WorldWideScience

Sample records for artificial learning approaches

  1. Machine learning an artificial intelligence approach

    CERN Document Server

    Banerjee, R; Bradshaw, Gary; Carbonell, Jaime Guillermo; Mitchell, Tom Michael; Michalski, Ryszard Spencer

    1983-01-01

    Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV a

  2. A Framework for Intelligent Instructional Systems: An Artificial Intelligence Machine Learning Approach.

    Science.gov (United States)

    Becker, Lee A.

    1987-01-01

    Presents and develops a general model of the nature of a learning system and a classification for learning systems. Highlights include the relationship between artificial intelligence and cognitive psychology; computer-based instructional systems; intelligent instructional systems; and the role of the learner's knowledge base in an intelligent…

  3. Reinforcement Learning Based Artificial Immune Classifier

    Directory of Open Access Journals (Sweden)

    Mehmet Karakose

    2013-01-01

    Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

  4. Study and reflections on the functional and organizational role of neuromessenger nitric oxide in learning: An artificial and biological approach

    Science.gov (United States)

    Suárez Araujo, C. P.

    2000-05-01

    We present in this work a theoretical and conceptual study and some reflections on a fundamental aspect concerning with the structure and brain function: the Cellular Communication. The main interests of our study are the signal transmission mechanisms and the neuronal mechanisms responsible to learning. We propose the consideration of a new kind of communication mechanisms, different to the synaptic transmission, "Diffusion or Volume Transmission." This new alternative is based on a diffusing messenger as nitric oxide (NO). Our study aims towards the design of a conceptual framework, which covers implications of NO in the artificial neural networks (ANNs), both in neural architecture and learning processing. This conceptual frame might be able to provide possible biological support for many aspects of ANNs and to generate new concepts to improve the structure and operation of them. Some of these new concepts are The Fast Diffusion Neural Propagation (FDNP), the Diffuse Neighborhood (DNB), (1), the Diffusive Hybrid Neuromodulation (DHN), the Virtual Weights. Finally we will propose a new mathematical formulation for the Hebb learning law, taking into account the NO effect. Along the same lines, we will reflect on the possibility of a new formal framework for learning processes in ANNs, which consist of slow and fast learning concerning with co-operation between the classical neurotransmission and FDNP. We will develop this work from a computational neuroscience point of view, proposing a global study framework of diffusion messenger NO (GSFNO), using a hybrid natural/artificial approach. Finally it is important to note that we can consider this paper the first paper of a set of scientific work on nitric oxide (NO) and artificial neural networks (ANNs): NO and ANNs Series. We can say that this paper has a character of search and query on both subjects their implications and co-existence.

  5. Artificial Intelligence and Second Language Learning: An Efficient Approach to Error Remediation

    Science.gov (United States)

    Dodigovic, Marina

    2007-01-01

    While theoretical approaches to error correction vary in the second language acquisition (SLA) literature, most sources agree that such correction is useful and leads to learning. While some point out the relevance of the communicative context in which the correction takes place, others stress the value of consciousness-raising. Trying to…

  6. Modular, Hierarchical Learning By Artificial Neural Networks

    Science.gov (United States)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  7. Smart-system of distance learning of visually impaired people based on approaches of artificial intelligence

    Science.gov (United States)

    Samigulina, Galina A.; Shayakhmetova, Assem S.

    2016-11-01

    Research objective is the creation of intellectual innovative technology and information Smart-system of distance learning for visually impaired people. The organization of the available environment for receiving quality education for visually impaired people, their social adaptation in society are important and topical issues of modern education.The proposed Smart-system of distance learning for visually impaired people can significantly improve the efficiency and quality of education of this category of people. The scientific novelty of proposed Smart-system is using intelligent and statistical methods of processing multi-dimensional data, and taking into account psycho-physiological characteristics of perception and awareness learning information by visually impaired people.

  8. Probabilistic machine learning and artificial intelligence.

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-28

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  9. Probabilistic machine learning and artificial intelligence

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-01

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  10. Meta-Learning Evolutionary Artificial Neural Networks

    OpenAIRE

    Abraham, Ajith

    2004-01-01

    In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventionally designed artificial neural networks for function approximation problems. To evaluate the compara...

  11. An artificial life approach to language.

    Science.gov (United States)

    Parisi, D

    1997-08-01

    The aim of the paper is to show that an Artificial Life approach to language tends to change the research agenda on language which has been shared by both the symbolic paradigm and classical connectionism. Artificial Life Neural Networks (ALNNs) are different from classical connectionist networks because they interact with an independent physical environment; are subject to evolutionary, developmental, and cultural change, and not only to learning; and are part of organisms that have a physical body, have a life (are born, develop, and die), and are members of genetic and sometimes, cultural populations. Using ALNNs to study language shifts the emphasis from research on linguistic forms and laboratory-like tasks to the investigation of the emergence and transmission of language, the use of language, its role in cognition, and language as a populational rather than as an individual phenomenon.

  12. Building Artificial Vision Systems with Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    LeCun, Yann [New York University

    2011-02-23

    Three questions pose the next challenge for Artificial Intelligence (AI), robotics, and neuroscience. How do we learn perception (e.g. vision)? How do we learn representations of the perceptual world? How do we learn visual categories from just a few examples?

  13. Artificial intelligence: Learning to see and act

    Science.gov (United States)

    Schölkopf, Bernhard

    2015-02-01

    An artificial-intelligence system uses machine learning from massive training sets to teach itself to play 49 classic computer games, demonstrating that it can adapt to a variety of tasks. See Letter p.529

  14. A new approach to artificial neural networks.

    Science.gov (United States)

    Baptista Filho, B D; Cabral, E L; Soares, A J

    1998-01-01

    A novel approach to artificial neural networks is presented. The philosophy of this approach is based on two aspects: the design of task-specific networks, and a new neuron model with multiple synapses. The synapses' connective strengths are modified through selective and cumulative processes conducted by axo-axonic connections from a feedforward circuit. This new concept was applied to the position control of a planar two-link manipulator exhibiting excellent results on learning capability and generalization when compared with a conventional feedforward network. In the present paper, the example shows only a network developed from a neuronal reflexive circuit with some useful artifices, nevertheless without the intention of covering all possibilities devised.

  15. An entropy model for artificial grammar learning

    Directory of Open Access Journals (Sweden)

    Emmanuel Pothos

    2010-06-01

    Full Text Available A model is proposed to characterize the type of knowledge acquired in Artificial Grammar Learning (AGL. In particular, Shannon entropy is employed to compute the complexity of different test items in an AGL task, relative to the training items. According to this model, the more predictable a test item is from the training items, the more likely it is that this item should be selected as compatible with the training items. The predictions of the entropy model are explored in relation to the results from several previous AGL datasets and compared to other AGL measures. This particular approach in AGL resonates well with similar models in categorization and reasoning which also postulate that cognitive processing is geared towards the reduction of entropy.

  16. Visual Feature Learning in Artificial Grammar Classification

    Science.gov (United States)

    Chang, Grace Y.; Knowlton, Barbara J.

    2004-01-01

    The Artificial Grammar Learning task has been used extensively to assess individuals' implicit learning capabilities. Previous work suggests that participants implicitly acquire rule-based knowledge as well as exemplar-specific knowledge in this task. This study investigated whether exemplar-specific knowledge acquired in this task is based on the…

  17. Emerging Artificial Societies Through Learning

    NARCIS (Netherlands)

    Gilbert, N.; Besten, M.; Bontovics, A.; Craenen, B.G.W.; Divina, F.; Eiben, A.E.; Griffioen, R.; Hévízi, G.; Lõrincz, A.; Paechter, B.; Schuster, S.; Schut, M.C.; Tzolov, C.; Vogt, P.; Yang, L.

    2006-01-01

    The NewTies project is implementing a simulation in which societies of agents are expected to develop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are intended

  18. Visual feature learning in artificial grammar classification.

    Science.gov (United States)

    Chang, Grace Y; Knowlton, Barbara J

    2004-05-01

    The Artificial Grammar Learning task has been used extensively to assess individuals' implicit learning capabilities. Previous work suggests that participants implicitly acquire rule-based knowledge as well as exemplar-specific knowledge in this task. This study investigated whether exemplar-specific knowledge acquired in this task is based on the visual features of the exemplars. When a change in the font and case occurred between study and test, there was no effect on sensitivity to grammatical rules in classification judgments. However, such a change did virtually eliminate sensitivity to training frequencies of letter bigrams and trigrams (chunk strength) in classification judgments. Performance of a secondary task during study eliminated this font sensitivity and generally reduced the contribution of chunk strength knowledge. The results are consistent with the idea that perceptual fluency makes a contribution to artificial grammar judgments.

  19. Measuring strategic control in artificial grammar learning.

    Science.gov (United States)

    Norman, Elisabeth; Price, Mark C; Jones, Emma

    2011-12-01

    In response to concerns with existing procedures for measuring strategic control over implicit knowledge in artificial grammar learning (AGL), we introduce a more stringent measurement procedure. After two separate training blocks which each consisted of letter strings derived from a different grammar, participants either judged the grammaticality of novel letter strings with respect to only one of these two grammars (pure-block condition), or had the target grammar varying randomly from trial to trial (novel mixed-block condition) which required a higher degree of conscious flexible control. Random variation in the colour and font of letters was introduced to disguise the nature of the rule and reduce explicit learning. Strategic control was observed both in the pure-block and mixed-block conditions, and even among participants who did not realise the rule was based on letter identity. This indicated detailed strategic control in the absence of explicit learning.

  20. Proactive learning for artificial cognitive systems

    Science.gov (United States)

    Lee, Soo-Young

    2010-04-01

    The Artificial Cognitive Systems (ACS) will be developed for human-like functions such as vision, auditory, inference, and behavior. Especially, computational models and artificial HW/SW systems will be devised for Proactive Learning (PL) and Self-Identity (SI). The PL model provides bilateral interactions between robot and unknown environment (people, other robots, cyberspace). For the situation awareness in unknown environment it is required to receive audiovisual signals and to accumulate knowledge. If the knowledge is not enough, the PL should improve by itself though internet and others. For human-oriented decision making it is also required for the robot to have self-identify and emotion. Finally, the developed models and system will be mounted on a robot for the human-robot co-existing society. The developed ACS will be tested against the new Turing Test for the situation awareness. The Test problems will consist of several video clips, and the performance of the ACSs will be compared against those of human with several levels of cognitive ability.

  1. Abstraction processes in artificial grammar learning.

    Science.gov (United States)

    Shanks, D R; Johnstone, T; Staggs, L

    1997-02-01

    Four experiments explored the extent the extent to which abstract knowledge may underlie subjects' performance when asked to judge the grammaticality of letter strings generated from an artificial grammar. In Experiment 1 and 2 subjects studied grammatical strings instantiated with one set of letters and were then tested on grammatical and ungrammatical strings formed either from the same or a changed letter-set. Even with a change of letter-set, subjects were found to be sensitive to a variety of violation of the grammar. In Experiments 3 and 4, the critical manipulation involved the way in which the training strings were studied: an incidental learning procedure was used for some subjects, and others engaged in an explicit code-breaking task to try to learn the rules of the grammar. When strings were generated from a biconditional (Experiment 4) but not from a standard finite-state grammar (Experiment 3), grammaticality judgements for test strings were independent of their surface similarity to specific studied strings. Overall, the results suggest that transfer in this simple memory task is mediated at least to some extent by abstract knowledge.

  2. Recent Theoretical Approaches to Minimal Artificial Cells

    Directory of Open Access Journals (Sweden)

    Fabio Mavelli

    2014-05-01

    Full Text Available Minimal artificial cells (MACs are self-assembled chemical systems able to mimic the behavior of living cells at a minimal level, i.e. to exhibit self-maintenance, self-reproduction and the capability of evolution. The bottom-up approach to the construction of MACs is mainly based on the encapsulation of chemical reacting systems inside lipid vesicles, i.e. chemical systems enclosed (compartmentalized by a double-layered lipid membrane. Several researchers are currently interested in synthesizing such simple cellular models for biotechnological purposes or for investigating origin of life scenarios. Within this context, the properties of lipid vesicles (e.g., their stability, permeability, growth dynamics, potential to host reactions or undergo division processes… play a central role, in combination with the dynamics of the encapsulated chemical or biochemical networks. Thus, from a theoretical standpoint, it is very important to develop kinetic equations in order to explore first—and specify later—the conditions that allow the robust implementation of these complex chemically reacting systems, as well as their controlled reproduction. Due to being compartmentalized in small volumes, the population of reacting molecules can be very low in terms of the number of molecules and therefore their behavior becomes highly affected by stochastic effects both in the time course of reactions and in occupancy distribution among the vesicle population. In this short review we report our mathematical approaches to model artificial cell systems in this complex scenario by giving a summary of three recent simulations studies on the topic of primitive cell (protocell systems.

  3. Layered learning of soccer robot based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

  4. Does Artificial Tutoring Foster Inquiry Based Learning?

    Science.gov (United States)

    Schmoelz, Alexander; Swertz, Christian; Forstner, Alexandra; Barberi, Alessandro

    2014-01-01

    This contribution looks at the Intelligent Tutoring Interface for Technology Enhanced Learning, which integrates multistage-learning and inquiry-based learning in an adaptive e-learning system. Based on a common pedagogical ontology, adaptive e-learning systems can be enabled to recommend learning objects and activities, which follow inquiry-based…

  5. Auditory artificial grammar learning in macaque and marmoset monkeys.

    Science.gov (United States)

    Wilson, Benjamin; Slater, Heather; Kikuchi, Yukiko; Milne, Alice E; Marslen-Wilson, William D; Smith, Kenny; Petkov, Christopher I

    2013-11-27

    Artificial grammars (AG) are designed to emulate aspects of the structure of language, and AG learning (AGL) paradigms can be used to study the extent of nonhuman animals' structure-learning capabilities. However, different AG structures have been used with nonhuman animals and are difficult to compare across studies and species. We developed a simple quantitative parameter space, which we used to summarize previous nonhuman animal AGL results. This was used to highlight an under-studied AG with a forward-branching structure, designed to model certain aspects of the nondeterministic nature of word transitions in natural language and animal song. We tested whether two monkey species could learn aspects of this auditory AG. After habituating the monkeys to the AG, analysis of video recordings showed that common marmosets (New World monkeys) differentiated between well formed, correct testing sequences and those violating the AG structure based primarily on simple learning strategies. By comparison, Rhesus macaques (Old World monkeys) showed evidence for deeper levels of AGL. A novel eye-tracking approach confirmed this result in the macaques and demonstrated evidence for more complex AGL. This study provides evidence for a previously unknown level of AGL complexity in Old World monkeys that seems less evident in New World monkeys, which are more distant evolutionary relatives to humans. The findings allow for the development of both marmosets and macaques as neurobiological model systems to study different aspects of AGL at the neuronal level.

  6. Machine Learning Optimization of Evolvable Artificial Cells

    DEFF Research Database (Denmark)

    Caschera, F.; Rasmussen, S.; Hanczyc, M.

    2011-01-01

    An evolvable artificial cell is a chemical or biological complex system assembled in laboratory. The system is rationally designed to show life-like properties. In order to achieve an optimal design for the emergence of minimal life, a high dimensional space of possible experimental combinations...... that artificial cells requires. In addition a replication cycle of oil in water emulsions is presented. They represent the container for the artificial cells. (C) Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V....

  7. Consequences of Lexical Stress on Learning an Artificial Lexicon

    Science.gov (United States)

    Creel, Sarah C.; Tanenhaus, Michael K.; Aslin, Richard N.

    2006-01-01

    Four experiments examined effects of lexical stress on lexical access for recently learned words. Participants learned artificial lexicons (48 words) containing phonologically similar items and were tested on their knowledge in a 4-alternative forced-choice (4AFC) referent-selection task. Lexical stress differences did not reduce confusions…

  8. The Relationship between Artificial and Second Language Learning

    Science.gov (United States)

    Ettlinger, Marc; Morgan-Short, Kara; Faretta-Stutenberg, Mandy; Wong, Patrick C. M.

    2016-01-01

    Artificial language learning (ALL) experiments have become an important tool in exploring principles of language and language learning. A persistent question in all of this work, however, is whether ALL engages the linguistic system and whether ALL studies are ecologically valid assessments of natural language ability. In the present study, we…

  9. Solution to reinforcement learning problems with artificial potential field

    Institute of Scientific and Technical Information of China (English)

    XIE Li-juan; XIE Guang-rong; CHEN Huan-wen; LI Xiao-li

    2008-01-01

    A novel method was designed to solve reinforcement learning problems with artificial potential field. Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field(APF), which was a very appropriate method to model a reinforcement learning problem. Secondly, a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept. The performance of this new method was tested by a gridworld problem named as key and door maze. The experimental results show that within 45 trials, good and deterministic policies are found in almost all simulations. In comparison with WIERING's HQ-learning system which needs 20 000 trials for stable solution, the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning. Therefore, the new method is simple and effective to give an optimal solution to the reinforcement learning problem.

  10. Semantics boosts syntax in artificial grammar learning tasks with recursion.

    Science.gov (United States)

    Fedor, Anna; Varga, Máté; Szathmáry, Eörs

    2012-05-01

    Center-embedded recursion (CER) in natural language is exemplified by sentences such as "The malt that the rat ate lay in the house." Parsing center-embedded structures is in the focus of attention because this could be one of the cognitive capacities that make humans distinct from all other animals. The ability to parse CER is usually tested by means of artificial grammar learning (AGL) tasks, during which participants have to infer the rule from a set of artificial sentences. One of the surprising results of previous AGL experiments is that learning CER is not as easy as had been thought. We hypothesized that because artificial sentences lack semantic content, semantics could help humans learn the syntax of center-embedded sentences. To test this, we composed sentences from 4 vocabularies of different degrees of semantic content due to 3 factors (familiarity, meaning of words, and semantic relationship between words). According to our results, these factors have no effect one by one but they make learning significantly faster when combined. This leads to the assumption that there were different mechanisms at work when CER was parsed in natural and in artificial languages. This finding questions the suitability of AGL tasks with artificial vocabularies for studying the learning and processing of linguistic CER.

  11. Knowledge representation an approach to artificial intelligence

    CERN Document Server

    Bench-Capon, TJM

    1990-01-01

    Although many texts exist offering an introduction to artificial intelligence (AI), this book is unique in that it places an emphasis on knowledge representation (KR) concepts. It includes small-scale implementations in PROLOG to illustrate the major KR paradigms and their developments.****back cover copy:**Knowledge representation is at the heart of the artificial intelligence enterprise: anyone writing a program which seeks to work by encoding and manipulating knowledge needs to pay attention to the scheme whereby he will represent the knowledge, and to be aware of the consequences of the ch

  12. The Relationship Between Artificial and Second Language Learning.

    Science.gov (United States)

    Ettlinger, Marc; Morgan-Short, Kara; Faretta-Stutenberg, Mandy; Wong, Patrick C M

    2016-05-01

    Artificial language learning (ALL) experiments have become an important tool in exploring principles of language and language learning. A persistent question in all of this work, however, is whether ALL engages the linguistic system and whether ALL studies are ecologically valid assessments of natural language ability. In the present study, we considered these questions by examining the relationship between performance in an ALL task and second language learning ability. Participants enrolled in a Spanish language class were evaluated using a number of different measures of Spanish ability and classroom performance, which was compared to IQ and a number of different measures of ALL performance. The results show that success in ALL experiments, particularly more complex artificial languages, correlates positively with indices of L2 learning even after controlling for IQ. These findings provide a key link between studies involving ALL and our understanding of second language learning in the classroom.

  13. Application of artificial neural network with extreme learning machine for economic growth estimation

    Science.gov (United States)

    Milačić, Ljubiša; Jović, Srđan; Vujović, Tanja; Miljković, Jovica

    2017-01-01

    The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.

  14. The P600 in Implicit Artificial Grammar Learning

    Science.gov (United States)

    Silva, Susana; Folia, Vasiliki; Hagoort, Peter; Petersson, Karl Magnus

    2017-01-01

    The suitability of the artificial grammar learning (AGL) paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax-related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features (e.g.,…

  15. Learning to Manipulate and Categorize in Human and Artificial Agents

    Science.gov (United States)

    Morlino, Giuseppe; Gianelli, Claudia; Borghi, Anna M.; Nolfi, Stefano

    2015-01-01

    This study investigates the acquisition of integrated object manipulation and categorization abilities through a series of experiments in which human adults and artificial agents were asked to learn to manipulate two-dimensional objects that varied in shape, color, weight, and color intensity. The analysis of the obtained results and the…

  16. "Memory foam" approach to unsupervised learning

    CERN Document Server

    Janson, Natalia B

    2011-01-01

    We propose an alternative approach to construct an artificial learning system, which naturally learns in an unsupervised manner. Its mathematical prototype is a dynamical system, which automatically shapes its vector field in response to the input signal. The vector field converges to a gradient of a multi-dimensional probability density distribution of the input process, taken with negative sign. The most probable patterns are represented by the stable fixed points, whose basins of attraction are formed automatically. The performance of this system is illustrated with musical signals.

  17. A Hybrid Approach Towards Intrusion Detection Based on Artificial Immune System and Soft Computing

    CERN Document Server

    Sanyal, Sugata

    2012-01-01

    A number of works in the field of intrusion detection have been based on Artificial Immune System and Soft Computing. Artificial Immune System based approaches attempt to leverage the adaptability, error tolerance, self- monitoring and distributed nature of Human Immune Systems. Whereas Soft Computing based approaches are instrumental in developing fuzzy rule based systems for detecting intrusions. They are computationally intensive and apply machine learning (both supervised and unsupervised) techniques to detect intrusions in a given system. A combination of these two approaches could provide significant advantages for intrusion detection. In this paper we attempt to leverage the adaptability of Artificial Immune System and the computation intensive nature of Soft Computing to develop a system that can effectively detect intrusions in a given network.

  18. Metrical presentation boosts implicit learning of artificial grammar.

    Science.gov (United States)

    Selchenkova, Tatiana; François, Clément; Schön, Daniele; Corneyllie, Alexandra; Perrin, Fabien; Tillmann, Barbara

    2014-01-01

    The present study investigated whether a temporal hierarchical structure favors implicit learning. An artificial pitch grammar implemented with a set of tones was presented in two different temporal contexts, notably with either a strongly metrical structure or an isochronous structure. According to the Dynamic Attending Theory, external temporal regularities can entrain internal oscillators that guide attention over time, allowing for temporal expectations that influence perception of future events. Based on this framework, it was hypothesized that the metrical structure provides a benefit for artificial grammar learning in comparison to an isochronous presentation. Our study combined behavioral and event-related potential measurements. Behavioral results demonstrated similar learning in both participant groups. By contrast, analyses of event-related potentials showed a larger P300 component and an earlier N2 component for the strongly metrical group during the exposure phase and the test phase, respectively. These findings suggests that the temporal expectations in the strongly metrical condition helped listeners to better process the pitch dimension, leading to improved learning of the artificial grammar.

  19. Artificial grammar learning of melody is constrained by melodic inconsistency: Narmour's principles affect melodic learning.

    Science.gov (United States)

    Rohrmeier, Martin; Cross, Ian

    2013-01-01

    Considerable evidence suggests that people acquire artificial grammars incidentally and implicitly, an indispensable capacity for the acquisition of music or language. However, less research has been devoted to exploring constraints affecting incidental learning. Within the domain of music, the extent to which Narmour's (1990) melodic principles affect implicit learning of melodic structure was experimentally explored. Extending previous research (Rohrmeier, Rebuschat & Cross, 2011), the identical finite-state grammar is employed having terminals (the alphabet) manipulated so that melodies generated systematically violated Narmour's principles. Results indicate that Narmour-inconsistent melodic materials impede implicit learning. This further constitutes a case in which artificial grammar learning is affected by prior knowledge or processing constraints.

  20. A PHILOSOPHICAL APPROACH TO ARTIFICIAL INTELLIGENCE AND ISLAMIC VALUES

    Directory of Open Access Journals (Sweden)

    Ali Akbar Ziaee

    2012-02-01

    Full Text Available Artificial Intelligence has the potential to empower humans through enhanced learning and performance. But if this potential is to be realized and accepted, the ethical aspects as well as the technical must be addressed. Many engineers claim that AI will be smarter than human brains, including scientific creativity, general wisdom and social skills, so we must consider it an important factor for making decisions in our social life and especially in our Islamic societies. The most important challenges will be the quality of representing the Islamic values like piety, obedience, Halal and Haram, and etc in the form of semantics. In this paper, I want to emphasize on the role of Divine Islamic values in the application of AI and discuss it according to philosophy of AI and Islamic perspective.Keywords- Value, expert, Community Development, Artificial Intelligence, Superintelligence, Friendly Artificial Intelligence

  1. Learning and geometry computational approaches

    CERN Document Server

    Smith, Carl

    1996-01-01

    The field of computational learning theory arose out of the desire to for­ mally understand the process of learning. As potential applications to artificial intelligence became apparent, the new field grew rapidly. The learning of geo­ metric objects became a natural area of study. The possibility of using learning techniques to compensate for unsolvability provided an attraction for individ­ uals with an immediate need to solve such difficult problems. Researchers at the Center for Night Vision were interested in solving the problem of interpreting data produced by a variety of sensors. Current vision techniques, which have a strong geometric component, can be used to extract features. However, these techniques fall short of useful recognition of the sensed objects. One potential solution is to incorporate learning techniques into the geometric manipulation of sensor data. As a first step toward realizing such a solution, the Systems Research Center at the University of Maryland, in conjunction with the C...

  2. DIGITAL SIMULATIONS FOR IMPROVING EDUCATION: Learning Through Artificial Teaching Environments

    OpenAIRE

    OZAN, Reviewed By Özlem

    2009-01-01

    DIGITAL SIMULATIONS FOR IMPROVING EDUCATION:Learning Through Artificial Teaching EnvironmentsGibson, David, Ed.D.; Information Science Reference, Hershey, PA,SBN-10: 1605663239, ISBN-13: 9781605663234, p.514 Jan 2009Reviewed byÖzlem OZANFaculty of Education, Eskişehir Osmangazi University,Eskisehir-TURKEYSimulations in education, both for children and adults,become popular with the development of computer technology, because they are fun and engaging and allow learners to internalize knowledg...

  3. Statistical learning of two artificial languages presented successively: How conscious?

    Directory of Open Access Journals (Sweden)

    Ana eFranco

    2011-09-01

    Full Text Available Statistical learning is assumed to occur automatically and implicitly, but little is known about the extent to which the representations acquired over training are available to conscious awareness. In this study, we focus whether the knowledge acquired in a statistical learning situation is conscious or not. Here, participants were first exposed to an artificial language presented auditorily. Immediately thereafter, they were exposed to a second artificial language. . Both languages were composed of the same corpus of syllables and differed only in the transitional probabilities between the latter. We first controlled that both languages were equally learnable (Experiment 1 and that participants could learn the two languages and differentiate between them (Experiment 2. In Experiment 3, we used an adaptation of the Process Dissociation Procedure (Jacoby, 1991 to explore whether knowledge of each language was consciously accessible and manipulable. Results suggest that statistical information can be used to parse and differentiate between two different artificial languages, and that the resulting representations are conscious.

  4. Implicit learning of a recursive rule in an artificial grammar.

    Science.gov (United States)

    Poletiek, Fenna H

    2002-11-01

    Participants performed an artificial grammar learning task, in which the standard finite state grammar (J. Verb. Learn. Verb. Behavior 6 (1967) 855) was extended with a recursive rule generating self-embedded sequences. We studied the learnability of such a rule in two experiments. The results verify the general hypothesis that recursivity can be learned in an artificial grammar learning task. However this learning seems to be rather based on recognising chunks than on abstract rule induction. First, performance was better for strings with more than one level of self-embedding in the sequence, uncovering more clearly the self-embedding pattern. Second, the infinite repeatability of the recursive rule application was not spontaneously induced from the training, but it was when an additional cue about this possibility was given. Finally, participants were able to verbalise their knowledge of the fragments making up the sequences-especially in the crucial front and back positions-, whereas knowledge of the underlying structure, to the extent it was acquired, was not articulatable. The results are discussed in relation to previous studies on the implicit learnability of complex and abstract rules.

  5. Human Robotic Swarm Interaction Using an Artificial Physics Approach

    Science.gov (United States)

    2014-12-01

    ARTIFICIAL PHYSICS APPROACH Brenton Campbell Lieutenant, United States Navy B.S., California Polytechnic State University, San Luis Obispo, 2006 Submitted... xvi Acknowledgments I would like to thank co-advisor Dr. Tim Chung for his guidance and vision. Without his prodding and focus, this thesis would

  6. Approaches toward learning in physiotherapy

    Directory of Open Access Journals (Sweden)

    L. Keiller

    2013-01-01

    Full Text Available The aim of this study was to investigate the approaches toward learning of undergraduate Physiotherapy students in a PBl module to enhance facilitation of learning at the Stellenbosch University, Division of Physiotherapy in South Africa. This quantitative, descriptive study utilized the revised Two-factor Study Process Questionnaire (r-SPQ-2f to evaluate the study cohorts’ approaches toward learning in the module. results of the data instruments were analysed statistically and discussed in a descriptive manner. There were a statistically significant greater number of students who adopted a deep approach toward learning at the commencement of the academic year. Students showed a trend toward an increase in their intrinsic interest in the learning material as the module progressed. Students in the Applied Physiotherapy module (ATP started to shift their focus from a surface learning approach to a deep learning approach. further research is needed to determine the long-term changes in approach toward learning and the possible determinants of these changes. This can be done in conjunction with the implementation of quality assurance mechanisms for learning material and earlier preparation of students for the change in the learning environment.

  7. An Approach to Structural Approximation Analysis by Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    陆金桂; 周济; 王浩; 陈新度; 余俊; 肖世德

    1994-01-01

    This paper theoretically proves that a three-layer neural network can be applied to implementing exactly the function between the stresses and displacements and the design variables of any elastic structure based on the Kolmogorov’s mapping neural network existence theorem. A new approach to the structural approximation analysis with the global characteristic based on artificial neural networks is presented. The computer simulation experiments made by this paper show that the new approach is effective.

  8. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

    Science.gov (United States)

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-07-05

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

  9. ARTIFICIAL NEURAL NETWORK APPROACH FOR HAND GESTURE RECOGNITION

    OpenAIRE

    MISS. SHWETA K. YEWALE,; MR. PANKAJ K. BHARNE

    2011-01-01

    Gesture recognition is an important for developing alternative human-computer interaction modalities. It enables human to interface with machine in a more natural way. For recognizing the gestures, there areseveral algorithms are available. There are several approaches for gesture recognition using MATLAB. Artificial Neural networks are flexible in a changing environment. This research paper gives the overview of ANN for gesture recognition. It also describes the process of gesture recognitio...

  10. Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    JinHyo Joseph Yun

    2016-08-01

    Full Text Available What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.

  11. Artificial grammar learning of melody is constrained by melodic inconsistency: Narmour's principles affect melodic learning.

    Directory of Open Access Journals (Sweden)

    Martin Rohrmeier

    Full Text Available Considerable evidence suggests that people acquire artificial grammars incidentally and implicitly, an indispensable capacity for the acquisition of music or language. However, less research has been devoted to exploring constraints affecting incidental learning. Within the domain of music, the extent to which Narmour's (1990 melodic principles affect implicit learning of melodic structure was experimentally explored. Extending previous research (Rohrmeier, Rebuschat & Cross, 2011, the identical finite-state grammar is employed having terminals (the alphabet manipulated so that melodies generated systematically violated Narmour's principles. Results indicate that Narmour-inconsistent melodic materials impede implicit learning. This further constitutes a case in which artificial grammar learning is affected by prior knowledge or processing constraints.

  12. Implicit learning in children with spelling disability: evidence from artificial grammar learning.

    Science.gov (United States)

    Ise, Elena; Arnoldi, Carolin J; Bartling, Jürgen; Schulte-Körne, Gerd

    2012-09-01

    The development of reading and spelling skills seems to be influenced by both explicit and implicit learning processes. The aim of this study was to investigate whether children with spelling difficulties show a deficit in the implicit learning of frequent letter chunks. This was done by comparing the performance of children with good and poor spelling skills on an artificial grammar learning task. The results show that children with poor spelling skills have difficulties recognizing previously presented letter strings. Moreover, they show impaired implicit learning of frequent letter chunks, particularly in letter strings that can be processed phonologically. Comparing children's performance with chance performance revealed that poor spellers demonstrated some implicit learning, but a significant group difference showed that implicit learning was less efficient in poor spellers as compared to good spellers. These findings support the idea that implicit learning deficits play a role in the development of poor literacy skills.

  13. The contribution of phonological short-term memory to artificial grammar learning.

    Science.gov (United States)

    Andrade, Jackie; Baddeley, Alan

    2011-05-01

    Three experiments investigated the contribution of phonological short-term memory (STM) to grammar learning by manipulating rehearsal during study of an auditory artificial grammar made up from a vocabulary of spoken Mandarin syllables. Experiment 1 showed that concurrent, irrelevant articulation impaired grammar learning compared with a nonverbal control task. Experiment 2 replicated and extended this finding, showing that repeating the grammatical strings at study improved grammar learning compared with suppressing rehearsal or remaining silent during learning. Experiment 3 found no effects of rehearsal on grammar learning once participants had learned the component syllables. The findings suggest that phonological STM aids artificial grammar learning via effects on vocabulary learning.

  14. Artificial grammar learning in individuals with severe aphasia.

    Science.gov (United States)

    Zimmerer, Vitor C; Cowell, Patricia E; Varley, Rosemary A

    2014-01-01

    One factor in syntactic impairment in aphasia might be damage to general structure processing systems. In such a case, deficits would be evident in the processing of syntactically structured non-linguistic information. To explore this hypothesis, we examined performances on artificial grammar learning (AGL) tasks in which the grammar was expressed in non-linguistic visual forms. In the first experiment, AGL behavior of four aphasic participants with severe syntactic impairment, five aphasic participants without syntactic impairment, and healthy controls was examined. Participants were trained on sequences of nonsense stimuli with the structure A(n)B(n). Data were analyzed at an individual level to identify different behavioral profiles and account for heterogeneity in aphasic as well as healthy groups. Healthy controls and patients without syntactic impairment were more likely to learn configurational (item order) than quantitative (counting) regularities. Quantitative regularities were only detected by individuals who also detected the configurational properties of the stimulus sequences. By contrast, two individuals with syntactic impairment learned quantitative regularities, but showed no sensitivity towards configurational structure. They also failed to detect configurational structure in a second experiment in which sequences were structured by the grammar A(+)B(+). We discuss the potential relationship between AGL and processing of word order as well as the potential of AGL in clinical practice.

  15. An artificial immune approach for optical image based vision inspection

    Institute of Scientific and Technical Information of China (English)

    Hong Zheng(郑宏); Nanfeng Xiao(肖南风); Jinhui Lan(蓝金辉)

    2003-01-01

    This paper presents a novel approach of visual inspection for texture surface defects. The approach usesartificial immune theory in learning the detection of texture defects. In this paper, texture defects areregards as non-self, and normal textures are regarded as self. Defect filters and segmentation thresholdsused for defect detection are regarded as antibodies. The clonal selection algorithm stemmed from thenatural immune system is employed to learn antibodies. Experimental results on textile image inspectionare presented to illustrate the merit and feasibility of the proposed method.

  16. Two modes of transfer in artificial grammar learning.

    Science.gov (United States)

    Tunney, R J; Altmann, G T

    2001-05-01

    Participants can transfer grammatical knowledge acquired implicitly in 1 vocabulary to new sequences instantiated in both the same and a novel vocabulary. Two principal theories have been advanced to account for these effects. One suggests that sequential dependencies form the basis for cross-domain transfer (e.g., Z. Dienes, G. T. M. Altmann, & S. J. Gao, 1999). Another argues that a form of episodic memory known as abstract analogy is sufficient (e.g., L. R. Brooks & J. R. Vokey, 1991). Three experiments reveal the contributions of the 2. In Experiment 1 sequential dependencies form the only basis for transfer. Experiment 2 demonstrates that this process is impaired by a change in the distributional properties of the language. Experiment 3 demonstrates that abstract analogy of repetition structure is relatively immune to such a change. These findings inform theories of artificial grammar learning and the transfer of grammatical knowledge.

  17. Information theory and artificial grammar learning: inferring grammaticality from redundancy.

    Science.gov (United States)

    Jamieson, Randall K; Nevzorova, Uliana; Lee, Graham; Mewhort, D J K

    2016-03-01

    In artificial grammar learning experiments, participants study strings of letters constructed using a grammar and then sort novel grammatical test exemplars from novel ungrammatical ones. The ability to distinguish grammatical from ungrammatical strings is often taken as evidence that the participants have induced the rules of the grammar. We show that judgements of grammaticality are predicted by the local redundancy of the test strings, not by grammaticality itself. The prediction holds in a transfer test in which test strings involve different letters than the training strings. Local redundancy is usually confounded with grammaticality in stimuli widely used in the literature. The confounding explains why the ability to distinguish grammatical from ungrammatical strings has popularized the idea that participants have induced the rules of the grammar, when they have not. We discuss the judgement of grammaticality task in terms of attribute substitution and pattern goodness. When asked to judge grammaticality (an inaccessible attribute), participants answer an easier question about pattern goodness (an accessible attribute).

  18. An Artificial Intelligence Approach for Gears Diagnostics in AUVs.

    Science.gov (United States)

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-04-12

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

  19. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg.

  20. An Artificial Intelligence Approach for Gears Diagnostics in AUVs

    Science.gov (United States)

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-01-01

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved. PMID:27077868

  1. An Artificial Intelligence Approach for Gears Diagnostics in AUVs

    Directory of Open Access Journals (Sweden)

    Graciliano Nicolás Marichal

    2016-04-01

    Full Text Available In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles, where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

  2. BOUNDARY ELEMENT ANALYSIS OF CONTACT PROBLEMS USING ARTIFICIAL BOUNDARY NODE APPROACH

    Institute of Scientific and Technical Information of China (English)

    Bahattin KANBER; Ibrahim H. GUZELBEY; Ahmet ERKLI

    2003-01-01

    An improved version of the regular boundary element method, the artificial boundary node approach, is derived. A simple contact algorithm is designed and implemented into the direct boundary element, regular boundary element and artificial boundary node approaches. The exisiting and derived approaches are tested using some case studies. The results of the artificial boundary node approach are compared with those of the existing boundary element program, the regular element approach, ANSYS and analytical solution whenever possible. The results show the effectiveness of the artificial boundary node approach for a wider range of boundary offsets.

  3. Quantum Interaction Approach in Cognition, Artificial Intelligence and Robotics

    CERN Document Server

    Aerts, Diederik; Sozzo, Sandro

    2011-01-01

    The mathematical formalism of quantum mechanics has been successfully employed in the last years to model situations in which the use of classical structures gives rise to problematical situations, and where typically quantum effects, such as 'contextuality' and 'entanglement', have been recognized. This 'Quantum Interaction Approach' is briefly reviewed in this paper focusing, in particular, on the quantum models that have been elaborated to describe how concepts combine in cognitive science, and on the ensuing identification of a quantum structure in human thought. We point out that these results provide interesting insights toward the development of a unified theory for meaning and knowledge formalization and representation. Then, we analyze the technological aspects and implications of our approach, and a particular attention is devoted to the connections with symbolic artificial intelligence, quantum computation and robotics.

  4. Modes of knowledge acquisition and retrieval in artificial grammar learning.

    Science.gov (United States)

    Poznanski, Yael; Tzelgov, Joseph

    2010-08-01

    The aim of this study was to conceptualize artificial grammar learning (AGL) in terms of two orthogonal dimensions--the mode of knowledge acquisition and the mode of knowledge retrieval--as was done by Perlman and Tzelgov (2006) for sequence learning. Experiment 1 was carried out to validate our experimental task; Experiments 2-4 tested, respectively, performance in the intentional, incidental, and automatic retrieval modes, for each of the three modes of acquisition. Furthermore, signal detection theory (SDT) was used as an analytic tool, consistent with our assumption that the processing of legality-relevant information involves decisions along a continuous dimension of fluency. The results presented support the analysis of AGL in terms of the proposed dimensions. They also indicate that knowledge acquired during training may include many aspects of the presented stimuli (whole strings, relations among elements, etc.). The contribution of the various components to performance depends on both the specific instruction in the acquisition phase and the requirements of the retrieval task.

  5. Artificial Intelligence.

    Science.gov (United States)

    Waltz, David L.

    1982-01-01

    Describes kinds of results achieved by computer programs in artificial intelligence. Topics discussed include heuristic searches, artificial intelligence/psychology, planning program, backward chaining, learning (focusing on Winograd's blocks to explore learning strategies), concept learning, constraint propagation, language understanding…

  6. The Wonder Approach to learning.

    Science.gov (United States)

    L'Ecuyer, Catherine

    2014-01-01

    Wonder, innate in the child, is an inner desire to learn that awaits reality in order to be awakened. Wonder is at the origin of reality-based consciousness, thus of learning. The scope of wonder, which occurs at a metaphysical level, is greater than that of curiosity. Unfortunate misinterpretations of neuroscience have led to false brain-based ideas in the field of education, all of these based on the scientifically wrong assumption that children's learning depends on an enriched environment. These beliefs have re-enforced the Behaviorist Approach to education and to parenting and have contributed to deadening our children's sense of wonder. We suggest wonder as the center of all motivation and action in the child. Wonder is what makes life genuinely personal. Beauty is what triggers wonder. Wonder attunes to beauty through sensitivity and is unfolded by secure attachment. When wonder, beauty, sensitivity and secure attachment are present, learning is meaningful. On the contrary, when there is no volitional dimension involved (no wonder), no end or meaning (no beauty) and no trusting predisposition (secure attachment), the rigid and limiting mechanical process of so-called learning through mere repetition become a deadening and alienating routine. This could be described as training, not as learning, because it does not contemplate the human being as a whole.

  7. The wonder approach to learning

    Directory of Open Access Journals (Sweden)

    Catherine eL'Ecuyer

    2014-10-01

    Full Text Available Wonder, innate in the child, is an inner desire to learn that awaits reality in order to be awakened. Wonder is at the origin of reality-based consciousness, thus of learning. The scope of wonder, which occurs at a metaphysical level, is greater than that of curiosity. Unfortunate misinterpretations of neuroscience have led to false brain-based ideas in the field of education, all of these based on the scientifically wrong assumption that children’s learning depends on an enriched environment. These beliefs have re-enforced the Behaviorist Approach to education and to parenting and have contributed to deadening our children’s sense of wonder. We suggest wonder as the center of all motivation and action in the child. Wonder is what makes life genuinely personal. Beauty is what triggers wonder. Wonder attunes to beauty through sensitivity and is unfolded by attachment. When wonder, beauty, sensitivity and secure attachment are present, learning is meaningful.On the contrary, when there is no volitional dimension involved (no wonder, no end or meaning (no beauty and no trusting predisposition (secure attachment, the rigid and limiting mechanical process of so-called learning through mere repetition become a deadening and alienating routine. This could be described as training, not as learning, because it does not contemplate the human being as a whole.

  8. The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing

    Directory of Open Access Journals (Sweden)

    Peng Zhang

    2015-01-01

    Full Text Available Ebola virus disease (EVD distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, in recent years, computational experiments based on artificial society appeared, providing a new approach to study the propagation of EVD and analyze the corresponding interventions. Therefore, the rationality of artificial society is the key to the accuracy and reliability of experiment results. Individuals’ behaviors along with travel mode directly affect the propagation among individuals. Firstly, artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals’ behaviors. Meanwhile, Ebola course model and propagation model are built, according to the parameters in West Africa. Subsequently, propagation mechanism of EVD is analyzed, epidemic scenario is predicted, and corresponding interventions are presented. Finally, by simulating the emergency responses of Chinese government, the conclusion is finally drawn that Ebola is impossible to outbreak in large scale in the city of Beijing.

  9. Artificial grammar learning meets formal language theory: an overview

    Science.gov (United States)

    Fitch, W. Tecumseh; Friederici, Angela D.

    2012-01-01

    Formal language theory (FLT), part of the broader mathematical theory of computation, provides a systematic terminology and set of conventions for describing rules and the structures they generate, along with a rich body of discoveries and theorems concerning generative rule systems. Despite its name, FLT is not limited to human language, but is equally applicable to computer programs, music, visual patterns, animal vocalizations, RNA structure and even dance. In the last decade, this theory has been profitably used to frame hypotheses and to design brain imaging and animal-learning experiments, mostly using the ‘artificial grammar-learning’ paradigm. We offer a brief, non-technical introduction to FLT and then a more detailed analysis of empirical research based on this theory. We suggest that progress has been hampered by a pervasive conflation of distinct issues, including hierarchy, dependency, complexity and recursion. We offer clarifications of several relevant hypotheses and the experimental designs necessary to test them. We finally review the recent brain imaging literature, using formal languages, identifying areas of convergence and outstanding debates. We conclude that FLT has much to offer scientists who are interested in rigorous empirical investigations of human cognition from a neuroscientific and comparative perspective. PMID:22688631

  10. Games and Machine Learning: A Powerful Combination in an Artificial Intelligence Course

    Science.gov (United States)

    Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

    2010-01-01

    Project MLeXAI [Machine Learning eXperiences in Artificial Intelligence (AI)] seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense--a simple real-time strategy game…

  11. Blended Learning: An Innovative Approach

    Science.gov (United States)

    Lalima; Dangwal, Kiran Lata

    2017-01-01

    Blended learning is an innovative concept that embraces the advantages of both traditional teaching in the classroom and ICT supported learning including both offline learning and online learning. It has scope for collaborative learning; constructive learning and computer assisted learning (CAI). Blended learning needs rigorous efforts, right…

  12. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam. Our tested data and experiments results shows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  13. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

    Directory of Open Access Journals (Sweden)

    Hiram Ponce

    2016-07-01

    Full Text Available Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

  14. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  15. Learning from Multiple Collaborating Intelligent Tutors: An Agent-based Approach.

    Science.gov (United States)

    Solomos, Konstantinos; Avouris, Nikolaos

    1999-01-01

    Describes an open distributed multi-agent tutoring system (MATS) and discusses issues related to learning in such open environments. Topics include modeling a one student-many teachers approach in a computer-based learning context; distributed artificial intelligence; implementation issues; collaboration; and user interaction. (Author/LRW)

  16. Artificial intelligence approach for spot application project system design

    Science.gov (United States)

    Lefevre, M. J.; Fisse, G.; Martin, E.; de Boissezon, H.; Galaup, M.

    1993-11-01

    Over the past four years, CNES has been engaged in a major programme focusing on the development of SPOT Operational Application Projects. With a total of sixty projects now complete, we can draw a number of meaningful conclusions and identify a number of objectives to be satisfied by advanced remote sensing methodology. One of the main conclusions points to the importance of human vision in studies on natural complex space imagery. This being so, visual recognition must be one of the main phases of the ``Pilot Project for the Application of Remote Sensing to Agricultural Statistics'': only human experts have the ability to make a meaningful analysis of Spot TM imagery. Non-expert operators will not be able to manage the subsequent rational production phase alone. The first part of this paper describes an approach to the formalization and modelling of expert know-how based on the use of artificial intelligence. The second part puts forward a cooperative operator/computer system based on a cognitive structure. Our proposal comprises 1) a specific knowledge base, 2) an ergonomic interface associated with functional software that is based on automatic image enhancement coupled with perception support functions.

  17. Artificial Neural Network Approach for Mapping Contrasting Tillage Practices

    Directory of Open Access Journals (Sweden)

    Terry Howell

    2010-02-01

    Full Text Available Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage information on individual fields over large areas. Numerous regression-based models are available to derive tillage information from remote sensing data. However, these models require information about the complex nature of underlying watershed characteristics and processes. Unlike regression-based models, Artificial Neural Network (ANN provides an efficient alternative to map complex nonlinear relationships between an input and output datasets without requiring a detailed knowledge of underlying physical relationships. Limited or no information currently exist quantifying ability of ANN models to identify contrasting tillage practices from remote sensing data. In this study, a set of Landsat TM-based ANN models was developed to identify contrasting tillage practices in the Texas High Plains. Observed tillage data from Moore and Ochiltree Counties were used to develop and evaluate the models, respectively. The overall classification accuracy for the 15 models developed with the Moore County dataset varied from 74% to 91%. Statistical evaluation of these models against the Ochiltree County dataset produced results with an overall classification accuracy varied from 66% to 80%. The ANN models based on TM band 5 or indices of TM Band 5 may provide consistent and accurate tillage information when applied to the Texas High Plains.

  18. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal someconfidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used forphishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mailscan be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spammingis growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb themind-peace, waste time and consume various resources e.g., memory space and network bandwidth, sofiltering of spam mails is a big issue in cyber security.This paper presents an novel approach of spam filtering which is based on some query generatedapproach on the knowledge base and also use some artificial neural network methods to detect the spammails based on their behavior. analysis of the mail header, cross validation. Proposed methodologyincludes the 7 several steps which are well defined and achieve the higher accuracy. It works well with allkinds of spam mails (text based spam as well as image spam. Our tested data and experiments resultsshows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  19. Doubly stochastic Poisson processes in artificial neural learning.

    Science.gov (United States)

    Card, H C

    1998-01-01

    This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.

  20. The Effects of Frequency, Distribution, Mode of Presentation, and First Language on Learning an Artificial Language

    Science.gov (United States)

    Miyata, Munehiko

    2011-01-01

    This dissertation presents results from a series of experiments investigating adult learning of an artificial language and the effects that input frequency (high vs. low token frequency), frequency distribution (skewed vs. balanced), presentation mode (structured vs. scrambled), and first language (English vs. Japanese) have on such learning.…

  1. The Influence of Consistency, Frequency, and Semantics on Learning to Read: An Artificial Orthography Paradigm

    Science.gov (United States)

    Taylor, J. S. H.; Plunkett, Kim; Nation, Kate

    2011-01-01

    Two experiments explored learning, generalization, and the influence of semantics on orthographic processing in an artificial language. In Experiment 1, 16 adults learned to read 36 novel words written in novel characters. Posttraining, participants discriminated trained from untrained items and generalized to novel items, demonstrating extraction…

  2. Using Dual-Task Methodology to Dissociate Automatic from Nonautomatic Processes Involved in Artificial Grammar Learning

    Science.gov (United States)

    Hendricks, Michelle A.; Conway, Christopher M.; Kellogg, Ronald T.

    2013-01-01

    Previous studies have suggested that both automatic and intentional processes contribute to the learning of grammar and fragment knowledge in artificial grammar learning (AGL) tasks. To explore the relative contribution of automatic and intentional processes to knowledge gained in AGL, we utilized dual-task methodology to dissociate automatic and…

  3. Image-Based Learning Approach Applied to Time Series Forecasting

    Directory of Open Access Journals (Sweden)

    J. C. Chimal-Eguía

    2012-06-01

    Full Text Available In this paper, a new learning approach based on time-series image information is presented. In order to implementthis new learning technique, a novel time-series input data representation is also defined. This input datarepresentation is based on information obtained by image axis division into boxes. The difference between this newinput data representation and the classical is that this technique is not time-dependent. This new information isimplemented in the new Image-Based Learning Approach (IBLA and by means of a probabilistic mechanism thislearning technique is applied to the interesting problem of time series forecasting. The experimental results indicatethat by using the methodology proposed in this article, it is possible to obtain better results than with the classicaltechniques such as artificial neuronal networks and support vector machines.

  4. Design and Evaluation of Two Blended Learning Approaches: Lessons Learned

    Science.gov (United States)

    Cheung, Wing Sum; Hew, Khe Foon

    2011-01-01

    In this paper, we share two blended learning approaches used at the National Institute of Education in Singapore. We have been using these two approaches in the last twelve years in many courses ranging from the diploma to graduate programs. For the first blended learning approach, we integrated one asynchronous communication tool with face to…

  5. Machine learning approaches in medical image analysis

    DEFF Research Database (Denmark)

    de Bruijne, Marleen

    2016-01-01

    Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols...

  6. Light-powered, artificial molecular pumps: a minimalistic approach

    Directory of Open Access Journals (Sweden)

    Giulio Ragazzon

    2015-11-01

    Full Text Available The realization of artificial molecular motors capable of converting energy into mechanical work is a fascinating challenge of nanotechnology and requires reactive systems that can operate away from chemical equilibrium. This article describes the design and construction of a simple, supramolecular ensemble in which light irradiation causes the directional transit of a macrocycle along a nonsymmetric molecular axle, thus forming the basis for the development of artificial molecular pumps.

  7. Error-correction learning for artificial neural networks using the Bayesian paradigm. Application to automated medical diagnosis.

    Science.gov (United States)

    Belciug, Smaranda; Gorunescu, Florin

    2014-12-01

    Automated medical diagnosis models are now ubiquitous, and research for developing new ones is constantly growing. They play an important role in medical decision-making, helping physicians to provide a fast and accurate diagnosis. Due to their adaptive learning and nonlinear mapping properties, the artificial neural networks are widely used to support the human decision capabilities, avoiding variability in practice and errors based on lack of experience. Among the most common learning approaches, one can mention either the classical back-propagation algorithm based on the partial derivatives of the error function with respect to the weights, or the Bayesian learning method based on posterior probability distribution of weights, given training data. This paper proposes a novel training technique gathering together the error-correction learning, the posterior probability distribution of weights given the error function, and the Goodman-Kruskal Gamma rank correlation to assembly them in a Bayesian learning strategy. This study had two main purposes; firstly, to develop anovel learning technique based on both the Bayesian paradigm and the error back-propagation, and secondly,to assess its effectiveness. The proposed model performance is compared with those obtained by traditional machine learning algorithms using real-life breast and lung cancer, diabetes, and heart attack medical databases. Overall, the statistical comparison results indicate that thenovellearning approach outperforms the conventional techniques in almost all respects.

  8. Approaches to e-learning

    DEFF Research Database (Denmark)

    Hartvig, Susanne Akrawi; Petersson, Eva

    2013-01-01

    E-learning has made its entrance into educational institutions. Compared to traditional learning methods, e-learning has the benefit of enabling educational institutions to attract more students. E-learning not only opens up for an increased enrollment, it also gives students who would otherwise...... not be able to take the education to now get the possibility to do so. This paper introduces Axel Honneth’s theory on the need for recognition as a framework to understand the role and function of interaction in relation to e-learning. The paper argues that an increased focus on the dialectic relationship...... between recognition and learning will enable an optimization of the learning conditions and the interactive affordances targeting students under e-learning programs. The paper concludes that the engagement and motivation to learn are not only influenced by but depending on recognition....

  9. What Artificial Grammar Learning Reveals about the Neurobiology of Syntax

    Science.gov (United States)

    Petersson, Karl-Magnus; Folia, Vasiliki; Hagoort, Peter

    2012-01-01

    In this paper we examine the neurobiological correlates of syntax, the processing of structured sequences, by comparing FMRI results on artificial and natural language syntax. We discuss these and similar findings in the context of formal language and computability theory. We used a simple right-linear unification grammar in an implicit artificial…

  10. What artificial grammar learning reveals about the neurobiology of syntax

    NARCIS (Netherlands)

    Petersson, K.M.; Vasiliki, F.; Hagoort, P.

    2012-01-01

    In this paper we examine the neurobiological correlates of syntax, the processing of structured sequences, by comparing FMRI results on artificial and natural language syntax. We discuss these and similar findings in the context of formal language and computability theory. We used a simple right-lin

  11. What artificial grammar learning reveals about the neurobiology of syntax

    NARCIS (Netherlands)

    Petersson, K.M.; Folia, V.; Hagoort, Peter

    2012-01-01

    : In this paper we examine the neurobiological correlates of syntax, the processing of structured sequences, by comparing FMRI results on artificial and natural language syntax. We discuss these and similar findings in the context of formal language and computability theory. We used a simple right-l

  12. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    Science.gov (United States)

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  13. Electrical stimulation of Broca's area enhances implicit learning of an artificial grammar.

    Science.gov (United States)

    de Vries, Meinou H; Barth, Andre C R; Maiworm, Sandra; Knecht, Stefan; Zwitserlood, Pienie; Flöel, Agnes

    2010-11-01

    Artificial grammar learning constitutes a well-established model for the acquisition of grammatical knowledge in a natural setting. Previous neuroimaging studies demonstrated that Broca's area (left BA 44/45) is similarly activated by natural syntactic processing and artificial grammar learning. The current study was conducted to investigate the causal relationship between Broca's area and learning of an artificial grammar by means of transcranial direct current stimulation (tDCS). Thirty-eight healthy subjects participated in a between-subject design, with either anodal tDCS (20 min, 1 mA) or sham stimulation, over Broca's area during the acquisition of an artificial grammar. Performance during the acquisition phase, presented as a working memory task, was comparable between groups. In the subsequent classification task, detecting syntactic violations, and specifically, those where no cues to superficial similarity were available, improved significantly after anodal tDCS, resulting in an overall better performance. A control experiment where 10 subjects received anodal tDCS over an area unrelated to artificial grammar learning further supported the specificity of these effects to Broca's area. We conclude that Broca's area is specifically involved in rule-based knowledge, and here, in an improved ability to detect syntactic violations. The results cannot be explained by better tDCS-induced working memory performance during the acquisition phase. This is the first study that demonstrates that tDCS may facilitate acquisition of grammatical knowledge, a finding of potential interest for rehabilitation of aphasia.

  14. Project Management Approaches for Online Learning Design

    Science.gov (United States)

    Eby, Gulsun; Yuzer, T. Volkan

    2013-01-01

    Developments in online learning and its design are areas that continue to grow in order to enhance students' learning environments and experiences. However, in the implementation of new technologies, the importance of properly and fairly overseeing these courses is often undervalued. "Project Management Approaches for Online Learning Design"…

  15. Effects of artificial gravity on the cardiovascular system: Computational approach

    Science.gov (United States)

    Diaz Artiles, Ana; Heldt, Thomas; Young, Laurence R.

    2016-09-01

    Artificial gravity has been suggested as a multisystem countermeasure against the negative effects of weightlessness. However, many questions regarding the appropriate configuration are still unanswered, including optimal g-level, angular velocity, gravity gradient, and exercise protocol. Mathematical models can provide unique insight into these questions, particularly when experimental data is very expensive or difficult to obtain. In this research effort, a cardiovascular lumped-parameter model is developed to simulate the short-term transient hemodynamic response to artificial gravity exposure combined with ergometer exercise, using a bicycle mounted on a short-radius centrifuge. The model is thoroughly described and preliminary simulations are conducted to show the model capabilities and potential applications. The model consists of 21 compartments (including systemic circulation, pulmonary circulation, and a cardiac model), and it also includes the rapid cardiovascular control systems (arterial baroreflex and cardiopulmonary reflex). In addition, the pressure gradient resulting from short-radius centrifugation is captured in the model using hydrostatic pressure sources located at each compartment. The model also includes the cardiovascular effects resulting from exercise such as the muscle pump effect. An initial set of artificial gravity simulations were implemented using the Massachusetts Institute of Technology (MIT) Compact-Radius Centrifuge (CRC) configuration. Three centripetal acceleration (artificial gravity) levels were chosen: 1 g, 1.2 g, and 1.4 g, referenced to the subject's feet. Each simulation lasted 15.5 minutes and included a baseline period, the spin-up process, the ergometer exercise period (5 minutes of ergometer exercise at 30 W with a simulated pedal cadence of 60 RPM), and the spin-down process. Results showed that the cardiovascular model is able to predict the cardiovascular dynamics during gravity changes, as well as the expected

  16. Learning Actions Models: Qualitative Approach

    DEFF Research Database (Denmark)

    Bolander, Thomas; Gierasimczuk, Nina

    2015-01-01

    —they are identifiable in the limit.We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning...... methods suited for finite identifiability of particular types of deterministic actions....

  17. Investigating the Effects of Personality on Second Language Learning through Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Meryem Karlık

    2016-06-01

    Full Text Available The aim of this research is to determine Second Language Acquisition and personality variable from affective factors analyzed by Artificial Neural Network in freshman class of both university students. This study presents an intelligent approach to the investigation of positive effects of personality on second language learning. For this purpose, watching TV, reading books, magazines, newspaper, listening to the radio, talking to a native English friend, and talking to people at school are investigated. The tool of our research is a survey (questionnaire to collect a data in order to quantify students ‘personality traits based on affective factors. The questionnaire consists of two parts. The first part consists of Yes/ No questions while the second part uses a 4 point Likert scale with 5 items that indicates what helped students personally to learn English. The participants were 160 students from two private universities in Bosnia and Herzegovina, International Burch University (90 students and International University of Sarajevo (70. The subjects’ major was English. The first part of the survey was analyzed using ANN, and the second part using statistical analysis. Both data analysis were processed by transferring answers to an Excel sheet. For each measure, mode, standard deviation, median were calculated to determine students’ personality factors. We used two different types of analysis in order to show that different kinds of analysis can be done.

  18. Artificial spider: eight-legged arachnid and autonomous learning of locomotion

    Science.gov (United States)

    Alshurafa, Nabil I.; Harmon, Justin T.

    2006-05-01

    Evolution has produced organisms whose locomotive agility and adaptivity mock the difficulty faced by robotic scientists. The problem of locomotion, which nature has solved so well, is surprisingly complex and difficult. We explore the ability of an artificial eight-legged arachnid, or animat, to autonomously learn a locomotive gait in a three-dimensional environment. We take a physics-based approach at modeling the world and the virtual body of the animat. The arachnid-like animat learns muscular control functions using simulated annealing techniques, which attempts to maximize forward velocity and minimize energy expenditure. We experiment with varying the weight of these parameters and the resulting locomotive gaits. We perform two experiments in which the first is a naive physics model of the body and world which uses point-masses and idealized joints and muscles. The second experiment is a more realistic simulation using rigid body elements with distributed mass, friction, motors, and mechanical joints. By emphasizing physical aspects we wish to minimize, a number of interesting gaits emerge.

  19. Learning Actions Models: Qualitative Approach

    DEFF Research Database (Denmark)

    Bolander, Thomas; Gierasimczuk, Nina

    2015-01-01

    identifiability (conclusively inferring the appropriate action model in finite time) and identifiability in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while non-deterministic actions require more learning power......—they are identifiable in the limit.We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning...

  20. Timing matters: The impact of immediate and delayed feedback on artificial language learning

    Directory of Open Access Journals (Sweden)

    Bertram Opitz

    2011-02-01

    Full Text Available In the present experiment, we used event-related potentials (ERP to investigate the role of immediate and delayed feedback in an artificial grammar learning task. Two groups of participants were engaged in classifying non-word strings according to an underlying rule system, not known to the participants. Visual feedback was provided after each classification either immediately or with a short delay of one second. Both groups were able to learn the artificial grammar system as indicated by an increase in classification performance. However, the gain in performance was significantly larger for the group receiving immediate feedback as compared to the group receiving delayed feedback. Learning was accompanied by an increase in P300 activity in the ERP for delayed as compared to immediate feedback. Irrespective of feedback delay, both groups exhibited learning related decreases in the feedback-related positivity (FRP elicited by positive feedback only. The feedback-related negativity (FRN, however, remained constant over the course of learning. These results suggest, first, that delayed feedback is less effective for artificial grammar learning as task requirements are very demanding, and second, that the FRP elicited by positive prediction errors decreases with learning while the FRN to negative prediction errors is elicited in an all-or-none fashion by negative feedback throughout the entire experiment.

  1. A time delay artificial neural network approach for flow routing in a river system

    Directory of Open Access Journals (Sweden)

    M. J. Diamantopoulou

    2006-09-01

    Full Text Available River flow routing provides basic information on a wide range of problems related to the design and operation of river systems. In this paper, three layer cascade correlation Time Delay Artificial Neural Network (TDANN models have been developed to forecast the one day ahead daily flow at Ilarionas station on the Aliakmon river, in Northern Greece. The networks are time lagged feed-formatted with delayed memory processing elements at the input layer. The network topology is using multiple inputs, which include the time lagged daily flow values further up at Siatista station on the Aliakmon river and at Grevena station on the Venetikos river, which is a tributary to the Aliakmon river and a single output, which are the daily flow values at Ilarionas station. The choice of the input variables introduced to the input layer was based on the cross-correlation. The use of cross-correlation between the ith input series and the output provides a short cut to the problem of the delayed memory determination. Kalman's learning rule was used to modify the artificial neural network weights. The networks are designed by putting weights between neurons, by using the hyperbolic-tangent function for training. The number of nodes in the hidden layer was determined based on the maximum value of the correlation coefficient. The results show a good performance of the TDANN approach for forecasting the daily flow values, at Ilarionas station and demonstrate its adequacy and potential for river flow routing. The TDANN approach introduced in this study is sufficiently general and has great potential to be applicable to many hydrological and environmental applications.

  2. Artificial Intelligence in Teaching and Learning: An Introduction.

    Science.gov (United States)

    Stubbs, Malcolm; Piddock, Peter

    1985-01-01

    Discussion of intelligent computer assisted learning (CAL) systems considers both those that offer natural language communication to the user and those that are adaptive, generative, or self-improving. Current interest in student-built learning environments (exemplified by work with LOGO and PROLOG) is examined, and obstacles to future intelligent…

  3. Application of Artificial Immune System Approach in MRI Classification

    Directory of Open Access Journals (Sweden)

    Gia-Hao Chang

    2008-05-01

    Full Text Available Numerous scholars have submitted the theory and research of artificial immune systems (AISs in recent years. Although AIS has been used in various fields, applying the AIS to medical images is very rare. The purpose of this study is using the clonal selection algorithm (CSA of artificial immune systems for classifying the brain MRI, and displaying a single organism image which can finally offer faster organism reference information to a doctor; hence reducing the time to ascertain large number of images, so that the doctor can diagnose the nidus more efficiently and accurately. In order to verify the feasibility and efficiency of this method, we adopt statistical theory for manifold assessment and compare with the perceptron network of double layers, FCM method. The result proves that the method of this study is both feasible and useful.

  4. Pap, gruel, and panada: early approaches to artificial infant feeding.

    Science.gov (United States)

    Obladen, Michael

    2014-01-01

    This paper collects information on artificial infant feeding published before 1860, the year when commercial formula became available. We have extensive artifactual evidence of thousands of feeding vessels since the Bronze Age. Special museum collections can be found in London, Paris, Cologne, Fécamp, Toronto, New Mexico, and elsewhere. The literature on the use of animal milk for infant feeding begins with Soranus in the 2nd century CE. Literature evidence from the very first printed books in the 15th century proves that physicians, surgeons, midwives, and the laity were aware of the opportunities and risks of artificial infant feeding. Most 17th to 19th century books on infant care contained detailed recipes for one or several of the following infant foods: pap, a semisolid food made of flour or bread crumbs cooked in water with or without milk; gruel, a thin porridge resulting from boiling cereal in water or milk, and panada, a preparation of various cereals or bread cooked in broth. During the 18th century, the published opinion on artificial feeding evolved from health concerns to a moral ideology. This view ignored the social and economic pressures which forced many mothers to forego or shorten breast-feeding. Bottle-feeding has been common practice throughout history.

  5. Study strategies and approaches to learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter

    Process Questionnaire to identify their approach to learning. It was hypothesised that the students’ learning approach would depend more on the quality of the study work than on the quantity; that an active and reflective study strategy was required to obtain deep conceptual understanding. The result...... showed a weak correlation between the student’s main learning approach as defined by the ratio of the deep approach score to the surface approach score and the student’s study intensity as identified by the ratio of non-scheduled independent activities to scheduled teacher-controlled activities....... There was however a much stronger linear correlation (significant at the 0.01 level) between the deep-surface ratio and the total study load. The same result was observed when measuring other students’ study strategy and learning approach for a single course. The empirical basis is still too limited to draw...

  6. Evaluation of the Artificial Neural Network for Color Discrimination : Discrimination of Non-learned Colors

    OpenAIRE

    Tayagaki, Yasuko; Sekiya, Satoko; Sekine, Seishi; Ohkawa, Masashi

    2004-01-01

    Our research purpose is to build an artificial neural network with an excellent color discrimination capability like human being on a computer. In this study, we built the network, which was trained to learn 10 colors with different hues in the Munsell color system. Then, we examined the response of the trained network when the network was interrogated about 10 non-learned colors. The network showed a good color discrimination capability, close to that of human being.

  7. Growing adaptive machines combining development and learning in artificial neural networks

    CERN Document Server

    Bredeche, Nicolas; Doursat, René

    2014-01-01

    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

  8. Phonetic richness can outweigh prosodically-driven phonological knowledge when learning words in an artificial language

    NARCIS (Netherlands)

    Kim, S.; Cho, T.; McQueen, J.M.

    2012-01-01

    How do Dutch and Korean listeners use acoustic–phonetic information when learning words in an artificial language? Dutch has a voiceless ‘unaspirated’ stop, produced with shortened Voice Onset Time (VOT) in prosodic strengthening environments (e.g., in domain-initial position and under prominence),

  9. The Metamorphosis of the Statistical Segmentation Output: Lexicalization during Artificial Language Learning

    Science.gov (United States)

    Fernandes, Tania; Kolinsky, Regine; Ventura, Paulo

    2009-01-01

    This study combined artificial language learning (ALL) with conventional experimental techniques to test whether statistical speech segmentation outputs are integrated into adult listeners' mental lexicon. Lexicalization was assessed through inhibitory effects of novel neighbors (created by the parsing process) on auditory lexical decisions to…

  10. A new artificial material approach for flat THz frequency lenses

    CERN Document Server

    Savini, Giorgio; Zhang, Jin; 10.1364/OE.20.025766

    2012-01-01

    Stacked layers of metal meshes embedded in a dielectric substrate are routinely used for providing spectral selection at THz frequencies. Recent work has shown that particular geometries allow the refractive index to be tuned to produce practical artificial materials. Here we show that by spatially grading in the plane of the mesh we can manufacture a Graded Index (GrIn) thin flat lens optimized for use at THz frequencies. Measurements on a prototype lens show we are able to obtain the parabolic profile of a Woods type lens which is dependent only on the mesh parameters. This technique could realize other exotic optical devices.

  11. Learning morphological phenomena of modern Greek an exploratory approach

    Directory of Open Access Journals (Sweden)

    Y. Kotsanis

    1996-12-01

    Full Text Available Educational technology is influenced by and closely related to the fields of generative epistemology, Artificial Intelligence, and the learning sciences. Relevant research literature refers to the term constructionism (Papert, 1993 and exploratory learning (diSessa et al, 1995. Constructionism and exploratory learning are a synthesis of the constructivist theory of Piaget and the opportunities offered by technology to education on thinking concretely, on learning while constructing intelligible entities, and on interacting with multimedia objects, rather than the direct acquisition of knowledge and facts. These views are based on the approach that learners can take substantial control of their own learning in an appropriately designed physical and cultural environment (Harel, 1991. In parallel, most of the studies of the Vygotskian framework focus on the role of language in the learning procedure, considering conceptual thought to be impossible outside an articulated verbal thinking. Moreover, the specific use of words is considered to be the most relevant cause for childhood and adolescent differentiation (Vygotsky, 1962.

  12. Simulation Techniques and Prosthetic Approach Towards Biologically Efficient Artificial Sense Organs- An Overview

    CERN Document Server

    Neogi, Biswarup; Mukherjee, Soumyajit; Das, Achintya; Tibarewala, D N

    2011-01-01

    An overview of the applications of control theory to prosthetic sense organs including the senses of vision, taste and odor is being presented in this paper. Simulation aspect nowadays has been the centre of research in the field of prosthesis. There have been various successful applications of prosthetic organs, in case of natural biological organs dis-functioning patients. Simulation aspects and control modeling are indispensible for knowing system performance, and to generate an original approach of artificial organs. This overview focuses mainly on control techniques, by far a theoretical overview and fusion of artificial sense organs trying to mimic the efficacies of biologically active sensory organs. Keywords: virtual reality, prosthetic vision, artificial

  13. Concurrence of rule- and similarity-based mechanisms in artificial grammar learning.

    Science.gov (United States)

    Opitz, Bertram; Hofmann, Juliane

    2015-03-01

    A current theoretical debate regards whether rule-based or similarity-based learning prevails during artificial grammar learning (AGL). Although the majority of findings are consistent with a similarity-based account of AGL it has been argued that these results were obtained only after limited exposure to study exemplars, and performance on subsequent grammaticality judgment tests has often been barely above chance level. In three experiments the conditions were investigated under which rule- and similarity-based learning could be applied. Participants were exposed to exemplars of an artificial grammar under different (implicit and explicit) learning instructions. The analysis of receiver operating characteristics (ROC) during a final grammaticality judgment test revealed that explicit but not implicit learning led to rule knowledge. It also demonstrated that this knowledge base is built up gradually while similarity knowledge governed the initial state of learning. Together these results indicate that rule- and similarity-based mechanisms concur during AGL. Moreover, it could be speculated that two different rule processes might operate in parallel; bottom-up learning via gradual rule extraction and top-down learning via rule testing. Crucially, the latter is facilitated by performance feedback that encourages explicit hypothesis testing.

  14. Learning Efficiency of Consciousness System for Robot Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Osama Shoubaky

    2014-12-01

    Full Text Available This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD. Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans.

  15. A Reinforcement Learning Approach to Control.

    Science.gov (United States)

    1997-05-31

    acquisition is inherently a partially observable Markov decision problem. This report describes an efficient, scalable reinforcement learning approach to the...deployment of refined intelligent gaze control techniques. This report first lays a theoretical foundation for reinforcement learning . It then introduces...perform well in both high and low SNR ATR environments. Reinforcement learning coupled with history features appears to be both a sound foundation and a practical scalable base for gaze control.

  16. All together now: concurrent learning of multiple structures in an artificial language.

    Science.gov (United States)

    Romberg, Alexa R; Saffran, Jenny R

    2013-01-01

    Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics influenced learning of both adjacent and non-adjacent dependencies. Additionally, though accuracy was similar for both types of structure, participants' knowledge of the deterministic non-adjacent dependencies was more explicit than their knowledge of the probabilistic adjacent dependencies. The results are discussed in the context of current theories of statistical learning and language acquisition.

  17. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    Directory of Open Access Journals (Sweden)

    Chandra Prasetyo Utomo

    2014-07-01

    Full Text Available 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 (BP ANN has some limitations. There are parameters to be set in the beginning, long time for training process, and possibility to be trapped in local minima. In this research, we implemented ANN with extreme learning techniques for diagnosing breast cancer based on Breast Cancer Wisconsin Dataset. Results showed that Extreme Learning Machine Neural Networks (ELM ANN has better generalization classifier model than BP ANN. The development of this technique is promising as intelligent component in medical decision support systems.

  18. Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.

    Science.gov (United States)

    Wilson, Benjamin; Smith, Kenny; Petkov, Christopher I

    2015-03-01

    Artificial grammars (AG) can be used to generate rule-based sequences of stimuli. Some of these can be used to investigate sequence-processing computations in non-human animals that might be related to, but not unique to, human language. Previous AG learning studies in non-human animals have used different AGs to separately test for specific sequence-processing abilities. However, given that natural language and certain animal communication systems (in particular, song) have multiple levels of complexity, mixed-complexity AGs are needed to simultaneously evaluate sensitivity to the different features of the AG. Here, we tested humans and Rhesus macaques using a mixed-complexity auditory AG, containing both adjacent (local) and non-adjacent (longer-distance) relationships. Following exposure to exemplary sequences generated by the AG, humans and macaques were individually tested with sequences that were either consistent with the AG or violated specific adjacent or non-adjacent relationships. We observed a considerable level of cross-species correspondence in the sensitivity of both humans and macaques to the adjacent AG relationships and to the statistical properties of the sequences. We found no significant sensitivity to the non-adjacent AG relationships in the macaques. A subset of humans was sensitive to this non-adjacent relationship, revealing interesting between- and within-species differences in AG learning strategies. The results suggest that humans and macaques are largely comparably sensitive to the adjacent AG relationships and their statistical properties. However, in the presence of multiple cues to grammaticality, the non-adjacent relationships are less salient to the macaques and many of the humans.

  19. Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories.

    Science.gov (United States)

    Fonteneau, Raphael; Murphy, Susan A; Wehenkel, Louis; Ernst, Damien

    2013-09-01

    In this paper, we consider the batch mode reinforcement learning setting, where the central problem is to learn from a sample of trajectories a policy that satisfies or optimizes a performance criterion. We focus on the continuous state space case for which usual resolution schemes rely on function approximators either to represent the underlying control problem or to represent its value function. As an alternative to the use of function approximators, we rely on the synthesis of "artificial trajectories" from the given sample of trajectories, and show that this idea opens new avenues for designing and analyzing algorithms for batch mode reinforcement learning.

  20. Template Approach for Adaptive Learning Strategies

    NARCIS (Netherlands)

    Abbing, Jana; Koidl, Kevin

    2006-01-01

    Please, cite this publication as: Abbing, J. & Koidl, K. (2006). Template Approach for Adaptive Learning Strategies. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

  1. Application of Artificial Intelligence to Reservoir Characterization - An Interdisciplinary Approach

    Energy Technology Data Exchange (ETDEWEB)

    Kelkar, B.G.; Gamble, R.F.; Kerr, D.R.; Thompson, L.G.; Shenoi, S.

    2000-01-12

    The primary goal of this project is to develop a user-friendly computer program to integrate geological and engineering information using Artificial Intelligence (AI) methodology. The project is restricted to fluvially dominated deltaic environments. The static information used in constructing the reservoir description includes well core and log data. Using the well core and the log data, the program identifies the marker beds, and the type of sand facies, and in turn, develops correlation's between wells. Using the correlation's and sand facies, the program is able to generate multiple realizations of sand facies and petrophysical properties at interwell locations using geostatistical techniques. The generated petrophysical properties are used as input in the next step where the production data are honored. By adjusting the petrophysical properties, the match between the simulated and the observed production rates is obtained.

  2. Artificial photosynthesis: biomimetic approaches to solar energy conversion and storage.

    Science.gov (United States)

    Kalyanasundaram, K; Graetzel, M

    2010-06-01

    Using sun as the energy source, natural photosynthesis carries out a number of useful reactions such as oxidation of water to molecular oxygen and fixation of CO(2) in the form of sugars. These are achieved through a series of light-induced multi-electron-transfer reactions involving chlorophylls in a special arrangement and several other species including specific enzymes. Artificial photosynthesis attempts to reconstruct these key processes in simpler model systems such that solar energy and abundant natural resources can be used to generate high energy fuels and restrict the amount of CO(2) in the atmosphere. Details of few model catalytic systems that lead to clean oxidation of water to H(2) and O(2), photoelectrochemical solar cells for the direct conversion of sunlight to electricity, solar cells for total decomposition of water and catalytic systems for fixation of CO(2) to fuels such as methanol and methane are reviewed here.

  3. The Point Approach and the Phrase Approach to Vocabulary Learning

    Institute of Scientific and Technical Information of China (English)

    刘梦媛

    2013-01-01

      As is known to all, vocabulary acquisition plays an essential role in English learning. However, it was supposed very dif⁃ficult to many Chinese learners. For the reason that so many kinds of approaches exists in the real life, English learners are always do not know which one is suitable and more effective. To solve this problem, the paper will analyze two approaches (point approach and phrase approach) for you.

  4. A Machine Learning Approach to Automated Negotiation

    Institute of Scientific and Technical Information of China (English)

    Zhang Huaxiang(张化祥); Zhang Liang; Huang Shangteng; Ma Fanyuan

    2004-01-01

    Automated negotiation between two competitive agents is analyzed, and a multi-issue negotiation model based on machine learning, time belief, offer belief and state-action pair expected Q value is developed. Unlike the widely used approaches such as game theory approach, heuristic approach and argumentation approach, This paper uses a machine learning method to compute agents' average Q values in each negotiation stage. The delayed reward is used to generate agents' offer and counteroffer of every issue. The effect of time and discount rate on negotiation outcome is analyzed. Theory analysis and experimental data show this negotiation model is practical.

  5. Machine learning a theoretical approach

    CERN Document Server

    Natarajan, Balas K

    2014-01-01

    This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks. The presentation

  6. An approach to elemental task learning

    Energy Technology Data Exchange (ETDEWEB)

    Belmans, P

    1990-01-01

    In this article we deal with the automated learning of tasks by a robotic system through observation of a human operator. Particularly, we explain what is meant by a learning ability in autonomous robots and in teleoperation systems, where several operators and several machines may work in cooperation to perform tasks. We discuss different approaches to learning in these systems and outline the features of the models they are based upon. This leads us to choose an analytical model suited for tasks analysis. We then present the software architecture for our proposed approach and show the first results obtained on sample tests. 5 refs., 9 figs.

  7. A Cultural Approach to Learning

    DEFF Research Database (Denmark)

    Rasmussen, Lauge Baungaard

    1998-01-01

    The this article learning is discussed in relation to different understanding of culture. In particular the dialectics of 'Enlightenment' inthe Western culture are reflected , as well aslow- and high-context communication and learningin different types of culture. Finaaly the Weberian methodology...

  8. A Cognitive Approach to e-Learning

    Energy Technology Data Exchange (ETDEWEB)

    Greitzer, Frank L.; Rice, Douglas M.; Eaton, Sharon L.; Perkins, Michael C.; Scott, Ryan T.; Burnette, John R.; Robertson, Sarah R.

    2003-12-01

    Like traditional classroom instruction, distributed learning derives from passive training paradigms. Just as student-centered classroom teaching methods have been applied over several decades of classroom instruction, interactive approaches have been encouraged for distributed learning. While implementation of multimedia-based training features may appear to produce active learning, sophisticated use of multimedia features alone does not necessarily enhance learning. This paper describes the results of applying cognitive science principles to enhance learning in a student-centered, distributed learning environment, and lessons learned in developing and delivering this training. Our interactive, scenario-based approach exploits multimedia technology within a systematic, cognitive framework for learning. The basis of the application of cognitive principles is the innovative use of multimedia technology to implement interaction elements. These simple multimedia interactions, which are used to support new concepts, are later combined with other interaction elements to create more complex, integrated practical exercises. This technology-based approach may be applied in a variety of training and education contexts, but is especially well suited for training of equipment operators and maintainers. For example, it has been used in a sustainment training application for the United States Army's Combat Support System Automated Information System Interface (CAISI). The CAISI provides a wireless communications capability that allows various logistics systems to communicate across the battlefield. Based on classroom training material developed by the CAISI Project Office, the Pacific Northwest National Laboratory designed and developed an interactive, student-centered distributed-learning application for CAISI operators and maintainers. This web-based CAISI training system is also distributed on CD media for use on individual computers, and material developed for the

  9. Applying artificial intelligence to clinical guidelines: the GLARE approach.

    Science.gov (United States)

    Terenziani, Paolo; Montani, Stefania; Bottrighi, Alessio; Molino, Gianpaolo; Torchio, Mauro

    2008-01-01

    We present GLARE, a domain-independent system for acquiring, representing and executing clinical guidelines (GL). GLARE is characterized by the adoption of Artificial Intelligence (AI) techniques in the definition and implementation of the system. First of all, a high-level and user-friendly knowledge representation language has been designed. Second, a user-friendly acquisition tool, which provides expert physicians with various forms of help, has been implemented. Third, a tool for executing GL on a specific patient has been made available. At all the levels above, advanced AI techniques have been exploited, in order to enhance flexibility and user-friendliness and to provide decision support. Specifically, this chapter focuses on the methods we have developed in order to cope with (i) automatic resource-based adaptation of GL, (ii) representation and reasoning about temporal constraints in GL, (iii) decision making support, and (iv) model-based verification. We stress that, although we have devised such techniques within the GLARE project, they are mostly system-independent, so that they might be applied to other guideline management systems.

  10. Modeling of methane emissions using artificial neural network approach

    Directory of Open Access Journals (Sweden)

    Stamenković Lidija J.

    2015-01-01

    Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

  11. Timing matters: the impact of immediate and delayed feedback on artificial language learning.

    Science.gov (United States)

    Opitz, Bertram; Ferdinand, Nicola K; Mecklinger, Axel

    2011-01-01

    In the present experiment, we used event-related potentials (ERP) to investigate the role of immediate and delayed feedback in an artificial grammar learning (AGL) task. Two groups of participants were engaged in classifying non-word strings according to an underlying rule system, not known to the participants. Visual feedback was provided after each classification either immediately or with a short delay of 1 s. Both groups were able to learn the artificial grammar system as indicated by an increase in classification performance. However, the gain in performance was significantly larger for the group receiving immediate feedback as compared to the group receiving delayed feedback. Learning was accompanied by an increase in P300 activity in the ERP for delayed as compared to immediate feedback. Irrespective of feedback delay, both groups exhibited learning related decreases in the feedback-related positivity (FRP) elicited by positive feedback only. The feedback-related negativity (FRN), however, remained constant over the course of learning. These results suggest, first, that delayed feedback is less effective for AGL as task requirements are very demanding, and second, that the FRP elicited by positive prediction errors decreases with learning while the FRN to negative prediction errors is elicited in an all-or-none fashion by negative feedback throughout the entire experiment.

  12. Artificial Intelligence in Education: An Exploration.

    Science.gov (United States)

    Cumming, Geoff

    1998-01-01

    Gives a brief outline of the development of Artificial Intelligence in Education (AIED) which includes psychology, education, cognitive science, computer science, and artificial intelligence. Highlights include learning environments; learner modeling; a situated approach to learning; and current examples of AIED research. (LRW)

  13. THE FACT APPROACH TO COLLEGE ENGLISH LEARNING

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    Introduction The proliferation of approaches and methods is a prominent characteristic of contemporary ESL or EFL teaching. From the Grammar Translation Method, the Direct Method, Situational Language Teaching, the Audiolingual Method to Communicative Language Teaching, Total Physical Response, the Silent Way, Community Language Learning, the Natural Approach and Suggestopedia, the discussion has lasted for more than a century. However, the wide variety of method or approach options confuses rather than comforts, because they seem to hold very different views of what language is and how a language is learned (Richards & Rodgers, 1991) . This confusing situation is mainly caused by the myth of the hu-

  14. Assessing Approaches to Learning in School Readiness

    Directory of Open Access Journals (Sweden)

    Otilia C. Barbu

    2015-07-01

    Full Text Available This study examines the psychometric properties of two assessments of children’s approaches to learning: the Devereux Early Childhood Assessment (DECA and a 13-item approaches to learning rating scale (AtL derived from the Arizona Early Learning Standards (AELS. First, we administered questionnaires to 1,145 randomly selected parents/guardians of first-time kindergarteners. Second, we employed confirmatory factor analysis (CFA with parceling for DECA to reduce errors due to item specificity and prevent convergence difficulties when simultaneously estimating DECA and AtL models. Results indicated an overlap of 55% to 72% variance between the domains of the two instruments and suggested that the new AtL instrument is an easily administered alternative to the DECA for measuring children’s approaches to learning. This is one of the first studies that investigated DECA’s approaches to learning dimension and explored the measurement properties of an instrument purposely derived from a state’s early learning guidelines.

  15. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Paul Tonelli

    Full Text Available A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1 the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2 synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT. Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1 in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2 whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  16. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Science.gov (United States)

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  17. Connections from Kafka: exposure to meaning threats improves implicit learning of an artificial grammar.

    Science.gov (United States)

    Proulx, Travis; Heine, Steven J

    2009-09-01

    In the current studies, we tested the prediction that learning of novel patterns of association would be enhanced in response to unrelated meaning threats. This prediction derives from the meaning-maintenance model, which hypothesizes that meaning-maintenance efforts may recruit patterns of association unrelated to the original meaning threat. Compared with participants in control conditions, participants exposed to either of two unrelated meaning threats (i.e., reading an absurd short story by Franz Kafka or arguing against one's own self-unity) demonstrated both a heightened motivation to perceive the presence of patterns within letter strings and enhanced learning of a novel pattern actually embedded within letter strings (artificial-grammar learning task). These results suggest that the cognitive mechanisms responsible for implicitly learning patterns are enhanced by the presence of a meaning threat.

  18. A Bayesian Approach for Structural Learning with Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Cen Li

    2002-01-01

    Full Text Available Hidden Markov Models(HMM have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.

  19. Bilingual Approaches to Language Learning

    Institute of Scientific and Technical Information of China (English)

    MaryMcgroarty; NorthernArizonaUniversity

    2003-01-01

    “Billingual Approaches to Language Learning” describes the various bilingual models found at different levels(elementary,secondary,post-secondary,and adult education),identifying key instructional features and emphasizing the drive for quality instruction.The paper makes reference to consideration of the political contexts as well as pedagogical factors affecting the choices and outcomes related to bilingual instruction.

  20. ASSESSMENT APPROACHES IN VIRTUAL LEARNING

    Directory of Open Access Journals (Sweden)

    Azam RASTGOO

    2010-01-01

    Full Text Available Today, the traditional assessment methods are not enough for measuring students’ ability. Internet and its technologies have a strong impact to change it and there are some new ways to measure students’ ability and knowledge. This article identifies some assessment methods and tools in online education and describes findings that show the importance of online assessment and online technologies. It also describes some advantages and disadvantages of new methods of assessment. Additionally this article review some valuable effects of using new methods such as e-portfolios, online self and peer-assessment in providing in time and good feedback for student, increasing students’ participation, improving students learning achievement and increasing their abilities in this area.

  1. The influence of consistency, frequency, and semantics on learning to read: an artificial orthography paradigm.

    Science.gov (United States)

    Taylor, J S H; Plunkett, Kim; Nation, Kate

    2011-01-01

    Two experiments explored learning, generalization, and the influence of semantics on orthographic processing in an artificial language. In Experiment 1, 16 adults learned to read 36 novel words written in novel characters. Posttraining, participants discriminated trained from untrained items and generalized to novel items, demonstrating extraction of individual character sounds. Frequency and consistency effects in learning and generalization showed that participants were sensitive to the statistics of their learning environment. In Experiment 2, 32 participants were preexposed to the sounds of all items (lexical phonology) and to novel definitions for half of these items (semantics). Preexposure to either lexical phonology or semantics boosted the early stages of orthographic learning relative to Experiment 1. By the end of training, facilitation was restricted to the semantic condition and to items containing low-frequency inconsistent vowels. Preexposure reduced generalization, suggesting that enhanced item-specific learning was achieved at the expense of character-sound abstraction. The authors' novel paradigm provides a new tool to explore orthographic learning. Although the present findings support the idea that semantic knowledge supports word reading processes, they also suggest that item-specific phonological knowledge is important in the early stages of learning to read.

  2. Learning and long-term retention of large-scale artificial languages.

    Directory of Open Access Journals (Sweden)

    Michael C Frank

    Full Text Available Recovering discrete words from continuous speech is one of the first challenges facing language learners. Infants and adults can make use of the statistical structure of utterances to learn the forms of words from unsegmented input, suggesting that this ability may be useful for bootstrapping language-specific cues to segmentation. It is unknown, however, whether performance shown in small-scale laboratory demonstrations of "statistical learning" can scale up to allow learning of the lexicons of natural languages, which are orders of magnitude larger. Artificial language experiments with adults can be used to test whether the mechanisms of statistical learning are in principle scalable to larger lexicons. We report data from a large-scale learning experiment that demonstrates that adults can learn words from unsegmented input in much larger languages than previously documented and that they retain the words they learn for years. These results suggest that statistical word segmentation could be scalable to the challenges of lexical acquisition in natural language learning.

  3. Artificial intelligence

    CERN Document Server

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  4. Artificial intelligence and tutoring systems computational and cognitive approaches to the communication of knowledge

    CERN Document Server

    Wenger, Etienne

    2014-01-01

    Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge focuses on the cognitive approaches, methodologies, principles, and concepts involved in the communication of knowledge. The publication first elaborates on knowledge communication systems, basic issues, and tutorial dialogues. Concerns cover natural reasoning and tutorial dialogues, shift from local strategies to multiple mental models, domain knowledge, pedagogical knowledge, implicit versus explicit encoding of knowledge, knowledge communication, and practical and theoretic

  5. Single Robot Localisation Approach for Indoor Robotic Systems through Integration of Odometry and Artificial Landmarks

    OpenAIRE

    Ņikitenko, A; Liekna, A; Ekmanis, M.; Kuļikovskis, G; Andersone, I

    2013-01-01

    we present an integrated approach for robot localization that allows to integrate for the artificial landmark localization data with odometric sensors and signal transfer function data to provide means for different practical application scenarios. The sensor data fusion deals with asynchronous sensor data using inverse Laplace transform. We demonstrate a simulation software system that ensures smooth integration of the odometry-based and signal transfer – based localization into one approach.

  6. Artificial intelligence approach in analysis of DNA sequences.

    Science.gov (United States)

    Brézillon, P J; Zaraté, P; Saci, F

    1993-01-01

    We present an approach for designing a knowledge-based system, called Sequence Acquisition In Context (SAIC), that will be able to cooperate with a biologist in the analysis of DNA sequences. The main task of the system is the acquisition of the expert knowledge that the biologist uses for solving ambiguities from gel autoradiograms, with the aim of re-using it later for solving similar ambiguities. The various types of expert knowledge constitute what we call the contextual knowledge of the sequence analysis. Contextual knowledge deals with the unavoidable problems that are common in the study of the living material (eg noise on data, difficulties of observations). Indeed, the analysis of DNA sequences from autoradiograms belongs to an emerging and promising area of investigation, namely reasoning with images. The SAIC project is developed in a theoretical framework that is shared with other applications. Not all tasks have the same importance in each application. We use this observation for designing an intelligent assistant system with three applications. In the SAIC project, we focus on knowledge acquisition, human-computer interaction and explanation. The project will benefit research in the two other applications. We also discuss our SAIC project in the context of large international projects that aim to re-use and share knowledge in a repository.

  7. A theoretical approach to artificial intelligence systems in medicine.

    Science.gov (United States)

    Spyropoulos, B; Papagounos, G

    1995-10-01

    The various theoretical models of disease, the nosology which is accepted by the medical community and the prevalent logic of diagnosis determine both the medical approach as well as the development of the relevant technology including the structure and function of the A.I. systems involved. A.I. systems in medicine, in addition to the specific parameters which enable them to reach a diagnostic and/or therapeutic proposal, entail implicitly theoretical assumptions and socio-cultural attitudes which prejudice the orientation and the final outcome of the procedure. The various models -causal, probabilistic, case-based etc. -are critically examined and their ethical and methodological limitations are brought to light. The lack of a self-consistent theoretical framework in medicine, the multi-faceted character of the human organism as well as the non-explicit nature of the theoretical assumptions involved in A.I. systems restrict them to the role of decision supporting "instruments" rather than regarding them as decision making "devices". This supporting role and, especially, the important function which A.I. systems should have in the structure, the methods and the content of medical education underscore the need of further research in the theoretical aspects and the actual development of such systems.

  8. Artificial Neural Network Approach in Radar Target Classification

    Directory of Open Access Journals (Sweden)

    N. K. Ibrahim

    2009-01-01

    Full Text Available Problem statement: This study unveils the potential and utilization of Neural Network (NN in radar applications for target classification. The radar system under test is a special of it kinds and known as Forward Scattering Radar (FSR. In this study the target is a ground vehicle which is represented by typical public road transport. The features from raw radar signal were extracted manually prior to classification process using Neural Network (NN. Features given to the proposed network model are identified through radar theoretical analysis. Multi-Layer Perceptron (MLP back-propagation neural network trained with three back-propagation algorithm was implemented and analyzed. In NN classifier, the unknown target is sent to the network trained by the known targets to attain the accurate output. Approach: Two types of classifications were analyzed. The first one is to classify the exact type of vehicle, four vehicle types were selected. The second objective is to grouped vehicle into their categories. The proposed NN architecture is compared to the K Nearest Neighbor classifier and the performance is evaluated. Results: Based on the results, the proposed NN provides a higher percentage of successful classification than the KNN classifier. Conclusion/Recommendation: The result presented here show that NN can be effectively employed in radar classification applications.

  9. Young Children's Approaches to Learning: A Sociocultural Perspective

    Science.gov (United States)

    Chen, Jie-Qi; Masur, Ann; McNamee, Gillian

    2011-01-01

    Recognising school readiness as a national priority, the National Education Goals Panel identified the development of young children's approaches to learning as essential for achieving readiness. Approaches to learning include inclinations, attitudes, and learning styles. Despite wide agreement that learning approaches are critical for school…

  10. A Learning Object Approach To Evidence based learning

    Directory of Open Access Journals (Sweden)

    Zabin Visram

    2005-06-01

    Full Text Available This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has meant increasingly, there is a desperate need to adopt wireless schemes, whereby bespoke courses can be developed to help practitioners keep up with expanding knowledge base. Evidently, without current best evidence, practice risks becoming rapidly out of date, to the detriment of the patient. There is a need to provide a tactical, operational and effective environment, which allows professional to update their education, and complete specialised training, just-in-time, in their own time and location. Following this demand in the marketplace the information engineering group, in combination with several medical and dental schools, set out to develop and design a conceptual framework which form the basis of pioneering research, which at last, enables practitioner's to adopt a philosophy of life long learning. The body and structure of this framework is subsumed under the term Object oriented approach to Evidence Based learning, Just-in-time, via Internet sustained by Reusable Learning Objects (The OEBJIRLO Progression. The technical pillars which permit this concept of life long learning are pivoted by the foundations of object oriented technology, Learning objects, Just-in-time education, Data Mining, intelligent Agent technology, Flash interconnectivity and remote wireless technology, which allow practitioners to update their professional skills, complete specialised training which leads to accredited qualifications. This paper sets out to develop and

  11. Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

    Science.gov (United States)

    Gupta, Shikha; Basant, Nikita; Rai, Premanjali; Singh, Kunwar P

    2015-11-01

    Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The

  12. Functional analysis from visual and compositional data. An artificial intelligence approach.

    Science.gov (United States)

    Barceló, J. A.; Moitinho de Almeida, V.

    Why archaeological artefacts are the way they are? In this paper we try to solve such a question by investigating the relationship between form and function. We propose new ways of studying the way behaviour in the past can be asserted on the examination of archaeological observables in the present. In any case, we take into account that there are also non-visual features characterizing ancient objects and materials (i.e., compositional information based on mass spectrometry data, chronological information based on radioactive decay measurements, etc.). Information that should make us aware of many functional properties of objects is multidimensional in nature: size, which makes reference to height, length, depth, weight and mass; shape and form, which make reference to the geometry of contours and volumes; texture, which refers to the microtopography (roughness, waviness, and lay) and visual appearance (colour variations, brightness, reflectivity and transparency) of surfaces; and finally material, meaning the combining of distinct compositional elements and properties to form a whole. With the exception of material data, the other relevant aspects for functional reasoning have been traditionally described in rather ambiguous terms, without taking into account the advantages of quantitative measurements of shape/form, and texture. Reasoning about the functionality of archaeological objects recovered at the archaeological site requires a cross-disciplinary investigation, which may also range from recognition techniques used in computer vision and robotics to reasoning, representation, and learning methods in artificial intelligence. The approach we adopt here is to follow current computational theories of object perception to ameliorate the way archaeology can deal with the explanation of human behaviour in the past (function) from the analysis of visual and non-visual data, taking into account that visual appearances and even compositional characteristics only

  13. Artificial grammar learning in primary school children with and without developmental dyslexia.

    Science.gov (United States)

    Pavlidou, Elpis V; Williams, Joanne M; Kelly, Louise M

    2009-06-01

    This paper explores implicit learning in typically developing and primary school children (9-12 years old) with developmental dyslexia using an artificial grammar learning (AGL) task. Two experiments were conducted, which differed in time of presentation and nature of the instructional set (experiment 1--implicit instructions vs experiment 2--explicit instructions). Repeated measures analysis of variance (group x grammaticality x chunk strength) showed a group effect only in experiment 1 (implicit instructions), with only the typically developing children showing evidence of AGL. There was a grammaticality effect (adherence to the rules) for both groups in the two experimental situations. We suggest that the typically developing children exhibited intact implicit learning as manifested in AGL performance, whereas children with developmental dyslexia failed to provide such evidence due to possible mediating cognitive developmental factors.

  14. PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Nick Z. Zacharis

    2016-09-01

    Full Text Available Along with the spreading of online education, the importance of active support of students involved in online learning processes has grown. The application of artificial intelligence in education allows instructors to analyze data extracted from university servers, identify patterns of student behavior and develop interventions for struggling students. This study used student data stored in a Moodle server and predicted student success in course, based on four learning activities - communication via emails, collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to predict student performance on a blended learning course environment. The model predicted the performance of students with correct classification rate, CCR, of 98.3%.

  15. The Three T's: Approaches to Environmental Learning.

    Science.gov (United States)

    Russell, Constance L.

    1999-01-01

    A model identified by educator Jack Miller identifies three dominant approaches to teaching: transmission (student as passive recipient of knowledge), transaction (student-centered active learning), and transformation (promoting personal growth and social change). While most current environmental-education theory and practice corresponds with the…

  16. Transformative Learning Approaches for Public Relations Pedagogy

    Science.gov (United States)

    Motion, Judy; Burgess, Lois

    2014-01-01

    Public relations educators are frequently challenged by students' flawed perceptions of public relations. Two contrasting case studies are presented in this paper to illustrate how socially-oriented paradigms may be applied to a real-client project to deliver a transformative learning experience. A discourse-analytic approach is applied within the…

  17. A Mixed Learning Approach in Mechatronics Education

    Science.gov (United States)

    Yilmaz, O.; Tuncalp, K.

    2011-01-01

    This study aims to investigate the effect of a Web-based mixed learning approach model on mechatronics education. The model combines different perception methods such as reading, listening, and speaking and practice methods developed in accordance with the vocational background of students enrolled in the course Electromechanical Systems in…

  18. Games and machine learning: a powerful combination in an artificial intelligence course

    Science.gov (United States)

    Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

    2010-03-01

    Project MLeXAI (Machine Learning eXperiences in Artificial Intelligence (AI)) seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense - a simple real-time strategy game and Checkers - a classic turn-based board game. From the instructors' prospective, we examine aspects of design and implementation as well as the challenges and rewards of using the curricula. We explore students' responses to the projects via the results of a common survey. Finally, we compare the student perceptions from the game-based projects to non-game based projects from the first phase of Project MLeXAI.

  19. Design of Learning Objects for Concept Learning: Effects of Multimedia Learning Principles and an Instructional Approach

    Science.gov (United States)

    Chiu, Thomas K. F.; Churchill, Daniel

    2016-01-01

    Literature suggests using multimedia learning principles in the design of instructional material. However, these principles may not be sufficient for the design of learning objects for concept learning in mathematics. This paper reports on an experimental study that investigated the effects of an instructional approach, which includes two teaching…

  20. Implicit learning and reading: insights from typical children and children with developmental dyslexia using the artificial grammar learning (AGL) paradigm.

    Science.gov (United States)

    Pavlidou, Elpis V; Williams, Joanne M

    2014-07-01

    We examined implicit learning in school-aged children with and without developmental dyslexia based on the proposal that implicit learning plays a significant role in mastering fluent reading. We ran two experiments with 16 typically developing children (9 to 11-years-old) and 16 age-matched children with developmental dyslexia using the artificial grammar learning (AGL) paradigm. In Experiment 1 (non-transfer task), children were trained on stimuli that followed patterns (rules) unknown to them. Subsequently, they were asked to decide from a novel set which stimuli follow the same rules (grammaticality judgments). In Experiment 2 (transfer task), training and testing stimuli differed in their superficial characteristics but followed the same rules. Again, children were asked to make grammaticality judgments. Our findings expand upon previous research by showing that children with developmental dyslexia show difficulties in implicit learning that are most likely specific to higher-order rule-like learning. These findings are discussed in relation to current theories of developmental dyslexia and of implicit learning.

  1. Semi-Supervised Learning Techniques in AO Applications: A Novel Approach To Drift Counteraction

    Science.gov (United States)

    De Vito, S.; Fattoruso, G.; Pardo, M.; Tortorella, F.; Di Francia, G.

    2011-11-01

    In this work we proposed and tested the use of SSL techniques in the AO domain. The SSL characteristics have been exploited to reduce the need for costly supervised samples and the effects of time dependant drift of state-of-the-art statistical learning approaches. For this purpose, an on-field recorded one year long atmospheric pollution dataset has been used. The semi-supervised approach benefitted from the use of updated unlabeled samples, adapting its knowledge to the slowly changing drift effects. We expect that semi-supervised learning can provide significant advantages to the performance of sensor fusion subsystems in artificial olfaction exhibiting an interesting drift counteraction effect.

  2. Implicit learning in aphasia: Evidence from serial reaction time and artificial grammar tasks

    Directory of Open Access Journals (Sweden)

    Julia Schuchard

    2014-04-01

    Full Text Available Introduction. Implicit learning involves extracting patterns through repeated exposure to stimuli. Little is known about this learning process in individuals with aphasia, although evidence suggests that implicit learning mechanisms remain intact in aphasia (Goschke et al., 2001; Schuchard & Thompson, 2014. The purpose of the present research was to test implicit learning in aphasia in two experiments. Method. Nine individuals with stroke-induced agrammatic aphasia and 21 age-matched healthy adults served as participants for both experiments. Experiment 1 examined nonverbal sequence learning, which required participants to perform a visuomotor serial reaction time task by pressing buttons corresponding to the location of an asterisk that appeared on a computer monitor. Unknown to participants, the location of the asterisk followed a repeating sequence until the final block of the experiment, in which the locations were randomized. Experiment 2 tested grammar learning. Participants were exposed to pseudowords ordered in short sentences according to the rules of an artificial phrase structure grammar (Saffran, 2002. On the first day of the study, all aphasic participants and twelve of the healthy participants received grammar training by listening to grammatical sentences in the artificial language for 30 minutes, followed by completion of a grammaticality judgment test. These participants returned the next day, during which they completed the grammaticality judgment test again, participated in a second session of training (i.e., listening to sentences, and completed a final administration of the judgment test. Untrained healthy control participants completed the three tests but did not receive the two training sessions. Results. Results from the serial reaction time task showed a significant increase in reaction time during the final randomized block compared to the preceding sequenced block, indicating implicit learning of the sequence, for both

  3. A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word-Order Universal

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul

    2012-01-01

    In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized…

  4. Revisiting the syntactic abilities of non-human animals: natural vocalizations and artificial grammar learning.

    Science.gov (United States)

    ten Cate, Carel; Okanoya, Kazuo

    2012-07-19

    The domain of syntax is seen as the core of the language faculty and as the most critical difference between animal vocalizations and language. We review evidence from spontaneously produced vocalizations as well as from perceptual experiments using artificial grammars to analyse animal syntactic abilities, i.e. abilities to produce and perceive patterns following abstract rules. Animal vocalizations consist of vocal units (elements) that are combined in a species-specific way to create higher order strings that in turn can be produced in different patterns. While these patterns differ between species, they have in common that they are no more complex than a probabilistic finite-state grammar. Experiments on the perception of artificial grammars confirm that animals can generalize and categorize vocal strings based on phonetic features. They also demonstrate that animals can learn about the co-occurrence of elements or learn simple 'rules' like attending to reduplications of units. However, these experiments do not provide strong evidence for an ability to detect abstract rules or rules beyond finite-state grammars. Nevertheless, considering the rather limited number of experiments and the difficulty to design experiments that unequivocally demonstrate more complex rule learning, the question of what animals are able to do remains open.

  5. A Bayesian Concept Learning Approach to Crowdsourcing

    DEFF Research Database (Denmark)

    Viappiani, Paolo Renato; Zilles, Sandra; Hamilton, Howard J.;

    2011-01-01

    techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing......We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation...... that our Bayesian strategies are effective even in large concept spaces with many uninformative experts....

  6. Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2011-04-01

    Full Text Available This paper proposes a global multi-level thresholding method for image segmentation. As a criterion for this, the traditional method uses the Shannon entropy, originated from information theory, considering the gray level image histogram as a probability distribution, while we applied the Tsallis entropy as a general information theory entropy formalism. For the algorithm, we used the artificial bee colony approach since execution of an exhaustive algorithm would be too time-consuming. The experiments demonstrate that: 1 the Tsallis entropy is superior to traditional maximum entropy thresholding, maximum between class variance thresholding, and minimum cross entropy thresholding; 2 the artificial bee colony is more rapid than either genetic algorithm or particle swarm optimization. Therefore, our approach is effective and rapid.

  7. Human Robotic Swarm Interaction Using An Artificial Physics Approach (Briefing Charts)

    Science.gov (United States)

    2014-12-01

    Human Robotic Swarm Interaction Using An Artificial Physics Approach LT Brenton Campbell ADVISORS: Asst Professor Dr. Timothy Chung Senior Lecturer...REPORT DATE DEC 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Human Robotic Swarm Interaction Using An...and R. Heil, “Distributed, physics-based control of swarms of vehicles,” Autonomous Robots, pp. 137–162, 2004. [Online]. Available: http

  8. Information theory and learning a physical approach

    CERN Document Server

    Nemenman, I M

    2000-01-01

    We try to establish a unified information theoretic approach to learning and to explore some of its applications. First, we define {\\em predictive information} as the mutual information between the past and the future of a time series, discuss its behavior as a function of the length of the series, and explain how other quantities of interest studied previously in learning theory - as well as in dynamical systems and statistical mechanics - emerge from this universally definable concept. We then prove that predictive information provides the {\\em unique measure for the complexity} of dynamics underlying the time series and show that there are classes of models characterized by {\\em power-law growth of the predictive information} that are qualitatively more complex than any of the systems that have been investigated before. Further, we investigate numerically the learning of a nonparametric probability density, which is an example of a problem with power-law complexity, and show that the proper Bayesian formul...

  9. Learning Matlab a problem solving approach

    CERN Document Server

    Gander, Walter

    2015-01-01

    This comprehensive and stimulating introduction to Matlab, a computer language now widely used for technical computing, is based on an introductory course held at Qian Weichang College, Shanghai University, in the fall of 2014.  Teaching and learning a substantial programming language aren’t always straightforward tasks. Accordingly, this textbook is not meant to cover the whole range of this high-performance technical programming environment, but to motivate first- and second-year undergraduate students in mathematics and computer science to learn Matlab by studying representative problems, developing algorithms and programming them in Matlab. While several topics are taken from the field of scientific computing, the main emphasis is on programming. A wealth of examples are completely discussed and solved, allowing students to learn Matlab by doing: by solving problems, comparing approaches and assessing the proposed solutions.

  10. Teaching European Studies: A Blended Learning Approach

    Directory of Open Access Journals (Sweden)

    Alina Christova

    2011-12-01

    Full Text Available This paper will be looking into the teaching method developed by the Institute for European Studies in Brussels, combining an e-learning tool- the E-modules- with face-to-face training sessions and webinars. The main aim is to analyse the three different components of this “blended learning” pedagogical approach, as well as the way they complement each other and to address a few of the challenges that have emerged from the experience of working with them so far. The E-modules are an e-learning platform that has been designed with the purpose of offering a structured and interactive way of learning how the European Union functions. The face-to-face training component currently takes the form of three days in-house seminars, covering in an intensive manner the most important areas of the curriculum. The lectures are held by a mix of academics and practitioners, hereby ensuring a balanced approach, in which theory and practice come together to facilitate the learning experience. The third element of the “blended learning” method is placed in-between online and face-to-face learning: interactive seminars and debates are held online, giving the participants the chance to deepen their knowledge in certain fields of interest and to discuss the content of the course with specialists and among themselves. The mixture of delivery and interaction methods was chosen in order to accommodate a large variety of target groups, ranging from students to professionals working with EU-related issues, with different backgrounds and geographical origins. One of the main challenges is to use each medium for the functionalities it is best designed for and to ensure that the various pieces of the pedagogical puzzle fit together perfectly, while allowing the learners the flexibility that had initially directed them towards “blended learning” instead of a classical classroom approach.

  11. Artificial trachea reconstruction with two-stage approach using memory-alloy mesh

    Institute of Scientific and Technical Information of China (English)

    赵凤瑞; 张银合; 刘世民; 余建军

    2003-01-01

    @@ Artificial tracheal prosthesis is now a challenge to the entire surgical field all over the world. Previously, all kinds of prosthesis were "inner stent" that could not be integrated with native trachea. Since there is an interface between smooth surface of the prosthesis and living tissues, and the inner side of the prosthesis is not covered with living membrane, infections always happen around the prosthesis. We then developed a new technique, which combined memory-alloy mesh with traditional operative procedures. Memory-alloy mesh is extensible, flexible and can maintain the shape of a tube. It has very good compatibility with tissues and no antigenicity. Thus, it is the most desirable material nowadays that can be found to make the frame of an artificial trachea by using a two-stage approach. That is imbedding a pre-shaped memory-alloy mesh under skin or endothelium (pleura or peritoneum), and then making the "sandwich" pedicle skin and muscle flap to a pedicled artificial trachea anastomosed with native trachea. After a two-year experimental study, a patient received the artificial trachea replacement on April 18, 2002, with a good result.

  12. Artificial Neural Network Approach to Predict Biodiesel Production in Supercritical tert-Butyl Methyl Ether

    Directory of Open Access Journals (Sweden)

    Obie Farobie

    2016-05-01

    Full Text Available In this study, for the first time artificial neural network was used to predict biodiesel yield in supercritical tert-butyl methyl ether (MTBE. The experimental data of biodiesel yield conducted by varying four input factors (i.e. temperature, pressure, oil-to-MTBE molar ratio, and reaction time were used to elucidate artificial neural network model in order to predict biodiesel yield. The main goal of this study was to assess how accurately this artificial neural network model to predict biodiesel yield conducted under supercritical MTBE condition. The result shows that artificial neural network is a powerful tool for modeling and predicting biodiesel yield conducted under supercritical MTBE condition that was proven by a high value of coefficient of determination (R of 0.9969, 0.9899, and 0.9658 for training, validation, and testing, respectively. Using this approach, the highest biodiesel yield was determined of 0.93 mol/mol (corresponding to the actual biodiesel yield of 0.94 mol/mol that was achieved at 400 °C, under the reactor pressure of 10 MPa, oil-to-MTBE molar ratio of 1:40 within 15 min of reaction time.

  13. Economic Gardening through Entrepreneurship Education: A Service-Learning Approach

    Science.gov (United States)

    Desplaces, David E.; Wergeles, Fred; McGuigan, Patrick

    2009-01-01

    This article outlines the implementation of a service-learning approach in an entrepreneurship programme using an "economic gardening" strategy. Economic Gardening through Service-Learning (EGS-L) is an approach to economic development that helps local businesses and students grow through a facilitated learning process. Learning is made possible…

  14. Understanding Fatty Acid Metabolism through an Active Learning Approach

    Science.gov (United States)

    Fardilha, M.; Schrader, M.; da Cruz e Silva, O. A. B.; da Cruz e Silva, E. F.

    2010-01-01

    A multi-method active learning approach (MALA) was implemented in the Medical Biochemistry teaching unit of the Biomedical Sciences degree at the University of Aveiro, using problem-based learning as the main learning approach. In this type of learning strategy, students are involved beyond the mere exercise of being taught by listening. Less…

  15. Connectionist models of artificial grammar learning: what type of knowledge is acquired?

    Science.gov (United States)

    Kinder, Annette; Lotz, Anja

    2009-09-01

    Two experiments are presented that test the predictions of two associative learning models of Artificial Grammar Learning. The two models are the simple recurrent network (SRN) and the competitive chunking (CC) model. The two experiments investigate acquisition of different types of knowledge in this task: knowledge of frequency and novelty of stimulus fragments (Experiment 1) and knowledge of letter positions, of small fragments, and of large fragments up to entire strings (Experiment 2). The results show that participants acquired all types of knowledge. Simulation studies demonstrate that the CC model explains the acquisition of all types of fragment knowledge but fails to account for the acquisition of positional knowledge. The SRN model, by contrast, accounts for the entire pattern of results found in the two experiments.

  16. Distal prosody affects learning of novel words in an artificial language.

    Science.gov (United States)

    Morrill, Tuuli H; McAuley, J Devin; Dilley, Laura C; Zdziarska, Patrycja A; Jones, Katherine B; Sanders, Lisa D

    2015-06-01

    The distal prosodic patterning established at the beginning of an utterance has been shown to influence downstream word segmentation and lexical access. In this study, we investigated whether distal prosody also affects word learning in a novel (artificial) language. Listeners were exposed to syllable sequences in which the embedded words were either congruent or incongruent with the distal prosody of a carrier phrase. Local segmentation cues, including the transitional probabilities between syllables, were held constant. During a test phase, listeners rated the items as either words or nonwords. Consistent with the perceptual grouping of syllables being predicted by distal prosody, congruent items were more likely to be judged as words than were incongruent items. The results provide the first evidence that perceptual grouping affects word learning in an unknown language, demonstrating that distal prosodic effects may be independent of lexical or other language-specific knowledge.

  17. Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

    CERN Document Server

    Ghosh, Arka

    2011-01-01

    Financial forecasting is an example of a signal processing problem which is challenging due to Small sample sizes, high noise, non-stationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model with Feedforward Multilayer Artificial Neural Network & Recurrent Time Delay Neural Network for the Financial Timeseries Prediction.This study is developed with the help of historical stockprice dataset made available by GoogleFinance.To develop this prediction model Backpropagation method with Gradient Descent learning has been implemented.Finally the Neural Net, learned with said algorithm is found to be skillful predictor for non-stationary noisy Financial Timeseries.

  18. ChemApproach: Validation of a Questionnaire to Assess the Learning Approaches of Chemistry Students

    Science.gov (United States)

    Lastusaari, Mika; Laakkonen, Eero; Murtonen, Mari

    2016-01-01

    The theory of learning approaches has proven to be one of the most powerful theories explaining university students' learning. However, learning approaches are sensitive to the situation and the content of learning. Chemistry has its own specific features that should be considered when exploring chemistry students' learning habits, specifically…

  19. Learning Strategies Under the Process Writing Approach

    Institute of Scientific and Technical Information of China (English)

    肖青芝

    2008-01-01

    Learning strategies play an important role in second language writing.It is even believed by some linguists that experienced writers and novice writers differ mainly in their writing strategies instead of language proficiency.Within the theoretical framework of the process writing approach,this article introduces the writing strategies with all attempt to increase students'awareness of strategy use and improve their English writing.

  20. OPUS One: An Intelligent Adaptive Learning Environment Using Artificial Intelligence Support

    Science.gov (United States)

    Pedrazzoli, Attilio

    2010-06-01

    AI based Tutoring and Learning Path Adaptation are well known concepts in e-Learning scenarios today and increasingly applied in modern learning environments. In order to gain more flexibility and to enhance existing e-learning platforms, the OPUS One LMS Extension package will enable a generic Intelligent Tutored Adaptive Learning Environment, based on a holistic Multidimensional Instructional Design Model (PENTHA ID Model), allowing AI based tutoring and adaptation functionality to existing Web-based e-learning systems. Relying on "real time" adapted profiles, it allows content- / course authors to apply a dynamic course design, supporting tutored, collaborative sessions and activities, as suggested by modern pedagogy. The concept presented combines a personalized level of surveillance, learning activity- and learning path adaptation suggestions to ensure the students learning motivation and learning success. The OPUS One concept allows to implement an advanced tutoring approach combining "expert based" e-tutoring with the more "personal" human tutoring function. It supplies the "Human Tutor" with precise, extended course activity data and "adaptation" suggestions based on predefined subject matter rules. The concept architecture is modular allowing a personalized platform configuration.

  1. Fronto-parietal contributions to phonological processes in successful artificial grammar learning

    Directory of Open Access Journals (Sweden)

    Dariya Goranskaya

    2016-11-01

    Full Text Available Sensitivity to regularities plays a crucial role in the acquisition of various linguistic features from spoken language input. Artificial grammar (AG learning paradigms explore pattern recognition abilities in a set of structured sequences (i.e. of syllables or letters. In the present study, we investigated the functional underpinnings of learning phonological regularities in auditorily presented syllable sequences. While previous neuroimaging studies either focused on functional differences between the processing of correct vs. incorrect sequences or between different levels of sequence complexity, here the focus is on the neural foundation of the actual learning success. During functional magnetic resonance imaging (fMRI, participants were exposed to a set of syllable sequences with an underlying phonological rule system, known to ensure performance differences between participants. We expected that successful learning and rule application would require phonological segmentation and phoneme comparison. As an outcome of four alternating learning and test fMRI sessions, participants split into successful learners and non-learners. Relative to non-learners, successful learners showed increased task-related activity in a fronto-parietal network of brain areas encompassing the left lateral premotor cortex as well as bilateral superior and inferior parietal cortices during both learning and rule application. These areas were previously associated with phonological segmentation, phoneme comparison and verbal working memory. Based on these activity patterns and the phonological strategies for rule acquisition and application, we argue that successful learning and processing of complex phonological rules in our paradigm is mediated via a fronto-parietal network for phonological processes.

  2. Fronto-Parietal Contributions to Phonological Processes in Successful Artificial Grammar Learning

    Science.gov (United States)

    Goranskaya, Dariya; Kreitewolf, Jens; Mueller, Jutta L.; Friederici, Angela D.; Hartwigsen, Gesa

    2016-01-01

    Sensitivity to regularities plays a crucial role in the acquisition of various linguistic features from spoken language input. Artificial grammar learning paradigms explore pattern recognition abilities in a set of structured sequences (i.e., of syllables or letters). In the present study, we investigated the functional underpinnings of learning phonological regularities in auditorily presented syllable sequences. While previous neuroimaging studies either focused on functional differences between the processing of correct vs. incorrect sequences or between different levels of sequence complexity, here the focus is on the neural foundation of the actual learning success. During functional magnetic resonance imaging (fMRI), participants were exposed to a set of syllable sequences with an underlying phonological rule system, known to ensure performance differences between participants. We expected that successful learning and rule application would require phonological segmentation and phoneme comparison. As an outcome of four alternating learning and test fMRI sessions, participants split into successful learners and non-learners. Relative to non-learners, successful learners showed increased task-related activity in a fronto-parietal network of brain areas encompassing the left lateral premotor cortex as well as bilateral superior and inferior parietal cortices during both learning and rule application. These areas were previously associated with phonological segmentation, phoneme comparison, and verbal working memory. Based on these activity patterns and the phonological strategies for rule acquisition and application, we argue that successful learning and processing of complex phonological rules in our paradigm is mediated via a fronto-parietal network for phonological processes. PMID:27877120

  3. A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems

    Directory of Open Access Journals (Sweden)

    Weixing Su

    2017-03-01

    Full Text Available There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

  4. A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems.

    Science.gov (United States)

    Wang, Zhongqiang; Ambrogio, Stefano; Balatti, Simone; Ielmini, Daniele

    2014-01-01

    Resistive (or memristive) switching devices based on metal oxides find applications in memory, logic and neuromorphic computing systems. Their small area, low power operation, and high functionality meet the challenges of brain-inspired computing aiming at achieving a huge density of active connections (synapses) with low operation power. This work presents a new artificial synapse scheme, consisting of a memristive switch connected to 2 transistors responsible for gating the communication and learning operations. Spike timing dependent plasticity (STDP) is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes. Experiments with integrated artificial synapses demonstrate STDP with stochastic behavior due to (i) the natural variability of set/reset processes in the nanoscale switch, and (ii) the different response of the switch to a given stimulus depending on the initial state. Experimental results are confirmed by model-based simulations of the memristive switching. Finally, system-level simulations of a 2-layer neural network and a simplified STDP model show random learning and recognition of patterns.

  5. A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning forneuromorphic systems

    Directory of Open Access Journals (Sweden)

    Zhongqiang eWang

    2015-01-01

    Full Text Available Resistive (or memristive switching devices based on metal oxides find applications in memory, logic and neuromorphic computing systems. Their small area, low power operation, and high functionality meet the challenges of brain-inspired computing aiming at achieving a huge density of active connections (synapses with low operation power. This work presents a new artificial synapse scheme, consisting of a memristive switch connected to 2 transistors responsible for gating the communication and learning operations. Spike timing dependent plasticity (STDP is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes. Experiments with integrated artificial synapses demonstrate STDP with stochastic behavior due to (i the natural variability of set/reset processes in the nanoscale switch, and (ii the different response of the switch to a given stimulus depending on the initial state. Experimental results are confirmed by model-based simulations of the memristive switching. Finally, system-level simulations of a 2-layer neural network and a simplified STDP model show random learning and recognition of patterns.

  6. A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

    Directory of Open Access Journals (Sweden)

    Hiram Ponce

    2016-10-01

    Full Text Available Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

  7. A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

    Science.gov (United States)

    Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

    2016-10-25

    Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

  8. Typological Asymmetries in Round Vowel Harmony: Support from Artificial Grammar Learning.

    Science.gov (United States)

    Finley, Sara

    2012-10-01

    Providing evidence for the universal tendencies of patterns in the world's languages can be difficult, as it is impossible to sample all possible languages, and linguistic samples are subject to interpretation. However, experimental techniques such as artificial grammar learning paradigms make it possible to uncover the psychological reality of claimed universal tendencies. This paper addresses learning of phonological patterns (systematic tendencies in the sounds in language). Specifically, I explore the role of phonetic grounding in learning round harmony, a phonological process in which words must contain either all round vowels ([o, u]) or all unround vowels ([i, e]). The phonetic precursors to round harmony are such that mid vowels ([o, e]), which receive the greatest perceptual benefit from harmony, are most likely to trigger harmony. High vowels ([i, u]), however, are cross-linguistically less likely to trigger round harmony. Adult participants were exposed to a miniature language that contained a round harmony pattern in which the harmony source triggers were either high vowels ([i, u]) (poor harmony source triggers) or mid vowels ([o, e]) (ideal harmony source triggers). Only participants who were exposed to the ideal mid vowel harmony source triggers were successfully able to generalize the harmony pattern to novel instances, suggesting that perception and phonetic naturalness play a role in learning.

  9. AN ARTIFICIAL INTELLIGENCE APPROACH FOR THE PREDICTION OF SURFACE ROUGHNESS IN CO2 LASER CUTTING

    Directory of Open Access Journals (Sweden)

    MILOŠ MADIĆ

    2012-12-01

    Full Text Available In laser cutting, the cut quality is of great importance. Multiple non-linear effects of process parameters and their interactions make very difficult to predict cut quality. In this paper, artificial intelligence (AI approach was applied to predict the surface roughness in CO2 laser cutting. To this aim, artificial neural network (ANN model of surface roughness was developed in terms of cutting speed, laser power and assist gas pressure. The experimental results obtained from Taguchi’s L25 orthogonal array were used to develop ANN model. The ANN mathematical model of surface roughness was expressed as explicit nonlinear function of the selected input parameters. Statistical results indicate that the ANN model can predict the surface roughness with good accuracy. It was showed that ANNs may be used as a good alternative in analyzing the effects of cutting parameters on the surface roughness.

  10. Forecasting the Colorado River Discharge Using an Artificial Neural Network (ANN) Approach

    CERN Document Server

    Mehrkesh, Amirhossein

    2014-01-01

    Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effectiveness of an Artificial Neural Network was examined to predict the Colorado River discharge. In this modeling process, an ANN model was used to relate the discharge of the Colorado River to such parameters as the amount of precipitation, ambient temperature and snowpack level at a specific time of the year. The model was able to precisely study the impact of climatic parameters on the flow rate of the Colorado River.

  11. Approaches to Learning to Control Dynamic Uncertainty

    Directory of Open Access Journals (Sweden)

    Magda Osman

    2015-10-01

    Full Text Available In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains or exploit (maximizing their short term gains? More to the point, how does this choice of learning strategy influence one’s later ability to control the environment? In the present study, we explore whether people’s self-reported learning strategies and levels of arousal (i.e., surprise, stress correspond to performance measures of controlling a Highly Uncertain or Moderately Uncertain dynamic environment. Generally, self-reports suggest a preference for exploring the environment to begin with. After which, those in the Highly Uncertain environment generally indicated they exploited more than those in the Moderately Uncertain environment; this difference did not impact on performance on later tests of people’s ability to control the dynamic environment. Levels of arousal were also differentially associated with the uncertainty of the environment. Going beyond behavioral data, our model of dynamic decision-making revealed that, in actual fact, there was no difference in exploitation levels between those in the highly uncertain or moderately uncertain environments, but there were differences based on sensitivity to negative reinforcement. We consider the implications of our findings with respect to learning and strategic approaches to controlling dynamic uncertainty.

  12. A two-level on-line learning algorithm of Artificial Neural Network with forward connections

    Directory of Open Access Journals (Sweden)

    Stanislaw Placzek

    2014-12-01

    Full Text Available An Artificial Neural Network with cross-connection is one of the most popular network structures. The structure contains: an input layer, at least one hidden layer and an output layer. Analysing and describing an ANN structure, one usually finds that the first parameter is the number of ANN’s layers. A hierarchical structure is a default and accepted way of describing the network. Using this assumption, the network structure can be described from a different point of view. A set of concepts and models can be used to describe the complexity of ANN’s structure in addition to using a two-level learning algorithm. Implementing the hierarchical structure to the learning algorithm, an ANN structure is divided into sub-networks. Every sub-network is responsible for finding the optimal value of its weight coefficients using a local target function to minimise the learning error. The second coordination level of the learning algorithm is responsible for coordinating the local solutions and finding the minimum of the global target function. In the article a special emphasis is placed on the coordinator’s role in the learning algorithm and its target function. In each iteration the coordinator has to send coordination parameters into the first level of subnetworks. Using the input X and the teaching Z vectors, the local procedures are working and finding their weight coefficients. At the same step the feedback information is calculated and sent to the coordinator. The process is being repeated until the minimum of local target functions is achieved. As an example, a two-level learning algorithm is used to implement an ANN in the underwriting process for classifying the category of health in a life insurance company.

  13. Pedagogical quality in e-learning - Designing e-learning from a learning theoretical approach

    Directory of Open Access Journals (Sweden)

    Christian Dalsgaard

    2005-02-01

    Full Text Available The article is concerned with design and use of e-learning technology to develop education qualitatively. The purpose is to develop a framework for a pedagogical evaluation of e-learning technology. The approach is that evaluation and design must be grounded in a learning theoretical approach, and it is argued that it is necessary to make a reflection of technology in relation to activities, learning principles, and a learning theory in order to qualitatively develop education. The article presents three frameworks developed on the basis of cognitivism, radical constructivism and activity theory. Finally, on the basis of the frameworks, the article discusses e-learning technology and, more specifically, design of virtual learning environments and learning objects. It is argued that e-learning technology is not pedagogically neutral, and that it is therefore necessary to focus on design of technology that explicitly supports a certain pedagogical approach. Further, it is argued that design should direct its focus away from organisation of content and towards design of activities.

  14. An adaptive online learning approach for Support Vector Regression: Online-SVR-FID

    Science.gov (United States)

    Liu, Jie; Zio, Enrico

    2016-08-01

    Support Vector Regression (SVR) is a popular supervised data-driven approach for building empirical models from available data. Like all data-driven methods, under non-stationary environmental and operational conditions it needs to be provided with adaptive learning capabilities, which might become computationally burdensome with large datasets cumulating dynamically. In this paper, a cost-efficient online adaptive learning approach is proposed for SVR by combining Feature Vector Selection (FVS) and Incremental and Decremental Learning. The proposed approach adaptively modifies the model only when different pattern drifts are detected according to proposed criteria. Two tolerance parameters are introduced in the approach to control the computational complexity, reduce the influence of the intrinsic noise in the data and avoid the overfitting problem of SVR. Comparisons of the prediction results is made with other online learning approaches e.g. NORMA, SOGA, KRLS, Incremental Learning, on several artificial datasets and a real case study concerning time series prediction based on data recorded on a component of a nuclear power generation system. The performance indicators MSE and MARE computed on the test dataset demonstrate the efficiency of the proposed online learning method.

  15. The Trialogical Learning Approach to innovate teaching

    Directory of Open Access Journals (Sweden)

    Nadia Sansone

    2016-11-01

    Full Text Available This article focuses on a case of implementing the Trialogical Learning Approach (TLA in two classes in the first year of a university school for future osteopaths (N = 36. The approach involves the creation of useful and tangible objects through alternation between individual and group activities, supported by digital technologies. The aim of the study is to observe the impact of TLA on the quality of learning products made by students and on teaching style, as well as to collect students’ views on activities. The collected data (individual and group products, notes inserted online, audio recordings of lessons, final questionnaires have been analyzed using a mixed qualitative and quantitative approach. The results show: a positive evolution in the quality of individual and group products; b progression from a transmissive teaching style towards one more oriented to collaboration and knowledge building; c general appreciation of the innovative method and its potential for fostering social skills useful for future employment.

  16. Social learning in Models and Cases - an Interdisciplinary Approach

    Science.gov (United States)

    Buhl, Johannes; De Cian, Enrica; Carrara, Samuel; Monetti, Silvia; Berg, Holger

    2016-04-01

    Our paper follows an interdisciplinary understanding of social learning. We contribute to the literature on social learning in transition research by bridging case-oriented research and modelling-oriented transition research. We start by describing selected theories on social learning in innovation, diffusion and transition research. We present theoretical understandings of social learning in techno-economic and agent-based modelling. Then we elaborate on empirical research on social learning in transition case studies. We identify and synthetize key dimensions of social learning in transition case studies. In the following we bridge between more formal and generalising modelling approaches towards social learning processes and more descriptive, individualising case study approaches by interpreting the case study analysis into a visual guide on functional forms of social learning typically identified in the cases. We then try to exemplarily vary functional forms of social learning in integrated assessment models. We conclude by drawing the lessons learned from the interdisciplinary approach - methodologically and empirically.

  17. Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

    Science.gov (United States)

    Chappell, Jackie; Hawes, Nick

    2012-10-05

    Do we fully understand the structure of the problems we present to our subjects in experiments on animal cognition, and the information required to solve them? While we currently have a good understanding of the behavioural and neurobiological mechanisms underlying associative learning processes, we understand much less about the mechanisms underlying more complex forms of cognition in animals. In this study, we present a proposal for a new way of thinking about animal cognition experiments. We describe a process in which a physical cognition task domain can be decomposed into its component parts, and models constructed to represent both the causal events of the domain and the information available to the agent. We then implement a simple set of models, using the planning language MAPL within the MAPSIM simulation environment, and applying it to a puzzle tube task previously presented to orangutans. We discuss the results of the models and compare them with the results from the experiments with orangutans, describing the advantages of this approach, and the ways in which it could be extended.

  18. Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

    Science.gov (United States)

    Chappell, Jackie; Hawes, Nick

    2012-01-01

    Do we fully understand the structure of the problems we present to our subjects in experiments on animal cognition, and the information required to solve them? While we currently have a good understanding of the behavioural and neurobiological mechanisms underlying associative learning processes, we understand much less about the mechanisms underlying more complex forms of cognition in animals. In this study, we present a proposal for a new way of thinking about animal cognition experiments. We describe a process in which a physical cognition task domain can be decomposed into its component parts, and models constructed to represent both the causal events of the domain and the information available to the agent. We then implement a simple set of models, using the planning language MAPL within the MAPSIM simulation environment, and applying it to a puzzle tube task previously presented to orangutans. We discuss the results of the models and compare them with the results from the experiments with orangutans, describing the advantages of this approach, and the ways in which it could be extended. PMID:22927571

  19. A NOVEL APPROACH FOR SELECTION OF LEARNING OBJECTS FOR PERSONALIZED DELIVERY OF E-LEARNING CONTENT

    OpenAIRE

    Anitha, D; C. DEISY

    2013-01-01

    Personalized E-learning, as an intelligent package of technology enhanced education tends to overrule the traditional practices of static web based E-learning systems. Delivering suitable learning objects according to the learners’ knowledge, preferences and learning styles makes up the personalized E-learning. This paper proposes a novel approach for classifying and selecting learning objects for different learning styles proposed by Felder and Silverman The methodology adhere...

  20. A Bayesian Approach to Learning Scoring Systems.

    Science.gov (United States)

    Ertekin, Şeyda; Rudin, Cynthia

    2015-12-01

    We present a Bayesian method for building scoring systems, which are linear models with coefficients that have very few significant digits. Usually the construction of scoring systems involve manual effort-humans invent the full scoring system without using data, or they choose how logistic regression coefficients should be scaled and rounded to produce a scoring system. These kinds of heuristics lead to suboptimal solutions. Our approach is different in that humans need only specify the prior over what the coefficients should look like, and the scoring system is learned from data. For this approach, we provide a Metropolis-Hastings sampler that tends to pull the coefficient values toward their "natural scale." Empirically, the proposed method achieves a high degree of interpretability of the models while maintaining competitive generalization performances.

  1. Course Management Systems and Blended Learning: An Innovative Learning Approach

    Science.gov (United States)

    Chou, Amy Y.; Chou, David C.

    2011-01-01

    This article utilizes Rogers' innovation-decision process model (2003) and Beckman and Berry's innovation process model (2007) to create an innovative learning map that illustrates three learning methods (i.e., face-to-face learning, online learning, and blended learning) in two types of innovation (i.e., incremental innovation and radical…

  2. Approaches to Learning and Study Orchestrations in High School Students

    Science.gov (United States)

    Cano, Francisco

    2007-01-01

    In the framework of the SAL (Students' approaches to learning) position, the learning experience (approaches to learning and study orchestrations) of 572 high school students was explored, examining its interrelationships with some personal and familial variables. Three major results emerged. First, links were found between family's intellectual…

  3. Artificial neural network approach to modeling of alcoholic fermentation of thick juice from sugar beet processing

    Directory of Open Access Journals (Sweden)

    Jokić Aleksandar I.

    2012-01-01

    Full Text Available In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells from thick juice as intermediate product of sugar beet processing was examined. The obtained results suggest that it is possible to decrease fermentation time for the cultivation medium based on thick juice with starting sugar content of 5-15 g kg-1. For the fermentation of cultivation medium based on thick juice with starting sugar content of 20 and 25 g kg-1 significant increase in ethanol content was attained during the whole fermentation process, resulting in 12.51 and 10.95 dm3 m-3 ethanol contents after 48 h, respectively. Other goals of this work were to investigate the possibilities for experimental results prediction using artificial neural networks (ANNs and to find its optimal topology. A feed-forward back-propagation artificial neural network was used to test the hypothesis. As input variables fermentation time and starting sugar content were used. Neural networks had one output value, ethanol content, yeast cell number or sugar content. There was one hidden layer and the optimal number of neurons was found to be nine for all selected network outputs. In this study transfer function was tansig and the selected learning rule was Levenberg-Marquardt. Results suggest that artificial neural networks are good prediction tool for selected network outputs. It was found that experimental results are in very good agreement with computed ones. The coefficient of determination (the R-squared was found to be 0.9997, 0.9997 and 0.9999 for ethanol content, yeast cell number and sugar content, respectively.

  4. The relationship between strategic control and conscious structural knowledge in artificial grammar learning.

    Science.gov (United States)

    Norman, Elisabeth; Scott, Ryan B; Price, Mark C; Dienes, Zoltan

    2016-05-01

    We address Jacoby's (1991) proposal that strategic control over knowledge requires conscious awareness of that knowledge. In a two-grammar artificial grammar learning experiment all participants were trained on two grammars, consisting of a regularity in letter sequences, while two other dimensions (colours and fonts) varied randomly. Strategic control was measured as the ability to selectively apply the grammars during classification. For each classification, participants also made a combined judgement of (a) decision strategy and (b) relevant stimulus dimension. Strategic control was found for all types of decision strategy, including trials where participants claimed to lack conscious structural knowledge. However, strong evidence of strategic control only occurred when participants knew or guessed that the letter dimension was relevant, suggesting that strategic control might be associated with - or even causally requires - global awareness of the nature of the rules even though it does not require detailed knowledge of their content.

  5. The Effects of Textisms on Learning, Study Time, and Instructional Perceptions in an Online Artificial Intelligence Instructional Module

    Science.gov (United States)

    Beasley, Robert; Bryant, Nathan L.; Dodson, Phillip T.; Entwistle, Kevin C.

    2013-01-01

    The purpose of this study was to investigate the effects of textisms (i.e., abbreviated spellings, acronyms, and other shorthand notations) on learning, study time, and instructional perceptions in an online artificial intelligence instructional module. The independent variable in this investigation was experimental condition. For the control…

  6. Developmental dyslexia and implicit learning in childhood: evidence using the artificial grammar learning paradigm

    OpenAIRE

    Pavlidou, Elpis V.

    2010-01-01

    This thesis explores implicit learning in children with developmental dyslexia. While specific cognitive abilities such as phonology and memory have been extensively explored in developmental dyslexia more global, fundamental abilities are rarely studied. A literature review is reported, which indicates that there is a gap in the study of more generic abilities highlighting at the same time, the need of investigating developmental dyslexia in the kind of contemporary context th...

  7. Learning approaches in accounting education: Towards deep learning

    Directory of Open Access Journals (Sweden)

    Yeng Wai Lau

    2015-09-01

    Full Text Available Deep learning facilitates development of generic skills pertinent to prepare graduates for employment. Accounting education with syllabuses burdened with accounting standards to be memorized and regurgitated in examinations does little to promote deep learning. This study conducted a questionnaire survey to examine the extent to which accounting undergraduates at a public university in Malaysia adopt deep learning. This study demonstrates that deep learning is not readily attainable. Surface learning, which promotes rote memorization, constitutes a stepping stone towards deep learning. Having a preference or thirst for meanings is also pertinent to motivate undergraduates to move from rote memorization to seek meanings and thus deep learning. Female undergraduates have been found to be more inclined to adopt deep learning. Much is still to be learned on how best to promote deep learning as learning is a life-long process where everyday life experiences, both on and off-campus, facilitate learning and development.

  8. Learning Pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data

    NARCIS (Netherlands)

    Di Mitri, Daniele; Scheffel, Maren; Drachsler, Hendrik; Börner, Dirk; Ternier, Stefaan; Specht, Marcus

    2017-01-01

    Learning Pulse explores whether using a machine learning approach on multimodal data such as heart rate, step count, weather condition and learning activity can be used to predict learning performance in self-regulated learning settings. An experiment was carried out lasting eight weeks involving Ph

  9. A genetic-neural artificial intelligence approach to resins optimization; Uma metodologia baseada em inteligencia artificial para otimizacao de resinas

    Energy Technology Data Exchange (ETDEWEB)

    Cabral, Denise C.; Barros, Marcio P.; Lapa, Celso M.F.; Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)]. E-mail: lapa@ien.gov.br; mbarros@ien.gov.br

    2005-07-01

    This work presents a preliminary study about the viability and adequacy of a new methodology for the definition of one of the main properties of ion exchange resins used for isotopic separation. Basically, the main problem is the definition of pelicule diameter in case of pelicular ion exchange resins, in order to achieve the best performance in the shortest time. In order to achieve this, a methodology was developed, based in two classic techniques of Artificial Intelligence (AI). At first, an artificial neural network (NN) was trained to map the existing relations between the nucleus radius and the resin's efficiency associated with the exchange time. Later on, a genetic algorithm (GA) was developed in order to find the best pelicule dimension. Preliminary results seem to confirm the potential of the method, and this can be used in any chemical process employing ion exchange resins. (author)

  10. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242

    Directory of Open Access Journals (Sweden)

    Ahmed R. J. Almusawi

    2016-01-01

    Full Text Available This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot’s joint angles.

  11. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

    Science.gov (United States)

    Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.

  12. Robot's Behavioral Learning Based on Artificial Emotion and CMAC Network%基于人工情感与CMAC网络的机器人行为学习

    Institute of Scientific and Technical Information of China (English)

    祝宇虹; 魏金海

    2012-01-01

    人工情感是一个新兴的研究方向,情感智能是人工智能对人类智能的重要逼近.该文利用模糊情感模型来指导机器人学习,在CMAC网络框架内实现机器人的新的行为学习方式.仿真实验结果表明基于人工情感与CMAC网络的机器人行为学习能够学习到一条好的行为策略,具有良好的学习性能.该方法对于提高机器人在恶劣环境下的生存能力和自主决策能力具有很大理论意义和实际应用价值.%Artificial emotion is a new and developing research area,affective intelligence is an important approach to human intelligence. This paper uses a fuzzy artificial emotion mode! Which is used for guiding robot behavior scheme learning and it is to realize a new behavior schem learning method in the frame of CMAC network. The result of simulation tells that the robot behavior scheme learning based on artificial emotion and CMAC network can generate a sheme good enough and the learning is of good performance. This method has great theoretical significance and high application value for enhancing robot' s survivability and autonomous decision - making capacity in harsh and complex environment.

  13. Stress impact on learning in rodents {--} ethological & fysiological approach

    OpenAIRE

    HAVLOVÁ, Jitka

    2009-01-01

    The review compares two methodological approaches of studying stress impact on learning in rodents. Particular forms of stress that can harm or facilitace memory and learning process are distinguished. Permanent exposure to hormone treatments results in space memory damage, whereas short-term exposure enforces the learning processes. Ethological methods have predominantly a negative effect on learning, particularly on space learning and reference memory. On the contrary, some methods can faci...

  14. Learning Approaches, Demographic Factors to Predict Academic Outcomes

    Science.gov (United States)

    Nguyen, Tuan Minh

    2016-01-01

    Purpose: The purpose of this paper is to predict academic outcome in math and math-related subjects using learning approaches and demographic factors. Design/Methodology/Approach: ASSIST was used as the instrumentation to measure learning approaches. The study was conducted in the International University of Vietnam with 616 participants. An…

  15. Cyber Asynchronous versus Blended Cyber Approach in Distance English Learning

    Science.gov (United States)

    Ge, Zi-Gang

    2012-01-01

    This study aims to compare the single cyber asynchronous learning approach with the blended cyber learning approach in distance English education. Two classes of 70 students participated in this study, which lasted one semester of about four months, with one class using the blended approach for their English study and the other only using the…

  16. The Anatomic Study on Replacement of Artificial Atlanto-odontoid Joint through Transoral Approach

    Institute of Scientific and Technical Information of China (English)

    Yong Hu; Shuhua YANG; Hui XIE; Xianfeng HE; Rongming XU; Weihu MA; Jianxiang FENG; Qiu CHEN

    2008-01-01

    In order to provide anatomical basis for transoral approach (TOA) in dealing with the ventro lesions of craniocervical junction, and the design and application of artificial atlanto-odontoid joint, microsurgical dissecting was performed on 8 fresh craniocervical specimens layer by layer through transoropharyngeal approach. The stratification of posterior pharyngeal wall, course of vertebral artery, adjacent relationship of atlas and axis and correlative anatomical parameters of replacement of artificial atlanto-odontoid joint were observed. Besides, 32 sets of atlanto-axial joint in adults' fresh bony specimens were measured with a digital caliper and a goniometer, including the width of bony window of anterior arch of atlas, the width of bony window of axis vertebra, the distance between superior and inferior two atlas screw inserting points, the distance between two axis screw inserting points etc. It was found that the width of atlas and axis which could be exposed were 40.2±3.5mm and 39.3±3.7mm respectively. The width and height of posterior pharyngeal wall which could be exposed were 40.1±5.2mm and 50.2±4.6mm respectively. The distance between superior and inferior two atlas screw inserting points was 28.0±2.9mm and 24.0±3.5mm respectively, and the distance of bilateral axis screw inserting points was 18.0±1.2mm. The operative exposure position through TOA ranged from inferior part of the clivus to the superior part of the C3 vertebral body. Posterior pharyngeal wall consisted of 5 layers and two interspaces: mucosa, submucosa, superficial muscular layer, anterior fascia of vertebrae, anterior muscular layer of vertebrae and posterior interspace of pharynx, anterior interspace of vertebrae. This study revealed that it had the advantages of short operative distance, good exposure and sufficient decompression in dealing with the ventro lesions from the upper cervical to the lower clivus through the TOA. The replacement of artificial atlanto-odontoid joint

  17. Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning

    OpenAIRE

    Carl Aberg, Kristoffer; Doell, Kimberly C.; Schwartz, Sophie

    2016-01-01

    Learning how to gain rewards (approach learning) and avoid punishments (avoidance learning) is fundamental for everyday life. While individual differences in approach and avoidance learning styles have been related to genetics and aging, the contribution of personality factors, such as traits, remains undetermined. Moreover, little is known about the computational mechanisms mediating differences in learning styles. Here, we used a probabilistic selection task with positive and negative feedb...

  18. Biological model of vision for an artificial system that learns to perceive its environment

    Energy Technology Data Exchange (ETDEWEB)

    Blackburn, M.R.; Nguyen, H.G.

    1989-06-01

    The objective is to design an artificial vision system for use in robotics applications. Because the desired performance is equivalent to that achieved by nature, the authors anticipate that the objective will be accomplished most efficiently through modeling aspects of the neuroanatomy and neurophysiology of the biological visual system. Information enters the biological visual system through the retina and is passed to the lateral geniculate and optic tectum. The lateral geniculate nucleus (LGN) also receives information from the cerebral cortex and the result of these two inflows is returned to the cortex. The optic tectum likewise receives the retinal information in a context of other converging signals and organizes motor responses. A computer algorithm is described which implements models of the biological visual mechanisms of the retina, thalamic lateral geniculate and perigeniculate nuclei, and primary visual cortex. Motion and pattern analyses are performed in parallel and interact in the cortex to construct perceptions. We hypothesize that motion reflexes serve as unconditioned pathways for the learning and recall of pattern information. The algorithm demonstrates this conditioning through a learning function approximating heterosynaptic facilitation.

  19. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  20. Development of an Artificial Intelligence Programming Course and Unity3d Based Framework to Motivate Learning in Artistic Minded Students

    DEFF Research Database (Denmark)

    Reng, Lars

    2012-01-01

    between technical and artistic minded students is, however, increased once the students reach the sixth semester. The complex algorithms of the artificial intelligence course seemed to demotivate the artistic minded students even before the course began. This paper will present the extensive changes made...... to the sixth semester artificial intelligence programming course, in order to provide a highly motivating direct visual feedback, and thereby remove the steep initial learning curve for artistic minded students. The framework was developed with close dialog to both the game industry and experienced master...

  1. Learning approaches and studies of effect of environmental factors

    Directory of Open Access Journals (Sweden)

    Mirkov Snežana

    2009-01-01

    Full Text Available There is a presentation of 3P model of learning (Presage-Process-Product, which comprises learning approaches placed in a wider context of the set of variables related to personality, environment, process and outcomes of learning. Three approaches to learning - surface, deep and achievement-oriented - consist of motives and the corresponding learning strategies. There is a discussion of the findings and implications of a great deal of research using the instruments based on this model. We analyzed research findings about the effect of instruction on learning approaches acquired by pupils, and especially students. It is shown how based on learning approach employed by pupils it is possible to draw conclusions about the quality of instruction. Testing the instruments on various samples indicates that the model is applicable in different cultures. Cross-cultural research opened up the problem of relation between memorising and understanding. Further research is necessary, both empirical and theoretical, that is, development of conceptualization of these constructs, and especially their role in education. Perspectives for further research also open up in the direction of studying various factors connected with personality and their relations with learning approaches. The role of learning approaches of teachers in developing the learning approaches of pupils is yet to be examined.

  2. Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression.

    Science.gov (United States)

    Aksu, Buket; Paradkar, Anant; de Matas, Marcel; Ozer, Ozgen; Güneri, Tamer; York, Peter

    2012-12-01

    The publication of the International Conference of Harmonization (ICH) Q8, Q9, and Q10 guidelines paved the way for the standardization of quality after the Food and Drug Administration issued current Good Manufacturing Practices guidelines in 2003. "Quality by Design", mentioned in the ICH Q8 guideline, offers a better scientific understanding of critical process and product qualities using knowledge obtained during the life cycle of a product. In this scope, the "knowledge space" is a summary of all process knowledge obtained during product development, and the "design space" is the area in which a product can be manufactured within acceptable limits. To create the spaces, artificial neural networks (ANNs) can be used to emphasize the multidimensional interactions of input variables and to closely bind these variables to a design space. This helps guide the experimental design process to include interactions among the input variables, along with modeling and optimization of pharmaceutical formulations. The objective of this study was to develop an integrated multivariate approach to obtain a quality product based on an understanding of the cause-effect relationships between formulation ingredients and product properties with ANNs and genetic programming on the ramipril tablets prepared by the direct compression method. In this study, the data are generated through the systematic application of the design of experiments (DoE) principles and optimization studies using artificial neural networks and neurofuzzy logic programs.

  3. Web-based e-learning and virtual lab of human-artificial immune system.

    Science.gov (United States)

    Gong, Tao; Ding, Yongsheng; Xiong, Qin

    2014-05-01

    Human immune system is as important in keeping the body healthy as the brain in supporting the intelligence. However, the traditional models of the human immune system are built on the mathematics equations, which are not easy for students to understand. To help the students to understand the immune systems, a web-based e-learning approach with virtual lab is designed for the intelligent system control course by using new intelligent educational technology. Comparing the traditional graduate educational model within the classroom, the web-based e-learning with the virtual lab shows the higher inspiration in guiding the graduate students to think independently and innovatively, as the students said. It has been found that this web-based immune e-learning system with the online virtual lab is useful for teaching the graduate students to understand the immune systems in an easier way and design their simulations more creatively and cooperatively. The teaching practice shows that the optimum web-based e-learning system can be used to increase the learning effectiveness of the students.

  4. Learning approaches in accounting education: Towards deep learning

    OpenAIRE

    Yeng Wai Lau; Shi Yee Lim

    2015-01-01

    Deep learning facilitates development of generic skills pertinent to prepare graduates for employment. Accounting education with syllabuses burdened with accounting standards to be memorized and regurgitated in examinations does little to promote deep learning. This study conducted a questionnaire survey to examine the extent to which accounting undergraduates at a public university in Malaysia adopt deep learning. This study demonstrates that deep learning is not readily attainable. Surface ...

  5. Enhancing Students' Approaches to Learning: The Added Value of Gradually Implementing Case-Based Learning

    Science.gov (United States)

    Baeten, Marlies; Dochy, Filip; Struyven, Katrien

    2013-01-01

    Previous research has shown the difficulty of enhancing students' approaches to learning, in particular the deep approach, through student-centred teaching methods such as problem- and case-based learning. This study investigates whether mixed instructional methods combining case-based learning and lectures have the power to enhance students'…

  6. Do Humans Really Learn A[superscript n] B[superscript n] Artificial Grammars from Exemplars?

    Science.gov (United States)

    Hochmann, Jean-Remy; Azadpour, Mahan; Mehler, Jacques

    2008-01-01

    An important topic in the evolution of language is the kinds of grammars that can be computed by humans and other animals. Fitch and Hauser (F&H; 2004) approached this question by assessing the ability of different species to learn 2 grammars, (AB)[superscript n] and A[superscript n] B[superscript n]. A[superscript n] B[superscript n] was taken to…

  7. River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches

    Science.gov (United States)

    Sivakumar, B.; Jayawardena, A. W.; Fernando, T. M. K. G.

    2002-08-01

    The use of two non-linear black-box approaches, phase-space reconstruction (PSR) and artificial neural networks (ANN), for forecasting river flow dynamics is studied and a comparison of their performances is made. This is done by attempting 1-day and 7-day ahead forecasts of the daily river flow from the Nakhon Sawan station at the Chao Phraya River basin in Thailand. The results indicate a reasonably good performance of both approaches for both 1-day and 7-day ahead forecasts. However, the performance of the PSR approach is found to be consistently better than that of ANN. One reason for this could be that in the PSR approach the flow series in the phase-space is represented step by step in local neighborhoods, rather than a global approximation as is done in ANN. Another reason could be the use of the multi-layer perceptron (MLP) in ANN, since MLPs may not be most appropriate for forecasting at longer lead times. The selection of training set for the ANN may also contribute to such results. A comparison of the optimal number of variables for capturing the flow dynamics, as identified by the two approaches, indicates a large discrepancy in the case of 7-day ahead forecasts (1 and 7 variables, respectively), though for 1-day ahead forecasts it is found to be consistent (3 variables). A possible explanation for this could be the influence of noise in the data, an observation also made from the 1-day ahead forecast results using the PSR approach. The present results lead to observation on: (1) the use of other neural networks for runoff forecasting, particularly at longer lead times; (2) the influence of training set used in the ANN; and (3) the effect of noise on forecast accuracy, particularly in the PSR approach.

  8. Discuss Optimal Approaches to Learning Strategy Instruction for EFL Learners

    Institute of Scientific and Technical Information of China (English)

    邢菊如

    2009-01-01

    Numerous research studies reveal that learning strategies have played an important role in language learning processes.This paper explores as English teachers.can we impmve students' language proficiency by giving them optimal learning strategy instruction and what approaches are most effective and efficient?

  9. Many-body approach to the dynamics of batch learning

    Science.gov (United States)

    Wong, K. Y. Michael; Li, S.; Tong, Y. W.

    2000-09-01

    Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples, widely applicable to general learning cost functions, and fully taking into account the temporal correlations introduced by the recycling of the examples. The approach is illustrated using the Adaline rule learning teacher-generated or random examples.

  10. Curriculum Design Requirements and Challenges of the Learning Society Approach

    Science.gov (United States)

    Karimi, Sedighe; Nasr, Ahmad-Reza; Sharif, Mostafa

    2012-01-01

    Entering the twenty-first century with the development of communities, they are faced with the necessity of moving towards a learning society. University must extend the learning opportunities and improve the quality of them with curriculum design by learning society approach to respond to the necessity. Researchers believe that some conditions…

  11. A Learning Progressions Approach to Early Algebra Research and Practice

    Science.gov (United States)

    Fonger, Nicole L.; Stephens, Ana; Blanton, Maria; Knuth, Eric

    2015-01-01

    We detail a learning progressions approach to early algebra research and how existing work around learning progressions and trajectories in mathematics and science education has informed our development of a four-component theoretical framework consisting of: a curricular progression of learning goals across big algebraic ideas; an instructional…

  12. University students' approaches to learning first-year mathematics.

    Science.gov (United States)

    Alkhateeb, Haitham M

    2003-12-01

    This study assessed reliability and validity of the Approaches to earning Mathematics Questionnaire, for 218 university students. The study also identified the relationship between subscales. Internal consistency as Cronbach alpha was .77 for the Surface Approach to Learning scale and .88 for the Deep Approach to Learning scale. Principal components analysis yielded a two-factor solution accounting for only 34.6% of variance. The factors were interpreted as Surface Approach and Deep Approach to learning mathematics, as in Australia. The former subscale scores were negatively correlated -.2 with the latter subscale scores.

  13. Computational Approaches to Modeling Artificial Emotion -– An overview of the Proposed Solutions

    Directory of Open Access Journals (Sweden)

    Zdzislaw eKOWALCZUK

    2016-04-01

    Full Text Available Cybernetic approach to modeling artificial emotion through the use of different theories of psychology is considered in this paper, presenting a review of twelve proposed solutions: ActAffAct, FLAME, EMA, ParleE, FearNot!, FAtiMA, WASABI, Cathexis, KARO, MAMID, FCM, and xEmotion. The main motivation for this study is founded on the hypothesis that emotions can play a definite utility role of scheduling variables in the construction of intelligent autonomous systems, agents and mobile robots. In this review we also include an innovative and panoptical, comprehensive system, referred to as the Intelligent System of Decision-making (ISD, which has been employed in practical applications of various autonomous units, and which applies as its part the xEmotion, taking into consideration the personal aspects of emotions, affects (short term emotions and mood (principally, long term emotions.

  14. Estimation of relative humidity based on artificial neural network approach in the Aegean Region of Turkey

    Science.gov (United States)

    Yasar, Abdulkadir; Simsek, Erdoğan; Bilgili, Mehmet; Yucel, Ahmet; Ilhan, Ilhami

    2012-01-01

    The aim of this study is to estimate the monthly mean relative humidity (MRH) values in the Aegean Region of Turkey with the help of the topographical and meteorological parameters based on artificial neural network (ANN) approach. The monthly MRH values were calculated from the measurement in the meteorological observing stations established in Izmir, Mugla, Aydin, Denizli, Usak, Manisa, Kutahya and Afyonkarahisar provinces between 2000 and 2006. Latitude, longitude, altitude, precipitation and months of the year were used in the input layer of the ANN network, while the MRH was used in output layer of the network. The ANN model was developed using MATLAB software, and then actual values were compared with those obtained by ANN and multi-linear regression methods. It seemed that the obtained values were in the acceptable error limits. It is concluded that the determination of relative humidity values is possible at any target point of the region where the measurement cannot be performed.

  15. Artificial neural network approach to modelling of metal contents in different types of chocolates.

    Science.gov (United States)

    Podunavac-Kuzmanović, Sanja; Jevrić, Lidija; Švarc-Gajić, Jaroslava; Kovačević, Strahinja; Vasiljević, Ivana; Kecojević, Isidora; Ivanović, Evica

    2015-01-01

    The relationships between the contents of various metals in different types of chocolates were studied using chemometric approach. Chemometric analysis was based on the application of artificial neural networks (ANN). ANN was performed in order to select the significant models for predicting the metal contents. ANN equations, that represent the content of one metal as a function of the contents of other metals were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. The obtained results indicate the possibility of predicting the metal contents in different types of chocolate.

  16. Optimizing parameters on alignment of PCL/PGA nanofibrous scaffold: An artificial neural networks approach.

    Science.gov (United States)

    Paskiabi, Farnoush Asghari; Mirzaei, Esmaeil; Amani, Amir; Shokrgozar, Mohammad Ali; Saber, Reza; Faridi-Majidi, Reza

    2015-11-01

    This paper proposes an artificial neural networks approach to finding the effects of electrospinning parameters on alignment of poly(ɛ-caprolactone)/poly(glycolic acid) blend nanofibers. Four electrospinning parameters, namely total polymer concentration, working distance, drum speed and applied voltage were considered as input and the standard deviation of the angles of nanofibers, introducing fibers alignments, as the output of the model. The results demonstrated that drum speed and applied voltage are two critical factors influencing nanofibers alignment, however their effect are entirely interdependent. Their effects also are not independent of other electrospinning parameters. In obtaining aligned electrospun nanofibers, the concentration and working distance can also be effective. In vitro cell culture study on random and aligned nanofibers showed directional growth of cells on aligned fibers.

  17. An integrated data envelopment analysis-artificial neural network approach for benchmarking of bank branches

    Science.gov (United States)

    Shokrollahpour, Elsa; Hosseinzadeh Lotfi, Farhad; Zandieh, Mostafa

    2016-02-01

    Efficiency and quality of services are crucial to today's banking industries. The competition in this section has become increasingly intense, as a result of fast improvements in Technology. Therefore, performance analysis of the banking sectors attracts more attention these days. Even though data envelopment analysis (DEA) is a pioneer approach in the literature as of an efficiency measurement tool and finding benchmarks, it is on the other hand unable to demonstrate the possible future benchmarks. The drawback to it could be that the benchmarks it provides us with, may still be less efficient compared to the more advanced future benchmarks. To cover for this weakness, artificial neural network is integrated with DEA in this paper to calculate the relative efficiency and more reliable benchmarks of one of the Iranian commercial bank branches. Therefore, each branch could have a strategy to improve the efficiency and eliminate the cause of inefficiencies based on a 5-year time forecast.

  18. NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    WANG Weizhong; ZHAO Jie; GAO Yongsheng; CAI Hegao

    2006-01-01

    A novel approach for collision-free path planning of a multiple degree-of-freedom (DOF)articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point (VNP), a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise fiom initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

  19. Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning.

    Science.gov (United States)

    Aberg, Kristoffer Carl; Doell, Kimberly C; Schwartz, Sophie

    2016-01-01

    Learning how to gain rewards (approach learning) and avoid punishments (avoidance learning) is fundamental for everyday life. While individual differences in approach and avoidance learning styles have been related to genetics and aging, the contribution of personality factors, such as traits, remains undetermined. Moreover, little is known about the computational mechanisms mediating differences in learning styles. Here, we used a probabilistic selection task with positive and negative feedbacks, in combination with computational modelling, to show that individuals displaying better approach (vs. avoidance) learning scored higher on measures of approach (vs. avoidance) trait motivation, but, paradoxically, also displayed reduced learning speed following positive (vs. negative) outcomes. These data suggest that learning different types of information depend on associated reward values and internal motivational drives, possibly determined by personality traits.

  20. Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning

    Science.gov (United States)

    Carl Aberg, Kristoffer; Doell, Kimberly C.; Schwartz, Sophie

    2016-01-01

    Learning how to gain rewards (approach learning) and avoid punishments (avoidance learning) is fundamental for everyday life. While individual differences in approach and avoidance learning styles have been related to genetics and aging, the contribution of personality factors, such as traits, remains undetermined. Moreover, little is known about the computational mechanisms mediating differences in learning styles. Here, we used a probabilistic selection task with positive and negative feedbacks, in combination with computational modelling, to show that individuals displaying better approach (vs. avoidance) learning scored higher on measures of approach (vs. avoidance) trait motivation, but, paradoxically, also displayed reduced learning speed following positive (vs. negative) outcomes. These data suggest that learning different types of information depend on associated reward values and internal motivational drives, possibly determined by personality traits. PMID:27851807

  1. Generational diversity: teaching and learning approaches.

    Science.gov (United States)

    Johnson, Susan A; Romanello, Mary L

    2005-01-01

    Nursing students represent multiple generations--Baby Boomers, Generation X, and now the Millennials. Each generation has its own set of values, ideas, ethics, beliefs, and learning styles. The authors describe the context, characteristics, and learning styles of each generation and provide suggestions for enhanced teaching and learning across multiple generations. Using generational diversity as a teaching tool in the classroom is also discussed.

  2. A Guided Discovery Approach for Learning Metabolic Pathways

    Science.gov (United States)

    Schultz, Emeric

    2005-01-01

    Learning the wealth of information in metabolic pathways is both challenging and overwhelming for students. A step-by-step guided discovery approach to the learning of the chemical steps in gluconeogenesis and the citric acid cycle is described. This approach starts from concepts the student already knows, develops these further in a logical…

  3. Learning Approaches - Final Report Sub-Project 4

    DEFF Research Database (Denmark)

    Dirckinck-Holmfeld, Lone; Rodríguez Illera, José Luis; Escofet, Anna

    2007-01-01

    The overall aim of Subproject 4 is to apply learning approaches that are appropriate and applicable using ICT. The task is made up of two components 4.1 dealing with learning approaches (see deliverable 4.1), and component 4.2 application of ICT (see deliverable 4.2, deliverable 4.3 & deliverable...

  4. Enhancing the Teaching-Learning Process: A Knowledge Management Approach

    Science.gov (United States)

    Bhusry, Mamta; Ranjan, Jayanthi

    2012-01-01

    Purpose: The purpose of this paper is to emphasize the need for knowledge management (KM) in the teaching-learning process in technical educational institutions (TEIs) in India, and to assert the impact of information technology (IT) based KM intervention in the teaching-learning process. Design/methodology/approach: The approach of the paper is…

  5. A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

    Directory of Open Access Journals (Sweden)

    Zhongbin Wang

    2016-01-01

    Full Text Available In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN and support vector machine (SVM methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.

  6. An adaptive locally linear embedding manifold learning approach for hyperspectral target detection

    Science.gov (United States)

    Ziemann, Amanda K.; Messinger, David W.

    2015-05-01

    Algorithms for spectral analysis commonly use parametric or linear models of the data. Research has shown, however, that hyperspectral data -- particularly in materially cluttered scenes -- are not always well-modeled by statistical or linear methods. Here, we propose an approach to hyperspectral target detection that is based on a graph theory model of the data and a manifold learning transformation. An adaptive nearest neighbor (ANN) graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation. The artificial target manifold helps to guide the separation of the target data from the background data in the new, transformed manifold coordinates. Then, target detection is performed in the manifold space using Spectral Angle Mapper. This methodology is an improvement over previous iterations of this approach due to the incorporation of ANN, the artificial target manifold, and the choice of detector in the transformed space. We implement our approach in a spatially local way: the image is delineated into square tiles, and the detection maps are normalized across the entire image. Target detection results will be shown using laboratory-measured and scene-derived target data from the SHARE 2012 collect.

  7. On the track of Artificial Intelligence: Learning with Intelligent Personal Assistants

    Directory of Open Access Journals (Sweden)

    Nil Goksel Canbek

    2016-01-01

    Full Text Available In a technology dominated world, useful and timely information can be accessed quickly via Intelligent Personal Assistants (IPAs.  By the use of these assistants built into mobile operating systems, daily electronic tasks of a user can be accomplished 24/7. Such tasks like taking dictation, getting turn-by-turn directions, vocalizing email messages, reminding daily appointments, setting reminders, responding any factual questions and invoking apps can be completed by  IPAs such as Apple’s Siri, Google Now and Microsoft Cortana. The mentioned assistants programmed within Artificial Intelligence (AI do create an interaction between human and computer through a natural language used in digital communication. In this regard, the overall purpose of this study is to examine the potential use of IPAs that use advanced cognitive computing technologies and Natural Language Processing (NLP for learning. To achieve this purpose, the working system of IPAs is reviewed briefly within the scope of AI that has recently become smarter to predict, comprehend and carry out multi-step and complex requests of users.

  8. Future Challenges of Robotics and Artificial Intelligence in Nursing: What Can We Learn from Monsters in Popular Culture?

    OpenAIRE

    2016-01-01

    It is highly likely that artificial intelligence (AI) will be implemented in nursing robotics in various forms, both in medical and surgical robotic instruments, but also as different types of droids and humanoids, physical reinforcements, and also animal/pet robots. Exploring and discussing AI and robotics in nursing and health care before these tools become commonplace is of great importance. We propose that monsters in popular culture might be studied with the hope of learning about situat...

  9. Utilizing an Artificial Outcrop to Scaffold Learning Between Laboratory and Field Experiences in a College-Level Introductory Geology Course

    Science.gov (United States)

    Wilson, Meredith

    Geologic field trips are among the most beneficial learning experiences for students as they engage the topic of geology, but they are also difficult environments to maximize learning. This action research study explored one facet of the problems associated with teaching geology in the field by attempting to improve the transition of undergraduate students from a traditional laboratory setting to an authentic field environment. Utilizing an artificial outcrop, called the GeoScene, during an introductory college-level non-majors geology course, the transition was studied. The GeoScene was utilized in this study as an intermediary between laboratory and authentic field based experiences, allowing students to apply traditional laboratory learning in an outdoor environment. The GeoScene represented a faux field environment; outside, more complex and tangible than a laboratory, but also simplified geologically and located safely within the confines of an educational setting. This exploratory study employed a mixed-methods action research design. The action research design allowed for systematic inquiry by the teacher/researcher into how the students learned. The mixed-methods approach garnered several types of qualitative and quantitative data to explore phenomena and support conclusions. Several types of data were collected and analyzed, including: visual recordings of the intervention, interviews, analytic memos, student reflections, field practical exams, and a pre/post knowledge and skills survey, to determine whether the intervention affected student comprehension and interpretation of geologic phenomena in an authentic field environment, and if so, how. Students enrolled in two different sections of the same laboratory course, sharing a common lecture, participated in laboratory exercises implementing experiential learning and constructivist pedagogies that focused on learning the basic geological skills necessary for work in a field environment. These laboratory

  10. A Bayesian model of biases in artificial language learning: the case of a word-order universal.

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul

    2012-01-01

    In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word-order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross-linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners' inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization-Greenberg's Universal 18-which bans a particular word-order pattern relating nouns, adjectives, and numerals.

  11. Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water

    Science.gov (United States)

    Li, Dong; Tang, Cheng; Xia, Chunlei; Zhang, Hua

    2017-02-01

    Artificial reefs (ARs) are effective means to maintain fishery resources and to restore ecological environment in coastal waters. ARs have been widely constructed along the Chinese coast. However, understanding of benthic habitats in the vicinity of ARs is limited, hindering effective fisheries and aquacultural management. Multibeam echosounder (MBES) is an advanced acoustic instrument capable of efficiently generating large-scale maps of benthic environments at fine resolutions. The objective of this study is to develop a technical approach to characterize, classify, and map shallow coastal areas with ARs using an MBES. An automated classification method is designed and tested to process bathymetric and backscatter data from MBES and transform the variables into simple, easily visualized maps. To reduce the redundancy in acoustic variables, a principal component analysis (PCA) is used to condense the highly collinear dataset. An acoustic benthic map of bottom sediments is classified using an iterative self-organizing data analysis technique (ISODATA). The approach is tested with MBES surveys in a 1.15 km2 fish farm with a high density of ARs off the Yantai coast in northern China. Using this method, 3 basic benthic habitats (sandy bottom, muddy sediments, and ARs) are distinguished. The results of the classification are validated using sediment samples and underwater surveys. Our study shows that the use of MBES is an effective method for acoustic mapping and classification of ARs.

  12. Approaches to Learning in First Year University Physics

    Directory of Open Access Journals (Sweden)

    Rachel Wilson

    2012-01-01

    Full Text Available Problem statement: In recent decades, discipline specific ways of thinking and knowing have gained increased recognition in understanding learning. However, there has been little empirical research examining student approaches to learning within specific disciplines or, even more specifically, within different streams of study or in response to different curriculum within a discipline. Approach: The aim of this study was to investigate student approaches to learning in physics. We explore whether different streams of study or exposure to different syllabi are associated with deep or surface approaches to learning. A total of 2,030 first year physics students at an Australian metropolitan university over three different year cohorts and three streams completed an adaptation of the Study Processes Questionnaire (SPQ which produces measures of Deep and Surface approaches to learning. Students studied within ‘Advanced’, ‘Regular’ and ‘Fundamentals’ streams, based upon prior experience in physics study. Students within the three cohorts were exposed to different senior high school syllabi, as the exam board introduced a new and innovative syllabus. We make comparison on approaches to learning across streams and across the three year cohorts. Results: Findings show that the behavior of the mean scale scores for students in different streams in first year physics is in agreement with expectations; advanced streams reported higher levels of deep approaches while Fundamentals streams reported higher levels of surface approaches. Furthermore, different year cohort performance on the scales reflects changes in senior high school syllabus; with a new syllabus reflecting a shift toward more deep approaches to learning. Conclusion/Recommendations: It is promising to see that revision of syllabi has a direct impact upon student approaches learning. A challenge lies in ways to best harness this power and to address the trends seen

  13. Does formal complexity reflect cognitive complexity? Investigating aspects of the Chomsky Hierarchy in an artificial language learning study.

    Science.gov (United States)

    Öttl, Birgit; Jäger, Gerhard; Kaup, Barbara

    2015-01-01

    This study investigated whether formal complexity, as described by the Chomsky Hierarchy, corresponds to cognitive complexity during language learning. According to the Chomsky Hierarchy, nested dependencies (context-free) are less complex than cross-serial dependencies (mildly context-sensitive). In two artificial grammar learning (AGL) experiments participants were presented with a language containing either nested or cross-serial dependencies. A learning effect for both types of dependencies could be observed, but no difference between dependency types emerged. These behavioral findings do not seem to reflect complexity differences as described in the Chomsky Hierarchy. This study extends previous findings in demonstrating learning effects for nested and cross-serial dependencies with more natural stimulus materials in a classical AGL paradigm after only one hour of exposure. The current findings can be taken as a starting point for further exploring the degree to which the Chomsky Hierarchy reflects cognitive processes.

  14. Problem Finding in Professional Learning Communities: A Learning Study Approach

    Science.gov (United States)

    Tan, Yuen Sze Michelle; Caleon, Imelda Santos

    2016-01-01

    This study marries collaborative problem solving and learning study in understanding the onset of a cycle of teacher professional development process within school-based professional learning communities (PLCs). It aimed to explore how a PLC carried out collaborative problem finding--a key process involved in collaborative problem solving--that…

  15. Lifelong Learning in Architectural Design Studio: The Learning Contract Approach

    Science.gov (United States)

    Hassanpour, B.; Che-Ani, A. I.; Usman, I. M. S.; Johar, S.; Tawil, N. M.

    2015-01-01

    Avant-garde educational systems are striving to find lifelong learning methods. Different fields and majors have tested a variety of proposed models and found varying difficulties and strengths. Architecture is one of the most critical areas of education because of its special characteristics, such as learning by doing and complicated evaluation…

  16. An Approach To Personalized e-Learning

    Directory of Open Access Journals (Sweden)

    Matteo Gaeta

    2013-02-01

    Full Text Available This paper focuses on the concept of personalized e-Learning for the computer science (or informatics education. Several authors have stated that personalization, in educational context, allows executing more efficient and effective learning processes. On the other side the use of Semantic Web technologies (e.g. ontologies is more and more often considered as a technological basis for personalization in e-Learning (the so-called self-regulated learning. In this paper we describe how personalization can be exploited in e-Learning systems, focusing on our proposal: the Intelligent Web Teacher (IWT. Therefore we present the evaluation of our personalization tools tested in real academic courses, where e-Learning activities are carried out to complement the traditional lectures.

  17. A Fuzzy Approach to Classify Learning Disability

    Directory of Open Access Journals (Sweden)

    Pooja Manghirmalani

    2012-05-01

    Full Text Available The endeavor of this work is to support the special education community in their quest to be with the mainstream. The initial segment of the paper gives an exhaustive study of the different mechanisms of diagnosing learning disability. After diagnosis of learning disability the further classification of learning disability that is dyslexia, dysgraphia or dyscalculia are fuzzy. Hence the paper proposes a model based on Fuzzy Expert System which enables the classification of learning disability into its various types. This expert system facilitates in simulating conditions which are otherwise imprecisely defined.

  18. Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Karin Kandananond

    2011-08-01

    Full Text Available Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA, artificial neural network (ANN and multiple linear regression (MLR—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance

  19. Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach.

    Science.gov (United States)

    Stamenković, Lidija J; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2015-12-01

    Ammonia emissions at the national level are frequently estimated by applying the emission inventory approach, which includes the use of emission factors, which are difficult and expensive to determine. Emission factors are therefore the subject of estimation, and as such they contribute to inherent uncertainties in the estimation of ammonia emissions. This paper presents an alternative approach for the prediction of ammonia emissions at the national level based on artificial neural networks and broadly available sustainability and economical/agricultural indicators as model inputs. The Multilayer Perceptron (MLP) architecture was optimized using a trial-and-error procedure, including the number of hidden neurons, activation function, and a back-propagation algorithm. Principal component analysis (PCA) was applied to reduce mutual correlation between the inputs. The obtained results demonstrate that the MLP model created using the PCA transformed inputs (PCA-MLP) provides a more accurate prediction than the MLP model based on the original inputs. In the validation stage, the MLP and PCA-MLP models were tested for ammonia emission predictions for up to 2 years and compared with a principal component regression model. Among the three models, the PCA-MLP demonstrated the best performance, providing predictions for the USA and the majority of EU countries with a relative error of less than 20%.

  20. An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas.

    Science.gov (United States)

    Shao, Jing; Yang, Lina; Peng, Ling; Chi, Tianhe; Wang, Xiaomeng

    2015-01-01

    China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC)-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining "replace" and "alter" operations, and the third is a "swap" strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China), and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing) was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures.

  1. An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas.

    Directory of Open Access Journals (Sweden)

    Jing Shao

    Full Text Available China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining "replace" and "alter" operations, and the third is a "swap" strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China, and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures.

  2. Dynamic Predictions from Time Series Data An Artificial Neural Network Approach

    CERN Document Server

    Kulkarni, D R; Parikh, J C

    1997-01-01

    A hybrid approach, incorporating concepts of nonlinear dynamics in artificial neural networks (ANN), is proposed to model time series generated by complex dynamic systems. We introduce well known features used in the study of dynamic systems - time delay $\\tau$ and embedding dimension $d$ - for ANN modelling of time series. These features provide a theoretical basis for selecting the optimal size for the number of neurons in the input layer. The main outcome for the number of neurons in the input layer. The main outcome of the new approach for such problems is that to a large extent it defines the ANN architecture and leads to better predictions. We illustrate our method by considering computer generated periodic and chaotic time series. The ANN model developed gave excellent quality of fit for the training and test sets as well as for iterative dynamic predictions for future values of the two time series. Further, computer experiments were conducted by introducing Gaussian noise of various degrees in the two...

  3. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    Science.gov (United States)

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  4. Undergraduate students' earth science learning: relationships among conceptions, approaches, and learning self-efficacy in Taiwan

    Science.gov (United States)

    Shen, Kuan-Ming; Lee, Min-Hsien; Tsai, Chin-Chung; Chang, Chun-Yen

    2016-06-01

    In the area of science education research, studies have attempted to investigate conceptions of learning, approaches to learning, and self-efficacy, mainly focusing on science in general or on specific subjects such as biology, physics, and chemistry. However, few empirical studies have probed students' earth science learning. This study aimed to explore the relationships among undergraduates' conceptions of, approaches to, and self-efficacy for learning earth science by adopting the structural equation modeling technique. A total of 268 Taiwanese undergraduates (144 females) participated in this study. Three instruments were modified to assess the students' conceptions of, approaches to, and self-efficacy for learning earth science. The results indicated that students' conceptions of learning made a significant contribution to their approaches to learning, which were consequently correlated with their learning self-efficacy. More specifically, students with stronger agreement that learning earth science involves applying the knowledge and skills learned to unknown problems were prone to possess higher confidence in learning earth science. Moreover, students viewing earth science learning as understanding earth science knowledge were more likely to adopt meaningful strategies to learn earth science, and hence expressed a higher sense of self-efficacy. Based on the results, practical implications and suggestions for future research are discussed.

  5. Vocation, motivation and approaches to learning: a comparative study

    OpenAIRE

    Arquero, Jose Luis; Fernandez-Polvillo, Carmen; Hassall, Trevor; Joyce, John

    2015-01-01

    Purpose – The individual characteristics of students can have a strong influence on the success of the adopted innovations in terms of their transferability and sustainability. The purpose of this paper is to compare the motivations and approaches to learning on degrees with differing vocational components. Design/methodology/approach – Self-Determination Theory (SDT) and approaches to learning framework were used as theoretical background. Questionnaires were used to generate dat...

  6. Morphological Analysis as Classification an Inductive-Learning Approach

    CERN Document Server

    Van den Bosch, A; Weijters, T; Bosch, Antal van den; Daelemans, Walter; Weijters, Ton

    1996-01-01

    Morphological analysis is an important subtask in text-to-speech conversion, hyphenation, and other language engineering tasks. The traditional approach to performing morphological analysis is to combine a morpheme lexicon, sets of (linguistic) rules, and heuristics to find a most probable analysis. In contrast we present an inductive learning approach in which morphological analysis is reformulated as a segmentation task. We report on a number of experiments in which five inductive learning algorithms are applied to three variations of the task of morphological analysis. Results show (i) that the generalisation performance of the algorithms is good, and (ii) that the lazy learning algorithm IB1-IG performs best on all three tasks. We conclude that lazy learning of morphological analysis as a classification task is indeed a viable approach; moreover, it has the strong advantages over the traditional approach of avoiding the knowledge-acquisition bottleneck, being fast and deterministic in learning and process...

  7. A Learning Approach to Optical Tomography

    CERN Document Server

    Shoreh, Morteza H; Papadopoulos, Ioannis N; Goy, Alexandre; Vonesch, Cedric; Unser, Michael; Psaltis, Demetri

    2015-01-01

    We describe a method for imaging 3D objects in a tomographic configuration implemented by training an artificial neural network to reproduce the complex amplitude of the experimentally measured scattered light. The network is designed such that the voxel values of the refractive index of the 3D object are the variables that are adapted during the training process. We demonstrate the method experimentally by forming images of the 3D refractive index distribution of cells.

  8. Child Development: An Active Learning Approach

    Science.gov (United States)

    Levine, Laura E.; Munsch, Joyce

    2010-01-01

    Within each chapter of this innovative topical text, the authors engage students by demonstrating the wide range of real-world applications of psychological research connected to child development. In particular, the distinctive Active Learning features incorporated throughout the book foster a dynamic and personal learning process for students.…

  9. Multi-dimensional technology-enabled social learning approach

    DEFF Research Database (Denmark)

    Petreski, Hristijan; Tsekeridou, Sofia; Prasad, Neeli R.

    2013-01-01

    in learning while socializing within their learning communities. However, their “educational” usage is still limited to facilitation of online learning communities and to collaborative authoring of learning material complementary to existing formal (e-) learning services. If the educational system doesn......’t respond to this systemic and structural changes and/or challenges and retains its status quo than it is jeopardizing its own existence or the existence of the education, as we know it. This paper aims to precede one step further by proposing a multi-dimensional approach for technology-enabled social...

  10. Use of Blended Approach in the Learning of Electromagnetic Induction

    CERN Document Server

    Chew, Charles

    2015-01-01

    This paper traces the importance of pedagogical content knowledge in the digital age to prepare today students for the 21st century. It highlights the need for ICT-based pedagogical models that are grounded in both the learning theories of constructivism and connectivism. One such suitable ICT-based pedagogical model is the TSOI Hybrid Learning Model. By means of a physics blended learning exemplar based on the TSOI Hybrid Learning Model, this paper argues for the use of blended learning approach as the way forward for 21st century teaching.

  11. A manifold learning approach to target detection in high-resolution hyperspectral imagery

    Science.gov (United States)

    Ziemann, Amanda K.

    Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to detect a specific material within a scene is of high importance to both civilian and defense applications. This may include identifying "targets" such as vehicles, buildings, or boats. Sensors that process hyperspectral images provide the high-dimensional spectral information necessary to perform such analyses. However, for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m manifold learning, which aims to characterize the embedded lower-dimensional, non-linear manifold upon which the hyperspectral data inherently lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data; this is particularly true when implementing traditional target detection approaches, and the limitations of these models are well-documented. With manifold learning based approaches, the only assumption is that the data reside on an underlying manifold that can be discretely modeled by a graph. The research presented here focuses on the use of graph theory and manifold learning in hyperspectral imagery. Early work explored various graph-building techniques with application to the background model of the Topological Anomaly Detection (TAD) algorithm, which is a graph theory based approach to anomaly detection. This led towards a focus on target detection, and in the development of a specific graph-based model of the data and subsequent dimensionality reduction using manifold learning. An adaptive graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation; the artificial target manifold helps to guide the

  12. Artificial sweeteners and metabolic dysregulation: Lessons learned from agriculture and the laboratory.

    Science.gov (United States)

    Shearer, Jane; Swithers, Susan E

    2016-06-01

    Escalating rates of obesity and public health messages to reduce excessive sugar intake have fuelled the consumption of artificial sweeteners in a wide range of products from breakfast cereals to snack foods and beverages. Artificial sweeteners impart a sweet taste without the associated energy and have been widely recommended by medical professionals since they are considered safe. However, associations observed in long-term prospective studies raise the concern that regular consumption of artificial sweeteners might actually contribute to development of metabolic derangements that lead to obesity, type 2 diabetes and cardiovascular disease. Obtaining mechanistic data on artificial sweetener use in humans in relation to metabolic dysfunction is difficult due to the long time frames over which dietary factors might exert their effects on health and the large number of confounding variables that need to be considered. Thus, mechanistic data from animal models can be highly useful because they permit greater experimental control. Results from animal studies in both the agricultural sector and the laboratory indicate that artificial sweeteners may not only promote food intake and weight gain but can also induce metabolic alterations in a wide range of animal species. As a result, simple substitution of artificial sweeteners for sugars in humans may not produce the intended consequences. Instead consumption of artificial sweeteners might contribute to increases in risks for obesity or its attendant negative health outcomes. As a result, it is critical that the impacts of artificial sweeteners on health and disease continue to be more thoroughly evaluated in humans.

  13. A Team Approach to Successful Learning: Peer Learning Coaches in Chemistry

    Science.gov (United States)

    Popejoy, Kate; Asala, Kathryn S.

    2013-01-01

    High failure rates in introductory large lecture chemistry courses for STEM majors have been of concern for years. Through our weekly Team Approach to Successful Learning (TASL) workshops, students learn and apply problem-solving strategies, coached by specially trained peer learning coaches (LCs). These coaches concurrently enroll in Chemistry…

  14. Undergraduate Students' Earth Science Learning: Relationships among Conceptions, Approaches, and Learning Self-Efficacy in Taiwan

    Science.gov (United States)

    Shen, Kuan-Ming; Lee, Min-Hsien; Tsai, Chin-Chung; Chang, Chun-Yen

    2016-01-01

    In the area of science education research, studies have attempted to investigate conceptions of learning, approaches to learning, and self-efficacy, mainly focusing on science in general or on specific subjects such as biology, physics, and chemistry. However, few empirical studies have probed students' earth science learning. This study aimed to…

  15. Student-Centred Learning Environments: An Investigation into Student Teachers' Instructional Preferences and Approaches to Learning

    Science.gov (United States)

    Baeten, Marlies; Dochy, Filip; Struyven, Katrien; Parmentier, Emmeline; Vanderbruggen, Anne

    2016-01-01

    The use of student-centred learning environments in education has increased. This study investigated student teachers' instructional preferences for these learning environments and how these preferences are related to their approaches to learning. Participants were professional Bachelor students in teacher education. Instructional preferences and…

  16. An Expert System-based Context-Aware Ubiquitous Learning Approach for Conducting Science Learning Activities

    Science.gov (United States)

    Wu, Po-Han; Hwang, Gwo-Jen; Tsai, Wen-Hung

    2013-01-01

    Context-aware ubiquitous learning has been recognized as being a promising approach that enables students to interact with real-world learning targets with supports from the digital world. Several researchers have indicated the importance of providing learning guidance or hints to individual students during the context-aware ubiquitous learning…

  17. A Machine Learning Approach to Test Data Generation

    DEFF Research Database (Denmark)

    Christiansen, Henning; Dahmcke, Christina Mackeprang

    2007-01-01

    Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by {\\em ad hoc......} techniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial data sets have...... been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM...

  18. First-year university students’ disposition and approaches to learning

    Directory of Open Access Journals (Sweden)

    María Victoria Pérez Villalobos

    2011-05-01

    Full Text Available Research on self-regulated learning has recognized cognitive processes that students select and execute to achieve their goals. When performing a task, the student analyzes the task characteristics, context and his own capacities, employing resource planning and management, adopting either deep or superficial learning approaches. To describe the relationship between “disposition to learning strategies and” and “deep and superficial learning approaches”, the “Cuestionario de Formas de Estudio” questionnaire was applied to 344 1st year students from eight study programs of a Chilean university. The results show significant correlation (r greater than 0.30, p lower than 0.001 between disposition to learning strategies and usage ofdeep learning approaches, and between the aforementioned variables andthe amount of weekly study time.

  19. Approaches to Learning and Kolb's Learning Styles of Undergraduates with Better Grades

    Science.gov (United States)

    Almeida, Patrícia; Teixeira-Dias, José Joaquim; Martinho, Mariana; Balasooriya, Chinthaka

    The purpose of this study is to investigate if the teaching, learning and assessment strategies conceived and implemented in a higher education chemistry course promote the development of conceptual understanding, as intended. Thus, our aim is to analyse the learning styles and the approaches to learning of chemistry undergraduates with better grades. The overall results show that the students with better grades possess the assimilator learning style, that is usually associated to the archetypal chemist. Moreover, the students with the highest grades revealed a conception of learning emphasising understanding. However, these students diverged both in their learning approaches and in their preferences for teaching strategies. The majority of students adopted a deep approach or a combination of a deep and a strategic approach, but half of them revealed their preference for teaching-centred strategies.

  20. Word Parts and a Systematic Approach to Medical Vocabulary Learning

    Institute of Scientific and Technical Information of China (English)

    田俊英; 蒋东坡

    2016-01-01

    This paper outlines four word parts of medical vocabulary—roots,prefixes,suffixes,and linking vowels(usually o)and put forward a systematic approach to medical vocabulary learning.To develop a high degree of proficiency in learning medical vocabulary,it is advisable to learn the basic roots and affixes so as to make informed guesses regarding the meanings of unfamiliar medical vocabulary.

  1. Artificial Intelligence and the 'Good Society': the US, EU, and UK approach.

    Science.gov (United States)

    Cath, Corinne; Wachter, Sandra; Mittelstadt, Brent; Taddeo, Mariarosaria; Floridi, Luciano

    2017-03-28

    In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.

  2. Artificial Intelligence and Semantics through the Prism of Structural, Post-Structural and Transcendental Approaches.

    Science.gov (United States)

    Gasparyan, Diana

    2016-12-01

    There is a problem associated with contemporary studies of philosophy of mind, which focuses on the identification and convergence of human and machine intelligence. This is the problem of machine emulation of sense. In the present study, analysis of this problem is carried out based on concepts from structural and post-structural approaches that have been almost entirely overlooked by contemporary philosophy of mind. If we refer to the basic definitions of "sign" and "meaning" found in structuralism and post-structuralism, we see a fundamental difference between the capabilities of a machine and the human brain engaged in the processing of a sign. This research will exemplify and provide additional evidence to support distinctions between syntactic and semantic aspects of intelligence, an issue widely discussed by adepts of contemporary philosophy of mind. The research will demonstrate that some aspect of a number of ideas proposed in relation to semantics and semiosis in structuralism and post-structuralism are similar to those we find in contemporary analytical studies related to the theory and philosophy of artificial intelligence. The concluding part of the paper offers an interpretation of the problem of formalization of sense, connected to its metaphysical (transcendental) properties.

  3. An Artificial Neural Network Approach For Ranking Quenching Parameters In Central Galaxies

    CERN Document Server

    Teimoorinia, Hossen; Ellison, Sara L

    2016-01-01

    We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a population of over 400,000 central galaxies taken from the Sloan Digital Sky Survey Data Release 7. We utilise a variety of physical galaxy properties for training the pattern recognition algorithm to recognise star forming and passive systems, for a `training set' of $\\sim$100,000 galaxies. We then apply the ANN model to a `verification set' of $\\sim$100,000 different galaxies, randomly chosen from the remaining sample. The success rate of each parameter singly, and in conjunction with other parameters, is taken as an indication of how important the parameters are to the process(es) of central galaxy quenching. We find that central velocity dispersion, bulge mass and B/T are excellent predictors of the passive state of the system, indicating that properties related to the...

  4. Artificial multiple criticality and phase equilibria: an investigation of the PC-SAFT approach.

    Science.gov (United States)

    Yelash, Leonid; Müller, Marcus; Paul, Wolfgang; Binder, Kurt

    2005-11-01

    The perturbed-chain statistical associating fluid theory (PC-SAFT) is studied for a wide range of temperature, T, pressure, p, and (effective) chain length, m, to establish the generic phase diagram of polymers according to this theory. In addition to the expected gas-liquid coexistence, two additional phase separations are found, termed "gas-gas" equilibrium (at very low densities) and "liquid-liquid" equilibrium (at densities where the system is expected to be solid already). These phase separations imply that in one-component polymer systems three critical points occur, as well as equilibria of three fluid phases at triple points. However, Monte Carlo simulations of the corresponding system yield no trace of the gas-gas and liquid-liquid equilibria, and we conclude that the latter are just artefacts of the PC-SAFT approach. Using PC-SAFT to correlate data for polybutadiene melts, we suggest that discrepancies in modelling the polymer density at ambient temperature and high pressure can be related to the presumably artificial liquid-liquid phase separation at lower temperatures. Thus, particular care is needed in engineering applications of the PC-SAFT theory that aims at predicting properties of macromolecular materials.

  5. Towards sustainability: artificial intelligent based approach for soil stabilization using various pozzolans

    KAUST Repository

    Ouf, M. S.

    2012-07-03

    Due to the gradual depletion in the conventional resources, searching for a more rational road construction approach aimed at reducing the dependence on imported materials while improving the quality and durability of the roads is necessary. A previous study carried out on a sample of Egyptian soil aimed at reducing the road construction cost, protect the environment and achieving sustainability. RoadCem, ground granulated blast furnace slag (GGBS), lime and ordinary Portland cement (OPC) were employed to stabilise the Egyptian clayey soil. The results revealed that the unconfined compressive strength (UCS) of the test soil increased while the free swelling percent (FSP) decreased with an increase in the total stabiliser and the curing period. This paper discusses attempts to reach optimum stabilization through: (1) Recognizing the relationship between the UCS/FSP of stabilized soil and the stabilization parameters using artificial neural network (ANN); and (2) Performing a backward optimization on the developed (ANN) model using general algorithm (GA) to meet practical design preferences. © 2012 WIT Press.

  6. An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Hong-Hai Tran

    2014-01-01

    Full Text Available Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM algorithm is utilized to classify grouting activities into two classes: success and  failure. Meanwhile, the differential evolution (DE optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.

  7. New Artificial Immune System Approach Based on Monoclonal Principle for Job Recommendation

    Directory of Open Access Journals (Sweden)

    Shaha Al-Otaibi

    2016-04-01

    Full Text Available Finding the best solution for an optimization problem is a tedious task, specifically in the presence of enormously represented features. When we handle a problem such as job recommendations that have a diversity of their features, we should rely to metaheuristics. For example, the Artificial Immune System which is a novel computational intelligence paradigm achieving diversification and exploration of the search space as well as exploitation of the good solutions were reached in reasonable time. Unfortunately, in problems with diversity nature such job recommendation, it produces a huge number of antibodies that causes a large number of matching processes affect the system efficiency. To leverage this issue, we present a new intelligence algorithm inspired by immunology based on monoclonal antibodies production principle that, up to our knowledge, has never applied in science and engineering problems. The proposed algorithm recommends ranked list of best applicants for a certain job. We discussed the design issues, as well as the immune system processes that should be applied to the problem. Finally, the experiments are conducted that shown an excellence of our approach.

  8. An artificial neural network approach for ranking quenching parameters in central galaxies

    Science.gov (United States)

    Teimoorinia, Hossen; Bluck, Asa F. L.; Ellison, Sara L.

    2016-04-01

    We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a population of over 400 000 central galaxies taken from the Sloan Digital Sky Survey Data Release 7. We utilize a variety of physical galaxy properties for training the pattern recognition algorithm to recognize star-forming and passive systems, for a `training set' of ˜100 000 galaxies. We then apply the ANN model to a `verification set' of ˜100 000 different galaxies, randomly chosen from the remaining sample. The success rate of each parameter singly, and in conjunction with other parameters, is taken as an indication of how important the parameters are to the process(es) of central galaxy quenching. We find that central velocity dispersion, bulge mass and bulge-to-total stellar mass ratio are excellent predictors of the passive state of the system, indicating that properties related to the central mass of the galaxy are most closely linked to the cessation of star formation. Larger scale galaxy properties (total or disc stellar masses), or those linked to environment (halo masses or δ5), perform significantly less well. Our results are plausibly explained by AGN feedback driving the quenching of central galaxies, although we discuss other possibilities as well.

  9. Changing the learning environment to promote deep learning approaches in first year accounting students

    OpenAIRE

    2004-01-01

    Developing deep approaches to learning is claimed to enhance students' engagement with their subject material and result in improved analytical and conceptual thinking skills. Numerous calls have been made for accounting educators to adopt strategies that produce such results. This paper reports on changes to the learning environment centring on the introduction of group learning activities that were designed to improve the quality of students' learning outcomes. The impact of changes in the ...

  10. A Challenge-Feedback Learning Approach to Teaching International Business

    Science.gov (United States)

    Sternad, Dietmar

    2015-01-01

    This article introduces a challenge-feedback learning (CFL) approach based on the goal-setting theory of human motivation, the deliberate practice theory of expert performance, and findings from the research on active and collaborative learning. The core of the teaching concept is the CFL cycle in which students repeatedly progress through four…

  11. Learning-by-doing approaches for skill acquisition

    NARCIS (Netherlands)

    Hristov, Ivo

    2006-01-01

    Please, cite this publication as: Hristov, I. (2006). Learning-by-doing approaches for skill acquisition. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria: TENCompetence. Retrieved June 30th, 20

  12. Developing a Competency-Based Assessment Approach for Student Learning

    Science.gov (United States)

    Dunning, Pamela T.

    2014-01-01

    Higher education accrediting bodies are increasing the emphasis on assessing student learning outcomes as opposed to teaching methodology. The purpose of this article is to describe the process used by Troy University's Master of Public Administration program to change their assessment approach from a course learning objective perspective to a…

  13. Learning and teaching as a game: A sabotage approach

    NARCIS (Netherlands)

    N. Gierasimczuk; L. Kurzen; F.R. Velázquez-Quesada

    2009-01-01

    In formal approaches to inductive learning, the ability to learn is understood as the ability to single out a correct hypothesis from a range of possibilities. Although most of the existing research focuses on the characteristics of the learner, in many paradigms the significance of the teacher’s ab

  14. Situated Poetry Learning Using Multimedia Resource Sharing Approach

    Science.gov (United States)

    Yang, Che-Ching; Tseng, Shian-Shyong; Liao, Anthony Y. H.; Liang, Tyne

    2013-01-01

    Educators have emphasized the importance of situating students in an authentic learning environment. By using such approach, teachers can encourage students to learn Chinese poems by browsing content resources and relevant online multimedia resources by using handheld devices. Nevertheless, students in heterogeneous network environments may have…

  15. Blended Learning in Higher Education: Three Different Design Approaches

    Science.gov (United States)

    Alammary, Ali; Sheard, Judy; Carbone, Angela

    2014-01-01

    Blended learning has been growing in popularity as it has proved to be an effective approach for accommodating an increasingly diverse student population whilst adding value to the learning environment through incorporation of online teaching resources. Despite this growing interest, there is ongoing debate about the definition of the concept of…

  16. Sound Foundations: Organic Approaches to Learning Notation in Beginning Band

    Science.gov (United States)

    West, Chad

    2016-01-01

    By starting with a foundation of sound before sight, we can help our students learn notation organically in a way that honors the natural process. This article describes five organic approaches to learning notation in beginning band: (1) iconic notation, (2) point and play, (3) student lead-sheet, (4) modeling, and (5) kid dictation. While…

  17. Inquiry Role Approach: A Model for Counselor Involvement in Learning.

    Science.gov (United States)

    Bingman, Richard M.; And Others

    The Inquiry Role Approach (IRA) is a strategy for classroom learning in which students work as 4-member teams and assume roles as Team Coordinator, Process Advisor, Data Recorder, and Technical Advisor. Cognitive as well as affective objectives are identified which relate to optimum learning and personal growth in the classroom. The counselor's…

  18. Sensorless speed estimation of an AC induction motor by using an artificial neural network approach

    Science.gov (United States)

    Alkhoraif, Abdulelah Ali

    Sensorless speed detection of an induction motor is an attractive area for researchers to enhance the reliability of the system and to reduce the cost of the components. This paper presents a simple method of estimating a rotational speed by utilizing an artificial neural network (ANN) that would be fed by a set of stator current frequencies that contain some saliency harmonics. This approach allows operators to detect the speed in induction motors such an approach also provides reliability, low cost, and simplicity. First, the proposed method is based on converting the stator current signals to the frequency domain and then applying a tracking algorithm to the stator current spectrum in order to detect frequency peaks. Secondly, the ANN has to be trained by the detected peaks; the training data must be from very precise data to provide an accurate rotor speed. Moreover, the desired output of the training is the speed, which is measured by a tachometer simultaneously with the stator current signal. The databases were collected at many different speeds from two different types of AC induction motors, wound rotor and squirrel cage. They were trained and tested, so when the difference between the desired speed value and the ANN output value reached the wanted accuracy, the system does not need to use the tachometer anymore. Eventually, the experimental results show that in an optimal ANN design, the speed of the wound rotor induction motor was estimated accurately, where the testing average error was 1 RPM. The proposed method has not succeeded to predict the rotor speed of the squirrel cage induction motor precisely, where the smallest testing­average error that was achieved was 5 RPM.

  19. Learning and Experience - a Psycho-societal Approach

    DEFF Research Database (Denmark)

    Olesen, Henning Salling

    2017-01-01

    Abstract: This chapter introduces a psycho-societal approach to theorizing learning, combining a materialist theory of socialization with a hermeneutic interpretation methodology. The term "approach" indicates the intrinsic connection between theory, empirical research process and epistemic subject....... Learning is theorized as dynamic subjective experience of (socially situated) realities, counting on individual subjectivity as well as subjective aspects of social interaction. This psycho-societal theory of subjective experiences conceptualizes individual psychic development as interactional experience...

  20. Medical students’ approaches to learning over a full degree programme

    Directory of Open Access Journals (Sweden)

    William A. Reid

    2012-08-01

    Full Text Available Students take three approaches to learning and studying: deep, surface and strategic, influenced by the learning environment. Following the General Medical Council's report "Tomorrow's Doctors," a deep approach was cultivated in Years 1 and 2 of a university undergraduate medical programme by introducing explicit written learning objectives constructed according to Biggs' SOLO taxonomy, problem-based learning and constructively aligned in-course assignments and examinations. The effect of these changes was measured with the Approaches to Study Skills Inventory for Students (ASSIST. Scores were highest for a deep approach and lowest for a surface approach and showed relatively little change during the degree programme, apart from a slight fall in the scores for a surface approach, particularly for students undertaking an intercalated science degree. Possible explanations include: students' approaches may be established prior to university entry; deep scores were already high at the beginning of the programme and may be difficult to increase further; the changes in learning environment may not be strong enough to alter approaches which students perceive as having been successful.

  1. Neutrosophic Logic Approach for Evaluating Learning Management Systems

    Directory of Open Access Journals (Sweden)

    Nouran Radwan

    2016-08-01

    Full Text Available Uncertainty in expert systems is essential research point in artificial intelligence domain. Uncertain knowledge representation and analysis in expert systems is one of the challenges that takes researchers concern as different uncertainty types which are imprecision, vagueness, ambiguity, and inconsistence need different handling models. This paper reviews some of the multivalued logic models which are fuzzy set, intuitionistic fuzzy set, and suggests a new approach which is neutrosophic set for handling uncertainty in expert systems to derive decisions.

  2. Self-structuring data learning approach

    Science.gov (United States)

    Ternovskiy, Igor; Graham, James; Carson, Daniel

    2016-05-01

    In this paper, we propose a hierarchical self-structuring learning algorithm based around the general principles of the Stanovich/Evans framework and "Quest" group definition of unexpected query. One of the main goals of our algorithm is for it to be capable of patterns learning and extrapolating more complex patterns from less complex ones. This pattern learning, influenced by goals, either learned or predetermined, should be able to detect and reconcile anomalous behaviors. One example of a proposed application of this algorithm would be traffic analysis. We choose this example, because it is conceptually easy to follow. Despite the fact that we are unlikely to develop superior traffic tracking techniques using our algorithm, a traffic based scenario remains a good starting point if only do to the easy availability of data and the number of other known techniques. In any case, in this scenario, the algorithm would observe and track all vehicular traffic in a particular area. After some initial time passes, it would begin detecting and learning the traffic's patters. Eventually the patterns would stabilize. At that point, "new" patterns could be considered anomalies, flagged, and handled accordingly. This is only one, particular application of our proposed algorithm. Ideally, we want to make it as general as possible, such that it can be applies to numerous different problems with varying types of sensory input and data types, such as IR, RF, visual, census data, meta data, etc.

  3. Determining the Approaches of High School Students to Learning Physics

    Directory of Open Access Journals (Sweden)

    Gamze Sezgin Selcuk

    2014-06-01

    Full Text Available The primary objective of this research was to adapt the Approaches to Learning Scale developed for the university level by Ellez & Sezgin (2000 to a high school level physics (Approaches to Learning Physics Scale. The secondary objective was to use the adapted scale to examine the approaches of high school students to learning physics and explore how this variable changes according to gender and level of achievement in physics. The adapted scale was applied to a total of 329 high school students in the province of İzmir, Turkey for the purpose of testing the scale's validity and reliability. The reliability coefficient for the whole of the scale was found to be 0.86. The data related to the secondary objective of the research were analyzed using frequencies, percentages, means, standard deviation, two-way multivariate analysis (two-way MANOVA, and follow-up tests. It was determined from the results of the analysis that the students' preference for both a deep and a surface approach to learning physics was slightly above average. It was found the students did not display a significant difference in their approaches to learning according to the gender variable but that there were significant differences between the students' approaches to learning according to the variable of achievement.

  4. An active learning approach to Bloom's Taxonomy.

    Science.gov (United States)

    Weigel, Fred K; Bonica, Mark

    2014-01-01

    As educators strive toward improving student learning outcomes, many find it difficult to instill their students with a deep understanding of the material the instructors share. One challenge lies in how to provide the material with a meaningful and engaging method that maximizes student understanding and synthesis. By following a simple strategy involving Active Learning across the 3 primary domains of Bloom's Taxonomy (cognitive, affective, and psychomotor), instructors can dramatically improve the quality of the lesson and help students retain and understand the information. By applying our strategy, instructors can engage their students at a deeper level and may even find themselves enjoying the process more.

  5. Relations between blended learning possibilities and teachers' approaches to blended learning

    DEFF Research Database (Denmark)

    Stenalt, Maria Hvid; Nielsen, Tobias Alsted; Bager-Elsborg, Anna

    suggest that in order to identify the level of impact of integrating technologies in teaching and learning, we need to understand the factors influencing approaches to design of courses for blended contexts. Participants in the teacher training project come from the Department of Law at Aarhus University......Higher Education has embraced blended learning as a way of enhancing quality in teaching and helping students to learn. This presentation addresses relations between blended learning possiblities presented to teachers in a teacher training project and teachers’ approaches to blended learning. We......: • Optain locally-embedded knowledge about blended learning • Develop opportunities for law students to receive (more) feedback • Comply with strategic aims The results so far suggest that teachers provide a disciplinary perspective on the key dimensions of blended learning, which influences...

  6. A Parallel and Incremental Approach for Data-Intensive Learning of Bayesian Networks.

    Science.gov (United States)

    Yue, Kun; Fang, Qiyu; Wang, Xiaoling; Li, Jin; Liu, Weiyi

    2015-12-01

    Bayesian network (BN) has been adopted as the underlying model for representing and inferring uncertain knowledge. As the basis of realistic applications centered on probabilistic inferences, learning a BN from data is a critical subject of machine learning, artificial intelligence, and big data paradigms. Currently, it is necessary to extend the classical methods for learning BNs with respect to data-intensive computing or in cloud environments. In this paper, we propose a parallel and incremental approach for data-intensive learning of BNs from massive, distributed, and dynamically changing data by extending the classical scoring and search algorithm and using MapReduce. First, we adopt the minimum description length as the scoring metric and give the two-pass MapReduce-based algorithms for computing the required marginal probabilities and scoring the candidate graphical model from sample data. Then, we give the corresponding strategy for extending the classical hill-climbing algorithm to obtain the optimal structure, as well as that for storing a BN by pairs. Further, in view of the dynamic characteristics of the changing data, we give the concept of influence degree to measure the coincidence of the current BN with new data, and then propose the corresponding two-pass MapReduce-based algorithms for BNs incremental learning. Experimental results show the efficiency, scalability, and effectiveness of our methods.

  7. A Cognitive Approach to Student-Centered e-Learning

    Energy Technology Data Exchange (ETDEWEB)

    Greitzer, Frank L.

    2002-09-30

    Like traditional classroom instruction, distance/electronic learning (e-Learning) derives from largely behaviorist computer-based instruction paradigms that tend to reflect passive training philosophies. Over the past thirty years, more flexible, student-centered classroom teaching methods have been advocated based on the concepts of ''discovery'' learning and ''active'' learning; student-centered approaches are likewise encouraged in the development of e-Learning applications. Nevertheless, many e-Learning applications that employ state-of-the art multimedia technology in which students interact with simulations, animations, video, and sounds still fail to meet their expected training potential. Implementation of multimedia-based training features may give the impression of engaging the student in more active forms of learning, but sophisticated use of multimedia features does not necessarily produce the desired effect. This paper briefly reviews some general guidelines for applying cognitive science principles to development of student-centered e-Learning applications and describes a cognitive approach to e-Learning development that is being undertaken for the US Army.

  8. Flipped Approach to Mobile Assisted Language Learning

    Science.gov (United States)

    Yamamoto, Junko

    2013-01-01

    There are abundant possibilities for using smart phones and tablet computers for foreign language learning. However, if there is an emphasis on memorization or on technology, language learners may not develop proficiency in their target language. Therefore, language teachers should be familiar with strategies for facilitating creative…

  9. Newton's First Law: A Learning Cycle Approach

    Science.gov (United States)

    McCarthy, Deborah

    2005-01-01

    To demonstrate how Newton's first law of motion applies to students' everyday lives, the author developed a learning cycle series of activities on inertia. The discrepant event at the heart of these activities is sure to elicit wide-eyed stares and puzzled looks from students, but also promote critical thinking and help bring an abstract concept…

  10. A Belgian Approach to Learning Disabilities.

    Science.gov (United States)

    Hayes, Cheryl W.

    The paper reviews Belgian philosophy toward the education of learning disabled students and cites the differences between American behaviorally-oriented theory and Belgian emphasis on identifying the underlying causes of the disability. Academic methods observed in Belgium (including psychodrama and perceptual motor training) are discussed and are…

  11. Bangladeshi EFL Learners' Approach towards Learning English

    Science.gov (United States)

    Fathema, Fawzia

    2015-01-01

    Bangladesh is a poverty stricken country with a huge population "unemployed' in respect of the definition of Economics including both male and female. Government is striving hard to make the people well-equipped with necessary skills and learning in order that they can prove themselves fit for the upcoming challenges of the global economy and…

  12. The Reading Approach & Second Language Learning

    Institute of Scientific and Technical Information of China (English)

    Dilian He Nicholson

    2004-01-01

    @@Learning a second language at the adult stage of life poses a complex situation of interacting already formed first tongue set in reflective mode of development and the new language with its all new:philosophy,syntax,spextra of idioms and implied meanings,humour,metaphors and rhythm.

  13. Learning disabilities and learned helplessness: a heuristic approach.

    Science.gov (United States)

    Hersh, C A; Stone, B J; Ford, L

    1996-02-01

    This study investigated whether students with learning disabilities exhibited learned helpless behavior at a greater rate than their normal achieving peers when confronted with reading failure. Forty-five third grade students from a suburban elementary schools were participants in the study. Thirty of the subjects were classified as having a learning disability (LD) and the remaining 15 subjects were from regular education (RE) classrooms. Fifteen of the students with LD were placed in the treatment group and the remaining fifteen were placed in the control group. All the regular education students were placed in the treatment group. After randomly assigning the students with LD into either a treatment (stressed) group or a control (nonstressed) group, the stressed students were administered a reading instrument in order to measure how they dealt with failure. A one-way ANCOVA was conducted to determine whether significant differences existed between the groups based on their posttest scores. The results indicate that stressed students with LD have a significantly more difficult time recovering from stress than their regular education peers.

  14. Motivational factors as predictors of student approach to learning

    DEFF Research Database (Denmark)

    Lassesen, Berit Irene

    Background and aim: Research indicates that both self-efficacy and test anxiety may influence student performance. There is also evidence to suggest that students´ approach to learn, i.e. whether they adopt a deep or surface approach influence learning outcome. There is, however, little research...... exploring the possible influences of self-efficacy and test anxiety on study behavior in higher education. Increasing our knowledge about these associations could improve our understanding of the processes and mechanisms involved in learning and academic performance. Methods: 1181 undergraduate and graduate...... and multivariate hierarchical regression analyses, adjusting for the remaining variables. Results: Both self-efficacy, test-anxiety, and perception of the teaching environment appeared to be strong independent predictors of student approaches to learning even when controlling for other motivational factors...

  15. A Mixed Learning Technology Approach for Continuing Medical Education

    Directory of Open Access Journals (Sweden)

    Vernon R. Curran

    2003-01-01

    Full Text Available Introduction: Distance learning technologies have been used for many years to provide CME to rural physicians. The purpose of this study was to evaluate the utility and acceptability of a mixed learning technology approach for providing distance CME. The approach combined audio teleconferencing instruction with a Web-based learning system enabling the live presentation and archiving of instructional material and media, asynchronous computer conferencing discussions, and access to supplemental online learning resources. Methodology: The study population was comprised of physicians and nurse practitioners who participated in audio teleconference sessions, but did not access the Web-based learning system (non-users; learners who participated in audio teleconferences and accessed the Web-based system (online users; and faculty. The evaluation focused upon faculty and learners’ experiences and perceptions of the mixed learning technology approach; the level of usage; and the effectiveness of the approach in fostering non-mandatory, computer-mediated discussions. Results and Discussion: The users of the Web-based learning system were satisfied with its features, ease of use, and the ability to access online CME instructional material. Learners who accessed the system reported a higher level of computer skill and comfort than those who did not, and the majority of these users accessed the system at times other than the live audio teleconference sessions. The greatest use of the system appeared to be for self-directed learning. The success of a mixed learning technology approach is dependent on Internet connectivity and computer access; learners and faculty having time to access and use the Web; comfort with computers; and faculty development in the area of Web-based teaching.

  16. A Socioeconomic Approach to the Development of E-Learning

    Directory of Open Access Journals (Sweden)

    Henrik Johannsen Duus

    2009-07-01

    Full Text Available A multitude of products, systems, approaches, views and notions characterize the field of e-learning. This article attempts to disentangle the field by using economic and sociological theories, theories of marketing management and strategy as well as practical experience gained by the author while working with leading edge suppliers of e-learning. On this basis, a distinction between knowledge creation e-learning and knowledge transfer e-learning is made. The various views are divided into four different ideal-typical paradigms, each with its own characteristics and limitations. Selecting the right paradigm to use in the development of an e-learning strategy may prove crucial to success. Implications for the development of an e-learning strategy in businesses and educational institutions are outlined.

  17. Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

    Science.gov (United States)

    Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

    2013-09-01

    The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds.

  18. Robust Fault-Tolerant Control for Satellite Attitude Stabilization Based on Active Disturbance Rejection Approach with Artificial Bee Colony Algorithm

    OpenAIRE

    Fei Song; Shiyin Qin

    2014-01-01

    This paper proposed a robust fault-tolerant control algorithm for satellite stabilization based on active disturbance rejection approach with artificial bee colony algorithm. The actuating mechanism of attitude control system consists of three working reaction flywheels and one spare reaction flywheel. The speed measurement of reaction flywheel is adopted for fault detection. If any reaction flywheel fault is detected, the corresponding fault flywheel is isolated and the spare reaction flywhe...

  19. Fullrmc, a rigid body Reverse Monte Carlo modeling package enabled with machine learning and artificial intelligence.

    Science.gov (United States)

    Aoun, Bachir

    2016-05-01

    A new Reverse Monte Carlo (RMC) package "fullrmc" for atomic or rigid body and molecular, amorphous, or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython, C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modeling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. In addition, fullrmc provides a unique way with almost no additional computational cost to recur a group's selection, allowing the system to go out of local minimas by refining a group's position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group.

  20. The Semiotic Approach and Language Teaching and Learning

    OpenAIRE

    Şenel, Müfit

    2007-01-01

    This study investigates the relation of the Foreign Language Teaching with the Semiotic Approach that gains more importance recently and tries to explain how this concept has been used as Semiotic Approach in Foreign Language Teaching and Learning and teacher-learner roles, strong-weak sides, types of activities, etc. have been handled.

  1. Computer Mediated Learning: An Example of an Approach.

    Science.gov (United States)

    Arcavi, Abraham; Hadas, Nurit

    2000-01-01

    There are several possible approaches in which dynamic computerized environments play a significant and possibly unique role in supporting innovative learning trajectories in mathematics in general and geometry in particular. Describes an approach based on a problem situation and some experiences using it with students and teachers. (Contains 15…

  2. 3 Colleges' Different Approaches Shape Learning in Econ 101

    Science.gov (United States)

    Berrett, Dan

    2012-01-01

    No matter the college, a class in the principles of microeconomics is likely to cover the discipline's greatest hits. The author attends three economics courses at three colleges, and finds three very different approaches. In this article, the author discusses three colleges' different approaches that shape learning in Econ 101.

  3. The scientific learning approach using multimedia-based maze game to improve learning outcomes

    Science.gov (United States)

    Setiawan, Wawan; Hafitriani, Sarah; Prabawa, Harsa Wara

    2016-02-01

    The objective of curriculum 2013 is to improve the quality of education in Indonesia, which leads to improving the quality of learning. The scientific approach and supported empowerment media is one approach as massaged of curriculum 2013. This research aims to design a labyrinth game based multimedia and apply in the scientific learning approach. This study was conducted in one of the Vocational School in Subjects of Computer Network on 2 (two) classes of experimental and control. The method used Mix Method Research (MMR) which combines qualitative in multimedia design, and quantitative in the study of learning impact. The results of a survey showed that the general of vocational students like of network topology material (68%), like multimedia (74%), and in particular, like interactive multimedia games and flash (84%). Multimediabased maze game developed good eligibility based on media and material aspects of each value 840% and 82%. Student learning outcomes as a result of using a scientific approach to learning with a multimediabased labyrinth game increase with an average of gain index about (58%) and higher than conventional multimedia with index average gain of 0.41 (41%). Based on these results the scientific approach to learning by using multimediabased labyrinth game can improve the quality of learning and increase understanding of students. Multimedia of learning based labyrinth game, which developed, got a positive response from the students with a good qualification level (75%).

  4. Feasibility Assessment of an ISS Artificial Gravity Conditioning Facility by Means of Multi-Body Approach

    Science.gov (United States)

    Toso, Mario; Baldesi, Gianluigi; Moratto, Claudio; De Wilde, Don; Bureo Dacal, Rafael; Castellsaguer, Joaquim

    2012-07-01

    Even though human exploration of Mars is a distant objective, it is well understood that, for human space voyages of several years duration, crews would be at risk of catastrophic consequences should any of the systems that provide adequate air, water, food, or thermal protection fail. Moreover, crews will face serious health and/or safety risks resulting from severe physiologic deconditioning associated with prolonged weightlessness. The principal ones are related to physical and functional deterioration of the regulation of the blood circulation, decreased aerobic capacity, impaired musculo-skeletal systems, and altered sensory- motor system performance. As the reliance of future space programmes on virtual modelling, simulation and justification has substantially grown together with the proto-flight hardware development approach, a range of simulation capabilities have become increasingly important in the requirements specification, design, verification, testing, launch and operation of new space systems. In this frame, multibody software is a key tool in providing a more coordinated and consistent approach from the preliminary development phases of the most complex systems. From a scientific prospective, an artificial gravity facility, such as the one evaluated in this paper, would be the first in-flight testing of the effectiveness and acceptability of short radius centrifuge as a countermeasure to human deconditioning on orbit. The ISS represents a unique opportunity to perform this research. From an engineering point of view, the preliminary assessment described in this paper, highlights the difficult engineering challenges of such a facility. The outcome proves that a human can be accommodated in the available volume, while respecting the human ergonomic basic requirements and preserving the global structural integrity of the hosting ISS module. In particular, analysis shows that, although the load capacity of the structural interfaces imposes a very low

  5. Approaches to learning and academic performance of Turkish university students

    Directory of Open Access Journals (Sweden)

    Leyla Harputlu *

    2011-12-01

    Full Text Available This paper reports findings with regard to approaches to learning of Turkish students. The term “approaches to learning” refers to the idea that learners perceive and process information in very different ways. The study is set out to (i explore and describe the approaches of learning of university students; (ii explore the relationship between approaches to learning constructs, (iii explore how the learning approaches of Turkish higher education students in combination with gender and academic discipline, year affect and academic performance; Employing a correlational research design- 44-item 1995 version of the RASI and the cumulative grade point, the study was conducted in two departments in two institutions of higher education: one humanities and one engineering. Total 160 students participated. This paper discusses firstly the findings of this study in the light of other research carried out in this area and secondly, and more importantly, in the light of its contribution towards a better understanding of the learning needs of Turkish university students.

  6. Multimodal approaches to use mobile, digital devices in learning practies

    DEFF Research Database (Denmark)

    Buhl, Mie

    , anthropology, psychology and sociology) and outlines the prospect of a trans-disciplinary learning mode. The learning mode reflects the current society where knowledge production is social collaborative process and is produced in formal as well as informal and non-formal contexts. My discussion’s theoretical......In this paper, I discuss the potential of multimodal approaches to enhance learning processes. I draw on a case based on Danish Master Courses in ICT and didactic designs where multimodal approaches are in the center of students’ practical design experience as well as in generation of theoretical...... knowledge. The design of the master courses takes its starting point in the assumption that theoretical knowledge generates from practical experiences. Thus, the organization of the students’ learning processes revolves around practical multimodal experiences followed by iterative reflexive sessions...

  7. Learning Approaches toward Title Word Selection on Indic Script

    Directory of Open Access Journals (Sweden)

    P.Vijayapal Reddy

    2011-03-01

    Full Text Available Title is a compact representation of a document which distill the important information from the document. In this paper we studied the selection words as title words by using different learning approachesnamely nearest neighbor approach (NN, Naive Bayes approach with limited-vocabulary (NBL, Naive Bayes approach with full vocabulary (NBF and by using a term weighing approach (tf-idf. We compare theperformance of these approaches by using F1 metric. We compare the F1 metric results both on English Script and Indic Script ' Telugu'. We concluded the influence of linguistic complexity in the process of Title word selection.

  8. A Collaborative Game-Based Learning Approach to Improving Students' Learning Performance in Science Courses

    Science.gov (United States)

    Sung, Han-Yu; Hwang, Gwo-Jen

    2013-01-01

    In this study, a collaborative game-based learning environment is developed by integrating a grid-based Mindtool to facilitate the students to share and organize what they have learned during the game-playing process. To evaluate the effectiveness of the proposed approach, an experiment has been conducted in an elementary school natural science…

  9. A Game-Based Learning Approach to Improving Students' Learning Achievements in a Nutrition Course

    Science.gov (United States)

    Yien, Jui-Mei; Hung, Chun-Ming; Hwang, Gwo-Jen; Lin, Yueh-Chiao

    2011-01-01

    The aim of this study was to explore the influence of applying a game-based learning approach to nutrition education. The quasi-experimental nonequivalent-control group design was adopted in a four-week learning activity. The participants included sixty-six third graders in two classes of an elementary school. One of the classes was assigned to be…

  10. The Effects of Computer Supported Problem Based Learning on Students' Approaches to Learning

    Science.gov (United States)

    Ak, Serife

    2011-01-01

    The purpose of this paper is to investigate the effects of computer supported problem based learning on students' approaches to learning. The research was conducted as a pre-test and posttest one-grouped design used to achieve the objectives of the study. The experimental process of study lasted 5 weeks and was carried out on 78 university…

  11. Meaningful Learning in the Teaching of Culture: The Project Based Learning Approach

    Science.gov (United States)

    Kean, Ang Chooi; Kwe, Ngu Moi

    2014-01-01

    This paper reports on a collaborative effort taken by a team of three teacher educators in using the Project Based Learning (PBL) approach in the teaching of Japanese culture with the aim to investigate the presence of actual "meaningful learning" among 15 students of a 12-Week Preparatory Japanese Language course under a teacher…

  12. To Learn More about Learning: The Value-Added Role of Qualitative Approaches to Assessment

    Science.gov (United States)

    Newhart, Daniel W.

    2015-01-01

    As we face increasing accountability in higher education, how we measure student learning should exceed the calls for an account of learning that places students at the center. Qualitative approaches to assessment and theoretical underpinnings gleaned from the qualitative research tradition may provide a way that we can support a more holistic…

  13. Adiabatic evolution of 1D shape resonances: an artificial interface conditions approach

    CERN Document Server

    Faraj, Ali; Nier, Francis

    2010-01-01

    Artificial interface conditions parametrized by a complex number $\\theta_{0}$ are introduced for 1D-Schr{\\"o}dinger operators. When this complex parameter equals the parameter $\\theta\\in i\\R$ of the complex deformation which unveils the shape resonances, the Hamiltonian becomes dissipative. This makes possible an adiabatic theory for the time evolution of resonant states for arbitrarily large time scales. The effect of the artificial interface conditions on the important stationary quantities involved in quantum transport models is also checked to be as small as wanted, in the polynomial scale $(h^N)_{N\\in \\N}$ as $h\\to 0$, according to $\\theta_{0}$.

  14. Learning from tutorials: a qualitative study of approaches to learning and perceptions of tutorial interaction

    DEFF Research Database (Denmark)

    Herrmann, Kim Jesper

    2014-01-01

    This study examines differences in university students’ approaches to learning when attending tutorials as well as variation in students’ perceptions of tutorials as an educational arena. In-depth qualitative analysis of semi-structured interviews with undergraduates showed how surface and deep...... approaches to learning were revealed in the students’ note-taking, listening, and engaging in dialogue. It was also shown how variation in the students’ approaches to learning were coherent with variation in the students’ perceptions of the tutors’ pedagogical role, the value of peer interaction......, and the overall purpose of tutorials. The results are discussed regarding the paradox that students relying on surface approaches to learning seemingly are the ones least likely to respond to tutorials in the way they were intended....

  15. A blended learning approach to teaching CVAD care and maintenance.

    Science.gov (United States)

    Hainey, Karen; Kelly, Linda J; Green, Audrey

    2017-01-26

    Nurses working within both acute and primary care settings are required to care for and maintain central venous access devices (CVADs). To support these nurses in practice, a higher education institution and local health board developed and delivered CVAD workshops, which were supported by a workbook and competency portfolio. Following positive evaluation of the workshops, an electronic learning (e-learning) package was also introduced to further support this clinical skill in practice. To ascertain whether this blended learning approach to teaching CVAD care and maintenance prepared nurses for practice, the learning package was evaluated through the use of electronic questionnaires. Results highlighted that the introduction of the e-learning package supported nurses' practice, and increased their confidence around correct clinical procedures.

  16. Students awareness of learning styles and their perceptions to a mixed method approach for learning

    Science.gov (United States)

    Bhagat, Anumeha; Vyas, Rashmi; Singh, Tejinder

    2015-01-01

    Background: Individualization of instructional method does not contribute significantly to learning outcomes although it is known that students have differing learning styles (LSs). Hence, in order to maximally enhance learning, one must try to use a mixed method approach. Hypothesis: Our hypothesis was that awareness of preferred LS and motivation to incorporate multiple learning strategies might enhance learning outcomes. Aim: Our aim was to determine the impact of awareness of LS among medical undergraduates and motivating students to use mixed methods of learning. Materials and Methods: Before awareness lecture, LS preferences were determined using Visual, Aural, Read/Write, and Kinesthetic (VARK) questionnaire. Awareness of LS was assessed using a validated questionnaire. Through a lecture, students were oriented to various LSs, impact of LS on their performance, and benefit of using mixed method approach for learning. Subsequently, group discussions were organized. After 3 months, VARK preferences and awareness of LSs were reassessed. Student narratives were collected. Qualitative analysis of the data was done. Results: There was a significant increase in the number of students who were aware of LS. The number of participants showing a change in VARK scores for various modalities of learning was also significant (P < 0.001). Conclusion: Thus, awareness of LSs motivated students to adapt other learning strategies and use mixed methods for learning. PMID:26380214

  17. Factors Influencing Sensitivity to Lexical Tone in an Artificial Language: Implications for Second Language Learning

    Science.gov (United States)

    Caldwell-Harris, Catherine L.; Lancaster, Alia; Ladd, D. Robert; Dediu, Dan; Christiansen, Morten H.

    2015-01-01

    This study examined whether musical training, ethnicity, and experience with a natural tone language influenced sensitivity to tone while listening to an artificial tone language. The language was designed with three tones, modeled after level-tone African languages. Participants listened to a 15-min random concatenation of six 3-syllable words.…

  18. A Communicative Approach to College English Grammar Teaching and Learning

    Institute of Scientific and Technical Information of China (English)

    LI Yong-xian

    2016-01-01

    In response to the misconception that Communicative Language Teaching means no teaching of grammar, it is argued that grammar is as important as traffic rules for safe and smooth traffic on the road. To achieve appropriate and effective commu-nication, a communicative approach to college grammar teaching and learning is proposed. Both teachers and learners should change their attitudes toward and conceptions about grammar teaching and learning;additionally, teaching grammar in the com-pany of reading and writing helps learners learn and acquire grammar in meaningful contexts.

  19. A Suggested approach to teaching and learning grammar

    Institute of Scientific and Technical Information of China (English)

    Yang Jijun; Niu Conglin

    2008-01-01

    The teaching and learning of grammar used to be processed at the sentence level with paying litttle attention to discourse and context.Students Were familiar with alot of gammatical terms and were able to remember many rules .However,they often wondered why they could not use English properly and fluently and fluently although they had learned the grammatical rules quite well.This essay,taking grammar as resource for communication,suggests an approach by using ideational frameworks to the teacxhing and learning of grammar in a communicative context.

  20. A CONTENT ANALYSIS ON PROBLEM-BASED LEARNING APPROACH

    OpenAIRE

    BİBER, Mahir; Esen ERSOY; KÖSE BİBER, Sezer

    2014-01-01

    Problem Based Learning is one of the learning models that contain the general principles of active learning and students can use scientific process skills. Within this research it was aimed to investigate in detail the postgraduate thesis held in Turkey about PBL approach. The content analysis method was used in the research. The study sample was consisted of a total of 64 masters and PhD thesis made between the years 2012-2013 and reached over the web. A “Content Analysis Template” prepared ...

  1. A CONTENT ANALYSIS ON PROBLEM-BASED LEARNING APPROACH

    OpenAIRE

    BİBER, Mahir; Esen ERSOY; KÖSE BİBER, Sezer

    2015-01-01

    Problem Based Learning is one of the learning models that contain the general principles of active learning and students can use scientific process skills. Within this research it was aimed to investigate in detail the postgraduate thesis held in Turkey about PBL approach. The content analysis method was used in the research. The study sample was consisted of a total of 64 masters and PhD thesis made between the years 2012-2013 and reached over the web. A “Content Analysis Template” prepared ...

  2. When to approach novel prey cues? Social learning strategies in frog-eating bats.

    Science.gov (United States)

    Jones, Patricia L; Ryan, Michael J; Flores, Victoria; Page, Rachel A

    2013-12-01

    Animals can use different sources of information when making decisions. Foraging animals often have access to both self-acquired and socially acquired information about prey. The fringe-lipped bat, Trachops cirrhosus, hunts frogs by approaching the calls that frogs produce to attract mates. We examined how the reliability of self-acquired prey cues affects social learning of novel prey cues. We trained bats to associate an artificial acoustic cue (mobile phone ringtone) with food rewards. Bats were assigned to treatments in which the trained cue was either an unreliable indicator of reward (rewarded 50% of the presentations) or a reliable indicator (rewarded 100% of the presentations), and they were exposed to a conspecific tutor foraging on a reliable (rewarded 100%) novel cue or to the novel cue with no tutor. Bats whose trained cue was unreliable and who had a tutor were significantly more likely to preferentially approach the novel cue when compared with bats whose trained cue was reliable, and to bats that had no tutor. Reliability of self-acquired prey cues therefore affects social learning of novel prey cues by frog-eating bats. Examining when animals use social information to learn about novel prey is key to understanding the social transmission of foraging innovations.

  3. A Digital Approach to Learning Petrology

    Science.gov (United States)

    Reid, M. R.

    2011-12-01

    In the undergraduate igneous and metamorphic petrology course at Northern Arizona University, we are employing petrographic microscopes equipped with relatively inexpensive ( $200) digital cameras that are linked to pen-tablet computers. The camera-tablet systems can assist student learning in a variety of ways. Images provided by the tablet computers can be used for helping students filter the visually complex specimens they examine. Instructors and students can simultaneously view the same petrographic features captured by the cameras and exchange information about them by pointing to salient features using the tablet pen. These images can become part of a virtual mineral/rock/texture portfolio tailored to individual student's needs. Captured digital illustrations can be annotated with digital ink or computer graphics tools; this activity emulates essential features of more traditional line drawings (visualizing an appropriate feature and selecting a representative image of it, internalizing the feature through studying and annotating it) while minimizing the frustration that many students feel about drawing. In these ways, we aim to help a student progress more efficiently from novice to expert. A number of our petrology laboratory exercises involve use of the camera-tablet systems for collaborative learning. Observational responsibilities are distributed among individual members of teams in order to increase interdependence and accountability, and to encourage efficiency. Annotated digital images are used to share students' findings and arrive at an understanding of an entire rock suite. This interdependence increases the individual's sense of responsibility for their work, and reporting out encourages students to practice use of technical vocabulary and to defend their observations. Pre- and post-course student interest in the camera-tablet systems has been assessed. In a post-course survey, the majority of students reported that, if available, they would use

  4. Artificial Neural Networks, and Evolutionary Algorithms as a systems biology approach to a data-base on fetal growth restriction.

    Science.gov (United States)

    Street, Maria E; Buscema, Massimo; Smerieri, Arianna; Montanini, Luisa; Grossi, Enzo

    2013-12-01

    One of the specific aims of systems biology is to model and discover properties of cells, tissues and organisms functioning. A systems biology approach was undertaken to investigate possibly the entire system of intra-uterine growth we had available, to assess the variables of interest, discriminate those which were effectively related with appropriate or restricted intrauterine growth, and achieve an understanding of the systems in these two conditions. The Artificial Adaptive Systems, which include Artificial Neural Networks and Evolutionary Algorithms lead us to the first analyses. These analyses identified the importance of the biochemical variables IL-6, IGF-II and IGFBP-2 protein concentrations in placental lysates, and offered a new insight into placental markers of fetal growth within the IGF and cytokine systems, confirmed they had relationships and offered a critical assessment of studies previously performed.

  5. A Bayesian Sampling Approach to Exploration in Reinforcement Learning

    CERN Document Server

    Asmuth, John; Littman, Michael L; Nouri, Ali; Wingate, David

    2012-01-01

    We present a modular approach to reinforcement learning that uses a Bayesian representation of the uncertainty over models. The approach, BOSS (Best of Sampled Set), drives exploration by sampling multiple models from the posterior and selecting actions optimistically. It extends previous work by providing a rule for deciding when to resample and how to combine the models. We show that our algorithm achieves nearoptimal reward with high probability with a sample complexity that is low relative to the speed at which the posterior distribution converges during learning. We demonstrate that BOSS performs quite favorably compared to state-of-the-art reinforcement-learning approaches and illustrate its flexibility by pairing it with a non-parametric model that generalizes across states.

  6. A Variance Based Active Learning Approach for Named Entity Recognition

    Science.gov (United States)

    Hassanzadeh, Hamed; Keyvanpour, Mohammadreza

    The cost of manually annotating corpora is one of the significant issues in many text based tasks such as text mining, semantic annotation and generally information extraction. Active Learning is an approach that deals with reduction of labeling costs. In this paper we proposed an effective active learning approach based on minimal variance that reduces manual annotation cost by using a small number of manually labeled examples. In our approach we use a confidence measure based on the model's variance that reaches a considerable accuracy for annotating entities. Conditional Random Field (CRF) is chosen as the underlying learning model due to its promising performance in many sequence labeling tasks. The experiments show that the proposed method needs considerably fewer manual labeled samples to produce a desirable result.

  7. A Probabilistic Approach for Learning Folksonomies from Structured Data

    CERN Document Server

    Plangprasopchok, Anon; Getoor, Lise

    2010-01-01

    Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, th...

  8. The Capability Approach: Enabling Musical Learning

    Science.gov (United States)

    Cameron, Kate

    2012-01-01

    Amartya Sen's capability approach offers a new perspective for educators throughout the curriculum. This new insight has the potential to promote a music education that is inherently tailored to the individual. In essence it asks the question: What is music education going to offer to this student? This article represents an initial enquiry into…

  9. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    Science.gov (United States)

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  10. A multiscale approach for modeling actuation response of polymeric artificial muscles.

    Science.gov (United States)

    Sharafi, Soodabeh; Li, Guoqiang

    2015-05-21

    Artificial muscles are emerging materials in the field of smart materials with applications in aerospace, robotic, and biomedical industries. Despite extensive experimental investigations in this field, there is a need for numerical modeling techniques that facilitate cutting edge research and development. This work aims at studying an artificial muscle made of twisted Nylon 6.6 fibers that are highly cold-drawn. A computationally efficient phenomenological thermo-mechanical constitutive model is developed in which several physical properties of the artificial muscles are incorporated to minimize the trial-and-error numerical curve fitting processes. Two types of molecular chains are considered at the micro-scale level that control training and actuation processes viz. (a) helically oriented chains which are structural switches that store a twisted shape in their low temperature phase and restore their random configuration during the thermal actuation process, and (b) entropic chains which are highly drawn chains that could actuate as soon as the muscle heats up, and saturates when coil contact temperature is reached. The thermal actuation response of the muscle over working temperatures has been elaborated in the Modeling section. The performance of the model is validated by available experiments in the literature. The model may provide a design platform for future artificial muscle developments.

  11. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  12. Gene prediction in metagenomic fragments: A large scale machine learning approach

    Directory of Open Access Journals (Sweden)

    Morgenstern Burkhard

    2008-04-01

    Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

  13. New approaches in buffalo artificial insemination programs with special reference to India.

    Science.gov (United States)

    Singh, Inderjeet; Balhara, A K

    2016-07-01

    Buffalo farming has made remarkable progress in productivity mainly because of controlled breeding with artificial insemination (AI) that has proved its worth in breed improvement and breeding managements across the livestock species. Artificial insemination is practiced very little in Europe and East Asian countries with coverage of only 5% buffaloes in Italy, 3.7% in Azerbaijan, 0.3% in Egypt, and 0.1% in Romania although in Bulgaria, 80% buffaloes in large cooperative state farms are subjected to AI. In Turkey, it began in 2002 near Hatay with Italian semen provided by the Food and Agriculture Organization (FAO) Network project. In India, where buffaloes are the most valuable livestock species, research on buffalo specific artificial breeding technologies and adoption of AI by buffalo owners are widely acknowledged. Resultantly, average milk yield of buffaloes in India increased from 3.4 kg in 1992 to 93 to 4.57 kg/day/buffalo in 2009 to 10. In the new millennium, mega projects such as the National Project for Cattle and Buffalo Breeding and the National Dairy Plan were initiated with focus on genetic upgradation of bovine and buffalo population through streamlining AI services and support system in the country. Artificial insemination started in India in the year 1939, and the frozen semen was introduced during late 1960s. During the year 2010 to 11, India produced 63 million bovine frozen semen straws including over one million buffalo semen straws through 49 semen stations. Artificial insemination services are provided through 71,341 AI stations clocking 52 million inseminations with overall conception rate of 35% in bovine and buffalo population. Research is being conducted for improved AI conception rates with synchronization programs and improved frozen-thawed semen quality, and success rates are at par with AI in cattle.

  14. A Wittgenstein Approach to the Learning of OO-modeling

    Science.gov (United States)

    Holmboe, Christian

    2004-12-01

    The paper uses Ludwig Wittgenstein's theories about the relationship between thought, language, and objects of the world to explore the assumption that OO-thinking resembles natural thinking. The paper imports from research in linguistic philosophy to computer science education research. I show how UML class diagrams (i.e., an artificial context-free language) correspond to the logically perfect languages described in Tractatus Logico-Philosophicus. In Philosophical Investigations Wittgenstein disputes his previous theories by showing that natural languages are not constructed by rules of mathematical logic, but are language games where the meaning of a word is constructed through its use in social contexts. Contradicting the claim that OO-thinking is easy to learn because of its similarity to natural thinking, I claim that OO-thinking is difficult to learn because of its differences from natural thinking. The nature of these differences is not currently well known or appreciated. I suggest how explicit attention to the nature and implications of different language games may improve the teaching and learning of OO-modeling as well as programming.

  15. Incidental Vocabulary Learning: A Semantic Field Approach

    Directory of Open Access Journals (Sweden)

    Parvaneh Khosravizadeh

    2011-10-01

    Full Text Available

    This study is an attempt to explore the difference between acquiring new words with different semantic fields to which they belong. In other words, the purpose of this study is to scrutinize the contribution of semantic field theory in learning new vocabulary items in an EFL setting. Thirty-eight students of three different levels of education took part in this research. They were exposed to some new words from four different semantic fields, and then they were tested on their acquisition of the words meaning. This exposure was through reading texts and the aim of reading was just comprehension, therefore the words were acquired incidentally. The outcome showed significant differences between groups with different levels of education regarding retention of words from different semantic fields.

  16. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Ling-Yu Duan

    2010-01-01

    Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  17. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Tian Yonghong

    2010-01-01

    Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  18. Introduce Lessons Learn Approach as A Phase in SDLC

    Directory of Open Access Journals (Sweden)

    Radhika D Amlani

    2013-01-01

    Full Text Available To understand what Lessons Learned is we must have to understand what is Lessons? Lessons can be derived from any activity. They are a product of operations, exercises, training, experiments, and day-to-day staff work. During the course of our activities most of us will recognize ways of doing things more easily or efficiently that can be passed on to our colleagues and successors to help them avoid problems and do even better than we did before. The term Lessons Learned is broadly used to describe act of learning from experience to achieve improvements. It is an activity used by people to gain benefit in current activity form past activity experience. The idea of lessons learned in an organization is that through a formal approach to learning, individual and the organization can reduce the risk of repeating mistakes and it would increase the chance of success. This means lessons learned approach will reduce failure risk, and improve operational effectiveness and also increase cost efficiency.

  19. The influence of social structure on the propagation of social information in artificial primate groups: a graph-based simulation approach.

    Science.gov (United States)

    Voelkl, Bernhard; Noë, Ronald

    2008-05-01

    Observations of primate groups have shown that social learning can lead to the development of temporal stable traditions or even proto-culture. The social structure of primate groups is highly diverse and it has been proposed that differences in the group structure shall influence the patterns of social information transmission. While empirical studies have mainly focused on the psychological mechanisms of social learning in individuals, the phenomenon of information propagation within the group has received relatively little attention. This might be due to the fact that formal theories that allow actual testing have not been formulated, or were kept too simple, ignoring the social dynamics of multi-agent societies. We want to propose a network approach to social information transmission that (1) preserves the complexity of the social structure of primate groups and (2) allows direct application to empirical data. Results from simulation experiments with artificial group structures confirm that association patterns of group-members influence the expected speed of information transmission during the propagation process. Introducing a forgetting rate shows that under certain conditions the proportion of informed individuals will reach a stable rate in some systems while it will drop to zero in others. This suggests that the likelihood to observe temporal stable traditions shall differ between social systems with different structure.

  20. Learning strategies and study approaches of postsecondary students with dyslexia.

    Science.gov (United States)

    Kirby, John R; Silvestri, Robert; Allingham, Beth H; Parrila, Rauno; La Fave, Chantal B

    2008-01-01

    The present study describes the self-reported learning strategies and study approaches of college and university students with and without dyslexia and examines the relationship of those characteristics with reading ability. Students with (n = 36) and without (n = 66) dyslexia completed tests measuring reading rate, reading comprehension, reading history, learning strategies, and learning approaches. The results indicated that students without dyslexia obtained significantly higher scores than students with dyslexia in their reported use of selecting main ideas and test taking strategies. Students with dyslexia reported significantly greater use of study aids and time management strategies in comparison to students without dyslexia. Moreover, university students with dyslexia were significantly more likely to report a deep approach to learning in comparison to university students without dyslexia. Reading ability correlated positively with selecting main ideas and test taking strategies and negatively with use of study aids. The authors interpret the learning strategy results as consequences of and compensations for the difficulties that students with dyslexia have in word reading.

  1. A developmental approach to learning causal models for cyber security

    Science.gov (United States)

    Mugan, Jonathan

    2013-05-01

    To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

  2. Factors Contributing to Changes in a Deep Approach to Learning in Different Learning Environments

    Science.gov (United States)

    Postareff, Liisa; Parpala, Anna; Lindblom-Ylänne, Sari

    2015-01-01

    The study explored factors explaining changes in a deep approach to learning. The data consisted of interviews with 12 students from four Bachelor-level courses representing different disciplines. We analysed and compared descriptions of students whose deep approach either increased, decreased or remained relatively unchanged during their courses.…

  3. Proposal for an Approach to Artificial Consciousness Based on Self-Consciousness

    OpenAIRE

    Menant, Mr Christophe

    2007-01-01

    Current research on artificial consciousness is focused on phenomenal consciousness and on functional consciousness. We propose to shift the focus to self-consciousness in order to open new areas of investigation. We use an existing scenario where self-consciousness is considered as the result of an evolution of representations. Application of the scenario to the possible build up of a conscious robot also introduces questions relative to emotions in robots. Areas of investigation...

  4. Biomimetic approaches to the design of materials for artificial tactile perception

    Science.gov (United States)

    de Rossi, Danilo

    While the computational and materials-synthesis aspects of artificial tactile perception remain rather abstract, three fields of investigation have been identified as promising: thermotactile interactions for sensory fusion, strain dilatation sensing for low-level computation, and strain-rate-to-impulse frequency information coding. Attention is also given to the possibilities of a 'pseudoepidermal layer' capable of resolving individual stress tensor components, and a 'pseudodermal pad' that can serve as a rheological skin-analog.

  5. The Effects of Discipline on Deep Approaches to Student Learning and College Outcomes

    Science.gov (United States)

    Nelson Laird, Thomas F.; Shoup, Rick; Kuh, George D.; Schwarz, Michael J.

    2008-01-01

    "Deep learning" represents student engagement in approaches to learning that emphasize integration, synthesis, and reflection. Because learning is a shared responsibility between students and faculty, it is important to determine whether faculty members emphasize deep approaches to learning and to assess how much students employ these approaches.…

  6. E-learning paradigms and applications agent-based approach

    CERN Document Server

    Jain, Lakhmi

    2014-01-01

    Teaching and learning paradigms have attracted increased attention especially in the last decade. Immense developments of different ICT technologies and services have paved the way for alternative but effective approaches in educational processes. Many concepts of the agent technology, such as intelligence, autonomy, and cooperation, have had a direct positive impact on many of the requests imposed on modern e-learning systems and educational processes. This book presents the state-of-the-art of e-learning and tutoring systems, and discusses their capabilities and benefits that stem from integrating software agents. We hope that the presented work will be of a great use to our colleagues and researchers interested in the e-learning and agent technology.    

  7. Designing e-learning solutions with a client centred approach

    DEFF Research Database (Denmark)

    Ørngreen, Rikke; Nielsen, Janni; Levinsen, Karin

    2008-01-01

    as the organisation that has initiated the e-learning project and needs to manage the e-learning system after its development. Through the Client Centred Design and in close collaboration with the client, three strategic issues are uncovered and strategic models are presented for each. These models are complementary......  This paper claims that the strategies applied in designing e-learning solutions tend to focus on how to proceed after the precondition, e.g., learners requirements, pedagogical choice, etc., have been decided upon. Investigating the HCI research field, we find that the methodological approaches...... perspectives in a Client Centred framework that is useable as the starting point for others in developing large scale e-learning projects....

  8. Learning about knowledge: A complex network approach

    CERN Document Server

    Costa, L F

    2006-01-01

    This article describes an approach to modeling of knowledge acquisition in terms of complex networks and walks. Each subset of knowledge is represented as a node, and relationship between such knowledge are represented as edges. Two types of edges are considered, corresponding to logical equivalence and implication. Multiple conditional implications are also considered, implying that a node can only be reached after visiting previously a set of nodes (the conditions). It is shown that hierarchical networks, involving a series of interconnected layers containing a connected subnetwork, provides a simple and natural means for avoiding deadlocks, i.e. unreachable nodes. The process of knowledge acquisition can then be simulated by considering a single agent moving along the nodes and edges, starting from the lowest layer. Several configurations of such hierarchical knowledge networks are simulated and the performance of the agent quantified in terms of the percentage of visited nodes after each movement. The Bar...

  9. A Web Mining Approach for Personalized E-Learning System

    Directory of Open Access Journals (Sweden)

    Manasi Chakurkar

    2014-01-01

    Full Text Available The Web Mining plays a very important role for the E-learning systems. In personalized E-Learning system, user customize the learning environment based on personal choices. In a general search process ,a hyperlink which is having maximum number of hits will get displayed first . For making a personalized system history of every user need to be saved in the form of user logs. In this paper we present a architecture with the use of Web mining for Web personalization. The proposed system provides a new approach with combination of web usage mining, HITS algorithm and web content mining. It combines hits results on user logs and web page contents with a clustering algorithm called as Lingo clustering algorithm. This proposed system with combined approach gives a better performance than a usage based system. Further the results are computed according to matrices computed from previous and proposed method.

  10. Considering a resource-light approach to learning verb valencies

    CERN Document Server

    Rudnick, Alex

    2012-01-01

    Here we describe work on learning the subcategories of verbs in a morphologically rich language using only minimal linguistic resources. Our goal is to learn verb subcategorizations for Quechua, an under-resourced morphologically rich language, from an unannotated corpus. We compare results from applying this approach to an unannotated Arabic corpus with those achieved by processing the same text in treebank form. The original plan was to use only a morphological analyzer and an unannotated corpus, but experiments suggest that this approach by itself will not be effective for learning the combinatorial potential of Arabic verbs in general. The lower bound on resources for acquiring this information is somewhat higher, apparently requiring a a part-of-speech tagger and chunker for most languages, and a morphological disambiguater for Arabic.

  11. Motivational factors as predictors of student approach to learning

    DEFF Research Database (Denmark)

    Lassesen, Berit

    about these associations could improve our understanding of the processes and mechanisms involved in learning and academic performance. Methods: 1181 undergraduate and graduate students from four major faculties at Aarhus University, Aarhus, Denmark (response rate: 87.5 %) completed a questionnaire......Abstract: Background and aim: Research indicates that both self-efficacy and test anxiety may influence student performance. There is also evidence to suggest that students´ approach to learn, i.e. whether they adopt a deep or surface approach affect the outcome of learning. There is, however......, little research exploring the possible influences of self-efficacy and test anxiety on study behavior in higher education, and current research stresses the importance of considering both cognitive and motivational factors in higher educational contexts (Dinther et.al., 2010) Increasing our knowledge...

  12. Can Virtual Museums Motivate Students? Toward a Constructivist Learning Approach

    Science.gov (United States)

    Katz, James E.; Halpern, Daniel

    2015-01-01

    This study aims to assess the effectiveness of immersive environments that have been implemented by museums to attract new visitors. Based on the frameworks introduced by telepresence and media richness theories, and following a constructivist-based learning approach, we argue that the greater the similarity of an online museum experience is to…

  13. Mathematical Critical Thinking Ability through Contextual Teaching and Learning Approach

    Science.gov (United States)

    Kurniati; Kusumah, Yaya S.; Sabandar, Jozua; Herman, Tatang

    2015-01-01

    This research aimed to examine the effect of the application of contextual teaching and learning (CTL) approach to the enhance of mathematical critical thinking ability (MCTA) of Primary School Teacher Students (PSTS). This research is an experimental study with the population of all students PSTS who took algebra subject matter of one university…

  14. Approaches to Learning Information Literacy: A Phenomenographic Study

    Science.gov (United States)

    Diehm, Rae-Anne; Lupton, Mandy

    2012-01-01

    This paper reports on an empirical study that explores the ways students approach learning to find and use information. Based on interviews with 15 education students in an Australian university, this study uses phenomenography as its methodological and theoretical basis. The study reveals that students use three main strategies for learning…

  15. Blended University Teaching Using Virtual Learning Environments: Conceptions and Approaches

    Science.gov (United States)

    Lameras, Petros; Levy, Philippa; Paraskakis, Iraklis; Webber, Sheila

    2012-01-01

    This paper reports findings from a phenomenographic investigation into blended university teaching using virtual learning environments (VLEs). Interviews with 25 Computer Science teachers in Greek universities illuminated a spectrum of teachers' conceptions and approaches from "teacher-focused and content-oriented", through "student-focused and…

  16. Approaches and Strategies in Next Generation Science Learning

    Science.gov (United States)

    Khine, Myint Swe, Ed.; Saleh, Issa M., Ed.

    2013-01-01

    "Approaches and Strategies in Next Generation Science Learning" examines the challenges involved in the development of modern curriculum models, teaching strategies, and assessments in science education in order to prepare future students in the 21st century economies. This comprehensive collection of research brings together science educators,…

  17. Human Resource Building--An Approach to Service Learning

    Science.gov (United States)

    Rajan, Sonika

    2009-01-01

    Background: Isabella Thoburn College at Lucknow, Uttar Pradesh, India has initiated Service Learning Program for its students through 4 issue based centers. One of the centers AIDS Awareness Center for Counseling, Education, and Training (AACCET) is in the field of HIV/AIDS. It follows 6 pronged approach to achieve its objectives and one of the…

  18. Assessing the Learning Path Specification: a Pragmatic Quality Approach

    NARCIS (Netherlands)

    Janssen, José; Berlanga, Adriana; Heyenrath, Stef; Martens, Harrie; Vogten, Hubert; Finders, Anton; Herder, Eelco; Hermans, Henry; Melero, Javier; Schaeps, Leon; Koper, Rob

    2010-01-01

    Janssen, J., Berlanga, A. J., Heyenrath, S., Martens, H., Vogten, H., Finders, A., Herder, E., Hermans, H., Melero Gallardo, J., Schaeps, L., & Koper, R. (2010). Assessing the Learning Path Specification: a Pragmatic Quality Approach. Journal of Universal Computer Science, 16(21), 3191-3209.

  19. Learning Strategies and Study Approaches of Postsecondary Students with Dyslexia

    Science.gov (United States)

    Kirby, John R.; Silvestri, Robert; Allingham, Beth H.; Parrila, Rauno; La Fave, Chantal B.

    2008-01-01

    The present study describes the self-reported learning strategies and study approaches of college and university students with and without dyslexia and examines the relationship of those characteristics with reading ability. Students with (n = 36) and without (n = 66) dyslexia completed tests measuring reading rate, reading comprehension, reading…

  20. Teaching and Learning Cycles in a Constructivist Approach to Instruction

    Science.gov (United States)

    Singer, Florence Mihaela; Moscovici, Hedy

    2008-01-01

    This study attempts to analyze and synthesize the knowledge collected in the area of conceptual models used in teaching and learning during inquiry-based projects, and to propose a new frame for organizing the classroom interactions within a constructivist approach. The IMSTRA model consists in three general phases: Immersion, Structuring,…

  1. The Learning Tree Montessori Child Care: An Approach to Diversity

    Science.gov (United States)

    Wick, Laurie

    2006-01-01

    In this article the author describes how she and her partners started The Learning Tree Montessori Child Care, a Montessori program with a different approach in Seattle in 1979. The author also relates that the other area Montessori schools then offered half-day programs, and as a result the children who attended were, for the most part,…

  2. Using an Active-Learning Approach to Teach Epigenetics

    Science.gov (United States)

    Colon-Berlingeri, Migdalisel

    2010-01-01

    Epigenetics involves heritable changes in gene expression that do not involve alterations in the DNA sequence. I developed an active-learning approach to convey this topic to students in a college genetics course. I posted a brief summary of the topic before class to stimulate exchange in cooperative groups. During class, we discussed the…

  3. Transferring Knowledge across Cultures: A Learning Competencies Approach

    Science.gov (United States)

    Kayes, Anna B.; Kayes, D. Christopher; Yamazaki, Yoshitaka

    2005-01-01

    At the heart of any successful cross-cultural knowledge transfer effort lies an individual or group of individuals with the skills to manage a complex, ambiguous and often stressful process. The ability to manage the knowledge transfer process depends as much on learning in real time as it does on rational planning. Yet, few approaches to…

  4. Re"modeling" College Algebra: An Active Learning Approach

    Science.gov (United States)

    Pinzon, D.; Pinzon, K.; Stackpole, M.

    2016-01-01

    In this paper, we discuss active learning in College Algebra at Georgia Gwinnett College. This approach has been used in more than 20 sections of College Algebra taught by the authors in the past four semesters. Students work in small, structured groups on guided inquiry activities after watching 15-20 minutes of videos before class. We discuss a…

  5. Understanding the Science-Learning Environment: A Genetically Sensitive Approach

    Science.gov (United States)

    Haworth, Claire M. A.; Davis, Oliver S. P.; Hanscombe, Ken B.; Kovas, Yulia; Dale, Philip S.; Plomin, Robert

    2013-01-01

    Previous studies have shown that environmental influences on school science performance increase in importance from primary to secondary school. Here we assess for the first time the relationship between the science-learning environment and science performance using a genetically sensitive approach to investigate the aetiology of this link. 3000…

  6. Taking Laptops Schoolwide: A Professional Learning Community Approach

    Science.gov (United States)

    Green, Tim; Donovan, Loretta; Bass, Kim

    2010-01-01

    A defined collaboration, such as a Professional Learning Community (PLC), can help expand a one-to-one program. In this article, the authors discuss four factors to consider in starting a collaborative approach at one's school: (1) school climate; (2) communication; (3) collaboration; and (4) progression of use.

  7. Adolescents and Learning Disability: An Approach to Treatment.

    Science.gov (United States)

    Pabis, Roman

    A treatment approach to adolescents with learning disabilities which incorporates remediation and psychotherapy is explored through two case studies of two youngsters (ages 14 and 17). After a review of earlier studies, the author focuses on the diagnosis and treatment of a 17 year old boy with a ninth grade education. Once a diagnosis of dyslexia…

  8. A Narrative Approach to Supporting Students Diagnosed with Learning Disabilities

    Science.gov (United States)

    Lambie, Glenn W.; Milsom, Amy

    2010-01-01

    Students diagnosed with learning disabilities experience many challenges that school counselors may address through narrative therapy. Narrative therapy is a postmodern, social constructionist approach based on the theoretical construct that individuals create their notions of truth and meaning of life through interpretive stories. This article…

  9. Instructor support for new learning approaches involving technology

    NARCIS (Netherlands)

    Bianco, Manuela; Collis, Betty; Cooke, Andy; Margaryan, Anoush

    2002-01-01

    New learning approaches involving technology are occurring in both universities and company training settings. Critical factors in regard to these changes are the professionals in an organisation responsible for course design, development, and delivery: the instructors and those who support them. In

  10. Evaluating Action Learning: A Critical Realist Complex Network Theory Approach

    Science.gov (United States)

    Burgoyne, John G.

    2010-01-01

    This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating. This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around…

  11. The Law Review Approach: What the Humanities Can Learn

    Science.gov (United States)

    Mendenhall, Allen

    2013-01-01

    Readers of this journal probably know how the peer review process works in the humanities disciplines and at various journals. Therefore the author explains how the law review process generally works and then what the humanities can learn and borrow from the law review process. He ends by advocating for a hybrid law review/peer review approach to…

  12. Teaching Mathematics to Mixed Ability Classes: A Mastery Learning Approach.

    Science.gov (United States)

    Herrington, Anthony J.; Wolff, Marj

    1985-01-01

    Describes the development, implementation, advantages, and disadvantages of a mastery learning program for teaching mathematics to mixed ability classes. Indicates that the strategy provides a successful approach while taking account of the realities of group-based instruction, a fixed curriculum with fixed time, use of a textbook, and grading.…

  13. Defining Leadership: Collegiate Women's Learning Circles: A Qualitative Approach

    Science.gov (United States)

    Preston-Cunningham, Tammie; Elbert, Chanda D.; Dooley, Kim E.

    2017-01-01

    The researchers employed qualitative methods to evaluate first-year female students' definition of "leadership" through involvement in the Women's Learning Circle. The findings revealed that students defined leadership in two dimensions: traits and behaviors. The qualitative findings explore a multidimensional approach to the voices of…

  14. The impact of teachers' approaches to teaching and students' learning styles on students' approaches to learning in college online biology courses

    Science.gov (United States)

    Hong, Yuh-Fong

    With the rapid growth of online courses in higher education institutions, research on quality of learning for online courses is needed. However, there is a notable lack of research in the cited literature providing evidence that online distance education promotes the quality of independent learning to which it aspires. Previous studies focused on academic outcomes and technology applications which do not monitor students' learning processes, such as their approaches to learning. Understanding students' learning processes and factors influencing quality of learning will provide valuable information for instructors and institutions in providing quality online courses and programs. The purpose of this study was to identify and investigate college biology teachers' approaches to teaching and students' learning styles, and to examine the impact of approaches to teaching and learning styles on students' approaches to learning via online instruction. Data collection included eighty-seven participants from five online biology courses at a community college in the southern area of Texas. Data analysis showed the following results. First, there were significant differences in approaches to learning among students with different learning styles. Second, there was a significant difference in students' approaches to learning between classes using different approaches to teaching. Three, the impact of learning styles on students' approaches to learning was not influenced by instructors' approaches to teaching. Two conclusions were obtained from the results. First, individuals with the ability to perceive information abstractly might be more likely to adopt deep approaches to learning than those preferring to perceive information through concrete experience in online learning environments. Second, Teaching Approach Inventory might not be suitable to measure approaches to teaching for online biology courses due to online instructional design and technology limitations. Based on

  15. A Reinforcement Learning Algorithm Using Multi-Layer Artificial Neural Networks for Semi-Markov Decision Problems

    Directory of Open Access Journals (Sweden)

    Mustafa Ahmet Beyazıt Ocaktan

    2013-06-01

    Full Text Available Real life problems are generally large-scale and difficult to model. Therefore, these problems can't be mostly solved by classical optimisation methods. This paper presents a reinforcement learning algorithm using a multi-layer artificial neural network to find an approximate solution for large-scale semi Markov decision problems. Performance of the developed algorithm is measured and compared to the classical reinforcement algorithm on a small-scale numerical example. According to results of numerical examples, a number of hidden layer are the key success factors, and average cost of the solution generated by the developed algorithm is approximately equal to that generated by the classical reinforcement algorithm.

  16. A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

    Indian Academy of Sciences (India)

    A Stanley Raj; Y Srinivas; D Hudson Oliver; D Muthuraj

    2014-03-01

    The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

  17. A Kernel Approach to Multi-Task Learning with Task-Specific Kernels

    Institute of Scientific and Technical Information of China (English)

    Wei Wu; Hang Li; Yun-Hua Hu; Rong Jin

    2012-01-01

    Several kernel-based methods for multi-task learning have been proposed,which leverage relations among tasks as regularization to enhance the overall learning accuracies.These methods assume that the tasks share the same kernel,which could limit their applications because in practice different tasks may need different kernels.The main challenge of introducing multiple kernels into multiple tasks is that models from different reproducing kernel Hilbert spaces (RKHSs) are not comparable,making it difficult to exploit relations among tasks.This paper addresses the challenge by formalizing the problem in the square integrable space (SIS).Specially,it proposes a kernel-based method which makes use of a regularization term defined in SIS to represent task relations.We prove a new representer theorem for the proposed approach in SIS.We further derive a practical method for solving the learning problem and conduct consistency analysis of the method.We discuss the relationship between our method and an existing method.We also give an SVM (support vector machine)-based implementation of our method for multi-label classification.Experiments on an artificial example and two real-world datasets show that the proposed method performs better than the existing method.

  18. Learning nursing through simulation: A case study approach towards an expansive model of learning.

    Science.gov (United States)

    Berragan, Liz

    2014-08-01

    This study explores the impact of simulation upon learning for undergraduate nursing students. The study objectives were (a) to explore the experiences of participating in simulation education for a small group of student nurses; and (b) to explore learning through simulation from the perspectives of the nursing students, the nurse educators and the nurse mentors. Conducted as a small-scale narrative case study, it tells the unique stories of a small number of undergraduate nursing students, nurse mentors and nurse educators and explores their experiences of learning through simulation. Data analysis through progressive focusing revealed that the nurse educators viewed simulation as a means of helping students to learn to be nurses, whilst, the nurse mentors suggested that simulation helped them to determine nursing potential. The students' narratives showed that they approached simulation learning in different ways resulting in a range of outcomes: those who were successfully becoming nurses, those who were struggling or working hard to become nurses and those who were not becoming nurses. Theories of professional practice learning and activity theory present an opportunity to articulate and theorise the learning inherent in simulation activities. They recognise the links between learning and the environment of work and highlight the possibilities for learning to inspire change and innovation.

  19. Learning Objects Recommendation System: Issues and Approaches for Retrieving, Indexing and Recomend Learning Objects

    Directory of Open Access Journals (Sweden)

    Ricardo AZAMBUJA SILVEIRA

    2016-07-01

    Full Text Available This paper discusses some important issues regarding the the management of Learning objects covering searching over repositories and different approaches of recommendation systems and presents a multiagent system based application model for indexing, retrieving and recommending learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata (data about data standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the relevance of the results we propose an information retrieval model based on a multiagent system approach and an ontological model to describe the covered knowledge domain.

  20. Discovery of a Recursive Principle: An Artificial Grammar Investigation of Human Learning of a Counting Recursion Language.

    Science.gov (United States)

    Cho, Pyeong Whan; Szkudlarek, Emily; Tabor, Whitney

    2016-01-01

    Learning is typically understood as a process in which the behavior of an organism is progressively shaped until it closely approximates a target form. It is easy to comprehend how a motor skill or a vocabulary can be progressively learned-in each case, one can conceptualize a series of intermediate steps which lead to the formation of a proficient behavior. With grammar, it is more difficult to think in these terms. For example, center embedding recursive structures seem to involve a complex interplay between multiple symbolic rules which have to be in place simultaneously for the system to work at all, so it is not obvious how the mechanism could gradually come into being. Here, we offer empirical evidence from a new artificial language (or "artificial grammar") learning paradigm, Locus Prediction, that, despite the conceptual conundrum, recursion acquisition occurs gradually, at least for a simple formal language. In particular, we focus on a variant of the simplest recursive language, a (n) b (n) , and find evidence that (i) participants trained on two levels of structure (essentially ab and aabb) generalize to the next higher level (aaabbb) more readily than participants trained on one level of structure (ab) combined with a filler sentence; nevertheless, they do not generalize immediately; (ii) participants trained up to three levels (ab, aabb, aaabbb) generalize more readily to four levels than participants trained on two levels generalize to three; (iii) when we present the levels in succession, starting with the lower levels and including more and more of the higher levels, participants show evidence of transitioning between the levels gradually, exhibiting intermediate patterns of behavior on which they were not trained; (iv) the intermediate patterns of behavior are associated with perturbations of an attractor in the sense of dynamical systems theory. We argue that all of these behaviors indicate a theory of mental representation in which recursive

  1. AN ARTIFICIAL FISH SWARM OPTIMIZED FUZZY MRI IMAGE SEGMENTATION APPROACH FOR IMPROVING IDENTIFICATION OF BRAIN TUMOUR

    Directory of Open Access Journals (Sweden)

    R.Jagadeesan

    2013-07-01

    Full Text Available In image processing, it is difficult to detect the abnormalities in brain especially in MRI brain images. Also the tumor segmentation from MRI image data is an important; however it is time consumingwhile carried out by medical specialists. A lot of methods have been proposed to solve MR images problems, quite difficult to develop an automated recognition system which could process on a large information of patient and provide a correct estimation. Hence enhanced k-means and fuzzy c-means with firefly algorithm for a segmentation of brain magnetic resonance images were developed. Thisalgorithm is based on maximum measure of the distance function which is found for cluster center detection process using the Mahalanobis concept. Particularly the firefly algorithm is implemented tooptimize the Fuzzy C-means membership function for better accuracy segmentation process. At the same time the convergence criteria is fixed for the efficient clustering method. The Firefly algorithmparameters are set fixed and they do not adjust by the time. As well Firefly algorithm does not memorize any history of better situation for each firefly and this reasons they travel in any case of it, and they miss their situations. So there is a need of better algorithm that could provide even better solution than the firefly algorithm. To attain this requirement as a proposed work the Artificial Fish Swarm Algorithm to optimize the fuzzy membership function. During surveying of the previous literature, it has been found out that no work has been done in segmentation of brain tumor using AFSA based clustering. In AFSA, artificial fishes for next movement act completely independent from past and next movement is justrelated to current position of artificial fish and its other companions which lead to select best initial centers for the MRI brain tumor segmentation. Experimental results show that presented method has an acceptable performance than the previous method.

  2. An Approach for Learning Expressive Ontologies in Medical Domain.

    Science.gov (United States)

    Rios-Alvarado, Ana B; Lopez-Arevalo, Ivan; Tello-Leal, Edgar; Sosa-Sosa, Victor J

    2015-08-01

    The access to medical information (journals, blogs, web-pages, dictionaries, and texts) has been increased due to availability of many digital media. In particular, finding an appropriate structure that represents the information contained in texts is not a trivial task. One of the structures for modeling the knowledge are ontologies. An ontology refers to a conceptualization of a specific domain of knowledge. Ontologies are especially useful because they support the exchange and sharing of information as well as reasoning tasks. The usage of ontologies in medicine is mainly focussed in the representation and organization of medical terminologies. Ontology learning techniques have emerged as a set of techniques to get ontologies from unstructured information. This paper describes a new ontology learning approach that consists of a method for the acquisition of concepts and its corresponding taxonomic relations, where also axioms disjointWith and equivalentClass are learned from text without human intervention. The source of knowledge involves files about medical domain. Our approach is divided into two stages, the first part corresponds to discover hierarchical relations and the second part to the axiom extraction. Our automatic ontology learning approach shows better results compared against previous work, giving rise to more expressive ontologies.

  3. Curriculum as a support to investigative approach in learning chemistry

    Directory of Open Access Journals (Sweden)

    Tomašević Biljana

    2009-01-01

    Full Text Available One of the main reasons for low achievement of our students in international tests is the lack of functional, applicable knowledge. Formation of such knowledge demands changing the usual way of implementation of instruction (transfer of ready-made knowledge to learning through performing simple research and practical work. Considering the fact that instruction, as an organised process, takes place in frameworks determined in advance, which are arranged and regulated on the national level by curricula, it is assumed that this kind of approach must originate precisely from curricula, which is not the case in our educational practice. The goal of this paper was to determine the way in which this kind of approach in instruction and learning of chemistry can be supported by the curriculum, in order for it to become a part of regular teaching practice on the national level. The paper presents how different structural components of curricula from eight different educational systems (four European countries, one Asian country, two American federal states and one Canadian province are used to promote and support the importance of research work in instruction and learning of chemistry. The curricula from Slovenia, England, Denmark, Malta, Singapore, North Carolina, Utah and Ontario were analyzed in order to determine the kind of information they offer within structural components and accordingly, the way in which each component promotes research approach to learning chemistry, how it guides the teacher in planning such activities in the classroom, organization and performing instruction, monitoring and evaluating students' achievements.

  4. Artificial Potential Field Approach to Path Tracking for a Non-Holonomic Mobile Robot

    DEFF Research Database (Denmark)

    Sørensen, M.J.

    2003-01-01

    This paper introduces a novel path tracking controller for an over-actuated robotic vehicle moving in an agricultural field. The vehicle itself is a four wheel steered, four wheel driven vehicle subject to the two non-holonomic constraints of free rolling and non-slipping wheels. A dynamic model...... of the vehicle is developed and used, together with an artificial potential field method, to synthesize a path tracking controller. The controller drives the vehicle to its destination way-point while avoiding crossing obstacles, e. g. crop rows. One of the key features of the controller is a novel method...

  5. Statistic Approach versus Artificial Intelligence for Rainfall Prediction Based on Data Series

    Directory of Open Access Journals (Sweden)

    Indrabayu

    2013-04-01

    Full Text Available This paper proposed a new idea in comparing two common predictors i.e. the statistic method and artificial intelligence (AI for rainfall prediction using empirical data series. The statistic method uses Auto- Regressive Integrated Moving (ARIMA and Adaptive Splines Threshold Autoregressive (ASTAR, most favorable statistic tools, while in the AI, combination of Genetic Algorithm-Neural Network (GA-NN is chosen. The results show that ASTAR gives best prediction compare to others, in term of root mean square (RMSE and following trend between prediction and actual.

  6. Residents’ perceptions of simulation as a clinical learning approach

    Science.gov (United States)

    Walsh, Catharine M.; Garg, Ankit; Ng, Stella L.; Goyal, Fenny; Grover, Samir C.

    2017-01-01

    Background Simulation is increasingly being integrated into medical education; however, there is little research into trainees’ perceptions of this learning modality. We elicited trainees’ perceptions of simulation-based learning, to inform how simulation is developed and applied to support training. Methods We conducted an instrumental qualitative case study entailing 36 semi-structured one-hour interviews with 12 residents enrolled in an introductory simulation-based course. Trainees were interviewed at three time points: pre-course, post-course, and 4–6 weeks later. Interview transcripts were analyzed using a qualitative descriptive analytic approach. Results Residents’ perceptions of simulation included: 1) simulation serves pragmatic purposes; 2) simulation provides a safe space; 3) simulation presents perils and pitfalls; and 4) optimal design for simulation: integration and tension. Key findings included residents’ markedly narrow perception of simulation’s capacity to support non-technical skills development or its use beyond introductory learning. Conclusion Trainees’ learning expectations of simulation were restricted. Educators should critically attend to the way they present simulation to learners as, based on theories of problem-framing, trainees’ a priori perceptions may delimit the focus of their learning experiences. If they view simulation as merely a replica of real cases for the purpose of practicing basic skills, they may fail to benefit from the full scope of learning opportunities afforded by simulation. PMID:28344719

  7. Multi-perspective Approaches of Vocabulary Teaching and Learning

    Institute of Scientific and Technical Information of China (English)

    王欣

    2016-01-01

    It is universally acknowledged that vocabulary is an essential component in language system. Nevertheless, in English teaching practice, imparting grammatical knowledge is highly emphasized but the vocabulary teaching is given little attention. In second language acquisition, proper application of vocabulary in communication is one of the important and difficult points for students. The paper aims to discuss the current problems in vocabulary teaching and learning, advocate a multi-perspective approach in teaching vocabulary so as to enhance the accuracy and fluency of language output, promote students’pragmatic and cross-cultural communicative competence and lay a solid foundation for their life-long learning.

  8. IMPROVING TRUST THROUGH ETHICAL LEADERSHIP: MOVING BEYOND THE SOCIAL LEARNING THEORY TO A HISTORICAL LEARNING APPROACH

    Directory of Open Access Journals (Sweden)

    Omoregie Charles Osifo

    2016-12-01

    Full Text Available The complex nature of trust and its evolving relative concepts require a more idealistic and simpler review. Ethical leadership is related to trust, honesty, transparency, compassion, empathy, results-orientedness, and many other behavioral attributes. Ethical leadership and good leadership are the same, because they represent practicing what one preaches or showing a way to the accomplishment of set goals. The outcomes and findings of many research papers on trust and ethical leadership report positive correlations between ethical leadership and trust. Improving trust from different rational standpoints requires moving and looking beyond the popular theoretical framework through which most results are derived in order to create a new thinking perspective. Social learning theory strongly emphasizes modelling while the new historical learning approach, proposed by the author, is defined as an approach that creates unique historical awareness among individuals, groups, institutions, societies, and nations to use previous experience(s or occurrence(s as a guide in developing positive opinion(s and framework(s in order to tackle the problems and issues of today and tomorrow. Social learning theory is seen as limited from the perspectives of balancing the equation between leadership and trust, the non-compatibility of the values of different generations at work, and other approaches and methods that support the historical approach. This paper is argumentative, adopts a writer´s perspective, and employs a logical analysis of the literature. The main contention is that a historical learning approach can inform an independent-learning to improve trust and its relatives (e.g. motivation and performance, because independent learning can positively shape the value of integrity, which is an integral part of ethical leadership. Historical learning can positively shape leadership in every perspective, because good leadership can develop based on history and

  9. Robust Fault-Tolerant Control for Satellite Attitude Stabilization Based on Active Disturbance Rejection Approach with Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Fei Song

    2014-01-01

    Full Text Available This paper proposed a robust fault-tolerant control algorithm for satellite stabilization based on active disturbance rejection approach with artificial bee colony algorithm. The actuating mechanism of attitude control system consists of three working reaction flywheels and one spare reaction flywheel. The speed measurement of reaction flywheel is adopted for fault detection. If any reaction flywheel fault is detected, the corresponding fault flywheel is isolated and the spare reaction flywheel is activated to counteract the fault effect and ensure that the satellite is working safely and reliably. The active disturbance rejection approach is employed to design the controller, which handles input information with tracking differentiator, estimates system uncertainties with extended state observer, and generates control variables by state feedback and compensation. The designed active disturbance rejection controller is robust to both internal dynamics and external disturbances. The bandwidth parameter of extended state observer is optimized by the artificial bee colony algorithm so as to improve the performance of attitude control system. A series of simulation experiment results demonstrate the performance superiorities of the proposed robust fault-tolerant control algorithm.

  10. Artificiality in Social Sciences

    OpenAIRE

    Rennard, Jean-Philippe

    2007-01-01

    This text provides with an introduction to the modern approach of artificiality and simulation in social sciences. It presents the relationship between complexity and artificiality, before introducing the field of artificial societies which greatly benefited from the computer power fast increase, gifting social sciences with formalization and experimentation tools previously owned by "hard" sciences alone. It shows that as "a new way of doing social sciences", artificial societies should undo...

  11. Artificial life and Piaget.

    Science.gov (United States)

    Mueller, Ulrich; Grobman, K H.

    2003-04-01

    Artificial life provides important theoretical and methodological tools for the investigation of Piaget's developmental theory. This new method uses artificial neural networks to simulate living phenomena in a computer. A recent study by Parisi and Schlesinger suggests that artificial life might reinvigorate the Piagetian framework. We contrast artificial life with traditional cognitivist approaches, discuss the role of innateness in development, and examine the relation between physiological and psychological explanations of intelligent behaviour.

  12. Effects of the Digital Game-Development Approach on Elementary School Students' Learning Motivation, Problem Solving, and Learning Achievement

    Science.gov (United States)

    Chu, Hui-Chun; Hung, Chun-Ming

    2015-01-01

    In this study, the game-based development approach is proposed for improving the learning motivation, problem solving skills, and learning achievement of students. An experiment was conducted on a learning activity of an elementary school science course to evaluate the performance of the proposed approach. A total of 59 sixth graders from two…

  13. Self-regulatory Behaviors and Approaches to Learning of Arts Students: A Comparison Between Professional Training and English Learning.

    Science.gov (United States)

    Tseng, Min-Chen; Chen, Chia-Cheng

    2016-11-17

    This study investigated the self-regulatory behaviors of arts students, namely memory strategy, goal-setting, self-evaluation, seeking assistance, environmental structuring, learning responsibility, and planning and organizing. We also explored approaches to learning, including deep approach (DA) and surface approach (SA), in a comparison between students' professional training and English learning. The participants consisted of 344 arts majors. The Academic Self-Regulation Questionnaire and the Revised Learning Process Questionnaire were adopted to examine students' self-regulatory behaviors and their approaches to learning. The results show that a positive and significant correlation was found in students' self-regulatory behaviors between professional training and English learning. The results indicated that increases in using self-regulatory behaviors in professional training were associated with increases in applying self-regulatory behaviors in learning English. Seeking assistance, self-evaluation, and planning and organizing were significant predictors for learning English. In addition, arts students used the deep approach more often than the surface approach in both their professional training and English learning. A positive correlation was found in DA, whereas a negative correlation was shown in SA between students' self-regulatory behaviors and their approaches to learning. Students with high self-regulation adopted a deep approach, and they applied the surface approach less in professional training and English learning. In addition, a SEM model confirmed that DA had a positive influence; however, SA had a negative influence on self-regulatory behaviors.

  14. [Innovative approach of teaching-learning of histology and embriology].

    Science.gov (United States)

    Nazer, R M; Tellez, T E; Bassan, N D; D'Ottavio, A E

    1977-01-01

    The paper presents an innovative approach to the teaching-learning of histology and embryology. The traditional teaching of the subject in the Faculty of Medical Sciences of Rosario National University, Santa Fe, Rosario, Argentina, up to 1974 is subjected to critical analysis, and on this basis the need for the innovation is propounded and the method for applying it proposed. A detailed account is given of the theoretical framework of the experiment reported, of the general and specific objectives of the teaching-learning technique, and of the thematic units into which the curriculum was divided. In the teaching-learning plan followed--described in the article--the conventional professorial lecture and its accompanying practical demonstrations are replaced by round tables and theoretical-practical tasks requiring active involvement and integrating theory and practice, in which teams tackle problems under teacher coordination. It also provides for evaluation of the students, teachers and course and eliminates the conventional examination.

  15. Approaches to probabilistic model learning for mobile manipulation robots

    CERN Document Server

    Sturm, Jürgen

    2013-01-01

    Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots that implement large parts of the housework, and versatile industrial assistants that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. This book presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The approaches presented in this book cover the following topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating,...

  16. Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge.

    Science.gov (United States)

    Gomez, R L; Gerken, L

    1999-03-01

    Four experiments used the head-turn preference procedure to assess whether infants could extract and remember information from auditory strings produced by a miniature artificial grammar. In all four experiments, infants generalized to new structure by discriminating new grammatical strings from ungrammatical ones after less than 2 min exposure to the grammar. Infants acquired specific information about the grammar as demonstrated by the ability to discriminate new grammatical strings from those with illegal endpoints (Experiment 1). Infants also discriminated new grammatical strings from those with string-internal pairwise violations (Experiments 2 and 3). Infants in Experiment 4 abstracted beyond specific word order as demonstrated by the ability to discriminate new strings produced by their training grammar from strings produced by another grammar despite a change in vocabulary between training and test. We discuss the implications of these findings for the study of language acquisition.

  17. Discovery of a Recursive Principle: An Artificial Grammar Investigation of Human Learning of a Counting Recursion Language

    Science.gov (United States)

    Cho, Pyeong Whan; Szkudlarek, Emily; Tabor, Whitney

    2016-01-01

    Learning is typically understood as a process in which the behavior of an organism is progressively shaped until it closely approximates a target form. It is easy to comprehend how a motor skill or a vocabulary can be progressively learned—in each case, one can conceptualize a series of intermediate steps which lead to the formation of a proficient behavior. With grammar, it is more difficult to think in these terms. For example, center embedding recursive structures seem to involve a complex interplay between multiple symbolic rules which have to be in place simultaneously for the system to work at all, so it is not obvious how the mechanism could gradually come into being. Here, we offer empirical evidence from a new artificial language (or “artificial grammar”) learning paradigm, Locus Prediction, that, despite the conceptual conundrum, recursion acquisition occurs gradually, at least for a simple formal language. In particular, we focus on a variant of the simplest recursive language, anbn, and find evidence that (i) participants trained on two levels of structure (essentially ab and aabb) generalize to the next higher level (aaabbb) more readily than participants trained on one level of structure (ab) combined with a filler sentence; nevertheless, they do not generalize immediately; (ii) participants trained up to three levels (ab, aabb, aaabbb) generalize more readily to four levels than participants trained on two levels generalize to three; (iii) when we present the levels in succession, starting with the lower levels and including more and more of the higher levels, participants show evidence of transitioning between the levels gradually, exhibiting intermediate patterns of behavior on which they were not trained; (iv) the intermediate patterns of behavior are associated with perturbations of an attractor in the sense of dynamical systems theory. We argue that all of these behaviors indicate a theory of mental representation in which recursive

  18. An Artificial Gravity Spacecraft Approach which Minimizes Mass, Fuel and Orbital Assembly Reg

    Science.gov (United States)

    Bell, L.

    2002-01-01

    The Sasakawa International Center for Space Architecture (SICSA) is undertaking a multi-year research and design study that is exploring near and long-term commercial space development opportunities. Space tourism in low-Earth orbit (LEO), and possibly beyond LEO, comprises one business element of this plan. Supported by a financial gift from the owner of a national U.S. hotel chain, SICSA has examined opportunities, requirements and facility concepts to accommodate up to 100 private citizens and crewmembers in LEO, as well as on lunar/planetary rendezvous voyages. SICSA's artificial gravity Science Excursion Vehicle ("AGSEV") design which is featured in this presentation was conceived as an option for consideration to enable round-trip travel to Moon and Mars orbits and back from LEO. During the course of its development, the AGSEV would also serve other important purposes. An early assembly stage would provide an orbital science and technology testbed for artificial gravity demonstration experiments. An ultimate mature stage application would carry crews of up to 12 people on Mars rendezvous missions, consuming approximately the same propellant mass required for lunar excursions. Since artificial gravity spacecraft that rotate to create centripetal accelerations must have long spin radii to limit adverse effects of Coriolis forces upon inhabitants, SICSA's AGSEV design embodies a unique tethered body concept which is highly efficient in terms of structural mass and on-orbit assembly requirements. The design also incorporates "inflatable" as well as "hard" habitat modules to optimize internal volume/mass relationships. Other important considerations and features include: maximizing safety through element and system redundancy; means to avoid destabilizing mass imbalances throughout all construction and operational stages; optimizing ease of on-orbit servicing between missions; and maximizing comfort and performance through careful attention to human needs. A

  19. Artificial Root Exudate System (ARES): a field approach to simulate tree root exudation in soils

    Science.gov (United States)

    Lopez-Sangil, Luis; Estradera-Gumbau, Eduard; George, Charles; Sayer, Emma

    2016-04-01

    The exudation of labile solutes by fine roots represents an important strategy for plants to promote soil nutrient availability in terrestrial ecosystems. Compounds exuded by roots (mainly sugars, carboxylic and amino acids) provide energy to soil microbes, thus priming the mineralization of soil organic matter (SOM) and the consequent release of inorganic nutrients into the rhizosphere. Studies in several forest ecosystems suggest that tree root exudates represent 1 to 10% of the total photoassimilated C, with exudation rates increasing markedly under elevated CO2 scenarios. Despite their importance in ecosystem functioning, we know little about how tree root exudation affect soil carbon dynamics in situ. This is mainly because there has been no viable method to experimentally control inputs of root exudates at field scale. Here, I present a method to apply artificial root exudates below the soil surface in small field plots. The artificial root exudate system (ARES) consists of a water container with a mixture of labile carbon solutes (mimicking tree root exudate rates and composition), which feeds a system of drip-tips covering an area of 1 m2. The tips are evenly distributed every 20 cm and inserted 4-cm into the soil with minimal disturbance. The system is regulated by a mechanical timer, such that artificial root exudate solution can be applied at frequent, regular daily intervals. We tested ARES from April to September 2015 (growing season) within a leaf-litter manipulation experiment ongoing in temperate deciduous woodland in the UK. Soil respiration was measured monthly, and soil samples were taken at the end of the growing season for PLFA, enzymatic activity and nutrient analyses. First results show a very rapid mineralization of the root exudate compounds and, interestingly, long-term increases in SOM respiration, with negligible effects on soil moisture levels. Large positive priming effects (2.5-fold increase in soil respiration during the growing

  20. Artificial cognition architectures

    CERN Document Server

    Crowder, James A; Friess, Shelli A

    2013-01-01

    The goal of this book is to establish the foundation, principles, theory, and concepts that are the backbone of real, autonomous Artificial Intelligence. Presented here are some basic human intelligence concepts framed for Artificial Intelligence systems. These include concepts like Metacognition and Metamemory, along with architectural constructs for Artificial Intelligence versions of human brain functions like the prefrontal cortex. Also presented are possible hardware and software architectures that lend themselves to learning, reasoning, and self-evolution

  1. Influence of open- and closed-book tests on medical students' learning approaches

    NARCIS (Netherlands)

    Heijne-Penninga, Marjolein; Kuks, Jan B. M.; Hofman, W. H. Adriaan; Cohen-Schotanus, Janke

    2008-01-01

    CONTEXT Two learning approaches are consistently distinguished in the literature: deep and surface learning. The deep learning approach is considered preferable. Open-book tests are expected to stimulate deep learning and to offer a possible way of handling the substantial growth in medical knowledg

  2. Synthesizing Technology Adoption and Learners' Approaches towards Active Learning in Higher Education

    Science.gov (United States)

    Chan, Kevin; Cheung, George; Wan, Kelvin; Brown, Ian; Luk, Green

    2015-01-01

    In understanding how active and blended learning approaches with learning technologies engagement in undergraduate education, current research models tend to undermine the effect of learners' variations, particularly regarding their styles and approaches to learning, on intention and use of learning technologies. This study contributes to further…

  3. Benefiting from Customer and Competitor Knowledge: A Market-Based Approach to Organizational Learning

    Science.gov (United States)

    Hoe, Siu Loon

    2008-01-01

    Purpose: The purpose of this paper is to review the organizational learning, market orientation and learning orientation concepts, highlight the importance of market knowledge to organizational learning and recommend ways in adopting a market-based approach to organizational learning. Design/methodology/approach: The extant organizational learning…

  4. An axiomatic approach to soft learning vector quantization and clustering.

    Science.gov (United States)

    Karayiannis, N B

    1999-01-01

    This paper presents an axiomatic approach to soft learning vector quantization (LVQ) and clustering based on reformulation. The reformulation of the fuzzy c-means (FCM) algorithm provides the basis for reformulating entropy-constrained fuzzy clustering (ECFC) algorithms. This analysis indicates that minimization of admissible reformulation functions using gradient descent leads to a broad variety of soft learning vector quantization and clustering algorithms. According to the proposed approach, the development of specific algorithms reduces to the selection of a generator function. Linear generator functions lead to the FCM and fuzzy learning vector quantization (FLVQ) algorithms while exponential generator functions lead to ECFC and entropy-constrained learning vector quantization (ECLVQ) algorithms. The reformulation of LVQ and clustering algorithms also provides the basis for developing uncertainty measures that can identify feature vectors equidistant from all prototypes. These measures are employed by a procedure developed to make soft LVQ and clustering algorithms capable of identifying outliers in the data set. This procedure is evaluated by testing the algorithms generated by linear and exponential generator functions on speech data.

  5. Long Range Forecast on South West Monsoon Rainfall using Artificial Neural Networks based on Clustering Approach

    Directory of Open Access Journals (Sweden)

    Maya L. Pai

    2014-06-01

    Full Text Available The purpose of this study is to forecast Southwest Indian Monsoon rainfall based on sea surface temperature, sea level pressure, humidity and zonal (u and meridional (v winds. With the aforementioned parameters given as input to an Artificial Neural Network (ANN, the rainfall within 10x10 grids of southwest Indian regions is predicted by means of one of the most efficient clustering methods, namely the Kohonen Self-Organizing Maps (SOM. The ANN is trained with input parameters spanning for 36 years (1960-1995 and tested and validated for a period of 9 years (1996-2004. It is further used to predict the rainfall for 6 years (2005-2010. The results show reasonably good accuracy for the summer monsoon periods June, July, August and September (JJAS of the validation years.

  6. Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

    Science.gov (United States)

    Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

    2014-06-01

    This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

  7. [Therapeutic approach to post-radiation xerostomia. A reservoir prosthesis and an artificial salivary gland].

    Science.gov (United States)

    Smatt, V; Briere, M; Cornebise-Drouhet, F; Maugey, N; Robin, M

    1989-01-01

    Radiation-induced xerostomia is an incapacitating sequela of salivary gland irradiation in patients receiving tumoricidal X-ray therapeutical doses for cancer of the upper respiratory and gastrointestinal tracts. All stimulant type therapeutics available are powerless to bring back wetness into the mouth of patients with asialia. Replacement therapy constitutes the only alternative for symptomatic treatment. Artificial salivary gland sprays have a proven unsustained, short-lasting efficacy. The administration of an oral mucine-containing salivary emollient solution drip, presented either in the form of an oral or external container, constitutes an original symptomatic treatment regimen, the efficacy of which has been established. The authors here review the concept and methodology of their palliative treatment protocol against chronic asialia.

  8. Concurrent study of stability and cytotoxicity of a novel nanoemulsion system - an artificial neural networks approach.

    Science.gov (United States)

    Seyedhassantehrani, Negar; Karimi, Roya; Tavoosidana, Gholamreza; Amani, Amir

    2017-05-01

    Problems commonly associated with using nanoemulsions are their cytotoxic effects and low stability profiles. Here, for the first time, concentrations of ingredients of a nanoemulsion system were investigated to obtain the most stable nanoemulsion system with the least cytotoxic effect on MCF7 cell line. Artificial neural networks (ANNs) were used to model the experimentally obtained data. Surfactant concentration was found to be the dominant factor in determining the stability - surfactant concentration above a critical point made the preparation unstable, while it appeared not to be influencing the cytotoxicity. Concentration of oil showed a direct relationship to the cytotoxicity with a minimum value required to provide an acceptable safety profile for the preparation. Co-surfactant appeared not to be considerably effective on neither stability nor cytotoxicity. To obtain the optimum preparation with maximum stability and minimum cytotoxicity, surfactant and oil values need to be kept at their maximum and minimum possible, respectively.

  9. Artificial neural network approach to assess selective flocculation on hematite and kaolinite

    Institute of Scientific and Technical Information of China (English)

    Lopamudra Panda; PK Banerjee; Surendra Kumar Biswal; R Venugopal; NR Mandre

    2014-01-01

    Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material. This process has not been extensively used commercially because of its complex dependency on process parameters. In this paper, a selective flocculation process, using synthetic mixtures of hematite and kaolinite in different ratios, was attempted, and the ad-sorption mechanism was investigated by Fourier transform infrared (FTIR) spectroscopy. A three-layer artificial neural network (ANN) model (4−4−3) was used to predict the separation performance of the process in terms of grade, Fe recovery, and separation efficiency. The model values were in good agreement with experimental values.

  10. Learning styles and disciplinary differences from a situated cognition approach

    Directory of Open Access Journals (Sweden)

    Ana Clara Ventura

    2013-06-01

    Full Text Available Students who enter college are invited to share language, habits and customs that may contradict your thoughts, knowledge and skills previously learned. This could be a problem for student because it could learn a new way of thinking and behaving to identify with their teachers. There are agreements in the scientific literature about the university learning of a discipline involves the appropriation of concepts and the acquisition of skills and typical learning styles. This process may be explained from situated cognition approach that consider a functional relationship between way of thinking and types of activities. The gap is what effects produces higher education on learning styles. Hence, the aim of this paper is to analyze the empirical studies that compare the learning styles of students from different disciplinary in Ibero American context. For this purpose, a descriptive review was carried out of the Scielo, Dialnet, Redalyc y Doaj data bases. Descriptors used in the research for information were the key words: learning styles, disciplinary differences and higher education. We found 9 specific empirical studies that complying with all the criteria for inclusion (time period 2000-2012, research article and university sample. However, the evidence does not sufficient to achieve a point of agreement on this problematic. These results, from a point of view scientific, could open to new research lines. On the other hand, from a pedagogical-didactic view this study allow introduce the discussion of different formats educational, can be distinguished teaching strategies towards acquisition of typical styles of own discipline or teaching strategies to strengthen with the diversity of styles preexisting of students.

  11. E-learning approaches in biometry and epidemiology

    Directory of Open Access Journals (Sweden)

    Ziegler, Andreas

    2010-01-01

    Full Text Available Education is an integral component of increasing our profession’s profile. While master and PhD level education in biostatistics and epidemiology is provided at high levels in several regions, there are parts of the world deserving proper specialized education. E-learning may be one option because traveling costs can be avoided, and the capacity of teachers can be multiplied by making appropriate use of e-learning tools. The aim of this work was to explore the availability of e-learning approaches in the areas of statistics, biometry, biostatistics, epidemiology, and genetic epidemiology by a systematic literature search and a search in databases. We identified a total of 25 courses. They differ with respect to target audience, content, amount and quality. Many of them had been developed at a time when technical aspects were the main hurdle at the stage of course development. Important hygiene and motivation factors were generally unknown at that time, and, subsequently, ignored. As a result, none of the courses provides exercises that generate individual feedback to motivate the student, and no varying degree of complexity is observed. Many courses do not fulfill modern needs for e-learning. In conclusion, the development of modern e-learning following recent didactical concepts is urgently required. Sustainability of these courses is crucial and can be best guaranteed by using available technological platforms. These allow the use of common didactical principles, robust and reliable technology.

  12. Team-Based Learning: A New Approach Toward Improving Education

    Directory of Open Access Journals (Sweden)

    Rita Rezaee

    2016-11-01

    Full Text Available  Team-based learning is designed to provide students with both conceptual and procedural knowledge, aiming to enhance active learning and critical thinking. In the present study, team-based learning and lecture methods in teaching the “hospital organization and management” course among hospital management students were compared. This quasi-experimental study was conducted on 25 undergraduate students of management. Teaching sessions were divided into two parts. The first part was taught with interactive lectures and the second part with team-based learning method. The students' knowledge was measured before, immediately and two months (late post-test after teaching. Finally, the mean scores of the final exam and students' satisfaction towards the methods of teaching were measured. There was an improvement in test scores of the students after the TBL sessions when compared to the test scores after lecture sessions (P<0.001. Also, TBL group had significantly a higher amount of knowledge retention compared to the lecture group (P<0.001, but no significant relationship was found between the mean scores of the final exam in the TBL and lecture groups (P=0.116. Finally, the majority of the respondents were more satisfied with TBL sessions compared to the ones held through lecture (P=0.037. The results indicated that TBL provides a better outcome for students. We found that the TBL approach allowed us to create an active learning environment that contributed to the improvement of the students’ performances.

  13. A reinforcement learning approach to gait training improves retention.

    Science.gov (United States)

    Hasson, Christopher J; Manczurowsky, Julia; Yen, Sheng-Che

    2015-01-01

    Many gait training programs are based on supervised learning principles: an individual is guided towards a desired gait pattern with directional error feedback. While this results in rapid adaptation, improvements quickly disappear. This study tested the hypothesis that a reinforcement learning approach improves retention and transfer of a new gait pattern. The results of a pilot study and larger experiment are presented. Healthy subjects were randomly assigned to either a supervised group, who received explicit instructions and directional error feedback while they learned a new gait pattern on a treadmill, or a reinforcement group, who was only shown whether they were close to or far from the desired gait. Subjects practiced for 10 min, followed by immediate and overnight retention and over-ground transfer tests. The pilot study showed that subjects could learn a new gait pattern under a reinforcement learning paradigm. The larger experiment, which had twice as many subjects (16 in each group) showed that the reinforcement group had better overnight retention than the supervised group (a 32% vs. 120% error increase, respectively), but there were no differences for over-ground transfer. These results suggest that encouraging participants to find rewarding actions through self-guided exploration is beneficial for retention.

  14. Using an Artificial Neural Network Approach for Supplier Evaluation Process and a Sectoral Application

    Directory of Open Access Journals (Sweden)

    A. Yeşim Yayla

    2011-02-01

    Full Text Available In this study, a-three layered feed-forward backpropagation Artificial Neural Network (ANN model is developed for the supplier firms in ceramic sector on the bases of user effectiveness for using concurrent engineering method. The developed model is also questioned for its usability in the supplier evaluation process. The network's independent variables of the developed model are considered as input variables of the network and dependent variables are used as output variables. The values of these variables are determined with factor analysis. For obtaining the date set to be used in the analysis, a questionnaire form with 34 questions explaining the network's input and output variables are prepared and sent out to 52 firms active in related sector. For obtaining more accurate results from the network, the questions having factor load below 0,6 are eliminated from the analysis. With the elimination of the questions from the analysis, the answers given for 22 questions explaining 8 input variables are used for the evaluation the network's inputs, the answers given for 3 questions explaining output variables are used for the evaluation the network's outputs. The data set of the network's are divided into four equal groups with k-fold method in order to get four different alternative network structures. As a conclusion, the forecasted firm scores giving the minimum error from the network test simulation and real firm scores are found to be very close to each other, thus, it is concluded that the developed artificial neural network model can be used effectively in the supplier evaluation process.

  15. Communicative – Activity Approach in Learning Foreign Language

    Directory of Open Access Journals (Sweden)

    Dariga A. Bekova

    2015-01-01

    Full Text Available The article is devoted communicative method of teaching foreign languages, which is the activity character. The task of the communicative approach – to interest of students in learning a foreign language through the accumulation and improvement their knowledge and experience. The main objective this method – free orienteering training in foreign language environment and the ability to adequately react in different situations, communication.

  16. The Past, Present, and Future of Artificial Life

    Directory of Open Access Journals (Sweden)

    Wendy eAguilar

    2014-10-01

    Full Text Available For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into fourteen themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning, ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields.

  17. Mathematical beauty in service of deep approach to learning

    DEFF Research Database (Denmark)

    Karamehmedovic, Mirza

    2015-01-01

    In the fall of 2014 I taught ‘02601 Introduction to Numerical Algorithms’ to a class of 86 engineering students at Technical University of Denmark. The course employed basic calculus and linear algebra to elucidate and analyse canonical algorithms of scientific computing. A major part of the course...... was hands-on MATLAB programming, where the algorithms were tested and applied to solve physical modelbased problems. To encourage a deep approach, and discourage a surface approach to learning, I introduced into the lectures a basic but rigorous mathematical treatment of crucial theoretical points...

  18. Could lectures be stimulating? An approach to encourage active learning

    Directory of Open Access Journals (Sweden)

    Bakoush O

    2008-01-01

    Full Text Available Medical education is the cornerstone of building an effective health care system. Newly qualified doctors have to be equipped with knowledge, skills and attitudes to face the challenges of treating patients. Therefore the majority of medical schools in Western countries adopted, to varying degrees, the Problem Based Learning (PBL approach. However the PBL is an expensive approach as it is based on small group teaching with a maximum of 8-10 students per group. This makes it difficult to apply in countries like Libya where the medical schools enrol larger numbers of students, often without appropriate resources. Hence the majority of the teaching is based on didactic lectures.

  19. Modern approaches in deep learning for SAR ATR

    Science.gov (United States)

    Wilmanski, Michael; Kreucher, Chris; Lauer, Jim

    2016-05-01

    Recent breakthroughs in computational capabilities and optimization algorithms have enabled a new class of signal processing approaches based on deep neural networks (DNNs). These algorithms have been extremely successful in the classification of natural images, audio, and text data. In particular, a special type of DNNs, called convolutional neural networks (CNNs) have recently shown superior performance for object recognition in image processing applications. This paper discusses modern training approaches adopted from the image processing literature and shows how those approaches enable significantly improved performance for synthetic aperture radar (SAR) automatic target recognition (ATR). In particular, we show how a set of novel enhancements to the learning algorithm, based on new stochastic gradient descent approaches, generate significant classification improvement over previously published results on a standard dataset called MSTAR.

  20. DO APPROACHES TO LEARNING AFFECT ACADEMIC PERFORMANCE OF BUSINESS ETHICS STUDENTS?

    OpenAIRE

    Zaza Eliza Mohd Redza; Suhaiza Ismail; Suhaimi Mhd. Sarif; Yusof Ismail

    2013-01-01

    The objectives of this study are to explore the approaches to learning Business Ethics course adopted by students and to examine the relationship between learning approaches and academic performances of Business Ethics course. A questionnaire survey was administered to 209 students taking Business Ethics course in a higher learning institution in Malaysia. The Approaches and Study Skills Inventory for Students (ASSIST) was used to assess the learning approaches adopted by students, whilst the...

  1. Personalized recommendation strategies for eLearning: An AHP approach

    Directory of Open Access Journals (Sweden)

    Ramo Sendelj

    2015-06-01

    Full Text Available eLearning has become a key adjunct to both, education in general and the business world; it isbecoming an important tool to allow the flexibility and quality requested by such a kind of learningprocess. One of recent challenges in eLearning industry is personalized learning (PL, aimed onmeeting the needs and aspirations of each individual learner. A PL can be considered as a facility foran individual to access, combine, configure and manage digital resources (knowledge assets andservices related to their present learning needs and interests. The role of teachers in PL is also enhanced, since they should monitor learners’ progress, make dynamic coherence between educational goals and students’ achievements, and provide all needed recourses accordingly. The variety of PL systems are already developed, the most attempts of learner personalization are focused on the level of knowledge, background and hyperspace experience, preferences and interests, or even learning styles and achievements. It still does not fully address the issue of inteligent personalized recommendations stimulated by the huge wealth of opportunities for collaboration and communication offered by semantic technologies and intelligent reasoning techniques. In this paper, focusing on the well-known Analytical Hierarchical Process (AHP method, we propose a framework for addressing different kinds of learners’ preferences in PL, integration with historical data and experiences, and making recommendations and personalization accordingly. Firstly, we are focused on making analyses of relevant kinds of preferences defined by both, learners and teachers over learning process in general (including indicators of progress, learning styles, pedagogice approach, etc, learning resources and learners’ interests and goals. Also, relevant historical data should be recognized with appropriate retrieving methods and potential web resources if applicable. Finally, semantic structure should

  2. Evolutionary artificial neural network approach for predicting properties of Cu- 15Ni-8Sn-0.4Si alloy

    Institute of Scientific and Technical Information of China (English)

    FANG Shan-feng; WANG Ming-pu; WANG Yan-hui; QI Wei-hong; LI Zhou

    2008-01-01

    A novel data mining approach, based on artificial neural network(ANN) using differential evolution(DE) training algorithm, was proposed to model the non-linear relationship between parameters of aging processes and mechanical and electrical properties of Cu-15Ni-8Sn-0.4Si alloy. In order to improve predictive accuracy of ANN model, the leave-one-out-cross-validation (LOOCV) technique was adopted to automatically determine the optimal number of neurons of the hidden layer. The forecasting performance of the proposed global optimization algorithm was compared with that of local optimization algorithm. The present calculated results are consistent with the experimental values, which suggests that the proposed evolutionary artificial neural network algorithm is feasible and efficient. Moreover, the experimental results illustrate that the DE training algorithm combined with gradient-based training algorithm achieves better convergence performance and the lowest forecasting errors and is therefore considered to be a promising alternative method to forecast the hardness and electrical conductivity of Cu- 15Ni-8Sn-0.4Si alloy.

  3. The Effect of Cooperative Learning on the Learning Approaches of Students with Different Learning Styles

    Science.gov (United States)

    Çolak, Esma

    2015-01-01

    Problem Statement: For this study, a cooperative learning process was designed in which students with different learning styles could help each other in heterogeneous groups to perform teamwork-based activities. One aspect deemed important in this context was whether the instructional environment designed to reach students with different learning…

  4. Students' Approaches to Learning and Assessment Preferences in a Portfolio-Based Learning Environment

    Science.gov (United States)

    Baeten, Marlies; Dochy, Filip; Struyven, Katrien

    2008-01-01

    This study focused on the relationships between experiences with portfolio assessment, students' approaches to learning and their assessment preferences by means of a pre- and post-test design in an authentic class setting. The participants were 138 first-year professional bachelor's degree students in office management. They were assessed by…

  5. A new method to learn to start in speed skating: A differencial learning approach

    NARCIS (Netherlands)

    Savelsbergh, J.P. Geert; Kamper, J. Willemiek; Rabius, Jorine; Schöllhorn, Wolfgang

    2010-01-01

    The aim of this study was to examine whether it is possible to utilize the fluctuations in human motor behaviour to induce a self-organizing process in the athlete, which takes advantage of individual movement and learning characteristics. This recently developed approach is known as differencial le

  6. A Critical Approach to Social Emotional Learning Instruction through Community-Based Service Learning

    Science.gov (United States)

    McKay-Jackson, Cassandra

    2014-01-01

    The traditional teaching of reading, writing, and arithmetic alone will not fully prepare students to lead with integrity, govern fairly, analyze problems, and work collectively with people different from themselves. Social emotional learning (SEL) has been described as one of the missing links in academic education, but a restrictive approach to…

  7. Epistasis analysis using artificial intelligence.

    Science.gov (United States)

    Moore, Jason H; Hill, Doug P

    2015-01-01

    Here we introduce artificial intelligence (AI) methodology for detecting and characterizing epistasis in genetic association studies. The ultimate goal of our AI strategy is to analyze genome-wide genetics data as a human would using sources of expert knowledge as a guide. The methodology presented here is based on computational evolution, which is a type of genetic programming. The ability to generate interesting solutions while at the same time learning how to solve the problem at hand distinguishes computational evolution from other genetic programming approaches. We provide a general overview of this approach and then present a few examples of its application to real data.

  8. Peak load forecasting using Bayesian regularization, Resilient and adaptive backpropagation learning based artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Saini, Lalit Mohan [Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana 136119 (India)

    2008-07-15

    Up to 7 days ahead electrical peak load forecasting has been done using feed forward neural network based on Steepest descent, Bayesian regularization, Resilient and adaptive backpropagation learning methods, by incorporating the effect of eleven weather parameters and past peak load information. To avoid trapping of network into a state of local minima, the optimization of user-defined parameters viz., learning rate and error goal has been performed. The sliding window concept has been incorporated for selection of training data set. It was then reduced as per relevant selection according to the day type and season for which the forecast is made. To reduce the dimensionality of input matrix, the Principal Component Analysis method of factor extraction or correlation analysis technique has been used and their performance has been compared. The resultant data set was used for training of three-layered neural network. In order to increase the learning speed, the weights and biases were initialized according to Nguyen and Widrow method. To avoid over fitting, early stopping of training was done at the minimum validation error. (author)

  9. Student Learning Approaches in the UAE: The Case for the Achieving Domain

    Science.gov (United States)

    McLaughlin, James; Durrant, Philip

    2017-01-01

    The deep versus surface learning approach dichotomy has dominated recent research in student learning approach dimensions. However, the achievement dimension may differ in importance in non-Western and vocational tertiary settings. The aim was to assess how Emirati tertiary students could be characterized in terms of their learning approaches. The…

  10. The Influence of Teachers' Teaching Approaches on Students' Learning Approaches: The Student Perspective

    Science.gov (United States)

    Beausaert, Simon A. J.; Segers, M. S. R; Wiltink, Danique P. A.

    2013-01-01

    Background: Research on the relation between teaching and learning approaches has been mainly conducted in higher education and it is not yet clear to what extent the results can be generalised when it comes to secondary education. Purpose: The purpose of this study was to research how students in secondary education perceive their teachers'…

  11. Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule

    Directory of Open Access Journals (Sweden)

    Ya-li Zhou

    2009-01-01

    Full Text Available In practical active noise control (ANC systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN-based simultaneous perturbation stochastic approximation (SPSA algorithm, which functions as a nonlinear mode-free (MF controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

  12. An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash

    Institute of Scientific and Technical Information of China (English)

    Okan KARAHAN; Harun TANYILDIZI; Cengiz D. ATIS

    2008-01-01

    In this study,an artificial neural network(ANN)model for studying the strength properties of steel fiber reinforced concrete(SFRC)containing fly ash was devised.The mixtures were prepared with 0 wt%,15 wt%,and 30 wt% of fly ash,at 0 vol.%,0.5 vol.%,1.0 vol.% and 1.5 vol.% of fiber,respectively.After being cured under the standard conditions for 7,28,90 and 365 d,the specimens of each mixture were tested to determine the corresponding compressive and flexural strengths.The pa-rameters such as the amounts of cement,fly ash replacement,sand,gravel,steel fiber,and the age of samples were selected as input variables,while the compressive and flexural strengths of the concrete were chosen as the output variables.The back propagation learning algorithm with three different variants,namely the Levenberg-Marquardt(LM),scaled conjugate gradient(SCG)and Fletcher-Powell conjugate gradient(CGF)algorithms were used in the network so that the best approach can be found.The results obtained from the model and the experiments were compared,and it was found that the suitable algorithm is the LM algorithm.Furthermore,the analysis of variance(ANOVA)method was used to determine how importantly the experimental parameters affect the strength of these mixtures.

  13. Evaluation of students' perception of their learning environment and approaches to learning

    Science.gov (United States)

    Valyrakis, Manousos; Cheng, Ming

    2015-04-01

    This work presents the results of two case studies designed to assess the various approaches undergraduate and postgraduate students undertake for their education. The first study describes the results and evaluation of an undergraduate course in Water Engineering which aims to develop the fundamental background knowledge of students on introductory practical applications relevant to the practice of water and hydraulic engineering. The study assesses the effectiveness of the course design and learning environment from the perception of students using a questionnaire addressing several aspects that may affect student learning, performance and satisfaction, such as students' motivation, factors to effective learning, and methods of communication and assessment. The second study investigates the effectiveness of supervisory arrangements based on the perceptions of engineering undergraduate and postgraduate students. Effective supervision requires leadership skills that are not taught in the University, yet there is rarely a chance to get feedback, evaluate this process and reflect. Even though the results are very encouraging there are significant lessons to learn in improving ones practice and develop an effective learning environment to student support and guidance. The findings from these studies suggest that students with high level of intrinsic motivation are deep learners and are also top performers in a student-centered learning environment. A supportive teaching environment with a plethora of resources and feedback made available over different platforms that address students need for direct communication and feedback has the potential to improve student satisfaction and their learning experience. Finally, incorporating a multitude of assessment methods is also important in promoting deep learning. These results have deep implications about student learning and can be used to further improve course design and delivery in the future.

  14. Simulation of artificial vision: II. Eccentric reading of full-page text and the learning of this task.

    Science.gov (United States)

    Sommerhalder, Jörg; Rappaz, Benjamin; de Haller, Raoul; Fornos, Angélica Pérez; Safran, Avinoam B; Pelizzone, Marco

    2004-01-01

    Reading of isolated words in conditions mimicking artificial vision has been found to be a difficult but feasible task. In particular at relatively high eccentricities, a significant adaptation process was required to reach optimal performances [Vision Res. 43 (2003) 269]. The present study addressed the task of full-page reading, including page navigation under control of subject's own eye movements. Conditions of artificial vision mimicking a retinal implant were simulated by projecting stimuli with reduced information content (lines of pixelised text) onto a restricted and eccentric area of the retina. Three subjects, naïve to the task, were trained for almost two months (about 1 h/day) to read full-page texts. Subjects had to use their own eye movements to displace a 10 degrees x 7 degrees viewing window, stabilised at 15 degrees eccentricity in their lower visual field. Initial reading scores were very low for two subjects (about 13% correctly read words), and astonishingly high for the third subject (86% correctly read words). However, all of them significantly improved their performance with time, reaching close to perfect reading scores (ranging from 86% to 98% correct) at the end of the training process. Reading rates were as low as 1-5 words/min at the beginning of the experiment and increased significantly with time to 14-28 words/min. Qualitative text understanding was also estimated. We observed that reading scores of at least 85% correct were necessary to achieve 'good' text understanding. Gaze position recordings, made during the experimental sessions, demonstrated that the control of eye movements, especially the suppression of reflexive vertical saccades, constituted an important part of the overall adaptive learning process. Taken together, these results suggest that retinal implants might restore full-page text reading abilities to blind patients. About 600 stimulation contacts, distributed on an implant surface of 3 x 2 mm2, appear to be a

  15. Teaching Approaches and Student Involvement in Learning to Write

    Directory of Open Access Journals (Sweden)

    Kyeongheui Kim

    2013-06-01

    Full Text Available This study examined the relationship between teaching approaches and the learning involvement of students from Korea in US college level classes by examining what these students did to complete writing assignments required for classes and what approaches professors adopted to assist students with English writing. It also examined how and why their involvement changed from active to less involvement to withdrawal or passive involvement to active involvement. In other words, this study examined how much professors’ teaching approaches influenced students’ attitudes towards English writing. Korean students who grew up in a culture where the whole society regards teachers highly expected more from their professors and were more dependent on professors. It appears that study participants’ English language proficiency also played a role in their dependency on their professors. There was a gap between these students’ expectations for professors and some of their professors’ teaching approaches. Also, there was some professors’ bias perceived by study participants, whether intentional or not, against non-native English speaking students and minority students, which disappointed and frustrated study participants and influenced these students’ degrees of involvement in learning.

  16. Application of artificial intelligence to reservoir characterization: An interdisciplinary approach. Final report, August 31, 1997

    Energy Technology Data Exchange (ETDEWEB)

    Kerr, D.R.; Thompson, L.G.; Shenoi, S.

    1998-03-01

    The primary goal of the project is to develop a user-friendly computer program to integrate geological and engineering information using Artificial Intelligence (AI) methodology. The project is restricted to fluvially dominated deltaic environments. The static information used in constructing the reservoir description includes well core and log data. Using the well core and the log data, the program identifies the marker beds, and the type of sand facies, and in turn, develops correlations between wells. Using the correlations and sand facies, the program is able to generate multiple realizations of sand facies and petrophysical properties at interwell locations using geostatistical techniques. The generated petrophysical properties are used as input in the next step where the production data are honored. By adjusting the petrophysical properties, the match between the simulated and the observed production rates is obtained. Although all the components within the overall system are functioning, the integration of dynamic data may not be practical due to the single-phase flow limitations and the computationally intensive algorithms. The future work needs to concentrate on making the dynamic data integration computationally efficient.

  17. A Comparative Approach to Hand Force Estimation using Artificial Neural Networks.

    Science.gov (United States)

    Mobasser, Farid; Hashtrudi-Zaad, Keyvan

    2012-01-01

    In many applications that include direct human involvement such as control of prosthetic arms, athletic training, and studying muscle physiology, hand force is needed for control, modeling and monitoring purposes. The use of inexpensive and easily portable active electromyography (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors which are often very expensive and require bulky frames. Among non-model-based estimation methods, Multilayer Perceptron Artificial Neural Networks (MLPANN) has widely been used to estimate muscle force or joint torque from different anatomical features in humans or animals. This paper investigates the use of Radial Basis Function (RBF) ANN and MLPANN for force estimation and experimentally compares the performance of the two methodologies for the same human anatomy, ie, hand force estimation, under an ensemble of operational conditions. In this unified study, the EMG signal readings from upper-arm muscles involved in elbow joint movement and elbow angular position and velocity are utilized as inputs to the ANNs. In addition, the use of the elbow angular acceleration signal as an input for the ANNs is also investigated.

  18. An artificial neural network approach to estimate evapotranspiration from remote sensing and AmeriFlux data

    Institute of Scientific and Technical Information of China (English)

    Zhuoqi CHEN; Runhe SHI; Shupeng Zhang

    2013-01-01

    A simple and accurate method to estimate evapotranspiration (ET) is essential for dynamic monitoring of the Earth system at a large scale.In this paper,we developed an artificial neural network (ANN) model forced by remote sensing and AmeriFlux data to estimate ET.First,the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data (normalized difference vegetation index,land surface temperature and surface net radiation) for the period 2002-2006.ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period.The validation sites covered five different vegetation types and were not involved in the ANN training.The coefficient of determination (R2) value for comparison between estimated and measured ET was 0.77,the root-meansquare error was 0.62 mm/d,and the mean residual was -0.28.The simple model developed in this paper captured the seasonal and interarnual variation features of ET on the whole.However,the accuracy of estimated ET depended on the vegetation types,among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.

  19. CONTEXTUAL TEACHING AND LEARNING APPROACH TO TEACHING WRITING

    Directory of Open Access Journals (Sweden)

    Intan Satriani

    2012-07-01

    Full Text Available Abstract: This article reports a study on the implementation of contextual teaching and learning approach to teaching English writing to second graders of a Junior High Shool in Bandung. The study aims to investigate the strategies of Contextual Teaching and Learning (CTL (as adapted from Crawford, 2001 and the advantages of using CTL approach. The study employed a qualitative case study research design. The data were obtained from several instruments, namely class observations, students’ interview and students’ writing products which were then analyzed using writing assessment criteria taken from Rose (2007, as cited by Emilia, 2011, p. 151. The findings revealed that the teaching writing program was successful to improve students’ recount writing skill. Specifically, they showed some improvement on schematic structure, grammar roles, and graphic features. Moreover, the data from observation, interview, and documentation of students’ text showed some benefits of CTL. These include: (1 engaging students in the writing activity; (2 increasing students’ motivation to participate actively in the writing class; (3 helping students to construct their writing; (4 helping students to solve their problems; (5 providing ways for students to discuss or interact with their friends; and (6 helping the students to summarize and reflect the lesson. Based on these findings, it is recommended that CTL be implemented in teaching writing.   Keywords: contextual teaching and learning, teaching writing

  20. Novel educational approaches to enhance learning and interest in nephrology.

    Science.gov (United States)

    Jhaveri, Kenar D; Sparks, Matthew A; Shah, Hitesh H

    2013-07-01

    The number of U.S. medical graduates pursuing careers in nephrology has declined over the last several years. Some of the proposed reasons for this declining interest include difficult-to-understand or unstimulating kidney pathophysiology courses in medical school; disheartening inpatient elective experiences; and few opportunities to experience the other aspects of nephrology careers such as outpatient nephrology clinics, outpatient dialysis, and kidney transplantation. Novel and alternative educational approaches should be considered by the nephrology training community to enhance the understanding of nephrology from medical school to fellowship training. Newer teaching methods and styles should also be incorporated to adapt to today's learner. These innovative educational approaches may not only increase interest in nephrology careers, but they may also enhance and maintain interest during nephrology fellowship. In this article, we will review several educational approaches that may enhance teaching and learning in nephrology.

  1. Learning the Task Management Space of an Aircraft Approach Model

    Science.gov (United States)

    Krall, Joseph; Menzies, Tim; Davies, Misty

    2014-01-01

    Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.

  2. Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment

    Science.gov (United States)

    Spierings, Michelle J.; ten Cate, Carel

    2016-01-01

    The ability to abstract a regularity that underlies strings of sounds is a core mechanism of the language faculty but might not be specific to language learning or even to humans. It is unclear whether and to what extent nonhuman animals possess the ability to abstract regularities defining the relation among arbitrary auditory items in a string and to generalize this abstraction to strings of acoustically novel items. In this study we tested these abilities in a songbird (zebra finch) and a parrot species (budgerigar). Subjects were trained in a go/no-go design to discriminate between two sets of sound strings arranged in an XYX or an XXY structure. After this discrimination was acquired, each subject was tested with test strings that were structurally identical to the training strings but consisted of either new combinations of known elements or of novel elements belonging to other element categories. Both species learned to discriminate between the two stimulus sets. However, their responses to the test strings were strikingly different. Zebra finches categorized test stimuli with previously heard elements by the ordinal position that these elements occupied in the training strings, independent of string structure. In contrast, the budgerigars categorized both novel combinations of familiar elements as well as strings consisting of novel element types by their underlying structure. They thus abstracted the relation among items in the XYX and XXY structures, an ability similar to that shown by human infants and indicating a level of abstraction comparable to analogical reasoning. PMID:27325756

  3. Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment.

    Science.gov (United States)

    Spierings, Michelle J; Ten Cate, Carel

    2016-07-05

    The ability to abstract a regularity that underlies strings of sounds is a core mechanism of the language faculty but might not be specific to language learning or even to humans. It is unclear whether and to what extent nonhuman animals possess the ability to abstract regularities defining the relation among arbitrary auditory items in a string and to generalize this abstraction to strings of acoustically novel items. In this study we tested these abilities in a songbird (zebra finch) and a parrot species (budgerigar). Subjects were trained in a go/no-go design to discriminate between two sets of sound strings arranged in an XYX or an XXY structure. After this discrimination was acquired, each subject was tested with test strings that were structurally identical to the training strings but consisted of either new combinations of known elements or of novel elements belonging to other element categories. Both species learned to discriminate between the two stimulus sets. However, their responses to the test strings were strikingly different. Zebra finches categorized test stimuli with previously heard elements by the ordinal position that these elements occupied in the training strings, independent of string structure. In contrast, the budgerigars categorized both novel combinations of familiar elements as well as strings consisting of novel element types by their underlying structure. They thus abstracted the relation among items in the XYX and XXY structures, an ability similar to that shown by human infants and indicating a level of abstraction comparable to analogical reasoning.

  4. A reinforcement learning approach to model interactions between landmarks and geometric cues during spatial learning.

    Science.gov (United States)

    Sheynikhovich, Denis; Arleo, Angelo

    2010-12-13

    In contrast to predictions derived from the associative learning theory, a number of behavioral studies suggested the absence of competition between geometric cues and landmarks in some experimental paradigms. In parallel to these studies, neurobiological experiments suggested the existence of separate independent memory systems which may not always interact according to classic associative principles. In this paper we attempt to combine these two lines of research by proposing a model of spatial learning that is based on the theory of multiple memory systems. In our model, a place-based locale strategy uses activities of modeled hippocampal place cells to drive navigation to a hidden goal, while a stimulus-response taxon strategy, presumably mediated by the dorso-lateral striatum, learns landmark-approaching behavior. A strategy selection network, proposed to reside in the prefrontal cortex, implements a simple reinforcement learning rule to switch behavioral strategies. The model is used to reproduce the results of a behavioral experiment in which an interaction between a landmark and geometric cues was studied. We show that this model, built on the basis of neurobiological data, can explain the lack of competition between the landmark and geometry, potentiation of geometry learning by the landmark, and blocking. Namely, we propose that the geometry potentiation is a consequence of cooperation between memory systems during learning, while blocking is due to competition between the memory systems during action selection.

  5. Application of artificial intelligence to reservoir characterization: An interdisciplinary approach. Annual report, October 1994--October 1995

    Energy Technology Data Exchange (ETDEWEB)

    Kerr, D.; Thompson, L.; Shenoi, S.

    1996-01-01

    The basis of this research is to apply novel techniques from Artificial Intelligence and Expert Systems in capturing, integrating and articulating key knowledge from geology, geostatistics, and petroleum engineering to develop accurate descriptions of petroleum reservoirs. The ultimate goal is to design and implement a single powerful expert system for use by small producers and independents to efficiently exploit reservoirs. The main challenge of the proposed research is to automate the generation of detailed reservoir descriptions honoring all the available soft and hard data that ranges from qualitative and semi-quantitative geological interpretations to numeric data obtained from cores, well tests, well logs and production statistics. Additional challenges are the verification and validation of the expert system, since much of the interpretation of the experts is based on extended experience in reservoir characterization. The overall project plan to design the system to create integrated reservoir descriptions begins by initially developing an AI-based methodology for producing large-scale reservoir descriptions generated interactively from geology and well test data. Parallel to this task is a second task that develops an AI-based methodology that uses facies-biased information to generate small-scale descriptions of reservoir properties such as permeability and porosity. The third task involves consolidation and integration of the large-scale and small-scale methodologies to produce reservoir descriptions honoring all the available data. The final task will be technology transfer. With this plan, the authors have carefully allocated and sequenced the activities involved in each of the tasks to promote concurrent progress towards the research objectives. Moreover, the project duties are divided among the faculty member participants. Graduate students will work in terms with faculty members.

  6. Bioprinting of artificial blood vessels: current approaches towards a demanding goal.

    Science.gov (United States)

    Hoch, Eva; Tovar, Günter E M; Borchers, Kirsten

    2014-11-01

    -linkability) functions, towards the demanding goal of biofabricating artificial blood vessels.

  7. Incorporation of iodine into apatite structure: a crystal chemistry approach using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jianwei eWang

    2015-06-01

    Full Text Available Materials with apatite crystal structure provide a great potential for incorporating the long-lived radioactive iodine isotope (129I in the form of iodide (I- from nuclear waste streams. Because of its durability and potentially high iodine content, the apatite waste form can reduce iodine release rate and minimize the waste volume. Crystal structure and composition of apatite was investigated for iodide incorporation into the channel of the structure using Artificial Neural Network. A total of 86 experimentally determined apatite crystal structures of different compositions were compiled from literature, and 46 of them were used to train the networks and 42 were used to test the performance of the trained networks. The results show that the performances of the networks are satisfactory for predictions of unit cell parameters a and c and channel size of the structure. The trained and tested networks were then used to predict unknown compositions of apatite that incorporates iodide. With a crystal chemistry consideration, chemical compositions that lead to matching the size of the structural channel to the size of iodide were then predicted to be able to incorporate iodide in the structural channel. The calculations suggest that combinations of A site cations of Ag+, K+, Sr2+, Pb2+, Ba2+, and Cs+, and X site cations, mostly formed tetrahedron, of Mn5+, As5+, Cr5+, V5+, Mo5+, Si4+, Ge4+, and Re7+ are possible apatite compositions that are able to incorporate iodide. The charge balance of different apatite compositions can be achieved by multiple substitutions at a single site or coupled substitutions at both A and X sites. The results give important clues for designing experiments to synthesize new apatite compositions and also provide a fundamental understanding how iodide is incorporated in the apatite structure. This understanding can provide important insights for apatite waste forms design by optimizing the chemical composition and synthesis

  8. A Hybrid Ensemble Learning Approach to Star-Galaxy Classification

    CERN Document Server

    Kim, Edward J; Kind, Matias Carrasco

    2015-01-01

    There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a more robust star-galaxy classification. To demonstrate this hybrid, ensemble approach, we combine a purely morphological classifier, a supervised machine learning method based on random forest, an unsupervised machine learning method based on self-organizing maps, and a hierarchical Bayesian template fitting method. Using data from the CFHTLenS survey, we consider different scenarios: when a high-quality training set is available with spectroscopic labels from DEEP2, SDSS, VIPERS, and VVDS, and when the demographics of sources in a low-quality training set do not match the demographics of objects in the test data set. We demonstrate that our Bayesian combination technique improves the overall performance over any individual classification method in these scenarios. Thus, s...

  9. A machine learning approach to nonlinear modal analysis

    Science.gov (United States)

    Worden, K.; Green, P. L.

    2017-02-01

    Although linear modal analysis has proved itself to be the method of choice for the analysis of linear dynamic structures, its extension to nonlinear structures has proved to be a problem. A number of competing viewpoints on nonlinear modal analysis have emerged, each of which preserves a subset of the properties of the original linear theory. From the geometrical point of view, one can argue that the invariant manifold approach of Shaw and Pierre is the most natural generalisation. However, the Shaw-Pierre approach is rather demanding technically, depending as it does on the analytical construction of a mapping between spaces, which maps physical coordinates into invariant manifolds spanned by independent subsets of variables. The objective of the current paper is to demonstrate a data-based approach motivated by Shaw-Pierre method which exploits the idea of statistical independence to optimise a parametric form of the mapping. The approach can also be regarded as a generalisation of the Principal Orthogonal Decomposition (POD). A machine learning approach to inversion of the modal transformation is presented, based on the use of Gaussian processes, and this is equivalent to a nonlinear form of modal superposition. However, it is shown that issues can arise if the forward transformation is a polynomial and can thus have a multi-valued inverse. The overall approach is demonstrated using a number of case studies based on both simulated and experimental data.

  10. Behavior generation strategy of artificial behavioral system by self-learning paradigm for autonomous robot tasks

    Science.gov (United States)

    Dağlarli, Evren; Temeltaş, Hakan

    2008-04-01

    In this study, behavior generation and self-learning paradigms are investigated for the real-time applications of multi-goal mobile robot tasks. The method is capable to generate new behaviors and it combines them in order to achieve multi goal tasks. The proposed method is composed from three layers: Behavior Generating Module, Coordination Level and Emotion -Motivation Level. Last two levels use Hidden Markov models to manage dynamical structure of behaviors. The kinematics and dynamic model of the mobile robot with non-holonomic constraints are considered in the behavior based control architecture. The proposed method is tested on a four-wheel driven and four-wheel steered mobile robot with constraints in simulation environment and results are obtained successfully.

  11. Future Challenges of Robotics and Artificial Intelligence in Nursing: What Can We Learn from Monsters in Popular Culture?

    Science.gov (United States)

    Erikson, Henrik; Salzmann-Erikson, Martin

    2016-01-01

    It is highly likely that artificial intelligence (AI) will be implemented in nursing robotics in various forms, both in medical and surgical robotic instruments, but also as different types of droids and humanoids, physical reinforcements, and also animal/pet robots. Exploring and discussing AI and robotics in nursing and health care before these tools become commonplace is of great importance. We propose that monsters in popular culture might be studied with the hope of learning about situations and relationships that generate empathic capacities in their monstrous existences. The aim of the article is to introduce the theoretical framework and assumptions behind this idea. Both robots and monsters are posthuman creations. The knowledge we present here gives ideas about how nursing science can address the postmodern, technologic, and global world to come. Monsters therefore serve as an entrance to explore technologic innovations such as AI. Analyzing when and why monsters step out of character can provide important insights into the conceptualization of caring and nursing as a science, which is important for discussing these empathic protocols, as well as more general insight into human knowledge. The relationship between caring, monsters, robotics, and AI is not as farfetched as it might seem at first glance. PMID:27455058

  12. Future Challenges of Robotics and Artificial Intelligence in Nursing: What Can We Learn from Monsters in Popular Culture?

    Science.gov (United States)

    Erikson, Henrik; Salzmann-Erikson, Martin

    2016-01-01

    It is highly likely that artificial intelligence (AI) will be implemented in nursing robotics in various forms, both in medical and surgical robotic instruments, but also as different types of droids and humanoids, physical reinforcements, and also animal/pet robots. Exploring and discussing AI and robotics in nursing and health care before these tools become commonplace is of great importance. We propose that monsters in popular culture might be studied with the hope of learning about situations and relationships that generate empathic capacities in their monstrous existences. The aim of the article is to introduce the theoretical framework and assumptions behind this idea. Both robots and monsters are posthuman creations. The knowledge we present here gives ideas about how nursing science can address the postmodern, technologic, and global world to come. Monsters therefore serve as an entrance to explore technologic innovations such as AI. Analyzing when and why monsters step out of character can provide important insights into the conceptualization of caring and nursing as a science, which is important for discussing these empathic protocols, as well as more general insight into human knowledge. The relationship between caring, monsters, robotics, and AI is not as farfetched as it might seem at first glance.

  13. A reinforcement learning approach to instrumental contingency degradation in rats.

    Science.gov (United States)

    Dutech, Alain; Coutureau, Etienne; Marchand, Alain R

    2011-01-01

    Goal-directed action involves a representation of action consequences. Adapting to changes in action-outcome contingency requires the prefrontal region. Indeed, rats with lesions of the medial prefrontal cortex do not adapt their free operant response when food delivery becomes unrelated to lever-pressing. The present study explores the bases of this deficit through a combined behavioural and computational approach. We show that lesioned rats retain some behavioural flexibility and stop pressing if this action prevents food delivery. We attempt to model this phenomenon in a reinforcement learning framework. The model assumes that distinct action values are learned in an incremental manner in distinct states. The model represents states as n-uplets of events, emphasizing sequences rather than the continuous passage of time. Probabilities of lever-pressing and visits to the food magazine observed in the behavioural experiments are first analyzed as a function of these states, to identify sequences of events that influence action choice. Observed action probabilities appear to be essentially function of the last event that occurred, with reward delivery and waiting significantly facilitating magazine visits and lever-pressing respectively. Behavioural sequences of normal and lesioned rats are then fed into the model, action values are updated at each event transition according to the SARSA algorithm, and predicted action probabilities are derived through a softmax policy. The model captures the time course of learning, as well as the differential adaptation of normal and prefrontal lesioned rats to contingency degradation with the same parameters for both groups. The results suggest that simple temporal difference algorithms with low learning rates can largely account for instrumental learning and performance. Prefrontal lesioned rats appear to mainly differ from control rats in their low rates of visits to the magazine after a lever press, and their inability to

  14. Novel approaches to address spectral distortions in photon counting x-ray CT using artificial neural networks

    Science.gov (United States)

    Touch, M.; Clark, D. P.; Barber, W.; Badea, C. T.

    2016-04-01

    Spectral CT using a photon-counting x-ray detector (PCXD) can potentially increase accuracy of measuring tissue composition. However, PCXD spectral measurements suffer from distortion due to charge sharing, pulse pileup, and Kescape energy loss. This study proposes two novel artificial neural network (ANN)-based algorithms: one to model and compensate for the distortion, and another one to directly correct for the distortion. The ANN-based distortion model was obtained by training to learn the distortion from a set of projections with a calibration scan. The ANN distortion was then applied in the forward statistical model to compensate for distortion in the projection decomposition. ANN was also used to learn to correct distortions directly in projections. The resulting corrected projections were used for reconstructing the image, denoising via joint bilateral filtration, and decomposition into three-material basis functions: Compton scattering, the photoelectric effect, and iodine. The ANN-based distortion model proved to be more robust to noise and worked better compared to using an imperfect parametric distortion model. In the presence of noise, the mean relative errors in iodine concentration estimation were 11.82% (ANN distortion model) and 16.72% (parametric model). With distortion correction, the mean relative error in iodine concentration estimation was improved by 50% over direct decomposition from distorted data. With our joint bilateral filtration, the resulting material image quality and iodine detectability as defined by the contrast-to-noise ratio were greatly enhanced allowing iodine concentrations as low as 2 mg/ml to be detected. Future work will be dedicated to experimental evaluation of our ANN-based methods using 3D-printed phantoms.

  15. Participant Approaches to and Reflections on Learning to Play a 12-Bar Blues in an Asynchronous E-Learning Environment

    Science.gov (United States)

    Seddon, Frederick; Biasutti, Michele

    2009-01-01

    This study investigated the viability of learning to play an improvised 12-bar blues on keyboard with both hands together in an asynchronous e-learning environment. The study also sought to reveal participant approaches to and reflections on this learning experience. Participants were video-taped as they engaged with six "Blues Activities",…

  16. Examining the Roles of Blended Learning Approaches in Computer-Supported Collaborative Learning (CSCL) Environments: A Delphi Study

    Science.gov (United States)

    So, Hyo-Jeong; Bonk, Curtis J.

    2010-01-01

    In this study, a Delphi method was used to identify and predict the roles of blended learning approaches in computer-supported collaborative learning (CSCL) environments. The Delphi panel consisted of experts in online learning from different geographic regions of the world. This study discusses findings related to (a) pros and cons of blended…

  17. ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings

    NARCIS (Netherlands)

    Drachsler, Hendrik; Pecceu, Dries; Arts, Tanja; Hutten, Edwin; Rutledge, Lloyd; Van Rosmalen, Peter; Hummel, Hans; Koper, Rob

    2009-01-01

    Drachsler, H., Peccau, D., Arts, T., Hutten, E., Rutledge, L., Van Rosmalen, P., Hummel, H. G. K., & Koper, R. (2009). ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings. Presentation at the 2nd Workshop Mash-Up Personal Learning

  18. High School Students' Approaches to Learning Physics with Relationship to Epistemic Views on Physics and Conceptions of Learning Physics

    Science.gov (United States)

    Chiou, Guo-Li; Lee, Min-Hsien; Tsai, Chin-Chung

    2013-01-01

    Background and purpose: Knowing how students learn physics is a central goal of physics education. The major purpose of this study is to examine the strength of the predictive power of students' epistemic views and conceptions of learning in terms of their approaches to learning in physics. Sample, design and method: A total of 279 Taiwanese high…

  19. Amp: A modular approach to machine learning in atomistic simulations

    Science.gov (United States)

    Khorshidi, Alireza; Peterson, Andrew A.

    2016-10-01

    Electronic structure calculations, such as those employing Kohn-Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning techniques can provide accurate potentials that can match the quality of electronic structure calculations, provided sufficient training data. These potentials can then be used to rapidly simulate large and long time-scale phenomena at similar quality to the parent electronic structure approach. Machine-learning potentials usually take a bias-free mathematical form and can be readily developed for a wide variety of systems. Electronic structure calculations have favorable properties-namely that they are noiseless and targeted training data can be produced on-demand-that make them particularly well-suited for machine learning. This paper discusses our modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package (Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems. Potentials developed through the atom-centered approach are simultaneously applicable for systems with various sizes. Interpolation can be enhanced by introducing custom descriptors of the local environment. We demonstrate this in the current work for Gaussian-type, bispectrum, and Zernike-type descriptors. Amp has an intuitive and modular structure with an interface through the python scripting language yet has parallelizable fortran components for demanding tasks; it is designed to integrate closely with the widely used Atomic Simulation Environment (ASE), which

  20. Comparison of Machine Learning Approaches on Arabic Twitter Sentiment Analysis

    Directory of Open Access Journals (Sweden)

    Merfat.M. Altawaier

    2016-12-01

    Full Text Available With the dramatic expansion of information over internet, users around the world express their opinion daily on the social network such as Facebook and Twitter. Large corporations nowadays invest on analyzing these opinions in order to assess their products or services by knowing the people feedback toward such business. The process of knowing users’ opinions toward particular product or services whether positive or negative is called sentiment analysis. Arabic is one of the common languages that have been addressed regarding sentiment analysis. In the literature, several approaches have been proposed for Arabic sentiment analysis and most of these approaches are using machine learning techniques. Machine learning techniques are various and have different performances. Therefore, in this study, we try to identifying a simple, but workable approach for Arabic sentiment analysis on Twitter. Hence, this study aims to investigate the machine learning technique in terms of Arabic sentiment analysis on Twitter. Three techniques have been used including Naïve Bayes, Decision Tree (DT and Support Vector Machine (SVM. In addition, two simple sub-tasks pre-processing have been also used; Term Frequency-Inverse Document Frequency (TF-IDF and Arabic stemming to get the heaviest weight term as the feature for tweet classification. TF-IDF aims to identify the most frequent words, whereas stemming aims to retrieve the stem of the word by removing the inflectional derivations. The dataset that has been used is Modern Arabic Corpus which consists of Arabic tweets. The performance of classification has been evaluated based on the information retrieval metrics precision, recall and f-measure. The experimental results have shown that DT has outperformed the other techniques by obtaining 78% of f-measure.

  1. Determination of Efficiency of Hybrid Photovoltaic Thermal Air Collectors Using Artificial Neural Network Approach for Different PV Technology

    Directory of Open Access Journals (Sweden)

    G. N. Tiwari

    2012-01-01

    Full Text Available In this paper an attempt has been made to determine efficiency of semi transparent hybrid photovoltaic thermal double pass air collector for different PV technology and compare it with single pass air collector using artificial neural network (ANN technique for New Delhi weather station of India. The MATLAB 7.1 neural networks toolbox has been used for defining and training of ANN for determination of thermal, electrical, overall thermal and overall exergy efficiency of the system. The ANN model uses ambient air temperature, number of sunshine hours, number of clear days, temperature coefficient, cell efficiency, global and diffuse radiation as input parameters. The transfer function, neural network configuration and learning parameters have been selected based on highest convergence during training and testing of network. About 2000 sets of data from four weather stations (Bangalore, Mumbai, Srinagar and Jodhpur have been given as input for training and data of the fifth weather station (New Delhi has been used for testing purpose. It has been observed that the best transfer function for a given configuration is logsig. The feed forward back-propagation algorithm has been used in this analysis. Further the results of ANN model have been compared with analytical values on the basis of root mean square error.

  2. FORECASTING CHINA'S FOREIGN TRADE VOLUME WITH A KERNEL-BASED HYBRID EC-ONOMETRIC-AI ENSEMBLE LEARNING APPROACH

    Institute of Scientific and Technical Information of China (English)

    Lean YU; Shouyang WANG; Kin Keung LAI

    2008-01-01

    Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal-ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en-semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic-tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.

  3. Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Holstein-Rathlou, N H; Marsh, D J

    1998-01-01

    via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. However, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general....

  4. Estimation of seismic quality factor: Artificial neural networks and current approaches

    Science.gov (United States)

    Yıldırım, Eray; Saatçılar, Ruhi; Ergintav, Semih

    2017-01-01

    The aims of this study are to estimate soil attenuation using alternatives to traditional methods, to compare results of using these methods, and to examine soil properties using the estimated results. The performances of all methods, amplitude decay, spectral ratio, Wiener filter, and artificial neural network (ANN) methods, are examined on field and synthetic data with noise and without noise. High-resolution seismic reflection field data from Yeniköy (Arnavutköy, İstanbul) was used as field data, and 424 estimations of Q values were made for each method (1,696 total). While statistical tests on synthetic and field data are quite close to the Q value estimation results of ANN, Wiener filter, and spectral ratio methods, the amplitude decay methods showed a higher estimation error. According to previous geological and geophysical studies in this area, the soil is water-saturated, quite weak, consisting of clay and sandy units, and, because of current and past landslides in the study area and its vicinity, researchers reported heterogeneity in the soil. Under the same physical conditions, Q value calculated on field data can be expected to be 7.9 and 13.6. ANN models with various structures, training algorithm, input, and number of neurons are investigated. A total of 480 ANN models were generated consisting of 60 models for noise-free synthetic data, 360 models for different noise content synthetic data and 60 models to apply to the data collected in the field. The models were tested to determine the most appropriate structure and training algorithm. In the final ANN, the input vectors consisted of the difference of the width, energy, and distance of seismic traces, and the output was Q value. Success rate of both ANN methods with noise-free and noisy synthetic data were higher than the other three methods. Also according to the statistical tests on estimated Q value from field data, the method showed results that are more suitable. The Q value can be estimated

  5. Perspective: Codesign for materials science: An optimal learning approach

    Science.gov (United States)

    Lookman, Turab; Alexander, Francis J.; Bishop, Alan R.

    2016-05-01

    A key element of materials discovery and design is to learn from available data and prior knowledge to guide the next experiments or calculations in order to focus in on materials with targeted properties. We suggest that the tight coupling and feedback between experiments, theory and informatics demands a codesign approach, very reminiscent of computational codesign involving software and hardware in computer science. This requires dealing with a constrained optimization problem in which uncertainties are used to adaptively explore and exploit the predictions of a surrogate model to search the vast high dimensional space where the desired material may be found.

  6. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing and Artificial Intelligence Models (ANN, SVM: The Case of Greek Electricity Market

    Directory of Open Access Journals (Sweden)

    George P. Papaioannou

    2016-08-01

    Full Text Available In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC technique and the traditional multiple regression (PC-regression, for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN and Support Vector Machines (SVM. Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

  7. Integrating transformative learning and action learning approaches to enhance ethical leadership for supervisors in the hotel business

    Directory of Open Access Journals (Sweden)

    Boonyuen Saranya

    2016-01-01

    Full Text Available Ethical leadership is now increasingly focused in leadership development. The main purpose of this study is to explore two methods of adult learning, action learning and transformative learning, and to use the methods to enhance ethical leadership. Building ethical leadership requires an approach that focuses on personal values, beliefs, or frames of references, which is transformative learning. Transformative learning requires a series of meetings to conduct critical discourse and to follow up the learning of learners. By organizing such action learning, human resource developers can optimize their time and effort more effectively. The authors have created a comprehensive model to integrate the two learning approaches in a general way that focuses not only on ethical leadership, but also on all kinds of behavioral transformation in the workplace in the hotel business or even other types of business.

  8. Inteligência biológica versus inteligência artificial: uma abordagem crítica Biologic intelligence versus artificial intelligence: a critical approach

    Directory of Open Access Journals (Sweden)

    Wilson Luiz Sanvito

    1995-09-01

    Full Text Available Após considerações iniciais sobre inteligência, um estudo comparativo entre inteligência biológica e inteligência artificial é feito. Os especialistas em Inteligência Artificial são de opinião que inteligência é simplesmente uma matéria de manipulação de símbolos físicos. Neste sentido, o objetivo da Inteligência Artificial é entender como a inteligência cerebral funciona em termos de conceitos e técnicas de engenharia. De modo diverso os filósofos da ciência acreditam que os computadores podem ter uma sintaxe, porém não têm uma semântica. No presente trabalho é ressaltado que o complexo cérebro/mente constitui um sistema monolítico, que funciona com funções emergentes em vários níveis de organização hierárquica. Esses níveis hierárquicos não são redutíveis um ao outro. Eles são, no mínimo, três (neuronal, funcional e semântico e funcionam dentro de um plano interacional. Do ponto de vista epistemológico, o complexo cérebro/mente se utiliza de mecanismos lógicos e não-lógicos para lidar com os problemas do dia-a-dia. A lógica é necessária para o processo do pensamento, porém não é suficiente. Ênfase é dada aos mecanismos não-lógicos (lógica nebulosa, heurística, raciocínio intuitivo, os quais permitem à mente desenvolver estratégias para encontrar soluções.After brief considerations about intelligence, a comparative study between biologic and artificial intelligence is made. The specialists in Artificial Intelligence found that intelligence is purely a matter of physical symbol manipulation. The enterprise of Artificial Intelligence aims to understand what we might call Brain Intelligence in terms of concepts and techniques of engineering. However the philosophers believed that computer-machine can have syntax, but can never have semantics. In other words, that they can follow rules, such as those of arithmetic or grammar, but not understand what to us are meanings of symbols

  9. [Artificial Inversion of the Left-Right Visceral Asymmetry in Vertebrates: Conceptual Approaches and Experimental Solutions].

    Science.gov (United States)

    Truleva, A S; Malashichev, E B; Ermakov, A S

    2015-01-01

    Externally, vertebrates are bilaterally symmetrical; however, left-right asymmetry is observed in the structure of their internal organs and systems of organs (circulatory, digestive, and respiratory). In addition to the asymmetry of internal organs (visceral), there is also functional (i.e., asymmetrical functioning of organs on the left and right sides of the body) and behavioral asymmetry. The question of a possible association between different types of asymmetry is still open. The study of the mechanisms of such association, in addition to the fundamental interest, has important applications for biomedicine, primarily for the understanding of the brain functioning in health and disease and for the development of methods of treatment of certain mental diseases, such as schizophrenia and autism, for which the disturbance of left-right asymmetry of the brain was shown. To study the deep association between different types of asymmetry, it is necessary to obtain adequate animal models (primarily animals with inverted visceral organs, situs inversus totalis). There are two main possible approaches to obtaining such model organisms: mutagenesis followed by selection of mutant strains with mutations in the genes that affect the formation of the left-right visceral asymmetry and experimental obtaining of animals with inverted internal organs. This review focuses on the second approach. We describe the theoretical models for establishing left-right asymmetry and possible experimental approaches to obtaining animals with inverted internal organs.

  10. College Students' Motivation and Learning Strategies Profiles and Academic Achievement: A Self-Determination Theory Approach

    Science.gov (United States)

    Liu, Woon Chia; Wang, Chee Keng John; Kee, Ying Hwa; Koh, Caroline; Lim, Boon San Coral; Chua, Lilian

    2014-01-01

    The development of effective self-regulated learning strategies is of interest to educationalists. In this paper, we examine inherent individual difference in self-regulated learning based on Motivated Learning for Learning Questionnaire (MLSQ) using the cluster analytic approach and examine cluster difference in terms of self-determination theory…

  11. Evaluation of impact of artificial reefs on artisanal fisheries: need for complementary approaches

    Directory of Open Access Journals (Sweden)

    Barbara Koeck

    2011-01-01

    Full Text Available In a general context of fisheries decline due to overfishing and to other phenomena such as climate change, it appears to be crucial to implement a sustainable management of natural resources by finding a balance between conservation and exploitation purposes. Artificial reefs (ARs have recently become one of the existing management tools, often in combination with fishing quotas or marine protected areas. To evaluate the effectiveness of the studied ARs, different methods have been used: (i visual census by SCUBA diving (AR scale, (ii fisheries landings survey (local scale and (iii external fish tagging (regional scale. Underwater visual census (UVC showed a significantly higher species richness and density in ARs than in the control site. Abundance, biomass and LPUE data (Landings Per Unit Effort issued from artisanal fisheries landings survey were not significantly different around the AR system from other fishing grounds of the French Catalan coast. The tagging experiments on Diplodus sargus suggested that the connectivity of demersal fish populations must be taken into account to evaluate the influence area of ARs and thus their indirect impacts on artisanal fisheries. The present study highlights the interest of combining methods covering different spatial scales in order to evaluate direct and indirect impacts of ARs on artisanal fisheries. Methods for the evaluation of AR efficiency are discussed.Dentro do atual contexto de redução nos estoques de peixes ligados à sobrepesca, e também à outros fenômenos tais como as mudanças climáticas, é indispensável implementar um plano de gestão durável para os recursos pesqueiros, conciliando sua exploração e conservação. Os recifes artificiais (RAs tem surgido nos dias atuais como uma importante ferramenta de gestão, freqüentemente combinada à cotas de pesca ou áreas marinhas protegidas. Com a finalidade de avaliar a eficiência dos recifes artificiais, utilizou-se os seguintes

  12. What's Wrong with Learning for the Exam? An Assessment-Based Approach for Student Engagement

    Science.gov (United States)

    Ito, Hiroshi

    2014-01-01

    It is now widely recognized that assessment and the feedback play key roles in the learning process. However, assessment-based learning approaches are not yet commonly practiced in Japan. This paper provides an example of an assessment-based approach to teaching and learning employed for a course entitled "English as an International…

  13. A Stochastic Approach for Automatic and Dynamic Modeling of Students' Learning Styles in Adaptive Educational Systems

    Science.gov (United States)

    Dorça, Fabiano Azevedo; Lima, Luciano Vieira; Fernandes, Márcia Aparecida; Lopes, Carlos Roberto

    2012-01-01

    Considering learning and how to improve students' performances, an adaptive educational system must know how an individual learns best. In this context, this work presents an innovative approach for student modeling through probabilistic learning styles combination. Experiments have shown that our approach is able to automatically detect and…

  14. Sequenced Integration and the Identification of a Problem-Solving Approach through a Learning Process

    Science.gov (United States)

    Cormas, Peter C.

    2016-01-01

    Preservice teachers (N = 27) in two sections of a sequenced, methodological and process integrated mathematics/science course solved a levers problem with three similar learning processes and a problem-solving approach, and identified a problem-solving approach through one different learning process. Similar learning processes used included:…

  15. A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

    Science.gov (United States)

    Humphrey, Greer B.; Gibbs, Matthew S.; Dandy, Graeme C.; Maier, Holger R.

    2016-09-01

    Monthly streamflow forecasts are needed to support water resources decision making in the South East of South Australia, where baseflow represents a significant proportion of the total streamflow and soil moisture and groundwater are important predictors of runoff. To address this requirement, the utility of a hybrid monthly streamflow forecasting approach is explored, whereby simulated soil moisture from the GR4J conceptual rainfall-runoff model is used to represent initial catchment conditions in a Bayesian artificial neural network (ANN) statistical forecasting model. To assess the performance of this hybrid forecasting method, a comparison is undertaken of the relative performances of the Bayesian ANN, the GR4J conceptual model and the hybrid streamflow forecasting approach for producing 1-month ahead streamflow forecasts at three key locations in the South East of South Australia. Particular attention is paid to the quantification of uncertainty in each of the forecast models and the potential for reducing forecast uncertainty by using the hybrid approach is considered. Case study results suggest that the hybrid models developed in this study are able to take advantage of the complementary strengths of both the ANN models and the GR4J conceptual models. This was particularly the case when forecasting high flows, where the hybrid models were shown to outperform the two individual modelling approaches in terms of the accuracy of the median forecasts, as well as reliability and resolution of the forecast distributions. In addition, the forecast distributions generated by the hybrid models were up to 8 times more precise than those based on climatology; thus, providing a significant improvement on the information currently available to decision makers.

  16. MULTI-TEMPORAL LAND USE ANALYSIS OF AN EPHEMERAL RIVER AREA USING AN ARTIFICIAL NEURAL NETWORK APPROACH ON LANDSAT IMAGERY

    Directory of Open Access Journals (Sweden)

    M. Aquilino

    2014-01-01

    The historical archive of LANDSAT imagery dating back to the launch of ERTS in 1972 provides a comprehensive and permanent data source for tracking change on the planet‟s land surface. In this study case the imagery acquisition dates of 1987, 2002 and 2011 were selected to cover a time trend of 24 years. Land cover categories were based on classes outlined by the Curve Number method with the aim of characterizing land use according to the level of surface imperviousness. After comparing two land use classification methods, i.e. Maximum Likelihood Classifier (MLC and Multi-Layer Perceptron (MLP neural network, the Artificial Neural Networks (ANN approach was found the best reliable and efficient method in the absence of ground reference data. The ANN approach has a distinct advantage over statistical classification methods in that it is non-parametric and requires little or no a priori knowledge on the distribution model of input data. The results quantify land cover change patterns in the river basin area under study and demonstrate the potential of multitemporal LANDSAT data to provide an accurate and cost-effective means to map and analyse land cover changes over time that can be used as input in land management and policy decision-making.

  17. Finding fossils in new ways: an artificial neural network approach to predicting the location of productive fossil localities.

    Science.gov (United States)

    Anemone, Robert; Emerson, Charles; Conroy, Glenn

    2011-01-01

    Chance and serendipity have long played a role in the location of productive fossil localities by vertebrate paleontologists and paleoanthropologists. We offer an alternative approach, informed by methods borrowed from the geographic information sciences and using recent advances in computer science, to more efficiently predict where fossil localities might be found. Our model uses an artificial neural network (ANN) that is trained to recognize the spectral characteristics of known productive localities and other land cover classes, such as forest, wetlands, and scrubland, within a study area based on the analysis of remotely sensed (RS) imagery. Using these spectral signatures, the model then classifies other pixels throughout the study area. The results of the neural network classification can be examined and further manipulated within a geographic information systems (GIS) software package. While we have developed and tested this model on fossil mammal localities in deposits of Paleocene and Eocene age in the Great Divide Basin of southwestern Wyoming, a similar analytical approach can be easily applied to fossil-bearing sedimentary deposits of any age in any part of the world. We suggest that new analytical tools and methods of the geographic sciences, including remote sensing and geographic information systems, are poised to greatly enrich paleoanthropological investigations, and that these new methods should be embraced by field workers in the search for, and geospatial analysis of, fossil primates and hominins.

  18. An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube

    Directory of Open Access Journals (Sweden)

    Ahadian Samad

    2009-01-01

    Full Text Available Abstract Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS is presented to solve this problem. The proposed ANFIS approach can construct an input–output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input–output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down.

  19. An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube

    Science.gov (United States)

    2009-01-01

    Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input–output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input–output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down. PMID:20596382

  20. Machine Learning Approaches: From Theory to Application in Schizophrenia

    Directory of Open Access Journals (Sweden)

    Elisa Veronese

    2013-01-01

    Full Text Available In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.