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298 results for “Neural networks”
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Research ArticleOpen accessPhrase in the titlePrediction of Double Layer Grids' Maximum Deflection Using Neural NetworksReza Kamyab Moghadas, Kok Keong ChoongEfficient neural networks models are trained to predict the maximum deflection of two-way on two-way grids with variable geometrical parameters (span and height) as well as cross-sectional areas of the element groups. Backpropagation (BP) and Radial Basis Function (RBF) neural networks are employed for the mentioned purpose. The inputs of the neural networks are the length of the spans, L, … …American Journal of Applied Sciences, Vol. 5, Issue 11 · 30 Nov 2008 · 6,488 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleDetection of Respiratory Abnormalities Using Artificial Neural NetworksMahdi J. Baemani, Amirhasan Monadjemi, Payman MoallemProblem Statement: Lung disease is a major threat to the human health regarding the industrial life, air pollution, smoking, and infections. Lung function tests are often performed using spirometry. Approach: The present study aims at detecting obstructive and restrictive pulmonary abnormalities. Lung function tests are often performed using spirometry. In this study, the data were obtained from 250 volunteers with … …Journal of Computer Science, Vol. 4, Issue 8 · 31 Aug 2008 · 6,634 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleRule 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 accessPhrase in the titleSpeaker Identification: A Hybrid Approach Using Neural Networks and Wavelet TransformMuzhir Shaban Al-Ani, Thabit Sultan Mohammed, Karim M. AljeboryIn speaker identification systems, a database is constructed from the speech samples of known speakers. The approach implemented in this paper is hybrid, where the wavelet transform and neural networks are used together to form a system with improved performance. Features are extracted by applying a discrete wavelet transform (DWT), while a neural network (NN) is used for formulating the … …Journal of Computer Science, Vol. 3, Issue 5 · 31 May 2007 · 7,072 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleNeural Networks for Interferences Suppression in DS/CDMA with Rayleigh Fading Channel and Power Control ErrorY. Jabrane, R. Iqdour, B. Ait Essaid et al.The exponential chip weighting waveforms have been designed with the purpose of multiple access interferences (MAI) rejection based on the concept that the optimum despreading sequence in reference emphasizes the transitions in the received spreading signal of interest. The despreading sequence weighted by exponential chip waveforms was determined by only one parameter. The objective of this study, was to introduce … …Journal of Computer Science, Vol. 3, Issue 3 · 31 Mar 2007 · 6,382 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleTAGN-IDC: Implicit Discrete Constraints for Tsunami Aware Graph Neural Networks in Spatial Evacuation RoutingSularno, Wendi Boy, Putri Anggraini et al.Tsunami evacuation route requires rapid identification of safe evacuation paths under dynamically changing hazard conditions. A major challenge is incorporating hard binary road-blockage constraints caused by tsunami inundation while maintaining end-to-end differentiability for deep learning models. Existing approaches commonly rely on continuous relaxations or stochastic sampling techniques, which either compromise physical feasibility or increase computational complexity. This study proposes the … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 704 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAn Efficient Video Compression Framework using Deep Convolutional Neural Networks (DCNN)Kommerla Siva Kumar, P. Bindhu Madhavi, K. JanakiIn the current world, video streaming has grown in popularity and now accounts for a large percentage of internet traffic, making it challenging for service providers to broadcast videos at high rates while utilizing less storage space. To follow inefficient analytical coding design, previous video compression prototypes require non-learning-based designs. As a result, we propose a DCNN technique that integrates … …Journal of Computer Science, Vol. 18, Issue 7 · 13 Jul 2022 · 6,153 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleNeural Networks Based Time-Delay Estimation using DCT CoefficientsSamir J. Shaltaf, Ahmad A. MohammadThis study dealt with the problem of estimating constant time delay embedded into a received signal that was noisy, delayed and damped image of a known reference signal. The received signal was filtered, normalized with respect to the peak value it achieved and then transformed by the Discrete Cosine Transform (DCT) into DCT coefficients. Those DCT coefficients that were most … …American Journal of Applied Sciences, Vol. 6, Issue 4 · 30 Apr 2009 · 7,479 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleEmpirical Studies on Applications of Neural Networks as Approximation OracleK. K. Aggarwal, Yogesh Singh, Arvinder KaurExpected results are essential to any test design. In testing, an Oracle is a trusted source of expected results. This research examines the performance of Artificial Neural Network (ANN) as Approximation Oracle. The supervised learning paradigm has been used and found to be suitable for the purpose with the help of case studies. …Journal of Computer Science, Vol. 1, Issue 3 · 30 Sep 2005 · 6,628 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleSystematic 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