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26 results for “Simulation Training”
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Research ArticleOpen accessPhrase in the titleGOSTE: Group Oriented Simulation Training EnvironmentKorichi Ahmed, Belattar BrahimThe purpose of this study is to explore the contribution of the CSCW (Computer Supported Cooperative Work) in the collaborative project conduct and more specifically in the conduct of a simulation training project. We justify the adequacy of the CSCW concepts with the method of conduct of a simulation project. The practice of the web-based simulation as well as the … …Journal of Computer Science, Vol. 3, Issue 10 · 31 Oct 2007 · 6,982 accessesView PDFCite
Research ArticleOpen accessIn the keywordsStochastic Estimator-Based Wireless Traffic Control SchemesFrancis Joseph Ogwu, Mohammad Talib, Ganiyu Adesola AderounmuRecent works on Available Bit Rate (ABR) traffic control have generated efficient control schemes for ABR traffic on Asynchronous Transfer Mode (ATM) network. This study examines the improved performance envisaged if these control schemes adjust dynamically to the varying ABR bandwidth capacity in a stochastic manner instead of conventional deterministic approach .The performance difference between setting explicit rate deterministically for … …Journal of Computer Science, Vol. 3, Issue 12 · 31 Dec 2007 · 7,099 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intrusion Detection System Using Optimized Deep Learning for IoT NetworksChempavathy B., Mouleeswaran S. K.In the rapidly evolving landscape of Internet of Things (IoT) networks, assuring security is paramount. Cyber-attacks on IoT networks are evolving in complexity and scale. Intruders employ diverse tactics, including malware, Denial of Service (DoS) attacks, along with unauthorized access. This research aims to propose an optimized Deep Learning (DL) based Intrusion Detection System (IDS) to bolster security within IoT … …Journal of Computer Science, Vol. 21, Issue 8 · 9 Oct 2025 · 2,993 accessesView PDFCite
Research ArticleOpen accessIn the abstractFeasibility of Hybrid Neuro-Fuzzy (ANFIS) Machine Learning Model with Classical Multi-Linear Regression (MLR) For the Simulation of Solar Radiation: A Case Study Abuja, NigeriaNajashi B. Gafai, Asia'u Talatu BelgoreThe extremely variable nature of solar radiation makes it difficult for solar power plants to keep up with predicted power output and demand curves. As a result, solar radiation simulation is crucial to the efficient design, administration, and operation of any solar power plant. With only partially satisfactory results, empirical models have been routinely employed in Nigeria to predict solar … …Energy Research Journal, Vol. 13, Issue 1 · 21 Jul 2022 · 10,032 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Attention-Based Stacked Bi-LSTM Model for Automated MultiImage CaptioningPaspula Ravinder, Saravanan SrinivasanIn recent days, the process of medical image captioning is become a prominent field. The distinct characteristics of medical imaging data provide a number of challenges when captioning medical images. Also, the variability in image modalities makes it difficult to generate an effective captioning process. Thus, the proposed study aims to design a novel Multi-image Captioning Hybrid Attention Model to … …Journal of Computer Science, Vol. 21, Issue 4 · 8 Mar 2025 · 5,157 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Evolving Autoregressive Predictor for Time Series ForecastingDe Z. Li, Wilson Wang, Fathy IsmailAutoregressive (AR) model is a common predictor that has been extensively used for time series forecasting. Many training methods can used to update AR model parameters, for instance, least square estimate and maximum likelihood estimate; however, both techniques are sensitive to noisy samples and outliers. To deal with the problems, an evolving AR predictor, EAR, is developed in this study … …American Journal of Engineering and Applied Sciences, Vol. 8, Issue 1 · 8 Apr 2015 · 6,965 accessesView PDFCite
Research ArticleOpen accessIn the abstractHALF OF THRESHOLD ALGORITHM: AN ENHANCED LINEAR ADAPTIVE SKIPPING TRAINING ALGORITHM OR MULTILAYER FEEDFORWARD NEURAL NETWORKSManjula Devi Ramasamy, Kuppuswami Subbaraya GounderMultilayer Feed Forward Neural Network (MFNN) has been successfully administered architectures for solving a wide range of supervised pattern recognition tasks. The most problematic task of MFNN is training phase which consumes very long training time on very huge training datasets. An enhanced linear adaptive skipping training algorithm for MFNN called Half of Threshold (HOT) is proposed in this research … …American Journal of Applied Sciences, Vol. 11, Issue 1 · 22 Nov 2013 · 7,016 accessesView PDFCite
Research ArticleOpen accessIn the abstractData-Driven Forecasting Schemes: Evaluation and ApplicationsJosip Vrbanek Jr, Wilson WangA reliable multi-step predictor is very useful to a wide array of applications to forecast the behavior of dynamic systems. The objective of this paper is to develop a more robust data-driven predictor for time series forecasting. Based on simulation analysis, it is found that multi-step-ahead forecasting schemes based on step inputs perform better than those based on sequential inputs. … …Journal of Computer Science, Vol. 3, Issue 9 · 30 Sep 2007 · 6,565 accessesView PDFCite
Research ArticleOpen accessIn the abstractRule Extraction from Radial Basis Functional Neural Networks by Using Particle Swarm OptimizationM. R. Senapati, I. Vijaya, P. K. DashRadial basis functional neural networks (RBFNN) provide an outstanding possibility for generating rules for solving pattern classification problems. One of the most important factors in RBFNN is finding out the center and spread. This paper examines rules extracted from RBF networks trained by Particle swarm Optimization (PSO). The selection of the RBFNN centers, spreads and the network weights can be … …Journal of Computer Science, Vol. 3, Issue 8 · 31 Aug 2007 · 6,784 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnergy and Carbon Flux Coupling: Multi-ecosystem Comparisons Using Artificial Neural NetworkAssefa M. Melesse, Rodney S. HanleyA multi-ecosystems carbon flux simulation from energy fluxes is presented. A new statistical learning technique based on Artificial Neural Network (ANN) back propagation algorithm and multi-layer perceptron architecture was used in the CO2 simulation. Four input layers (net radiation, soil heat flux, sensible and latent heat flux) were used for training (calibration) and testing (verification) of model outputs. The 15-days … …American Journal of Applied Sciences, Vol. 2, Issue 2 · 28 Feb 2005 · 7,347 accessesView PDFCite