Collaborative CALL Task

    Science.gov (United States)

    Leahy, Christine

    2016-01-01

    This research study explores the learning potential of a computer-assisted language learning (CALL) activity. Research suggests that the dual emphasis on content development and language accuracy, as well as the complexity of L2 production in natural settings, can potentially create cognitive overload. This study poses the question whether, and…

  8. Social Learning: Medical Student Perceptions of Geriatric House Calls

    Science.gov (United States)

    Abbey, Linda; Willett, Rita; Selby-Penczak, Rachel; McKnight, Roberta

    2010-01-01

    Bandura's social learning theory provides a useful conceptual framework to understand medical students' perceptions of a house calls experience at Virginia Commonwealth University School of Medicine. Social learning and role modeling reflect Liaison Committee on Medical Education guidelines for "Medical schools (to) ensure that the learning…

  9. Computer Assisted Language Learning (CALL): Using Internet for Effective Language Learning

    NARCIS (Netherlands)

    Kremenska, Anelly

    2006-01-01

    Please, cite this publication as: Kremenska, A. (2006). Computer Assisted Language Learning (CALL): Using Internet for Effective Language Learning. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia,

  10. Social learning: medical student perceptions of geriatric house calls.

    Science.gov (United States)

    Abbey, Linda; Willett, Rita; Selby-Penczak, Rachel; McKnight, Roberta

    2010-01-01

    Bandura's social learning theory provides a useful conceptual framework to understand medical students' perceptions of a house calls experience at Virginia Commonwealth University School of Medicine. Social learning and role modeling reflect Liaison Committee on Medical Education guidelines for "Medical schools (to) ensure that the learning environment for medical students promotes the development of explicit and appropriate professional attributes (attitudes, behaviors, and identity) in their medical students." This qualitative study reports findings from open-ended survey questions from 123 medical students who observed a preceptor during house calls to elderly homebound patients. Their comments included reflections on the medical treatment as well as interactions with family and professional care providers. Student insights about the social learning process they experienced during house calls to geriatric patients characterized physician role models as dedicated, compassionate, and communicative. They also described patient care in the home environment as comprehensive, personalized, more relaxed, and comfortable. Student perceptions reflect an appreciation of the richness and complexity of details learned from home visits and social interaction with patients, families, and caregivers.

  11. Complexity Theory and CALL Curriculum in Foreign Language Learning

    Directory of Open Access Journals (Sweden)

    Hassan Soleimani

    2014-05-01

    Full Text Available Complexity theory literally indicates the complexity of a system, behavior, or a process. Its connotative meaning, while, implies dynamism, openness, sensitivity to initial conditions and feedback, and adaptation properties of a system. Regarding English as a Foreign/ Second Language (EFL/ESL this theory emphasizes on the complexity of the process of teaching and learning, including all the properties of a complex system. The purpose of the current study is to discuss the role of CALL as a modern technology in simplifying the process of teaching and learning a new language while integrating into the complexity theory. Nonetheless, the findings obtained from reviewing previously conducted studies in this field confirmed the usefulness of CALL curriculum in EFL/ESL contexts. These findings can also provide pedagogical implications for employing computer as an effective teaching and learning tool.

  12. Wild birds learn to eavesdrop on heterospecific alarm calls.

    Science.gov (United States)

    Magrath, Robert D; Haff, Tonya M; McLachlan, Jessica R; Igic, Branislav

    2015-08-03

    Many vertebrates gain critical information about danger by eavesdropping on other species' alarm calls [1], providing an excellent context in which to study information flow among species in animal communities [2-4]. A fundamental but unresolved question is how individuals recognize other species' alarm calls. Although individuals respond to heterospecific calls that are acoustically similar to their own, alarms vary greatly among species, and eavesdropping probably also requires learning [1]. Surprisingly, however, we lack studies demonstrating such learning. Here, we show experimentally that individual wild superb fairy-wrens, Malurus cyaneus, can learn to recognize previously unfamiliar alarm calls. We trained individuals by broadcasting unfamiliar sounds while simultaneously presenting gliding predatory birds. Fairy-wrens in the experiment originally ignored these sounds, but most fled in response to the sounds after two days' training. The learned response was not due to increased responsiveness in general or to sensitization following repeated exposure and was independent of sound structure. Learning can therefore help explain the taxonomic diversity of eavesdropping and the refining of behavior to suit the local community. In combination with previous work on unfamiliar predator recognition (e.g., [5]), our results imply rapid spread of anti-predator behavior within wild populations and suggest methods for training captive-bred animals before release into the wild [6]. A remaining challenge is to assess the importance and consequences of direct association of unfamiliar sounds with predators, compared with social learning-such as associating unfamiliar sounds with conspecific alarms. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Sustainability in CALL Learning Environments: A Systemic Functional Grammar Approach

    Directory of Open Access Journals (Sweden)

    Peter McDonald

    2014-09-01

    Full Text Available This research aims to define a sustainable resource in Computer-Assisted Language Learning (CALL. In order for a CALL resource to be sustainable it must work within existing educational curricula. This feature is a necessary prerequisite of sustainability because, despite the potential for educational change that digitalization has offered since the nineteen nineties, curricula in traditional educational institutions have not fundamentally changed, even as we move from a pre-digital society towards a digital society. Curricula have failed to incorporate CALL resources because no agreed-upon pedagogical language enables teachers to discuss CALL classroom practices. Systemic Functional Grammar (SFG can help to provide this language and bridge the gap between the needs of the curriculum and the potentiality of CALL-based resources. This paper will outline how SFG principles can be used to create a pedagogical language for CALL and it will give practical examples of how this language can be used to create sustainable resources in classroom contexts.

  14. Constructivism, the so-called semantic learning theories, and situated cognition versus the psychological learning theories.

    Science.gov (United States)

    Aparicio, Juan José; Rodríguez Moneo, María

    2005-11-01

    In this paper, the perspective of situated cognition, which gave rise both to the pragmatic theories and the so-called semantic theories of learning and has probably become the most representative standpoint of constructivism, is examined. We consider the claim of situated cognition to provide alternative explanations of the learning phenomenon to those of psychology and, especially, to those of the symbolic perspective, currently predominant in cognitive psychology. The level of analysis of situated cognition (i.e., global interactive systems) is considered an inappropriate approach to the problem of learning. From our analysis, it is concluded that the pragmatic theories and the so-called semantic theories of learning which originated in situated cognition can hardly be considered alternatives to the psychological learning theories, and they are unlikely to add anything of interest to the learning theory or to contribute to the improvement of our knowledge about the learning phenomenon.

  15. m-Learning and holography: Compatible techniques?

    Science.gov (United States)

    Calvo, Maria L.

    2014-07-01

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

  16. Exploration of Textual Interactions in CALL Learning Communities: Emerging Research and Opportunities

    Science.gov (United States)

    White, Jonathan R.

    2017-01-01

    Computer-assisted language learning (CALL) has greatly enhanced the realm of online social interaction and behavior. In language classrooms, it allows the opportunity for students to enhance their learning experiences. "Exploration of Textual Interactions in CALL Learning Communities: Emerging Research and Opportunities" is an ideal…

  17. Virtual Learning Environments on the Go: CALL Meets MALL

    Science.gov (United States)

    Arús Hita, Jorge

    2016-01-01

    This paper presents "Eating out," a Moodle-based digital learning resource for English as a Foreign Language (EFL) teaching that can be run both on computers and mobile devices. It is argued that Mobile Assisted Language Learning (MALL) resources do not necessarily need to be specifically designed for such platforms. Rather, a carefully…

  18. Information systems performance evaluation, introducing a two-level technique: Case study call centers

    Directory of Open Access Journals (Sweden)

    Hesham A. Baraka

    2015-03-01

    The objective of this paper was to introduce a new technique that can support decision makers in the call centers industry to evaluate, and analyze the performance of call centers. The technique presented is derived from the research done on measuring the success or failure of information systems. Two models are mainly adopted namely: the Delone and Mclean model first introduced in 1992 and the Design Reality Gap model introduced by Heeks in 2002. Two indices are defined to calculate the performance of the call center; the success index and the Gap Index. An evaluation tool has been developed to allow call centers managers to evaluate the performance of their call centers in a systematic analytical approach; the tool was applied on 4 call centers from different areas, simple applications such as food ordering, marketing, and sales, technical support systems, to more real time services such as the example of emergency control systems. Results showed the importance of using information systems models to evaluate complex systems as call centers. The models used allow identifying the dimensions for the call centers that are facing challenges, together with an identification of the individual indicators in these dimensions that are causing the poor performance of the call center.

  19. Journaling; an active learning technique.

    Science.gov (United States)

    Blake, Tim K

    2005-01-01

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

  20. Is CALL Obsolete? Language Acquisition and Language Learning Revisited in a Digital Age

    Science.gov (United States)

    Jarvis, Huw; Krashen, Stephen

    2014-01-01

    In this article, Huw Jarvis and Stephen Krashen ask "Is CALL Obsolete?" When the term CALL (Computer-Assisted Language Learning) was introduced in the 1960s, the language education profession knew only about language learning, not language acquisition, and assumed the computer's primary contribution to second language acquisition…

  1. Translating and transforming (a) CALL for leadership for learning

    DEFF Research Database (Denmark)

    Weinreich, Elvi; Bjerg, Helle

    2015-01-01

    "The paper pursues the argument that the process of translation is not solely a linguistic exercise. It also implies methodological and conceptual questions related to the translation and as such transformation of general and theoretical research based models of leadership for learning...

  2. Computer-Assisted Language Learning (CALL) in Support of (Re)-Learning Native Languages: The Case of Runyakitara

    Science.gov (United States)

    Katushemererwe, Fridah; Nerbonne, John

    2015-01-01

    This study presents the results from a computer-assisted language learning (CALL) system of Runyakitara (RU_CALL). The major objective was to provide an electronic language learning environment that can enable learners with mother tongue deficiencies to enhance their knowledge of grammar and acquire writing skills in Runyakitara. The system…

  3. Stimulating Deep Learning Using Active Learning Techniques

    Science.gov (United States)

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

    2016-01-01

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

  4. Developing an effective corrective action process : lessons learned from operating a confidential close call reporting system

    Science.gov (United States)

    2013-03-05

    In 2007, the Federal Railroad Administration (FRA) launched : C3RS, the Confidential Close Call Reporting System, as a : demonstration project to learn how to facilitate the effective : reporting and implementation of corrective actions, and assess t...

  5. Attitudes of Jordanian Undergraduate Students towards Using Computer Assisted Language Learning (CALL)

    Science.gov (United States)

    Saeed, Farah Jamal Abed Alrazeq; Al-Zayed, Norma Nawaf

    2018-01-01

    The study aimed at investigating the attitudes of Jordanian undergraduate students towards using computer assisted-language learning (CALL) and its effectiveness in the process of learning the English language. In order to fulfill the study's objective, the researchers used a questionnaire to collect data, followed-up with semi-structured…

  6. CALL AND COOPERATIVE LEARNING: A SOLUTION TO DEVELOP STUDENTS‟ LISTENING ABILITY

    OpenAIRE

    Delsa Miranty

    2017-01-01

    This paper aims to provide some ideas both for English teachers and target learners about how to apply CALL and Cooperative Learning as the solution to develop students‘ listening activities in the classroom. Since teachers need to understand about students‘ needs, background, age and expectations when they learn English as the foreign language in the classroom. Therefore, the English teacher should provide environment which facilitates the children to have fun di the teaching learning proces...

  7. The Effectiveness of Using Contextual Clues, Dictionary Strategy and Computer Assisted Language Learning (Call In Learning Vocabulary

    Directory of Open Access Journals (Sweden)

    Zuraina Ali

    2013-07-01

    Full Text Available This study investigates the effectiveness of three vocabulary learning methods that are Contextual Clues, Dictionary Strategy, and Computer Assisted Language Learning (CALL in learning vocabulary among ESL learners. First, it aims at finding which of the vocabulary learning methods namely Dictionary Strategy, Contextual Clues, and CALL that may result in the highest number of words learnt in the immediate and delayed recall tests. Second, it compares the results of the Pre-test and the Delayed Recall Post-test to determine the differences of learning vocabulary using the methods. A quasi-experiment that tested the effectiveness of learning vocabulary using Dictionary Strategy, Contextual clues, and CALL involved 123 first year university students. Qualitative procedures included the collection of data from interviews which were conducted to triangulate the data obtain from the quantitative inquiries. Findings from the study using ANOVA revealed that there were significant differences when students were exposed to Dictionary Strategy, Contextual Clues and CALL in the immediate recall tests but not in the Delayed Recall Post-test. Also, there were significant differences when t test was used to compare the scores between the Pre-test and the Delayed Recall Post-test in using the three methods of vocabulary learning. Although many researchers have advocated the relative effectiveness of Dictionary Strategy, Contextual Clues, and CALL in learning vocabulary, the study however, is still paramount since there is no study has ever empirically investigated the relative efficacy of these three methods in a single study.

  8. A Call to Action for Research in Digital Learning: Learning without Limits of Time, Place, Path, Pace…or Evidence

    Science.gov (United States)

    Cavanaugh, Cathy; Sessums, Christopher; Drexler, Wendy

    2015-01-01

    This essay is a call for rethinking our approach to research in digital learning. It plots a path founded in social trends and advances in education. A brief review of these trends and advances is followed by discussion of what flattened research might look like at scale. Scaling research in digital learning is crucial to advancing understanding…

  9. Investigating Language Learning Activity Using a CALL Task in the Self-access Centre

    Directory of Open Access Journals (Sweden)

    Carlos Montoro

    2011-09-01

    Full Text Available This article describes a small study of the language learning activity of individual learners using a CALL task in a self-access environment. The research focuses on the nature of the language learning activity, the most salient elements that make up its structure and major disturbances observed between and within some of those elements. It is set in the context of computer-assisted language learning (CALL and activity theory. A CALL task designed by the authors was made available online to be used as a research and learning tool. Empirical data was collected from two participants using ethnographic tools, such as participant observation and stimulated recall sessions. The analysis focuses on disturbances mainly involving the subject (i.e., the learner, mediating artefacts (e.g., the CALL task, the community (e.g., management and other self-access centre users and the object of the activity (i.e., learning English. It is recommended that future studies should look deeper into contradictions in the learning activity from a cultural-historical perspective.

  10. Machine Learning Techniques in Clinical Vision Sciences.

    Science.gov (United States)

    Caixinha, Miguel; Nunes, Sandrina

    2017-01-01

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

  11. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  12. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  13. Neural Correlates of Threat Perception: Neural Equivalence of Conspecific and Heterospecific Mobbing Calls Is Learned

    Science.gov (United States)

    Avey, Marc T.; Hoeschele, Marisa; Moscicki, Michele K.; Bloomfield, Laurie L.; Sturdy, Christopher B.

    2011-01-01

    Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise [1]–[2]. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators [3]. Mobbing calls produced in response to smaller, higher-threat predators contain more “D” notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators [4]. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned. PMID:21909363

  14. Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

    Science.gov (United States)

    Avey, Marc T; Hoeschele, Marisa; Moscicki, Michele K; Bloomfield, Laurie L; Sturdy, Christopher B

    2011-01-01

    Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

  15. Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

    Directory of Open Access Journals (Sweden)

    Marc T Avey

    Full Text Available Songbird auditory areas (i.e., CMM and NCM are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

  16. Pre-Service Teachers' Uses of and Barriers from Adopting Computer-Assisted Language Learning (CALL) Programs

    Science.gov (United States)

    Samani, Ebrahim; Baki, Roselan; Razali, Abu Bakar

    2014-01-01

    Success in implementation of computer-assisted language learning (CALL) programs depends on the teachers' understanding of the roles of CALL programs in education. Consequently, it is also important to understand the barriers teachers face in the use of computer-assisted language learning (CALL) programs. The current study was conducted on 14…

  17. From Computer Assisted Language Learning (CALL) to Mobile Assisted Language Use (MALU)

    Science.gov (United States)

    Jarvis, Huw; Achilleos, Marianna

    2013-01-01

    This article begins by critiquing the long-established acronym CALL (Computer Assisted Language Learning). We then go on to report on a small-scale study which examines how student non-native speakers of English use a range of digital devices beyond the classroom in both their first (L1) and second (L2) languages. We look also at the extent to…

  18. Learning Based Approach for Optimal Clustering of Distributed Program's Call Flow Graph

    Science.gov (United States)

    Abofathi, Yousef; Zarei, Bager; Parsa, Saeed

    Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Learning automatas (LAs) are search tools which are used for solving many NP-Complete problems. In this paper a learning based algorithm is proposed to optimal clustering of call flow graph and appropriate distributing of programs in network level. The algorithm uses learning feature of LAs to search in state space. It has been shown that the speed of reaching to solution increases remarkably using LA in search process, and it also prevents algorithm from being trapped in local minimums. Experimental results show the superiority of proposed algorithm over others.

  19. Active learning techniques for librarians practical examples

    CERN Document Server

    Walsh, Andrew

    2010-01-01

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

  20. Storytelling: a teaching-learning technique.

    Science.gov (United States)

    Geanellos, R

    1996-03-01

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

  1. Optimizing of verification photographs by using the so-called tangential field technique

    International Nuclear Information System (INIS)

    Proske, H.; Merte, H.; Kratz, H.

    1991-01-01

    When irradiating under high voltage condition, verification photographs prove to be difficult to take if the Gantry position is not aligned to 0deg respectively 180deg, since the patient is being irradiated diagonally. Under these conditions it is extremely difficult to align the X-ray-cartridge vertically to the central beam of the therapeutic radiation. This results in, amongst others, misprojections, so that definite dimensions of portrayed organ structures become practical impossible to determine. This paper describes how we have solved these problems on our high voltage units (tele-gamma cobalt unit and linear-accelerator). By using simple accessories, determination of dimensions of organ structures, as shown on the verification photographs, are made possible. We illustrate our method by using the so-called tangential fields technique when irradiating mamma carcinoma. (orig.) [de

  2. Attitudes of Jordanian Undergraduate Students towards Using Computer Assisted Language Learning (CALL

    Directory of Open Access Journals (Sweden)

    Farah Jamal Abed Alrazeq Saeed

    2018-01-01

    Full Text Available The study aimed at investigating the attitudes of Jordanian undergraduate students towards using computer assisted -language learning (CALL and its effectiveness in the process of learning the English language.  In order to fulfill the study’s objective, the researchers used a questionnaire to collect data, followed-up with semi-structured interviews to investigate the students’ beliefs towards CALL. Twenty- one of Jordanian BA students majoring in English language and literature were selected according to simple random sampling. The results revealed positive attitudes towards CALL in facilitating the process of writing assignments, gaining information; making learning enjoyable; improving their creativity, productivity, academic achievement, critical thinking skills, and enhancing their knowledge about vocabulary grammar, and culture. Furthermore, they believed that computers can motivate them to learn English language and help them to communicate and interact with their teachers and colleagues. The researchers recommended conducting a research on the same topic, taking into consideration the variables of age, gender, experience in using computers, and computer skills.

  3. CALL AND COOPERATIVE LEARNING: A SOLUTION TO DEVELOP STUDENTS‟ LISTENING ABILITY

    Directory of Open Access Journals (Sweden)

    Delsa Miranty

    2017-04-01

    Full Text Available This paper aims to provide some ideas both for English teachers and target learners about how to apply CALL and Cooperative Learning as the solution to develop students‘ listening activities in the classroom. Since teachers need to understand about students‘ needs, background, age and expectations when they learn English as the foreign language in the classroom. Therefore, the English teacher should provide environment which facilitates the children to have fun di the teaching learning process, nice atmosphere, comfort and enjoyable to learn English and practice it both in the classroom and in the laboratory. Furthermore, this paper will provide what the teachers should do related activities such as: listening to the songs, movies, cartoon by applying STAD (Students Teams – Achievement Divisions in the classroom in order to develop students‘ listening ability both in the classroom and laboratory.

  4. A Reinforcement Learning Approach to Call Admission Control in HAPS Communication System

    Directory of Open Access Journals (Sweden)

    Ni Shu Yan

    2017-01-01

    Full Text Available The large changing of link capacity and number of users caused by the movement of both platform and users in communication system based on high altitude platform station (HAPS will resulting in high dropping rate of handover and reduce resource utilization. In order to solve these problems, this paper proposes an adaptive call admission control strategy based on reinforcement learning approach. The goal of this strategy is to maximize long-term gains of system, with the introduction of cross-layer interaction and the service downgraded. In order to access different traffics adaptively, the access utility of handover traffics and new call traffics is designed in different state of communication system. Numerical simulation result shows that the proposed call admission control strategy can enhance bandwidth resource utilization and the performances of handover traffics.

  5. The Adaptation Study of the Questionnaires of the Attitude towards CALL (A-CALL), the Attitude towards CAL (A-CAL), the Attitude towards Foreign Language Learning (A-FLL) to Turkish Language

    Science.gov (United States)

    Erdem, Cahit; Saykili, Abdullah; Kocyigit, Mehmet

    2018-01-01

    This study primarily aims to adapt the Foreign Language Learning (FLL), Computer assisted Learning (CAL) and Computer assisted Language Learning (CALL) scales developed by Vandewaetere and Desmet into Turkish context. The instrument consists of three scales which are "the attitude towards CALL questionnaire" ("A-CALL")…

  6. Editorial (special issue on CALL, e-learning and m-learning

    Directory of Open Access Journals (Sweden)

    Jo Mynard

    2011-09-01

    Full Text Available Technology has, in one form of another, been a part of self-access learning since the very first self-access centres (SACs of the 1980s. Some of the better-funded centres featured elaborate listening and recording machinery and (occasionally early personal computers. Early software programmes and language-learning websites available for self-access use tended to be aimed at individual study, initially following the language lab model, and were often designed to teach or test discrete language points. Of course, in 2011 programmes aimed at individual study do still exist and certainly have a place in self-access learning, particularly if a learner has identified a target language area that the software or website covers. However, in this special issue we go beyond language learning software and look at tools and technologies currently available to the learner as self-access resources.

  7. Three visual techniques to enhance interprofessional learning.

    Science.gov (United States)

    Parsell, G; Gibbs, T; Bligh, J

    1998-07-01

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

  8. Learning Physics through Project-Based Learning Game Techniques

    Science.gov (United States)

    Baran, Medine; Maskan, Abdulkadir; Yasar, Seyma

    2018-01-01

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

  9. Skype™ Conference Calls: A Way to Promote Speaking Skills in the Teaching and Learning of English

    Directory of Open Access Journals (Sweden)

    Yeferson Romaña Correa

    2015-01-01

    Full Text Available This article presents the results of a research project on the teaching and learning of English through the use of Skype™ conference calls. The research was carried out with a group of 12 English as a foreign language adult learners in the language institute of Universidad Distrital Francisco José de Caldas, Bogotá, Colombia. The findings of this study suggest that Skype™ conference calls might be considered as an influential computer-mediated communication tool in order to promote English as a foreign language adult A1 learners’ speaking skill, especially for social interaction purposes and oral reinforcement of both language fluency and course contents outside of classroom settings.

  10. MACHINE LEARNING TECHNIQUES USED IN BIG DATA

    Directory of Open Access Journals (Sweden)

    STEFANIA LOREDANA NITA

    2016-07-01

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

  11. Learning curve estimation techniques for nuclear industry

    International Nuclear Information System (INIS)

    Vaurio, Jussi K.

    1983-01-01

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

  12. Machine learning techniques for optical communication system optimization

    DEFF Research Database (Denmark)

    Zibar, Darko; Wass, Jesper; Thrane, Jakob

    In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction.......In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction....

  13. The Differences across Distributed Leadership Practices by School Position According to the Comprehensive Assessment of Leadership for Learning (CALL)

    Science.gov (United States)

    Blitz, Mark H.; Modeste, Marsha

    2015-01-01

    The Comprehensive Assessment of Leadership for Learning (CALL) is a multi-source assessment of distributed instructional leadership. As part of the validation of CALL, researchers examined differences between teacher and leader ratings in assessing distributed leadership practices. The authors utilized a t-test for equality of means for the…

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

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

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

  15. Analysing CMS transfers using Machine Learning techniques

    CERN Document Server

    Diotalevi, Tommaso

    2016-01-01

    LHC experiments transfer more than 10 PB/week between all grid sites using the FTS transfer service. In particular, CMS manages almost 5 PB/week of FTS transfers with PhEDEx (Physics Experiment Data Export). FTS sends metrics about each transfer (e.g. transfer rate, duration, size) to a central HDFS storage at CERN. The work done during these three months, here as a Summer Student, involved the usage of ML techniques, using a CMS framework called DCAFPilot, to process this new data and generate predictions of transfer latencies on all links between Grid sites. This analysis will provide, as a future service, the necessary information in order to proactively identify and maybe fix latency issued transfer over the WLCG.

  16. On some surprising statistical properties of a DNA fingerprinting technique called AFLP

    NARCIS (Netherlands)

    Gort, G.

    2010-01-01

    AFLP is a widely used DNA fingerprinting technique, resulting in band absence - presence profiles, like a bar code. Bands represent DNA fragments, sampled from the genome of an individual plant or other organism. The DNA fragments travel through a lane of an electrophoretic gel or microcapillary

  17. Exploring the Earth Using Deep Learning Techniques

    Science.gov (United States)

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

    2016-12-01

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

  18. Opportunities to Create Active Learning Techniques in the Classroom

    Science.gov (United States)

    Camacho, Danielle J.; Legare, Jill M.

    2015-01-01

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

  19. CRDM motion analysis using machine learning technique

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  20. The Significant Incidents and Close Calls in Human Space Flight Chart: Lessons Learned Gone Viral

    Science.gov (United States)

    Wood, Bill; Pate, Dennis; Thelen, David

    2010-01-01

    This presentation will explore the surprising history and events that transformed a mundane spreadsheet of historical spaceflight incidents into a popular and widely distributed visual compendium of lessons learned. The Significant Incidents and Close Calls in Human Space Flight Chart (a.k.a. The Significant Incidents Chart) is a popular and visually captivating reference product that has arisen from the work of the Johnson Space Center (JSC) Safety and Mission Assurance (S&MA) Flight Safety Office (FSO). It began as an internal tool intended to increase our team s awareness of historical and modern space flight incidents. Today, the chart is widely recognized across the agency as a reference tool. It appears in several training and education programs. It is used in familiarization training in the JSC Building 9 Mockup Facility and is seen by hundreds of center visitors each week. The chart visually summarizes injuries, fatalities, and close calls sustained during the continuing development of human space flight. The poster-sized chart displays over 100 total events that have direct connections to human space flight endeavors. The chart is updated periodically. The update process itself has become a collaborative effort. Many people, spanning multiple NASA organizations, have provided suggestions for additional entries. The FSO maintains a growing list of subscribers who have requested to receive updates. The presenters will discuss the origins and motivations behind the significant incidents chart. A review of the inclusion criteria used to select events will be offered. We will address how the chart is used today by S&MA and offer a vision of how it might be used by other organizations now and in the future. Particular emphasis will be placed on features of the chart that have met with broad acceptance and have helped spread awareness of the most important lessons in human spaceflight.

  1. The colloquial approach: An active learning technique

    Science.gov (United States)

    Arce, Pedro

    1994-09-01

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

  2. Application of Machine Learning Techniques in Aquaculture

    OpenAIRE

    Rahman, Akhlaqur; Tasnim, Sumaira

    2014-01-01

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

  3. Call mimicry by eastern towhees and its significance in relation to auditory learning

    Science.gov (United States)

    Jon S. Greenlaw; Clifford E. Shackelford; Raymond E. Brown

    1998-01-01

    The authors document cases of eastern towhees (Pipilo erythrophthalmus) using mimicked alarm calls from three presumptive models (blue jay (Cyanocitta cristata), brown thrasher (Toxostoma rufum), and American robin (Turdus migratorius)). In four instances, male towhees employed heterospecific calls without substitution in their own call repertoires. Three birds (New...

  4. EFL Teachers' Knowledge of the Use and Development of Computer-Assisted Language Learning (CALL) Materials

    Science.gov (United States)

    Dashtestani, Reza

    2014-01-01

    Even though there are a plethora of CALL materials available to EFL teachers nowadays, very limited attention has been directed toward the issue that most EFL teachers are merely the consumers of CALL materials. The main challenge is to equip EFL teachers with the required CALL materials development skills to enable them to be contributors to CALL…

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

    Science.gov (United States)

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

    2009-01-01

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

  6. Challenges of Using Learning Analytics Techniques to Support Mobile Learning

    Science.gov (United States)

    Arrigo, Marco; Fulantelli, Giovanni; Taibi, Davide

    2015-01-01

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

  7. SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

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

  8. Performance, Cognitive Load, and Behaviour of Technology-Assisted English Listening Learning: From CALL to MALL

    Science.gov (United States)

    Chang, Chi-Cheng; Warden, Clyde A.; Liang, Chaoyun; Chou, Pao-Nan

    2018-01-01

    This study examines differences in English listening comprehension, cognitive load, and learning behaviour between outdoor ubiquitous learning and indoor computer-assisted learning. An experimental design, employing a pretest-posttest control group is employed. Randomly assigned foreign language university majors joined either the experimental…

  9. BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

    OpenAIRE

    Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

    2015-01-01

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

  10. IoT Security Techniques Based on Machine Learning

    OpenAIRE

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

    2018-01-01

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

  11. Machine Learning Techniques in Optimal Design

    Science.gov (United States)

    Cerbone, Giuseppe

    1992-01-01

    Many important applications can be formalized as constrained optimization tasks. For example, we are studying the engineering domain of two-dimensional (2-D) structural design. In this task, the goal is to design a structure of minimum weight that bears a set of loads. A solution to a design problem in which there is a single load (L) and two stationary support points (S1 and S2) consists of four members, E1, E2, E3, and E4 that connect the load to the support points is discussed. In principle, optimal solutions to problems of this kind can be found by numerical optimization techniques. However, in practice [Vanderplaats, 1984] these methods are slow and they can produce different local solutions whose quality (ratio to the global optimum) varies with the choice of starting points. Hence, their applicability to real-world problems is severely restricted. To overcome these limitations, we propose to augment numerical optimization by first performing a symbolic compilation stage to produce: (a) objective functions that are faster to evaluate and that depend less on the choice of the starting point and (b) selection rules that associate problem instances to a set of recommended solutions. These goals are accomplished by successive specializations of the problem class and of the associated objective functions. In the end, this process reduces the problem to a collection of independent functions that are fast to evaluate, that can be differentiated symbolically, and that represent smaller regions of the overall search space. However, the specialization process can produce a large number of sub-problems. This is overcome by deriving inductively selection rules which associate problems to small sets of specialized independent sub-problems. Each set of candidate solutions is chosen to minimize a cost function which expresses the tradeoff between the quality of the solution that can be obtained from the sub-problem and the time it takes to produce it. The overall solution

  12. Data Mining Practical Machine Learning Tools and Techniques

    CERN Document Server

    Witten, Ian H; Hall, Mark A

    2011-01-01

    Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place

  13. A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.

    Science.gov (United States)

    van den Akker, Jeroen; Mishne, Gilad; Zimmer, Anjali D; Zhou, Alicia Y

    2018-04-17

    Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist. For this reason, many clinical laboratories confirm NGS results using orthogonal technologies such as Sanger sequencing. Here, we report the development of a deterministic machine-learning-based model to differentiate between these two types of variant calls: those that do not require confirmation using an orthogonal technology (high confidence), and those that require additional quality testing (low confidence). This approach allows reliable NGS-based calling in a clinical setting by identifying the few important variant calls that require orthogonal confirmation. We developed and tested the model using a set of 7179 variants identified by a targeted NGS panel and re-tested by Sanger sequencing. The model incorporated several signals of sequence characteristics and call quality to determine if a variant was identified at high or low confidence. The model was tuned to eliminate false positives, defined as variants that were called by NGS but not confirmed by Sanger sequencing. The model achieved very high accuracy: 99.4% (95% confidence interval: +/- 0.03%). It categorized 92.2% (6622/7179) of the variants as high confidence, and 100% of these were confirmed to be present by Sanger sequencing. Among the variants that were categorized as low confidence, defined as NGS calls of low quality that are likely to be artifacts, 92.1% (513/557) were found to be not present by Sanger sequencing. This work shows that NGS data contains sufficient characteristics for a machine-learning-based model to

  14. Maximizing Reading Narrative Text Ability by Probing Prompting Learning Technique

    Directory of Open Access Journals (Sweden)

    Wiwied Pratiwi

    2017-12-01

    Full Text Available The objective of this research was to know whether Probing Prompting Learning Technique can be used to get the maximum effect of students’ reading narrative ability in teaching and learning process. This research was applied collaborative action reEsearch, this research was done in two cycle. The subject of this research was 23 students at tenth grade of SMA Kartikatama Metro. The result of the research showed that the Probing Prompting Learning Technique is useful and effective to help students get maximum effect of their reading. Based on the results of the questionnaire obtained an average percentage of 95%, it indicated that application of Probing Prompting Learning Technique in teaching l reading was appropriately applied. In short that students’ responses toward Probing Prompting Learning Technique in teaching reading was positive. In conclusion, Probing Prompting Learning Technique can get maximum effect of students’ reading ability. In relation to the result of the reserach, some suggestion are offered to english teacher, that  the use of Probing Prompting learning Technique in teaching reading will get the maximum effect of students’ reading abilty.

  15. Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls

    NARCIS (Netherlands)

    Shamir, L.; Carol Yerby, C.; Simpson, R.; Benda-Beckmann, A.M. von; Tyack, P.; Samarra, F.; Miller, P.; Wallin, J.

    2014-01-01

    Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds

  16. The Unreasonable Effectiveness of CALL: What Have We Learned in Two Decades of Research?

    Science.gov (United States)

    Felix, Uschi

    2008-01-01

    This paper presents a comprehensive picture of what has been investigated in terms of CALL effectiveness over the period 1981-2005 throwing light on why this question is still such a difficult one to answer unequivocally. The author looks at both strengths and weaknesses in this body of work, highlighting pitfalls and paradoxes in research…

  17. E-learning systems intelligent techniques for personalization

    CERN Document Server

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

    2017-01-01

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

  18. A Comparative Analysis of Machine Learning Techniques for Credit Scoring

    OpenAIRE

    Nwulu, Nnamdi; Oroja, Shola; İlkan, Mustafa

    2012-01-01

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

  19. Precision Learning Assessment: An Alternative to Traditional Assessment Techniques.

    Science.gov (United States)

    Caltagirone, Paul J.; Glover, Christopher E.

    1985-01-01

    A continuous and curriculum-based assessment method, Precision Learning Assessment (PLA), which integrates precision teaching and norm-referenced techniques, was applied to a math computation curriculum for 214 third graders. The resulting districtwide learning curves defining average annual progress through the computation curriculum provided…

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

    Science.gov (United States)

    Wiles, Amy M

    2016-07-08

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

  1. AGE GROUP CLASSIFICATION USING MACHINE LEARNING TECHNIQUES

    OpenAIRE

    Arshdeep Singh Syal*1 & Abhinav Gupta2

    2017-01-01

    A human face provides a lot of information that allows another person to identify characteristics such as age, sex, etc. Therefore, the challenge is to develop an age group prediction system using the automatic learning method. The task of estimating the age group of the human from their frontal facial images is very captivating, but also challenging because of the pattern of personalized and non-linear aging that differs from one person to another. This paper examines the problem of predicti...

  2. Prostate Cancer Probability Prediction By Machine Learning Technique.

    Science.gov (United States)

    Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

    2017-11-26

    The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

  3. The Role of Computer-Assisted Language Learning (CALL) in Promoting Learner Autonomy

    Science.gov (United States)

    Mutlu, Arzu; Eroz-Tuga, Betil

    2013-01-01

    Problem Statement: Teaching a language with the help of computers and the Internet has attracted the attention of many practitioners and researchers in the last 20 years, so the number of studies that investigate whether computers and the Internet promote language learning continues to increase. These studies have focused on exploring the beliefs…

  4. Leaders Learning Orientation and the HCM-turn in call centres

    DEFF Research Database (Denmark)

    Gnaur, Dorina

    2013-01-01

    as a significant leadership quality that promotes reflexivity in the ongoing processes of interpretation and meaning creation enhancing the human dimension in the production of service. Learning orientation will be related to high-commitment management (HCM) as a way to reconcile the logics of efficiency...

  5. "I Learned that There's a State Called Victoria and He Has Six Blue-Tongued Lizards!"

    Science.gov (United States)

    Charron, Nancy Necora

    2007-01-01

    In the study described in this article, fourth-grade teachers and students of different abilities and language backgrounds were interviewed before, during, and after participating in an Internet pen pal program. Results reveal that the program's authentic tasks and texts facilitated communication and enabled students to learn about a different…

  6. Making Skills Everyone's Business: A Call to Transform Adult Learning in the United States

    Science.gov (United States)

    Strawn, Julie

    2015-01-01

    To address the need to connect Americans with learning opportunities, the Office of Career, Technical, and Adult Education released the present report. Grounded in evidence and informed by effective and emerging practices, "Making Skills Everyone's Business" offers seven strategies that hold great promise for improving the conditions…

  7. Modern machine learning techniques and their applications in cartoon animation research

    CERN Document Server

    Yu, Jun

    2013-01-01

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

  8. Modelling tick abundance using machine learning techniques and satellite imagery

    DEFF Research Database (Denmark)

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

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

  9. Enhancing E-Learning with VRML Techniques

    OpenAIRE

    Sangeetha Senthilkumar; E. Kirubakaran

    2011-01-01

    Virtual Reality (VR) is a computer-generated three-dimensional space that is multi-sensorial, interactive and engaging. Virtual reality is an artificial environment that is created with software and presented to the user in such a way that the user suspends belief and accepts it as a real environment. On a computer, virtual reality is primarily experienced through two of the five senses: sight and sound. This research paper is focused on enhancing E-Learning using the three dimensional Web Te...

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

    African Journals Online (AJOL)

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

  11. Global learning on carbon capture and storage: A call for strong international cooperation on CCS demonstration

    International Nuclear Information System (INIS)

    Coninck, Heleen de; Stephens, Jennie C.; Metz, Bert

    2009-01-01

    Closing the gap between carbon dioxide capture and storage (CCS) rhetoric and technical progress is critically important to global climate mitigation efforts. Developing strong international cooperation on CCS demonstration with global coordination, transparency, cost-sharing and communication as guiding principles would facilitate efficient and cost-effective collaborative global learning on CCS, would allow for improved understanding of the global capacity and applicability of CCS, and would strengthen global trust, awareness and public confidence in the technology.

  12. DESIGNING AND BUILDING EXERCISE MODEL OF TECHNICAL ENGLISH VOCABULARIES USING CALL (COMPUTER ASSISTED LANGUAGE LEARNING

    Directory of Open Access Journals (Sweden)

    Yogi Widiawati

    2017-11-01

    Full Text Available The research is aimed to assist and facilitate the students of Electrical and Electronics Department of Politeknik Negeri Jakarta (State Polytechnics of Jakarta, Indonesia, in learning technical English vocabulary. As technical students, they study ESP (English for Specific Purposes and they find some obstacles in memorizing technical vocabularies which are very important in order to read and understand manual books for laboratory and workshop. Some English technical vocabularies among others are “generate”, “pile”, “bench”, et cetera. The research outcome is software which will be beneficial for technical students, especially electrical and electronics students. This software can be used to practice their vocabulary skills, so they will be more skillful and knowledgeable. This software is designed by using the program of Rapid E-Learning Suite Version 5.2 and Flash CS3. The software practice contains some exercises on reading text and reading comprehension questions and presented with the multiple answers. This software is handy and flexible because students can bring it anywhere and be studied anytime. It is handy because this software is put and saved in CD (compact disc, so the students can take it with them anywhere and anytime they want to learn. In other words, they have flexibility to learn and practice English Technical Vocabularies. As a result, the students are found one of the ways to overcome their problems of memorizing vocabularies. The product is a kind of software which is easily used and portable so that the students can use the software anywhere and anytime. It consists of 3 (three sections of exercises. At the end of each exercise, the students are evaluated automatically by looking at the scoring system. These will encourage them to get good score by repeating it again and again. So the technical words are not problem for them. Furthermore, the students can practice technical English vocabulary both at home and

  13. Machine learning techniques to examine large patient databases.

    Science.gov (United States)

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

    2009-03-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  15. APPLICABILITY OF COOPERATIVE LEARNING TECHNIQUES IN DIFFERENT CLASSROOM CONTEXTS

    Directory of Open Access Journals (Sweden)

    Dr. Issy Yuliasri

    2017-04-01

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

  16. Machine learning techniques for razor triggers

    CERN Document Server

    Kolosova, Marina

    2015-01-01

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

  17. Predicting radiotherapy outcomes using statistical learning techniques

    International Nuclear Information System (INIS)

    El Naqa, Issam; Bradley, Jeffrey D; Deasy, Joseph O; Lindsay, Patricia E; Hope, Andrew J

    2009-01-01

    Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model

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

    Science.gov (United States)

    Makahinda, T.

    2018-02-01

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

  19. Case studies in nanny state name-calling: what can we learn?

    Science.gov (United States)

    Magnusson, R S

    2015-08-01

    The 'nanny state' has become a popular metaphor in debates about public health regulation. It fulfils a particular role in that debate: to caution government against taking action. This paper presents case studies of nanny state criticisms, using them to identify a series of contextual features that may assist in better understanding, evaluating and where appropriate, resisting the rhetorical force of nanny state criticisms. The case studies presented include Rush Limbaugh's reactions to Michelle Obama's efforts to encourage American food companies to market healthier food to children; Christopher Hitchens' critique of New York City Mayor Michael Bloomberg's public health policies; and the reaction of neoliberal think tanks to Australia's plain tobacco packaging legislation. These case studies do not provide a basis for making generalisations about the practice of 'nanny state name-calling'. Nor do they preclude debate about the appropriate limits of government action. However, in appropriate cases they may assist policy-makers and public health advocates to contest the framing of public health interventions as unwarranted incursions into the private lives of individuals. One important lesson from these case studies is that the principal concern of nanny state critics is not loss of freedom as such, but the role of the state. The nanny state critique is ultimately a call for the state to be agnostic about the health of citizens, allowing market forces to dominate. Although the nanny state critique is not new, it is a significant challenge to government efforts to address lifestyle-influenced risk factors for non-communicable diseases, including tobacco use, harmful use of alcohol, and unhealthy diet. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

  20. Contemporary machine learning: techniques for practitioners in the physical sciences

    Science.gov (United States)

    Spears, Brian

    2017-10-01

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

  1. Event Streams Clustering Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Hanen Bouali

    2015-10-01

    Full Text Available Data streams are usually of unbounded lengths which push users to consider only recent observations by focusing on a time window, and ignore past data. However, in many real world applications, past data must be taken in consideration to guarantee the efficiency, the performance of decision making and to handle data streams evolution over time. In order to build a selectively history to track the underlying event streams changes, we opt for the continuously data of the sliding window which increases the time window based on changes over historical data. In this paper, to have the ability to access to historical data without requiring any significant storage or multiple passes over the data. In this paper, we propose a new algorithm for clustering multiple data streams using incremental support vector machine and data representative points’ technique. The algorithm uses a sliding window model for the most recent clustering results and data representative points to model the old data clustering results. Our experimental results on electromyography signal show a better clustering than other present in the literature

  2. Civic Education and the Learning Behaviors of Youth in the Online Environment: A Call for Reform

    Directory of Open Access Journals (Sweden)

    Barbara A. Jansen

    2011-11-01

    Full Text Available Scholarly discourse in political science and communication studies is replete with empirical evidence lamenting the decline in civic engagement and political participation among adolescents and young adults. Scholars offer a variety of factors contributing to the disengagement of youth from the civic and political process including lack of attention paid to youth by politicians and the political process, the limited experience and a narrow frame of reference of young people in the political process, their aversion to traditional politics, and to poor quality courses and a decline in civic education in schools. Youth frequently lack civic and political knowledge as well as information and communications technology and social skills needed to engage in public life due in large part to the superficial coverage of substantive civic topics in textbooks and concentrating on knowledge level information that focuses on rights to the exclusion of obligations and participation. Civics curriculum often lacks opportunities for young people to embrace and communicate about politics on their own terms and frequently has little connection between the academic presentation of politics and the acquisition of skills that might help develop engaged citizens. Current approaches to civic education are at odds with young people’s experiences of informal participation with their peers in a nonhierarchical network. Traditional civics curriculum often treats subject matter as another academic subject with right or wrong answers arbitrated by the teacher as central authority and students in competition for grades. A growing body of literature discusses the affinity that youth have for Internet use and the possibilities of new media to address disengagement and to enhance new forms of citizenship calling for pedagogical reform in civic education.

  3. Why the changing American economy calls for twenty-first century learning: answers to educators' questions.

    Science.gov (United States)

    Levy, Frank; Murnane, Richard J

    2006-01-01

    While struggling with the current pressures of educational reform, some educators will ask whether their efforts make economic sense. Questioning the future makeup of the nation's workforce, many wonder how the educational system should be tempered to better prepare today's youth. This chapter answers educators' and parents' questions around the effect of fluctuations in the American economy on the future of education. The authors offer reassurance that good jobs will always be available, but warn that those jobs will require a new level of skills: expert thinking and complex communication. Schools need to go beyond their current curriculum and prepare students to use reading, math, and communication skills to build a deeper and more thoughtful understanding of subject matter. To explain the implications of the nation's changing economy on jobs, technology, and therefore education, the authors address a range of vital questions. Citing occupational distribution data, the chapter explores the supply and range of jobs in the future, as well as why changes in the U.S. job distribution have taken place. As much of the explanation for the shift in job distribution over the past several decades is due to the computerization of the workforce, the authors discuss how computers will affect the future composition of the workforce. The chapter also addresses the consequences of educational improvement on earnings distribution. The authors conclude that beyond workforce preparedness, students need to learn how to be contributing members of a democracy.

  4. Machine learning techniques for persuasion dectection in conversation

    OpenAIRE

    Ortiz, Pedro.

    2010-01-01

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

  5. Instructional Television: Visual Production Techniques and Learning Comprehension.

    Science.gov (United States)

    Silbergleid, Michael Ian

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

  6. Generating a Spanish Affective Dictionary with Supervised Learning Techniques

    Science.gov (United States)

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

    2016-01-01

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

  7. Tourism websites in English as a source for the autonomous learning of specialized terminology: A CALL application

    Directory of Open Access Journals (Sweden)

    Ángel Felices Lago

    2016-04-01

    Full Text Available For years now, it has been an unquestioned fact that a large majority of textbooks available in English for Tourism, either in book format, CD-Rom or web site access are based on situations and professional contexts connected with the Anglo-Saxon environment, even though the vast majority of graduates in Tourism in Spain (and other countries end up working in the region (autonomous community of origin or in the province of reference for studies. There is, therefore, a clear dysfunction between the textbooks available in the market and the materials and situations that the students will face in their professional future. However, the Internet now allows us to exploit the availability of vast quantities of local resources (websites, blogs, etc. with their corresponding versions in English, which include tourist information referring to, for example, hotels, restaurants, historical and artistic heritage sites, tour operators, travel agencies, trade fairs or specialized services at the national, regional or communal levels. All these sites offer a special showcase of all the linguistic resources available (be they lexical, syntactic or terminological that the learners must acquire for their professional development. Consequently, the purpose of this study is to offer the results of the computer-assisted language learning (CALL project entitled Autonomous Learning of Specialized Vocabulary in English for Tourism (http://wdb.ugr.es/~afelices/, which takes into consideration the previous premises in order to promote, as its title indicates, autonomous learning in a more realistic professional context and to serve as a model for the development of similar e-learning platforms in other regions or countries.

  8. Computer-aided auscultation learning system for nursing technique instruction.

    Science.gov (United States)

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

    2008-01-01

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

  9. Practising What We Teach: Vocational Teachers Learn to Research through Applying Action Learning Techniques

    Science.gov (United States)

    Lasky, Barbara; Tempone, Irene

    2004-01-01

    Action learning techniques are well suited to the teaching of organisation behaviour students because of their flexibility, inclusiveness, openness, and respect for individuals. They are no less useful as a tool for change for vocational teachers, learning, of necessity, to become researchers. Whereas traditional universities have always had a…

  10. Simulation-based optimization parametric optimization techniques and reinforcement learning

    CERN Document Server

    Gosavi, Abhijit

    2003-01-01

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

  11. Machine Learning Techniques for Stellar Light Curve Classification

    Science.gov (United States)

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

    2018-07-01

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

  12. Toward accelerating landslide mapping with interactive machine learning techniques

    Science.gov (United States)

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

    2013-04-01

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

  13. The Learning Called Psychomotor

    Science.gov (United States)

    Banville, Tom

    1976-01-01

    The nature of psychomotor skills and their relationship to academic achievement and positive self concept are discussed. Illustrations of program implementation and instructor preparation in several schools are presented. (RW)

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

    Directory of Open Access Journals (Sweden)

    Astiti Kade kAyu

    2018-01-01

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

  15. Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

    Science.gov (United States)

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

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

  16. Data mining practical machine learning tools and techniques

    CERN Document Server

    Witten, Ian H

    2005-01-01

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

  17. Using Machine Learning Techniques in the Analysis of Oceanographic Data

    Science.gov (United States)

    Falcinelli, K. E.; Abuomar, S.

    2017-12-01

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

  18. Statistical Techniques Used in Three Applied Linguistics Journals: "Language Learning,""Applied Linguistics" and "TESOL Quarterly," 1980-1986: Implications for Readers and Researchers.

    Science.gov (United States)

    Teleni, Vicki; Baldauf, Richard B., Jr.

    A study investigated the statistical techniques used by applied linguists and reported in three journals, "Language Learning,""Applied Linguistics," and "TESOL Quarterly," between 1980 and 1986. It was found that 47% of the published articles used statistical procedures. In these articles, 63% of the techniques used could be called basic, 28%…

  19. Memorization techniques: Using mnemonics to learn fifth grade science terms

    Science.gov (United States)

    Garcia, Juan O.

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

  20. Applying perceptual and adaptive learning techniques for teaching introductory histopathology

    Directory of Open Access Journals (Sweden)

    Sally Krasne

    2013-01-01

    Full Text Available Background: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. Methods: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses that appear during a learning session based on each learner′s accuracy and response time (RT. We developed a perceptual and adaptive learning module (PALM that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. Results: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1 st -year students, but not significantly so for 2 nd -year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1 st and 2 nd year students suggesting good retention of pattern recognition. Student evaluations were very favorable. Conclusion: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students.

  1. Our experience with the so-called pull-through technique combined with liposuction for management of gynecomastia.

    Science.gov (United States)

    Bracaglia, Roberto; Fortunato, Regina; Gentileschi, Stefano; Seccia, Antonio; Farallo, Eugenio

    2004-07-01

    Gynecomastia is a benign enlargement of male breast, common in adolescents and adults. To treat this deformity, we have been carrying out liposuction through small cutaneous incisions placed in the axilla and on the sternum. If necessary, we performed a surgical excision of glandular tissue through a periareolar incision. From 1995, we started to perform surgical excision of glandular tissue, if necessary, through the small incisions made for liposuction, thus avoiding the periareolar scars. We describe our experience with this technique, which we believe excellent for the correction of glandular and fatty glandular gynecomastia, obtaining excellent esthetic results and minimal local scarring.

  2. Learning-curve estimation techniques for nuclear industry

    Energy Technology Data Exchange (ETDEWEB)

    Vaurio, J.K.

    1983-01-01

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

  3. Learning curve estimation techniques for the nuclear industry

    International Nuclear Information System (INIS)

    Vaurio, J.K.

    1983-01-01

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

  4. Learning-curve estimation techniques for nuclear industry

    International Nuclear Information System (INIS)

    Vaurio, J.K.

    1983-01-01

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

  5. Subliminal Cues While Teaching: HCI Technique for Enhanced Learning

    Directory of Open Access Journals (Sweden)

    Pierre Chalfoun

    2011-01-01

    Full Text Available This paper presents results from an empirical study conducted with a subliminal teaching technique aimed at enhancing learner's performance in Intelligent Systems through the use of physiological sensors. This technique uses carefully designed subliminal cues (positive and miscues (negative and projects them under the learner's perceptual visual threshold. A positive cue, called answer cue, is a hint aiming to enhance the learner's inductive reasoning abilities and projected in a way to help them figure out the solution faster but more importantly better. A negative cue, called miscue, is also used and aims at obviously at the opposite (distract the learner or lead them to the wrong conclusion. The latest obtained results showed that only subliminal cues, not miscues, could significantly increase learner performance and intuition in a logic-based problem-solving task. Nonintrusive physiological sensors (EEG for recording brainwaves, blood volume pressure to compute heart rate and skin response to record skin conductivity were used to record affective and cerebral responses throughout the experiment. The descriptive analysis, combined with the physiological data, provides compelling evidence for the positive impact of answer cues on reasoning and intuitive decision making in a logic-based problem-solving paradigm.

  6. Techniques to Promote Reflective Practice and Empowered Learning.

    Science.gov (United States)

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

    2018-02-01

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

  7. Active Learning Techniques Applied to an Interdisciplinary Mineral Resources Course.

    Science.gov (United States)

    Aird, H. M.

    2015-12-01

    An interdisciplinary active learning course was introduced at the University of Puget Sound entitled 'Mineral Resources and the Environment'. Various formative assessment and active learning techniques that have been effective in other courses were adapted and implemented to improve student learning, increase retention and broaden knowledge and understanding of course material. This was an elective course targeted towards upper-level undergraduate geology and environmental majors. The course provided an introduction to the mineral resources industry, discussing geological, environmental, societal and economic aspects, legislation and the processes involved in exploration, extraction, processing, reclamation/remediation and recycling of products. Lectures and associated weekly labs were linked in subject matter; relevant readings from the recent scientific literature were assigned and discussed in the second lecture of the week. Peer-based learning was facilitated through weekly reading assignments with peer-led discussions and through group research projects, in addition to in-class exercises such as debates. Writing and research skills were developed through student groups designing, carrying out and reporting on their own semester-long research projects around the lasting effects of the historical Ruston Smelter on the biology and water systems of Tacoma. The writing of their mini grant proposals and final project reports was carried out in stages to allow for feedback before the deadline. Speakers from industry were invited to share their specialist knowledge as guest lecturers, and students were encouraged to interact with them, with a view to employment opportunities. Formative assessment techniques included jigsaw exercises, gallery walks, placemat surveys, think pair share and take-home point summaries. Summative assessment included discussion leadership, exams, homeworks, group projects, in-class exercises, field trips, and pre-discussion reading exercises

  8. Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

    Directory of Open Access Journals (Sweden)

    Shuibo Hu

    2018-03-01

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

  9. Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

    OpenAIRE

    Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

    2015-01-01

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

  10. Deep Learning Techniques for Top-Quark Reconstruction

    CERN Document Server

    Naderi, Kiarash

    2017-01-01

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

  11. Machine Learning Techniques for Prediction of Early Childhood Obesity.

    Science.gov (United States)

    Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

    2015-01-01

    This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

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

    Science.gov (United States)

    Xue, Hongsheng; Zhang, Jin

    2017-08-12

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

  13. Machine-learning techniques applied to antibacterial drug discovery.

    Science.gov (United States)

    Durrant, Jacob D; Amaro, Rommie E

    2015-01-01

    The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. © 2015 John Wiley & Sons A/S.

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

    CSIR Research Space (South Africa)

    Krige, PD

    2001-12-01

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

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

    Science.gov (United States)

    Takemura, Atsushi

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Shereen H. Ali

    2016-03-01

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

  17. The Effect of Computer Assisted Language Learning (CALL) on Performance in the Test of English for International Communication (TOEIC) Listening Module

    Science.gov (United States)

    van Han, Nguyen; van Rensburg, Henriette

    2014-01-01

    Many companies and organizations have been using the Test of English for International Communication (TOEIC) for business and commercial communication purpose in Vietnam and around the world. The present study investigated the effect of Computer Assisted Language Learning (CALL) on performance in the Test of English for International Communication…

  18. Improving face image extraction by using deep learning technique

    Science.gov (United States)

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

    2016-03-01

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

  19. Convergence of calls as animals form social bonds, active compensation for noisy communication channels, and the evolution of vocal learning in mammals.

    Science.gov (United States)

    Tyack, Peter L

    2008-08-01

    The classic evidence for vocal production learning involves imitation of novel, often anthropogenic sounds. Among mammals, this has been reported for dolphins, elephants, harbor seals, and humans. A broader taxonomic distribution has been reported for vocal convergence, where the acoustic properties of calls from different individuals converge when they are housed together in captivity or form social bonds in the wild. Vocal convergence has been demonstrated for animals as diverse as songbirds, parakeets, hummingbirds, bats, elephants, cetaceans, and primates. For most species, call convergence is thought to reflect a group-distinctive identifier, with shared calls reflecting and strengthening social bonds. A ubiquitous function for vocal production learning that is starting to receive attention involves modifying signals to improve communication in a noisy channel. Pooling data on vocal imitation, vocal convergence, and compensation for noise suggests a wider taxonomic distribution of vocal production learning among mammals than has been generally appreciated. The wide taxonomic distribution of this evidence for vocal production learning suggests that perhaps more of the neural underpinnings for vocal production learning are in place in mammals than is usually recognized. (c) 2008 APA, all rights reserved

  20. eLearning techniques supporting problem based learning in clinical simulation.

    Science.gov (United States)

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

    2005-08-01

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

  1. Study of CT image texture using deep learning techniques

    Science.gov (United States)

    Dutta, Sandeep; Fan, Jiahua; Chevalier, David

    2018-03-01

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

  2. Sculpting with people – An experiential learning technique

    DEFF Research Database (Denmark)

    Andersen, Helle Elisabeth; Larsen, Kirsten Vendelbo

    2015-01-01

    At Department of Nursing, University College Lillebaelt in Denmark we use an experiential technique called sculpting in our simulation program. Sculpting is a kind of non-verbal role play in which participants are given a certain character and create a 'sculpture' by arranging family members......, social circles and professionals in ways which reflect the quality of the relationships of the people involved. The aim of this study is to further describe the sculpting exercise and present a small scale evaluation study using a qualitative descriptive design. An evaluation sheet was formulated...... by the authors and filled out by 114 Danish third-year nursing students. The results show that sculpting is experienced as emotionally demanding, but in a good way. It is experienced as an eye-opener that helps to identify the possible complex and emotional dynamics in a family experiencing critical illness...

  3. Locomotion training of legged robots using hybrid machine learning techniques

    Science.gov (United States)

    Simon, William E.; Doerschuk, Peggy I.; Zhang, Wen-Ran; Li, Andrew L.

    1995-01-01

    In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible

  4. THE PUZZLE TECHNIQUE, COOPERATIVE LEARNING STRATEGY TO IMPROVE ACADEMIC PERFORMANCE

    Directory of Open Access Journals (Sweden)

    M.ª José Mayorga Fernández

    2012-04-01

    Full Text Available This  article  presents  an  innovative  experience  carried  out  in  the  subject Pedagogical Bases of Special Education, a 4.5 credit core subject taught at the second year of the Degree in Physical Education Teacher Training (to be extinguish, based on the use of a methodological strategic in accordance with the new demands of the EEES. With the experience we pursue a double purpose: firstly, to present the technique of jigsaw or puzzle as a useful methodological strategy for university learning and, on the other hand, to show whether this strategy improves students results. Comparing the results with students previous year results shows that the performance of students who participated in the innovative experience has improved considerably, increasing their motivation and involvement towards the task.

  5. Classifying Structures in the ISM with Machine Learning Techniques

    Science.gov (United States)

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

    2011-01-01

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

  6. Using machine learning techniques to differentiate acute coronary syndrome

    Directory of Open Access Journals (Sweden)

    Sougand Setareh

    2015-02-01

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

  7. Reinforcement learning techniques for controlling resources in power networks

    Science.gov (United States)

    Kowli, Anupama Sunil

    As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

  8. Classification of Phishing Email Using Random Forest Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    Andronicus A. Akinyelu

    2014-01-01

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

  9. Estimation of Alpine Skier Posture Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Bojan Nemec

    2014-10-01

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

  10. Calle Blanco

    Directory of Open Access Journals (Sweden)

    Gonzalo Cerda Brintrup

    1988-06-01

    Full Text Available Importante arteria, que comunica el sector del puerto con la plaza. Las más imponentes construcciones se sucedían de un modo continuo, encaramándose a ambos lados de la empinada calle. Antes del gran incendio de 1936 grandes casonas de madera destacaban en calle Irarrázabal y en la esquina de ésta con calle Blanco, la más hermosa construcción pertenecía a don Alberto Oyarzún y la casa vecina hacia Blanco era de don Mateo Miserda, limitada por arriba con la casa de don Augusto Van Der Steldt y ésta era seguida de la casa de don David Barrientos provista de cuatro cúpulas en las esquinas y de un amplio corredor en el frontis. Todas estas construcciones de madera fueron destruidas en el gran incendio de 1936.

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

    Science.gov (United States)

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

    2017-07-01

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

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

    CSIR Research Space (South Africa)

    Ngxande, Mkhuseli

    2017-11-01

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

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

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Koper, Rob

    2007-01-01

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

  14. Exercise, character strengths, well-being, and learning climate in the prediction of performance over a 6-month period at a call center.

    Science.gov (United States)

    Moradi, Saleh; Nima, Ali A; Rapp Ricciardi, Max; Archer, Trevor; Garcia, Danilo

    2014-01-01

    Performance monitoring might have an adverse influence on call center agents' well-being. We investigate how performance, over a 6-month period, is related to agents' perceptions of their learning climate, character strengths, well-being (subjective and psychological), and physical activity. Agents (N = 135) self-reported perception of the learning climate (Learning Climate Questionnaire), character strengths (Values In Action Inventory Short Version), well-being (Positive Affect, Negative Affect Schedule, Satisfaction With Life Scale, Psychological Well-Being Scales Short Version), and how often/intensively they engaged in physical activity. Performance, "time on the phone," was monitored for 6 consecutive months by the same system handling the calls. Performance was positively related to having opportunities to develop, the character strengths clusters of Wisdom and Knowledge (e.g., curiosity for learning, perspective) and Temperance (e.g., having self-control, being prudent, humble, and modest), and exercise frequency. Performance was negatively related to the sense of autonomy and responsibility, contentedness, the character strengths clusters of Humanity and Love (e.g., helping others, cooperation) and Justice (e.g., affiliation, fairness, leadership), positive affect, life satisfaction and exercise Intensity. Call centers may need to create opportunities to develop to increase agents' performance and focus on individual differences in the recruitment and selection of agents to prevent future shortcomings or worker dissatisfaction. Nevertheless, performance measurement in call centers may need to include other aspects that are more attuned with different character strengths. After all, allowing individuals to put their strengths at work should empower the individual and at the end the organization itself. Finally, physical activity enhancement programs might offer considerable positive work outcomes.

  15. Exercise, Character Strengths, Well-Being and Learning Climate in the Prediction of Performance over a Six-Month Period at a Call Center

    Directory of Open Access Journals (Sweden)

    Saleh eMoradi

    2014-06-01

    Full Text Available Background: Performance monitoring might have an adverse influence on call center agents’ well-being. We investigate how performance, over a six-month period, is related to agents’ perceptions of their learning climate, character strengths, well-being (subjective and psychological, and physical activity.Method: Agents (N = 135 self-reported perception of the learning climate (Learning Climate Questionnaire, character strengths (Values In Action Inventory Short Version, well-being (Positive Affect, Negative Affect Schedule, Satisfaction With Life Scale, Psychological Well-Being Scales Short Version, and how often/intensively they engaged in physical activity. Performance, time on the phone, was monitored for six consecutive months by the same system handling the calls. Results: Performance was positively related to having opportunities to develop, the character strengths clusters of Wisdom and Knowledge (e.g., curiosity for learning, perspective and Temperance (e.g., having self-control, being prudent, humble, and modest, and exercise frequency. Performance was negatively related to the sense of autonomy and responsibility, contentedness, the character strengths clusters of Humanity and Love (e.g., helping others, cooperation and Justice (e.g., affiliation, fairness, leadership, positive affect, life satisfaction and exercise Intensity.Conclusion: Call centers may need to create opportunities to develop to increase agents’ performance and focus on individual differences in the recruitment and selection of agents to prevent future shortcomings or worker dissatisfaction. Nevertheless, performance measurement in call centers may need to include other aspects that are more attuned with different character strengths. After all, allowing individuals to put their strengths at work should empower the individual and at the end the organization itself. Finally, physical activity enhancement programs might offer considerable positive work outcomes.

  16. Machine Learning Techniques for Arterial Pressure Waveform Analysis

    Directory of Open Access Journals (Sweden)

    João Cardoso

    2013-05-01

    Full Text Available The Arterial Pressure Waveform (APW can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1 a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2 the acquired position and amplitude of onset, Systolic Peak (SP, Point of Inflection (Pi and Dicrotic Wave (DW were used for the computation of some morphological attributes; (3 pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4 classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic, J48 (decision tree and RIPPER (rule-based induction; and (5 we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx. Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95% and high area under the curve (AUC of a Receiver Operating Characteristic (ROC curve (0.961. Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.

  17. Learning Strategies in Play during Basic Training for Medal of Honor and Call of Duty Video Games

    Science.gov (United States)

    Ziaeehezarjeribi, Yadi

    2010-01-01

    This study, based on experiential play methodology was used to explore student engagement while playing "Medal of Honor (2002)" and "Call of Duty (2003)". It identifies some of the key issues related to the use of video games and simulations during the training phase of game play. Research into the effects of gaming in education has been extremely…

  18. Predicting the Failure of Dental Implants Using Supervised Learning Techniques

    Directory of Open Access Journals (Sweden)

    Chia-Hui Liu

    2018-05-01

    Full Text Available Prosthodontic treatment has been a crucial part of dental treatment for patients with full mouth rehabilitation. Dental implant surgeries that replace conventional dentures using titanium fixtures have become the top choice. However, because of the wide-ranging scope of implant surgeries, patients’ body conditions, surgeons’ experience, and the choice of implant system should be considered during treatment. The higher price charged by dental implant treatments compared to conventional dentures has led to a rush among medical staff; therefore, the future impact of surgeries has not been analyzed in detail, resulting in medial disputes. Previous literature on the success factors of dental implants is mainly focused on single factors such as patients’ systemic diseases, operation methods, or prosthesis types for statistical correlation significance analysis. This study developed a prediction model for providing an early warning mechanism to reduce the chances of dental implant failure. We collected the clinical data of patients who received artificial dental implants at the case hospital for a total of 8 categories and 20 variables. Supervised learning techniques such as decision tree (DT, support vector machines, logistic regressions, and classifier ensembles (i.e., Bagging and AdaBoost were used to analyze the prediction of the failure of dental implants. The results show that DT with both Bagging and Adaboost techniques possesses the highest prediction performance for the failure of dental implant (area under the receiver operating characteristic curve, AUC: 0.741; the analysis also revealed that the implant systems affect dental implant failure. The model can help clinical surgeons to reduce medical failures by choosing the optimal implant system and prosthodontics treatments for their patients.

  19. Pedagogical Scholarship in Public Health: A Call for Cultivating Learning Communities to Support Evidence-Based Education.

    Science.gov (United States)

    Merzel, Cheryl; Halkitis, Perry; Healton, Cheryl

    Public health education is experiencing record growth and transformation. The current emphasis on learning outcomes necessitates attention to creating and evaluating the best curricula and learning methods for helping public health students develop public health competencies. Schools and programs of public health would benefit from active engagement in pedagogical research and additional platforms to support dissemination and implementation of educational research findings. We reviewed current avenues for sharing public health educational research, curricula, and best teaching practices; we identified useful models from other health professions; and we offered suggestions for how the field of public health education can develop communities of learning devoted to supporting pedagogy. Our goal was to help advance an agenda of innovative evidence-based public health education, enabling schools and programs of public health to evaluate and measure success in meeting the current and future needs of the public health profession.

  20. EMERGENCY CALLS

    CERN Multimedia

    Medical Service

    2001-01-01

    IN URGENT NEED OF A DOCTOR GENEVA EMERGENCY SERVICES GENEVA AND VAUD 144 FIRE BRIGADE 118 POLICE 117 CERN FIREMEN 767-44-44 ANTI-POISONS CENTRE Open 24h/24h 01-251-51-51 Patient not fit to be moved, call family doctor, or: GP AT HOME, open 24h/24h 748-49-50 Association Of Geneva Doctors Emergency Doctors at home 07h-23h 322 20 20 Patient fit to be moved: HOPITAL CANTONAL CENTRAL 24 Micheli-du-Crest 372-33-11 ou 382-33-11 EMERGENCIES 382-33-11 ou 372-33-11 CHILDREN'S HOSPITAL 6 rue Willy-Donzé 372-33-11 MATERNITY 32 bvd.de la Cluse 382-68-16 ou 382-33-11 OPHTHALMOLOGY 22 Alcide Jentzer 382-33-11 ou 372-33-11 MEDICAL CENTRE CORNAVIN 1-3 rue du Jura 345 45 50 HOPITAL DE LA TOUR Meyrin EMERGENCIES 719-61-11 URGENCES PEDIATRIQUES 719-61-00 LA TOUR MEDICAL CENTRE 719-74-00 European Emergency Call 112 FRANCE EMERGENCY SERVICES 15 FIRE BRIGADE 18 POLICE 17 CERN FIREMEN AT HOME 00-41-22-767-44-44 ANTI-POISONS CENTRE Open 24h/24h 04-72-11-69-11 All doctors ...

  1. Under which conditions does ICT have a positive effect on teaching and learning? A Call to Action

    NARCIS (Netherlands)

    Voogt, Joke; Knezek, G.; Cox, M.; Knezek, D.; ten Brummelhuis, A.C.A.

    2013-01-01

    Under which conditions does ICT have a positive effect on teaching and learning?’ This was the leading question of the International EDUsummIT in The Hague, the Netherlands. The bases for the discussion were the scholarly findings of the International Handbook of Information Technology in Primary

  2. Under Which Conditions Does ICT Have a Positive Effect on Teaching and Learning? A Call to Action

    Science.gov (United States)

    Voogt, J.; Knezek, G.; Cox, M.; Knezek, D.; ten Brummelhuis, A.

    2013-01-01

    "Under which conditions does ICT have a positive effect on teaching and learning?" This was the leading question of the International EDUsummIT in The Hague, the Netherlands. The bases for the discussion were the scholarly findings of the International Handbook of Information Technology in Primary and Secondary Education, a synthesis of research…

  3. Critically Evaluating Prensky in a Language Learning Context: The "Digital Natives/Immigrants Debate" and Its Implications for CALL

    Science.gov (United States)

    Benini, Silvia; Murray, Liam

    2013-01-01

    More than 10 years have passed since the first introduction of the term "digital natives" in Prensky's (2001a, 2001b) two seminal articles. Prensky argues that students today, having grown up in the Digital Age, learn differently from their predecessors, or "digital immigrants". As such, the pedagogical tools and methods used…

  4. Reciprocal Learning Strategy in CALL Environment: A Case Study of EFL Teaching at X University in Shanghai

    Science.gov (United States)

    Liu, An; Bu, Yuhua

    2016-01-01

    Colleges and universities in China have been bent on remolding the existing unitary teacher-centered education mode and enhancing students' individualized and autonomous learning with the help of multimedia and cyber technology in order to meet the College English Curriculum Requirements instituted by the Ministry of Education in 2004. Admittedly…

  5. The Learner, the Media and the Community: How Does Learning Take Place in the Other CALL Triangle?

    Science.gov (United States)

    Sockett, Geoffrey

    2012-01-01

    In this research project, students in applied linguistics were asked to keep blogs over a three-month period in which they reported on their online informal learning of English through activities such as social networking, downloading films and TV series and listening to music on demand. The study is situated within the framework of complexity…

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

    Directory of Open Access Journals (Sweden)

    Runisah Runisah

    2017-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Supalak Nakhornsri

    2016-09-01

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

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

    Directory of Open Access Journals (Sweden)

    Guillermo A. SANCHEZ PRIETO

    2018-01-01

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

  9. Adaptive Landmark-Based Navigation System Using Learning Techniques

    DEFF Research Database (Denmark)

    Zeidan, Bassel; Dasgupta, Sakyasingha; Wörgötter, Florentin

    2014-01-01

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

  10. Minimizing Barriers in Learning for On-Call Radiology Residents-End-to-End Web-Based Resident Feedback System.

    Science.gov (United States)

    Choi, Hailey H; Clark, Jennifer; Jay, Ann K; Filice, Ross W

    2018-02-01

    Feedback is an essential part of medical training, where trainees are provided with information regarding their performance and further directions for improvement. In diagnostic radiology, feedback entails a detailed review of the differences between the residents' preliminary interpretation and the attendings' final interpretation of imaging studies. While the on-call experience of independently interpreting complex cases is important to resident education, the more traditional synchronous "read-out" or joint review is impossible due to multiple constraints. Without an efficient method to compare reports, grade discrepancies, convey salient teaching points, and view images, valuable lessons in image interpretation and report construction are lost. We developed a streamlined web-based system, including report comparison and image viewing, to minimize barriers in asynchronous communication between attending radiologists and on-call residents. Our system provides real-time, end-to-end delivery of case-specific and user-specific feedback in a streamlined, easy-to-view format. We assessed quality improvement subjectively through surveys and objectively through participation metrics. Our web-based feedback system improved user satisfaction for both attending and resident radiologists, and increased attending participation, particularly with regards to cases where substantive discrepancies were identified.

  11. A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

    Directory of Open Access Journals (Sweden)

    Pengfei Jia

    2016-03-01

    Full Text Available When an electronic nose (E-nose is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde. Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

  12. Peer Feedback, Learning, and Improvement: Answering the Call of the Institute of Medicine Report on Diagnostic Error.

    Science.gov (United States)

    Larson, David B; Donnelly, Lane F; Podberesky, Daniel J; Merrow, Arnold C; Sharpe, Richard E; Kruskal, Jonathan B

    2017-04-01

    In September 2015, the Institute of Medicine (IOM) published a report titled "Improving Diagnosis in Health Care," in which it was recommended that "health care organizations should adopt policies and practices that promote a nonpunitive culture that values open discussion and feedback on diagnostic performance." It may seem counterintuitive that a report addressing a highly technical skill such as medical diagnosis would be focused on organizational culture. The wisdom becomes clearer, however, when examined in the light of recent advances in the understanding of human error and individual and organizational performance. The current dominant model for radiologist performance improvement is scoring-based peer review, which reflects a traditional quality assurance approach, derived from manufacturing in the mid-1900s. Far from achieving the goals of the IOM, which are celebrating success, recognizing mistakes as an opportunity to learn, and fostering openness and trust, we have found that scoring-based peer review tends to drive radiologists inward, against each other, and against practice leaders. Modern approaches to quality improvement focus on using and enhancing interpersonal professional relationships to achieve and maintain high levels of individual and organizational performance. In this article, the authors review the recommendations set forth by the recent IOM report, discuss the science and theory that underlie several of those recommendations, and assess how well they fit with the current dominant approach to radiology peer review. The authors also offer an alternative approach to peer review: peer feedback, learning, and improvement (or more succinctly, "peer learning"), which they believe is better aligned with the principles promoted by the IOM. © RSNA, 2016.

  13. A call for a multifaceted approach to language learning motivation research: Combining complexity, humanistic, and critical perspectives

    Directory of Open Access Journals (Sweden)

    Julian Pigott

    2012-10-01

    Full Text Available In this paper I give an overview of recent developments in the L2 motivation field, in particular the movement away from quantitative, questionnaire-based methodologies toward smaller-scale qualitative studies incorporating concepts from complexity theory. While complexity theory provides useful concepts for exploring motivation in new ways, it has nothing to say about ethics, morality, ideology, politics, power or educational purpose. Furthermore, calls for its use come primarily from researchers from the quantitative tradition whose aim in importing this paradigm from the physical sciences appears to be to conceptualize and model motivation more accurately. The endeavor therefore remains a fundamentally positivist one. Rather than being embraced as a self-contained methodology, I argue that complexity theory should be used cautiously and prudently alongside methods grounded in other philosophical traditions. Possibilities abound, but here I suggest one possible multifaceted approach combining complexity theory, a humanisticconception of motivation, and a critical perspective.

  14. Approaching Assessment from a Learning Perspective: Elevating Assessment beyond Technique

    Science.gov (United States)

    Simms, Michele; George, Beena

    2014-01-01

    Assessment is a key process in assuring quality education but how is it linked to the scholarship of teaching and learning (SoTL)? How can we join teaching and learning to the assessment process rather than view it as a stand-alone component in course and/or program development? This paper explores the relationship between assessment and the SoTL…

  15. Learning Faults Detection by AIS Techniques in CSCL Environments

    Science.gov (United States)

    Zedadra, Amina; Lafifi, Yacine

    2015-01-01

    By the increase of e-learning platforms, huge data sets are made from different kinds of the collected traces. These traces differ from one learner to another according to their characteristics (learning styles, preferences, performed actions, etc.). Learners' traces are very heterogeneous and voluminous, so their treatments and exploitations are…

  16. Celebrating variability and a call to limit systematisation: the example of the Behaviour Change Technique Taxonomy and the Behaviour Change Wheel.

    Science.gov (United States)

    Ogden, Jane

    2016-09-01

    Within any discipline there is always a degree of variability. For medicine it takes the form of Health Professional's behaviour, for education it's the style and content of the classroom, and for health psychology, it can be found in patient's behaviour, the theories used and clinical practice. Over recent years, attempts have been made to reduce this variability through the use of the Behaviour Change Technique Taxonomy, the COM-B and the Behaviour Change Wheel. This paper argues that although the call for better descriptions of what is done is useful for clarity and replication, this systematisation may be neither feasible nor desirable. In particular, it is suggested that the gaps inherent in the translational process from coding a protocol to behaviour will limit the effectiveness of reducing patient variability, that theory variability is necessary for the health and well-being of a discipline and that practice variability is central to the professional status of our practitioners. It is therefore argued that we should celebrate rather than remove this variability in order for our discipline to thrive and for us to remain as professionals rather than as technicians.

  17. MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques

    Directory of Open Access Journals (Sweden)

    Cerqueira Fabio R

    2012-10-01

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

  18. Machine learning in Python essential techniques for predictive analysis

    CERN Document Server

    Bowles, Michael

    2015-01-01

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

  19. Evaluation and Implementation of Distance Learning: Technologies, Tools and Techniques

    Directory of Open Access Journals (Sweden)

    Figen UNAL

    2004-04-01

    Full Text Available This book is published by Idea Group Publishing. The book consistsof seven chapters, a bibliography and references section, fourappendices, an index, and author biography. In appendix A, thereare three data forms those can used by distance learning coursedesigners. In appendix B, under the title of ‘definitions’, there is a dictionary consists of Internet and e-learning terms. In appendixC, there is a table relevant to infrastructure survey and upgraderequirements. Finally appendix D contains a list of web sites thatoffer discussions of distance learning issues and concepts.

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

    Directory of Open Access Journals (Sweden)

    Sugiarto Indar

    2018-01-01

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

  1. Educating patients: understanding barriers, learning styles, and teaching techniques.

    Science.gov (United States)

    Beagley, Linda

    2011-10-01

    Health care delivery and education has become a challenge for providers. Nurses and other professionals are challenged daily to assure that the patient has the necessary information to make informed decisions. Patients and their families are given a multitude of information about their health and commonly must make important decisions from these facts. Obstacles that prevent easy delivery of health care information include literacy, culture, language, and physiological barriers. It is up to the nurse to assess and evaluate the patient's learning needs and readiness to learn because everyone learns differently. This article will examine how each of these barriers impact care delivery along with teaching and learning strategies will be examined. Copyright © 2011 American Society of PeriAnesthesia Nurses. Published by Elsevier Inc. All rights reserved.

  2. Machine learning techniques applied to system characterization and equalization

    DEFF Research Database (Denmark)

    Zibar, Darko; Thrane, Jakob; Wass, Jesper

    2016-01-01

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

  3. Interactive Multimedia Instruction for Training Self-Directed Learning Techniques

    Science.gov (United States)

    2016-06-01

    feedback and input on the content, format, and pedagogical approach of the lesson. This survey could be e-mailed to the principal ARI researcher for...peers in self-directed learning. Some examples of the metaphorical relationships and common examples woven into this IMI are identified in Table 1...20 Table 1 Metaphorical Relationships and Illustrations Used in Self-Directed Learning Training Military or Common Example Self-Directed

  4. Mobile Robot Navigation Based on Q-Learning Technique

    Directory of Open Access Journals (Sweden)

    Lazhar Khriji

    2011-03-01

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

  5. An empirical study on the performance of spectral manifold learning techniques

    DEFF Research Database (Denmark)

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

    2011-01-01

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

  6. Action Research to Improve the Learning Space for Diagnostic Techniques

    Directory of Open Access Journals (Sweden)

    Ellen Ariel

    2015-08-01

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

  7. Action Research to Improve the Learning Space for Diagnostic Techniques.

    Science.gov (United States)

    Ariel, Ellen; Owens, Leigh

    2015-12-01

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

  8. Impacts of Vocabulary Acquisition Techniques Instruction on Students' Learning

    Science.gov (United States)

    Orawiwatnakul, Wiwat

    2011-01-01

    The objectives of this study were to determine how the selected vocabulary acquisition techniques affected the vocabulary ability of 35 students who took EN 111 and investigate their attitudes towards the techniques instruction. The research study was one-group pretest and post-test design. The instruments employed were in-class exercises…

  9. Comparing visualization techniques for learning second language prosody

    DEFF Research Database (Denmark)

    Niebuhr, Oliver; Alm, Maria Helena; Schümchen, Nathalie

    2017-01-01

    We tested the usability of prosody visualization techniques for second language (L2) learners. Eighteen Danish learners realized target sentences in German based on different visualization techniques. The sentence realizations were annotated by means of the phonological Kiel Intonation Model...... and then analyzed in terms of (a) prosodic-pattern consistency and (b) correctness of the prosodic patterns. In addition, the participants rated the usability of the visualization techniques. The results from the phonological analysis converged with the usability ratings in showing that iconic techniques......, in particular the stylized “hat pattern” visualization, performed better than symbolic techniques, and that marking prosodic information beyond intonation can be more confusing than instructive. In discussing our findings, we also provide a description of the new Danish-German learner corpus we created: DANGER...

  10. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  11. Combining machine learning and matching techniques to improve causal inference in program evaluation.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Program evaluations often utilize various matching approaches to emulate the randomization process for group assignment in experimental studies. Typically, the matching strategy is implemented, and then covariate balance is assessed before estimating treatment effects. This paper introduces a novel analytic framework utilizing a machine learning algorithm called optimal discriminant analysis (ODA) for assessing covariate balance and estimating treatment effects, once the matching strategy has been implemented. This framework holds several key advantages over the conventional approach: application to any variable metric and number of groups; insensitivity to skewed data or outliers; and use of accuracy measures applicable to all prognostic analyses. Moreover, ODA accepts analytic weights, thereby extending the methodology to any study design where weights are used for covariate adjustment or more precise (differential) outcome measurement. One-to-one matching on the propensity score was used as the matching strategy. Covariate balance was assessed using standardized difference in means (conventional approach) and measures of classification accuracy (ODA). Treatment effects were estimated using ordinary least squares regression and ODA. Using empirical data, ODA produced results highly consistent with those obtained via the conventional methodology for assessing covariate balance and estimating treatment effects. When ODA is combined with matching techniques within a treatment effects framework, the results are consistent with conventional approaches. However, given that it provides additional dimensions and robustness to the analysis versus what can currently be achieved using conventional approaches, ODA offers an appealing alternative. © 2016 John Wiley & Sons, Ltd.

  12. LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data.

    Science.gov (United States)

    Nakamura, Munehiro; Kajiwara, Yusuke; Otsuka, Atsushi; Kimura, Haruhiko

    2013-10-02

    Over-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometimes worse result than the simplest SMOTE. In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization. In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes. To tackle this problem, our over-sampling method generates synthetic samples which occupy more feature space than the other SMOTE algorithms. Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets. Experiments on eight real-world imbalanced datasets demonstrate that our proposed over-sampling method performs better than the simplest SMOTE on four of five standard classification algorithms. Moreover, it is seen that the performance of our method increases if the latest SMOTE called MWMOTE is used in our algorithm. Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses. The proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data. Besides, the proposed over-sampling method is basically compatible with basic classification algorithms and the existing over-sampling methods.

  13. Investigating the Effectiveness of Computer-Assisted Language Learning (CALL) Using Google Documents in Enhancing Writing--A Study on Senior 1 Students in a Chinese Independent High School

    Science.gov (United States)

    Ambrose, Regina Maria; Palpanathan, Shanthini

    2017-01-01

    Computer-assisted language learning (CALL) has evolved through various stages in both technology as well as the pedagogical use of technology (Warschauer & Healey, 1998). Studies show that the CALL trend has facilitated students in their English language writing with useful tools such as computer based activities and word processing. Students…

  14. An Exploration of Prospective Teachers' Learning of Clinical Interview Techniques

    Science.gov (United States)

    Groth, Randall E.; Bergner, Jennifer A.; Burgess, Claudia R.

    2016-01-01

    The present study followed four prospective teachers through the process of learning to interview during an undergraduate research project experience. Participants conducted and video recorded a series of interviews with children. They also carried out guided analyses of the videos and written artefacts from the interviews to formulate conjectures…

  15. Cooperative Learning Technique through Internet Based Education: A Model Proposal

    Science.gov (United States)

    Ozkan, Hasan Huseyin

    2010-01-01

    Internet is gradually becoming the most valuable learning environment for the people which form the information society. That the internet provides written, oral and visual communication between the participants who are at different places, that it enables the students' interaction with other students and teachers, and that it does these so fast…

  16. Software Engineering Techniques for Computer-Aided Learning.

    Science.gov (United States)

    Ibrahim, Bertrand

    1989-01-01

    Describes the process for developing tutorials for computer-aided learning (CAL) using a programing language rather than an authoring system. The workstation used is described, the use of graphics is discussed, the role of a local area network (LAN) is explained, and future plans are discussed. (five references) (LRW)

  17. Using Deep Learning Techniques to Forecast Environmental Consumption Level

    Directory of Open Access Journals (Sweden)

    Donghyun Lee

    2017-10-01

    Full Text Available Artificial intelligence is a promising futuristic concept in the field of science and technology, and is widely used in new industries. The deep-learning technology leads to performance enhancement and generalization of artificial intelligence technology. The global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems such as climate change, but few environmental applications have so far been developed. This study uses deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN model. To verify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial neural network models. The RNN model predicts the pro-environmental consumption index better than any other model. We expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly sophisticated as the volume of data grows. Moreover, the framework of this study could be useful in environmental forecasting to prevent damage caused by climate change.

  18. Using the Technique of Journal Writing to Learn Emergency Psychiatry

    Science.gov (United States)

    Bhuvaneswar, Chaya; Stern, Theodore; Beresin, Eugene

    2009-01-01

    Objective: The authors discuss journal writing in learning emergency psychiatry. Methods: The journal of a psychiatry intern rotating through an emergency department is used as sample material for analysis that could take place in supervision or a resident support group. A range of articles are reviewed that illuminate the relevance of journal…

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

    CSIR Research Space (South Africa)

    van Zyl, TL

    2014-07-01

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

  20. Application of machine learning techniques to lepton energy reconstruction in water Cherenkov detectors

    Science.gov (United States)

    Drakopoulou, E.; Cowan, G. A.; Needham, M. D.; Playfer, S.; Taani, M.

    2018-04-01

    The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found that applying these techniques leads to an improvement of more than 50% in the energy resolution for all lepton energies compared to an approach based upon lookup tables. Machine learning techniques can be easily applied to different detector configurations and the results are comparable to likelihood-function based techniques that are currently used.

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

    Science.gov (United States)

    Wong, Lung-Hsiang; Looi, Chee-Kit

    2012-01-01

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

  2. Clustering: An Interactive Technique to Enhance Learning in Biology.

    Science.gov (United States)

    Ambron, Joanna

    1988-01-01

    Explains an interdisciplinary approach to biology and writing which increases students' mastery of vocabulary, scientific concepts, creativity, and expression. Describes modifications of the clustering technique used to summarize lectures, integrate reading and understand textbook material. (RT)

  3. Predicting breast screening attendance using machine learning techniques.

    Science.gov (United States)

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

    2011-03-01

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

  4. Use of machine learning techniques for modeling of snow depth

    Directory of Open Access Journals (Sweden)

    G. V. Ayzel

    2017-01-01

    Full Text Available Snow exerts significant regulating effect on the land hydrological cycle since it controls intensity of heat and water exchange between the soil-vegetative cover and the atmosphere. Estimating of a spring flood runoff or a rain-flood on mountainous rivers requires understanding of the snow cover dynamics on a watershed. In our work, solving a problem of the snow cover depth modeling is based on both available databases of hydro-meteorological observations and easily accessible scientific software that allows complete reproduction of investigation results and further development of this theme by scientific community. In this research we used the daily observational data on the snow cover and surface meteorological parameters, obtained at three stations situated in different geographical regions: Col de Porte (France, Sodankyla (Finland, and Snoquamie Pass (USA.Statistical modeling of the snow cover depth is based on a complex of freely distributed the present-day machine learning models: Decision Trees, Adaptive Boosting, Gradient Boosting. It is demonstrated that use of combination of modern machine learning methods with available meteorological data provides the good accuracy of the snow cover modeling. The best results of snow cover depth modeling for every investigated site were obtained by the ensemble method of gradient boosting above decision trees – this model reproduces well both, the periods of snow cover accumulation and its melting. The purposeful character of learning process for models of the gradient boosting type, their ensemble character, and use of combined redundancy of a test sample in learning procedure makes this type of models a good and sustainable research tool. The results obtained can be used for estimating the snow cover characteristics for river basins where hydro-meteorological information is absent or insufficient.

  5. Machine learning and evolutionary techniques in interplanetary trajectory design

    OpenAIRE

    Izzo, Dario; Sprague, Christopher; Tailor, Dharmesh

    2018-01-01

    After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth-Mars orbital transfer, extend the findings made previously for landing ...

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

    CERN Document Server

    Mason, James Eric; Woungang, Isaac

    2016-01-01

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

  7. 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.

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

    Science.gov (United States)

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

    2017-11-01

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

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

    Directory of Open Access Journals (Sweden)

    Wei-Chien Wang

    2018-05-01

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

  10. Applying effective teaching and learning techniques to nephrology education.

    Science.gov (United States)

    Rondon-Berrios, Helbert; Johnston, James R

    2016-10-01

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

  11. "PowerPoint[R] Engagement" Techniques to Foster Deep Learning

    Science.gov (United States)

    Berk, Ronald A.

    2011-01-01

    The purpose of this article is to describe a bunch of strategies with which teachers may already be familiar and, perhaps, use regularly, but not always in the context of a formal PowerPoint[R] presentation. Here are the author's top 10 engagement techniques that fit neatly within any version of PowerPoint[R]. Some of these may also be used with…

  12. Integrative Teaching Techniques and Improvement of German Speaking Learning Skills

    Science.gov (United States)

    Litualy, Samuel Jusuf

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Anne Elliott

    2003-07-01

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

  14. The learning technique. Theoretical considerations for planning lessons wit h a strategic learning approach

    Directory of Open Access Journals (Sweden)

    Dania Regueira Martínez

    2014-03-01

    Full Text Available This article presents the learning task considered as the unit of smaller organization level in the teaching-learning process that conditions in its systemic structuring, the learning actions, for the students acquisition of the content, by means of the development of the reflection and the metacognitiv e regulation when they conscious ly or partially plan different types of learning strategies in the ir realization, with the objective to solv e the pedagogic professional problems that are p resented in the disciplines they receive and in its research task during the direction o f the teaching-learning process.

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Science.gov (United States)

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

    2018-05-25

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

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

    Science.gov (United States)

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

    2017-12-01

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

  18. Sentiment Analysis in Geo Social Streams by using Machine Learning Techniques

    OpenAIRE

    Twanabasu, Bikesh

    2018-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Ester López Donoso

    2008-09-01

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

  20. Classification of Phishing Email Using Random Forest Machine Learning Technique

    OpenAIRE

    Akinyelu, Andronicus A.; Adewumi, Aderemi O.

    2013-01-01

    Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learnin...

  1. Barriers to calling 911 and learning and performing cardiopulmonary resuscitation for residents of primarily Latino, high-risk neighborhoods in Denver, Colorado.

    Science.gov (United States)

    Sasson, Comilla; Haukoos, Jason S; Ben-Youssef, Leila; Ramirez, Lorenzo; Bull, Sheana; Eigel, Brian; Magid, David J; Padilla, Ricardo

    2015-05-01

    Individuals in neighborhoods composed of minority and lower socioeconomic status populations are more likely to have an out-of-hospital cardiac arrest event, less likely to have bystander cardiopulmonary resuscitation (CPR) performed, and less likely to survive. Latino cardiac arrest victims are 30% less likely than whites to have bystander CPR performed. The goal of this study is to identify barriers and facilitators to calling 911, and learning and performing CPR in 5 low-income, Latino neighborhoods in Denver, CO. Six focus groups and 9 key informant interviews were conducted in Denver during the summer of 2012. Purposeful and snowball sampling, conducted by community liaisons, was used to recruit participants. Two reviewers analyzed the data to identify recurrent and unifying themes. A qualitative content analysis was used with a 5-stage iterative process to analyze each transcript. Six key barriers to calling 911 were identified: fear of becoming involved because of distrust of law enforcement, financial, immigration status, lack of recognition of cardiac arrest event, language, and violence. Seven cultural barriers were identified that may preclude performance of bystander CPR: age, sex, immigration status, language, racism, strangers, and fear of touching someone. Participants suggested that increasing availability of tailored education in Spanish, increasing the number of bilingual 911 dispatchers, and policy-level changes, including CPR as a requirement for graduation and strengthening Good Samaritan laws, may serve as potential facilitators in increasing the provision of bystander CPR. Distrust of law enforcement, language concerns, lack of recognition of cardiac arrest, and financial issues must be addressed when community-based CPR educational programs for Latinos are implemented. Copyright © 2014 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

  2. Greedy Deep Dictionary Learning

    OpenAIRE

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

    2016-01-01

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

  3. Applying machine learning techniques for forecasting flexibility of virtual power plants

    DEFF Research Database (Denmark)

    MacDougall, Pamela; Kosek, Anna Magdalena; Bindner, Henrik W.

    2016-01-01

    network as well as the multi-variant linear regression. It is found that it is possible to estimate the longevity of flexibility with machine learning. The linear regression algorithm is, on average, able to estimate the longevity with a 15% error. However, there was a significant improvement with the ANN...... approach to investigating the longevity of aggregated response of a virtual power plant using historic bidding and aggregated behaviour with machine learning techniques. The two supervised machine learning techniques investigated and compared in this paper are, multivariate linear regression and single...... algorithm achieving, on average, a 5.3% error. This is lowered 2.4% when learning for the same virtual power plant. With this information it would be possible to accurately offer residential VPP flexibility for market operations to safely avoid causing further imbalances and financial penalties....

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

    Science.gov (United States)

    Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

    2015-01-01

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

  5. Functional discrimination of membrane proteins using machine learning techniques

    Directory of Open Access Journals (Sweden)

    Yabuki Yukimitsu

    2008-03-01

    Full Text Available Abstract Background Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters. Results We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network method discriminated the channels/pores, electrochemical potential-driven transporters and active transporters with the 5-fold cross validation accuracy of 64% in a data set of 1718 membrane proteins. The application of amino acid occurrence improved the overall accuracy to 68%. In addition, we have discriminated transporters from other α-helical and β-barrel membrane proteins with the accuracy of 85% using k-nearest neighbor method. The classification of transporters and all other proteins (globular and membrane showed the accuracy of 82%. Conclusion The performance of discrimination with amino acid occurrence is better than that with amino acid composition. We suggest that this method could be effectively used to discriminate transporters from all other globular and membrane proteins, and classify them into channels/pores, electrochemical and active transporters.

  6. Relaxation techniques for stress

    Science.gov (United States)

    ... raise your heart rate. This is called the stress response. Relaxation techniques can help your body relax and lower your blood pressure ... also many other types of breathing techniques you can learn. In many cases, you do not need much ... including those that cause stress. Meditation has been practiced for thousands of years, ...

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

    Science.gov (United States)

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

    2013-04-01

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

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

    OpenAIRE

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

    2004-01-01

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

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

    OpenAIRE

    Torregrosa García, Blas

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Manojit Chattopadhyay

    2018-05-01

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

  11. DIAGNOSIS OF DIABETIC RETINOPATHY USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    R. Priya

    2013-07-01

    Full Text Available Diabetic retinopathy (DR is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely non-proliferative diabetic retinopathy (NPDR and proliferative diabetic retinopathy (PDR. In this paper, to diagnose diabetic retinopathy, three models like Probabilistic Neural network (PNN, Bayesian Classification and Support vector machine (SVM are described and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. The features like blood vessels, haemmoraghes of NPDR image and exudates of PDR image are extracted from the raw images using the image processing techniques and fed to the classifier for classification. A total of 350 fundus images were used, out of which 100 were used for training and 250 images were used for testing. Experimental results show that PNN has an accuracy of 89.6 % Bayes Classifier has an accuracy of 94.4% and SVM has an accuracy of 97.6%. This infers that the SVM model outperforms all other models. Also our system is also run on 130 images available from “DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy” and the results show that PNN has an accuracy of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has an accuracy of 95.38%.

  12. 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.

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

    Directory of Open Access Journals (Sweden)

    Juri. S. Ezrokh

    2014-01-01

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

  14. Call Forecasting for Inbound Call Center

    Directory of Open Access Journals (Sweden)

    Peter Vinje

    2009-01-01

    Full Text Available In a scenario of inbound call center customer service, the ability to forecast calls is a key element and advantage. By forecasting the correct number of calls a company can predict staffing needs, meet service level requirements, improve customer satisfaction, and benefit from many other optimizations. This project will show how elementary statistics can be used to predict calls for a specific company, forecast the rate at which calls are increasing/decreasing, and determine if the calls may stop at some point.

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

    Science.gov (United States)

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

    2018-05-24

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

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

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2017-01-01

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

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

    Science.gov (United States)

    Xinogalos, Stelios

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

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

    Science.gov (United States)

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

    2013-12-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

  20. Integrating SQ4R Technique with Graphic Postorganizers in the Science Learning of Earth and Space

    OpenAIRE

    Djudin, Tomo; Amir, R

    2018-01-01

    This study examined the effect of integrating SQ4R reading technique with graphic post organizers on the students' Earth and Space Science learning achievement and development of metacognitive knowledge. The pretest-posttest non-equivalent control group design was employed in this quasi-experimental method. The sample which consists of 103 seventh grade of secondary school students of SMPN 1 Pontianak was drawn by using intact group random sampling technique. An achievement test and a questio...

  1. Investigating CALL in the Classroom: Situational Variables to Consider

    Directory of Open Access Journals (Sweden)

    Darlene Liutkus

    2012-01-01

    Full Text Available A new paradigm in second language pedagogy has Computer Assisted Language Learning (CALL playing a significant role. Much of the literature to-date claims that CALL can have a positive impact on students’ second language acquisition (SLA. Mixed method of research produces data to investigate if CALL positively affects student language proficiency, motivation and autonomy. Classroom observation of participants in their natural environment is a qualitative technique used but has situational variables that could skew results if not structured. A questionnaire is a quantitative tool that can offer insight regarding participants’ perception of performance but can contradict what the researcher has observed. This paper will take an in-depth look at variables such as: instructor’s pedagogical application; blending CALL into the curriculum; types of CALL implemented; feedback received and their implications for design of the data collection tools

  2. Status of the Usage of Active Learning and Teaching Method and Techniques by Social Studies Teachers

    Science.gov (United States)

    Akman, Özkan

    2016-01-01

    The purpose of this study was to determine the active learning and teaching methods and techniques which are employed by the social studies teachers working in state schools of Turkey. This usage status was assessed using different variables. This was a case study, wherein the research was limited to 241 social studies teachers. These teachers…

  3. A Severe Weather Laboratory Exercise for an Introductory Weather and Climate Class Using Active Learning Techniques

    Science.gov (United States)

    Grundstein, Andrew; Durkee, Joshua; Frye, John; Andersen, Theresa; Lieberman, Jordan

    2011-01-01

    This paper describes a new severe weather laboratory exercise for an Introductory Weather and Climate class, appropriate for first and second year college students (including nonscience majors), that incorporates inquiry-based learning techniques. In the lab, students play the role of meteorologists making forecasts for severe weather. The…

  4. 76 FR 45334 - Innovative Techniques for Delivering ITS Learning; Request for Information

    Science.gov (United States)

    2011-07-28

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

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

    NARCIS (Netherlands)

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

    2009-01-01

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

  6. Phishtest: Measuring the Impact of Email Headers on the Predictive Accuracy of Machine Learning Techniques

    Science.gov (United States)

    Tout, Hicham

    2013-01-01

    The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning…

  7. Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

    Science.gov (United States)

    Chee, Brant Wah Kwong

    2011-01-01

    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

  8. Critique: Can Children with AD/HD Learn Relaxation and Breathing Techniques through Biofeedback Video Games?

    Science.gov (United States)

    Wright, Craig; Conlon, Elizabeth

    2009-01-01

    This article presents a critique on K. Amon and A. Campbell's "Can children with AD/HD learn relaxation and breathing techniques through biofeedback video games?". Amon and Campbell reported a successful trial of a commercially available biofeedback program, "The Wild Divine", in reducing symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD)…

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

    Directory of Open Access Journals (Sweden)

    Cristina Poyatos

    2011-02-01

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

  10. COMPUTER ASSISTED LANGUAGE LEARNING (CALL: ITS PROSPECTS AND CONSEQUENCES FOR NIGERIAN LANGUAGES / L'APPRENTISSAGE DE LA LANGUE À L'AIDE DES TICE: PERSPECTIVES ET CONSÉQUENCES POUR LES LANGUES NIGÉRIENNES / ÎNVĂŢAREA LIMBII CU TIC: PERSPECTIVE ŞI CONSECINŢE PENTRU LIMBILE NIGERIENE

    Directory of Open Access Journals (Sweden)

    Oyè Táíwò

    2014-11-01

    Full Text Available The shift in language learning today is from “classical teaching environment” to “self-learning environment”. In Nigeria today, although CALL efforts are made by schools and individuals, these effort are geared towards the English language learning other than Nigerian languages. This paper seeks to explore the development of CALL for Nigerian Languages and the challenges of running CALL in Nigeria. The results indicate that CALL for Nigerian languages is needed and should be promoted. CALL in Nigerian can only be successful if the shortcomings of CALL are recognized and the mitigating circumstances tackled. Adequate arrangements must be made to manage CALL and Teacher-Assisted language learning (TALL in consideration of the socioeconomic impact of CALL on the teachers Nigerian languages. The attitude of Nigerians towards Nigerian languages should be positive. The government, corporate bodies and individuals must intervene in CALL programs in schools so as to control the resulting high tuition fee.

  11. Cultivating ICT Students' Interpersonal Soft Skills in Online Learning Environments Using Traditional Active Learning Techniques

    Science.gov (United States)

    Myers, Trina S.; Blackman, Anna; Andersen, Trevor; Hay, Rachel; Lee, Ickjai; Gray, Heather

    2014-01-01

    Flexible online delivery of tertiary ICT programs is experiencing rapid growth. Creating an online environment that develops team building and interpersonal skills is difficult due to factors such as student isolation and the individual-centric model of online learning that encourages discrete study rather than teamwork. Incorporating teamwork…

  12. An Effective Performance Analysis of Machine Learning Techniques for Cardiovascular Disease

    Directory of Open Access Journals (Sweden)

    Vinitha DOMINIC

    2015-03-01

    Full Text Available Machine learning techniques will help in deriving hidden knowledge from clinical data which can be of great benefit for society, such as reduce the number of clinical trials required for precise diagnosis of a disease of a person etc. Various areas of study are available in healthcare domain like cancer, diabetes, drugs etc. This paper focuses on heart disease dataset and how machine learning techniques can help in understanding the level of risk associated with heart diseases. Initially, data is preprocessed then analysis is done in two stages, in first stage feature selection techniques are applied on 13 commonly used attributes and in second stage feature selection techniques are applied on 75 attributes which are related to anatomic structure of the heart like blood vessels of the heart, arteries etc. Finally, validation of the reduced set of features using an exhaustive list of classifiers is done.In parallel study of the anatomy of the heart is done using the identified features and the characteristics of each class is understood. It is observed that these reduced set of features are anatomically relevant. Thus, it can be concluded that, applying machine learning techniques on clinical data is beneficial and necessary.

  13. An Island Called Cuba

    Directory of Open Access Journals (Sweden)

    Jean Stubbs

    2011-06-01

    Full Text Available Review of: An Island Called Home: Returning to Jewish Cuba. Ruth Behar, photographs by Humberto Mayol. New Brunswick NJ: Rutgers University Press, 2007. xiii + 297 pp. (Cloth US$ 29.95 Fidel Castro: My Life: A Spoken Autobiography. Fidel Castro & Ignacio Ramonet. New York: Scribner/Simon & Schuster, 2008. vii + 724 pp. (Paper US$ 22.00, e-book US$ 14.99 Cuba: What Everyone Needs to Know. Julia E. Sweig. New York: Oxford University Press, 2009. xiv + 279 pp. (Paper US$ 16.95 [First paragraph] These three ostensibly very different books tell a compelling story of each author’s approach, as much as the subject matter itself. Fidel Castro: My Life: A Spoken Autobiography is based on a series of long interviews granted by the then-president of Cuba, Fidel Castro, to Spanish-Franco journalist Ignacio Ramonet. Cuba: What Everyone Needs to Know, by U.S. political analyst Julia Sweig, is one of a set country series, and, like Ramonet’s, presented in question/answer format. An Island Called Home: Returning to Jewish Cuba, with a narrative by Cuban-American anthropologist Ruth Behar and photographs by Cuban photographer Humberto Mayol, is a retrospective/introspective account of the Jewish presence in Cuba. While from Ramonet and Sweig we learn much about the revolutionary project, Behar and Mayol convey the lived experience of the small Jewish community against that backdrop.

  14. Changing teaching techniques and adapting new technologies to improve student learning in an introductory meteorology and climate course

    Directory of Open Access Journals (Sweden)

    E. M. Cutrim

    2006-01-01

    Full Text Available Responding to the call for reform in science education, changes were made in an introductory meteorology and climate course offered at a large public university. These changes were a part of a larger project aimed at deepening and extending a program of science content courses that model effective teaching strategies for prospective middle school science teachers. Therefore, revisions were made to address misconceptions about meteorological phenomena, foster deeper understanding of key concepts, encourage engagement with the text, and promote inquiry-based learning. Techniques introduced include: use of a flash cards, student reflection questionnaires, writing assignments, and interactive discussions on weather and forecast data using computer technology such as Integrated Data Viewer (IDV. The revision process is described in a case study format. Preliminary results (self-reflection by the instructor, surveys of student opinion, and measurements of student achievement, suggest student learning has been positively influenced. This study is supported by three grants: NSF grant No. 0202923, the Unidata Equipment Award, and the Lucia Harrison Endowment Fund.

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

    Science.gov (United States)

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

    2012-05-15

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

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

    Science.gov (United States)

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

    2015-08-01

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

  17. Developing an instrument to measure emotional behaviour abilities of meaningful learning through the Delphi technique.

    Science.gov (United States)

    Cadorin, Lucia; Bagnasco, Annamaria; Tolotti, Angela; Pagnucci, Nicola; Sasso, Loredana

    2017-09-01

    To identify items for a new instrument that measures emotional behaviour abilities of meaningful learning, according to Fink's Taxonomy. Meaningful learning is an active process that promotes a wider and deeper understanding of concepts. It is the result of an interaction between new and previous knowledge and produces a long-term change of knowledge and skills. To measure meaningful learning capability, it is very important in the education of health professionals to identify problems or special learning needs. For this reason, it is necessary to create valid instruments. A Delphi Study technique was implemented in four phases by means of e-mail. The study was conducted from April-September 2015. An expert panel consisting of ten researchers with experience in Fink's Taxonomy was established to identify the items of the instrument. Data were analysed for conceptual description and item characteristics and attributes were rated. Expert consensus was sought in each of these phases. An 87·5% consensus cut-off was established. After four rounds, consensus was obtained for validation of the content of the instrument 'Assessment of Meaningful learning Behavioural and Emotional Abilities'. This instrument consists of 56 items evaluated on a 6-point Likert-type scale. Foundational Knowledge, Application, Integration, Human Dimension, Caring and Learning How to Learn were the six major categories explored. This content validated tool can help educators (teachers, trainers and tutors) to identify and improve the strategies to support students' learning capability, which could increase their awareness of and/or responsibility in the learning process. © 2017 John Wiley & Sons Ltd.

  18. An Evaluation Framework for CALL

    Science.gov (United States)

    McMurry, Benjamin L.; Williams, David Dwayne; Rich, Peter J.; Hartshorn, K. James

    2016-01-01

    Searching prestigious Computer-assisted Language Learning (CALL) journals for references to key publications and authors in the field of evaluation yields a short list. The "American Journal of Evaluation"--the flagship journal of the American Evaluation Association--is only cited once in both the "CALICO Journal and Language…

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

    Science.gov (United States)

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

    2013-06-01

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

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

    Directory of Open Access Journals (Sweden)

    VIMALA C.

    2015-05-01

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

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  2. Indico CONFERENCE: Define the Call for Abstracts

    CERN Multimedia

    CERN. Geneva; Ferreira, Pedro

    2017-01-01

    In this tutorial, you will learn how to define and open a call for abstracts. When defining a call for abstracts, you will be able to define settings related to the type of questions asked during a review of an abstract, select the users who will review the abstracts, decide when to open the call for abstracts, and more.

  3. "I experienced freedom within the frame of my own narrative": The contribution of psychodrama techniques to experiential learning in teacher training

    Science.gov (United States)

    ter Avest, Ina

    2017-02-01

    To prepare Dutch students in education for critical situations in their professional life as a teacher, part of their training is to ask them to reflect upon their own experiences in their life as a child, a pupil and a student - experiences of crucial moments or with significant others which are still of the utmost importance to them. This article underlines the significance of so-called "experiential learning" in student career counselling. In this context, experiential learning is understood as an extension of in- depth reflection on critical incidents and critical persons in the biography of pre-service teachers. This reflection - customary and effective in Dutch teacher training - is a verbal process. However, this technique does not seem to be adequate for many students from other cultural backgrounds (e.g. second-generation descendants of migrant workers). By consequence, some of these students are not able to take newly offered information on board, but remain imprisoned in their own culture-related narrative, their own ethnic society of mind. Research has shown that for these students, psychodrama techniques, focusing on non-verbal and playful aspects of reflection, seem to be more suitable. The author of this article presents a sample case from a pilot study which used one of the psychodrama techniques called the empty chair. The findings of the pilot study are promising in the sense that experiencing different I- positions does seem to help students from other cultural backgrounds to develop agency in responding to hitherto unfamiliar and confusing situations.

  4. Calibration and statistical techniques for building an interactive screen for learning of alphabets by children

    Directory of Open Access Journals (Sweden)

    Riby Abraham Boby

    2017-05-01

    Full Text Available This article focuses on the implementation details of a portable interactive device called Image-projective Desktop Varnamala Trainer. The device uses a projector to produce a virtual display on a flat surface. For enabling interaction, the information about a user’s hand movement is obtained from a single two-dimensional scanning laser range finder in contrast with a camera sensor used in many earlier applications. A generalized calibration process to obtain exact transformation from projected screen coordinate system to sensor coordinate system is proposed in this article and implemented for enabling interaction. This permits production of large interactive displays with minimal cost. Additionally, it makes the entire system portable, that is, display can be produced on any planar surface like floor, tabletop, and so on. The calibration and its performance have been evaluated by varying screen sizes and the number of points used for calibration. The device was successfully calibrated for different screens. A novel learning-based methodology for predicting a user’s behaviour was then realized to improve the system’s performance. This has been experimentally evaluated, and the overall accuracy of prediction was about 96%. An application was then designed for this set-up to improve the learning of alphabets by the children through an interactive audiovisual feedback system. It uses a game-based methodology to help students learn in a fun way. Currently, it has bilingual (Hindi and English user interface to enable learning of alphabets and elementary mathematics. A user survey was conducted after demonstrating it to school children. The survey results are very encouraging. Additionally, a study to ascertain the improvement in the learning outcome of the children was done. The results clearly indicate an improvement in the learning outcome of the children who used the device over those who did not.

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

    CERN Document Server

    Ratner, Bruce

    2011-01-01

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

  6. The Application of the Complex Field of Geodesy to an Entrance Level College Course using Cognitive Learning Techniques.

    Science.gov (United States)

    Menard, J.; Beall King, A.; Larson, P. B.

    2017-12-01

    The study of the shape of the Earth is called geodesy. It is a complex and rich field, encompassing GPS, the development of satellites to measure Earth, and the many applications of these measurements to better understand our planet. What is the best way to explain complex concepts to an entry-level college student, such as geodesy or gravitation? What is the most efficient way to peek a student's interest in an abstract field? Two people are walking side by side on a crowded street. Do they talk? Do they look at each other? Do they laugh together? Do they touch? Even though the bond between these two people cannot necessarily be physically seen, it is possible, by looking at their behavior towards each other, to determine whether or not they know each other. If they do, they are attracted to one another, walking together in the same direction, exchanging ideas or laughs. The Moon attracts the Earth's oceans, forming tides. The Earth attracts the Moon into staying in orbit. They are attracted to each other by the invisible yet quantifiable force of gravitation. In order to ensure that first year college students understand the concept and applications of geodesy, and find interest in the field, several teaching and learning techniques must be used. These techniques are compared to one another in terms of efficiency both by comparing the students' success through quizzes and discussions, and by comparing the students' enjoyment of and interest in the class through evaluations at the beginning and end of each class in order to assess how much material was learned, understood, and retained. This study is conducted via a short course with volunteer students. The course is a combination of lecture, discussion, experiments, and field work. Quizzes are used to evaluate not the students, but their improvement as a result of the efficacy of the teaching method. In class group and one on one discussions are used as the main part of the final grade.

  7. A Cultural Psychological Approach to Analyze Intercultural Learning: Potential and Limits of the Structure Formation Technique

    Directory of Open Access Journals (Sweden)

    Doris Weidemann

    2009-01-01

    Full Text Available Despite the huge interest in sojourner adjustment, there is still a lack of qualitative as well as of longitudinal research that would offer more detailed insights into intercultural learning processes during overseas stays. The present study aims to partly fill that gap by documenting changes in knowledge structures and general living experiences of fifteen German sojourners in Taiwan in a longitudinal, cultural-psychological study. As part of a multimethod design a structure formation technique was used to document subjective theories on giving/losing face and their changes over time. In a second step results from this study are compared to knowledge-structures of seven long-term German residents in Taiwan, and implications for the conceptualization of intercultural learning will be proposed. Finally, results from both studies serve to discuss the potential and limits of structure formation techniques in the field of intercultural communication research. URN: urn:nbn:de:0114-fqs0901435

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

    International Nuclear Information System (INIS)

    Rocco S, Claudio M.; Muselli, Marco

    2005-01-01

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

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

    KAUST Repository

    AlQuerm, Ismail A.

    2018-02-21

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

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

    Directory of Open Access Journals (Sweden)

    Laura Cornejo-Bueno

    2017-11-01

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

  11. Kin Signatures Learned in the Egg? Red-Backed Fairy-Wren Songs Are Similar to Their Mother's In-Nest Calls and Songs

    OpenAIRE

    Dowling, Jenélle L.; Colombelli-Négrel, Diane; Webster, Michael S.

    2016-01-01

    Many vocal animals recognize kin using vocal cues, in territorial contexts and in rearing young, but little is known about the developmental and evolutionary mechanisms that produce vocal kin recognition systems. In the cooperatively breeding red-backed fairy-wren (Malurus melanocephalus), females give specific “in-nest calls” while incubating their eggs. Elements from these calls are incorporated into chicks' begging calls, and appear to be used by parents for recognition. This is likely a r...

  12. Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

    Science.gov (United States)

    Zhao, Xiaowei; Ning, Qiao; Chai, Haiting; Ma, Zhiqiang

    2015-06-07

    As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Daniah ALABBASI

    2017-07-01

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

  14. Science Teachers' Views and Stereotypes of Religion, Scientists and Scientific Research: A Call for Scientist-Science Teacher Partnerships to Promote Inquiry-Based Learning

    Science.gov (United States)

    Mansour, Nasser

    2015-01-01

    Despite a growing consensus regarding the value of inquiry-based learning (IBL) for students' learning and engagement in the science classroom, the implementation of such practices continues to be a challenge. If science teachers are to use IBL to develop students' inquiry practices and encourage them to think and act as scientists, a better…

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

    Directory of Open Access Journals (Sweden)

    Anjali Goyal

    2017-07-01

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

  16. Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

    Directory of Open Access Journals (Sweden)

    Manoja Kumar Behera

    2018-06-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2009-05-01

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

  19. Learning the „Look-at-you-go” Moment in Corporate Governance Negotiation Techniques

    Directory of Open Access Journals (Sweden)

    Clara VOLINTIRU

    2015-06-01

    Full Text Available This article explores in an interdisciplinary manner the way concepts are learned or internalized, depending on the varying means of transmission, as well as on the sequencing in which the information is transmitted. In this sense, we build on the constructivist methodology framework in assessing concept acquisition in academic disciplines, at an advanced level. We also present the evolution of certain negotiation techniques, from traditional setting, to less predictable ones. This assessment is compared to a specific Pop Culture case study in which we find an expressive representation of negotiation techniques. Our methodology employs both focus groups and experimental design to test the relative positioning of theoretical concept acquisition (TCA as opposed to expressive concept-acquisition (ECA. Our findings suggest that while expressive concept acquisition (ECA via popular culture representations enhances the students understanding of negotiation techniques, this can only happen in circumstances in which a theoretical concept acquisition (TCA is pre-existent.

  20. Machine Learning Techniques for Modelling Short Term Land-Use Change

    Directory of Open Access Journals (Sweden)

    Mileva Samardžić-Petrović

    2017-11-01

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

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

    Directory of Open Access Journals (Sweden)

    S Vyawahare

    2013-01-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Abdus Salam

    2015-07-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

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

    Directory of Open Access Journals (Sweden)

    Hua KL

    2015-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Land Walker H

    2011-01-01

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

  8. Laparoscopic colorectal surgery in learning curve: Role of implementation of a standardized technique and recovery protocol. A cohort study

    Directory of Open Access Journals (Sweden)

    Gaetano Luglio

    2015-06-01

    Conclusion: Proper laparoscopic colorectal surgery is safe and leads to excellent results in terms of recovery and short term outcomes, even in a learning curve setting. Key factors for better outcomes and shortening the learning curve seem to be the adoption of a standardized technique and training model along with the strict supervision of an expert colorectal surgeon.

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

    Science.gov (United States)

    Prabakaran, S.; Mitra, Shilpa

    2018-04-01

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

  10. Particle identification at LHCb: new calibration techniques and machine learning classification algorithms

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic uncertainties. This talk highlights recent developments in PID that use innovative machine learning techniques, as well as novel data-driven approaches which ensure that PID performance is well reproduced in simulation.

  11. Research On C4.5 As One Of The Inductive Learning Techniques

    OpenAIRE

    Yıldırım, Savaş

    2003-01-01

    The thesis in hand deals with C4.5 (Decision Tree Construction Algorithm) as one of the most significant techniques of machine learning, and how it differs from its older version ID3. With this aim in mind, not only the approaches provided by C4.5 but also other approaches are examined. The decision tree algorithms are useful in a variety of spheres from defense to medicine or economics; and bear a vital importance for decision support systems in these areas. Written by Quinlan in 1993 in C p...

  12. Analysis on the Metrics used in Optimizing Electronic Business based on Learning Techniques

    Directory of Open Access Journals (Sweden)

    Irina-Steliana STAN

    2014-09-01

    Full Text Available The present paper proposes a methodology of analyzing the metrics related to electronic business. The drafts of the optimizing models include KPIs that can highlight the business specific, if only they are integrated by using learning-based techniques. Having set the most important and high-impact elements of the business, the models should get in the end the link between them, by automating business flows. The human resource will be found in the situation of collaborating more and more with the optimizing models which will translate into high quality decisions followed by profitability increase.

  13. Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

    Directory of Open Access Journals (Sweden)

    Palika Chopra

    2018-01-01

    Full Text Available A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT model, random forest (RF model, and neural network (NN model have been used and compared with the help of coefficient of determination (R2 and root-mean-square error (RMSE, and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

  14. Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

    Directory of Open Access Journals (Sweden)

    Jose M. Bernal-de-Lázaro

    2016-05-01

    Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

  15. Call Center Capacity Planning

    DEFF Research Database (Denmark)

    Nielsen, Thomas Bang

    in order to relate the results to the service levels used in call centers. Furthermore, the generic nature of the approximation is demonstrated by applying it to a system incorporating a dynamic priority scheme. In the last paper Optimization of overflow policies in call centers, overflows between agent......The main topics of the thesis are theoretical and applied queueing theory within a call center setting. Call centers have in recent years become the main means of communication between customers and companies, and between citizens and public institutions. The extensively computerized infrastructure...... in modern call centers allows for a high level of customization, but also induces complicated operational processes. The size of the industry together with the complex and labor intensive nature of large call centers motivates the research carried out to understand the underlying processes. The customizable...

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

    Directory of Open Access Journals (Sweden)

    EJ van Aswegen

    2001-09-01

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

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

    Science.gov (United States)

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

    2001-11-01

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

  18. A data-driven predictive approach for drug delivery using machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Li

    Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

  19. Help Options in CALL: A Systematic Review

    Science.gov (United States)

    Cardenas-Claros, Monica S.; Gruba, Paul A.

    2009-01-01

    This paper is a systematic review of research investigating help options in the different language skills in computer-assisted language learning (CALL). In this review, emerging themes along with is-sues affecting help option research are identified and discussed. We argue that help options in CALL are application resources that do not only seem…

  20. Validation and Test-Retest Reliability of New Thermographic Technique Called Thermovision Technique of Dry Needling for Gluteus Minimus Trigger Points in Sciatica Subjects and TrPs-Negative Healthy Volunteers

    Science.gov (United States)

    Rychlik, Michał; Samborski, Włodzimierz

    2015-01-01

    The aim of this study was to assess the validity and test-retest reliability of Thermovision Technique of Dry Needling (TTDN) for the gluteus minimus muscle. TTDN is a new thermography approach used to support trigger points (TrPs) diagnostic criteria by presence of short-term vasomotor reactions occurring in the area where TrPs refer pain. Method. Thirty chronic sciatica patients (n=15 TrP-positive and n=15 TrPs-negative) and 15 healthy volunteers were evaluated by TTDN three times during two consecutive days based on TrPs of the gluteus minimus muscle confirmed additionally by referred pain presence. TTDN employs average temperature (T avr), maximum temperature (T max), low/high isothermal-area, and autonomic referred pain phenomenon (AURP) that reflects vasodilatation/vasoconstriction. Validity and test-retest reliability were assessed concurrently. Results. Two components of TTDN validity and reliability, T avr and AURP, had almost perfect agreement according to κ (e.g., thigh: 0.880 and 0.938; calf: 0.902 and 0.956, resp.). The sensitivity for T avr, T max, AURP, and high isothermal-area was 100% for everyone, but specificity of 100% was for T avr and AURP only. Conclusion. TTDN is a valid and reliable method for T avr and AURP measurement to support TrPs diagnostic criteria for the gluteus minimus muscle when digitally evoked referred pain pattern is present. PMID:26137486

  1. Development of self-learning Monte Carlo technique for more efficient modeling of nuclear logging measurements

    International Nuclear Information System (INIS)

    Zazula, J.M.

    1988-01-01

    The self-learning Monte Carlo technique has been implemented to the commonly used general purpose neutron transport code MORSE, in order to enhance sampling of the particle histories that contribute to a detector response. The parameters of all the biasing techniques available in MORSE, i.e. of splitting, Russian roulette, source and collision outgoing energy importance sampling, path length transformation and additional biasing of the source angular distribution are optimized. The learning process is iteratively performed after each batch of particles, by retrieving the data concerning the subset of histories that passed the detector region and energy range in the previous batches. This procedure has been tested on two sample problems in nuclear geophysics, where an unoptimized Monte Carlo calculation is particularly inefficient. The results are encouraging, although the presented method does not directly minimize the variance and the convergence of our algorithm is restricted by the statistics of successful histories from previous random walk. Further applications for modeling of the nuclear logging measurements seem to be promising. 11 refs., 2 figs., 3 tabs. (author)

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

    Wang, Ding; Liu, Derong

    2018-06-01

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

  4. Markerless gating for lung cancer radiotherapy based on machine learning techniques

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  5. The Impact of Using Note Taking's Techniques on the Students' Learning

    Directory of Open Access Journals (Sweden)

    Asrar Jabir Edan

    2017-03-01

    Full Text Available It is often said that the worst pen is better than the best memory and regardless of how good the students' memory might be, they need to take notes during the lesson or lecture because it is impossible to remember all the details later on. This is so easy to use technique which requires a brief record of important information can help students not only recall what has been said in the class, but also to achieve their learning goals and provide a useful summary of the material to be revised especially before the test. Unfortunately, it is noticed that most of the students, especially at the secondary stage, neglect this important skill. Most of them don’t often write notes unless they are told to do so by the teacher or depend only on the textbooks forgetting that not all the material mentioned during the lesson found in them as some are explanations to the complex and abstract ones and others are related to the teacher's experience in the subject matter. In fact, note taking skill is part of the learning process and to be useful, students need to learn how to do it effectively and what to record because not all what is said is important. This requires acquiring more than one skill on the part of the learners and more effort on the part of the teacher to teach them how to do it properly. For the above reasons, more light will be shed in this research on this topic followed by an experiment and a test to evaluate its effectiveness in learning

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

    Science.gov (United States)

    Miyadahira, A M

    2001-12-01

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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  8. Medical students benefit from the use of ultrasound when learning peripheral IV techniques.

    Science.gov (United States)

    Osborn, Scott R; Borhart, Joelle; Antonis, Michael S

    2012-03-06

    Recent studies support high success rates after a short learning period of ultrasound IV technique, and increased patient and provider satisfaction when using ultrasound as an adjunct to peripheral IV placement. No study to date has addressed the efficacy for instructing ultrasound-naive providers. We studied the introduction of ultrasound to the teaching technique of peripheral IV insertion on first- and second-year medical students. This was a prospective, randomized, and controlled trial. A total of 69 medical students were randomly assigned to the control group with a classic, landmark-based approach (n = 36) or the real-time ultrasound-guided group (n = 33). Both groups observed a 20-min tutorial on IV placement using both techniques and then attempted vein cannulation. Students were given a survey to report their results and observations by a 10-cm visual analog scale. The survey response rate was 100%. In the two groups, 73.9% stated that they attempted an IV previously, and 63.7% of students had used an ultrasound machine prior to the study. None had used ultrasound for IV access prior to our session. The average number of attempts at cannulation was 1.42 in either group. There was no difference between the control and ultrasound groups in terms of number of attempts (p = 0.31). In both groups, 66.7% of learners were able to cannulate in one attempt, 21.7% in two attempts, and 11.6% in three attempts. The study group commented that they felt they gained more knowledge from the experience (p students feel they learn more when using ultrasound after a 20-min tutorial to place IVs and cannulation of the vein feels easier. Success rates are comparable between the traditional and ultrasound teaching approaches.

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

    Science.gov (United States)

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

    2014-06-01

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

  10. Callings and Organizational Behavior

    Science.gov (United States)

    Elangovan, A. R.; Pinder, Craig C.; McLean, Murdith

    2010-01-01

    Current literature on careers, social identity and meaning in work tends to understate the multiplicity, historical significance, and nuances of the concept of calling(s). In this article, we trace the evolution of the concept from its religious roots into secular realms and develop a typology of interpretations using occupation and religious…

  11. Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

    Science.gov (United States)

    Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

    2017-08-01

    The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

  12. Training of passive systems matter using the Project Oriented Learning technique in Ciudad de Mexico del Tecnologico de Monterey; Ensenanza de la materia de sistemas pasivos en el cuarto semestre de la carrera de arquitectura plan 99 bajo la tecnica didactica project oriented learning (Pol), en el campus Ciudad de Mexico del Tecnologico de Monterrey

    Energy Technology Data Exchange (ETDEWEB)

    Aguayo, G. R.

    2004-07-01

    Environmental design or solar architecture is one of the subjects taught in the fourth semester of Architecture at the Instituto Tecnologico y de Estudios Superiores de Monterrey (ITESM), using the Project Oriented Learning technique. This paper explains the way Environmental Design is part of what is called the 4th semester block in the Architecture curriculum using POL technique, the way this subject was taught before as opposed to the way it is currently taught, which are the roles of the actors involved, the main characteristics of the technique itself, and how the above responds to the 2005 Mission Statement of the ITESM. (Author)

  13. Science Teachers' Views and Stereotypes of Religion, Scientists and Scientific Research: A call for scientist-science teacher partnerships to promote inquiry-based learning

    Science.gov (United States)

    Mansour, Nasser

    2015-07-01

    Despite a growing consensus regarding the value of inquiry-based learning (IBL) for students' learning and engagement in the science classroom, the implementation of such practices continues to be a challenge. If science teachers are to use IBL to develop students' inquiry practices and encourage them to think and act as scientists, a better understanding of factors that influence their attitudes towards scientific research and scientists' practices is very much needed. Within this context there is a need to re-examine the science teachers' views of scientists and the cultural factors that might have an impact on teachers' views and pedagogical practices. A diverse group of Egyptian science teachers took part in a quantitative-qualitative study using a questionnaire and in-depth interviews to explore their views of scientists and scientific research, and to understand how they negotiated their views of scientists and scientific research in the classroom, and how these views informed their practices of using inquiry in the classroom. The findings highlighted how the teachers' cultural beliefs and views of scientists and scientific research had constructed idiosyncratic pedagogical views and practices. The study suggested implications for further research and argued for teacher professional development based on partnerships with scientists.

  14. The simulated early learning of cervical spine manipulation technique utilising mannequins.

    Science.gov (United States)

    Chapman, Peter D; Stomski, Norman J; Losco, Barrett; Walker, Bruce F

    2015-01-01

    Trivial pain or minor soreness commonly follows neck manipulation and has been estimated at one in three treatments. In addition, rare catastrophic events can occur. Some of these incidents have been ascribed to poor technique where the neck is rotated too far. The aims of this study were to design an instrument to measure competency of neck manipulation in beginning students when using a simulation mannequin, and then examine the suitability of using a simulation mannequin to teach the early psychomotor skills for neck chiropractic manipulative therapy. We developed an initial set of questionnaire items and then used an expert panel to assess an instrument for neck manipulation competency among chiropractic students. The study sample comprised all 41 fourth year 2014 chiropractic students at Murdoch University. Students were randomly allocated into either a usual learning or mannequin group. All participants crossed over to undertake the alternative learning method after four weeks. A chi-square test was used to examine differences between groups in the proportion of students achieving an overall pass mark at baseline, four weeks, and eight weeks. This study was conducted between January and March 2014. We successfully developed an instrument of measurement to assess neck manipulation competency in chiropractic students. We then randomised 41 participants to first undertake either "usual learning" (n = 19) or "mannequin learning" (n = 22) for early neck manipulation training. There were no significant differences between groups in the overall pass rate at baseline (χ(2) = 0.10, p = 0.75), four weeks (χ(2) = 0.40, p = 0.53), and eight weeks (χ(2) = 0.07, p = 0.79). This study demonstrates that the use of a mannequin does not affect the manipulation competency grades of early learning students at short term follow up. Our findings have potentially important safety implications as the results indicate that students could initially

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

    Science.gov (United States)

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

    2013-12-01

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

  16. Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

    Science.gov (United States)

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

    2017-05-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jonas eKaplan

    2015-03-01

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

  19. submitter Studies of CMS data access patterns with machine learning techniques

    CERN Document Server

    De Luca, Silvia

    This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy ove...

  20. A hybrid stock trading framework integrating technical analysis with machine learning techniques

    Directory of Open Access Journals (Sweden)

    Rajashree Dash

    2016-03-01

    Full Text Available In this paper, a novel decision support system using a computational efficient functional link artificial neural network (CEFLANN and a set of rules is proposed to generate the trading decisions more effectively. Here the problem of stock trading decision prediction is articulated as a classification problem with three class values representing the buy, hold and sell signals. The CEFLANN network used in the decision support system produces a set of continuous trading signals within the range 0–1 by analyzing the nonlinear relationship exists between few popular technical indicators. Further the output trading signals are used to track the trend and to produce the trading decision based on that trend using some trading rules. The novelty of the approach is to engender the profitable stock trading decision points through integration of the learning ability of CEFLANN neural network with the technical analysis rules. For assessing the potential use of the proposed method, the model performance is also compared with some other machine learning techniques such as Support Vector Machine (SVM, Naive Bayesian model, K nearest neighbor model (KNN and Decision Tree (DT model.

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

    Science.gov (United States)

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

    2018-06-01

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

  2. An analysis of a digital variant of the Trail Making Test using machine learning techniques.

    Science.gov (United States)

    Dahmen, Jessamyn; Cook, Diane; Fellows, Robert; Schmitter-Edgecombe, Maureen

    2017-01-01

    The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility. This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities. Using digital Trail Making Test (dTMT) data collected from (N = 54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features. Predicted TMT scores correlate well with clinical digital test scores (r = 0.98) and paper time to completion scores (r = 0.65). Predicted TICS exhibited a small correlation with clinically derived TICS scores (r = 0.12 Part A, r = 0.10 Part B). Predicted FAB scores exhibited a small correlation with clinically derived FAB scores (r = 0.13 Part A, r = 0.29 for Part B). Digitally derived features were also used to predict diagnosis (AUC of 0.65). Our findings indicate that the dTMT is capable of measuring the same aspects of cognition as the paper-based TMT. Furthermore, the dTMT's additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone.

  3. Overview of manifold learning techniques for the investigation of disruptions on JET

    International Nuclear Information System (INIS)

    Cannas, B; Fanni, A; Pau, A; Sias, G; Murari, A

    2014-01-01

    Identifying a low-dimensional embedding of a high-dimensional data set allows exploration of the data structure. In this paper we tested some existing manifold learning techniques for discovering such embedding within the multidimensional operational space of a nuclear fusion tokamak. Among the manifold learning methods, the following approaches have been investigated: linear methods, such as principal component analysis and grand tour, and nonlinear methods, such as self-organizing map and its probabilistic variant, generative topographic mapping. In particular, the last two methods allow us to obtain a low-dimensional (typically two-dimensional) map of the high-dimensional operational space of the tokamak. These maps provide a way of visualizing the structure of the high-dimensional plasma parameter space and allow discrimination between regions characterized by a high risk of disruption and those with a low risk of disruption. The data for this study comes from plasma discharges selected from 2005 and up to 2009 at JET. The self-organizing map and generative topographic mapping provide the most benefits in the visualization of very large and high-dimensional datasets. Some measures have been used to evaluate their performance. Special emphasis has been put on the position of outliers and extreme points, map composition, quantization errors and topological errors. (paper)

  4. Assessing Uncertainty in Deep Learning Techniques that Identify Atmospheric Rivers in Climate Simulations

    Science.gov (United States)

    Mahesh, A.; Mudigonda, M.; Kim, S. K.; Kashinath, K.; Kahou, S.; Michalski, V.; Williams, D. N.; Liu, Y.; Prabhat, M.; Loring, B.; O'Brien, T. A.; Collins, W. D.

    2017-12-01

    Atmospheric rivers (ARs) can be the difference between CA facing drought or hurricane-level storms. ARs are a form of extreme weather defined as long, narrow columns of moisture which transport water vapor outside the tropics. When they make landfall, they release the vapor as rain or snow. Convolutional neural networks (CNNs), a machine learning technique that uses filters to recognize features, are the leading computer vision mechanism for classifying multichannel images. CNNs have been proven to be effective in identifying extreme weather events in climate simulation output (Liu et. al. 2016, ABDA'16, http://bit.ly/2hlrFNV). Here, we compare three different CNN architectures, tuned with different hyperparameters and training schemes. We compare two-layer, three-layer, four-layer, and sixteen-layer CNNs' ability to recognize ARs in Community Atmospheric Model version 5 output, and we explore the ability of data augmentation and pre-trained models to increase the accuracy of the classifier. Because pre-training the model with regular images (i.e. benches, stoves, and dogs) yielded the highest accuracy rate, this strategy, also known as transfer learning, may be vital in future scientific CNNs, which likely will not have access to a large labelled training dataset. By choosing the most effective CNN architecture, climate scientists can build an accurate historical database of ARs, which can be used to develop a predictive understanding of these phenomena.

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

    Directory of Open Access Journals (Sweden)

    Evanthia E. Tripoliti

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

  6. The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Jianning Wu

    2015-01-01

    Full Text Available The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

  7. The novel quantitative technique for assessment of gait symmetry using advanced statistical learning algorithm.

    Science.gov (United States)

    Wu, Jianning; Wu, Bin

    2015-01-01

    The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

  8. Call cultures in orang-utans?

    Directory of Open Access Journals (Sweden)

    Serge A Wich

    Full Text Available BACKGROUND: Several studies suggested great ape cultures, arguing that human cumulative culture presumably evolved from such a foundation. These focused on conspicuous behaviours, and showed rich geographic variation, which could not be attributed to known ecological or genetic differences. Although geographic variation within call types (accents has previously been reported for orang-utans and other primate species, we examine geographic variation in the presence/absence of discrete call types (dialects. Because orang-utans have been shown to have geographic variation that is not completely explicable by genetic or ecological factors we hypothesized that this will be similar in the call domain and predict that discrete call type variation between populations will be found. METHODOLOGY/PRINCIPAL FINDINGS: We examined long-term behavioural data from five orang-utan populations and collected fecal samples for genetic analyses. We show that there is geographic variation in the presence of discrete types of calls. In exactly the same behavioural context (nest building and infant retrieval, individuals in different wild populations customarily emit either qualitatively different calls or calls in some but not in others. By comparing patterns in call-type and genetic similarity, we suggest that the observed variation is not likely to be explained by genetic or ecological differences. CONCLUSION/SIGNIFICANCE: These results are consistent with the potential presence of 'call cultures' and suggest that wild orang-utans possess the ability to invent arbitrary calls, which spread through social learning. These findings differ substantially from those that have been reported for primates before. First, the results reported here are on dialect and not on accent. Second, this study presents cases of production learning whereas most primate studies on vocal learning were cases of contextual learning. We conclude with speculating on how these findings might

  9. Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

    Science.gov (United States)

    Foulquier, Nathan; Redou, Pascal; Le Gal, Christophe; Rouvière, Bénédicte; Pers, Jacques-Olivier; Saraux, Alain

    2018-05-17

    Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

  10. Distance learning strategies for weight management utilizing social media: A comparison of phone conference call versus social media platform. Rationale and design for a randomized study.

    Science.gov (United States)

    Willis, Erik A; Szabo-Reed, Amanda N; Ptomey, Lauren T; Steger, Felicia L; Honas, Jeffery J; Al-Hihi, Eyad M; Lee, Robert; Vansaghi, Lisa; Washburn, Richard A; Donnelly, Joseph E

    2016-03-01

    Management of obesity in the context of the primary care physician visit is of limited efficacy in part because of limited ability to engage participants in sustained behavior change between physician visits. Therefore, healthcare systems must find methods to address obesity that reach beyond the walls of clinics and hospitals and address the issues of lifestyle modification in a cost-conscious way. The dramatic increase in technology and online social networks may present healthcare providers with innovative ways to deliver weight management programs that could have an impact on health care at the population level. A randomized study will be conducted on 70 obese adults (BMI 30.0-45.0 kg/m(2)) to determine if weight loss (6 months) is equivalent between weight management interventions utilizing behavioral strategies by either a conference call or social media approach. The primary outcome, body weight, will be assessed at baseline and 6 months. Secondary outcomes including waist circumference, energy and macronutrient intake, and physical activity will be assessed on the same schedule. In addition, a cost analysis and process evaluation will be completed. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. Deep learning ensemble with asymptotic techniques for oscillometric blood pressure estimation.

    Science.gov (United States)

    Lee, Soojeong; Chang, Joon-Hyuk

    2017-11-01

    This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and Monte-Carlo approach. While the former is used to estimate SBP and DBP, the latter attempts to determine confidence intervals (CIs) for SBP and DBP based on oscillometric BP measurements. This work originally employs deep belief networks (DBN)-deep neural networks (DNN) to effectively estimate BPs based on oscillometric measurements. However, there are some inherent problems with these methods. First, it is not easy to determine the best DBN-DNN estimator, and worthy information might be omitted when selecting one DBN-DNN estimator and discarding the others. Additionally, our input feature vectors, obtained from only five measurements per subject, represent a very small sample size; this is a critical weakness when using the DBN-DNN technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To address these problems, an ensemble with an asymptotic approach (based on combining the bootstrap with the DBN-DNN technique) is utilized to generate the pseudo features needed to estimate the SBP and DBP. In the first stage, the bootstrap-aggregation technique is used to create ensemble parameters. Afterward, the AdaBoost approach is employed for the second-stage SBP and DBP estimation. We then use the bootstrap and Monte-Carlo techniques in order to determine the CIs based on the target BP estimated using the DBN-DNN ensemble regression estimator with the asymptotic technique in the third stage. The proposed method can mitigate the estimation uncertainty such as large the standard deviation of error (SDE) on comparing the proposed DBN-DNN ensemble regression estimator with the DBN-DNN single regression estimator, we identify that the SDEs of the SBP and DBP are reduced by 0.58 and 0.57  mmHg, respectively. These

  12. Learner Personas in CALL

    Science.gov (United States)

    Heift, Trude

    2007-01-01

    In examining the titles of this year's conference presentations, the author noticed quite a few papers that focus on learner-specific issues, for instance, papers that address learning styles, learner needs, personality and learning, learner modeling and, more generally, pedagogical issues that deal with individual learner differences in…

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

    OpenAIRE

    Sugiarto Indar; Pasila Felix

    2018-01-01

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

  14. Massively collaborative machine learning

    NARCIS (Netherlands)

    Rijn, van J.N.

    2016-01-01

    Many scientists are focussed on building models. We nearly process all information we perceive to a model. There are many techniques that enable computers to build models as well. The field of research that develops such techniques is called Machine Learning. Many research is devoted to develop

  15. Assessing the Effectiveness of Inquiry-based Learning Techniques Implemented in Large Classroom Settings

    Science.gov (United States)

    Steer, D. N.; McConnell, D. A.; Owens, K.

    2001-12-01

    assessments of knowledge-level learning included evaluations of student responses to pre- and post-instruction conceptual test questions, short group exercises and content-oriented exam questions. Higher level thinking skills were assessed when students completed exercises that required the completion of Venn diagrams, concept maps and/or evaluation rubrics both during class periods and on exams. Initial results indicate that these techniques improved student attendance significantly and improved overall retention in the course by 8-14% over traditional lecture formats. Student scores on multiple choice exam questions were slightly higher (1-3%) for students taught in the active learning environment and short answer questions showed larger gains (7%) over students' scores in a more traditional class structure.

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

    Directory of Open Access Journals (Sweden)

    J. Fischer

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

  17. Femtosecond laser-assisted cataract surgery with bimanual technique: learning curve for an experienced cataract surgeon.

    Science.gov (United States)

    Cavallini, Gian Maria; Verdina, Tommaso; De Maria, Michele; Fornasari, Elisa; Volpini, Elisa; Campi, Luca

    2017-11-29

    To describe the intraoperative complications and the learning curve of microincision cataract surgery assisted by femtosecond laser (FLACS) with bimanual technique performed by an experienced surgeon. It is a prospective, observational, comparative case series. A total of 120 eyes which underwent bimanual FLACS by the same experienced surgeon during his first experience were included in the study; we considered the first 60 cases as Group A and the second 60 cases as Group B. In both groups, only nuclear sclerosis of grade 2 or 3 was included; an intraocular lens was implanted through a 1.4-mm incision. Best-corrected visual acuity (BCVA), surgically induced astigmatism (SIA), central corneal thickness and endothelial cell loss (ECL) were evaluated before and at 1 and 3 months after surgery. Intraoperative parameters, and intra- and post-operative complications were recorded. In Group A, we had femtosecond laser-related minor complications in 11 cases (18.3%) and post-operative complications in 2 cases (3.3%); in Group B, we recorded 2 cases (3.3%) of femtosecond laser-related minor complications with no post-operative complications. Mean effective phaco time (EPT) was 5.32 ± 3.68 s in Group A and 4.34 ± 2.39 s in Group B with a significant difference (p = 0.046). We recorded a significant mean BCVA improvement at 3 months in both groups (p  0.05). Finally, we found significant ECL in both groups with a significant difference between the two groups (p = 0.042). FLACS with bimanual technique and low-energy LDV Z8 is associated with a necessary initial learning curve. After the first adjustments in the surgical technique, this technology seems to be safe and effective with rapid visual recovery and it helps surgeons to standardize the crucial steps of cataract surgery.

  18. Assessing call centers’ success:

    Directory of Open Access Journals (Sweden)

    Hesham A. Baraka

    2013-07-01

    This paper introduces a model to evaluate the performance of call centers based on the Delone and McLean Information Systems success model. A number of indicators are identified to track the call center’s performance. Mapping of the proposed indicators to the six dimensions of the D&M model is presented. A Weighted Call Center Performance Index is proposed to assess the call center performance; the index is used to analyze the effect of the identified indicators. Policy-Weighted approach was used to assume the weights with an analysis of different weights for each dimension. The analysis of the different weights cases gave priority to the User satisfaction and net Benefits dimension as the two outcomes from the system. For the input dimensions, higher priority was given to the system quality and the service quality dimension. Call centers decision makers can use the tool to tune the different weights in order to reach the objectives set by the organization. Multiple linear regression analysis was used in order to provide a linear formula for the User Satisfaction dimension and the Net Benefits dimension in order to be able to forecast the values for these two dimensions as function of the other dimensions

  19. A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

    Energy Technology Data Exchange (ETDEWEB)

    Xu Xiaoying; Ho, Shirley; Trac, Hy; Schneider, Jeff; Ntampaka, Michelle [McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States); Poczos, Barnabas [School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States)

    2013-08-01

    We investigate machine learning (ML) techniques for predicting the number of galaxies (N{sub gal}) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N{sub gal}. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N{sub gal} by training our algorithms on the following six halo properties: number of particles, M{sub 200}, {sigma}{sub v}, v{sub max}, half-mass radius, and spin. For Millennium, our predicted N{sub gal} values have a mean-squared error (MSE) of {approx}0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to {approx}5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N{sub gal}. Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M{sub star}, low M{sub star}). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs.

  20. A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

    International Nuclear Information System (INIS)

    Xu Xiaoying; Ho, Shirley; Trac, Hy; Schneider, Jeff; Ntampaka, Michelle; Poczos, Barnabas

    2013-01-01

    We investigate machine learning (ML) techniques for predicting the number of galaxies (N gal ) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N gal . In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N gal by training our algorithms on the following six halo properties: number of particles, M 200 , σ v , v max , half-mass radius, and spin. For Millennium, our predicted N gal values have a mean-squared error (MSE) of ∼0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to ∼5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N gal . Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M star , low M star ). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs

  1. A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

    Science.gov (United States)

    Tsipouras, Markos G; Giannakeas, Nikolaos; Tzallas, Alexandros T; Tsianou, Zoe E; Manousou, Pinelopi; Hall, Andrew; Tsoulos, Ioannis; Tsianos, Epameinondas

    2017-03-01

    Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  2. Care and calls

    DEFF Research Database (Denmark)

    Paasch, Bettina Sletten

    -centred care through the use of tactile resources and embodied orientations while they attend to the phone call. Experienced nurses Thus perform multiactivity by distributing attention towards both the patient and the phone, and the analysis shows that their concrete ways of doing so depend on the complex...... they are telephoned during interactions with patients are not universal. Indeed different strategies have evolved in other hospital departments. Not only does this thesis contribute insights into the way nurses manage phone calls during interactions with patients, but by subscribing to a growing body of embodied...... of human interaction....

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

    Science.gov (United States)

    Hancher, M.

    2017-12-01

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

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

    OpenAIRE

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

    2005-01-01

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

  5. Current Techniques of Teaching and Learning in Bariatric Surgical Procedures: A Systematic Review.

    Science.gov (United States)

    Kaijser, Mirjam; van Ramshorst, Gabrielle; van Wagensveld, Bart; Pierie, Jean-Pierre

    The gastric sleeve resection and gastric bypass are the 2 most commonly performed bariatric procedures. This article provides an overview of current teaching and learning methods of those techniques in resident and fellow training. A database search was performed on Pubmed, Embase, and the Education Resources Information Center (ERIC) to identify the methods used to provide training in bariatric surgery worldwide. After exclusion based on titles and abstracts, full texts of the selected articles were assessed. Included articles were reviewed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. In total, 2442 titles were identified and 14 full text articles met inclusion criteria. Four publications described an ex vivo training course, and 6 focused on at least 1 step of the gastric bypass procedure. Two randomized controlled trials (RCT) provided high-quality evidence on training aspects. Surgical coaching caused significant improvement of Bariatric Objective Structured Assessment of Technical Skills (BOSATS) scores (3.60 vs. 3.90, p = 0.017) and reduction of technical errors (18 vs. 10, p = 0.003). A preoperative warm-up increased global rating scales (GRS) scores on depth perception (p = 0.02), bimanual dexterity (p = 0.01), and efficiency of movements (p = 0.03). Stepwise education, surgical coaching, warming up, Internet-based knowledge modules, and ex vivo training courses are effective in relation to bariatric surgical training of residents and fellows, possibly shortening their learning curves. Copyright © 2018 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

  6. Partitioning a call graph

    NARCIS (Netherlands)

    Bisseling, R.H.; Byrka, J.; Cerav-Erbas, S.; Gvozdenovic, N.; Lorenz, M.; Pendavingh, R.A.; Reeves, C.; Röger, M.; Verhoeven, A.; Berg, van den J.B.; Bhulai, S.; Hulshof, J.; Koole, G.; Quant, C.; Williams, J.F.

    2006-01-01

    Splitting a large software system into smaller and more manageable units has become an important problem for many organizations. The basic structure of a software system is given by a directed graph with vertices representing the programs of the system and arcs representing calls from one program to

  7. CALLING AQUARIUM LOVERS...

    CERN Multimedia

    2002-01-01

    CERN's anemones will soon be orphans. We are looking for someone willing to look after the aquarium in the main building, for one year. If you are interested, or if you would like more information, please call 73830. (The anemones living in the aquarium thank you in anticipation.)

  8. A call for surveys

    DEFF Research Database (Denmark)

    Bernstein, Philip A.; Jensen, Christian S.; Tan, Kian-Lee

    2012-01-01

    The database field is experiencing an increasing need for survey papers. We call on more researchers to set aside time for this important writing activity. The database field is growing in population, scope of topics covered, and the number of papers published. Each year, thousands of new papers ...

  9. Call for Research

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

    Marie-Isabelle Beyer

    2014-10-03

    Oct 3, 2014 ... 5.Submission process. 6.Eligibility criteria. 7.Selection Process. 8. Format and requirements. 9.Evaluation criteria. 10.Country clearance requirements. 11. .... It is envisaged that through this call a single consortium will undertake 6-8 projects within a total budget of up to ... principle qualify for IDRC's support.

  10. Too close to call

    DEFF Research Database (Denmark)

    Kurrild-Klitgaard, Peter

    2012-01-01

    a number of other frequent explanations and is found to be quite robust. When augmented with approval ratings for incumbent presidents, the explanatory power increases to 83 pct. and only incorrectly calls one of the last 15 US presidential elections. Applied to the 2012 election as a forecasting model...

  11. Applying machine-learning techniques to Twitter data for automatic hazard-event classification.

    Science.gov (United States)

    Filgueira, R.; Bee, E. J.; Diaz-Doce, D.; Poole, J., Sr.; Singh, A.

    2017-12-01

    The constant flow of information offered by tweets provides valuable information about all sorts of events at a high temporal and spatial resolution. Over the past year we have been analyzing in real-time geological hazards/phenomenon, such as earthquakes, volcanic eruptions, landslides, floods or the aurora, as part of the GeoSocial project, by geo-locating tweets filtered by keywords in a web-map. However, not all the filtered tweets are related with hazard/phenomenon events. This work explores two classification techniques for automatic hazard-event categorization based on tweets about the "Aurora". First, tweets were filtered using aurora-related keywords, removing stop words and selecting the ones written in English. For classifying the remaining between "aurora-event" or "no-aurora-event" categories, we compared two state-of-art techniques: Support Vector Machine (SVM) and Deep Convolutional Neural Networks (CNN) algorithms. Both approaches belong to the family of supervised learning algorithms, which make predictions based on labelled training dataset. Therefore, we created a training dataset by tagging 1200 tweets between both categories. The general form of SVM is used to separate two classes by a function (kernel). We compared the performance of four different kernels (Linear Regression, Logistic Regression, Multinomial Naïve Bayesian and Stochastic Gradient Descent) provided by Scikit-Learn library using our training dataset to build the SVM classifier. The results shown that the Logistic Regression (LR) gets the best accuracy (87%). So, we selected the SVM-LR classifier to categorise a large collection of tweets using the "dispel4py" framework.Later, we developed a CNN classifier, where the first layer embeds words into low-dimensional vectors. The next layer performs convolutions over the embedded word vectors. Results from the convolutional layer are max-pooled into a long feature vector, which is classified using a softmax layer. The CNN's accuracy

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

    Directory of Open Access Journals (Sweden)

    Marselina Karina Purnomo

    2017-03-01

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

  13. Enhancing Peer Acceptance of Children with Learning Difficulties: Classroom Goal Orientation and Effects of a Storytelling Programme with Drama Techniques

    Science.gov (United States)

    Law, Yin-kum; Lam, Shui-fong; Law, Wilbert; Tam, Zoe W. Y.

    2017-01-01

    Peer acceptance is an important facilitator for the success of inclusive education. The aim of the current study is twofold: (1) to examine how classroom goal orientation is associated with children's acceptance of peers with learning difficulties; and (2) to evaluate the effectiveness of a storytelling programme with drama techniques on…

  14. Classification of Cytochrome P450 1A2 Inhibitors and Non-Inhibitors by Machine Learning Techniques

    DEFF Research Database (Denmark)

    Vasanthanathan, Poongavanam; Taboureau, Olivier; Oostenbrink, Chris

    2009-01-01

    of CYP1A2 inhibitors and non-inhibitors. Training and test sets consisted of about 400 and 7000 compounds, respectively. Various machine learning techniques, like binary QSAR, support vector machine (SVM), random forest, kappa nearest neighbors (kNN), and decision tree methods were used to develop...

  15. Children's Negotiations of Visualization Skills during a Design-Based Learning Experience Using Nondigital and Digital Techniques

    Science.gov (United States)

    Smith, Shaunna

    2018-01-01

    In the context of a 10-day summer camp makerspace experience that employed design-based learning (DBL) strategies, the purpose of this descriptive case study was to better understand the ways in which children use visualization skills to negotiate design as they move back and forth between the world of nondigital design techniques (i.e., drawing,…

  16. Comparison of Two Different Techniques of Cooperative Learning Approach: Undergraduates' Conceptual Understanding in the Context of Hormone Biochemistry

    Science.gov (United States)

    Mutlu, Ayfer

    2018-01-01

    The purpose of the research was to compare the effects of two different techniques of the cooperative learning approach, namely Team-Game Tournament and Jigsaw, on undergraduates' conceptual understanding in a Hormone Biochemistry course. Undergraduates were randomly assigned to Group 1 (N = 23) and Group 2 (N = 29). Instructions were accomplished…

  17. Effect of Ability Grouping in Reciprocal Teaching Technique of Collaborative Learning on Individual Achievements and Social Skills

    Science.gov (United States)

    Sumadi; Degeng, I Nyoman S.; Sulthon; Waras

    2017-01-01

    This research focused on effects of ability grouping in reciprocal teaching technique of collaborative learning on individual achievements dan social skills. The results research showed that (1) there are differences in individual achievement significantly between high group of homogeneous, middle group of homogeneous, low group of homogeneous,…

  18. Examining Mobile Learning Trends 2003-2008: A Categorical Meta-Trend Analysis Using Text Mining Techniques

    Science.gov (United States)

    Hung, Jui-Long; Zhang, Ke

    2012-01-01

    This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four…

  19. The Effectiveness of Using WhatsApp Messenger as One of Mobile Learning Techniques to Develop Students' Writing Skills

    Science.gov (United States)

    Fattah, Said Fathy El Said Abdul

    2015-01-01

    The present study was an attempt to determine the effectiveness of using a WhatsApp Messenger as one of mobile learning techniques to develop students' writing skills. Participants were 30 second year college students, English department from a private university in Saudi Arabia. The experimental group (N = 15) used WhatsApp technology to develop…

  20. Negotiating the Rules of Engagement: Exploring Perceptions of Dance Technique Learning through Bourdieu's Concept of "Doxa"

    Science.gov (United States)

    Rimmer, Rachel

    2017-01-01

    This article presents the findings from a focus group discussion conducted with first year undergraduate dance students in March 2015. The focus group concluded a cycle of action research during which the researcher explored the use of enquiry-based learning approaches to teaching dance technique in higher education. Grounded in transformative and…

  1. Conventional and Piecewise Growth Modeling Techniques: Applications and Implications for Investigating Head Start Children's Early Literacy Learning

    Science.gov (United States)

    Hindman, Annemarie H.; Cromley, Jennifer G.; Skibbe, Lori E.; Miller, Alison L.

    2011-01-01

    This article reviews the mechanics of conventional and piecewise growth models to demonstrate the unique affordances of each technique for examining the nature and predictors of children's early literacy learning during the transition from preschool through first grade. Using the nationally representative Family and Child Experiences Survey…

  2. Using the IGCRA (Individual, Group, Classroom Reflective Action) Technique to Enhance Teaching and Learning in Large Accountancy Classes

    Science.gov (United States)

    Poyatos Matas, Cristina; Ng, Chew; Muurlink, Olav

    2011-01-01

    First year accounting has generally been perceived as one of the more challenging first year business courses for university students. Various Classroom Assessment Techniques (CATs) have been proposed to attempt to enrich and enhance student learning, with these studies generally positioning students as learners alone. This paper uses an…

  3. Is There a Relationship between the Usage of Active and Collaborative Learning Techniques and International Students' Study Anxiety?

    Science.gov (United States)

    Khoshlessan, Rezvan

    2013-01-01

    This study was designed to explore the relationships between the international students' perception of professors' instructional practices (the usage of active and collaborative learning techniques in class) and the international students' study anxiety. The dominant goal of this research was to investigate whether the professors' usage of active…

  4. The Effect of Semantic Mapping as a Vocabulary Instruction Technique on EFL Learners with Different Perceptual Learning Styles

    Directory of Open Access Journals (Sweden)

    Esmaeel Abdollahzadeh

    2009-05-01

    Full Text Available Traditional and modern vocabulary instruction techniques have been introduced in the past few decades to improve the learners’ performance in reading comprehension. Semantic mapping, which entails drawing learners’ attention to the interrelationships among lexical items through graphic organizers, is claimed to enhance vocabulary learning significantly. However, whether this technique suits all types of learners has not been adequately investigated. This study examines the effectiveness of employing semantic mapping versus traditional approaches in vocabulary instruction to EFL learners with different perceptual modalities. A modified version of Reid’s (1987 perceptual learning style questionnaire was used to determine the learners’ modality types. The results indicate that semantic mapping in comparison to the traditional approaches significantly enhances vocabulary learning of EFL learners. However, although visual learners slightly outperformed other types of learners on the post-test, no significant differences were observed among intermediate learners with different perceptual modalities employing semantic mapping for vocabulary practice.

  5. The learning curve of the three-port two-instrument complete thoracoscopic lobectomy for lung cancer—A feasible technique worthy of popularization

    Directory of Open Access Journals (Sweden)

    Yu-Jen Cheng

    2015-07-01

    Conclusion: Three-port complete thoracoscopic lobectomy with the two-instrument technique is feasible for lung cancer treatment. The length of the learning curve consisted of 28 cases. This TPTI technique should be popularized.

  6. A comparison of machine learning techniques for detection of drug target articles.

    Science.gov (United States)

    Danger, Roxana; Segura-Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

    2010-12-01

    Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure. Copyright © 2010 Elsevier Inc. All rights reserved.

  7. Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

    KAUST Repository

    Fernandes, José Antonio

    2015-01-01

    The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.

  8. Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques.

    Science.gov (United States)

    Cui, De-Mi; Yan, Weizhong; Wang, Xiao-Quan; Lu, Lie-Min

    2017-10-25

    Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT's turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts' interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology's effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.

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

    Science.gov (United States)

    Erol, B.; Erol, S.

    2013-03-01

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

  10. A comparison of machine learning techniques for survival prediction in breast cancer.

    Science.gov (United States)

    Vanneschi, Leonardo; Farinaccio, Antonella; Mauri, Giancarlo; Antoniotti, Mauro; Provero, Paolo; Giacobini, Mario

    2011-05-11

    The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

  11. A comparison of machine learning techniques for survival prediction in breast cancer

    Directory of Open Access Journals (Sweden)

    Vanneschi Leonardo

    2011-05-01

    Full Text Available Abstract Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

  12. Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Carla Iglesias

    2017-01-01

    Full Text Available The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470:2008, fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines were tested. Classification and regression trees (CART was the most accurate model for the prediction of pulp ISO brightness (R = 0.85. The other parameters could be predicted with fair results (R = 0.64–0.75 by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.

  13. Online Learning Behaviors for Radiology Interns Based on Association Rules and Clustering Technique

    Science.gov (United States)

    Chen, Hsing-Shun; Liou, Chuen-He

    2014-01-01

    In a hospital, clinical teachers must also care for patients, so there is less time for the teaching of clinical courses, or for discussing clinical cases with interns. However, electronic learning (e-learning) can complement clinical skills education for interns in a blended-learning process. Students discuss and interact with classmates in an…

  14. Dynamic Learning Style Prediction Method Based on a Pattern Recognition Technique

    Science.gov (United States)

    Yang, Juan; Huang, Zhi Xing; Gao, Yue Xiang; Liu, Hong Tao

    2014-01-01

    During the past decade, personalized e-learning systems and adaptive educational hypermedia systems have attracted much attention from researchers in the fields of computer science Aand education. The integration of learning styles into an intelligent system is a possible solution to the problems of "learning deviation" and…

  15. Care and Calls

    DEFF Research Database (Denmark)

    Paasch, Bettina Sletten

    on the enactment of care but also on patient safety. Nurses working in various hospital departments have developed different strategies for handling mobile phone calls when with a patient. Additional research into the ways nurses successfully or unsuccessfully enact care and ensure patient safety when they answer......In Danish hospitals, nurses have been equipped with a mobile work phone to improve their availability and efficiency. On the phones nurses receive internal and external phone conversations, patient calls, and alarms from electronic surveillance equipment. For safety reasons the phones cannot...... be switched off or silenced; they consequently ring during all activities and also during interactions with patients. A possible tension thus arises when nurses have to be both caring and sensitive towards the patient and simultaneously be efficient and available and answer their phone. The present paper...

  16. Flight calls and orientation

    DEFF Research Database (Denmark)

    Larsen, Ole Næsbye; Andersen, Bent Bach; Kropp, Wibke

    2008-01-01

    flight calls was simulated by sequential computer controlled activation of five loudspeakers placed in a linear array perpendicular to the bird's migration course. The bird responded to this stimulation by changing its migratory course in the direction of that of the ‘flying conspecifics' but after about......  In a pilot experiment a European Robin, Erithacus rubecula, expressing migratory restlessness with a stable orientation, was video filmed in the dark with an infrared camera and its directional migratory activity was recorded. The flight overhead of migrating conspecifics uttering nocturnal...... 30 minutes it drifted back to its original migration course. The results suggest that songbirds migrating alone at night can use the flight calls from conspecifics as additional cues for orientation and that they may compare this information with other cues to decide what course to keep....

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

    Science.gov (United States)

    Victoroff, Kristin Zakariasen; Hogan, Sarah

    2006-02-01

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

  18. "Galileo Calling Earth..."

    Science.gov (United States)

    National Aeronautics and Space Administration, Washington, DC.

    This guide presents an activity for helping students understand how data from the Galileo spacecraft is sent to scientists on earth. Students are asked to learn about the concepts of bit-rate and resolution and apply them to the interpretation of images from the Galileo Orbiter. (WRM)

  19. Mining FDA drug labels using an unsupervised learning technique--topic modeling.

    Science.gov (United States)

    Bisgin, Halil; Liu, Zhichao; Fang, Hong; Xu, Xiaowei; Tong, Weida

    2011-10-18

    The Food and Drug Administration (FDA) approved drug labels contain a broad array of information, ranging from adverse drug reactions (ADRs) to drug efficacy, risk-benefit consideration, and more. However, the labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the labeling text in a consistent and accurate fashion for comparative analysis across drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive. In this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the drug labeling with a goal of discovering "topics" that group drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved drug labels were used in this study. First, the three labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of drugs. The results demonstrate that drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P<0.05). The identified topics have distinct context that can be directly linked to specific adverse events (e.g., liver injury or kidney injury) or therapeutic application (e.g., antiinfectives for systemic use). We were also able to identify potential adverse events that might arise from specific

  20. Mining FDA drug labels using an unsupervised learning technique - topic modeling

    Science.gov (United States)

    2011-01-01

    Background The Food and Drug Administration (FDA) approved drug labels contain a broad array of information, ranging from adverse drug reactions (ADRs) to drug efficacy, risk-benefit consideration, and more. However, the labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the labeling text in a consistent and accurate fashion for comparative analysis across drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive. Method In this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the drug labeling with a goal of discovering “topics” that group drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved drug labels were used in this study. First, the three labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of drugs. Results The results demonstrate that drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P<0.05). The identified topics have distinct context that can be directly linked to specific adverse events (e.g., liver injury or kidney injury) or therapeutic application (e.g., antiinfectives for systemic use). We were also able to identify potential adverse events that

  1. Does Teaching Mnemonics for Vocabulary Learning Make a Difference? Putting the Keyword Method and the Word Part Technique to the Test

    Science.gov (United States)

    Wei, Zheng

    2015-01-01

    The present research tested the effectiveness of the word part technique in comparison with the keyword method and self-strategy learning. One hundred and twenty-one Chinese year-one university students were randomly assigned to one of the three learning conditions: word part, keyword or self-strategy learning condition. Half of the target words…

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

    Directory of Open Access Journals (Sweden)

    Apinya Phonpinyo

    2017-03-01

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

  3. FERMILAB: Call for physics

    International Nuclear Information System (INIS)

    Anon.

    1994-01-01

    Several hundred physicists attended a special Fermilab 'All Experimenter's Meeting' on November 20 to hear Director John Peoples call for new Tevatron Collider proposals for the years 2000-2005, when the new Main Injector will be complete. At the Tevatron proton-antiproton collider, the CDF and DO experiments are currently completing improvements for Run II to use the Tevatron when the Main Injector is complete later in this decade. New proposals would be aimed at a Collider Run III to follow these CDF and DO efforts

  4. To be called upon

    DEFF Research Database (Denmark)

    Kublitz, Anja

    2015-01-01

    When Danish Muslims explain what made them decide to travel to the Middle East and take up arms in the wake of the Arab Spring, they say that they were called upon. Displayed on videos on social media, women and sometimes children begged them to come to their rescue. In light of some...... to the mass if it is no longer a causal phenomenon that expands from small to big, but rather a simultaneous multitude of one to one relations that are neither local nor global? How are the one and the many related in this specific setting? Furthermore, many of the videos display dead bodies. How can we...

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

    Directory of Open Access Journals (Sweden)

    Meike Imelda Wachyu

    2017-12-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

  7. Collaborative Autonomous English Language Learning in CALL

    OpenAIRE

    Alghammas, Abdurrazzag

    2010-01-01

    The explosive growth of technology has affected almost all cities and villages around the globe. This century might be deemed to be the era of technology. Computers were amazing key technological invention of the 20st century and were first used for military purposes many years ago in both America and Britain. Now that we are living in a technological village, each successive generation is more dependent on technology. There is at least one computer and one mobile phone in most homes and it i...

  8. Current breathomics-a review on data pre-processing techniques and machine learning in metabolomics breath analysis

    DEFF Research Database (Denmark)

    Smolinska, A.; Hauschild, A. C.; Fijten, R. R. R.

    2014-01-01

    been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start...... different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus...

  9. Modelling risk of tick exposure in southern Scandinavia using machine learning techniques, satellite imagery, and human population density maps

    DEFF Research Database (Denmark)

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

    30 sites (forests and meadows) in each of Denmark, southern Norway and south-eastern Sweden. At each site we measured presence/absence of ticks, and used the data obtained along with environmental satellite images to run Boosted Regression Tree machine learning algorithms to predict overall spatial...... and Sweden), areas with high population densities tend to overlap with these zones.Machine learning techniques allow us to predict for larger areas without having to perform extensive sampling all over the region in question, and we were able to produce models and maps with high predictive value. The results...

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

    Directory of Open Access Journals (Sweden)

    Ivana Šemanjski

    2015-12-01

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

  11. CALL in the Year 2000: A Look Back from 2016

    Science.gov (United States)

    Chapelle, Carol A.

    2016-01-01

    This commentary offers a brief reflection on the state of CALL in 1997, when "Language Learning & Technology" was launched with my paper entitled "CALL in the year 2000: Still in search of research paradigms?" The point of my 1997 paper was to suggest the potential value of research on second language learning for the study…

  12. Impact of Using CALL on Iranian EFL Learners' Vocabulary Knowledge

    Science.gov (United States)

    Yunus, Melor Md; Salehi, Hadi; Amini, Mahdi

    2016-01-01

    Computer Assisted Language Learning (CALL) integration in EFL contexts has intensified noticeably in recent years. This integration might be in different ways and for different purposes such as vocabulary acquisition, grammar learning, phonology, writing skills, etc. More explicitly, this study is an attempt to explore the effect of using CALL on…

  13. Hubble Tarantula Treasury Project - VI. Identification of Pre-Main-Sequence Stars using Machine Learning techniques

    Science.gov (United States)

    Ksoll, Victor F.; Gouliermis, Dimitrios A.; Klessen, Ralf S.; Grebel, Eva K.; Sabbi, Elena; Anderson, Jay; Lennon, Daniel J.; Cignoni, Michele; de Marchi, Guido; Smith, Linda J.; Tosi, Monica; van der Marel, Roeland P.

    2018-05-01

    The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire star-burst region of 30 Doradus down to the half Solar mass limit. We use the deep stellar catalogue of HTTP to identify all the pre-main-sequence (PMS) stars of the region, i.e., stars that have not started their lives on the main-sequence yet. The photometric distinction of these stars from the more evolved populations is not a trivial task due to several factors that alter their colour-magnitude diagram positions. The identification of PMS stars requires, thus, sophisticated statistical methods. We employ Machine Learning Classification techniques on the HTTP survey of more than 800,000 sources to identify the PMS stellar content of the observed field. Our methodology consists of 1) carefully selecting the most probable low-mass PMS stellar population of the star-forming cluster NGC2070, 2) using this sample to train classification algorithms to build a predictive model for PMS stars, and 3) applying this model in order to identify the most probable PMS content across the entire Tarantula Nebula. We employ Decision Tree, Random Forest and Support Vector Machine classifiers to categorise the stars as PMS and Non-PMS. The Random Forest and Support Vector Machine provided the most accurate models, predicting about 20,000 sources with a candidateship probability higher than 50 percent, and almost 10,000 PMS candidates with a probability higher than 95 percent. This is the richest and most accurate photometric catalogue of extragalactic PMS candidates across the extent of a whole star-forming complex.

  14. Education techniques for lifelong learning: giving a PowerPoint presentation: the art of communicating effectively.

    Science.gov (United States)

    Collins, Jannette

    2004-01-01

    Effectiveness of an oral presentation depends on the ability of the speaker to communicate with the audience. An important part of this communication is focusing on two to five key points and emphasizing those points during the presentation. Every aspect of the presentation should be purposeful and directed at facilitating learners' achievement of the objectives. This necessitates that the speaker has carefully developed the objectives and built the presentation around attainment of the objectives. The best presentations are rehearsed, not so that the speaker memorizes exactly what he or she will say, but to facilitate the speaker's ability to interact with the audience and portray a relaxed, professional, and confident demeanor. Rehearsal also helps alleviate stage fright. The most useful method of controlling nervousness is to visualize success. When showing images, it is important to orient the audience with an adequate description, point out the relevant findings, and allow enough time for the audience to assimilate the information before moving on. This can be facilitated with appropriate use of a laser pointer, cursor, or use of builds and transitioning. A presentation should be designed to include as much audience participation as possible, no matter the size of the audience. Techniques to encourage audience participation include questioning, brainstorming, small-group activities, role-playing, case-based examples, and directed listening. It is first necessary to motivate and gain attention of the learner for learning to take place. This can be accomplished through appropriate use of humor, anecdotes, and quotations. Attention should be given to posture, body movement, eye contact, and voice when speaking, as how one appears to the audience will have an impact on their reaction to what is presented. Copyright RSNA, 2004

  15. Change detection of medical images using dictionary learning techniques and principal component analysis.

    Science.gov (United States)

    Nika, Varvara; Babyn, Paul; Zhu, Hongmei

    2014-07-01

    Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between [Formula: see text] and [Formula: see text] norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the [Formula: see text] norm over the [Formula: see text] norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient's position and other acquisition artifacts.

  16. Change detection of medical images using dictionary learning techniques and PCA

    Science.gov (United States)

    Nika, Varvara; Babyn, Paul; Zhu, Hongmei

    2014-03-01

    Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.

  17. MEDICAL SERVICE - URGENT CALLS

    CERN Multimedia

    Service Médical

    2000-01-01

    IN URGENT NEED OF A DOCTOR GENEVA: EMERGENCY SERVICES GENEVA AND VAUD 144 FIRE BRIGADE 118 POLICE 117 CERN FIREMEN 767-44-44 ANTI-POISONS CENTRE Open 24h/24h 01-251-51-51 Patient not fit to be moved, call family doctor, or: GP AT HOME: Open 24h/24h 748-49-50 AMG- Association Of Geneva Doctors: Emergency Doctors at home 07h-23h 322 20 20 Patient fit to be moved: HOPITAL CANTONAL CENTRAL 24 Micheli-du-Crest 372-33-11 ou 382-33-11 EMERGENCIES 382-33-11 ou 372-33-11 CHILDREN'S HOSPITAL 6 rue Willy-Donzé 372-33-11 MATERNITY 32 bvd.de la Cluse 382-68-16 ou 382-33-11 OPHTHALMOLOGY 22 Alcide Jentzer 382-33-11 ou 372-33-11 MEDICAL CENTRE CORNAVIN 1-3 rue du Jura 345 45 50 HOPITAL DE LA TOUR Meyrin 719-61-11 EMERGENCIES 719-61-11 CHILDREN'S EMERGENCIES 719-61-00 LA TOUR MEDICAL CENTRE 719-74-00 European Emergency Call 112   FRANCE: EMERGENCY SERVICES 15 FIRE BRIGADE 18 POLICE 17 CERN FIREMEN AT HOME 00-41-22-767-44-44 ...

  18. Call for volunteers

    CERN Document Server

    2008-01-01

    CERN is calling for volunteers from all members of the Laboratory for organizing the two exceptional Open days.CERN is calling for volunteers from all members of the Laboratory’s personnel to help with the organisation of these two exceptional Open Days, for the visits of CERN personnel and their families on the Saturday and above all for the major public Open Day on the Sunday. As for the 50th anniversary in 2004, the success of the Open Days will depend on a large number of volunteers. All those working for CERN as well as retired members of the personnel can contribute to making this event a success. Many guides will be needed at the LHC points, for the activities at the surface and to man the reception and information points. The aim of these major Open Days is to give the local populations the opportunity to discover the fruits of almost 20 years of work carried out at CERN. We are hoping for some 2000 volunteers for the two Open Days, on the Saturday from 9 a.m. to ...

  19. Machine Learning Techniques for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Thrane, Jakob; Wass, Jesper; Piels, Molly

    2017-01-01

    Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal...... detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical...

  20. Don't Call It School

    Science.gov (United States)

    Robb, Daniel

    2006-01-01

    "Homeschooling," "deschooling," and "unschooling" are commonly used terms in the alternative-education world, but each lacks specificity. In this article, the author describes what he discovered during several visits to North Star. Known officially as North Star: Self-Directed Learning for Teens, it is not as structured as a so-called "free"…

  1. Scoping Study of Machine Learning Techniques for Visualization and Analysis of Multi-source Data in Nuclear Safeguards

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Yonggang

    2018-05-07

    In implementation of nuclear safeguards, many different techniques are being used to monitor operation of nuclear facilities and safeguard nuclear materials, ranging from radiation detectors, flow monitors, video surveillance, satellite imagers, digital seals to open source search and reports of onsite inspections/verifications. Each technique measures one or more unique properties related to nuclear materials or operation processes. Because these data sets have no or loose correlations, it could be beneficial to analyze the data sets together to improve the effectiveness and efficiency of safeguards processes. Advanced visualization techniques and machine-learning based multi-modality analysis could be effective tools in such integrated analysis. In this project, we will conduct a survey of existing visualization and analysis techniques for multi-source data and assess their potential values in nuclear safeguards.

  2. Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

    Science.gov (United States)

    Crossley, Scott A.

    2013-01-01

    This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

  3. Incorporating Service-Learning, Technology, and Research Supportive Teaching Techniques into the University Chemistry Classroom

    Science.gov (United States)

    Saitta, E. K. H.; Bowdon, M. A.; Geiger, C. L.

    2011-01-01

    Technology was integrated into service-learning activities to create an interactive teaching method for undergraduate students at a large research institution. Chemistry students at the University of Central Florida partnered with high school students at Crooms Academy of Information Technology in interactive service learning projects. The…

  4. Seeing the System through the End Users' Eyes: Shadow Expert Technique for Evaluating the Consistency of a Learning Management System

    Science.gov (United States)

    Holzinger, Andreas; Stickel, Christian; Fassold, Markus; Ebner, Martin

    Interface consistency is an important basic concept in web design and has an effect on performance and satisfaction of end users. Consistency also has significant effects on the learning performance of both expert and novice end users. Consequently, the evaluation of consistency within a e-learning system and the ensuing eradication of irritating discrepancies in the user interface redesign is a big issue. In this paper, we report of our experiences with the Shadow Expert Technique (SET) during the evaluation of the consistency of the user interface of a large university learning management system. The main objective of this new usability evaluation method is to understand the interaction processes of end users with a specific system interface. Two teams of usability experts worked independently from each other in order to maximize the objectivity of the results. The outcome of this SET method is a list of recommended changes to improve the user interaction processes, hence to facilitate high consistency.

  5. Blended call center with idling times during the call service

    NARCIS (Netherlands)

    Legros, Benjamin; Jouini, Oualid; Koole, Ger

    We consider a blended call center with calls arriving over time and an infinitely backlogged amount of outbound jobs. Inbound calls have a non-preemptive priority over outbound jobs. The inbound call service is characterized by three successive stages where the second one is a break; i.e., there is

  6. Analysis and design of machine learning techniques evolutionary solutions for regression, prediction, and control problems

    CERN Document Server

    Stalph, Patrick

    2014-01-01

    Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. Nevertheless, motor skills are not easy to learn for humans and this is also an active research topic in robotics. However, most solutions are optimized for industrial applications and, thus, few are plausible explanations for human learning. The fundamental challenge, that motivates Patrick Stalph, originates from the cognitive science: How do humans learn their motor skills? The author makes a connection between robotics and cognitive sciences by analyzing motor skill learning using implementations that could be found in the human brain – at least to some extent. Therefore three suitable machine learning algorithms are selected – algorithms that are plausible from a cognitive viewpoint and feasible for the roboticist. The power and scalability of those algorithms is evaluated in theoretical simulations and more realistic scenarios with the iCub humanoid robot. Convincing results confirm the...

  7. Game Art Complete All-in-One; Learn Maya, 3ds Max, ZBrush, and Photoshop Winning Techniques

    CERN Document Server

    Gahan, Andrew

    2008-01-01

    A compilation of key chapters from the top Focal game art books available today - in the areas of Max, Maya, Photoshop, and ZBrush. The chapters provide the CG Artist with an excellent sampling of essential techniques that every 3D artist needs to create stunning game art. Game artists will be able to master the modeling, rendering, rigging, and texturing techniques they need - with advice from Focal's best and brightest authors. Artists can learn hundreds of tips, tricks and shortcuts in Max, Maya, Photoshop, ZBrush - all within the covers of one complete, inspiring reference

  8. Machine-learning techniques for family demography: an application of random forests to the analysis of divorce determinants in Germany

    OpenAIRE

    Arpino, Bruno; Le Moglie, Marco; Mencarini, Letizia

    2018-01-01

    Demographers often analyze the determinants of life-course events with parametric regression-type approaches. Here, we present a class of nonparametric approaches, broadly defined as machine learning (ML) techniques, and discuss advantages and disadvantages of a popular type known as random forest. We argue that random forests can be useful either as a substitute, or a complement, to more standard parametric regression modeling. Our discussion of random forests is intuitive and...

  9. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques

    OpenAIRE

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-01-01

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content...

  10. Call 1 FAQ (FR)

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

    Francine Sinzinkayo

    Le terme « amélioration » utilisé dans cet appel à propositions fait référence à toutes les innovations utilisées par les ... Le candidat principal est chargé de la conception intellectuelle et de la mise en oeuvre de l'idée, .... de vaccins, notamment les nouvelles techniques mises au point en biologie synthétique, ou ce type de ...

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

    Science.gov (United States)

    Mutlu, Ayfer

    2018-03-01

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

  12. Machine learning techniques in disease forecasting: a case study on rice blast prediction

    Directory of Open Access Journals (Sweden)

    Kapoor Amar S

    2006-11-01

    Full Text Available Abstract Background Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG approach achieved an average correlation coefficient (r of 0.50, which increased to 0.60 and percent mean absolute error (%MAE decreased from 65.42 to 52.24 when back-propagation neural network (BPNN was used. With generalized regression neural network (GRNN, the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also

  13. Some aspects of using new techniques of teaching/learning in education in optics (Abstract only)

    Science.gov (United States)

    Suchanska, Malgorzata

    2003-11-01

    The deep learning in Optics can be encouraged by stimulating and considerate teaching. It means that teacher should demonstrate his/her personal commitment to the subject and stress its meaning, relevance and importance to the students. It is also important to allow students to be creative in solving problems and in interpretation of its contents. In order to help the students to become more creative persons it is necessary to enhance the learning process of modern knowledge in Optics, to design and conduct experiments, stimulate passions and interests, allow an access to the e-learning system (Internet) and introduce the psychological training (creativity, communication, lateral thinking etc.) (Abstract only available)

  14. Using Participatory Learning & Action (PLA) research techniques for inter-stakeholder dialogue in primary healthcare: an analysis of stakeholders' experiences.

    Science.gov (United States)

    de Brún, T; O'Reilly-de Brún, M; Van Weel-Baumgarten, E; Burns, N; Dowrick, C; Lionis, C; O'Donnell, C; Mair, F S; Papadakaki, M; Saridaki, A; Spiegel, W; Van Weel, C; Van den Muijsenbergh, M; MacFarlane, A

    2017-01-01

    It is important for health care workers to know the needs and expectations of their patients. Therefore, service users have to be involved in research. To achieve a meaningful dialogue between service users, healthcare workers and researchers, participatory methods are needed. This paper describes how the application of a specific participatory methodology, Participatory Learning and Action (PLA) can lead to such a meaningful dialogue. In PLA all stakeholders are regarded as equal partners and collaborators in research.During 2011-2015, a European project called RESTORE used PLA in Austria, Greece, Ireland, The Netherlands and the UK to investigate how communication between primary health care workers and their migrant patients could be improved.Seventy eight migrants, interpreters, doctors, nurses and other key stakeholders (see Table 2) participated in 62 PLA sessions. These dialogues (involving discussions, activities, PLA techniques and evaluations) were generally 2-3 h long and were recorded and analysed by the researchers.Participants reported many positive experiences about their dialogues with other stakeholders. There was a positive, trusting atmosphere in which all stakeholders could express their views despite differences in social power. This made for better understanding within and across stakeholder groups. For instance a doctor changed her view on the use of interpreters after a migrant explained why this was important. Negative experiences were rare: some doctors and healthcare workers thought the PLA sessions took a lot of time; and despite the good dialogue, there was disappointment that very few migrants used the new interpreting service. Background In order to be effective, primary healthcare must understand the health needs, values and expectations of the population it serves. Recent research has shown that the involvement of service users and other stakeholders and gathering information on their perspectives can contribute positively to many

  15. Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

    DEFF Research Database (Denmark)

    Zibar, Darko; de Carvalho, Luis Henrique Hecker; Piels, Molly

    2015-01-01

    In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms...

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

    Directory of Open Access Journals (Sweden)

    Steren Chabert

    2017-01-01

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

  17. Using Elearning techniques to support problem based learning within a clinical simulation laboratory.

    Science.gov (United States)

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

    2004-01-01

    This paper details the results of the first phase of a project that used eLearning to support students' learning within a simulated environment. The locus was a purpose built Clinical Simulation Laboratory (CSL) where the School's newly adopted philosophy of Problem Based Learning (PBL) was challenged through lecturers reverting to traditional teaching methods. The solution, a student-centred, problem-based approach to the acquisition of clinical skills was developed using learning objects embedded within web pages that substituted for lecturers providing instruction and demonstration. This allowed lecturers to retain their facilitator role, and encouraged students to explore, analyse and make decisions within the safety of a clinical simulation. Learning was enhanced through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that an elearning approach can support PBL in delivering a student centred learning experience.

  18. Game Design Narrative for Learning: Appropriating Adventure Game Design Narrative Devices and Techniques for the Design of Interactive Learning Environments

    Science.gov (United States)

    Dickey, Michele D.

    2006-01-01

    The purpose of this conceptual analysis is to investigate how contemporary video and computer games might inform instructional design by looking at how narrative devices and techniques support problem solving within complex, multimodal environments. Specifically, this analysis presents a brief overview of game genres and the role of narrative in…

  19. Student Teachers and CALL: Personal and Pedagogical Uses and Beliefs

    Science.gov (United States)

    Hlas, Anne Cummings; Conroy, Kelly; Hildebrandt, Susan A.

    2017-01-01

    The student teaching semester affords teacher candidates the chance to apply what they have learned during their teacher preparation coursework. Therefore, it can be a prime opportunity for student teachers to use technology for their own language learning and to implement computer assisted language learning (CALL) in their instruction. This study…

  20. The Ghost in the Machine: Are "Teacherless" CALL Programs Really Possible?

    Science.gov (United States)

    Davies, Ted; Williamson, Rodney

    1998-01-01

    Reflects critically on pedagogical issues in the production of computer-assisted language learning (CALL) courseware and ways CALL has affected the practice of language learning. Concludes that if CALL is to reach full potential, it must be more than a simple medium of information; it should provide a teaching/learning process, with the real…

  1. The use of indigenous techniques of communication for language learning: The case of Cameroon

    OpenAIRE

    Ebong, Balbina

    2004-01-01

    This study aimed at determining whether the use of indigenous techniques of communication can have a positive impact on the motivation of the learner of English as a foreign language in Cameroon. By indigenous techniques of communication we mean techniques like role-play, songs, the telling of folktales, riddles and proverbs. This work is intended as a contribution to the search for improvement of student motivation and enthusiasm, whereby they can be more responsive as they participate spont...

  2. Machine Learning Techniques for Characterizing IEEE 802.11b Encrypted Data Streams

    National Research Council Canada - National Science Library

    Henson, Michael

    2004-01-01

    .... Even though there have been major advancements in encryption technology, security protocols and packet header obfuscation techniques, other distinguishing characteristics do exist in wireless network traffic...

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

    Science.gov (United States)

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

    2017-06-14

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

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

    Science.gov (United States)

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

    2017-10-01

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

  5. Learning Design Implementation for Distance e-Learning: Blending Rapid e-Learning Techniques with Activity-Based Pedagogies to Design and Implement a Socio-Constructivist Environment

    Science.gov (United States)

    Santally, Mohammad Issack; Rajabalee, Yousra; Cooshna-Naik, Dorothy

    2012-01-01

    This paper discusses how modern technologies are changing the teacher-student-content relationships from the conception to the delivery of so-called "distance" education courses. The concept of Distance Education has greatly evolved in the digital era of 21st Century. With the widespread use and access to the Internet, exponential growth…

  6. Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Shirin Enshaeifar

    Full Text Available The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM. TIHM is a technology assisted monitoring system that uses Internet of Things (IoT enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

  7. Design of a Bahasa Melayu Grammar Online Learning Portal for Form Two Students Using Delphi Technique

    Science.gov (United States)

    Leng, Chin Hai; Siraj, Saedah; Asmawi, Adelina; Dewitt, Dorothy; Ranee, Alina

    2013-01-01

    This study was aimed at developing a Bahasa Melayu grammar learning portal for Form Two students (BMGLP). A developmental approach was used in this study. Needs analysis was carried out on the Bahasa Melayu teachers and Form Two students. The results of needs analysis on Form Two students showed that they preferred topics such as question…

  8. Which Technique Is Most Effective for Learning Declarative Concepts--Provided Examples, Generated Examples, or Both?

    Science.gov (United States)

    Zamary, Amanda; Rawson, Katherine A.

    2018-01-01

    Students in many courses are commonly expected to learn declarative concepts, which are abstract concepts denoted by key terms with short definitions that can be applied to a variety of scenarios as reported by Rawson et al. ("Educational Psychology Review" 27:483-504, 2015). Given that declarative concepts are common and foundational in…

  9. A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

    OpenAIRE

    Ademujimi , Toyosi ,; Brundage , Michael ,; Prabhu , Vittaldas ,

    2017-01-01

    Part 6: Intelligent Diagnostics and Maintenance Solutions; International audience; Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learnin...

  10. Active Learning in PhysicsTechnology and Research-based Techniques Emphasizing Interactive Lecture Demonstrations

    Science.gov (United States)

    Thornton, Ronald

    2010-10-01

    Physics education research has shown that learning environments that engage students and allow them to take an active part in their learning can lead to large conceptual gains compared to traditional instruction. Examples of successful curricula and methods include Peer Instruction, Just in Time Teaching, RealTime Physics, Workshop Physics, Scale-Up, and Interactive Lecture Demonstrations (ILDs). An active learning environment is often difficult to achieve in lecture sessions. This presentation will demonstrate the use of sequences of Interactive Lecture Demonstrations (ILDs) that use real experiments often involving real-time data collection and display combined with student interaction to create an active learning environment in large or small lecture classes. Interactive lecture demonstrations will be done in the area of mechanics using real-time motion probes and the Visualizer. A video tape of students involved in interactive lecture demonstrations will be shown. The results of a number of research studies at various institutions (including international) to measure the effectiveness of ILDs and guided inquiry conceptual laboratories will be presented.

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

    Science.gov (United States)

    Alabbasi, Daniah

    2017-01-01

    Teachers and educational institutions are attempting to find an appropriate strategy to motivate as well as engage students in the learning process. Institutions are encouraging the use of gamification in education for the purpose of improving the intrinsic motivation as well as engagement. However, the students' perspective of the issue is…

  12. Modeling as a Technique for Promoting Classroom Learning and Prosocial Behavior. Theoretical Paper No. 39.

    Science.gov (United States)

    Frayer, Dorothy A.; Klausmeier, Herbert J.

    Research has shown that a behavior may be acquired through observing and imitating a model. A behavior which has already been acquired may be inhibited, disinhibited, or elicited by observing and imitating. A definition of imitation is given, and the effects of imitation on learning and performance are summarized. Research on factors which affect…

  13. Measurement of Learning Process by Semantic Annotation Technique on Bloom's Taxonomy Vocabulary

    Science.gov (United States)

    Yanchinda, Jirawit; Yodmongkol, Pitipong; Chakpitak, Nopasit

    2016-01-01

    A lack of science and technology knowledge understanding of most rural people who had the highest education at elementary education level more than others level is unsuccessfully transferred appropriate technology knowledge for rural sustainable development. This study provides the measurement of the learning process by on Bloom's Taxonomy…

  14. Vocabulary Learning through Viewing Video: The Effect of Two Enhancement Techniques

    Science.gov (United States)

    Montero Perez, Maribel; Peters, Elke; Desmet, Piet

    2018-01-01

    While most studies on L2 vocabulary learning through input have addressed learners' vocabulary uptake from written text, this study focuses on audio-visual input. In particular, we investigate the effects of enhancing video by (1) adding different types of L2 subtitling (i.e. no captioning, full captioning, keyword captioning, and glossed keyword…

  15. Development of an Advanced, Automatic, Ultrasonic NDE Imaging System via Adaptive Learning Network Signal Processing Techniques

    Science.gov (United States)

    1981-03-13

    UNCLASSIFIED SECURITY CLAS,:FtfC ’i OF TH*!’ AGC W~ct P- A* 7~9r1) 0. ABSTRACT (continued) onuing in concert with a sophisticated detector has...and New York, 1969. Whalen, M.F., L.J. O’Brien, and A.N. Mucciardi, "Application of Adaptive Learning Netowrks for the Characterization of Two

  16. Laparoscopic colorectal surgery in learning curve: Role of implementation of a standardized technique and recovery protocol. A cohort study

    Science.gov (United States)

    Luglio, Gaetano; De Palma, Giovanni Domenico; Tarquini, Rachele; Giglio, Mariano Cesare; Sollazzo, Viviana; Esposito, Emanuela; Spadarella, Emanuela; Peltrini, Roberto; Liccardo, Filomena; Bucci, Luigi

    2015-01-01

    Background Despite the proven benefits, laparoscopic colorectal surgery is still under utilized among surgeons. A steep learning is one of the causes of its limited adoption. Aim of the study is to determine the feasibility and morbidity rate after laparoscopic colorectal surgery in a single institution, “learning curve” experience, implementing a well standardized operative technique and recovery protocol. Methods The first 50 patients treated laparoscopically were included. All the procedures were performed by a trainee surgeon, supervised by a consultant surgeon, according to the principle of complete mesocolic excision with central vascular ligation or TME. Patients underwent a fast track recovery programme. Recovery parameters, short-term outcomes, morbidity and mortality have been assessed. Results Type of resections: 20 left side resections, 8 right side resections, 14 low anterior resection/TME, 5 total colectomy and IRA, 3 total panproctocolectomy and pouch. Mean operative time: 227 min; mean number of lymph-nodes: 18.7. Conversion rate: 8%. Mean time to flatus: 1.3 days; Mean time to solid stool: 2.3 days. Mean length of hospital stay: 7.2 days. Overall morbidity: 24%; major morbidity (Dindo–Clavien III): 4%. No anastomotic leak, no mortality, no 30-days readmission. Conclusion Proper laparoscopic colorectal surgery is safe and leads to excellent results in terms of recovery and short term outcomes, even in a learning curve setting. Key factors for better outcomes and shortening the learning curve seem to be the adoption of a standardized technique and training model along with the strict supervision of an expert colorectal surgeon. PMID:25859386

  17. The Effect of Jigsaw Technique on 6th Graders' Learning of Force and Motion Unit and Their Science Attitudes and Motivation

    Science.gov (United States)

    Ural, Evrim; Ercan, Orhan; Gençoglan, Durdu Mehmet

    2017-01-01

    The study aims to investigate the effects of jigsaw technique on 6th graders' learning of "Force and Motion" unit, their science learning motivation and their attitudes towards science classes. The sample of the study consisted of 49 6th grade students from two different classes taking the Science and Technology course at a government…

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

    Science.gov (United States)

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

    2015-01-01

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

  19. Reverse engineering smart card malware using side channel analysis with machine learning techniques

    CSIR Research Space (South Africa)

    Djonon Tsague, Hippolyte

    2016-12-01

    Full Text Available as much variance of the original data as possible. Among feature extraction techniques, PCA and LDA are very common dimensionality reduction algorithms that have successfully been applied in many classification problems like face recognition, character...

  20. High Classification Rates for Continuous Cow Activity Recognition using Low-cost GPS Positioning Sensors and Standard Machine Learning Techniques

    DEFF Research Database (Denmark)

    Godsk, Torben; Kjærgaard, Mikkel Baun

    2011-01-01

    activities. By preprocessing the raw cow position data, we obtain high classification rates using standard machine learning techniques to recognize cow activities. Our objectives were to (i) determine to what degree it is possible to robustly recognize cow activities from GPS positioning data, using low...... and their activities manually logged to serve as ground truth. For our dataset we managed to obtain an average classification success rate of 86.2% of the four activities: eating/seeking (90.0%), walking (100%), lying (76.5%), and standing (75.8%) by optimizing both the preprocessing of the raw GPS data...

  1. Children’s Negotiations of Visualization Skills During a Design-Based Learning Experience Using Nondigital and Digital Techniques

    OpenAIRE

    Smith, Shaunna

    2018-01-01

    In the context of a 10-day summer camp makerspace experience that employed design-based learning (DBL) strategies, the purpose of this descriptive case study was to better understand the ways in which children use visualization skills to negotiate design as they move back and forth between the world of nondigital design techniques (i.e., drawing, 3-D drawing with hot glue, sculpture, discussion, writing) and digital technologies (i.e., 3-D scanning, 3-D modeling, 3-D printing). Participants i...

  2. Skype™ Conference Calls: A Way to Promote Speaking Skills in the Teaching and Learning of English (Llamadas para conferencia en Skype™: una forma de promover la habilidad de habla en la enseñanza y aprendizaje del inglés)

    Science.gov (United States)

    Romaña Correa, Yeferson

    2015-01-01

    This article presents the results of a research project on the teaching and learning of English through the use of Skype™ conference calls. The research was carried out with a group of 12 English as a foreign language adult learners in the language institute of Universidad Distrital Francisco José de Caldas, Bogotá, Colombia. The findings of this…

  3. Call Centre- Computer Telephone Integration

    Directory of Open Access Journals (Sweden)

    Dražen Kovačević

    2012-10-01

    Full Text Available Call centre largely came into being as a result of consumerneeds converging with enabling technology- and by the companiesrecognising the revenue opportunities generated by meetingthose needs thereby increasing customer satisfaction. Regardlessof the specific application or activity of a Call centre, customersatisfaction with the interaction is critical to the revenuegenerated or protected by the Call centre. Physical(v, Call centreset up is a place that includes computer, telephone and supervisorstation. Call centre can be available 24 hours a day - whenthe customer wants to make a purchase, needs information, orsimply wishes to register a complaint.

  4. Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning

    CERN Document Server

    Demetriadis, Stavros; Xhafa, Fatos

    2012-01-01

    Adaptation and personalization have been extensively studied in CSCL research community aiming to design intelligent systems that adaptively support eLearning processes and collaboration. Yet, with the fast development in Internet technologies, especially with the emergence of new data technologies and the mobile technologies, new opportunities and perspectives are opened for advanced adaptive and personalized systems. Adaptation and personalization are posing new research and development challenges to nowadays CSCL systems. In particular, adaptation should be focused in a multi-dimensional way (cognitive, technological, context-aware and personal). Moreover, it should address the particularities of both individual learners and group collaboration. As a consequence, the aim of this book is twofold. On the one hand, it discusses the latest advances and findings in the area of intelligent adaptive and personalized learning systems. On the other hand it analyzes the new implementation perspectives for intelligen...

  5. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    OpenAIRE

    Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

    2014-01-01

    Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

  6. Exploration of machine learning techniques in predicting multiple sclerosis disease course

    OpenAIRE

    Zhao, Yijun; Healy, Brian C.; Rotstein, Dalia; Guttmann, Charles R. G.; Bakshi, Rohit; Weiner, Howard L.; Brodley, Carla E.; Chitnis, Tanuja

    2017-01-01

    Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 1693 CLIMB study patients were classified as increased EDSS?1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up. Results Baseline data...

  7. The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm

    OpenAIRE

    Wu, Jianning; Wu, Bin

    2015-01-01

    The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of...

  8. Support techniques in the learning process: the case of antiplagiarism systems

    OpenAIRE

    Jorge Matías-Pereda; Gustavo Lannelongue Nieto

    2013-01-01

    The widespread use of the Internet has given university students access to information resources on a level never experienced in the past. The bad news is that this issue increased the inappropriate use of those resources. In this paper we discuss the experience of using the Turnitin anti-plagiarism license. In total, 358 assignments were analyzed. We partly noticed a learning effect among students between assignment deliveries. The main conclusions drawn from the use of Turnitin anti-plag...

  9. 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.

  10. Adaptive critic learning techniques for engine torque and air-fuel ratio control.

    Science.gov (United States)

    Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

    2008-08-01

    A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

  11. Investigating the Effects of Group Investigation (GI and Cooperative Integrated Reading and Comprehension (CIRC as the Cooperative Learning Techniques on Learner's Reading Comprehension

    Directory of Open Access Journals (Sweden)

    Mohammad Amin Karafkan

    2015-11-01

    Full Text Available Cooperative learning consists of some techniques for helping students work together more effectively. This study investigated the effects of Group Investigation (GI and Cooperative Integrated Reading and Composition (CIRC as cooperative learning techniques on Iranian EFL learners’ reading comprehension at an intermediate level. The participants of the study were 207 male students who studied at an intermediate level at ILI. The participants were randomly assigned into three equal groups: one control group and two experimental groups. The control group was instructed via conventional technique following an individualistic instructional approach. One experimental group received GI technique. The other experimental group received CIRC technique. The findings showed that there was a meaningful difference between the mean of the reading comprehension score of GI experimental group and CRIC experimental group. CRIC technique is more effective than GI technique in enhancing the reading comprehension test scores of students.

  12. Estimating Global Seafloor Total Organic Carbon Using a Machine Learning Technique and Its Relevance to Methane Hydrates

    Science.gov (United States)

    Lee, T. R.; Wood, W. T.; Dale, J.

    2017-12-01

    Empirical and theoretical models of sub-seafloor organic matter transformation, degradation and methanogenesis require estimates of initial seafloor total organic carbon (TOC). This subsurface methane, under the appropriate geophysical and geochemical conditions may manifest as methane hydrate deposits. Despite the importance of seafloor TOC, actual observations of TOC in the world's oceans are sparse and large regions of the seafloor yet remain unmeasured. To provide an estimate in areas where observations are limited or non-existent, we have implemented interpolation techniques that rely on existing data sets. Recent geospatial analyses have provided accurate accounts of global geophysical and geochemical properties (e.g. crustal heat flow, seafloor biomass, porosity) through machine learning interpolation techniques. These techniques find correlations between the desired quantity (in this case TOC) and other quantities (predictors, e.g. bathymetry, distance from coast, etc.) that are more widely known. Predictions (with uncertainties) of seafloor TOC in regions lacking direct observations are made based on the correlations. Global distribution of seafloor TOC at 1 x 1 arc-degree resolution was estimated from a dataset of seafloor TOC compiled by Seiter et al. [2004] and a non-parametric (i.e. data-driven) machine learning algorithm, specifically k-nearest neighbors (KNN). Built-in predictor selection and a ten-fold validation technique generated statistically optimal estimates of seafloor TOC and uncertainties. In addition, inexperience was estimated. Inexperience is effectively the distance in parameter space to the single nearest neighbor, and it indicates geographic locations where future data collection would most benefit prediction accuracy. These improved geospatial estimates of TOC in data deficient areas will provide new constraints on methane production and subsequent methane hydrate accumulation.

  13. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

    Science.gov (United States)

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-08-15

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

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

    Directory of Open Access Journals (Sweden)

    Avilner Rafael Páez Pereira

    2017-11-01

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

  15. Technique adaptation, strategic replanning, and team learning during implementation of MR-guided brachytherapy for cervical cancer.

    Science.gov (United States)

    Skliarenko, Julia; Carlone, Marco; Tanderup, Kari; Han, Kathy; Beiki-Ardakani, Akbar; Borg, Jette; Chan, Kitty; Croke, Jennifer; Rink, Alexandra; Simeonov, Anna; Ujaimi, Reem; Xie, Jason; Fyles, Anthony; Milosevic, Michael

    MR-guided brachytherapy (MRgBT) with interstitial needles is associated with improved outcomes in cervical cancer patients. However, there are implementation barriers, including magnetic resonance (MR) access, practitioner familiarity/comfort, and efficiency. This study explores a graded MRgBT implementation strategy that included the adaptive use of needles, strategic use of MR imaging/planning, and team learning. Twenty patients with cervical cancer were treated with high-dose-rate MRgBT (28 Gy in four fractions, two insertions, daily MR imaging/planning). A tandem/ring applicator alone was used for the first insertion in most patients. Needles were added for the second insertion based on evaluation of the initial dosimetry. An interdisciplinary expert team reviewed and discussed the MR images and treatment plans. Dosimetry-trigger technique adaptation with the addition of needles for the second insertion improved target coverage in all patients with suboptimal dosimetry initially without compromising organ-at-risk (OAR) sparing. Target and OAR planning objectives were achieved in most patients. There were small or no systematic differences in tumor or OAR dosimetry between imaging/planning once per insertion vs. daily and only small random variations. Peer review and discussion of images, contours, and plans promoted learning and process development. Technique adaptation based on the initial dosimetry is an efficient approach to implementing MRgBT while gaining comfort with the use of needles. MR imaging and planning once per insertion is safe in most patients as long as applicator shifts, and large anatomical changes are excluded. Team learning is essential to building individual and programmatic competencies. Copyright © 2017 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

  16. Accuracy comparison among different machine learning techniques for detecting malicious codes

    Science.gov (United States)

    Narang, Komal

    2016-03-01

    In this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Naïve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.

  17. Mindfulness for Singers: The Effects of a Targeted Mindfulness Course on Learning Vocal Technique

    Science.gov (United States)

    Czajkowski, Anne-Marie L.; Greasley, Alinka E.

    2015-01-01

    This paper reports the development and implementation of a unique Mindfulness for Singers (MfS) course designed to improve singers' vocal technique. Eight university students completed the intervention. Five Facet Mindfulness Questionnaire (FFMQ) scores showed general improvement across all five facets of mindfulness. Qualitative results showed…

  18. The Effective Use of Symbols in Teaching Word Recognition to Children with Severe Learning Difficulties: A Comparison of Word Alone, Integrated Picture Cueing and the Handle Technique.

    Science.gov (United States)

    Sheehy, Kieron

    2002-01-01

    A comparison is made between a new technique (the Handle Technique), Integrated Picture Cueing, and a Word Alone Method. Results show using a new combination of teaching strategies enabled logographic symbols to be used effectively in teaching word recognition to 12 children with severe learning difficulties. (Contains references.) (Author/CR)

  19. Incorporating Service-Learning, Technology, and Research Supportive Teaching Techniques into the University Chemistry Classroom

    Science.gov (United States)

    Saitta, E. K. H.; Bowdon, M. A.; Geiger, C. L.

    2011-12-01

    Technology was integrated into service-learning activities to create an interactive teaching method for undergraduate students at a large research institution. Chemistry students at the University of Central Florida partnered with high school students at Crooms Academy of Information Technology in interactive service learning projects. The projects allowed UCF students to teach newly acquired content knowledge and build upon course lecture and lab exercises. Activities utilized the web-conferencing tool Adobe Connect Pro to enable interaction with high school students, many of whom have limited access to supplemental educational opportunities due to low socioeconomic status. Seventy chemistry I students created lessons to clarify high school students' misconceptions through the use of refutational texts. In addition, 21 UCF students enrolled in the chemistry II laboratory course acted as virtual lab partners with Crooms students in an interactive guided inquiry experiment focused on chemical kinetics. An overview of project's design, implementation, and assessments are detailed in the case study and serve as a model for future community partnerships. Emerging technologies are emphasized as well as a suggested set of best practices for future projects.

  20. The Da Vinci Xi and robotic radical prostatectomy-an evolution in learning and technique.

    Science.gov (United States)

    Goonewardene, S S; Cahill, D

    2017-06-01

    The da Vinci Xi robot has been introduced as the successor to the Si platform. The promise of the Xi is to open the door to new surgical procedures. For robotic-assisted radical prostatectomy (RARP)/pelvic surgery, the potential is better vision and longer instruments. How has the Xi impacted on operative and pathological parameters as indicators of surgical performance? This is a comparison of an initial series of 42 RARPs with the Xi system in 2015 with a series using the Si system immediately before Xi uptake in the same calendar year, and an Si series by the same surgeon synchronously as the Xi series using operative time, blood loss, and positive margins as surrogates of surgical performance. Subjectively and objectively, there is a learning curve to Xi uptake in longer operative times but no impact on T2 positive margins which are the most reflective single measure of RARP outcomes. Subjectively, the vision of the Xi is inferior to the Si system, and the integrated diathermy system and automated setup are quirky. All require experience to overcome. There is a learning curve to progress from the Si to Xi da Vinci surgical platforms, but this does not negatively impact the outcome.

  1. Identifying tropical dry forests extent and succession via the use of machine learning techniques

    Science.gov (United States)

    Li, Wei; Cao, Sen; Campos-Vargas, Carlos; Sanchez-Azofeifa, Arturo

    2017-12-01

    Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages.

  2. The Effect of Mnemonic and Mapping Techniques on L2 Vocabulary Learning

    Directory of Open Access Journals (Sweden)

    Abbas Ali Zarei

    2016-01-01

    Full Text Available The present study investigated the effects of selected presentation techniques including the keyword method, the peg word method, the loci method, argument mapping, concept mapping and mind mapping on L2 vocabulary comprehension and production. To this end, a sample of 151 Iranian female students from a public pre-university school in Islam Shahr was selected. They were assigned to six groups. Each group was randomly assigned to one of the afore-mentioned treatment conditions. After the experimental period, two post-tests in multiple choice and fill-in-the-blanks formats were administered to assess the participants’ vocabulary comprehension and production. Two independent One-Way ANOVA procedures were used to analyze the obtained data. The results showed that the differences among the effects of the above-mentioned techniques were statistically significant in both vocabulary comprehension and production. These findings can have implications for learners, teachers, and materials’ developers.

  3. A model for teaching and learning spinal thrust manipulation and its effect on participant confidence in technique performance.

    Science.gov (United States)

    Wise, Christopher H; Schenk, Ronald J; Lattanzi, Jill Black

    2016-07-01

    Despite emerging evidence to support the use of high velocity thrust manipulation in the management of lumbar spinal conditions, utilization of thrust manipulation among clinicians remains relatively low. One reason for the underutilization of these procedures may be related to disparity in training in the performance of these techniques at the professional and post professional levels. To assess the effect of using a new model of active learning on participant confidence in the performance of spinal thrust manipulation and the implications for its use in the professional and post-professional training of physical therapists. A cohort of 15 DPT students in their final semester of entry-level professional training participated in an active training session emphasizing a sequential partial task practice (SPTP) strategy in which participants engaged in partial task practice over several repetitions with different partners. Participants' level of confidence in the performance of these techniques was determined through comparison of pre- and post-training session surveys and a post-session open-ended interview. The increase in scores across all items of the individual pre- and post-session surveys suggests that this model was effective in changing overall participant perception regarding the effectiveness and safety of these techniques and in increasing student confidence in their performance. Interviews revealed that participants greatly preferred the SPTP strategy, which enhanced their confidence in technique performance. Results indicate that this new model of psychomotor training may be effective at improving confidence in the performance of spinal thrust manipulation and, subsequently, may be useful for encouraging the future use of these techniques in the care of individuals with impairments of the spine. Inasmuch, this method of instruction may be useful for training of physical therapists at both the professional and post-professional levels.

  4. We'll Make You a Better Teacher: Learning from Guitar Techniques

    Science.gov (United States)

    Greenbowe, Thomas J.

    2008-02-01

    It is worth noting that there are more resources and more uses of technology available world-wide to help individuals become better guitar players than there are resources available to help individuals become better science teachers. Providing resources and services to help individuals become effective chemistry teachers and improve their chemistry teaching and expand their range of techniques is a worthwhile endeavor. This commentary proposes that a new magazine should be developed and designed to complement and augment the Journal of Chemical Education , the Examinations Institute, the BCCEs, and programming at regional, national, and international meetings. We need to be making use of the expertise of chemical educators from around the world to convey the best practices of teaching chemistry. This magazine would feature topics directly relating to teaching chemistry in the classroom and it would include master teachers explaining and discussing chemistry education techniques. A Web site and perhaps a DVD would have digital movies of master chemistry teachers illustrating how they implement a specific technique with students. The Web site would serve as a repository for resources. It would serve as an alternative site for professional development.

  5. Machine learning techniques for medical diagnosis of diabetes using iris images.

    Science.gov (United States)

    Samant, Piyush; Agarwal, Ravinder

    2018-04-01

    Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for disease diagnosis is reliable and fastest growing field in biomedical. Proposed model is an attempt to evaluate diagnostic validity of an old complementary and alternative medicine technique, iridology for diagnosis of type-2 diabetes using soft computing methods. Investigation was performed over a close group of total 338 subjects (180 diabetic and 158 non-diabetic). Infra-red images of both the eyes were captured simultaneously. The region of interest from the iris image was cropped as zone corresponds to the position of pancreas organ according to the iridology chart. Statistical, texture and discrete wavelength transformation features were extracted from the region of interest. The results show best classification accuracy of 89.63% calculated from RF classifier. Maximum specificity and sensitivity were absorbed as 0.9687 and 0.988, respectively. Results have revealed the effectiveness and diagnostic significance of proposed model for non-invasive and automatic diabetes diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

    Directory of Open Access Journals (Sweden)

    H Kimura

    2009-04-01

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

  7. Classification of fMRI resting-state maps using machine learning techniques: A comparative study

    Science.gov (United States)

    Gallos, Ioannis; Siettos, Constantinos

    2017-11-01

    We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

  8. Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

    Science.gov (United States)

    Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

    The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

  9. Learning from social media: utilizing advanced data extraction techniques to understand barriers to breast cancer treatment.

    Science.gov (United States)

    Freedman, Rachel A; Viswanath, Kasisomayajula; Vaz-Luis, Ines; Keating, Nancy L

    2016-07-01

    Past examinations of breast cancer treatment barriers have typically included registry, claims-based, and smaller survey studies. We examined treatment barriers using a novel, comprehensive, social media analysis of online, candid discussions about breast cancer. Using an innovative toolset to search postings on social networks, message boards, patient communities, and topical sites, we performed a large-scale qualitative analysis. We examined the sentiments and barriers expressed about breast cancer treatments by Internet users during 1 year (2/1/14-1/31/15). We categorized posts based on thematic patterns and examined trends in discussions by race/ethnicity (white/black/Hispanic) when this information was available. We identified 1,024,041 unique posts related to breast cancer treatment. Overall, 57 % of posts expressed negative sentiments. Using machine learning software, we assigned treatment barriers for 387,238 posts (38 %). Barriers included emotional (23 % of posts), preferences and spiritual/religious beliefs (21 %), physical (18 %), resource (15 %), healthcare perceptions (9 %), treatment processes/duration (7 %), and relationships (7 %). Black and Hispanic (vs. white) users more frequently reported barriers related to healthcare perceptions, beliefs, and pre-diagnosis/diagnosis organizational challenges and fewer emotional barriers. Using a novel analysis of diverse social media users, we observed numerous breast cancer treatment barriers that differed by race/ethnicity. Social media is a powerful tool, allowing use of real-world data for qualitative research, capitalizing on the rich discussions occurring spontaneously online. Future research should focus on how to further employ and learn from this type of social intelligence research across all medical disciplines.

  10. Incorporating Experiential Learning Techniques to Improve Self-Efficacy in Clinical Special Care Dentistry Education.

    Science.gov (United States)

    Watters, Amber L; Stabulas-Savage, Jeanine; Toppin, James D; Janal, Malvin N; Robbins, Miriam R

    2015-09-01

    The New York University College of Dentistry has introduced a clinical rotation for fourth-year dental students that focuses on treating people with special health care needs (PSN). The aim of this study was to investigate the hypothesis that clinical experience in treating patients with special health care needs during predoctoral education is associated with increased self-assessed student ability and comfort and therefore self-efficacy. The study also investigated whether other characteristics, such as prior personal or volunteer experience with this population, service-mindedness, and/or the inclination to treat underserved populations, were associated with comfort in treating PSN. A survey was used to assess changes in students' perceived knowledge, beliefs, and attitudes regarding treating PSN before and after the clinical experience for July 2012-June 2013. The survey included questions about students' service-mindedness, comfort, perceptions of abilities of PSN and educational importance of learning to treat PSN, desire for clinical experience, and future intent or interest in treating PSN. Out of 364 students invited to participate, 127 surveys were returned, for a response rate of 34.9%. The results showed statistically significant increases on six items following training: impressions about the importance of oral health among PSN, comfort in treating people with cognitive disabilities and with medical complexities, intent to treat PSN in future practice, interest in including PSN in postgraduate training, and belief that PSN could be treated in the private practice setting. These students reported preferring to learn in the clinical setting over didactic instruction. This clinical experience was associated with improved self-efficacy in treating PSN and increased intentions to treat this population in future practice. Improvements were particularly evident among those with the least prior experience with PSN and were independent of other aspects of the

  11. Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study

    Directory of Open Access Journals (Sweden)

    Alberto Corbi

    2014-09-01

    Full Text Available Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal, relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI, detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below.

  12. “Computer Assisted Language Learning” (CALL

    Directory of Open Access Journals (Sweden)

    Nazlı Gündüz

    2005-10-01

    Full Text Available This article will provide an overview of computers; an overview of the history of CALL, itspros and cons, the internet, World Wide Web, Multimedia, and research related to the uses of computers in the language classroom. Also, it also aims to provide some background for the beginnerson using the Internet in language classes today. It discusses some of the common types of Internetactivities that are being used today, what the minimum requirements are for using the Internet forlanguage learning, and some easy activities you can adapt for your classes. Some special terminology related to computers will also be used in this paper. For example, computer assisted language learning(CALL refers to the sets of instructions which need to be loaded into the computer for it to be able to work in the language classroom. It should be borne in mind that CALL does not refer to the use of acomputer by a teacher to type out a worksheet or a class list or preparing his/her own teaching alone.Hardware refers to any computer equipment used, including the computer itself, the keyboard, screen (or the monitor, the disc-drive, and the printer. Software (computer programs refers to the sets of instructions which need to be loaded into the computer for it to be able to work.

  13. Developing a Virtual Teach-To-Goal™ Inhaler Technique Learning Module: A Mixed Methods Approach.

    Science.gov (United States)

    Wu, Meng; Woodrick, Nicole M; Arora, Vineet M; Farnan, Jeanne M; Press, Valerie G

    Most hospitalized patients with asthma or chronic obstructive pulmonary disease misuse respiratory inhalers. An in-person educational strategy, teach-to-goal (TTG), improves inpatients' inhaler technique. To develop an effective, portable education intervention that remains accessible to hospitalized patients postdischarge for reinforcement of proper inhaler technique. A mixed methods approach at an urban academic hospital was used to iteratively develop, modify, and test a virtual teach-to-goal ™ (V-TTG ™ ) educational intervention using patient end-user feedback. A survey examined access and willingness to use technology for self-management education. Focus groups evaluated patients' feedback on access, functionality, and quality of V-TTG ™ . Forty-eight participants completed the survey, with most reporting having Internet access; 77% used the Internet at home and 82% used the Internet at least once every few weeks. More than 80% reported that they were somewhat or very likely to use V-TTG ™ to gain skills to improve their health. Most participants reported smartphone access (73%); half owned laptop computers (52%). Participants with asthma versus chronic obstructive pulmonary disease were more likely to own a smartphone, have a data plan, and have daily Internet use (P platform and delivery, Internet access, and technological literacy; functionality-usefulness, content, and teaching strategy; and quality-clarity, ease of use, length, and likability. V-TTG ™ is a promising educational tool for improving patients' inhaler technique, iteratively developed and refined with patient input. Patients in our urban, academic hospital overwhelmingly reported access to platforms and willingness to use V-TTG ™ for health education. Copyright © 2017 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-07-01

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

  16. The difficult medical emergency call

    DEFF Research Database (Denmark)

    Møller, Thea Palsgaard; Kjærulff, Thora Majlund; Viereck, Søren

    2017-01-01

    BACKGROUND: Pre-hospital emergency care requires proper categorization of emergency calls and assessment of emergency priority levels by the medical dispatchers. We investigated predictors for emergency call categorization as "unclear problem" in contrast to "symptom-specific" categories and the ......BACKGROUND: Pre-hospital emergency care requires proper categorization of emergency calls and assessment of emergency priority levels by the medical dispatchers. We investigated predictors for emergency call categorization as "unclear problem" in contrast to "symptom-specific" categories...... and the effect of categorization on mortality. METHODS: Register-based study in a 2-year period based on emergency call data from the emergency medical dispatch center in Copenhagen combined with nationwide register data. Logistic regression analysis (N = 78,040 individuals) was used for identification...

  17. Criteria for Evaluating a Game-Based CALL Platform

    Science.gov (United States)

    Ní Chiaráin, Neasa; Ní Chasaide, Ailbhe

    2017-01-01

    Game-based Computer-Assisted Language Learning (CALL) is an area that currently warrants attention, as task-based, interactive, multimodal games increasingly show promise for language learning. This area is inherently multidisciplinary--theories from second language acquisition, games, and psychology must be explored and relevant concepts from…

  18. Integrating CALL into an Iranian EAP Course: Constraints and Affordances

    Science.gov (United States)

    Mehran, Parisa; Alizadeh, Mehrasa

    2015-01-01

    Iranian universities have recently displayed a growing interest in integrating Computer-Assisted Language Learning (CALL) into teaching/learning English. The English for Academic Purposes (EAP) context, however, is not keeping pace with the current changes since EAP courses are strictly text-based and exam-oriented, and little research has thus…

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  20. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

    Science.gov (United States)

    Yassin, Nisreen I R; Omran, Shaimaa; El Houby, Enas M F; Allam, Hemat

    2018-03-01

    The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation

    Science.gov (United States)

    Lee, Kit-Hang; Fu, Denny K.C.; Leong, Martin C.W.; Chow, Marco; Fu, Hing-Choi; Althoefer, Kaspar; Sze, Kam Yim; Yeung, Chung-Kwong

    2017-01-01

    Abstract Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments. PMID:29251567

  2. Exploration of machine learning techniques in predicting multiple sclerosis disease course.

    Directory of Open Access Journals (Sweden)

    Yijun Zhao

    Full Text Available To explore the value of machine learning methods for predicting multiple sclerosis disease course.1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening or not (non-worsening at up to five years after baseline visit. Support vector machines (SVM were used to build the classifier, and compared to logistic regression (LR using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%. Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

  3. Training veterinary students in shelter medicine: a service-learning community-classroom technique.

    Science.gov (United States)

    Stevens, Brenda J; Gruen, Margaret E

    2014-01-01

    Shelter medicine is a rapidly developing field of great importance, and shelters themselves provide abundant training opportunities for veterinary medical students. Students trained in shelter medicine have opportunities to practice zoonotic and species-specific infectious disease control, behavioral evaluation and management, primary care, animal welfare, ethics, and public policy issues. A range of sheltering systems now exists, from brick-and-mortar facilities to networks of foster homes with no centralized facility. Exposure to a single shelter setting may not allow students to understand the full range of sheltering systems that exist; a community-classroom approach introduces students to a diverse array of sheltering systems while providing practical experience. This article presents the details and results of a series of 2-week elective clinical rotations with a focus on field and service learning in animal shelters. The overall aim was to provide opportunities that familiarized students with sheltering systems and delivered primary-care training. Other priorities included increasing awareness of public health concerns and equipping students to evaluate shelters on design, operating protocols, infectious disease control, animal enrichment, and community outreach. Students were required to participate in rounds and complete a project that addressed a need recognized by them during the rotation. This article includes costs associated with the rotation, a blueprint for how the rotation was carried out at our institution, and details of shelters visited and animals treated, including a breakdown of treatments provided. Also discussed are the student projects and student feedback on this valuable clinical experience.

  4. "Digit anatomy": a new technique for learning anatomy using motor memory.

    Science.gov (United States)

    Oh, Chang-Seok; Won, Hyung-Sun; Kim, Kyong-Jee; Jang, Dong-Su

    2011-01-01

    Gestural motions of the hands and fingers are powerful tools for expressing meanings and concepts, and the nervous system has the capacity to retain multiple long-term motor memories, especially including movements of the hands. We developed many sets of successive movements of both hands, referred to as "digit anatomy," and made students practice the movements which express (1) the aortic arch, subclavian, and thoracoacromial arteries and their branches, (2) the celiac trunk, superior mesenteric artery and their branches, and formation of the portal vein, (3) the heart and the coronary arteries, and (4) the brachial, lumbar, and sacral plexuses. A feedback survey showed that digit anatomy was helpful for the students not only in memorizing anatomical structures but also in understanding their functions. Out of 40 students, 34 of them who learned anatomy with the help of digit anatomy were "very satisfied" or "generally satisfied" with this new teaching method. Digit anatomy that was used to express the aortic arch, subclavian, and thoracoacromial arteries and their branches was more helpful than those representing other structures. Although the movements of digit anatomy are expected to be remembered longer than the exact meaning of each movement, invoking the motor memory of the movement may help to make relearning of the same information easier and faster in the future. Copyright © 2011 American Association of Anatomists.

  5. Control of a reactive batch distillation process using an iterative learning technique

    International Nuclear Information System (INIS)

    Ahn, Hyunsoo; Lee, Kwang Soon; Kim, Mansuk; Lee, Juhyun

    2014-01-01

    Quadratic criterion-based iterative learning control (QILC) was applied to a numerical reactive batch distillation process, in which methacrylic anhydride (MAN) is produced through the reaction of methacrylic acid with acetic anhydride. The role of distillation is to shift the equilibrium conversion toward the direction of the product by removing acetic acid (AcH), a by-product of the reaction. Two temperatures at both ends of the column were controlled by individual control loops. A nonlinear PID controller manipulating the reflux ratio was employed to regulate the top temperature at the boiling point of AcH. A constrained QILC was used for the tracking of the reactor temperature. A time-varying reference trajectory for the reactor temperature that satisfies the target conversion and purity of MAN was obtained through repeated simulations and confirmation experiments in the pilot plant. The QILC achieved satisfactory tracking in several batch runs with gentle control movements, while the PID control as a substitute of the QILC in a comparative study exhibited unacceptable performance

  6. Comparative Analysis of River Flow Modelling by Using Supervised Learning Technique

    Science.gov (United States)

    Ismail, Shuhaida; Mohamad Pandiahi, Siraj; Shabri, Ani; Mustapha, Aida

    2018-04-01

    The goal of this research is to investigate the efficiency of three supervised learning algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550 square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Wavelet Regression (WR). The forecasting models predict the monthly river flow obtained from the three models individually for river flow data and the accuracy of the all models were then compared against each other. The monthly river flow of the said river has been forecasted using these three models. The obtained results were compared and statistically analysed. Then, the results of this analytical comparison showed that LSSVM model is more precise in the monthly river flow forecasting. It was found that LSSVM has he higher r with the value of 0.934 compared to other models. This indicate that LSSVM is more accurate and efficient as compared to the ANN and WR model.

  7. Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.

    Science.gov (United States)

    Nilsson, M; Herlin, A H; Ardö, H; Guzhva, O; Åström, K; Bergsten, C

    2015-11-01

    In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640 × 480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness.

  8. CERN Technical Training 2002: Learning for the LHC ! HeREF-2002 : Helium Refrigeration Techniques

    CERN Multimedia

    Davide Vitè

    2002-01-01

    Theory, Technology, Maintenance and Control of Helium Refrigerators HeREF-2002 is a new course, in the framework of the 2002 Technical Training Programme, that will provide a complete introduction to Helium refrigeration, with a practical approach to theory, technology, maintenance and control of Helium refrigeration installations. Theoretical aspects and equations will be limited to a minimum. HeREF-2002 targets an audience of technicians and operators of Helium refrigeration plants at CERN, as well as physicists and engineers needing an overview of current Helium refrigeration techniques. HeREF-2002 will address, among other, issues related to component technology, installation maintenance, process control and Helium purity. A commented visit to a couple of CERN Helium refrigeration or liquefaction plants will also take place. Duration: 7 half days (5 mornings and 2 afternoons), 21-25 October 2002. Estimated cost: 300.- CHF Language: Bilingual English-French. The course support will be in English, the...

  9. CERN Technical Training 2002: Learning for the LHC! HEREF-2002 : HELIUM REFRIGERATION TECHNIQUES

    CERN Multimedia

    Davide Vitè

    2002-01-01

    Theory, Technology, Maintenance and Control of Helium Refrigerators HeREF-2002 is a new course, in the framework of the 2002 Technical Training Programme, that will provide a complete introduction to Helium refrigeration, with a practical approach to theory, technology, maintenance and control of Helium refrigeration installations. Theoretical aspects and equations will be limited to a minimum. HeREF-2002 targets an audience of technicians and operators of Helium refrigeration plants at CERN, as well as physicists and engineers needing an overview of current Helium refrigeration techniques. HeREF-2002 will address, among other, issues related to component technology, installation maintenance, process control and Helium purity. A commented visit to a couple of CERN Helium refrigeration or liquefaction plants will also take place. Duration: 7 half days (5 mornings and 2 afternoons), 21-25 October, 2002. Estimated cost: 300.- CHF Language: Bilingual English-French. The course support will be in English, the ...

  10. Application of Mind Map-based abstracting technique in pedagogical strategy for ESP teaching/learning

    Directory of Open Access Journals (Sweden)

    Ekaterina Choporova

    2014-09-01

    Full Text Available The work presents some theoretical and practical results of the abstracting practice carried out by the teachers and cadets of Voronezh Institute of the Ministry of Interior of Russia. The sources used in the experiment were of British and American origin, equally authentic, and were mainly of engineering content because of the cadets’ speciality. The main purpose of the experiment was focused on the primary source adequate abstract making as a product of a keen understanding of social and professional aspects, views, and anticipations of English-speaking nations. The authors analyzed a number of current approaches towards abstract making procedures and offered an original system of the education strategy by means of Mind Map building technique.

  11. 3D Cloud Field Prediction using A-Train Data and Machine Learning Techniques

    Science.gov (United States)

    Johnson, C. L.

    2017-12-01

    Validation of cloud process parameterizations used in global climate models (GCMs) would greatly benefit from observed 3D cloud fields at the size comparable to that of a GCM grid cell. For the highest resolution simulations, surface grid cells are on the order of 100 km by 100 km. CloudSat/CALIPSO data provides 1 km width of detailed vertical cloud fraction profile (CFP) and liquid and ice water content (LWC/IWC). This work utilizes four machine learning algorithms to create nonlinear regressions of CFP, LWC, and IWC data using radiances, surface type and location of measurement as predictors and applies the regression equations to off-track locations generating 3D cloud fields for 100 km by 100 km domains. The CERES-CloudSat-CALIPSO-MODIS (C3M) merged data set for February 2007 is used. Support Vector Machines, Artificial Neural Networks, Gaussian Processes and Decision Trees are trained on 1000 km of continuous C3M data. Accuracy is computed using existing vertical profiles that are excluded from the training data and occur within 100 km of the training data. Accuracy of the four algorithms is compared. Average accuracy for one day of predicted data is 86% for the most successful algorithm. The methodology for training the algorithms, determining valid prediction regions and applying the equations off-track is discussed. Predicted 3D cloud fields are provided as inputs to the Ed4 NASA LaRC Fu-Liou radiative transfer code and resulting TOA radiances compared to observed CERES/MODIS radiances. Differences in computed radiances using predicted profiles and observed radiances are compared.

  12. Flip-J: Development of the System for Flipped Jigsaw Supported Language Learning

    Science.gov (United States)

    Yamada, Masanori; Goda, Yoshiko; Hata, Kojiro; Matsukawa, Hideya; Yasunami, Seisuke

    2016-01-01

    This study aims to develop and evaluate a language learning system supported by the "flipped jigsaw" technique, called "Flip-J". This system mainly consists of three functions: (1) the creation of a learning material database, (2) allocation of learning materials, and (3) formation of an expert and jigsaw group. Flip-J was…

  13. House Calls in Private Practice.

    Science.gov (United States)

    Whittington, Ronaele

    1985-01-01

    Relates the experiences of a social worker in private practice who offered house calls as an ongoing setting for counseling and psychotherapy to individuals and families. Describes advantages and disadvantages, liability, and target populations. (JAC)

  14. Support techniques in the learning process: the case of antiplagiarism systems

    Directory of Open Access Journals (Sweden)

    Jorge Matías-Pereda

    2013-02-01

    Full Text Available 0 0 1 194 1069 USAL 8 2 1261 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} The widespread use of the Internet has given university students access to information resources on a level never experienced in the past. The bad news is that this issue increased the inappropriate use of those resources. In this paper we discuss the experience of using the Turnitin anti-plagiarism license. In total, 358 assignments were analyzed. We partly noticed a learning effect among students between assignment deliveries. The main conclusions drawn from the use of Turnitin anti-plagiarism license are positive and we have seen how students have become aware of the existence of this tool in the evaluation process, which in turn has led to greater attention and dedication to the writing process and to the development of ideas and concepts. It has also allowed to diminish suspicions about the authenticity of the work handed by the students, valuing the real effort, improving their relationship with the teacher reinforcing the authority of the latter. The incorporation of the license can also lead to some negative aspects: The generation of a feeling of rejection by the student by the exaggerated perception of control, the adaptation of written work to Turnitin requirements and the legal vacuum surrounding the dissemination of student work.

  15. Coconut Model for Learning First Steps of Craniotomy Techniques and Cerebrospinal Fluid Leak Avoidance.

    Science.gov (United States)

    Drummond-Braga, Bernardo; Peleja, Sebastião Berquó; Macedo, Guaracy; Drummond, Carlos Roberto S A; Costa, Pollyana H V; Garcia-Zapata, Marco T; Oliveira, Marcelo Magaldi

    2016-12-01

    Neurosurgery simulation has gained attention recently due to changes in the medical system. First-year neurosurgical residents in low-income countries usually perform their first craniotomy on a real subject. Development of high-fidelity, cheap, and largely available simulators is a challenge in residency training. An original model for the first steps of craniotomy with cerebrospinal fluid leak avoidance practice using a coconut is described. The coconut is a drupe from Cocos nucifera L. (coconut tree). The green coconut has 4 layers, and some similarity can be seen between these layers and the human skull. The materials used in the simulation are the same as those used in the operating room. The coconut is placed on the head holder support with the face up. The burr holes are made until endocarp is reached. The mesocarp is dissected, and the conductor is passed from one hole to the other with the Gigli saw. The hook handle for the wire saw is positioned, and the mesocarp and endocarp are cut. After sawing the 4 margins, mesocarp is detached from endocarp. Four burr holes are made from endocarp to endosperm. Careful dissection of the endosperm is done, avoiding liquid albumen leak. The Gigli saw is passed through the trephine holes. Hooks are placed, and the endocarp is cut. After cutting the 4 margins, it is dissected from the endosperm and removed. The main goal of the procedure is to remove the endocarp without fluid leakage. The coconut model for learning the first steps of craniotomy and cerebrospinal fluid leak avoidance has some limitations. It is more realistic while trying to remove the endocarp without damage to the endosperm. It is also cheap and can be widely used in low-income countries. However, the coconut does not have anatomic landmarks. The mesocarp makes the model less realistic because it has fibers that make the procedure more difficult and different from a real craniotomy. The model has a potential pedagogic neurosurgical application for

  16. Learning from the experts: exploring playground experience and activities using a write and draw technique.

    Science.gov (United States)

    Knowles, Zoe Rebecca; Parnell, Daniel; Stratton, Gareth; Ridgers, Nicola Diane

    2013-03-01

    Qualitative research into the effect of school recess on children's physical activity is currently limited. This study used a write and draw technique to explore children's perceptions of physical activity opportunities during recess. 299 children age 7-11 years from 3 primary schools were enlisted. Children were grouped into Years 3 & 4 and Years 5 & 6 and completed a write and draw task focusing on likes and dislikes. Pen profiles were used to analyze the data. Results indicated 'likes' focused on play, positive social interaction, and games across both age groups but showed an increasing dominance of games with an appreciation for being outdoors with age. 'Dislikes' focused on dysfunctional interactions linked with bullying, membership, equipment, and conflict for playground space. Football was a dominant feature across both age groups and 'likes/dislikes' that caused conflict and dominated the physically active games undertaken. Recess was important for the development of conflict management and social skills and contributed to physical activity engagement. The findings contradict suggestions that time spent in recess should be reduced because of behavioral issues.

  17. Assessment techniques for a learning-centered curriculum: evaluation design for adventures in supercomputing

    Energy Technology Data Exchange (ETDEWEB)

    Helland, B. [Ames Lab., IA (United States); Summers, B.G. [Oak Ridge National Lab., TN (United States)

    1996-09-01

    As the classroom paradigm shifts from being teacher-centered to being learner-centered, student assessments are evolving from typical paper and pencil testing to other methods of evaluation. Students should be probed for understanding, reasoning, and critical thinking abilities rather than their ability to return memorized facts. The assessment of the Department of Energy`s pilot program, Adventures in Supercomputing (AiS), offers one example of assessment techniques developed for learner-centered curricula. This assessment has employed a variety of methods to collect student data. Methods of assessment used were traditional testing, performance testing, interviews, short questionnaires via email, and student presentations of projects. The data obtained from these sources have been analyzed by a professional assessment team at the Center for Children and Technology. The results have been used to improve the AiS curriculum and establish the quality of the overall AiS program. This paper will discuss the various methods of assessment used and the results.

  18. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Science.gov (United States)

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  19. A New Project-Based Curriculum of Design Thinking with Systems Engineering Techniques

    NARCIS (Netherlands)

    Haruyama, S.; Kim, S.K.; Beiter, K.A.; Dijkema, G.P.J.; De Weck, O.L.

    2012-01-01

    We developed a new education curriculum called "ALPS" (Active Learning Project Sequence) at Keio University that emphasizes team project-based learning and design thinking with systems engineering techniques. ALPS is a 6 month course, in which students work as a team and design and propose

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-03-20

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