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37 articles for “Neural network modeling”
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Research ArticleOpen accessIn the abstractA Dynamic Temporal Neuro Fuzzy Inference System for Mining Medical DatabasesR. Sethukkarasi, A. KannanThe analysis and representation of temporal data are becoming increasingly important in many areas of research and application. The existing Fuzzy Cognitive Maps (FCMs) are efficient modeling method for knowledge representation and fuzzy reasoning in time series analysis. In the past, it was used to represent a complex causal system as a collection of concepts and causal relationships among concepts. … …Journal of Computer Science, Vol. 8, Issue 11 · 7 Nov 2012 · 6,358 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnvironmental Modeling for Traffic Noise in Urban AreaFrancis Cirianni, Giovanni LeonardiTraffic noise can be classified among the worst factors in terms of damage to people’s health and well-being. The trend of noise pollution modeling variable from the smart result of classic regressive models in the performance of many assessment models based on mathematical expressions, genetic algorithms and neural networks (of GRNN type, General Regression Neural Network). A methodological approach for … …American Journal of Environmental Sciences, Vol. 8, Issue 4 · 18 Jun 2012 · 7,438 accessesView PDFCite
Research ArticleOpen accessIn the abstractElectricity Load Forecasting based on Framelet Neural Network TechniqueMohammed K. AbdLoad forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This study shows Electricity Load Forecasting modeling based on Framelet Neural Network Technique (FNN) for Baghdad City. Framelet technique is implemented to the time series data, decomposing the data into number of Framelet coefficient signals. The decomposed signals … …American Journal of Applied Sciences, Vol. 6, Issue 5 · 31 May 2009 · 8,274 accessesView PDFCite
Research ArticleOpen accessIn the abstractANN Based Prediction of Effect of Reinforcements on Abrasive Wear Loss and Hardness in a Hybrid MMCP. Sathyabalan, V. Selladurai, P. SakthivelProblem statement: The reinforcements added to an alloy lead to variation in properties. The content and size of the reinforcement influences the properties of composites. Very little research has been carried out in hybrid composites. Work on hybrid LM6 aluminium alloy metal matrix composites (MMC) with flyash and SiC has been initiated here. The effect of the four parameters, size … …American Journal of Engineering and Applied Sciences, Vol. 2, Issue 1 · 31 Mar 2009 · 7,551 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference SystemsSamira Chabaa, Abdelouhab ZeroualProblem statement: The statistical modeling for predicting network traffic has now become a major tool used for network and is of significant interest in many domains: Adaptive application, congestion and admission control, wireless, network management and network anomalies. To comprehend the properties of IP-network traffic and system conditions, many kinds of reports based on measured network traffic data have been … …Journal of Computer Science, Vol. 5, Issue 2 · 28 Feb 2009 · 7,533 accessesView PDFCite
Research ArticleOpen accessIn the abstractPower Enhancement of Weightlifters during Snatch through Reducing Torque on Joints by Particle Swarm OptimizationFirooz Salaami, Nima Jamshidi, Mostafa Rostami, Siamak NajarianIn this research, an athlete's body on sagittal plane in tension phase of snatch weightlifting has been modeled in two dimensions for calculating the generated torques in joints. The error back propagation multi-layer perceptrons has been used for modeling the torque through changing the angular velocity, angular acceleration and absolute angle of each segment. Finally, the torque in joints has … …American Journal of Applied Sciences, Vol. 5, Issue 12 · 31 Dec 2008 · 7,101 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting Air Temperatures Using Time Series Models and Neural-based AlgorithmsMahmoud M. Smadi, Farouq S. MjalliRecently, neural network models have been popular and found useful for forecasting a wide variety of time series data in many disciplines. This is due to their favorable modeling properties of simplicity, fault and noise tolerance and their capability to adapt to process changes. Nevertheless, applications in climatology have been less widespread than other disciplines such as economics. In this … …Journal of Mathematics and Statistics, Vol. 3, Issue 2 · 30 Jun 2007 · 7,272 accessesView PDFCite
Research ArticleOpen accessIn the abstractQualitative Ferromagnetic Hysteresis ModelingM. Mordjaoui, M. Chabane, B. Boudjema, R. DairaIn determining the electromagnetic properties of magnetic materials, hysteresis modeling is of high importance. Many models are available to investigate those characteristics but they tend to be complex and difficult to implement. A new qualitative hysteresis model for ferromagnetic core presented, based on the function approximation capabilities of adaptive neuro-fuzzy inference system (ANFIS). The proposed ANFIS model combined the neural … …Journal of Computer Science, Vol. 3, Issue 6 · 30 Jun 2007 · 7,076 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric ModelsAbdel K.M. Baareh, Alaa F. Sheta, Khaled A. KhnaifesForecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN) became quite successful tool to handle time series modeling problem. This paper … …Journal of Computer Science, Vol. 2, Issue 10 · 31 Oct 2006 · 6,962 accessesView PDFCite
Research ArticleOpen accessAll words, across the recordSystematic Literature Review of Graph Neural Networks in Disaster Management: Methods, Applications, and Future DirectionsSularno, Wendi Boy, Putri Anggraini et al.The increasing frequency and complexity of natural disasters underscore the urgent need for intelligent systems that support timely and effective decision-making. Graph Neural Networks (GNNs) have emerged as a powerful deep-learning paradigm for modeling spatial and relational data, offering distinct advantages for disaster management. This study presents a Systematic Literature Review (SLR) of GNN-based approaches for disaster mitigation, emergency response, … …Journal of Computer Science, Vol. 22, Issue 2 · 17 Feb 2026 · 2,036 accessesView PDFCite