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  Subjects -> COMPUTER SCIENCE (Total: 1964 journals)
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COMPUTER SCIENCE (1143 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 11)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 62)
ACM Computing Surveys     Hybrid Journal   (Followers: 21)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 6)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 10)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 7)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 10)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 2)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 12)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 2)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 9)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 7)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 19)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 22)
Advanced Science Letters     Full-text available via subscription   (Followers: 4)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 14)
Advances in Artificial Neural Systems     Open Access   (Followers: 3)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 4)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 13)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 42)
Advances in Engineering Software     Hybrid Journal   (Followers: 23)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 9)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Human-Computer Interaction     Open Access   (Followers: 18)
Advances in Materials Sciences     Open Access   (Followers: 15)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 34)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access  
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 7)
African Journal of Information and Communication     Open Access   (Followers: 7)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 3)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal  
Algorithms     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 2)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 5)
American Journal of Sensor Technology     Open Access   (Followers: 1)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 5)
Analysis in Theory and Applications     Hybrid Journal  
Animation Practice, Process & Production     Hybrid Journal   (Followers: 4)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 6)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 5)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 11)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 32)
Applied Medical Informatics     Open Access   (Followers: 9)
Applied Numerical Analysis & Computational Mathematics     Hybrid Journal   (Followers: 5)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 13)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 3)
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 5)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 1)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 4)
Automatica     Hybrid Journal   (Followers: 8)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Basin Research     Hybrid Journal   (Followers: 3)
Behaviour & Information Technology     Hybrid Journal   (Followers: 53)
Bioinformatics     Hybrid Journal   (Followers: 199)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 11)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 30)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 44)
British Journal of Educational Technology     Hybrid Journal   (Followers: 107)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 9)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 12)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
Cell Communication and Signaling     Open Access   (Followers: 1)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 2)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 13)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 17)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 2)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 2)
Communications of the ACM     Full-text available via subscription   (Followers: 46)
Communications of the Association for Information Systems     Open Access   (Followers: 19)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 1)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access  
Computational Biology and Chemistry     Hybrid Journal   (Followers: 10)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access  
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 8)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 12)
Computational Linguistics     Open Access   (Followers: 22)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 1)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 27)
Computer     Full-text available via subscription   (Followers: 76)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 7)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 20)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 13)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 7)
Computer Science and Engineering     Open Access   (Followers: 16)
Computer Science and Information Technology     Open Access   (Followers: 10)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 21)
Computer Science Master Research     Open Access   (Followers: 9)
Computer Science Review     Hybrid Journal   (Followers: 10)
Computer Standards & Interfaces     Hybrid Journal   (Followers: 4)
Computer Supported Cooperative Work (CSCW)     Hybrid Journal   (Followers: 9)
Computer-aided Civil and Infrastructure Engineering     Hybrid Journal   (Followers: 9)
Computer-Aided Design and Applications     Hybrid Journal   (Followers: 2)

        1 2 3 4 5 6 | Last

Journal Cover Advances in Artificial Neural Systems
  [3 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1687-7594 - ISSN (Online) 1687-7608
   Published by Hindawi Homepage  [334 journals]
  • A State-Based Sensitivity Analysis for Distinguishing the Global
           Importance of Predictor Variables in Artificial Neural Networks

    • Abstract: Artificial neural networks (ANNs) are powerful empirical approaches used to model databases with a high degree of accuracy. Despite their recognition as universal approximators, many practitioners are skeptical about adopting their routine usage due to lack of model transparency. To improve the clarity of model prediction and correct the apparent lack of comprehension, researchers have utilized a variety of methodologies to extract the underlying variable relationships within ANNs, such as sensitivity analysis (SA). The theoretical basis of local SA (that predictors are independent and inputs other than variable of interest remain “fixed” at predefined values) is challenged in global SA, where, in addition to altering the attribute of interest, the remaining predictors are varied concurrently across their respective ranges. Here, a regression-based global methodology, state-based sensitivity analysis (SBSA), is proposed for measuring the importance of predictor variables upon a modeled response within ANNs. SBSA was applied to network models of a synthetic database having a defined structure and exhibiting multicollinearity. SBSA achieved the most accurate portrayal of predictor-response relationships (compared to local SA and Connected Weights Analysis), closely approximating the actual variability of the modeled system. From this, it is anticipated that skepticisms concerning the delineation of predictor influences and their uncertainty domains upon a modeled output within ANNs will be curtailed.
      PubDate: Sun, 07 Aug 2016 13:51:49 +000
       
  • Modified STDP Triplet Rule Significantly Increases Neuron Training
           Stability in the Learning of Spatial Patterns

    • Abstract: Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are based firmly on biological evidence. STDP learning is capable of detecting spatiotemporal patterns highly obscured by noise. This feature appears attractive from the point of view of machine learning. In this paper three different additive STDP models of spike interactions were compared in respect to training performance when the neuron is exposed to a recurrent spatial pattern injected into Poisson noise. The STDP models compared were all-to-all interaction, nearest-neighbor interaction, and the nearest-neighbor triplet interaction. The parameters of the neuron model and STDP training rules were optimized for a range of spatial patterns of different sizes by the means of heuristic algorithm. The size of the pattern, that is, the number of synapses containing the pattern, was gradually decreased from what amounted to a relatively easy task down to a single synapse. Optimization was performed for each size of the pattern. The parameters were allowed to evolve freely. The triplet rule, in most cases, performed better by far than the other two rules, while the evolutionary algorithm immediately switched the polarity of the triplet update. The all-to-all rule achieved moderate results.
      PubDate: Mon, 01 Aug 2016 07:27:20 +000
       
  • Artificial Neural Network Estimation of Thermal Insulation Value of
           Children’s School Wear in Kuwait Classroom

    • Abstract: Artificial neural network (ANN) was utilized to predict the thermal insulation values of children’s school wear in Kuwait. The input thermal insulation data of the different children’s school wear used in Kuwait classrooms were obtained from study using thermal manikins. The lowest mean squared error (MSE) value for the validation data was 1.5 × 10−5 using one hidden layer of six neurons and one output layer. The R2 values for the training, validation, and testing data were almost equal to 1. The values from ANN prediction were compared with McCullough’s equation and the standard tables’ methods. Results suggested that the ANN is able to give more accurate prediction of the clothing thermal insulation values than the regression equation and the standard tables methods. The effect of the different input variables on the thermal insulation value was examined using Garson algorithm and sensitivity analysis and it was found that the cloths weight, the body surface area nude (BSA0), and body surface area covered by one layer of clothing (BSAC1) have the highest effect on the thermal insulation value with about 29%, 27%, and 23%, respectively.
      PubDate: Wed, 28 Oct 2015 08:37:20 +000
       
  • Water Quality Modeling in Reservoirs Using Multivariate Linear Regression
           and Two Neural Network Models

    • Abstract: In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical errors, including the mean absolute error, the root mean square error, and the correlation coefficient, computed from the measured and the model-simulated DO, TP, Chl a, and SD values. The results indicate that the performance of the ANFIS model is superior to those of the MLR and RBFN models. The study results show that the neural network using the ANFIS model is suitable for simulating the water quality variables with reasonable accuracy, suggesting that the ANFIS model can be used as a valuable tool for reservoir management in Taiwan.
      PubDate: Tue, 09 Jun 2015 06:15:00 +000
       
  • Application of Neural Network Modeling to Identify Auditory Processing
           Disorders in School-Age Children

    • Abstract: P300 Auditory Event-Related Potentials (P3AERPs) were recorded in nine school-age children with auditory processing disorders and nine age- and gender-matched controls in response to tone burst stimuli presented at varying rates (1/second or 3/second) under varying levels of competing noise (0 dB, 40 dB, or 60 dB SPL). Neural network modeling results indicated that speed of information processing and task-related demands significantly influenced P3AERP latency in children with auditory processing disorders. Competing noise and rapid stimulus rates influenced P3AERP amplitude in both groups.
      PubDate: Thu, 30 Apr 2015 13:17:06 +000
       
  • Generalisation over Details: The Unsuitability of Supervised
           Backpropagation Networks for Tetris

    • Abstract: We demonstrate the unsuitability of Artificial Neural Networks (ANNs) to the game of Tetris and show that their great strength, namely, their ability of generalization, is the ultimate cause. This work describes a variety of attempts at applying the Supervised Learning approach to Tetris and demonstrates that these approaches (resoundedly) fail to reach the level of performance of hand-crafted Tetris solving algorithms. We examine the reasons behind this failure and also demonstrate some interesting auxiliary results. We show that training a separate network for each Tetris piece tends to outperform the training of a single network for all pieces; training with randomly generated rows tends to increase the performance of the networks; networks trained on smaller board widths and then extended to play on bigger boards failed to show any evidence of learning, and we demonstrate that ANNs trained via Supervised Learning are ultimately ill-suited to Tetris.
      PubDate: Mon, 27 Apr 2015 09:08:34 +000
       
  • Sensorless Direct Power Control of Induction Motor Drive Using Artificial
           Neural Network

    • Abstract: This paper proposes the design of sensorless induction motor drive based on direct power control (DPC) technique. It is shown that DPC technique enjoys all advantages of pervious methods such as fast dynamic and ease of implementation, without having their problems. To reduce the cost of drive and enhance the reliability, an effective sensorless strategy based on artificial neural network (ANN) is developed to estimate rotor’s position and speed of induction motor. Developed sensorless scheme is a new model reference adaptive system (MRAS) speed observer for direct power control induction motor drives. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Simulink. Some simulations are carried out for the closed-loop speed control systems under various load conditions to verify the proposed methods. Simulation results confirm the performance of ANN based sensorless DPC induction motor drive in various conditions.
      PubDate: Mon, 30 Mar 2015 09:14:24 +000
       
  • Stochastic Search Algorithms for Identification, Optimization, and
           Training of Artificial Neural Networks

    • Abstract: This paper presents certain stochastic search algorithms (SSA) suitable for effective identification, optimization, and training of artificial neural networks (ANN). The modified algorithm of nonlinear stochastic search (MN-SDS) has been introduced by the author. Its basic objectives are to improve convergence property of the source defined nonlinear stochastic search (N-SDS) method as per Professor Rastrigin. Having in mind vast range of possible algorithms and procedures a so-called method of stochastic direct search (SDS) has been practiced (in the literature is called stochastic local search-SLS). The MN-SDS convergence property is rather advancing over N-SDS; namely it has even better convergence over range of gradient procedures of optimization. The SDS, that is, SLS, has not been practiced enough in the process of identification, optimization, and training of ANN. Their efficiency in some cases of pure nonlinear systems makes them suitable for optimization and training of ANN. The presented examples illustrate only partially operatively end efficiency of SDS, that is, MN-SDS. For comparative method backpropagation error (BPE)method was used.
      PubDate: Sat, 28 Feb 2015 07:30:27 +000
       
  • Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector
           Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes

    • Abstract: There is a necessity for analysis of a large amount of data in many fields such as healthcare, business, industries, and agriculture. Therefore, the need of the feature selection (FS) technique for the researchers is quite evident in many fields of science, especially in computer science. Furthermore, an effective FS technique that is best suited to a particular learning algorithm is of great help for the researchers. Hence, this paper proposes a hybrid feature selection (HFS) based efficient disease diagnostic model for Breast Cancer, Hepatitis, and Diabetes. A HFS is an efficient method that combines the positive aspects of both Filter and Wrapper FS approaches. The proposed model adopts weighted least squares twin support vector machine (WLSTSVM) as a classification approach, sequential forward selection (SFS) as a search strategy, and correlation feature selection (CFS) to evaluate the importance of each feature. This model not only selects relevant feature subset but also efficiently deals with the data imbalance problem. The effectiveness of the HFS based WLSTSVM approach is examined on three well-known disease datasets taken from UCI repository with the help of predictive accuracy, sensitivity, specificity, and geometric mean. The experiment confirms that our proposed HFS based WLSTSVM disease diagnostic model can result in positive outcomes.
      PubDate: Wed, 21 Jan 2015 14:30:32 +000
       
  • Neural Virtual Sensors for Adaptive Magnetic Localization of Autonomous
           Dataloggers

    • Abstract: The surging advance in micro- and nanotechnologies allied with neural learning systems allows the realization of miniaturized yet extremely powerful multisensor systems and networks for wide application fields, for example, in measurement, instrumentation, automation, and smart environments. Time and location context is particularly relevant to sensor swarms applied for distributed measurement in industrial environment, such as, for example, fermentation tanks. Common RF solutions face limits here, which can be overcome by magnetic systems. Previously, we have developed the electronic system for an integrated data logger swarm with magnetic localization and sensor node timebase synchronization. The focus of this work is on an approach to improving both localization accuracy and flexibility by the application of artificial neural networks applied as virtual sensors and classifiers in a hybrid dedicated learning system. Including also data from an industrial brewery environment, the best investigated neural virtual sensor approach has achieved an advance in localization accuracy of a factor of 4 compared to state-of-the-art numerical methods and, thus, results in the order of less than 5 cm meeting industrial expectations on a feasible solution for the presented integrated localization system solution.
      PubDate: Tue, 30 Dec 2014 08:15:28 +000
       
  • An Overview of Transmission Line Protection by Artificial Neural Network:
           Fault Detection, Fault Classification, Fault Location, and Fault Direction
           Discrimination

    • Abstract: Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.
      PubDate: Sun, 28 Dec 2014 09:22:34 +000
       
  • Optimal Design of PID Controller for the Speed Control of DC Motor by
           Using Metaheuristic Techniques

    • Abstract: DC motors are used in numerous industrial applications like servo systems and speed control applications. For such systems, the Proportional+Integral+Derivative (PID) controller is usually the controller of choice due to its ease of implementation, ruggedness, and easy tuning. All the classical methods for PID controller design and tuning provide initial workable values for , , and which are further manually fine-tuned for achieving desired performance. The manual fine tuning of the PID controller parameters is an arduous job which demands expertise and comprehensive knowledge of the domain. In this research work, some metaheuristic algorithms are explored for designing PID controller and a comprehensive comparison is made between these algorithms and classical techniques as well for the purpose of selecting the best technique for PID controller design and parameters tuning.
      PubDate: Wed, 10 Dec 2014 00:10:05 +000
       
  • Architecture Analysis of an FPGA-Based Hopfield Neural Network

    • Abstract: Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA) hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.
      PubDate: Tue, 09 Dec 2014 00:10:05 +000
       
  • ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance
           Theory

    • Abstract: This paper proposes a novel neural network architecture based on adaptive resonance theory (ART) called ARTgrid that can perform both online and offline clustering of 2D object structures. The main novelty of the proposed architecture is a two-level categorization and search mechanism that can enhance computation speed while maintaining high performance in cases of higher vigilance values. ARTgrid is developed for specific robotic applications for work in unstructured environments with diverse work objects. For that reason simulations are conducted on random generated data which represents actual manipulation objects, that is, their respective 2D structures. ARTgrid verification is done through comparison in clustering speed with the fuzzy ART algorithm and Adaptive Fuzzy Shadow (AFS) network. Simulation results show that by applying higher vigilance values () clustering performance of ARTgrid is considerably better, while lower vigilance values produce comparable results with the original fuzzy ART algorithm.
      PubDate: Wed, 03 Dec 2014 07:28:57 +000
       
  • An Electronic Circuit Model of the Interpostsynaptic Functional LINK
           Designed to Study the Formation of Internal Sensations in the Nervous
           System

    • Abstract: The nervous system makes changes in response to the continuous arrival of associative learning stimuli from the environment and executes behavioral motor activities after making predictions based on past experience. The system exhibits dynamic plasticity changes that involve the formation of the first-person internal sensations of perception, memory, and consciousness to which only the owner of the nervous system has access. These properties of natural intelligence need to be verified for their mechanism of formation using engineered systems so that a third person can access them. In the presence of a synaptic junctional delay of up to two milliseconds, we anticipate that the systems property of formation of internal sensations is likely independent of the mode of conduction along the neuronal processes. This allows testing for the formation of internal sensations using electronic circuits. The present work describes the neurobiological context for the formation of the basic units of inner sensations that occur through the reactivation of interpostsynaptic functional LINKs and its connection to motor activity. These mechanisms are translated to an analogue circuit unit for the development of robotic systems.
      PubDate: Wed, 03 Dec 2014 06:48:37 +000
       
  • Virtual Sensor for Calibration of Thermal Models of Machine Tools

    • Abstract: Machine tools are important parts of high-complex industrial manufacturing. Thus, the end product qualitystrictly depends on the accuracy of these machines, but they are prone to deformation caused by their own heat. The deformation needs to be compensated in order to assure accurate production. So an adequate model of the high-dimensional thermal deformation process must be created and parameters of this model must be evaluated. Unfortunately, such parameters are often unknown and cannot be calculated a priori. Parameter identification during real experiments is not an option for these models because of its high engineering and machine time effort. The installation of additional sensors to measure these parameters directly is uneconomical. Instead, an effective calibration of thermal models can be reached by combining real and virtual measurements on a machine tool during its real operation, without additional sensors installation. In this paper, a new approach for thermal model calibration is presented. The expected results are very promising and can be recommended as an effective solution for this class of problems.
      PubDate: Thu, 27 Nov 2014 14:03:15 +000
       
  • Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial
           Neural Networks

    • Abstract: Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA) stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.
      PubDate: Tue, 11 Nov 2014 06:03:21 +000
       
  • Global Stability, Bifurcation, and Chaos Control in a Delayed Neural
           Network Model

    • Abstract: Conditions for the global asymptotic stability of delayed artificial neural network model of n (≥3) neurons have been derived. For bifurcation analysis with respect to delay we have considered the model with three neurons and used suitable transformation on multiple time delays to reduce it to a system with single delay. Bifurcation analysis is discussed with respect to single delay. Numerical simulations are presented to verify the analytical results. Using numerical simulation, the role of delay and neuronal gain parameter in changing the dynamics of the neural network model has been discussed.
      PubDate: Wed, 08 Oct 2014 06:51:42 +000
       
  • Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and
           Teaching Learning Based Optimization Algorithm

    • Abstract: Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using -fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.
      PubDate: Wed, 17 Sep 2014 00:00:00 +000
       
  • Long Time Behavior for a System of Differential Equations with
           Non-Lipschitzian Nonlinearities

    • Abstract: We consider a general system of nonlinear ordinary differential equations of first order. The nonlinearities involve distributed delays in addition to the states. In turn, the distributed delays involve nonlinear functions of the different variables and states. An explicit bound for solutions is obtained under some rather reasonable conditions. Several special cases of this system may be found in neural network theory. As a direct application of our result it is shown how to obtain global existence and, more importantly, convergence to zero at an exponential rate in a certain norm. All these nonlinearities (including the activation functions) may be non-Lipschitz and unbounded.
      PubDate: Sun, 14 Sep 2014 00:00:00 +000
       
  • A Hybrid Intelligent Method of Predicting Stock Returns

    • Abstract: This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of both a linear and a nonlinear model. The model has been tested on stock data obtained from National Stock Exchange of India. The results indicate that the proposed model can be a promising approach in predicting future stock movements.
      PubDate: Sun, 07 Sep 2014 08:26:42 +000
       
  • Downscaling Statistical Model Techniques for Climate Change Analysis
           Applied to the Amazon Region

    • Abstract: The Amazon is an area covered predominantly by dense tropical rainforest with relatively small inclusions of several other types of vegetation. In the last decades, scientific research has suggested a strong link between the health of the Amazon and the integrity of the global climate: tropical forests and woodlands (e.g., savannas) exchange vast amounts of water and energy with the atmosphere and are thought to be important in controlling local and regional climates. Consider the importance of the Amazon biome to the global climate changes impacts and the role of the protected area in the conservation of biodiversity and state-of-art of downscaling model techniques based on ANN Calibrate and run a downscaling model technique based on the Artificial Neural Network (ANN) that is applied to the Amazon region in order to obtain regional and local climate predicted data (e.g., precipitation). Considering the importance of the Amazon biome to the global climate changes impacts and the state-of-art of downscaling techniques for climate models, the shower of this work is presented as follows: the use of ANNs good similarity with the observation in the cities of Belém and Manaus, with correlations of approximately 88.9% and 91.3%, respectively, and spatial distribution, especially in the correction process, representing a good fit.
      PubDate: Thu, 29 May 2014 13:26:24 +000
       
  • Exponential Stability of Periodic Solution to Wilson-Cowan Networks with
           Time-Varying Delays on Time Scales

    • Abstract: We present stability analysis of delayed Wilson-Cowan networks on time scales. By applying the theory of calculus on time scales, the contraction mapping principle, and Lyapunov functional, new sufficient conditions are obtained to ensure the existence and exponential stability of periodic solution to the considered system. The obtained results are general and can be applied to discrete-time or continuous-time Wilson-Cowan networks.
      PubDate: Wed, 02 Apr 2014 11:56:19 +000
       
  • Oscillatory Behavior on a Three-Node Neural Network Model with Discrete
           and Distributed Delays

    • Abstract: This paper investigates the oscillatory behavior of the solutions for a three-node neural network with discrete and distributed delays. Two theorems are provided to determine the conditions for oscillating solutions of the model. The criteria for selecting the parameters in this network are derived. Some simulation examples are presented to illustrate the effectiveness of the results.
      PubDate: Mon, 24 Feb 2014 13:56:51 +000
       
  • Novel Discrete Compactness-Based Training for Vector Quantization
           Networks: Enhancing Automatic Brain Tissue Classification

    • Abstract: An approach for nonsupervised segmentation of Computed Tomography (CT) brain slices which is based on the use of Vector Quantization Networks (VQNs) is described. Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood. The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC) which is a factor that provides information about the shape of an object. One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object. We will present, compare, and discuss some examples of segmentation networks trained under Kohonen’s original algorithm and also under our similarity metric. Some experiments are established in order to measure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.
      PubDate: Mon, 30 Dec 2013 08:53:57 +000
       
  • Estimation of Static Pull-In Instability Voltage of Geometrically
           Nonlinear Euler-Bernoulli Microbeam Based on Modified Couple Stress Theory
           by Artificial Neural Network Model

    • Abstract: In this study, the static pull-in instability of beam-type micro-electromechanical system (MEMS) is theoretically investigated. Considering the mid-plane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. Two supervised neural networks, namely, back propagation (BP) and radial basis function (RBF), have been used for modeling the static pull-in instability of microcantilever beam. These networks have four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data employed for training the networks and capabilities of the models in predicting the pull-in instability behavior has been verified. Based on verification errors, it is shown that the radial basis function of neural network is superior in this particular case and has the average errors of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations show a good agreement, which also proves the feasibility and effectiveness of the adopted approach.
      PubDate: Thu, 26 Dec 2013 13:29:13 +000
       
  • Comparison of Artificial Neural Network Architecture in Solving Ordinary
           Differential Equations

    • Abstract: This paper investigates the solution of Ordinary Differential Equations (ODEs) with initial conditions using Regression Based Algorithm (RBA) and compares the results with arbitrary- and regression-based initial weights for different numbers of nodes in hidden layer. Here, we have used feed forward neural network and error back propagation method for minimizing the error function and for the modification of the parameters (weights and biases). Initial weights are taken as combination of random as well as by the proposed regression based model. We present the method for solving a variety of problems and the results are compared. Here, the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression fitting. For this, the input and output data are fitted first with various degree polynomials using regression analysis and the coefficients involved are taken as initial weights to start with the neural training. Fixing of the hidden nodes depends upon the degree of the polynomial. For the example problems, the analytical results have been compared with neural results with arbitrary and regression based weights with four, five, and six nodes in hidden layer and are found to be in good agreement.
      PubDate: Sun, 15 Dec 2013 14:56:15 +000
       
  • Artificial Neural Network Modeling for Biological Removal of Organic
           Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch
           Reactor

    • Abstract: The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and -N level of 2000 ± 100 mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models “A,” “B,” and “C”) using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models “A,” “B,” and “C”) were trained and tested reasonably well to predict COD and -N removal efficiently with 3.33% experimental error.
      PubDate: Thu, 12 Dec 2013 07:54:34 +000
       
  • The Classification of Valid and Invalid Beats of Three-Dimensional
           Nystagmus Eye Movement Signals Using Machine Learning Methods

    • Abstract: Nystagmus recordings frequently include eye blinks, noise, or other corrupted segments that, with the exception of noise, cannot be dampened by filtering. We measured the spontaneous nystagmus of 107 otoneurological patients to form a training set for machine learning-based classifiers to assess and separate valid nystagmus beats from artefacts. Video-oculography was used to record three-dimensional nystagmus signals. Firstly, a procedure was implemented to accept or reject nystagmus beats according to the limits for nystagmus variables. Secondly, an expert perused all nystagmus beats manually. Thirdly, both the machine and the manual results were united to form the third variation of the training set for the machine learning-based classification. This improved accuracy results in classification; high accuracy values of up to 89% were obtained.
      PubDate: Tue, 10 Dec 2013 08:13:00 +000
       
  • Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A
           Low Cost Alternative to Experimentation

    • Abstract: Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs) are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.
      PubDate: Wed, 23 Oct 2013 08:55:06 +000
       
 
 
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