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. 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…

  3. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  4. 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.

  5. 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.

  6. Comparison of Two Machine Learning Regression Approaches (Multivariate Relevance Vector Machine and Artificial Neural Network) Coupled with Wavelet Decomposition to Forecast Monthly Streamflow in Peru

    Science.gov (United States)

    Ticlavilca, A. M.; Maslova, I.; McKee, M.

    2011-12-01

    This research presents a modeling approach that incorporates wavelet-based analysis techniques used in statistical signal processing and multivariate machine learning regression to forecast monthly streamflow in Peru. Two machine learning regression approaches, Multivariate Relevance Vector Machine and Artificial Neural Network, are compared in terms of performance and robustness. The inputs of the model utilize information of streamflow and Pacific sea surface temperature (SST). The monthly Pacific SST data (from 1950 to 2010) are obtained from the NOAA Climate Prediction Center website. The inputs are decomposed into meaningful components formulated in terms of wavelet multiresolution analysis (MRA). The outputs are the forecasts of streamflow two, four and six months ahead simultaneously. The proposed hybrid modeling approach of wavelet decomposition and machine learning regression can capture sufficient information at meaningful temporal scales to improve the performance of the streamflow forecasts in Peru. A bootstrap analysis is used to explore the robustness of the hybrid modeling approaches.

  7. The Emergence of Artificial Intelligence: Learning to Learn

    OpenAIRE

    de Bock, Peter

    1985-01-01

    The classical approach to the acquisition of knowledge and reason in artificial intelligence is to program the facts and rules into the machine. Unfortunately, the amount of time required to program the equivalent of human intelligence is prohibitively large. An alternative approach allows an automaton to learn to solve problems through iterative trial-and-error interaction with its environment, much as humans do. To solve a problem posed by the environment, the automaton generates a sequence...

  8. An Artificial Intelligence Approach to Transient Stability Assessment

    OpenAIRE

    Akella, Vijay Ahaskar; Khincha, HP; Kumar, Sreerama R

    1991-01-01

    An artificial intelligence approach to online transient stability assessment is briefly discussed, and some crucial requirements for this algorithm are identified. Solutions to these are proposed. Some new attributes are suggested so as to reflect machine dynamics and changes in the network. Also a new representative learning set algorithm has been developed.

  9. Artificial knowledge an evolutionary approach

    OpenAIRE

    McMullin, Finbar Vincent

    1992-01-01

    I present a new analysis of the problem, situation in Artificial Intelligence (AI), grounded in a Popperian epistemology. I first review arguments purporting to establish that no purely “computational” system can realise genuine mentality. I conclude that the question is still open; but that the more pressing question is whether such a system can even exhibit intelligent behaviour. Attention is thus directed at the computational embodiment of knowledge, and its growth. I suggest that much...

  10. Artificial grammar learning: preference for acoustic or visual modality?

    OpenAIRE

    Bernardo, Ana Isabel Santos

    2013-01-01

    This thesis transmits the artificial grammar learning paradigm as an acquisition and processing model of language, within implicit learning investigation. The number of investigations about language and it’s acquisition has increased throughout the years, specifically in the implicit learning ability and artificial grammar learning as the most adequate ...

  11. 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.

  12. Artificial intelligence approaches to software engineering

    Science.gov (United States)

    Johannes, James D.; Macdonald, James R.

    1988-01-01

    Artificial intelligence approaches to software engineering are examined. The software development life cycle is a sequence of not so well-defined phases. Improved techniques for developing systems have been formulated over the past 15 years, but pressure continues to attempt to reduce current costs. Software development technology seems to be standing still. The primary objective of the knowledge-based approach to software development presented in this paper is to avoid problem areas that lead to schedule slippages, cost overruns, or software products that fall short of their desired goals. Identifying and resolving software problems early, often in the phase in which they first occur, has been shown to contribute significantly to reducing risks in software development. Software development is not a mechanical process but a basic human activity. It requires clear thinking, work, and rework to be successful. The artificial intelligence approaches to software engineering presented support the software development life cycle through the use of software development techniques and methodologies in terms of changing current practices and methods. These should be replaced by better techniques that that improve the process of of software development and the quality of the resulting products. The software development process can be structured into well-defined steps, of which the interfaces are standardized, supported and checked by automated procedures that provide error detection, production of the documentation and ultimately support the actual design of complex programs.

  13. 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.

  14. 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.

  15. 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…

  16. Optimal control learning with artificial neural networks

    International Nuclear Information System (INIS)

    This paper shows neural networks capabilities in optimal control applications of non linear dynamic systems. Our method is issued of a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of multi layered networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable to define an optimal control in relation to a temporal criterion. (orig.)

  17. 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.

  18. Machine Learning Optimization of Evolvable Artificial Cells

    DEFF Research Database (Denmark)

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

    2011-01-01

    and an in vitro cell-free expression system are presented as examples of optimization of molecular interactions in high dimensional space of compositions [1,4]. These represent, for instance, the modules or subsystems that could be optimized by "mixing the protocols" to achieve the high level of...... sophistication 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.......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...

  19. Approaches to the artificial heart. Invited speaker.

    Science.gov (United States)

    Pierce, W S; Myers, J L; Donachy, J H; Rosenberg, G; Landis, D L; Prophet, G A; Snyder, A J

    1981-08-01

    Over the last two decades, the implantable artificial heart has evolved from an idea to a device capable of completely supporting the circulation for periods now exceeding 5 months. Although initial animal studies were limited by thromboembolism and device breakage, the usual causes of death in experimental animals are now infection, atrioventricular valve obstruction, elastomer bladder calcification, or inadequate cardiac output because of the relatively rapid growth of the young calves. As a result of the bulky nature of the energy converter and the substantial risk of infection with large diameter percutaneous tubes, clinical use of their air-powered artificial hearts will be limited to patients who are awaiting or being prepared for heart transplantation. Artificial hearts with implanted energy converters are being developed for permanent heart replacement. These devices require well-designed, durable mechanical components and sophisticated control systems. Although initial designs centered around thermal engines powered by a completely implantable nuclear energy source, the excessive cost and potential dangers have shifted the focus away from the nuclear system. Several electrically driven artificial hearts, based on samarium-cobalt magnet brushless direct-current motors, are now undergoing bench testing and will be ready for long-term animal studies within 2 years. This research will culminate with the availability of an "off-the-shelf" electrically powered artificial heart for use in patients with a wide range of nonrepairable forms of end-stage heart disease. PMID:7256534

  20. 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

  1. Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

    OpenAIRE

    Atris Suyantohadi; Mochamad Hariadi; Mauridhi Hery Purnomo

    2010-01-01

    The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr) plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN) and Lindenmayer System (L-System) methods. Research aimed to design and to visualize plant growth modeling...

  2. Artificial intelligence approach to legal reasoning

    International Nuclear Information System (INIS)

    For artificial intelligence, understanding the forms of human reasoning is a central goal. Legal reasoning is a form that makes a new set of demands on artificial intelligence methods. Most importantly, a computer program that reasons about legal problems must be able to distinguish between questions it is competent to answer and questions that human lawyers could seriously argue either way. In addition, a program for analyzing legal problems should be able to use both general legal rules and decisions in past cases; and it should be able to work with technical concepts that are only partly defined and subject to shifts of meaning. Each of these requirements has wider applications in artificial intelligence, beyond the legal domain. This dissertation presents a computational framework for legal reasoning, within which such requirements can be accommodated. The development of the framework draws significantly on the philosophy of law, in which the elucidation of legal reasoning is an important topic. A key element of the framework is the legal distinction between hard cases and clear cases. In legal writing, this distinction has been taken for granted more often than it has been explored. Here, some initial heuristics are proposed by which a program might make the distinction

  3. Artificial intelligence approach to accelerator control systems

    International Nuclear Information System (INIS)

    An experiment was recently started at LAMPF to evaluate the power and limitations of using artificial intelligence techniques to solve problems in accelerator control and operation. A knowledge base was developed to describe the characteristics and the relationships of the first 30 devices in the LAMPF H+ beam line. Each device was categorized and pertinent attributes for each category defined. Specific values were assigned in the knowledge base to represent each actual device. Relationships between devices are modeled using the artificial intelligence techniques of rules, active values, and object-oriented methods. This symbolic model, built using the Knowledge Engineering Environment (KEE) system, provides a framework for analyzing faults, tutoring trainee operators, and offering suggestions to assist in beam tuning. Based on information provided by the domain expert responsible for tuning this portion of the beam line, additional rules were written to describe how he tunes, how he analyzes what is actually happening, and how he deals with failures. Initial results have shown that artificial intelligence techniques can be a useful adjunct to traditional methods of numerical simulation. Successful and efficient operation of future accelerators may depend on the proper merging of symbolic reasoning and conventional numerical control algorithms

  4. Optimizing radiologic workup: An artificial intelligence approach

    International Nuclear Information System (INIS)

    The increasing complexity of diagnostic imaging is presenting an ever-expanding variety of radiologic test options to referring clinicians, making it more difficult for them to plan efficient workup. Diagnosis-oriented reimbursement systems are providing new incentives for hospitals and radiologists to use imaging modalities judiciously. This paper describes DxCON, a developmental artificial intelligence-based computer system, which gives advice to physicians about the optimum sequencing of radiologic tests. DxCON analyzes a physician's proposed workup plan and discusses its strengths and weaknesses. The domain chosen for this research is the imaging workup of obstructive jaundice

  5. "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.

  6. Numerical approaches for approximating technical effectiveness of artificial recharge structures

    OpenAIRE

    Neumann, I.; Barker, J; MacDonald, D; Gale, I.

    2004-01-01

    This report describes various numerical approaches to quantify technical effectiveness of low-technology artificial recharge structures as seen commonly in rural environment and communities in semi-arid developing countries. The described methodologies enable benefits of artificial recharge facilities, i.e. their ability to replenish the aquifer, to be approximated. Technical effectiveness of recharge facilities is thereby evaluated on three scales: On a recharge basin scale, the rate of i...

  7. Numerical approaches for approximating technical effectivessness of artificial recharge structures

    OpenAIRE

    Neumann, I.; Barker, J; MacDonald, D; Gale, I.

    2004-01-01

    This report describes various numerical approaches to quantify technical effectiveness of low-technology artificial recharge structures as seen commonly in rural environment and communities in semi-arid developing countries. The described methodologies enable benefits of artificial recharge facilities, i.e. their ability to replenish the aquifer, to be approximated. Technical effectiveness of recharge facilities is thereby evaluated on three scales: On a recharge basin scale, the rate of i...

  8. An artificial intelligence approach towards disturbance analysis

    International Nuclear Information System (INIS)

    Scale and degree of sophistication of technological plants, e.g. nuclear power plants, have been essentially increased during the last decades. Conventional disturbance analysis systems have proved to work successfully in well-known situations. But in cases of emergencies, the operator needs more advanced assistance in realizing diagnosis and therapy control. The significance of introducing artificial intelligence (AI) methods in nuclear power technology is emphasized. Main features of the on-line disturbance analysis system SAAP-2 are reported about. It is being developed for application to nuclear power plants. Problems related to man-machine communication will be gone into more detail, because their solution will influence end-user acceptance considerably. (author)

  9. Artificial Immune System Approaches for Aerospace Applications

    Science.gov (United States)

    KrishnaKumar, Kalmanje; Koga, Dennis (Technical Monitor)

    2002-01-01

    Artificial Immune Systems (AIS) combine a priori knowledge with the adapting capabilities of biological immune system to provide a powerful alternative to currently available techniques for pattern recognition, modeling, design, and control. Immunology is the science of built-in defense mechanisms that are present in all living beings to protect against external attacks. A biological immune system can be thought of as a robust, adaptive system that is capable of dealing with an enormous variety of disturbances and uncertainties. Biological immune systems use a finite number of discrete "building blocks" to achieve this adaptiveness. These building blocks can be thought of as pieces of a puzzle which must be put together in a specific way-to neutralize, remove, or destroy each unique disturbance the system encounters. In this paper, we outline AIS models that are immediately applicable to aerospace problems and identify application areas that need further investigation.

  10. Learning comunication strategies for distributed artificial intelligence

    Science.gov (United States)

    Kinney, Michael; Tsatsoulis, Costas

    1992-08-01

    We present a methodology that allows collections of intelligent system to automatically learn communication strategies, so that they can exchange information and coordinate their problem solving activity. In our methodology communication between agents is determined by the agents themselves, which consider the progress of their individual problem solving activities compared to the communication needs of their surrounding agents. Through learning, communication lines between agents might be established or disconnected, communication frequencies modified, and the system can also react to dynamic changes in the environment that might force agents to cease to exist or to be added. We have established dynamic, quantitative measures of the usefulness of a fact, the cost of a fact, the work load of an agent, and the selfishness of an agent (a measure indicating an agent's preference between transmitting information versus performing individual problem solving), and use these values to adapt the communication between intelligent agents. In this paper we present the theoretical foundations of our work together with experimental results and performance statistics of networks of agents involved in cooperative problem solving activities.

  11. 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

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

    OpenAIRE

    Reviewed by Özlem OZAN

    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...

  13. 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.

  14. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  15. 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...

  16. Sentence Processing in an Artificial Language: Learning and Using Combinatorial Constraints

    Science.gov (United States)

    Amato, Michael S.; MacDonald, Maryellen C.

    2010-01-01

    A study combining artificial grammar and sentence comprehension methods investigated the learning and online use of probabilistic, nonadjacent combinatorial constraints. Participants learned a small artificial language describing cartoon monsters acting on objects. Self-paced reading of sentences in the artificial language revealed comprehenders'…

  17. A PHILOSOPHICAL APPROACH TO ARTIFICIAL INTELLIGENCE AND ISLAMIC VALUES

    OpenAIRE

    2012-01-01

    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 ch...

  18. Artificial Grammar Learning of Melody Is Constrained by Melodic Inconsistency: Narmour's Principles Affect Melodic Learning

    OpenAIRE

    Martin Rohrmeier; Ian Cross

    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 iden...

  19. A tensor artificial viscosity using a finite element approach

    Science.gov (United States)

    Kolev, Tz. V.; Rieben, R. N.

    2009-12-01

    We derive a tensor artificial viscosity suitable for use in a 2D or 3D unstructured arbitrary Lagrangian-Eulerian (ALE) hydrodynamics code. This work is similar in nature to that of Campbell and Shashkov [1]; however, our approach is based on a finite element discretization that is fundamentally different from the mimetic finite difference framework. The finite element point of view leads to novel insights as well as improved numerical results. We begin with a generalized tensor version of the Von Neumann-Richtmyer artificial viscosity, then convert it to a variational formulation and apply a Galerkin discretization process using high order Gaussian quadrature to obtain a generalized nodal force term and corresponding zonal heating (or shock entropy) term. This technique is modular and is therefore suitable for coupling to a traditional staggered grid discretization of the momentum and energy conservation laws; however, we motivate the use of such finite element approaches for discretizing each term in the Euler equations. We review the key properties that any artificial viscosity must possess and use these to formulate specific constraints on the total artificial viscosity force term as well as the artificial viscosity coefficient. We also show, that under certain simplifying assumptions, the two-dimensional scheme from [1] can be viewed as an under-integrated version of our finite element method. This equivalence holds on general distorted quadrilateral grids. Finally, we present computational results on some standard shock hydro test problems, as well as some more challenging problems, indicating the advantages of the new approach with respect to symmetry preservation for shock wave propagation over general grids.

  20. 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.

  1. The equity premium puzzle: an artificial neural network approach

    OpenAIRE

    Shee Q. Wong; Nik R. Hassan; Ehsan Feroz

    2007-01-01

    Purpose – In recent years, equity premiums have been unusually large and efforts to forecast them have been largely unsuccessful. This paper presents evidence suggesting that artificial neural networks (ANNs) outperform traditional statistical methods and can forecast equity premiums reasonably well. Design/methodology/approach – This study replicates out-of-sample estimates of regression using ANN with economic fundamentals as inputs. The theory states that recent large equity premium values...

  2. 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. PMID:23874388

  3. 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.

  4. An artificial boundary approach for short-ranged interactions

    Science.gov (United States)

    Jacobs, David M.

    2016-07-01

    Real physical systems are only understood, experimentally or theoretically, to a finite resolution so in their analysis there is generally an ignorance of possible short-range phenomena. It is also well-known that the boundary conditions of wavefunctions and fields can be used to model short-range interactions when those interactions are expected, a priori. Here, a real-space approach is described wherein an artificial boundary of ignorance is imposed to explicitly exclude from analysis the region of a system wherein short-distance effects may be obscure. The (artificial) boundary conditions encode those short-distance effects by parameterizing the possible UV completions of the wavefunction. Since measurable quantities, such as spectra and cross sections, must be independent of the position of the artificial boundary, the boundary conditions must evolve with the radius of the boundary in a particular way. As examples of this approach, an analysis is performed of the non-relativistic free particle, harmonic oscillator, and Coulomb potential, and some known results for point-like (contact) interactions are recovered, however from a novel perspective. Generically, observables differ from their canonical values and symmetries are anomalously broken compared to those of idealized models. Connections are made to well-studied physical systems, such as the binding of light nuclei and cold atomic systems. This method is arguably more physically transparent and mathematically easier to use than other techniques that require the regularization and renormalization of delta-function potentials, and may offer further generalizations of practical use.

  5. Artificial Life of Soybean Plant Growth Modeling Using Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Atris Suyantohadi

    2010-03-01

    Full Text Available The natural process on plant growth system has a complex system and it has could be developed on characteristic studied using intelligent approaches conducting with artificial life system. The approaches on examining the natural process on soybean (Glycine Max L.Merr plant growth have been analyzed and synthesized in these research through modeling using Artificial Neural Network (ANN and Lindenmayer System (L-System methods. Research aimed to design and to visualize plant growth modeling on the soybean varieties which these could help for studying botany of plant based on fertilizer compositions on plant growth with Nitrogen (N, Phosphor (P and Potassium (K. The soybean plant growth has been analyzed based on the treatments of plant fertilizer compositions in the experimental research to develop plant growth modeling. By using N, P, K fertilizer compositions, its capable result on the highest production 2.074 tons/hectares. Using these models, the simulation on artificial life for describing identification and visualization on the characteristic of soybean plant growth could be demonstrated and applied.

  6. Control approach development for variable recruitment artificial muscles

    Science.gov (United States)

    Jenkins, Tyler E.; Chapman, Edward M.; Bryant, Matthew

    2016-04-01

    This study characterizes hybrid control approaches for the variable recruitment of fluidic artificial muscles with double acting (antagonistic) actuation. Fluidic artificial muscle actuators have been explored by researchers due to their natural compliance, high force-to-weight ratio, and low cost of fabrication. Previous studies have attempted to improve system efficiency of the actuators through variable recruitment, i.e. using discrete changes in the number of active actuators. While current variable recruitment research utilizes manual valve switching, this paper details the current development of an online variable recruitment control scheme. By continuously controlling applied pressure and discretely controlling the number of active actuators, operation in the lowest possible recruitment state is ensured and working fluid consumption is minimized. Results provide insight into switching control scheme effects on working fluids, fabrication material choices, actuator modeling, and controller development decisions.

  7. 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; Floel, Agnes

    2010-01-01

    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…

  8. Artificial Bee Colony Algorithm Based on Information Learning.

    Science.gov (United States)

    Gao, Wei-Feng; Huang, Ling-Ling; Liu, San-Yang; Dai, Cai

    2015-12-01

    Inspired by the fact that the division of labor and cooperation play extremely important roles in the human history development, this paper develops a novel artificial bee colony algorithm based on information learning (ILABC, for short). In ILABC, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of subpopulation is dynamically adjusted based on the last search experience, which results in a clear division of labor. Furthermore, the two search mechanisms are designed to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively, which acts as the cooperation. Finally, the comparison results on a number of benchmark functions demonstrate that the proposed method performs competitively and effectively when compared to the selected state-of-the-art algorithms. PMID:25594992

  9. 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.

  10. Acquisition and manipulation of mental structures : Investigations on artificial grammar learning and implicit sequence processing

    OpenAIRE

    Forkstam, Christian

    2010-01-01

    This thesis introduces repetitive artificial grammar learning as a paradigm in the investigation of sequential implicit learning, in particular as a model for language acquisition and processing. Implicit learning of sequential structure captures an essential cognitive processing capacity of interest from a larger cognitive neuroscience perspective. We investigate in this thesis the underlying neural processing architecture for implicit learning/acquisition to acquire and ...

  11. Fractal Globules: A New Approach to Artificial Molecular Machines

    Science.gov (United States)

    Avetisov, Vladik A.; Ivanov, Viktor A.; Meshkov, Dmitry A.; Nechaev, Sergei K.

    2014-01-01

    The over-damped relaxation of elastic networks constructed by contact maps of hierarchically folded fractal (crumpled) polymer globules was investigated in detail. It was found that the relaxation dynamics of an anisotropic fractal globule is very similar to the behavior of biological molecular machines like motor proteins. When it is perturbed, the system quickly relaxes to a low-dimensional manifold, M, with a large basin of attraction and then slowly approaches equilibrium, not escaping M. Taking these properties into account, it is suggested that fractal globules, even those made by synthetic polymers, are artificial molecular machines that can transform perturbations into directed quasimechanical motion along a defined path. PMID:25418305

  12. IMBALANCED DATA LEARNING APPROACHES REVIEW

    Directory of Open Access Journals (Sweden)

    Mohamed Bekkar

    2013-07-01

    Full Text Available The present work deals with a well-known problem in machine learning, that classes have generally skewed prior probabilities distribution. This situation of imbalanced data is a handicap when trying to identify the minority classes , usually more interesting one In real world applications. This paper is an attempt to list the different approachs proposed in scientific research to deal with the imbalanced data learning, as well a comparison between various applications cases performed on this subject.

  13. 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. PMID:27297046

  14. 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.

  15. 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

  16. 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

  17. 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. PMID:20063258

  18. E-LEARNING EXPERIENCE WITH ARTIFICIAL INTELLIGENCE SUPPORTED SOFTWARE: An International Application on English Language Courses

    OpenAIRE

    Kose, Utku; Arslan, Ahmet

    2015-01-01

    Nowadays, artificial intelligence supported e-learning scenarios are widely employed by educational institutions in order to ensure better teaching and learning experiences along educational activities. In the context of performed scientific studies, positive results often encourage such institutions to apply their intelligent e-learning systems on different types of courses and report advantages of artificial intelligent in especially education field. It seems that the future of education w...

  19. An intercomparison of artificial intelligence approaches for polar scene identification

    Science.gov (United States)

    Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

    1993-01-01

    The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

  20. Learning and liking of melody and harmony: Further studies in artificial grammar learning

    OpenAIRE

    Loui, Psyche

    2012-01-01

    Much of what we know and love about music is based on implicitly acquired mental representations of musical pitches and the relationships between them. While previous studies have shown that these mental representations of music can be acquired rapidly and can influence preference, it is still unclear which aspects of music influence learning and preference formation. This article reports two experiments that use an artificial musical system to examine two questions: 1) which aspects of music...

  1. An integrated artificial neural networks approach for predicting global radiation

    International Nuclear Information System (INIS)

    This article presents an integrated artificial neural network (ANN) approach for predicting solar global radiation by climatological variables. The integrated ANN trains and tests data with multi layer perceptron (MLP) approach which has the lowest mean absolute percentage error (MAPE). The proposed approach is particularly useful for locations where no available measurement equipment. Also, it considers all related climatological and meteorological parameters as input variables. To show the applicability and superiority of the integrated ANN approach, monthly data were collected for 6 years (1995-2000) in six nominal cities in Iran. Separate model for each city is considered and the quantity of solar global radiation in each city is calculated. Furthermore an integrated ANN model has been introduced for prediction of solar global radiation. The acquired results of the integrated model have shown high accuracy of about 94%. The results of the integrated model have been compared with traditional angstrom's model to show its considerable accuracy. Therefore, the proposed approach can be used as an efficient tool for prediction of solar radiation in the remote and rural locations with no direct measurement equipment.

  2. The wonder approach to learning

    Directory of Open Access Journals (Sweden)

    Catherine L'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.

  3. 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. PMID:25339882

  4. Transfer learning approach for financial applications

    OpenAIRE

    Stamate, Cosmin; Magoulas, George D.; Thomas, Michael S.C.

    2015-01-01

    Artificial neural networks learn how to solve new problems through a computationally intense and time consuming process. One way to reduce the amount of time required is to inject preexisting knowledge into the network. To make use of past knowledge, we can take advantage of techniques that transfer the knowledge learned from one task, and reuse it on another (sometimes unrelated) task. In this paper we propose a novel selective breeding technique that extends the transfer learning with behav...

  5. Modeling Expectations with GENEFER -- an Artificial Intelligence Approach

    OpenAIRE

    Eric Ringhut; Stefan Kooths

    2003-01-01

    Economic modeling of financial markets attempts to model highly complex systems in which expectations can be among the dominant driving forces. It is necessary, then, to focus on how agents form expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. Agents' bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule Bases. For example if a single agent believes the exchange rate is determined by a set of possible input...

  6. 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

  7. 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). PMID:25828458

  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. 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…

  10. Automatic voice recognition using traditional and artificial neural network approaches

    Science.gov (United States)

    Botros, Nazeih M.

    1989-01-01

    The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

  11. Timing Matters: The Impact of Immediate and Delayed Feedback on Artificial Language Learning

    OpenAIRE

    Axel Mecklinger

    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 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...

  12. An Artificial Neural Network Approach to the Solution of Molecular Chemical Equilibrium

    CERN Document Server

    Ramos, A A

    2005-01-01

    A novel approach is presented for the solution of instantaneous chemical equilibrium problems. The chemical equilibrium can be considered, due to its intrinsically local character, as a mapping of the three-dimensional parameter space spanned by the temperature, hydrogen density and electron density into many one-dimensional spaces representing the number density of each species. We take advantage of the ability of artificial neural networks to approximate non-linear functions and construct neural networks for the fast and efficient solution of the chemical equilibrium problem in typical stellar atmosphere physical conditions. The neural network approach has the advantage of providing an analytic function, which can be rapidly evaluated. The networks are trained with a learning set (that covers the entire parameter space) until a relative error below 1% is reached. It has been verified that the networks are not overtrained by using an additional verification set. The networks are then applied to a snapshot of...

  13. A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

    Science.gov (United States)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao

    2016-04-01

    In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

  14. 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.

  15. 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

    2016-01-01

    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. PMID:27399696

  16. 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

    2016-01-01

    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. PMID:27399696

  17. MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH

    OpenAIRE

    Stefan Kooths; Eric Ringhut

    2000-01-01

    Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and...

  18. 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)

  19. 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.

  20. 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.

  1. An artificial intelligence approach in designing new materials

    Directory of Open Access Journals (Sweden)

    W. Sitek

    2006-04-01

    Full Text Available Purpose: The paper presents the computer aided method of chemical composition designing the metallic materials with a required property.Design/methodology/approach: The purpose has been achieved in two stages. In the first stage a neural network model for calculating the Jominy curve on the basis of the chemical composition has been worked out. This model made possible to prepare, in the second stage, a representative set of data and to work out the neural classifier that would aid the selection of steel grade with the required hardenability.Findings: Obtained results show that AI tools used are effective and very useful in designing new metallic materials.Research limitations/implications: The presented models may be used in the ranges of mass concentrations of alloying elements presented in the paper. The methodology presented in the paper makes it possible to add new grades of steel to the models.Practical implications: The worked out models may be used in computer systems of steel selection and designing for the heat-treated machine parts.Originality/value: The use of the artificial intelligence method, particularly the neural networks as a tool for designing the chemical composition of steels with the required properties.

  2. Design and evaluation of two blended learning approaches: Lessons learned

    OpenAIRE

    Cheung, WS; Hew, KF

    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 face tutorials, classroom discussions, and a reflection session. For the second blended learning approach, we integrated two asynchronous tools with fa...

  3. 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.

    2009-01-01

    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…

  4. An artificial neural network approach to laser-induced breakdown spectroscopy quantitative analysis

    International Nuclear Information System (INIS)

    The usual approach to laser-induced breakdown spectroscopy (LIBS) quantitative analysis is based on the use of calibration curves, suitably built using appropriate reference standards. More recently, statistical methods relying on the principles of artificial neural networks (ANN) are increasingly used. However, ANN analysis is often used as a ‘black box’ system and the peculiarities of the LIBS spectra are not exploited fully. An a priori exploration of the raw data contained in the LIBS spectra, carried out by a neural network to learn what are the significant areas of the spectrum to be used for a subsequent neural network delegated to the calibration, is able to throw light upon important information initially unknown, although already contained within the spectrum. This communication will demonstrate that an approach based on neural networks specially taylored for dealing with LIBS spectra would provide a viable, fast and robust method for LIBS quantitative analysis. This would allow the use of a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and provide a fully automatizable approach for the analysis of a large number of samples. - Highlights: • A methodological approach to neural network analysis of LIBS spectra is proposed. • The architecture of the network and the number of inputs are optimized. • The method is tested on bronze samples already analyzed using a calibration-free LIBS approach. • The results are validated, compared and discussed

  5. Robot Learning Using Learning Classifier Systems Approach

    OpenAIRE

    Jabin, Suraiya

    2010-01-01

    In this chapter, I have presented Learning Classifier Systems, which add to the classical Reinforcement Learning framework the possibility of representing the state as a vector of attributes and finding a compact expression of the representation so induced. Their formalism conveys a nice interaction between learning and evolution, which makes them a class of particularly rich systems, at the intersection of several research domains. As a result, they profit from the accumulated extensions of ...

  6. 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…

  7. AGL StimSelect: Software for automated selection of stimuli for artificial grammar learning

    OpenAIRE

    Bailey, T. M.; Pothos, E. M.

    2008-01-01

    Artificial Grammar Learning (AGL) is an experimental paradigm that has been used extensively in cognitive research for many years to study implicit learning, associative learning, and generalization based either on similarity or rules. Without computer assistance it is virtually impossible to generate appropriate grammatical training stimuli along with grammatical or non-grammatical test stimuli that control relevant psychological variables. We present the first flexible, fully automated soft...

  8. A Learning Approach for Adaptive Image Segmentation

    OpenAIRE

    Martin, Vincent; Thonnat, Monique

    2007-01-01

    In this chapter, we have proposed a learning approach for three major issues of image segmentation: context adaptation, algorithm selection and parameter tuning according to the image content and the application need. This supervised learning approach relies on hand-labelled samples. The learning process is guided by the goal of the segmentation and therefore makes the approach reliable for a broad range of applications. The user effort is restrained compared to other supervised methods since...

  9. 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)

  10. 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…

  11. 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....

  12. The relationship between students approach to learning and lifelong learning

    OpenAIRE

    Rita Barros; Angélica Monteiro; Fouad Nejmedinne; José António Moreira

    2013-01-01

    The current investigation proposes to analyse the relationship between learning, from the appropriation students’ make of the different ways of learning and studying, and their willingness to be involved in life- long learning (LL) activities. The theoretical rationale is inscribed in the Biggs’ Theory (1987), concern- ing the student’s approach to learning, and under the guiding principle of LL. The concept of LL has been understood and formalised in a distinct way, translated into different...

  13. 50 years of artificial intelligence: a neuronal approach

    OpenAIRE

    Fernández Caballero, Antonio; Deco, Gustavo; Mira Mira, José

    2008-01-01

    Recently, the 50th anniversary of the birth of Artificial Intelligence (AI) has been celebrated worldwide, and about 65 years ago (1943) its foundational works on Biocybernetics and Bionics were published due to movements led by McCulloch and Pitts and Wiener.

  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. 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.

  16. 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.

  17. Machine Learning Approaches for Music Information Retrieval

    OpenAIRE

    Li, Tao; Ogihara, Mitsunori; Shao, Bo; DingdingWang,

    2009-01-01

    We discussed the following machine learning approaches used in music information retrieval: (1) multi-class classification methods for music genre categorization; (2) multi-label classification methods for emotion detection; (3) clustering methods for music style identification; and (4) semi-supervised learning methods for music recommendation. Experimental results are also presented to evaluate the approaches.

  18. 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...... methods suited for finite identifiability of particular types of deterministic actions....

  19. Cognitive Expert Systems and Machine Learning: Artificial Intelligence Research at the University of Connecticut

    OpenAIRE

    Selfridge, Mallory; Dickerson, Donald J.; Biggs, Stanley F.

    1987-01-01

    In order for next-generation expert systems to demonstrate the performance, robustness, flexibility, and learning ability of human experts, they will have to be based on cognitive models of expert human reasoning and learning. We call such next-generation systems cognitive expert systems. Research at the Artificial Intelligence Laboratory at the University of Connecticut is directed toward understanding the principles underlying cognitive expert systems and developing computer programs embody...

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

    OpenAIRE

    Fonteneau, Raphaël; Murphy, Susan A.; Wehenkel, Louis; Ernst, Damien

    2013-01-01

    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 ...

  1. A genetic-neural artificial intelligence approach to resins optimization

    International Nuclear Information System (INIS)

    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)

  2. A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

    OpenAIRE

    Wenping Zou; Yunlong Zhu; Hanning Chen; Xin Sui

    2010-01-01

    Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorit...

  3. 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...

  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. PMID:24577423

  5. An artificial intelligence approach to accelerator control systems

    International Nuclear Information System (INIS)

    An experiment was recently started at LAMPF to evaluate the power and limitations of using artificial intelligence techniques to solve problems in accelerator control and operation. A knowledge base was developed to describe the characteristics and the relationships of the first 30 devices in the LAMPF H+ beam line. Each device was categorized and pertinent attributes for each category defined. Specific values were assigned in the knowledge base to represent each actual device. Relationships between devices are modeled using the artificial intelligence techniques of rules, active values, and object-oriented methods. This symbolic model, built using the Knowledge Engineering Environment (KEE) system, provides a framework for analyzing faults, tutoring trainee operators, and offering suggestions to assist in beam tuning. Based on information provided by the domain expert responsible for tuning this portion of the beam line, additional rules were written to describe how he tunes, how he analyzes what is actually happening, and how he deals with failures. Initial results have shown that artificial intelligence techniques can be a useful adjunct to traditional methods of numerical simulation. Successful and efficient operation of future accelerators may depend on the proper merging of symbolic reasoning and conventional numerical control algorithms

  6. Lab classes in chemistry learning an artificial intelligence view

    OpenAIRE

    Figueiredo, Margarida; Esteves, M. Lurdes; Neves, José; Vicente, Henrique (Orientador)

    2014-01-01

    The teaching methodology used in lab classes in Chemistry Learning was studied for a cohort of 702 students in the 10th grade of Portuguese Secondary Schools. The k-Means Clustering Method, with k values ranging between 2 (two) and 4 (four), was used in order to segment the data. Decision Trees were used for the development of explanatory models of the segmentation. The results obtained showed that the majority of the answerers considered that experimentation is central on Chemistry learning....

  7. 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…

  8. Karst and artificial recharge: Theoretical and practical problems. A preliminary approach to artificial recharge assessment

    Science.gov (United States)

    Daher, Walid; Pistre, Séverin; Kneppers, Angeline; Bakalowicz, Michel; Najem, Wajdi

    2011-10-01

    SummaryManaged Aquifer Recharge (MAR) is an emerging sustainable technique that has already generated successful results and is expected to solve many water resource problems, especially in semi-arid and arid zones. It is of great interest for karst aquifers that currently supply 20-25% of the world's potable water, particularly in Mediterranean countries. However, the high heterogeneity in karst aquifers is too complex to be able to locate and describe them simply via field observations. Hence, as compared to projects in porous media, MAR is still marginal in karst aquifers. Accordingly, the present work presents a conceptual methodology for Aquifer Rechargeability Assessment in Karst - referred to as ARAK. The methodology was developed noting that artificial recharge in karst aquifers is considered an improbable challenge to solve since karst conduits may drain off recharge water without any significant storage, or recharge water may not be able to infiltrate. The aim of the ARAK method is to determine the ability of a given karst aquifer to be artificially recharged and managed, and the best sites for implementing artificial recharge from the surface. ARAK is based on multi-criteria indexation analysis modeled on karst vulnerability assessment methods. ARAK depends on four independent criteria, i.e. Epikarst, Rock, Infiltration and Karst. After dividing the karst domain into grids, these criteria are indexed using geological and topographic maps refined by field observations. ARAK applies a linear formula that computes the intrinsic rechargeability index based on the indexed map for every criterion, coupled with its attributed weighting rate. This index indicates the aptitude for recharging a given karst aquifer, as determined by studying its probability first on a regional scale for the whole karst aquifer, and then by characterizing the most favorable sites. Subsequently, for the selected sites, a technical and economic feasibility factor is applied, weighted

  9. 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...

  10. Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach

    International Nuclear Information System (INIS)

    This study presents an application of the artificial neural network (ANN) model using the back propagation (BP) learning algorithm to predict the performance (suction line outlet temperature and mass flow rate) of a non-adiabatic capillary tube suction line heat exchanger, basically used as a throttling device in small household refrigeration systems. Comparative studies were made by using an ANN model, experimental results and correlations to predict the performance. These studies showed that the proposed approach could successfully be used for performance prediction for the exchanger

  11. ICON: An artificial intelligence approach to radiologic differential diagnosis

    International Nuclear Information System (INIS)

    ICON is a computer system, developed using artificial intelligence techniques, that is designed to help radiologists manage the large body of knowledge needed to perform differential diagnosis in radiology. The system's domain is lung disease in patients with lymphoproliferative disorders. The radiologist proposes a diagnostic hypothesis which he or she thinks explains the known clinical and chest radiographic findings. ICON responds with an English-language prose critique that discusses how and why the proposed diagnosis is or is not supported by the clinical literature and suggests further findings or clinical information that might make the diagnosis more secure

  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. Predicting oil price movements: A dynamic Artificial Neural Network approach

    International Nuclear Information System (INIS)

    Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks. - Highlights: • Nonlinear Auto Regressive model with eXogenous (NARX) inputs is developed for predicting oil prices. • The results of NARX model in oil price forecasting is more accurate than those of time series and Artificial Neural Network. • The NARX model predicts the price shocks in the oil market. • The NARX model is dynamic and accounts for the factor of time

  14. 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.

  15. Learning to discriminate complex movements: biological versus artificial trajectories

    OpenAIRE

    Jastorff, Jan; Kourtzi, Zoe; Giese, Martin A

    2006-01-01

    The recognition of complex body movements and actions is a fundamental visual capacity very important for social communication. It seems possible that movement recognition is based on a general capability of the visual system to learn complex visual motion patterns. Alternatively, this visual function might exploit specialized mechanisms for the analysis of biologically relevant movements, for example, of humans or animals. To investigate this question, we trained human observers to discrimin...

  16. A Learning Approach to Auctions

    OpenAIRE

    Hon-Snir, Shlomit; Monderer, Dov; Sela, Aner

    1996-01-01

    We analyze a repeated first-price auction in which the types of the players are determined before the first round. It is proved that if every player is using either a belief-based learning scheme with bounded recall or a generalized fictitious play learning scheme, then for sufficiently large time, the players' bids are in equilibrium in the one-shot auction in which the types are commonly known.

  17. 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.

  18. A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Wenping Zou

    2010-01-01

    Full Text Available Artificial Bee Colony (ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC, which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO, and its cooperative version (CPSO are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

  19. Retrieving Atmospheric Precipitable Water Vapor Using Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Wang Xin

    2013-07-01

    Full Text Available Discussing of water vapor and its variation is the important issue for synoptic meteorology and meteorology. In physical Atmospheric, the moisture content of the earth atmosphere is one of the most important parameters, it is hard to represent water vapor because of its space-time variation. High-spectral resolution Atmospheric Infrared Sounder (AIRS data can be used to retrieve the small scale vertical structure of air temperature, which provided a more accurate and good initial field for the numerical forecasting and the large-scale weather analysis. This paper proposes an artificial neural network to retrieve the clear sky atmospheric radiation data from AIRS and comparing with the AIRS Level-2 standard product, and gain a good inversion results.

  20. A Hybrid Approach to Active Learning.

    Science.gov (United States)

    Ramsier, R. D.

    2001-01-01

    Describes an approach to incorporate active learning strategies into the first semester of a university-level introductory physics course. Combines cooperative and peer-based methods inside the classroom with project-based learning outside the classroom in an attempt to develop students' transferable skills as well as improving their understanding…

  1. Volume learning algorithm artificial neural networks for 3D QSAR studies.

    Science.gov (United States)

    Tetko, I V; Kovalishyn, V V; Livingstone, D J

    2001-07-19

    The current study introduces a new method, the volume learning algorithm (VLA), for the investigation of three-dimensional quantitative structure-activity relationships (QSAR) of chemical compounds. This method incorporates the advantages of comparative molecular field analysis (CoMFA) and artificial neural network approaches. VLA is a combination of supervised and unsupervised neural networks applied to solve the same problem. The supervised algorithm is a feed-forward neural network trained with a back-propagation algorithm while the unsupervised network is a self-organizing map of Kohonen. The use of both of these algorithms makes it possible to cluster the input CoMFA field variables and to use only a small number of the most relevant parameters to correlate spatial properties of the molecules with their activity. The statistical coefficients calculated by the proposed algorithm for cannabimimetic aminoalkyl indoles were comparable to, or improved, in comparison to the original study using the partial least squares algorithm. The results of the algorithm can be visualized and easily interpreted. Thus, VLA is a new convenient tool for three-dimensional QSAR studies. PMID:11448223

  2. Classification of Two Phase Flow Patterns in a Horizontal Pipe Using Artificial Neural Network by Learning Vector Quantization

    International Nuclear Information System (INIS)

    Classification of two phase flow patterns in a horizontal pipe has been done based on the vibration signal using learning vector quantization method of artificial neural network. The flow patterns classified consist of slug, plug and stratified wavy flow. The developed artificial neural network contains 128 of neurons on hidden layer, with learning rate 0.031. This configuration reached 99.3% of right classification for training data as the input and 90.5% for unknown data. These result shows that the learning vector quantization method of artificial neural network is capable to classify two phase flow patterns in a horizontal pipe

  3. Social Learning in Bumblebees (Bombus impatiens): Worker Bumblebees Learn to Manipulate and Forage at Artificial Flowers by Observation and Communication within the Colony

    OpenAIRE

    Hamida B. Mirwan; Peter G. Kevan

    2013-01-01

    Social learning occurs when one individual learns from another, mainly conspecific, often by observation, imitation, or communication. Using artificial flowers, we studied social learning by allowing test bumblebees to (a) see dead bumblebees arranged in foraging positions or (b) watch live bumblebees actually foraging or (c) communicate with nestmates within their colony without having seen foraging. Artificial flowers made from 1.5 mL microcentrifuge tubes with closed caps were inserted thr...

  4. Evaluation of Learning Approaches for Prospective Physics

    OpenAIRE

    SELÇUK, Gamze SEZGİN; ÇALIŞKAN, Serap; Erol, Mustafa

    2007-01-01

    The purpose of this research is to determine prospective physics teachers\\' learning approaches and to investigate the relationships among this variable, students\\' gender, class level and academic success. Total number of 141 students, Physics Education Department, Education Faculty of Buca, Dokuz Eylul University, is participated to this research. Data of the research were collected by Learning Approaches Scale (α=0,.81). The analysis of the data clearly indicates that prospectiv...

  5. A Methodological approach to supporting organisational learning

    OpenAIRE

    Mulholland, Paul; Zdrahal, Zdenek; Domingue, John; Hatala, Marek; Bernardi, Ansgar

    2001-01-01

    Many organizations need to respond quickly to change and their workers need to regularly develop new knowledge and skills. The prevailing approach to meeting these demands is on-the-job training, but this is known to be highly ineffective, cause stress and devalue workplace autonomy. Conversely, organizational learning is a process through which workers learn gradually in the work context through experience, reflection on work practice and collaboration with colleagues. Our approach aims to s...

  6. 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.

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

    OpenAIRE

    Dalsgaard, Christian

    2005-01-01

    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 pr...

  8. 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

  9. 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.

  10. Artificial neural networks approach on solar parabolic dish cooker

    International Nuclear Information System (INIS)

    This paper presents heat transfer analysis of solar parabolic dish cooker using Artificial Neural Network (ANN). The objective of this study to envisage thermal performance parameters such as receiver plate and pot water temperatures of the solar parabolic dish cooker by using the ANN for experimental data. An experiment is conducted under two cases (1) cooker with plain receiver and (2) cooker with porous receiver. The Back Propagation (BP) algorithm is used to train and test networks and ANN predictions are compared with experimental results. Different network configurations are studied by the aid of searching a relatively better network for prediction. The results showed a good regression analysis with the correlation coefficients in the range of 0.9968-0.9992 and mean relative errors (MREs) in the range of 1.2586-4.0346% for the test data set. Thus ANN model can successfully be used for the prediction of the thermal performance parameters of parabolic dish cooker with reasonable degree of accuracy. (authors)

  11. 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

  12. 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...... of using ideal types as ananalytical tool is presented and discussed....

  13. Cultural differences in learning approaches

    NARCIS (Netherlands)

    Tempelaar, D.T.; Rienties, B.C.; Giesbers, S.J.H.; Schim van der Loeff, S.; Van den Bossche, P.; Gijselaers, W.H.; Milter, R.G.

    2013-01-01

    Cultural differences in learning-related dispositions are investigated amongst 7,300 first year students from 81 different nationalities, using the framework of Hofstede (Culture’s consequences: international differences in work-related values. Sage, Beverly Hills, 1980). Comparing levels and interc

  14. LEARNING ALGORITHM EFFECT ON MULTILAYER FEED FORWARD ARTIFICIAL NEURAL NETWORK PERFORMANCE IN IMAGE CODING

    Directory of Open Access Journals (Sweden)

    OMER MAHMOUD

    2007-08-01

    Full Text Available One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.

  15. Artificial Neural Network for Transfer Function Placental Development: DCT and DWT Approach

    OpenAIRE

    Mohammad Ayache; Mohamad Khalil; Francois Tranquart

    2011-01-01

    The aim of our study is to propose an approach for transfer function placental development using ultrasound images. This approach is based to the selection of tissues, feature extraction by discrete cosine transform DCT, discrete wavelet transform DWT and classification of different grades of placenta by artificial neural network and especially the multi layer perceptron MLP. The proposed approach is tested for ultrasound images of placenta, resulting in 75% success rate of classification usi...

  16. Concept Learning for Achieving Personalized Ontologies: An Active Learning Approach

    Science.gov (United States)

    Şensoy, Murat; Yolum, Pinar

    In many multiagent approaches, it is usual to assume the existence of a common ontology among agents. However, in dynamic systems, the existence of such an ontology is unrealistic and its maintenance is cumbersome. Burden of maintaining a common ontology can be alleviated by enabling agents to evolve their ontologies personally. However, with different ontologies, agents are likely to run into communication problems since their vocabularies are different from each other. Therefore, to achieve personalized ontologies, agents must have a means to understand the concepts used by others. Consequently, this paper proposes an approach that enables agents to teach each other concepts from their ontologies using examples. Unlike other concept learning approaches, our approach enables the learner to elicit most informative examples interactively from the teacher. Hence, the learner participates to the learning process actively. We empirically compare the proposed approach with the previous concept learning approaches. Our experiments show that using the proposed approach, agents can learn new concepts successfully and with fewer examples.

  17. An Open Learning Environment for the Diagnosis, Assistance and Evaluation of Students Based on Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Maria Samarakou

    2014-05-01

    Full Text Available The personalized diagnosis, assistance and evaluation of students in open learning environments can be a challenging task, especially in cases that the processes need to be taking place in real-time, classroom conditions. This paper describes the design of an open learning environment under development, designed to monitor the comprehension of students, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate their performance by using artificial intelligence. A trial test has been performed, with the participation of 20 students, which displayed promising results.

  18. 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.

  19. Personality, Approaches to Learning and Achievement

    Science.gov (United States)

    Swanberg, Anne Berit; Martinsen, Oyvind Lund

    2010-01-01

    The present study investigated the relationships between the five-factor model of personality, approaches to learning and academic achievement. Based on the previous research, we expected approaches to have a mediating effect between personality and academic achievement. Six hundred and eighty-seven business students participated in a survey; 56%…

  20. 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. PMID:19656338

  1. Heutagogy: An alternative practice based learning approach.

    Science.gov (United States)

    Bhoyrub, John; Hurley, John; Neilson, Gavin R; Ramsay, Mike; Smith, Margaret

    2010-11-01

    Education has explored and utilised multiple approaches in attempts to enhance the learning and teaching opportunities available to adult learners. Traditional pedagogy has been both directly and indirectly affected by andragogy and transformational learning, consequently widening our understandings and approaches toward view teaching and learning. Within the context of nurse education, a major challenge has been to effectively apply these educational approaches to the complex, unpredictable and challenging environment of practice based learning. While not offered as a panacea to such challenges, heutagogy is offered in this discussion paper as an emerging and potentially highly congruent educational framework to place around practice based learning. Being an emergent theory its known conceptual underpinnings and possible applications to nurse education need to be explored and theoretically applied. Through placing the adult learner at the foreground of grasping learning opportunities as they unpredictability emerge from a sometimes chaotic environment, heutagogy can be argued as offering the potential to minimise many of the well published difficulties of coordinating practice with faculty teaching and learning. PMID:20554249

  2. A Learning Object Approach To Evidence based learning

    OpenAIRE

    Zabin Visram; Bruce Elson; Patricia Reynolds

    2005-01-01

    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 mea...

  3. Neutron spectrometry and dosimetry based on a new approach called Genetic Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks and Genetic Algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. The structure of a neural network is a significant contributing factor to its performance and the structure is generally heuristically chosen. The use of evolutionary algorithms as search techniques has allowed different properties of neural networks to be evolved. This paper focuses on the intersection on neural networks and evolutionary computation, namely on how evolutionary algorithms can be used to assist neural network design and training, as a novel approach. In this research, a new evolvable artificial neural network modelling approach is presented, which utilizes an optimization process based on the combination of genetic algorithms and artificial neural networks, and is applied in the design of a neural network, oriented to solve the neutron spectrometry and simultaneous dosimetry problems, using only the count rates measured with a Bonner spheres spectrometer system as entrance data. (author)

  4. Modelling fed-batch fermentation processes : an approach based on artificial neural networks

    OpenAIRE

    Valente, Eduardo; Rocha, I; Rocha, Miguel

    2009-01-01

    Publicado em "2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)", ISBN 978-3-540-85860-7 Artificial Neural Networks (ANNs) have shown to be powerful tools for solving several problems which, due to their complexity, are extremely difficult to unravel with other methods. Their capabilities of massive parallel processing and learning from the environment make these structures ideal for prediction of nonlinear events. In this work,...

  5. 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.

  6. An artificial intelligence approach in designing new materials

    OpenAIRE

    W. Sitek; J. Trzaska; L.A. Dobrzański

    2006-01-01

    Purpose: The paper presents the computer aided method of chemical composition designing the metallic materials with a required property.Design/methodology/approach: The purpose has been achieved in two stages. In the first stage a neural network model for calculating the Jominy curve on the basis of the chemical composition has been worked out. This model made possible to prepare, in the second stage, a representative set of data and to work out the neural classifier that would aid the select...

  7. Artificial Neural Network Approach for Mapping Contrasting Tillage Practices

    OpenAIRE

    Terry Howell; Indrajeet Chaubey; Prasanna Gowda; K. P. Sudheer

    2010-01-01

    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 remo...

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

    OpenAIRE

    Aerts, Diederik; Czachor, Marek; 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...

  9. Intelligent process control operator aid -- An artificial intelligence approach

    International Nuclear Information System (INIS)

    This paper describes an approach for designing intelligent process and power plant control operator aids. It is argued that one of the key aspects of an intelligent operator aid is the capability for dynamic procedure synthesis with incomplete definition of initial state, unknown goal states, and the dynamic world situation. The dynamic world state is used to determine the goal, select appropriate plan steps from prespecified procedures to achieve the goal, control the execution of the synthesized plan, and provide for dynamic recovery from failure often using a goal hierarchy. The dynamic synthesis of a plan requires integration of various problems solving capabilities such as plan generation, plan synthesis, plan modification, and failure recovery from a plan. The programming language for implementing the DPS framework provides a convenient tool for developing applications. An application of the DPS approach to a Nuclear Power Plant emergency procedure synthesis is also described. Initial test results indicate that the approach is successful in dynamically synthesizing the procedures. The authors realize that the DPS framework is not a solution for all control tasks. However, many existing process and plant control problems satisfy the requirements discussed in the paper and should be able to benefit from the framework described

  10. A Bayesian Concept Learning Approach to Crowdsourcing

    DEFF Research Database (Denmark)

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

    2011-01-01

    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...... 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...

  11. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    Science.gov (United States)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1995-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

  12. 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

  13. 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.

  14. 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.

  15. Artificial Neural Network Based Approach for short load forecasting

    Directory of Open Access Journals (Sweden)

    Mr. Rajesh Deshmukh

    2011-12-01

    Full Text Available Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short term load forecasting is a essential part of electric power system planning and operation forecasting made for unit commitment and security assessment, which have a direct impact on operational casts and system security. Conventional ANN based load forecasting method deal with 24 hour ahead load forecasting by using forecasted temp. This can lead to high forecasting errors in case of rapid temperature changes. This paper present a neural network based approach for short term load forecasting considering data for training, validation and testing of neural network.

  16. Measuring approaches to learning in a problem based learning context

    Directory of Open Access Journals (Sweden)

    Diana H. Dolmans

    2010-07-01

    Full Text Available Objectives: Students in Problem-Based Learning (PBL are assumed to adopt a deep learning approach and not a surface approach. This study investigated: 1 the reliability and validity of version of the Revised Study Process Questionnaire adapted to the PBL context (PBL-R-SPQ and 2 the extent to which PBL students use deep or surface approaches, and whether this differs between first and second year students. Methods: The items of the R-SPQ were reformulated to better fit with a PBL environment, resulting in the PBL-R-SPQ. In total 262 students from MaastrichtMedicalSchool responded to the PBL-R-SPQ. Results: A Confirmatory Factor Analysis (CFA demon-strated that a 9-item Deep Approach scale and a 9-item Surface Approach scale fitted the observed set of data well. Cronbach alphas for the Deep and Surface scales were 0.76 and 0.74, respectively. First year students reported signifi cantly higher Deep Approach scores (M = 3.60, SD = .48 than second year students (M = 3.40, SD = .48 (p = .001, d = .42. Conversely, second year students reported signifi-cantly higher Surface Approach scores, (M = 2.45, SD = .48 than first year students (M = 2.26, SD = .52 (p = .003, d = .38. Conclusions: The 18-item PBL-R-SPQ provides a valid and reliable tool to measure students' learning approach in PBL. In addition, PBL students tended to adopt a deep approach rather than a surface approach, which is in line with the assumptions behind PBL, although the second year students have a somewhat less deep approach than the first year students.

  17. An artificial neural network approach to reconstruct the source term of a nuclear accident

    International Nuclear Information System (INIS)

    This work makes use of one of the main features of artificial neural networks, which is their ability to 'learn' from sets of known input and output data. Indeed, a trained artificial neural network can be used to make predictions on the input data when the output is known, and this feedback process enables one to reconstruct the source term from field observations. With this aim, an artificial neural networks has been trained, using the projections of a segmented plume atmospheric dispersion model at fixed points, simulating a set of gamma detectors located outside the perimeter of a nuclear facility. The resulting set of artificial neural networks was used to determine the release fraction and rate for each of the noble gases, iodines and particulate fission products that could originate from a nuclear accident. Model projections were made using a large data set consisting of effective release height, release fraction of noble gases, iodines and particulate fission products, atmospheric stability, wind speed and wind direction. The model computed nuclide-specific gamma dose rates. The locations of the detectors were chosen taking into account both building shine and wake effects, and varied in distance between 800 and 1200 m from the reactor.The inputs to the artificial neural networks consisted of the measurements from the detector array, atmospheric stability, wind speed and wind direction; the outputs comprised a set of release fractions and heights. Once trained, the artificial neural networks was used to reconstruct the source term from the detector responses for data sets not used in training. The preliminary results are encouraging and show that the noble gases and particulate fission product release fractions are well determined

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. A new approach to thermodynamic analysis of ejector-absorption cycle: artificial neural networks

    International Nuclear Information System (INIS)

    Thermodynamic analysis of absorption thermal systems is too complex because of analytic functions calculating the thermodynamic properties of fluid couples involving the solution of complex differential equations. To simplify this complex process, the use of artificial neural networks (ANNs) has been proposed for the analysis of ejector-absorption refrigeration systems (EARSs). ANNs approach was used to determine the properties of liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiBr), which does not cause ozone depletion for EARS. The back-propagation learning algorithm with three different variants and logistic sigmoid transfer function was used in the network. In addition, this paper presents a comparative performance study of the EARS using both analytic functions and prediction of ANN for properties of the fluid couple. After training, it was found that average error is less than 1.3% and R2 values are about 0.9999. Additionally, when the results of analytic equations obtained by using experimental data and by means of ANN were compared, deviations in coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F) for all working temperatures were found to be less than 1.8%, 4%, 0.2%, respectively. Deviations for COP, ECOP and F at a generator temperature of ∼90 deg. C for which the COP of the system is maximum are 1%, 2%, 0.1%, respectively, for other working temperatures. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable uncertainties

  3. Approaches to learning of Irish students studying accounting

    OpenAIRE

    Byrne, Marann; Flood, Barbara; Willis, Pauline

    1999-01-01

    Several reports on accounting education have identified the development of students' learning to learn as the primary objective of accounting education. Higher education research identifies the approach to learning as a significant factor in the overall student learning experience. If accounting educators are to find ways to improve the educational experience of their students, they must understand how students learn and the effects of the learning context on learning approaches. This ...

  4. 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

  5. Polyhedral approach to statistical learning graphical models

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Hemmecke, R.; Vomlel, Jiří; Lindner, S.

    Osaka : JST CREST, 2010. s. 1-4. [The 2nd CREST-SBM International Conference "Harmony of Groebner Bases and the Moderm Industrial Socienty". 28.06.2010-02.07.2010, Hotel Hankyu Expo Park, Osaka] Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian network * polyhedral approach * imset Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2010/MTR/studeny-polyhedral approach to statistical learning graphical models.pdf

  6. A Sociocultural Approach to Recognition and Learning

    Directory of Open Access Journals (Sweden)

    Peter Musaeus

    2006-04-01

    Full Text Available This is a case study of goldsmith craft apprenticeship learning and recognition. The study includes 13 participants in a goldsmith's workshop. The theoretical approach to recognition and learning is inspired by sociocultural theory. In this article recognition is defined with reference to Hegel’s understanding of the concept as a transformed struggle of granting acknowledgement to another person plus receiving acknowledgement as a person. It is argued that the notion of recognition can enhance sociocultural notions of learning. In analysing the case study of apprenticeship learning, the article suggests that recognition is expressed in the act of participants staking their lives to prove their autonomy, in work activity in terms of the role of artefacts and in the form of abstract and concrete recognition. Finally recognition is discussed in relation to learning and development. The study concludes that recognition is an important category not only to explain apprenticeship learning but also to give a sociocultural explanation of learning in general.

  7. Chaotic artificial immune approach applied to economic dispatch of electric energy using thermal units

    International Nuclear Information System (INIS)

    The economic dispatch problem (EDP) is an optimization problem useful in power systems operation. The objective of the EDP of electric power generation, whose characteristics are complex and highly non-linear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying system constraints. Recently, as an alternative to the conventional mathematical approaches, modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. As special mechanism to avoid being trapped in local minimum, the ergodicity property of chaotic sequences has been used as optimization technique in EDPs. Based on the chaos theory, this paper discusses the design and validation of an optimization procedure based on a chaotic artificial immune network approach based on Zaslavsky's map. The optimization approach based on chaotic artificial immune network is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results and comparisons show that the chaotic artificial immune network approach is competitive in performance with other optimization approaches presented in literature and is also an attractive tool to be used on applications in the power systems field.

  8. 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…

  9. 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…

  10. 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…

  11. Intrusion detection a machine learning approach

    CERN Document Server

    Tsai, Jeffrey JP

    2011-01-01

    This important book introduces the concept of intrusion detection, discusses various approaches for intrusion detection systems (IDS), and presents the architecture and implementation of IDS. It emphasizes on the prediction and learning algorithms for intrusion detection and highlights techniques for intrusion detection of wired computer networks and wireless sensor networks. The performance comparison of various IDS via simulation will also be included.

  12. Effect of signal noise on the learning capability of an artificial neural network

    International Nuclear Information System (INIS)

    Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

  13. An artificial economy based on reinforcement learning and agent based modeling

    OpenAIRE

    Fernando Lozano; Jaime Lozano; Mario García

    2007-01-01

    In this paper we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on convention. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions meaning that a firm is likely to behave as it neighbors if it observes that their actions lead to a good pay-off. On the other hand, we propose the use of reinfo...

  14. An Artificial Intelligence Approach to the Symbolic Factorization of Multivariable Polynomials. Technical Report No. CS74019-R.

    Science.gov (United States)

    Claybrook, Billy G.

    A new heuristic factorization scheme uses learning to improve the efficiency of determining the symbolic factorization of multivariable polynomials with interger coefficients and an arbitrary number of variables and terms. The factorization scheme makes extensive use of artificial intelligence techniques (e.g., model-building, learning, and…

  15. Artificial Neural Network for Transfer Function Placental Development: DCT and DWT Approach

    Directory of Open Access Journals (Sweden)

    Mohammad Ayache

    2011-09-01

    Full Text Available The aim of our study is to propose an approach for transfer function placental development using ultrasound images. This approach is based to the selection of tissues, feature extraction by discrete cosine transform DCT, discrete wavelet transform DWT and classification of different grades of placenta by artificial neural network and especially the multi layer perceptron MLP. The proposed approach is tested for ultrasound images of placenta, resulting in 75% success rate of classification using DCT and 92% using DWT. The method based on multi resolution decomposition analysis and on supervised neural network technique MLP, seems a good method to study the transfer function of placental development in ultrasound.

  16. A Geometric Approach to Learning BN Structures

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Vomlel, Jiří

    Aalborg : Aalborg University, 2008 - (Jaeger, M.; Nielsen, T.), s. 281-288 [the Fourth European Workshop on Probabilistic Graphical Models (PGM'08). Hirtshals (DK), 17.09.2008-19.09.2008] R&D Projects: GA MŠk 1M0572; GA ČR GA201/08/0539 Grant ostatní: GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian network * machine learning Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2008/MTR/studeny-vomlel-a%20geometric%20approach%20to%20learning%20bn%20structures.pdf

  17. 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.

  18. Learning Approaches of Successful Students and Factors Affecting Their Learning Approaches

    OpenAIRE

    İlhan Beyaztaş, Dilek; Erzincan Üniversitesi, Eğitim Fakültesi, İlköğretim Bölümü; Senemoğlu, Nuray; Hacettepe Üniversitesi, Eğitim Fakültesi, Eğitim Bilimleri Bölümü

    2015-01-01

    The purpose of this descriptive study is to identify learning approaches (deep, surface, or strategic) among successful undergraduate students and the factors that affect and shape their learning approaches. The study sample comprised 90 freshman students who were ranked in the top one percent portion of the 2013 University Placement Exam (UPE) in Turkey. Students were variously attending faculties of education, law or medicine and were grouped in subject areas of Literacy-Social (LS), Litera...

  19. IDENTIFICATION OF ERYTHEMATO-SQUAMOUS SKIN DISEASES USING EXTREME LEARNING MACHINE AND ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Sunday Olusanya Olatunji

    2013-10-01

    Full Text Available In this work, a new identification model, based on extreme learning machine (ELM, to better identify Erythemato – Squamous skin diseases have been proposed and implemented and the results compared to that of the classical artificial neural network (ANN. ELMs provide solutions to single- and multi- hidden layer feed-forward neural networks. ELMs can achieve high learning speed, good generalization performance, and ease of implementation. Experimental results indicated that ELM outperformed the classical ANN in all fronts both for the training and testing cases. The effect of varying size of training and testing set on the performance of classifiers were also investigated in this study. The proposed classifier demonstrated to be a viable tool in this germane field of medical diagnosis as indicated by its high accuracy and consistency of result.

  20. 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.

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

    OpenAIRE

    Yudong Zhang; Lenan Wu

    2011-01-01

    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 ex...

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

    OpenAIRE

    Hong-Hai Tran; Nhat-Duc Hoang

    2014-01-01

    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 u...

  3. 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.

  4. 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.

  5. 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...

  6. 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.

  7. 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.

  8. 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.

  9. 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…

  10. Business goal oriented approach for Adaptive Learning System

    OpenAIRE

    Houda Zouari Ounaies

    2013-01-01

    Several adaptive learning systems are currently available. Nevertheless, most existing e-learning platforms lack efficient alignment to decision makers. Our approach is two-fold. Firstly, we aim to integrate business goals in the selection process of learning concepts. Secondly, we make use of Case Based Reasoning (CBR) approach to learn from past experiences and construct effective adaptive system.

  11. SCREEN Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

    CERN Document Server

    Wermter, S; Wermter, Stefan; Weber, Volker

    1997-01-01

    In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processi...

  12. ABC Tester - Artificial Bee Colony Based Software Test Suite Optimization Approach

    Directory of Open Access Journals (Sweden)

    D. Jeya Mala

    2009-07-01

    Full Text Available In this paper we present a new, non-pheromone-based test suite optimization approach inspired by the behavior of biological bees. Our proposed approach is based on ABC (Artificial Bee Colony Optimization which is motivated by the intelligent behavior of honey bees. In our proposed system, the sites are the nodes in the Software under Test (SUT, the artificial bees modify the test cases with time and the bee?s aim is to discover the places of nodes with higher coverage and finally the one with the highest usage by the given test case. Since ABC system combines local search methods carried out by employed bees with global search methods managed by onlookers and scouts, we attain near global optima. We investigate whether this new approach outperforms existing test optimization approach based on Genetic Algorithms (GA in the task of software test optimization. Taking into account the results of our experiments, we conclude that (i the proposed approach uses fewer iterations to complete the task; (ii is more scalable, i.e., it requires less computation time to complete the task, and finally (iii our approach is best in achieving near global optimal solution.

  13. A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting

    OpenAIRE

    Zhaoxuan Li; SM Mahbobur Rahman; Rolando Vega; Bing Dong

    2016-01-01

    We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statisti...

  14. Cultivating Collaborative Improvement: An Action Learning Approach

    OpenAIRE

    Middel, Rick; McNichols, Timothy

    2004-01-01

    As competitive pressure mounts to innovate in the global knowledge economy, many organizations are exploring new ways of collaborating with their supply chain partners. However, the process of implementing collaborative initiatives across disparate members of supply networks is fraught with difficulties. One approach designed to tackle the difficulties of organizational change and inter-organizational improvement in practice is `action learning¿. This paper examines the experiential lessons t...

  15. 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.

  16. A biomimetic approach for synthesizing artificial light-harvesting systems using self-assembly

    Energy Technology Data Exchange (ETDEWEB)

    Bhise, A.D.

    2005-10-01

    Photosynthesis is an extremely important process on Earth as it is the only natural source of food and fossil fuel, which fulfil our daily needs. After a certain period, the natural source of food and energy will decrease due to rapid consumption. Therefore, future generations will require alternative food and fuel sources. This represents a strong driving force to do research in construction of artificial light-harvesting (or antenna) systems. Synthetic antennas can be achieved either by covalent or non-covalent approaches by employing different strategies. This work throws light on the non-covalent approach i.e. a supramolecular approach in quest of artificial antenna systems wherein self-assembly and self-aggregation are at the focus. Furthermore this approach is biomimetic in nature as it is inspired by the antenna system which operates in green photosynthetic bacteria. Bacteriochlorophyll-c, d and e were selected as models for the syntheses of artificial mimics. The supramolecular interactions which are, the ligation of the central Mg atom by the 3{sup 1}-hydroxy group of another molecule; cooperative hydrogen bonding of the same OH group to the 13{sup 1}-carbonyl group of a third BChl-c molecule; and {pi}-{pi} interactions between the macrocycles are responsible for self-assembly of the building blocks or tectons. Well-defined architectures of self-assembling porphyrins find applications in mimicking the functions of light-harvesting. Porphyrins that are equipped with the same functional groups that are responsible for the self-assembly of bacteriochlorophylls-c, d and e within the chlorosomal antenna of some green photosynthetic bacteria, have been selectively synthesized from easily available and cheap starting materials, 10,20-Bis(3,5-di-t-butylphenyl)porphinato copper. All the target compounds were obtained after four to eight synthetic steps in good yields by employing different synthetic procedures involving also novel reactions. These fully synthetic

  17. A classical Master equation approach to modeling an artificial protein motor

    International Nuclear Information System (INIS)

    Inspired by biomolecular motors, as well as by theoretical concepts for chemically driven nanomotors, there is significant interest in constructing artificial molecular motors. One driving force is the opportunity to create well-controlled model systems that are simple enough to be modeled in detail. A remaining challenge is the fact that such models need to take into account processes on many different time scales. Here we describe use of a classical Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model an existing artificial molecular motor concept, the Tumbleweed, across many time scales. This enables us to study how interdependencies between motor processes, such as center-of-mass diffusion and track binding/unbinding, affect motor performance. Results from our model help guide the experimental realization of the proposed motor, and potentially lead to insights that apply to a wider class of molecular motors.

  18. 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.

  19. Survey on Machine Learning Approaches for Solar Irradiation Prediction

    Directory of Open Access Journals (Sweden)

    U.Divya*

    2014-10-01

    Full Text Available Renewable energy technologies are clean sources of energy that have a much lower environmental impact than conventional energy generation methods. Researches focusing on different energy generation techniques are gaining much importance worldwide, to manage exponential increase in the energy requirements. Solar energy is used in various applications like solar charged sensor nodes, solar charged vehicles, agriculture, electricity production etc. This solar energy can be harnessed using a range of technologies such as solar heating, solar photovoltaic cells, solar thermal electricity, solar architecture and artificial photosynthesis. The need for solar energy requires the estimation of solar energy production at various atmospherical conditions. This estimation involves the prediction of solar irradiation. Machine learning techniques based on Support Vector Machine (SVM, Neural Networks, Multilayer Perception(MLP, etc as well as Gaussian Process Regression method are normally applied for learning and predicting solar parameters. These models make use of parameters like air temperature, wind direction, relative humidity, and total rainfall as input to predict the temperature for a particular day. This paper highlights on the features of these different approaches for prediction and various metrics that are normally used for measuring the accuracy of the prediction process.

  20. SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

    CERN Document Server

    Wermter, S

    2008-01-01

    Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken- language systems still use a relatively brittle, hand-coded symbolic grammar or symbolic semantic component. In contrast, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, lea...

  1. 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.

  2. ClimateLearn: A machine-learning approach for climate prediction using network measures

    OpenAIRE

    Feng, Qing Yi; Vasile, Ruggero; Segond, Marc; Gozolchiani, Avi; Wang, Yang; Abel, Markus; Havlin, Shilomo; Bunde, Armin; Dijkstra, Henk A.

    2016-01-01

    We present the toolbox ClimateLearn to tackle problems in climate prediction using machine learning techniques and climate network analysis. The package allows basic operations of data mining, i.e. reading, merging, and cleaning data, and running machine learning algorithms such as multilayer artificial neural networks and symbolic regression with genetic programming. Because spatial temporal information on climate variability can be efficiently represented by complex network measures, such d...

  3. 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.

  4. A Conceptual Model of Relationships among Constructivist Learning Environment Perceptions, Epistemological Beliefs, and Learning Approaches

    Science.gov (United States)

    Ozkal, Kudret; Tekkaya, Ceren; Cakiroglu, Jale; Sungur, Semra

    2009-01-01

    This study proposed a conceptual model of relationships among constructivist learning environment perception variables (Personal Relevance, Uncertainty, Critical Voice, Shared Control, and Student Negotiation), scientific epistemological belief variables (fixed and tentative), and learning approach. It was proposed that learning environment…

  5. A pedagogical approach to the design of Learning Objects

    OpenAIRE

    Busetti, Emanuela; Dettori, Giuliana; Forcheri, Paola; Ierardi, Maria Grazia

    2005-01-01

    In this paper we describe an approach to the design of learning objects (LOs) suitable to support learning in complex domains at university level. Our proposal is centred on a costructivist approach where learning is viewed as resulting from personal activity and comparison with the activity of others. Our pedagogical approach to knowledge acquisition and to the use of technological tools is realized by means of didactical units which can be implemented as Learning Objects (LOs) with a variet...

  6. 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.

  7. Transfer function approach for artificial tracer test interpretation in karstic systems

    Science.gov (United States)

    Labat, D.; Mangin, A.

    2015-10-01

    A karstic formation consists in a three-dimensional hydrological system which involves horizontal and vertical, diphasic or saturated water transfers characterised by a large range of velocity. These subsurface flow processes correspond to various water pathways through fractured, fissured, and underground streams or conduits leading to a nonlinear global behaviour of the system. An efficient way of investigating of a karstic system behaviour consists in the injection of artificial tracer tests at loss points and in careful analysis of the recovery tracer fluxes at one or several outlets of the systems. These injections are also an efficient way of providing hypotheses on characteristic time of contaminant transfer in these type of aquifers. Here, we propose a Laplace-transform transfer function of the Residence Time Distribution function that allows to discriminate between a quick-flow advection-dominated component and a slow-flow advection-dispersion/dominated component in the artificial tracer transfer in the system. We apply this transfer function on five high resolution sampling rate artificial tracer tests operated on the Baget system in the Pyrenees (France) in order to illustrate the advantages and limitations of this approach. We provide then an interpretation of the relationship between tracer test recovery shape and karstic system organisation between inlet and outlet site.

  8. Learning from Reward as an emergent property of Physics-like interactions between neurons in an artificial neural network.

    OpenAIRE

    Davesne, Frédéric

    2004-01-01

    We study a class of artificial neural networks in which a physics-like conservation law upon the activity of connected neurons is imposed at each time. We postulate that the modification of the network activities may be interpreted as a learning capability if a judicious conservation law is chosen. We illustrate our claim by modeling a rat behavior in a labyrinth: the exploration of the labyrinth permits to create connections between neurons (latent learning), whereas the discovery of food in...

  9. 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. PMID:27055066

  10. 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...

  11. 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.

  12. 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…

  13. Bridging the Gaps between Learning and Teaching through Recognition of Students' Learning Approaches: A Case Study

    Science.gov (United States)

    Malie, Senian; Akir, Oriah

    2012-01-01

    Learning approaches, learning methods and learning environments have different effects on students? academic performance. However, they are not the sole factors that impact students? academic achievement. The aims of this research are three-fold: to determine the learning approaches preferred by most students and the impact of the learning…

  14. Polyhedral approach to statistical learning graphical models

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Haws, D.; Hemmecke, R.; Lindner, S.

    Singapore : World Scientific Press, 2012, s. 346-372. ISBN 978-981-4383-45-5. [The 2nd CREST-SBM International Conference "Harmony of Groebner Bases and the Modern Industrial Society". Osaka (JP), 28.06.2012-2.07.2012] R&D Projects: GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : Bayesian network stucture * standard imset * characteristic imset * polyhedral geometry Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2012/MTR/studeny-polyhedral approach to statistical learning graphical models.pdf

  15. A NEW RULE-BASED APPROACH FOR COMPUTER CHESS PROGRAMMING USING GP-ARTIFICIAL TECHNIQUES : PECP

    OpenAIRE

    HANEDAN, Y.Güney; SERTBAŞ, Ahmet

    2012-01-01

    In this paper, we use a brand new chess engine programming technique which we name PECP (Positional Evolutionary Chess Programming), that brings the Artificial Intelligence and Genetic Programming approaches together, to construct a chess endgame analyzing engine. Throughout the paper, the technique and the algorithm are discussed in detail. Also,  using PECP, an  example program (RETI V1.0)  aimed to prove the correctness and performance of the rule-based theory and algorithm is written in P...

  16. 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.

  17. 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...

  18. Component-Based Approach in Learning Management System Development

    Science.gov (United States)

    Zaitseva, Larisa; Bule, Jekaterina; Makarov, Sergey

    2013-01-01

    The paper describes component-based approach (CBA) for learning management system development. Learning object as components of e-learning courses and their metadata is considered. The architecture of learning management system based on CBA being developed in Riga Technical University, namely its architecture, elements and possibilities are…

  19. Concept Based Approach for Adaptive Personalized Course Learning System

    Science.gov (United States)

    Salahli, Mehmet Ali; Özdemir, Muzaffer; Yasar, Cumali

    2013-01-01

    One of the most important factors for improving the personalization aspects of learning systems is to enable adaptive properties to them. The aim of the adaptive personalized learning system is to offer the most appropriate learning path and learning materials to learners by taking into account their profiles. In this paper, a new approach to…

  20. 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…

  1. Integrating Adult Learning and Technologies for Effective Education: Strategic Approaches

    Science.gov (United States)

    Wang, Victor C. X.

    2010-01-01

    As adult learners and educators pioneer the use of technology in the new century, attention has been focused on developing strategic approaches to effectively integrate adult learning and technology in different learning environments. "Integrating Adult Learning and Technologies for Effective Education: Strategic Approaches" provides innovative…

  2. 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.

  3. Cooperative Learning: An Effective Approach to College English Learning

    OpenAIRE

    You Lv

    2014-01-01

    Cooperative Learning (or Collaborative Learning), has become one of mainstream learning strategies in the world. As China continues to push forward and lucubrate into the reform of college English, many educational theorists and teachers who work on college English try to use the cooperative learning, a creative and effective learning strategy, into the current college English learning system, and have made some achievements. This paper also tries to sum up some relevant studies on cooperativ...

  4. 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.

  5. 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)

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

    Science.gov (United States)

    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. PMID:27610129

  7. 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. PMID:27610129

  8. 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.

  9. A Pareto evolutionary artificial neural network approach for remote sensing image classification

    Science.gov (United States)

    Liu, Fujiang; Wu, Xincai; Guo, Yan; Sun, Huashan; Zhou, Feng; Mei, Linlu

    2006-10-01

    This paper presents a Pareto evolutionary artificial neural network (Pareto-EANN) approach based on the evolutionary algorithms for multiobjective optimization augmented with local search for the classification of remote sensing image. Its novelty lies in the use of a multiobjective genetic algorithm where single hidden layers Multilayer Perceptrons (MLP) are employed to indicate the accuracy/complexity trade-off. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy of the classifier and number of hidden units. We compared Pareto-EANN classifiers results of the classification of remote sensing image against standard backpropagation neural network classifiers and EANN classifiers; we show experimentally the efficiency of the proposed methodology.

  10. Delaunay variables approach to the elimination of the perigee in Artificial Satellite Theory

    Science.gov (United States)

    Lara, Martin; San-Juan, Juan F.; López-Ochoa, Luis M.

    2014-09-01

    Analytical integration in Artificial Satellite Theory may benefit from different canonical simplification techniques, like the elimination of the parallax, the relegation of the nodes, or the elimination of the perigee. These techniques were originally devised in polar-nodal variables, an approach that requires expressing the geopotential as a Pfaffian function in certain invariants of the Kepler problem. However, it has been recently shown that such sophisticated mathematics are not needed if implementing both the relegation of the nodes and the parallax elimination directly in Delaunay variables. Proceeding analogously, it is shown here how the elimination of the perigee can be carried out also in Delaunay variables. In this way the construction of the simplification algorithm becomes elementary, on one hand, and the computation of the transformation series is achieved with considerable savings, on the other, reducing the total number of terms of the elimination of the perigee to about one third of the number of terms required in the classical approach.

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

    OpenAIRE

    Reid, William A.; Evans, Phillip; Duvall, Edward

    2012-01-01

    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 t...

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

    OpenAIRE

    Reid, William A.; Phillip Evans; Edward Duvall

    2012-01-01

    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 examinatio...

  13. Deep Multi-task Representation Learning: A Tensor Factorisation Approach

    OpenAIRE

    Yang, Yongxin; Hospedales, Timothy

    2016-01-01

    Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end...

  14. 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...

  15. The Relationship between Intelligence, Approaches to Learning and Academic Achievement.

    Science.gov (United States)

    Diseth, Age

    2002-01-01

    Administered three tests of intelligence and the Approaches and Study Skills Inventory for Students (Entwhistle, 1997) to 89 Norwegian undergraduates to study the relationships among intelligence, approaches of learning, and academic achievement. Findings support the construct validity of approaches to learning because of its independence from…

  16. 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…

  17. A New Approach to Image Reconstruction in Positron Emission Tomography Using Artificial Neural Networks

    Science.gov (United States)

    Bevilacqua, A.; Bollini, D.; Campanini, R.; Lanconelli, N.; Galli, M.

    This study investigates the possibility of using an Artificial Neural Network (ANN) for reconstructing Positron Emission Tomography (PET) images. The network is trained with simulated data which include physical effects such as attenuation and scattering. Once the training ends, the weights of the network are held constant. The network is able to reconstruct every type of source distribution contained inside the area mapped during the learning. The reconstruction of a simulated brain phantom in a noiseless case shows an improvement if compared with Filtered Back-Projection reconstruction (FBP). In noisy cases there is still an improvement, even if we do not compensate for noise fluctuations. These results show that it is possible to reconstruct PET images using ANNs. Initially we used a Dec Alpha; then, due to the high data parallelism of this reconstruction problem, we ported the learning on a Quadrics (SIMD) machine, suited for the realization of a small medical dedicated system. These results encourage us to continue in further studies that will make possible reconstruction of images of bigger dimension than those used in the present work (32 × 32 pixels).

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

    OpenAIRE

    Gamze Sezgin Selcuk; Gülbin Özkan

    2014-01-01

    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...

  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. Edu-mining: A Machine Learning Approach

    Science.gov (United States)

    Srimani, P. K.; Patil, Malini M.

    2011-12-01

    Mining Educational data is an emerging interdisciplinary research area that mainly deals with the development of methods to explore the data stored in educational institutions. The educational data is referred as Edu-DATA. Queries related to Edu-DATA are of practical interest as SQL approach is insufficient and needs to be focused in a different way. The paper aims at developing a technique called Edu-MINING which converts raw data coming from educational institutions using data mining techniques into useful information. The discovered knowledge will have a great impact on the educational research and practices. Edu-MINING explores Edu-DATA, discovers new knowledge and suggests useful methods to improve the quality of education with regard to teaching-learning process. This is illustrated through a case study.

  1. 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. PMID:24816704

  2. 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…

  3. Feasibility report: Delivering case-study based learning using artificial intelligence and gaming technologies

    OpenAIRE

    Cresswell, Stephen; Prigmore, Martyn

    2008-01-01

    This document describes an investigation into the technical feasibility of a game to support learning based on case studies. Information systems students using the game will conduct fact-finding interviews with virtual characters. We survey relevant technologies in computational linguistics and games. We assess the applicability of the various approaches and propose an architecture for the game based on existing techniques. We propose a phased development plan for the development of the game.

  4. Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks.

    Science.gov (United States)

    Chon, K H; Holstein-Rathlou, N H; Marsh, D J; Marmarelis, V Z

    1998-01-01

    Volterra models have been increasingly popular in modeling studies of nonlinear physiological systems. In this paper, feedforward artificial neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks (sigmoidal and polynomial) and the Volterra models are comparable in terms of normalized mean-square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. Nonetheless, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general, since they may exhibit different strengths and weaknesses depending on the specific characteristics of each application. PMID:18252466

  5. 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. PMID:22956056

  6. Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

    Directory of Open Access Journals (Sweden)

    Ch. Sanjay

    2014-12-01

    Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

  7. 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.

  8. Artificial neural network approach for moiré fringe center determination

    Science.gov (United States)

    Woo, Wing Hon; Ratnam, Mani Maran; Yen, Kin Sam

    2015-11-01

    The moiré effect has been used in high-accuracy positioning and alignment systems for decades. Various methods have been proposed to identify and locate moiré fringes in order to relate the pattern information to dimensional and displacement measurement. These methods can be broadly categorized into manual interpretation based on human knowledge and image processing based on computational algorithms. An artificial neural network (ANN) is proposed to locate moiré fringe centers within circular grating moiré patterns. This ANN approach aims to mimic human decision making by eliminating complex mathematical computations or time-consuming image processing algorithms in moiré fringe recognition. A feed-forward backpropagation ANN architecture was adopted in this work. Parametric studies were performed to optimize the ANN architecture. The finalized ANN approach was able to determine the location of the fringe centers with average deviations of 3.167 pixels out of 200 pixels (≈1.6%) and 6.166 pixels out of 200 pixels (≈3.1%) for real moiré patterns that lie within and outside the training intervals, respectively. In addition, a reduction of 43.4% in the computational time was reported using the ANN approach. Finally, the applicability of the ANN approach for moiré fringe center determination was confirmed.

  9. AN ARTIFICIAL NEURAL NETWORK APPROACH – PERFORMANCE MEASURE OF A RE-ENTRANT LINE IN A REFLOW SCREENING OPERATION

    Directory of Open Access Journals (Sweden)

    SURESH KUMAR

    2010-12-01

    Full Text Available This paper presents an artificial neural network (ANN method applied to a multistage re-entrant line system. Generally, queuing networks adopt analytical methods or use simulation packages to determine their performance measure. The contribution of this paper is the development of an alternate solution method using ANN approach to determine performance measure namely the total cycle time for a Reflow Screening (RS operation in a semiconductor assembly plant. Performance measure of an operation is an important aspect in management decision making. In order to validate the proposed method, comparison results were made using the analytical method based on mean value analysis (MVA technique for the re-entrant line and with some historical data collected from the operation. In this paper, Back Propagation Network (BPN learning algorithm is proposed for the computation of the total cycle time with respect to the number of lots circulating in the system. Extensive training and testing of the proposed ANN method is performed which enables the BPN model to be used to determine the required total cycle time.

  10. 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'…

  11. Do Learning Approaches of Medical Students Affect Their Satisfaction with Problem-Based Learning?

    Science.gov (United States)

    Gurpinar, Erol; Kulac, Esin; Tetik, Cihat; Akdogan, Ilgaz; Mamakli, Sumer

    2013-01-01

    The aim of this research was to determine the satisfaction of medical students with problem-based learning (PBL) and their approaches to learning to investigate the effect of learning approaches on their levels of satisfaction. The study group was composed of medical students from three different universities, which apply PBL at different levels…

  12. Multiple Regression Analysis of Epistemological Beliefs, Learning Approaches, and Self-Regulated Learning

    Science.gov (United States)

    Phan, Huy P.

    2008-01-01

    Introduction: Recent research in educational psychology has explored student approaches to learning (SAL) and epistemological beliefs within the theoretical framework of self-regulated learning. The focus of this research study seeks to explore the predictiveness of learning approaches and epistemological beliefs on students' self-regulatory…

  13. 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 ...

  14. Artificial neural network modeling of fixed bed biosorption using radial basis approach

    Science.gov (United States)

    Saha, Dipendu; Bhowal, Avijit; Datta, Siddhartha

    2010-04-01

    In modern day scenario, biosorption is a cost effective separation technology for the removal of various pollutants from wastewater and waste streams from various process industries. The difficulties associated in rigorous mathematical modeling of a fixed bed bio-adsorbing systems due to the complexities of the process often makes the development of pure black-box artificial neural network (ANN) models particularly useful in this field. In this work, radial basis function network has been employed as ANN to model the breakthrough curves in fixed bed biosorption. The prediction has been compared to the experimental breakthrough curves of Cadmium, Lanthanum and a dye available in the literature. Results show that this network gives fairly accurate representation of the actual breakthrough curves. The results obtained from ANN modeling approach shows the better agreement between experimental and predicted breakthrough curves as the error for all these situations are within 6%.

  15. 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.

  16. 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.

  17. 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.

  18. (Re)Conceptualizing Design Approaches for Mobile Language Learning

    Science.gov (United States)

    Hoven, Debra; Palalas, Agnieszka

    2011-01-01

    An exploratory study conducted at George Brown College in Toronto, Canada between 2007 and 2009 investigated language learning with mobile devices as an approach to augmenting ESP learning by taking learning outside the classroom into the real-world context. In common with findings at other community colleges, this study identified inadequate…

  19. 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?

  20. 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.

  1. A novel artificial bee colony algorithm based on modified search equation and orthogonal learning.

    Science.gov (United States)

    Gao, Wei-feng; Liu, San-yang; Huang, Ling-ling

    2013-06-01

    The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions. PMID:23086528

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

    OpenAIRE

    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. ...

  3. Water level forecasting through fuzzy logic and artificial neural network approaches

    Directory of Open Access Journals (Sweden)

    S. Alvisi

    2006-01-01

    Full Text Available In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the same input and output variables. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time. The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy. It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach.

  4. A Fuzzy Approach to Classify Learning Disability

    OpenAIRE

    Pooja Manghirmalani; Darshana More; Kavita Jain

    2012-01-01

    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...

  5. Blended learning approaches enhance student academic performance

    OpenAIRE

    Morris, NP

    2010-01-01

    Blended learning, or technology enhanced learning, is increasingly becoming an expectation for higher education students. Blended learning allows for the enhancement of face-to-face interaction between tutors and students, using internet or computer based tools. In this paper, a range of case studies are described which illustrate methods to engage students with technology enhanced learning and improve academic performance and student satisfaction. In the first case study, first year undergra...

  6. SUPERVISED MACHINE LEARNING APPROACHES: A SURVEY

    OpenAIRE

    Iqbal Muhammad; Zhu Yan

    2015-01-01

    One of the core objectives of machine learning is to instruct computers to use data or past experience to solve a given problem. A good number of successful applications of machine learning exist already, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through Supervised ...

  7. The Effect of 5E Learning Model on Seventh Grade Students’ Approaches to Learning

    Directory of Open Access Journals (Sweden)

    Eylem Yıldız Feyzioğlu

    2012-06-01

    Full Text Available The purpose of this study is to investigate the effect of 5E learning model on three seventh grade students’ approaches to learning. The students’ approaches to “remembering what s/he learned”, “the goal of learning” and “encountering the difficulty in learning” before and after the application of 5E model is presented. Data was gathered through these students’ scores on the scale of “Deep Approaches to Learning” and “Surface Approaches to Learning”. Pre and post interviews were also conducted with the students. The results show that the change in students’ approaches to learning varies from each other. Two of the three students used both “deep approaches” and “surface approaches” to learning before the application of 5E model, then the students developed their approaches to learning deeply. The third student maintained both “deep approach” and “surface approach” to learning. The reason why two of the three students changed their approaches to learning deeply is that through 5E learning model, these students become active participants in the process of learning. This study also indicates that creating the constructivist learning environment may not cause the same effects for each of these students. Longitudinal studies should be designed to change students’ approaches to learning deeply.

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

    Science.gov (United States)

    Wang, Zhongbin; Xu, Xihua; Si, Lei; Ji, Rui; Liu, Xinhua; Tan, Chao

    2016-01-01

    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. PMID:27123002

  9. 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.

  10. 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

  11. An Improved Metric Learning Approach for Degraded Face Recognition

    OpenAIRE

    2014-01-01

    To solve the matching problem of the elements in different data collections, an improved coupled metric learning approach is proposed. First, we improved the supervised locality preserving projection algorithm and added the within-class and between-class information of the improved algorithm to coupled metric learning, so a novel coupled metric learning method is proposed. Furthermore, we extended this algorithm to nonlinear space, and the kernel coupled metric learning method based on superv...

  12. An Efficient Dual Approach to Distance Metric Learning

    OpenAIRE

    Shen, Chunhua; Kim, Junae; Liu, Fayao; Wang, Lei; Hengel, Anton van den

    2013-01-01

    Distance metric learning is of fundamental interest in machine learning because the distance metric employed can significantly affect the performance of many learning methods. Quadratic Mahalanobis metric learning is a popular approach to the problem, but typically requires solving a semidefinite programming (SDP) problem, which is computationally expensive. Standard interior-point SDP solvers typically have a complexity of $O(D^{6.5})$ (with $D$ the dimension of input data), and can thus onl...

  13. Outdoor Education: An Alternative Approach in Teaching and Learning Science

    OpenAIRE

    Tuan Mastura Tuan Soh; Tamby Subahan Mohd. Meerah

    2013-01-01

    To understand fully and aware of children’s science learning, one should look not only at learning that takes place in the kindergarten and primary school but also in learning that takes place outside the classroom. This paper aims to discuss outdoor education: an alternative approach in teaching and learning science in the Malaysian context. In this 21st century, the exposure and experience in the field of science and technology are needed in nurturing interest among students who are involve...

  14. 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…

  15. Students' Approaches to Learning a New Mathematical Model

    Science.gov (United States)

    Flegg, Jennifer A.; Mallet, Daniel G.; Lupton, Mandy

    2013-01-01

    In this article, we report on the findings of an exploratory study into the experience of undergraduate students as they learn new mathematical models. Qualitative and quantitative data based around the students' approaches to learning new mathematical models were collected. The data revealed that students actively adopt three approaches to…

  16. Forward-Oriented Design for Learning: Illustrating the Approach

    Science.gov (United States)

    Dimitriadis, Yannis; Goodyear, Peter

    2013-01-01

    This paper concerns sustainable approaches to design for learning, emphasising the need for designs to be able to thrive outside of the protective niches of project-based innovation. It builds on the "in medias res" framework and more specifically on a forward-oriented approach to design for learning: one that takes a pro-active design…

  17. Approaches to Learning in a Second Year Chemical Engineering Course.

    Science.gov (United States)

    Case, Jennifer M.; Gunstone, Richard F.

    2003-01-01

    Investigates student approaches to learning in a second year chemical engineering course by means of a qualitative research project which utilized interview and journal data from a group of 11 students. Identifies three approaches to learning: (1) conceptual; (2) algorithmic; and (3) information-based. Presents student responses to a series of…

  18. Approach to Learning of Sub-Degree Students in Hong Kong

    Science.gov (United States)

    Chan, Yiu Man; Chan, Christine Mei Sheung

    2010-01-01

    The learning approaches and learning experiences of 404 sub-degree students were assessed by using a Study Process Questionnaire and a Learning Experience Questionnaire. While the learning approaches in this study meant whether students used a deep learning or surface learning approach, the learning experiences referred to students' perceptions…

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

    OpenAIRE

    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 med...

  20. Predicting manual arm strength: A direct comparison between artificial neural network and multiple regression approaches.

    Science.gov (United States)

    La Delfa, Nicholas J; Potvin, Jim R

    2016-02-29

    In ergonomics, strength prediction has typically been accomplished using linked-segment biomechanical models, and independent estimates of strength about each axis of the wrist, elbow and shoulder joints. It has recently been shown that multiple regression approaches, using the simple task-relevant inputs of hand location and force direction, may be a better method for predicting manual arm strength (MAS) capabilities. Artificial neural networks (ANNs) also serve as a powerful data fitting approach, but their application to occupational biomechanics and ergonomics is limited. Therefore, the purpose of this study was to perform a direct comparison between ANN and regression models, by evaluating their ability to predict MAS with identical sets of development and validation MAS data. Multi-directional MAS data were obtained from 95 healthy female participants at 36 hand locations within the reach envelope. ANN and regression models were developed using a random, but identical, sample of 85% of the MAS data (n=456). The remaining 15% of the data (n=80) were used to validate the two approaches. When compared to the development data, the ANN predictions had a much higher explained variance (90.2% vs. 66.5%) and much lower RMSD (9.3N vs. 17.2N), vs. the regression model. The ANN also performed better with the independent validation data (r(2)=78.6%, RMSD=15.1) compared to the regression approach (r(2)=65.3%, RMSD=18.6N). These results suggest that ANNs provide a more accurate and robust alternative to regression approaches, and should be considered more often in biomechanics and ergonomics evaluations. PMID:26876987

  1. 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.......4 (in Spanish), and deliverable 4.5. (in Spanish), which are attached in as Annex 1, 2, 3 and 4. Deliverable 4.1 provides a conceptual framework that has inspired the learning approaches in ELAC. The deliverable presents an overview of the overall approach and methodology used within the project......, followed by a presentation of learning approaches, and the identification of pedagogic concepts and tools applied in e-Learning. The deliverable moreover has a list of produced working papers and articles from partners within the ELAC project with relevance for deliverable. Deliverable 4.2 focus...

  2. DEVELOPING A MATH LEARNING ENVIRONMENT – A LEARNING OBJECT APPROACH

    OpenAIRE

    Stanica Justina Lavinia

    2011-01-01

    Implementing a software architecture, that provides the learning content in a dynamic manner, would allow educational developers to use the same content more than one time, at a very structured level. The concept underlying this architecture is that of Learning Objects, a promising technology, which allows the separation of data, logic and presentation levels, offering the potential for interoperability, combination and reusability. In this context, emerged the idea to define a learning objec...

  3. Measuring students’ approaches to learning in different clinical rotations

    Directory of Open Access Journals (Sweden)

    Emilia Ova

    2012-11-01

    Full Text Available Abstract Background Many studies have explored approaches to learning in medical school, mostly in the classroom setting. In the clinical setting, students face different conditions that may affect their learning. Understanding students’ approaches to learning is important to improve learning in the clinical setting. The aim of this study was to evaluate the Study Process Questionnaire (SPQ as an instrument for measuring clinical learning in medical education and also to show whether learning approaches vary between rotations. Methods All students involved in this survey were undergraduates in their clinical phase. The SPQ was adapted to the clinical setting and was distributed in the last week of the clerkship rotation. A longitudinal study was also conducted to explore changes in learning approaches. Results Two hundred and nine students participated in this study (response rate 82.0%. The SPQ findings supported a two-factor solution involving deep and surface approaches. These two factors accounted for 45.1% and 22.5%, respectively, of the variance. The relationships between the two scales and their subscales showed the internal consistency and factorial validity of the SPQ to be comparable with previous studies. The clinical students in this study had higher scores for deep learning. The small longitudinal study showed small changes of approaches to learning with different rotation placement but not statistically significant. Conclusions The SPQ was found to be a valid instrument for measuring approaches to learning among clinical students. More students used a deep approach than a surface approach. Changes of approach not clearly occurred with different clinical rotations.

  4. A PSO based Artificial Neural Network approach for short term unit commitment problem

    Directory of Open Access Journals (Sweden)

    AFTAB AHMAD

    2010-10-01

    Full Text Available Unit commitment (UC is a non-linear, large scale, complex, mixed-integer combinatorial constrained optimization problem. This paper proposes, a new hybrid approach for generating unit commitment schedules using swarm intelligence learning rule based neural network. The training data has been generated using dynamic programming for machines without valve point effects and using genetic algorithm for machines with valve point effects. A set of load patterns as inputs and the corresponding unit generation schedules as outputs are used to train the network. The neural network fine tunes the best results to the desired targets. The proposed approach has been validated for three thermal machines with valve point effects and without valve point effects. The results are compared with the approaches available in the literature. The PSO-ANN trained model gives better results which show the promise of the proposed methodology.

  5. SUPERVISED MACHINE LEARNING APPROACHES: A SURVEY

    Directory of Open Access Journals (Sweden)

    Iqbal Muhammad

    2015-04-01

    Full Text Available One of the core objectives of machine learning is to instruct computers to use data or past experience to solve a given problem. A good number of successful applications of machine learning exist already, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through Supervised learning, Unsupervised learning and Reinforcement Learning but in this study we will focus on strength and weakness of supervised learning classification algorithms. The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. We are optimistic that this study will help new researchers to guiding new research areas and to compare the effectiveness and impuissance of supervised learning algorithms.

  6. 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

  7. 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…

  8. A medical expert system approach using artificial neural networks for standardized treatment planning

    International Nuclear Information System (INIS)

    Purpose: Many radiotherapy treatment plans involve some level of standardization (e.g., in terms of beam ballistics, collimator settings, and wedge angles), which is determined primarily by tumor site and stage. If patient-to-patient variations in the size and shape of relevant anatomical structures for a given treatment site are adequately sampled, then it would seem possible to develop a general method for automatically mapping individual patient anatomy to a corresponding set of treatment variables. A medical expert system approach to standardized treatment planning was developed that should lead to improved planning efficiency and consistency. Methods and Materials: The expert system was designed to specify treatment variables for new patients based upon a set of templates (a database of treatment plans for previous patients) and a similarity metric for determining the goodness of fit between the relevant anatomy of new patients and patients in the database. A set of artificial neural networks was used to optimize the treatment variables to the individual patient. A simplified example, a four-field box technique for prostate treatments based upon a single external contour, was used to test the viability of the approach. Results: For a group of new prostate patients, treatment variables specified by the expert system were compared to treatment variables chosen by the dosimetrists. Performance criteria included dose uniformity within the target region and dose to surrounding critical organs. For this standardized prostate technique, a database consisting of approximately 75 patient records was required for the expert system performance to approach that of the dosimetrists. Conclusions: An expert system approach to standardized treatment planning has the potential of improving the overall efficiency of the planning process by reducing the number of iterations required to generate an optimized dose distribution, and to function most effectively, should be closely

  9. 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.

  10. Identification of time-varying structural dynamic systems - An artificial intelligence approach

    Science.gov (United States)

    Glass, B. J.; Hanagud, S.

    1992-01-01

    An application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.

  11. 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%. PMID:26201663

  12. 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

  13. An Improved Metric Learning Approach for Degraded Face Recognition

    Directory of Open Access Journals (Sweden)

    Guofeng Zou

    2014-01-01

    Full Text Available To solve the matching problem of the elements in different data collections, an improved coupled metric learning approach is proposed. First, we improved the supervised locality preserving projection algorithm and added the within-class and between-class information of the improved algorithm to coupled metric learning, so a novel coupled metric learning method is proposed. Furthermore, we extended this algorithm to nonlinear space, and the kernel coupled metric learning method based on supervised locality preserving projection is proposed. In kernel coupled metric learning approach, two elements of different collections are mapped to the unified high dimensional feature space by kernel function, and then generalized metric learning is performed in this space. Experiments based on Yale and CAS-PEAL-R1 face databases demonstrate that the proposed kernel coupled approach performs better in low-resolution and fuzzy face recognition and can reduce the computing time; it is an effective metric method.

  14. 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…

  15. 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.

  16. IMPROVE AFFECTIVE LEARNING WITH EEG APPROACH

    OpenAIRE

    Xiaowei Li; Qinglin Zhao; Li Liu; Hong Peng; Yanbing Qi; Chengsheng Mao; Zheng Fang; Quanying Liu; Bin Hu

    2010-01-01

    With the development of computer science, cognitive science and psychology, a new paradigm, affective learning, has emerged into e-learning domain. Although scientists and researchers have achieved fruitful outcomes in exploring the ways of detecting and understanding learners affect, e.g. eyes motion, facial expression etc., it sounds still necessary to deepen the recognition of learners affect in learning procedure with innovative methodologies. Our research focused on using bio-signals bas...

  17. A collaborative learning approach and its evaluation

    OpenAIRE

    Ishitani, Lucila; Guimarães, Silvio J. F.; Bruegger, Gisele

    2006-01-01

    The use of new technologies does not mean that the applied education model is modern. New technologies can be used in a way that follows the traditional education model, with all its deficiencies. The collaborative education model involves students in reflection, participation, and construction of their knowledge, or to collaboratively learn. This article aims to present mechanisms to stimulate collaborative learning, in present education, through the aid of virtual learning environments.

  18. 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. PMID:16170263

  19. Novel reinforcement learning approach for difficult control problems

    Science.gov (United States)

    Becus, Georges A.; Thompson, Edward A.

    1997-09-01

    We review work conducted over the past several years and aimed at developing reinforcement learning architectures for solving difficult control problems and based on and inspired by associative control process (ACP) networks. We briefly review ACP networks able to reproduce many classical instrumental conditioning test results observed in animal research and to engage in real-time, closed-loop, goal-seeking interactions with their environment. Chronologically, our contributions include the ideally interfaced ACP network which is endowed with hierarchical, attention, and failure recognition interface mechanisms which greatly enhanced the capabilities of the original ACP network. When solving the cart-pole problem, it achieves 100 percent reliability and a reduction in training time similar to that of Baird and Klopf's modified ACP network and additionally an order of magnitude reduction in number of failures experienced for successful training. Next we introduced the command and control center/internal drive (Cid) architecture for artificial neural learning systems. It consists of a hierarchy of command and control centers governing motor selection networks. Internal drives, similar hunger, thirst, or reproduction in biological systems, are formed within the controller to facilitate learning. Efficiency, reliability, and adjustability of this architecture were demonstrated on the benchmark cart-pole control problem. A comparison with other artificial learning systems indicates that it learns over 100 times faster than Barto, et al's adaptive search element/adaptive critic element, experiencing less failures by more than an order of magnitude while capable of being fine-tuned by the user, on- line, for improved performance without additional training. Finally we present work in progress on a 'peaks and valleys' scheme which moves away from the one-dimensional learning mechanism currently found in Cid and shows promises in solving even more difficult learning control

  20. 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.

  1. El aprendizaje basado en problemas: De herejía artificial a res popularis Problem based learning: From artificial heresy to res popularis

    Directory of Open Access Journals (Sweden)

    L.A. Branda

    2009-03-01

    Full Text Available La extensa implementación del aprendizaje basado en problemas (ABP en el proceso enseñanza-aprendizaje ha resultado en su transformación de herejía artificial a res popularis con la consecuente proliferación de publicaciones, libros y congresos sobre el tema. A menudo, esta avalancha de información, ha creado una confusión en la comprensión de qué es el ABP como estrategia de aprendizaje. Este artículo presenta al lector una definición de lo que se consideró que era el ABP y su extensión, además de incluir la resolución de problemas. Se indica la importancia de los objetivos de aprendizaje (resultados del aprendizaje y se presentan algunos pasos que se deben seguir en la preparación de situaciones/escenarios/problemas/casos. De forma general, se describen la evaluación de los estudiantes fundamentalmente formativa, basada en las observaciones hechas en las sesiones de tutoría, y la evaluación de carácter sumativo. La descripción de las etapas más comunes en el ABP tiene el propósito de indicar lo que los estudiantes pueden hacer y no que deben hacer. Si se consideran las limitaciones de recursos que tienen la mayoría de las instituciones que desean implementar el ABP, se describe la aplicación de esta estrategia en grupos grandes. Se discute el rol del tutor facilitador y se indican las características de sus intervenciones en un continuo que va desde jerárquica a facilitadora de la autonomía del estudiante en su aprendizaje. Este artículo finaliza con una reflexión sobre el aprendizaje autodirigido y su relación con el aprendizaje autorregulado.The vast use of problem based learning (PBL in the teaching-learning process has resulted in its transformation from an artificial heresy to a res popularis with the subsequent proliferation of publications, books and congresses on the subject. This deluge of information, very often, has created confusion on the comprehension of what PBL is as learning strategy. This

  2. Adaptive Approaches to Context Aware Mobile Learning Applications

    Directory of Open Access Journals (Sweden)

    P. Govindarajulu

    2008-04-01

    Full Text Available Learning has gone through major changes from its inception in the human race. Among all such major changes mobile learning is the latest to happen with the advent of mobile learning technologies that have the potential to revolutionize distance education by bringing the concept of anytime and anywhere to reality. From the learner’s perceptive, mobile learning is “any sort of leaning that happens when the learner is not at a fixed, pre-determined location or learning that happens when the learner takes advantage of learning opportunities offered by mobile technologies”. Research in context aware mobile learning has concentrated on how to adapt applications to context. This paper reviews and discusses few mobile learning systems the approach in implementing context awareness and adaptation

  3. Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm.

    Science.gov (United States)

    Ji, Junzhong; Liu, Jinduo; Liang, Peipeng; Zhang, Aidong

    2016-01-01

    Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC. In the proposed algorithm, a brain effective connectivity network is mapped onto an antibody, and four immune operators are employed to perform the optimization process of antibodies, including clonal selection operator, crossover operator, mutation operator and suppression operator, and finally gets an antibody with the highest K2 score as the solution. AIAEC is then tested on Smith's simulated datasets, and the effect of the different factors on AIAEC is evaluated, including the node number, session length, as well as the other potential confounding factors of the blood oxygen level dependent (BOLD) signal. It was revealed that, as contrast to other existing methods, AIAEC got the best performance on the majority of the datasets. It was also found that AIAEC could attain a relative better solution under the influence of many factors, although AIAEC was differently affected by the aforementioned factors. AIAEC is thus demonstrated to be an effective method for detecting the brain effective connectivity. PMID:27045295

  4. 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.

  5. 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.…

  6. 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…

  7. A first approach to “Learning Dashboards” in formal learning contexts

    NARCIS (Netherlands)

    Verpoorten, Dominique; Westera, Wim; Specht, Marcus

    2012-01-01

    Verpoorten, D., Westera, W., & Specht, M. (2011, 20 September). A first approach to “Learning Dashboards” in formal learning contexts. Paper presented at the ADVTEL workshop (1st International Workshop on Enhancing Learning with Ambient Displays and Visualization Techniques) at EC-TEL 2011, Palermo,

  8. 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…

  9. Investigations of Students' Motivation Towards Learning Secondary School Physics through Mastery Learning Approach

    Science.gov (United States)

    Changeiywo, Johnson M.; Wambugu, P. W.; Wachanga, S. W.

    2011-01-01

    Teaching method is a major factor that affects students' motivation to learn physics. This study investigated the effects of using mastery learning approach (MLA) on secondary school students' motivation to learn physics. Solomon four non-equivalent control group design under the quasi-experimental research method was used in which a random sample…

  10. 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…

  11. 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…

  12. Machine Teaching -- A Machine Learning Approach to Technology Enhanced Learning

    OpenAIRE

    Weimer, Markus

    2010-01-01

    Many applications of Technology Enhanced Learning are based on strong assumptions: Knowledge needs to be standardized, structured and most of all externalized into learning material that preferably is annotated with meta-data for efficient re-use. A vast body of valuable knowledge does not meet these assumptions, including informal knowledge such as experience and intuition that is key to many complex activities. We notice that knowledge, even if not standardized, structured and externalized,...

  13. Outdoor Education: An Alternative Approach in Teaching and Learning Science

    Directory of Open Access Journals (Sweden)

    Tuan Mastura Tuan Soh

    2013-11-01

    Full Text Available To understand fully and aware of children’s science learning, one should look not only at learning that takes place in the kindergarten and primary school but also in learning that takes place outside the classroom. This paper aims to discuss outdoor education: an alternative approach in teaching and learning science in the Malaysian context. In this 21st century, the exposure and experience in the field of science and technology are needed in nurturing interest among students who are involved in this field. The advantages of experience-based learning which includes hands-on activities and on-site learning should be empowered. Thus, informal and non-formal science learning plays an important role in assisting all levels of society, regardless of age in exploring science and technology. Informal and non-formal learning of science is a complement to formal learning and occur in a variety of different places through various channels, such as the entertainment media, television and film; science centres and museums; zoos and aquariums, botanical gardens, and etc. This paper discusses the concept of science learning outside the classroom; the non-formal and informal science learning which covers the institutions/organizations involved in the non-formal science learning in Malaysia; and the potential of non-formal science centre setting in complement with the formal science education setting. It is hoped that this paper will provide an insight towards science learning out of school in Malaysian context.

  14. 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.

  15. 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.

  16. The systems approach for applying artificial intelligence to space station automation (Invited Paper)

    Science.gov (United States)

    Grose, Vernon L.

    1985-12-01

    The progress of technology is marked by fragmentation -- dividing research and development into ever narrower fields of specialization. Ultimately, specialists know everything about nothing. And hope for integrating those slender slivers of specialty into a whole fades. Without an integrated, all-encompassing perspective, technology becomes applied in a lopsided and often inefficient manner. A decisionary model, developed and applied for NASA's Chief Engineer toward establishment of commercial space operations, can be adapted to the identification, evaluation, and selection of optimum application of artificial intelligence for space station automation -- restoring wholeness to a situation that is otherwise chaotic due to increasing subdivision of effort. Issues such as functional assignments for space station task, domain, and symptom modules can be resolved in a manner understood by all parties rather than just the person with assigned responsibility -- and ranked by overall significance to mission accomplishment. Ranking is based on the three basic parameters of cost, performance, and schedule. This approach has successfully integrated many diverse specialties in situations like worldwide terrorism control, coal mining safety, medical malpractice risk, grain elevator explosion prevention, offshore drilling hazards, and criminal justice resource allocation -- all of which would have otherwise been subject to "squeaky wheel" emphasis and support of decision-makers.

  17. Prediction of hydrate formation temperature by both statistical models and artificial neural network approaches

    International Nuclear Information System (INIS)

    In this study, various estimation methods have been reviewed for hydrate formation temperature (HFT) and two procedures have been presented. In the first method, two general correlations have been proposed for HFT. One of the correlations has 11 parameters, and the second one has 18 parameters. In order to obtain constants in proposed equations, 203 experimental data points have been collected from literatures. The Engineering Equation Solver (EES) and Statistical Package for the Social Sciences (SPSS) soft wares have been employed for statistical analysis of the data. Accuracy of the obtained correlations also has been declared by comparison with experimental data and some recent common used correlations. In the second method, HFT is estimated by artificial neural network (ANN) approach. In this case, various architectures have been checked using 70% of experimental data for training of ANN. Among the various architectures multi layer perceptron (MLP) network with trainlm training algorithm was found as the best architecture. Comparing the obtained ANN model results with 30% of unseen data confirms ANN excellent estimation performance. It was found that ANN is more accurate than traditional methods and even our two proposed correlations for HFT estimation.

  18. 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.

  19. Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine - an artificial neural network approach.

    Science.gov (United States)

    Zaqoot, Hossam Adel; Ansari, Abdul Khalique; Unar, Mukhtiar Ali; Khan, Shaukat Hyat

    2009-01-01

    Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels. PMID:19955628

  20. 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...

  1. 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.

  2. Towards an Adaptive Learning System Based on a New Learning Object Granularity Approach

    OpenAIRE

    Amal Battou; Ali El Mezouary; Driss Mammass

    2011-01-01

    To achieve the adaptability required in ALS, adaptive learning system (ALS) takes advantage of granular and reusable content. The main goal of this paper is to examine the learning object granularity issue which is directly related with Learning Object (LO) reusability and the adaptability process required in ALS. For that purpose, we present the learning objects approach and the related technologies. Then, we discuss the fine-grained as a fundamental characteristic to reach the adaptabil...

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

    OpenAIRE

    Hall, Matthew; Ramsay, Alan; Raven, John

    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 ...

  4. Learning from project experiences using a legacy-based approach

    Science.gov (United States)

    Cooper, Lynne P.; Majchrzak, Ann; Faraj, Samer

    2005-01-01

    As project teams become used more widely, the question of how to capitalize on the knowledge learned in project teams remains an open issue. Using previous research on shared cognition in groups, an approach to promoting post-project learning was developed. This Legacy Review concept was tested on four in tact project teams. The results from those test sessions were used to develop a model of team learning via group cognitive processes. The model and supporting propositions are presented.

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

    OpenAIRE

    J. C. Chimal-Eguía; K. Ramírez-Amáro

    2012-01-01

    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 data representation is based on information obtained by image axis division into boxes. The difference between this new input data representation and the classical is that this technique is not time-dependent. This new information is implemented in the new Image-Based Learning A...

  6. Web-Based Teaching and Learning Approach (WBTLA) Usability in Institutions of Higher Learning in Malaysia

    Science.gov (United States)

    Nordin, Abu Bakar; Alias, Norlidah

    2013-01-01

    Today teachers in schools and lecturers in institutions of higher learning are endowed with a wide range of new teaching experiences through web-based teaching and learning approaches (WBTLA), which was not possible before through the traditional classroom approach. With the use of WBTLA emerged problems related to usability in technical,…

  7. Learning from Tutorials: A Qualitative Study of Approaches to Learning and Perceptions of Tutorial Interaction

    Science.gov (United States)

    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…

  8. Holistic approaches to e-learning accessibility

    Directory of Open Access Journals (Sweden)

    Lawrie Phipps

    2006-12-01

    Full Text Available The importance of accessibility to digital e-learning resources is widely acknowledged. The World Wide Web Consortium Web Accessibility Initiative has played a leading role in promoting the importance of accessibility and developing guidelines that can help when developing accessible web resources. The accessibility of e-learning resources provides additional challenges. While it is important to consider the technical and resource related aspects of e-learning when designing and developing resources for students with disabilities, there is a need to consider pedagogic and contextual issues as well. A holistic framework is therefore proposed and described, which in addition to accessibility issues takes into account learner needs, learning outcomes, local factors, infrastructure, usability and quality assurance. The practical application and implementation of this framework is discussed and illustrated through the use of examples and case studies.

  9. Student Approaches to Learning Chinese Vocabulary

    OpenAIRE

    Fu, I-Ping P.

    2005-01-01

    This research focuses on the strategies that native English speakers use as they learn to speak and write Chinese vocabulary words in the first year of an elementary Chinese class. The main research question was: what strategies do native English-speaking beginning learners of Chinese use to learn Chinese vocabulary words in their speaking and writing? The study was conducted at a medium-sized comprehensive university in the Southeastern U.S. The study drew from concepts and theories in s...

  10. Epistemological Belief and Learning Approaches of Students in Higher Institutions of Learning in Malaysia

    OpenAIRE

    Habsah Ismail; Aminuddin Hassan; Mohd. Mokhtar Muhamad; Wan Zah Wan Ali; Mohd Majid Konting

    2013-01-01

    This is an investigation of the students’ beliefs about the nature of knowledge or epistemological beliefs, and the relation of these beliefs on their learning approaches. Students chosen as samples of the study were from both public and private higher institutions of learning in Malaysia. The instrument used in the study consists of 49 items measuring students’ epistemological beliefs and 20 items on their learning approaches. Items on epistemological belief were adapted and modified from...

  11. A New Design Approach to game or play based learning

    DEFF Research Database (Denmark)

    Larsen, Lasse Juel

    Abstract: The present paper proposes a new design perspective for game based learning. The general idea is to abandon the long and sought after dream of designing a closed learning system, where students from elementary school to high school without teachers’ interference could learn whatever...... on collision course with of-the-shelf-commercial entertainment games. This paper wants to promote two different yet interconnected ideas. The first aims at describing a different design perspective for game based learning which in many ways will provoke not only the above mentioned latent dream of a closed...... to ground the students sense of meaning. This paper proposes another approach: using visualization in immersive 3D-worlds as documentation of learning progress while at the same time constituting a reward system which motivate further learning. The overall design idea is to build a game based learning...

  12. Social Learning in Bumblebees (Bombus impatiens: Worker Bumblebees Learn to Manipulate and Forage at Artificial Flowers by Observation and Communication within the Colony

    Directory of Open Access Journals (Sweden)

    Hamida B. Mirwan

    2013-01-01

    Full Text Available Social learning occurs when one individual learns from another, mainly conspecific, often by observation, imitation, or communication. Using artificial flowers, we studied social learning by allowing test bumblebees to (a see dead bumblebees arranged in foraging positions or (b watch live bumblebees actually foraging or (c communicate with nestmates within their colony without having seen foraging. Artificial flowers made from 1.5 mL microcentrifuge tubes with closed caps were inserted through the centres of blue 7 cm plastic discs as optical signals through which the bees could not forage. The reinforcer reward syrup was accessible only through holes in the sides of the tubes beneath the blue discs. Two colonies (A and B were used in tandem along with control (C and D colonies. No bee that was not exposed (i.e., from the control colonies (C and D to social learning discovered the access holes. Inside colony B, we imprisoned a group of bees that were prevented from seeing or watching. Bees that saw dead bumblebees in foraging positions, those that watched nest-mates foraging, and those that had only in-hive communication with successful foragers all foraged successfully. The means of in-hive communication are not understood and warrant intense investigation.

  13. 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

    2010-03-01

    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 emphasis is placed on transmitting information and the focus is shifted toward developing higher order thinking (analysis, synthesis, and evaluation). However, MALA should always involve clearly identified objectives and well-defined targets. Understanding fatty acid metabolism was one of the proposed goals of the Medical Biochemistry unit. To this end, students were challenged with a variety of learning strategies to develop skills associated with group conflict resolution, critical thinking, information access, and retrieval, as well as oral and written communication skills. Overall, students and learning facilitators were highly motivated by the diversity of learning activities, particularly due to the emphasis on correlating theoretical knowledge with human health and disease. As a quality control exercise, the students were asked to answer a questionnaire on their evaluation of the whole teaching/learning experience. Our initial analysis of the learning outcomes permits us to conclude that the approach undertaken yields results that surpass the traditional teaching methods. PMID:21567798

  14. Mini Anchors: A Universal Design for Learning Approach

    Science.gov (United States)

    Zydney, Janet Mannheimer; Hasselbring, Ted S.

    2014-01-01

    Teachers are challenged to create flexible learning environments that prepare students with diverse learning needs for adaptable thinking in a fast-paced and changing society. To address this need, we used a design-based research approach to develop a technology-based solution to individualize mathematical problem solving instruction to students…

  15. 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…

  16. Learning Objects Update: Review and Critical Approach to Content Aggregation

    Science.gov (United States)

    Balatsoukas, Panos; Morris, Anne; O'Brien, Ann

    2008-01-01

    The structure and composite nature of a learning object is still open to interpretation. Although several theoretical studies advocate integrated approaches to the structure and aggregation level of learning objects, in practice, many content specifications, such as SCORM, IMS Content Packaging, and course authoring tools, do not explicitly state…

  17. 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…

  18. Approaches to Learning: Supporting Brain Development for School Success

    Science.gov (United States)

    Petersen, Sandra

    2012-01-01

    Prenatally and in infants and toddlers, the brain is being constructed as a foundation for all later learning. Positive early experiences contribute to the formation of a brain that is capable, early in infancy, of utilizing and strengthening the basic processes of learning. Throughout a lifetime, a person will repeatedly use these approaches to…

  19. Exploring the Behavioural Patterns of Entrepreneurial Learning: A Competency Approach

    Science.gov (United States)

    Man, Thomas Wing Yan

    2006-01-01

    Purpose: The purpose of this study is to empirically explore the behavioural patterns involved in entrepreneurial learning through a conceptualization of entrepreneurial learning as a "competency". Design/methodology/approach: Semi-structured interviews to 12 entrepreneurs were conducted with a focus on the critical incidents in which significant…

  20. 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…

  1. Teaching Math in the Primary Grades: The Learning Trajectories Approach

    Science.gov (United States)

    Sarama, Julie; Clements, Douglas

    2009-01-01

    Children's thinking follows natural developmental paths in learning math. When teachers understand those paths and offer activities based on children's progress along them, they build developmentally appropriate math environments. The authors explain math learning trajectories and why teaching math using the trajectories approach is effective. A…

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

    Directory of Open Access Journals (Sweden)

    D. Anitha

    2013-02-01

    Full Text Available 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 adheres to the IEEE LOM standard and maps the IEEE LO Metadata to the identified learning styles based on rule based classification of learning objects. A pilot study on the research work is performed and evaluation of the system gives an encouraging result.

  3. Learning emergence: adaptive cellular automata façade trained by artificial neural networks

    OpenAIRE

    Skavara, M. M. E.

    2009-01-01

    This thesis looks into the possibilities of controlling the emergent behaviour of Cellular Automata (CA) to achieve specific architectural goals. More explicitly, the objective is to develop a performing, adaptive building facade, which is fed with the history of its achievements and errors, to provide optimum light conditions in buildings’ interiors. To achieve that, an artificial Neural Network (NN) is implemented. However, can an artificial NN cope with the complexity of suc...

  4. On the Future Possibilities of Artificial Intelligence Based M-Learning Content Development

    OpenAIRE

    KÖSE, Utku; TÜFEKÇİ, Aslıhan

    2015-01-01

    Abstract—Artificial Intelligence is widely used in almost every field of the modern life; in order to provide effective solutions for real-world problems. It can be definitely said that this research field has a remarkable power on shaping the future of the humankind. When we take today's technologies into consideration, it is also seen that usage of Artificial Intelligence and mobile applications together is a key element for many future applications. At this point, main objective of this st...

  5. Learning from the application of the systematic approach to training

    International Nuclear Information System (INIS)

    The paper describes the objectives, lessons learned, key accomplishments and related activities of the application of the systematic approach to training initiated by DOE in Russia and Ukraine in 1992 focused on single facility in each country

  6. Learning and Experience - a Psycho-societal Approach

    DEFF Research Database (Denmark)

    Olesen, Henning Salling

    2016-01-01

    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...

  7. Exploring changes in ocupational therapy students' approaches to learning

    OpenAIRE

    Chapman, J.; Watson, J.; Adams, J.

    2006-01-01

    This article describes a longitudinal cohort study that examined the preferred approaches to learning of pre-registration occupational therapy students (N=55) as they progressed through the three years of an undergraduate BSc (Hons) programme. Students’ orientations to learning were measured using the Short Inventory of Approaches to Studying (ASI) (Entwistle 1981) and results were compared descriptively across repeat measures undertaken during each year of study. Inferential statistics are ...

  8. Service-Learning Pedagogy: Benefits of a Learning Community Approach

    Science.gov (United States)

    Flinders, Brooke A.

    2013-01-01

    Service-learning is, by nature, continually evolving. Seifer (1996) stressed the importance of partnerships between communities and schools, and stated that reflection should facilitate the connection between practice and theory, and lead to critical thinking. Before these reflective activities occur, however, much can be done to maximize…

  9. 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.

  10. A Machine Learning Approach to Test Data Generation

    DEFF Research Database (Denmark)

    Christiansen, Henning; Dahmcke, Christina Mackeprang

    2007-01-01

    } 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...... system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here...

  11. Interaction Profiles for an Artificial Conversational Companion

    OpenAIRE

    Höhn, Sviatlana; Busemann, Stephan; Max, Charles; Schommer, Christoph; Ziegler, Gudrun

    2015-01-01

    Using Artificial Companions for tasks requiring long-term interaction like language learning or coaching can be approached by creating local computational models for particular interaction structures, and models reflecting changes in interaction over time. An Artificial Conversational Companion (ACC) that helps to practice conversation in a foreign language is expected to play the role of a language expert in conversation. We apply methods of Conversation Analysis to obtain data- driven model...

  12. A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Raju Dara

    2014-10-01

    Full Text Available Many diversified applications do exist in science & technology, which make use of the primary theory of a recognition phenomenon as one of its solutions. Recognition scenario is incorporated with a set of decisions and the action according to the decision purely relies on the quality of extracted information on utmost applications. Thus, the quality decision making absolutely reckons on processing momentum and precision which are entirely coupled with recognition methodology. In this article, a latest rule is formulated based on the degree of correlation to characterize the generalized recognition constraint and the application is explored with respect to image based information extraction. Machine learning based perception called feed forward architecture of Artificial Neural Network has been applied to attain the expected eminence of elucidation. The proposed method furnishes extraordinary advantages such as less memory requirements, extremely high level security for storing data, exceptional speed and gentle implementation approach.

  13. 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.

  14. Neural Machine Learning Approaches: Q-Learning and Complexity Estimation Based Information Processing System

    OpenAIRE

    Chebira, Abdennasser; MELLOUK, Abdelhamid; Madani, Kurosh; Hoceini, Said

    2009-01-01

    Due the complexity of the actual systems based on heterogeneous methods, artificial neural networks approaches can reduce this complexity by modeling the environment as stochastic. Algorithms based on Neural Networks can take into account the dynamics of these environments with no model of dynamics to be given. Main idea of the approaches developed in this chapter is to take advantage from distributed processing and task simplification by dividing an initially complex processing task into a s...

  15. Inverse Problem Solution in Acoustic Emission Source Analysis : Classical and Artificial Neural Network Approach

    Czech Academy of Sciences Publication Activity Database

    Převorovský, Zdeněk; Chlada, Milan; Vodička, Josef

    New York : Springer, 2006 - (Delsanto, P.), s. 515-529 ISBN 0-387-33860-8 Institutional research plan: CEZ:AV0Z20760514 Keywords : acoustic emission * artificial neural network s * inverse problems Subject RIV: BI - Acoustics

  16. Using an artificial neural network approach to estimate surface-layer optical turbulence at Mauna Loa, Hawaii.

    Science.gov (United States)

    Wang, Yao; Basu, Sukanta

    2016-05-15

    In this Letter, an artificial neural network (ANN) approach is proposed for the estimation of optical turbulence (Cn2) in the atmospheric surface layer. Five routinely available meteorological variables are used as the inputs. Observed Cn2 data near the Mauna Loa Observatory, Hawaii are utilized for validation. The proposed approach has demonstrated its prowess by capturing the temporal evolution of Cn2 remarkably well. More interestingly, this ANN approach is found to outperform a widely used similarity theory-based conventional formulation for all the prevalent atmospheric conditions (including strongly stratified conditions). PMID:27176996

  17. An Autoencoder Approach to Learning Bilingual Word Representations

    OpenAIRE

    P, Sarath Chandar A; Lauly, Stanislas; Larochelle, Hugo; Khapra, Mitesh M.; Ravindran, Balaraman; Raykar, Vikas; Saha, Amrita

    2014-01-01

    Cross-language learning allows us to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning of vectorial word representations that are aligned between two languages, while not relying on word-level alignments. We show that by simply learning to reconstruct the bag-of-w...

  18. A multi-objective approach to evolving artificial neural networks for coronary heart disease classification

    OpenAIRE

    Shenfield, Alex; Rostami, Shahin

    2015-01-01

    The optimisation of the accuracy of classifiers in pattern recognition is a complex problem that is often poorly understood. Whilst numerous techniques exist for the optimisation of weights in artificial neural networks (e.g. the Widrow-Hoff least mean squares algorithm and back propagation techniques), there do not exist any hard and fast rules for choosing the structure of an artificial neural network - in particular for choosing both the number of the hidden layers used in the network and ...

  19. Automatic speech recognition a deep learning approach

    CERN Document Server

    Yu, Dong

    2015-01-01

    This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition.

  20. Interrelations among University Students' Approaches to Learning, Regulation of Learning, and Cognitive and Attributional Strategies: A Person Oriented Approach

    Science.gov (United States)

    Heikkila, Annamari; Niemivirta, Markku; Nieminen, Juha; Lonka, Kirsti

    2011-01-01

    This study investigated the relationships among approaches to learning, regulation of learning, cognitive and attributional strategies, stress, exhaustion, and study success. University students (N = 437) from three faculties filled in a questionnaire concerning their self-reported study behaviour, cognitive strategies, and well-being. Their…

  1. 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. PMID:25622335

  2. 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. PMID:26800289

  3. 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 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 separation of the target data from the background data in the new, lower-dimensional manifold coordinates. Then, target detection is performed in the

  4. 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. PMID:23764236

  5. 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…

  6. 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…

  7. 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…

  8. 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.

  9. 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.

  10. 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.

  11. New Trends in Computing Anticipatory Systems : Emergence of Artificial Conscious Intelligence with Machine Learning Natural Language

    Science.gov (United States)

    Dubois, Daniel M.

    2008-10-01

    This paper deals with the challenge to create an Artificial Intelligence System with an Artificial Consciousness. For that, an introduction to computing anticipatory systems is presented, with the definitions of strong and weak anticipation. The quasi-anticipatory systems of Robert Rosen are linked to open-loop controllers. Then, some properties of the natural brain are presented in relation to the triune brain theory of Paul D. MacLean, and the mind time of Benjamin Libet, with his veto of the free will. The theory of the hyperincursive discrete anticipatory systems is recalled in view to introduce the concept of hyperincursive free will, which gives a similar veto mechanism: free will as unpredictable hyperincursive anticipation The concepts of endo-anticipation and exo-anticipation are then defined. Finally, some ideas about artificial conscious intelligence with natural language are presented, in relation to the Turing Machine, Formal Language, Intelligent Agents and Mutli-Agent System.

  12. Learner Performance in Multimedia Learning Arrangements: An Analysis across Instructional Approaches

    Science.gov (United States)

    Eysink, Tessa H. S.; de Jong, Ton; Berthold, Kirsten; Kolloffel, Bas; Opfermann, Maria; Wouters, Pieter

    2009-01-01

    In this study, the authors compared four multimedia learning arrangements differing in instructional approach on effectiveness and efficiency for learning: (a) hypermedia learning, (b) observational learning, (c) self-explanation-based learning, and (d) inquiry learning. The approaches all advocate learners' active attitude toward the learning…

  13. 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

  14. Artificial Instruction. A Method for Relating Learning Theory to Instructional Design.

    Science.gov (United States)

    Ohlsson, Stellan

    Prior research on learning has been linked to instruction by the derivation of general principles of instructional design from learning theories. However, such design principles are often difficult to apply to particular instructional issues. A new method for relating research on learning to instructional design is proposed: Different ways of…

  15. 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%).

  16. Mathematical beauty in service of deep approach to learning

    DEFF Research Database (Denmark)

    Karamehmedovic, Mirza

    2015-01-01

    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...

  17. 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.

  18. The Semiotic Approach and Language Teaching and Learning

    Directory of Open Access Journals (Sweden)

    Müfit Şenel

    2007-04-01

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

  19. Galaxy morphology - an unsupervised machine learning approach

    CERN Document Server

    Schutter, Andrew

    2015-01-01

    Structural properties posses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of similarities between galaxy morphological types, and automatically deduce a morphological sequence of galaxies. Application of the method to the EFIGI catalog show that the morphological scheme produced by the algorithm is largely in agreement with the De Vaucouleurs system, demonstrating the ability of computer vision and machine learning methods to automatically profile galaxy morphological sequences. The unsupervised analysis method is based on comprehensive computer vision techniques that compute the visual similarities between the different morphological types. Rather than relying on human cognition, the proposed system deduces the similarities between sets of galaxy images in an automatic manner, and is therefore not limited by the number of galaxies being ...

  20. Galaxy morphology - an unsupervised machine learning approach

    OpenAIRE

    Schutter, Andrew; Shamir, Lior

    2015-01-01

    Structural properties posses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of similarities between galaxy morphological types, and automatically deduce a morphological sequence of galaxies. Application of the method to the EFIGI catalog show that the morphological scheme produced by the algorithm is largely in agreement wi...

  1. 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…

  2. Modeling Human Infant Learning in Embodied Artificial Entities to Produce Grounded Concepts

    OpenAIRE

    Berkowitz, Eric

    2003-01-01

    I present a system for concept development in an artificial entity. The concept development is designed around the foundations of human cognition while at the same time remaining grounded in the agent or robot’s own perception of its world.

  3. Three Years of Using Robots in an Artificial Intelligence Course: Lessons Learned

    Science.gov (United States)

    Kumar, Amruth N.

    2004-01-01

    We have been using robots in our artificial intelligence course since fall 2000. We have been using the robots for open-laboratory projects. The projects are designed to emphasize high-level knowledge-based AI algorithms. After three offerings of the course, we paused to analyze the collected data and to see if we could answer the following…

  4. 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.…

  5. Case-based approaches for knowledge application and organisational learning

    DEFF Research Database (Denmark)

    Wang, Chengbo; Johansen, John; Luxhøj, James T.;

    2005-01-01

    practices and activity patterns are based on learning and applying the knowledge internal and external to an organisation. To ensure their smooth formulation process, there are two important techniques designed – an expert adaptation approach and an expert evaluation approach. These two approaches provide...... structured processes to execute the organisational learning and knowledge application, which intend to guide the practitioners during the process of manufacturing competence development and improvement. They are based on Case-Based Reasoning (CBR) methodology and rely on cases as the primary knowledge supply....... This paper aims to present the two approaches; introduce two types of tests on these approaches to verify their functionality: role-play testing and real world application testing; and summarises the applicability of the two approaches....

  6. Epistemological Belief and Learning Approaches of Students in Higher Institutions of Learning in Malaysia

    Science.gov (United States)

    Ismail, Habsah; Hassan, Aminuddin; Muhamad, Mohd. Mokhtar; Ali, Wan Zah Wan; Konting, Mohd. Majid

    2013-01-01

    This is an investigation of the students' beliefs about the nature of knowledge or epistemological beliefs, and the relation of these beliefs on their learning approaches. Students chosen as samples of the study were from both public and private higher institutions of learning in Malaysia. The instrument used in the study consists of 49 items…

  7. Evoked Prior Learning Experience and Approach to Learning as Predictors of Academic Achievement

    Science.gov (United States)

    Trigwell, Keith; Ashwin, Paul; Millan, Elena S.

    2013-01-01

    Background: In separate studies and research from different perspectives, five factors are found to be among those related to higher quality outcomes of student learning (academic achievement). Those factors are higher self-efficacy, deeper approaches to learning, higher quality teaching, students' perceptions that their workload is…

  8. Associations between the Classroom Learning Environment and Student Engagement in Learning 1: A Rasch Model Approach

    Science.gov (United States)

    Cavanagh, Rob

    2012-01-01

    This report is about one of two phases in an investigation into associations between student engagement in classroom learning and the classroom learning environment. Both phases applied the same instrumentation to the same sample. The difference between the phases was in the measurement approach applied. This report is about application of the…

  9. 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....

  10. Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM

    Directory of Open Access Journals (Sweden)

    Arash Ebrahimabadi

    2015-10-01

    Full Text Available Application of mechanical excavators is one of the most commonly used excavation methods because it can bring the project more productivity, accuracy and safety. Among the mechanical excavators, roadheaders are mechanical miners which have been extensively used in tunneling, mining and civil industries. Performance prediction is an important issue for successful roadheader application and generally deals with machine selection, production rate and bit consumption. The main aim of this research is to investigate the cutting performance (instantaneous cutting rates (ICRs of medium-duty roadheaders by using artificial neural network (ANN approach. There are different categories for ANNs, but based on training algorithm there are two main kinds: supervised and unsupervised. The multi-layer perceptron (MLP and Kohonen self-organizing feature map (KSOFM are the most widely used neural networks for supervised and unsupervised ones, respectively. For gaining this goal, a database was primarily provided from roadheaders' performance and geomechanical characteristics of rock formations in tunnels and drift galleries in Tabas coal mine, the largest and the only fully-mechanized coal mine in Iran. Then the database was analyzed in order to yield the most important factor for ICR by using relatively important factor in which Garson equation was utilized. The MLP network was trained by 3 input parameters including rock mass properties, rock quality designation (RQD, intact rock properties such as uniaxial compressive strength (UCS and Brazilian tensile strength (BTS, and one output parameter (ICR. In order to have more validation on MLP outputs, KSOFM visualization was applied. The mean square error (MSE and regression coefficient (R of MLP were found to be 5.49 and 0.97, respectively. Moreover, KSOFM network has a map size of 8 × 5 and final quantization and topographic errors were 0.383 and 0.032, respectively. The results show that MLP neural networks

  11. Prediction of roadheaders’ performance using artificial neural network approaches (MLP and KOSFM)

    Institute of Scientific and Technical Information of China (English)

    Arash Ebrahimabadi; Mohammad Azimipour; Ali Bahreini

    2015-01-01

    Application of mechanical excavators is one of the most commonly used excavation methods because it can bring the project more productivity, accuracy and safety. Among the mechanical excavators, road-headers are mechanical miners which have been extensively used in tunneling, mining and civil in-dustries. Performance prediction is an important issue for successful roadheader application and generally deals with machine selection, production rate and bit consumption. The main aim of this research is to investigate the cutting performance (instantaneous cutting rates (ICRs)) of medium-duty roadheaders by using artificial neural network (ANN) approach. There are different categories for ANNs, but based on training algorithm there are two main kinds: supervised and unsupervised. The multi-layer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM) are the most widely used neural networks for supervised and unsupervised ones, respectively. For gaining this goal, a database was primarily provided from roadheaders’ performance and geomechanical characteristics of rock formations in tunnels and drift galleries in Tabas coal mine, the largest and the only fully-mechanized coal mine in Iran. Then the database was analyzed in order to yield the most important factor for ICR by using relatively important factor in which Garson equation was utilized. The MLP network was trained by 3 input parameters including rock mass properties, rock quality designation (RQD), intact rock properties such as uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS), and one output parameter (ICR). In order to have more validation on MLP outputs, KSOFM visu-alization was applied. The mean square error (MSE) and regression coefficient (R) of MLP were found to be 5.49 and 0.97, respectively. Moreover, KSOFM network has a map size of 8 ? 5 and final quantization and topographic errors were 0.383 and 0.032, respectively. The results show that MLP neural networks have a

  12. Multi-dimensional technology-enabled social learning approach

    DEFF Research Database (Denmark)

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

    2013-01-01

    content on the Web, using social networks to keep in touch, express, distribute and publish their experiences, views and ideas. Although, since their birth, most of the social media tools were not intended for educational purposes, educational organizations have started to recognize their added value 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...

  13. 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.

  14. Galaxy morphology - An unsupervised machine learning approach

    Science.gov (United States)

    Schutter, A.; Shamir, L.

    2015-09-01

    Structural properties poses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a network of similarities between galaxy morphological types, and automatically deduce a morphological sequence of galaxies. Application of the method to the EFIGI catalog show that the morphological scheme produced by the algorithm is largely in agreement with the De Vaucouleurs system, demonstrating the ability of computer vision and machine learning methods to automatically profile galaxy morphological sequences. The unsupervised analysis method is based on comprehensive computer vision techniques that compute the visual similarities between the different morphological types. Rather than relying on human cognition, the proposed system deduces the similarities between sets of galaxy images in an automatic manner, and is therefore not limited by the number of galaxies being analyzed. The source code of the method is publicly available, and the protocol of the experiment is included in the paper so that the experiment can be replicated, and the method can be used to analyze user-defined datasets of galaxy images.

  15. Learning Theory Approach to Minimum Error Entropy Criterion

    OpenAIRE

    Hu, Ting; Fan, Jun; Wu, Qiang; Zhou, Ding-Xuan

    2012-01-01

    We consider the minimum error entropy (MEE) criterion and an empirical risk minimization learning algorithm in a regression setting. A learning theory approach is presented for this MEE algorithm and explicit error bounds are provided in terms of the approximation ability and capacity of the involved hypothesis space when the MEE scaling parameter is large. Novel asymptotic analysis is conducted for the generalization error associated with Renyi's entropy and a Parzen window function, to over...

  16. An Easy and Effective Approach to English Vocabulary Learning

    Institute of Scientific and Technical Information of China (English)

    陈以欣; 陈建章

    2013-01-01

    Vocabulary poses a challenge to most English learners. There are some misconcepts and mismanagements in this area. In view of the problems arising, this article makes particular points addressing the issues concerned. Firstly, it discusses the position of vocabulary in English language and some important notions concerning vocabulary learning. Furthermore, it examines the condition of learnt vocabulary in the mental space of learners. Finally, it proposes some useful strategies and approaches applicable to vocabulary learning process.

  17. SOCIAL ENTREPRENEURSHIP: A GROUNDED LEARNING APPROACH TO SOCIAL VALUE CREATION

    OpenAIRE

    BRETT R. SMITH; TERRI FELDMAN BARR; SAULO D. BARBOSA; JILL R. KICKUL

    2008-01-01

    The value of the inclusion of social entrepreneurship in entrepreneurship education courses and programs is considered in light of the increase in social entrepreneurial ventures worldwide as well as changing business school requirements. Using a grounded learning theory approach as a foundation, we consider factors unique to social entrepreneurship and present a live case social venture which provides hands-on experience to students. Student comments regarding their learning through this exp...

  18. Stochastic learning in co-ordination games : a simulation approach

    OpenAIRE

    Zaninotto, Enrico; Rossi, Alessandro; Gaio, Loris

    1999-01-01

    In the presence of externalities, consumption behaviour depends on the solution of a co-ordination problem. In our paper we suggest a learning approach to the study of co-ordination in consumption contexts where agents adjust their choices on the basis of the reinforcement (payoff) they receive during the game. The results of simulations allowed us to distinguish the roles of different aspects of learning in enabling co-ordination within a population of agents. Our main results highlight: 1. ...

  19. Inverse Problem Solution in Acoustic Emission Source Analysis: Classical and Artificial Neural Network Approaches

    Czech Academy of Sciences Publication Activity Database

    Převorovský, Zdeněk; Chlada, Milan; Vodička, Josef

    Torino : Springer, 2007 - (Delsanto, P.), s. 515-529 ISBN 0-387-33860-8 R&D Projects: GA ČR GA205/03/0071; GA ČR GA201/04/2102 Institutional research plan: CEZ:AV0Z20760514 Keywords : acoustic emission * artificial neural network s * inverse problems Subject RIV: BI - Acoustics

  20. 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…

  1. Scientific approaches and techniques for negotiation : a game theoretic and artificial intelligence perspective

    NARCIS (Netherlands)

    Gerding, E.H.; Bragt, D.D.B. van; La Poutré, J.A.

    2000-01-01

    Due to the rapid growth of electronic environments (such as the Internet) much research is currently being performed on autonomous trading mechanisms. This report contains an overview of the current literature on negotiations in the fields of game theory and artificial intelligence (AI). Game theori

  2. 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…

  3. 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.

  4. 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.

  5. Learning discourse discursive approaches to research in mathematics education

    CERN Document Server

    Kieran, C

    2007-01-01

    Guest Editorial. Acknowledgements. There is more to discourse than meets the ears: Looking at thinking as communicating to learn more about mathematical learning; A. Sfard. Educational forms of initiation in mathematical culture; B. van Oers. Cultural, discursive psychology: A socio-cultural approach to studying the teaching and learning of mathematics; S. Lerman. The multiple voices of a mathematics classroom community; E. Forman, E. Ansell. 'Can any fraction be turned into a decimal?' A case study of a mathematical group discussion; M.C. O'Connor. The mathematical discourse of 13-ye

  6. The Effect of 5E Learning Model on Seventh Grade Students’ Approaches to Learning

    OpenAIRE

    Eylem Yıldız Feyzioğlu; Ömer Ergin

    2012-01-01

    The purpose of this study is to investigate the effect of 5E learning model on three seventh grade students’ approaches to learning. The students’ approaches to “remembering what s/he learned”, “the goal of learning” and “encountering the difficulty in learning” before and after the application of 5E model is presented. Data was gathered through these students’ scores on the scale of “Deep Approaches to Learning” and “Surface Approaches to Learning”. Pre and post interviews were also conducte...

  7. Forward-oriented design for learning: illustrating the approach

    Directory of Open Access Journals (Sweden)

    Yannis Dimitriadis

    2013-08-01

    Full Text Available This paper concerns sustainable approaches to design for learning, emphasising the need for designs to be able to thrive outside of the protective niches of project-based innovation. It builds on the “in medias res” framework and more specifically on a forward-oriented approach to design for learning: one that takes a pro-active design stance with respect to each of the phases of an extended lifecycle. We draw on fieldwork notes and interview data to describe two cases that illustrate some of the key features of the approach. Recommendations for further R&D in the area of design for learning are provided, derived from the theoretical framework, and illustrated in this paper.

  8. 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. PMID:27155729

  9. 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...

  10. Methodological approaches to learning self-education

    OpenAIRE

    BORANBAYEVA AKTOLKYN; BERKIMBAYEV KAMALBEK; ARYMBAYEVA KULIMKHAN

    2016-01-01

    In article reveals the concepts «self-education» and components of self-education, revealed scientific and methodological approaches to education and self-education. Defined functions of personally focused education and self-education activity of future teacher.

  11. 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

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

    OpenAIRE

    ten Cate, Carel; Okanoya, Kazuo

    2012-01-01

    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 highe...

  13. 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.

  14. New approach to the ecotoxicological risk assessment of artificial outdoor sporting grounds.

    Science.gov (United States)

    Krüger, O; Kalbe, U; Richter, E; Egeler, P; Römbke, J; Berger, W

    2013-04-01

    Artificial surfaces for outdoor sporting grounds may pose environmental and health hazards that are difficult to assess due to their complex chemical composition. Ecotoxicity tests can indicate general hazardous impacts. We conducted growth inhibition (Pseudokirchneriella subcapitata) and acute toxicity tests (Daphnia magna) with leachates obtained from batch tests of granular infill material and column tests of complete sporting ground assemblies. Ethylene propylene diene monomer rubber (EPDM) leachate showed the highest effect on Daphnia magna (EC(50) scrap tires made of styrene butadiene rubber (SBR) had the highest effect on P. subcapitata (EC(10) = 4.2% leachate; EC(50) = 15.6% leachate). We found no correlations between ecotoxicity potential of leachates and zinc and PAH concentrations. Leachates obtained from column tests revealed lower ecotoxicological potential. Leachates of column tests of complete assemblies may be used for a reliable risk assessment of artificial sporting grounds. PMID:23337354

  15. Modelling of Biotechnological Processes - An approach based on Artificial Neural Networks

    OpenAIRE

    Valente, Eduardo; Rocha, Miguel; Ferreira, E.C.; Rocha, I

    2009-01-01

    In this chapter we describe a software tool for modelling fermentation processes, the FerMoANN, which allows researchers in biology and biotechnology areas to access the potential of Artificial Neural Networks (ANNs) for this task. The FerMoANN is tested and validated using two fermentation processes, an Escherichia coli recombinant protein production and the production of a secreted protein with Saccharomyces cerevisiae in fed-batch reactors. The application to these two case studies, tested...

  16. A Power Market Forward Curve with Hydrology Dependence - An Approach based on Artificial Neural Networks

    OpenAIRE

    Green, Rikard

    2014-01-01

    This paper develops an hourly forward curve for power markets where the intra-day and intra-week shapes (profiles) depend on the level of the hydrological balance. The shaping model is based on a feed-forward Artificial Neural Network (ANN), which is trained on a historical data set of hourly electricity spot prices from the Nord Pool market and weekly measurements of the Nordic hydrological balance. The yearly seasonal cycle is estimated with historical electricity forward prices from the...

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

    OpenAIRE

    Menant, 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...

  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. 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.

  20. 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.

  1. Safety core parameters prediction in research reactors using artificial neural networks: A comparative study of various learning algorithms

    International Nuclear Information System (INIS)

    In recent years, Artificial Neural Networks (ANNs) were applied successfully as an advanced and promising tool for simulating several reactor physics parameters in nuclear engineering applications. The main objective in using such Artificial Intelligent (AI) methods, in the field of nuclear engineering, is to develop simple and 1st estimate models capable of simulating adequately, with reasonable error, important reactor physics parameters in relatively short time comparatively to time consuming and cumbersome reactor physics computer codes. The feasibility of this application has been demonstrated through a previous work done for a typical benchmark 10 Mw IAEA LEU (Low Enriched Uranium) core research reactor, using an adaptive learning rate procedure in a typical back-propagation algorithm in the training process. However, even tough the predictive results achieved are within ±0.7% for Keff and within ±8.5% for Pmax, the convergence time spent during the training phase were of about 36 and 24 hours, respectively for both cited parameters, on a small computational system (300 Mhz Pentium II PC). Hence, this paper suggests one of the suitable ways explored to speed up the training process and to improve neural networks performances by carrying out a comprehensive sensitivity studies on an iterative and multistage calculation process using Neural Network MATLAB Toolbox

  2. 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.

  3. Environmental Factors and Students’ Learning Approaches: A Survey on Malaysian Polytechnics Students

    OpenAIRE

    Ramlee Mustapha; Seri Bunian Mokhtar; Saemah Rahman; Mohd Yusof Husain; Rahayu Ahamad Bahtiar

    2014-01-01

    Several studies have shown the impact of environmental factors on student learning approaches. Despite the importance of such studies, studies on technical learners are few. Thus, this study aimed to determine the influence of learning environment on Polytechnics students’ learning approaches in Malaysia. Learning environment plays an important role in the cognitive, effective and social domains of students because it could improve students’ learning outcomes.  Learning approaches refer to th...

  4. MAN, MACHINE & MUSIC:-A QUALITATIVE APPROACH FOR EMOTIONAL MACHINE LEARNING

    OpenAIRE

    Hardeep Singh

    2012-01-01

    Machine learning can be comes from the music. We as well as machine can have ability to learn from music. Music is all around us. We have music everywhere in our life. Music can be a beneficial in learning process. Our machine should be intelligent. Machine learning is a rising field in artificial intelligence. So many researches show how quick a machine can learn from experiences and knowledge. Research shows music is also a powerful tool in machine learning. Music is also working as a thera...

  5. 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.

  6. 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.…

  7. 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.…

  8. Does Instructional Approach Matter? How Elaboration Plays a Crucial Role in Multimedia Learning

    Science.gov (United States)

    Eysink, Tessa H. S.; de Jong, Ton

    2012-01-01

    This study compared the affordances of 4 multimedia learning environments for specific learning processes. The environments covered the same domain but used different instructional approaches: (a) hypermedia learning, (b) observational learning, (c) self-explanation-based learning, and (d) inquiry learning. Although they all promote an active…

  9. A Machine Learning Approach to Preference Strategies for Anaphor Resolution

    OpenAIRE

    Stuckardt, Roland

    2005-01-01

    In the last years, much effort went into the design of robust anaphor resolution algorithms. Many algorithms are based on antecedent filtering and preference strategies that are manually designed. Along a different line of research, corpus-based approaches have been investigated that employ machine-learning techniques for deriving strategies automatically. Since the knowledge-engineering effort for designing and optimizing the strategies is reduced, the latter approaches are considered partic...

  10. A Novel Privacy Preserving Supervised Learning Approach in Data mining

    OpenAIRE

    K Raghaveswara rao, V Sangeeta

    2013-01-01

    In this paper we are proposing an efficient privacy preserving supervised learning approach with ID3 and Advanced Encryption Standard(AES). The main objective of the approach is to provide security during the mining of data over the networks, Confidentiality and sensitivity of data provided with our architecture during mining of data. Data owner can securely achieve his classified results without losing integrity of data after receiving the mined results from the analyst.

  11. 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.    

  12. A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting

    Directory of Open Access Journals (Sweden)

    Zhaoxuan Li

    2016-01-01

    Full Text Available We evaluate and compare two common methods, artificial neural networks (ANN and support vector regression (SVR, for predicting energy productions from a solar photovoltaic (PV system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE, mean absolute error (MAE, root mean square error (RMSE, relative MBE (rMBE, mean percentage error (MPE and relative RMSE (rRMSE. This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.

  13. Actor-critic models of reinforcement learning in the basal ganglia: From natural to artificial rats

    OpenAIRE

    Khamassi, Mehdi; Lachèze, Loïc; Girard, Benoît; Berthoz, Alain; Guillot, Agnès

    2005-01-01

    International audience Since 1995, numerous Actor–Critic architectures for reinforcement learning have been proposed as models of dopamine-like reinforcement learning mechanisms in the rat's basal ganglia. However, these models were usually tested in different tasks, and it is then difficult to compare their efficiency for an autonomous animat. We present here the comparison of four architectures in an animat as it per forms the same reward-seeking task. This will illustrate the consequenc...

  14. Motivational factors as predictors of student approach to learning

    DEFF Research Database (Denmark)

    Lassesen, Berit Irene

    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......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...... students (response rate: 87.5 %) completed a questionnaire package assessing self-efficacy and test anxiety, together with a Danish version of the revised Study Process Questionnaire (R-SPQ-2F) and a number of other variables. The associations were analyzed separately with linear regressions...

  15. Motivational factors as predictors of student approach to learning

    DEFF Research Database (Denmark)

    Lassesen, Berit

    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...... 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...

  16. 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.

  17. 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.

  18. Epistemological Belief and Learning Approaches of Students in Higher Institutions of Learning in Malaysia

    Directory of Open Access Journals (Sweden)

    Habsah Ismail

    2013-01-01

    Full Text Available This is an investigation of the students’ beliefs about the nature of knowledge or epistemological beliefs, and the relation of these beliefs on their learning approaches. Students chosen as samples of the study were from both public and private higher institutions of learning in Malaysia. The instrument used in the study consists of 49 items measuring students’ epistemological beliefs and 20 items on their learning approaches. Items on epistemological belief were adapted and modified from Schommer’s Epistemological Questionnaire (1990 and Schraw, Bendixen and Dunkle (2000 Epistemic Beliefs Inventory that assesses students' beliefs about simple knowledge, certain knowledge, quick learning, and fixed ability to learn. Items on learning approaches were adapted from Bigg’s forty-two-item Study Process Questionnaire (SPQ, designed for tertiary-level students. The instrument was administered to 1405 students of higher institutions of learning both public and private. Differences in epistemological beliefs among students of these higher institutions, ethnic and between genders were examined.

  19. E-learning for Newcomers on the IAEA Milestones Approach

    International Nuclear Information System (INIS)

    Background to E-learning modules: • Member States requesting assistance in introducing nuclear power programs; • Implement training for a broad audience at an overview level; • Foundation to better understand the IAEA Milestones approach; • MS may have problems providing satisfactory (nuclear) Education and Training; • Funded by Republic of Korea

  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…

  1. Students’ approaches to learning from other students’ oral presentations

    OpenAIRE

    Grape, Sophie; Jacobsson Svärd, Staffan; Jansson, Peter; Österlund, Michael

    2013-01-01

    A phenomenographic study has been performed in order to investigate students’ approaches to learning from other students’ oral presentations in the context of a compulsory seminar on nuclear accidents in the third year of the nuclear engineering programme at Uppsala University.

  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. A Child-Centred Approach to Learning about Healthy Eating

    Science.gov (United States)

    Telford, Francesca

    2013-01-01

    Science involves children in exploring and gaining understanding about the world they live in. Use of a creative and imaginative approach to science can enhance this learning in many ways (Coates and Wilson, 2003). When presented with the challenge of teaching a series of science lessons on food and nutrition to a mixed class of years 4 and 5…

  4. 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…

  5. A Problem-Based Learning Approach to Entrepreneurship Education

    Science.gov (United States)

    Tan, Siok San; Ng, C. K. Frank

    2006-01-01

    Purpose: While it is generally acknowledged that entrepreneurship can be taught, many differ in their opinions about the appropriate methodologies to teach and equip students with the requisite entrepreneurial skills. This paper presents a case to suggest that a problem-based learning (PBL) approach practised at the Republic Polytechnic in…

  6. Enhancing the Scholarship of Teaching and Learning: An Organic Approach

    Science.gov (United States)

    Adcroft, Andy; Lockwood, Andrew

    2010-01-01

    The aim of this paper is to report on an experiment in the School of Management at the University of Surrey whereby the Scholarship of Teaching and Learning is being promoted through an approach which is organic in nature. The paper argues that the nature of such scholarship means that its promotion is more likely to be successful when the…

  7. 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…

  8. Cognitive Ability, Learning Approaches and Personality Correlates of General Knowledge

    Science.gov (United States)

    Furnham, Adrian; Swami, Viren; Arteche, Adriane; Chamorro-Premuzic, Tomas

    2008-01-01

    The relationship between general knowledge (GK) and cognitive ability (IQ and abstract reasoning), learning approaches, and personality ("big five" traits and typical intellectual engagement) was investigated in a sample of 101 British undergraduates. As predicted, GK was positively correlated with cognitive ability (more so with IQ than with…

  9. Students Approach to Learning and Their Use of Lecture Capture

    Science.gov (United States)

    Vajoczki, Susan; Watt, Susan; Marquis, Nick; Liao, Rose; Vine, Michelle

    2011-01-01

    This study examined lecture capture as a way of enhancing university education, and explored how students with different learning approaches used lecture capturing (i.e., podcasts and vodcasts). Results indicate that both deep and surface learners report increased course satisfaction and better retention of knowledge in courses with traditional…

  10. 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…

  11. 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…

  12. 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…

  13. 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…

  14. 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.

  15. Parallelization of learning problems by artificial neural networks. Application in external radiotherapy

    International Nuclear Information System (INIS)

    This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)

  16. A new approach for estimation of PVT properties of pure gases based on artificial neural network model

    Directory of Open Access Journals (Sweden)

    A. R. Moghadassi

    2009-03-01

    Full Text Available Equations of state are useful for description of fluid properties such as pressure-volume-temperature (PVT. However, the success estimation of such correlations depends mainly on the range of data which have originated. Therefore new models are highly required. In this work a new method is proposed based on Artificial Neural Network (ANN for estimation of PVT properties of compounds. The data sets were collected from Perry's Chemical Engineers' Handbook. Different training schemes for the back-propagation learning algorithm, such as; Scaled Conjugate Gradient (SCG, Levenberg-Marquardt (LM and Resilient back Propagation (RP methods were used. The accuracy and trend stability of the trained networks were tested against unseen data. The LM algorithm with sixty neurons in the hidden layer has proved to be the best suitable algorithm with the minimum Mean Square Error (MSE of 0.000606. The ANN's capability to estimate the PVT properties is one of the best estimating method with high performance.

  17. 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. PMID:24713128

  18. Learning about knowledge: A complex network approach

    International Nuclear Information System (INIS)

    An approach to modeling knowledge acquisition in terms of walks along complex networks is described. Each subset of knowledge is represented as a node, and relations between such knowledge are expressed as edges. Two types of edges are considered, corresponding to free and conditional transitions. The latter case implies that a node can only be reached after visiting previously a set of nodes (the required conditions). The process of knowledge acquisition can then be simulated by considering the number of nodes visited as a single agent moves along the network, starting from its lowest layer. It is shown that hierarchical networks--i.e., networks composed of successive interconnected layers--are related to compositions of the prerequisite relationships between the nodes. In order to avoid deadlocks--i.e., unreachable nodes--the subnetwork in each layer is assumed to be a connected component. Several configurations of such hierarchical knowledge networks are simulated and the performance of the moving agent quantified in terms of the percentage of visited nodes after each movement. The Barabasi-Albert and random models are considered for the layer and interconnecting subnetworks. Although all subnetworks in each realization have the same number of nodes, several interconnectivities, defined by the average node degree of the interconnection networks, have been considered. Two visiting strategies are investigated: random choice among the existing edges and preferential choice to so far untracked edges. A series of interesting results are obtained, including the identification of a series of plateaus of knowledge stagnation in the case of the preferential movement strategy in the presence of conditional edges

  19. A first approach in applying Artificial Potential Fields in Car Games

    OpenAIRE

    Uusitalo, Tim

    2011-01-01

    In car racing simulation games, finishing first is the main goal. To reach that goal, it is required to go around a racing track, competing against other cars aiming for the same goal. Implementing a bot for doing so may have its difficulties, although using a technique called multi-agent systems combined with artificial potential field, let- ting the agents take care of subtasks like keeping the car on the track, minimize how much the car turns in a curvature and basics in navigation around ...

  20. 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...

  1. 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.

  2. Changing Students' Approaches to Learning: A Two-Year Study within a University Teacher Training Course

    Science.gov (United States)

    Gijbels, David; Coertjens, Liesje; Vanthournout, Gert; Struyf, Elke; Van Petegem, Peter

    2009-01-01

    Inciting a deep approach to learning in students is difficult. The present research poses two questions: can a constructivist learning-assessment environment change students' approaches towards a more deep approach? What effect does additional feedback have on the changes in learning approaches? Two cohorts of students completed questionnaires…

  3. Effectiveness of Active Learning Approach in Teaching English Language of Standard IX Students

    OpenAIRE

    Khevna Trivedi

    2013-01-01

    The present study aims to compare Active Learning Approach and tradition method of teaching. 200 students of English medium school were the sample of the study. Two equivalent group pre test post test experimental design was used. It was found that Active Learning Approach had been more effective than Conventional Approach. Key words: Active Learning Approach, Standard IX students, English subject

  4. 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. PMID:26077127

  5. Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

    Science.gov (United States)

    Srimani, P. K.; Parimala, Y. G.

    2011-12-01

    A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

  6. 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.

  7. 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…

  8. A New Approach for Transmission Network Expansion Planning Considering Actual Worth of Adequacy Using Modified Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Seyed Mahdi Mazhari

    2012-02-01

    Full Text Available Expansion planning of the electric power systems has significant importance considering increases in electricity load demand. Transmission network expansion planning (TNEP is an important part of the electric power system development. In this paper, the TNEP is investigated as a new framework considering actual worth of network adequacy. To do so, a new economic based definition is introduced for network adequacy and statistical studies are developed to calculate the actual worth of network adequacy via proposed formulation. Next, the TNEP is investigated as an optimization problem, with two objectives, using Artificial Bee Colony algorithm. Moreover, new heuristic approaches are presented to enhance the optimization process. Detailed numerical studies and comparisons presented in the paper show that the proposed approach could improve the quality of problem solutions and can be used as a new framework for TNEP within actual networks.

  9. 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

  10. 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

  11. 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

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

    DEFF Research Database (Denmark)

    Ørngreen, Rikke; Nielsen, Janni; Levinsen, Karin

    2008-01-01

    focus on end-users and primarily address the phases succeeding the initial pre-analysis. The HCI approach lacks pre-analyses, including focusing on the client as a user of the product. With the point of departure in our study a private educational organisation within healthcare, we understand the client...... 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 perspectives in a Client Centred framework that is useable as the starting point for others in developing large scale e-learning projects....

  13. 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,...

  14. Occupational therapy students' approaches to learning: considering the impact of culture

    OpenAIRE

    Watson, Jo; Chapman, Judith; Adams, Jo; Nila, Ummey Hamila

    2006-01-01

    Learning approaches describe the way individuals approach tasks or learning situations and are influenced by individual characteristics and specific learning contexts. Cultural factors are likely to impact on various aspects of learning, yet the literature disagrees over the extent to which culture influences approaches to learning. With increasing cultural diversity in student cohorts and the contributions of western therapists to occupational therapy programmes in developing nations, this i...

  15. 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.

  16. 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.

  17. 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.

  18. Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks

    International Nuclear Information System (INIS)

    Spike-timing-dependent synaptic plasticity (STDP) is demonstrated in a synapse device based on a ferroelectric-gate field-effect transistor (FeFET). STDP is a key of the learning functions observed in human brains, where the synaptic weight changes only depending on the spike timing of the pre- and post-neurons. The FeFET is composed of the stacked oxide materials with ZnO/Pr(Zr,Ti)O3 (PZT)/SrRuO3. In the FeFET, the channel conductance can be altered depending on the density of electrons induced by the polarization of PZT film, which can be controlled by applying the gate voltage in a non-volatile manner. Applying a pulse gate voltage enables the multi-valued modulation of the conductance, which is expected to be caused by a change in PZT polarization. This variation depends on the height and the duration of the pulse gate voltage. Utilizing these characteristics, symmetric and asymmetric STDP learning functions are successfully implemented in the FeFET-based synapse device by applying the non-linear pulse gate voltage generated from a set of two pulses in a sampling circuit, in which the two pulses correspond to the spikes from the pre- and post-neurons. The three-terminal structure of the synapse device enables the concurrent learning, in which the weight update can be performed without canceling signal transmission among neurons, while the neural networks using the previously reported two-terminal synapse devices need to stop signal transmission for learning.

  19. Internet-based computer-aided learning for artificial neural networks

    OpenAIRE

    Keeling, Paul J

    1998-01-01

    This thesis presents research performed to mvestigate the potential offered by the Internet for the implementation of an Engineering Computer-Aided Learning (CAL) environment. The research comprises two categories, a detailed literature survey of CAL and its application through the medium of the Internet environment. As a direct result of the literature survey, the scope of CAL can be considered to comprise the use of text, graphics, animations and sound. It is through the use of the CAL ...

  20. 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…

  1. The Epistemological Beliefs, Learning Approaches and Study Orchestrations of University Students

    Science.gov (United States)

    Rodriguez, Lourdes; Cano, Francisco

    2006-01-01

    This study examined the learning experience (learning approaches, study orchestrations and epistemological beliefs) of 388 university students. Data analysis revealed two main results. First, the different aspects of students' learning experience were related: learning approaches and epistemological beliefs (two pairs of canonical variates…

  2. 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

  3. School health approach to teaching and learning of students

    Directory of Open Access Journals (Sweden)

    Lukianova Yu.S.

    2015-01-01

    Full Text Available Purpose: disclosure of health-ways for teaching and learning of students. Material: analysis of the publications of domestic and foreign authors. Results: The article is devoted to the implementation of healthy way approach to the educational process, namely, the rational organization of training aimed at keeping the dynamics of human health, the prevention of mental fatigue and overload, increase adaptive reserves of the body of the person; intensification of teaching and learning of students (application-is controversial dialogue, training, game forms and methods of training, participation in project activities, the work of pedagogical workshops that stimulates emotional accommodation and understanding of knowledge, helps students acquire personal-relevant knowledge and experience; use of health effect of artistic and practical (music, painting activities of students. Conclusions: highlights the key towards the implementation of health-promoting approach to the educational process.

  4. 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.

  5. 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.

  6. Does instructional approach matter? How elaboration plays a crucial role in multimedia learning.

    NARCIS (Netherlands)

    Eysink, T.H.S.; Jong, de T.

    2012-01-01

    This study compared the affordances of 4 multimedia learning environments for specific learning processes. The environments covered the same domain but used different instructional approaches: (a) hypermedia learning, (b) observational learning, (c) self-explanation-based learning, and (d) inquiry l

  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. PMID:25403541

  8. Qualitative detection of oil adulteration with machine learning approaches

    OpenAIRE

    Jin, Xiao-Bo; LU Qiang; Wang, Feng; Huo, Quan-gong

    2013-01-01

    The study focused on the machine learning analysis approaches to identify the adulteration of 9 kinds of edible oil qualitatively and answered the following three questions: Is the oil sample adulterant? How does it constitute? What is the main ingredient of the adulteration oil? After extracting the high-performance liquid chromatography (HPLC) data on triglyceride from 370 oil samples, we applied the adaptive boosting with multi-class Hamming loss (AdaBoost.MH) to distinguish the oil adulte...

  9. Practice Makes Perfect ? The Practice Approach in E-Learning

    OpenAIRE

    Norm Friesen

    2009-01-01

    [English] Anyone involved in e-learning is certain to have run across the word practice in connection with a number of notable expressions or phrases : communities of practice, best practices, and teaching practices among others. However, there have been few definitions or discussions that address exactly what practice is. This article provides a short overview of the practice approach, focusing first on its origin in the philosophy of Heidegger and the sociology of Bourdieu. It then provides...

  10. More like this : machine learning approaches to music similarity

    OpenAIRE

    McFee, Brian

    2012-01-01

    The rise of digital music distribution has provided users with unprecedented access to vast song catalogs. In order to help users cope with large collections, music information retrieval systems have been developed to automatically analyze, index, and recommend music based on a user's preferences or search criteria. This dissertation proposes machine learning approaches to content-based, query-by-example search, and investigates applications in music information retrieval. The proposed method...

  11. Machine Learning Approaches : from Theory to Application in Schizophrenia

    OpenAIRE

    Elisa Veronese; Umberto Castellani; Denis Peruzzo; Marcella Bellani; Paolo Brambilla

    2013-01-01

    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, comp...

  12. 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.

  13. 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...

  14. 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…

  15. SENTIMENT CLASSIFICATION OF MOVIE REVIEWS BY SUPERVISED MACHINE LEARNING APPROACHES

    Directory of Open Access Journals (Sweden)

    P.Kalaivani

    2013-08-01

    Full Text Available Large volumes of data are available in the web. The discussion forum, review sites, blogs and news corpora are some of the opinion rich resources. The opinions obtained from those can be classified and used for gathering online customer’s preferences. Techniques are being applied to design a system that identifies and classify opinions spread largely in the internet. Few different problems such as sentiment classification, feature based classification and handling negotiations are predominating this research community. This paper studies online movie reviews using sentiment analysising approaches. In this study, sentiment classification techniques wereapplied to movie reviews. Specifically, we compared three supervised machine learning approaches SVM, Navie Bayes and kNN for Sentiment Classification of Reviews. Empirical results states that SVM approachoutperformed the Navie Bayes and kNN approaches, and the training dataset had a large number of reviews, SVM approach reached accuracies of atleast 80%.

  16. 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…

  17. Relations between Students' Approaches to Learning, Experienced Emotions and Outcomes of Learning

    Science.gov (United States)

    Trigwell, Keith; Ellis, Robert A.; Han, Feifei

    2012-01-01

    Quantitative analyses conducted on the self-reports of first year university students suggest that there is a relationship between the ways they emotionally experience their course and the approach they take to the learning of that course. Students who more strongly experience positive emotions, such as hope and pride, and more weakly experience…

  18. 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…

  19. 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.

  20. 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

    2016-07-01

    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