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34 articles for “Training Speed”
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Research ArticleOpen accessIn the abstractAdaptive and Neural Network Based Control of Pitch of Unmanned Aerial VehiclesMackenzie T. Matthews, Sun YiThe objective is to develop a new control strategy for quadrotor types of Unmanned Aerial Systems (UAVs) that assist in closing the existing research gaps between the undesired uncertainties and current control systems. This research investigates a system modeling and the effectiveness of error elimination through adaptation in the change of the plant/system’s output. The UAV is controlled via Wi-Fi … …Journal of Mechatronics and Robotics, Vol. 3, Issue 1 · 18 Nov 2019 · 6,489 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Enhanced Training- Based Arabic Sign Language Virtual Interpreter Using Parallel Recurrent Neural NetworksMohamed A. AbdouIntelligent machine translation systems have a remarkable importance in integrating people with disabilities in community. Arabic to Arabic sign language systems are limited. Deep Learning (DL) was successfully applied to problems related to music information retrieval, image recognition and text recognition, but its use in sign language recognition is rare. This paper introduces an automatic virtual translation system from Arabic … …Journal of Computer Science, Vol. 14, Issue 2 · 20 Feb 2018 · 7,628 accessesView PDFCite
Research ArticleOpen accessIn the abstractPARALLEL IMPLEMENTATION OF EXPECTATION-MAXIMISATION ALGORITHM FOR THE TRAINING OF GAUSSIAN MIXTURE MODELSG. F. Araújo, H. T. Macedo, M. T. Chella et al.Most machine learning algorithms need to handle large data sets. This feature often leads to limitations on processing time and memory. The Expectation-Maximization (EM) is one of such algorithms, which is used to train one of the most commonly used parametric statistical models, the Gaussian Mixture Models (GMM). All steps of the algorithm are potentially parallelizable once they iterate over … …Journal of Computer Science, Vol. 10, Issue 10 · 9 Jul 2014 · 6,937 accessesView PDFCite
Research ArticleOpen accessIn the abstractRegularization Method for Solving Denoising and Inpainting Task Using Stacked Sparse Denoising AutoencodersPavel Vyacheslavovich Skribtsov, Sergey Olegovich SurikovThis article offers a regularization method for training stacked sparse denoising autoencoders aimed at designing model description of objects used for image denoising and inpainting. The offered regularization method allows increasing the generalizing ability of model description, which results in greater stability of denoising methods using it with regard to variation of the noise type. This makes the offered method … …American Journal of Applied Sciences, Vol. 13, Issue 1 · 13 Jan 2016 · 7,609 accessesView PDFCite
Research ArticleOpen accessIn the abstractHYBRID FEATURE SELECTION ALGORITHM FOR INTRUSION DETECTION SYSTEMSeyed Reza Hasani, Zulaiha Ali Othman, Seyed Mostafa Mousavi KahakiNetwork security is a serious global concern. Usefulness Intrusion Detection Systems (IDS) are increasing incredibly in Information Security research using Soft computing techniques. In the previous researches having irrelevant and redundant features are recognized causes of increasing the processing speed of evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates dimension of data and reduce redundancy … …Journal of Computer Science, Vol. 10, Issue 6 · 31 Jan 2014 · 7,350 accessesView PDFCite
Research ArticleOpen accessIn the abstractA COMPARATIVE ANALYSIS OF OPTIMIZATION TECHNIQUES FOR ARTIFICIAL NEURAL NETWORK IN BIO MEDICAL APPLICATIONSV. Saishanmuga Raja, S. P. RajagopalanIn this study we compare the performance of three evolutionary algorithms such as Genetic Algorithm (GA) Particle Swarm Optimization (PSO) and Ant-Colony Optimization (ACO) which are used to optimize the Artificial Neural Network (ANN). Optimization of Neural Networks improves speed of recall and may also improve the efficiency of training. Here we have used the Ant colony optimization, Particle Swarm … …Journal of Computer Science, Vol. 10, Issue 1 · 12 Nov 2013 · 7,287 accessesView PDFCite
Research ArticleOpen accessIn the abstractResearches Concerning the using of Moven© Inertial Navigation System in Monitoring the Male Triple Jump EventMihilescu Liliana, Mihai Ilie, Bidiugan RaduProblem statement: This study aims to highlight ways of using Moven© inertial navigation system applied in the monitoring of the male triple jump event technique. Inertial navigation system involves the use of some elements to determine the coordinates and speed of a body by processing information related to its acceleration. Approach: The research results point out that this equipment can … …Journal of Social Sciences, Vol. 7, Issue 3 · 16 Jul 2011 · 7,365 accessesView PDFCite
Research ArticleOpen accessIn the abstractImproving the Performance of Backpropagation Neural Network Algorithm for Image Compression/Decompression SystemOmaima N.A. AL-AllafProblem statement: The problem inherent to any digital image is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop algorithms that compress images to lower data rates with better quality. Artificial neural networks are becoming attractive in image processing where high computational performance and parallel architectures are required. … …Journal of Computer Science, Vol. 6, Issue 11 · 26 Oct 2010 · 8,370 accessesView PDFCite
Research ArticleOpen accessIn the abstractMethod of Object Detection for Mobile RobotSurachai PanichProblem statement: In general, there are two steps of object detection, which are object generation, where the locations of possible objects are in an image and object verification, where tests are performed to verify the presence of object in an image. Approach: The purpose of the proposed object recognition system was to detect the object which is in front the … …Journal of Computer Science, Vol. 6, Issue 10 · 24 Aug 2010 · 7,428 accessesView PDFCite
Research ArticleOpen accessIn the abstractPrediction of the Daily Mean PM10 Concentrations Using Linear ModelsJ. C.M. Pires, F. G. Martins, S. I.V. Sousa et al.The performance of five linear models to predict the daily mean PM10 concentrations was compared. The linear models proposed were: (i) multiple linear regression; (ii) principal component regression; (iii) independent component regression; (iv) quantile regression; and (v) partial least squares regression. The study was based on data from an urban site in Oporto Metropolitan Area and the analysed period was … …American Journal of Environmental Sciences, Vol. 4, Issue 5 · 31 Oct 2008 · 7,132 accessesView PDFCite