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154 results for “network optimization”
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Research ArticleOpen accessIn the keywordsA Genetic Based Neuro Fuzzy Technique for Process Grain Sized Scheduling of Parallel JobsSadasivam Vijayakumar Sudha, Keppanagowder ThanushkodiProblem statement: In this study, we present the development of genetic algorithm based neuro fuzzy technique for process grain sized in scheduling of parallel jobs with the help of real lIfe workload data. Approach: The study uses the rule based scheduling strategy for the scheduling and classIfies all possible scheduling strategies. The rule bases are developed with the help of … …Journal of Computer Science, Vol. 8, Issue 1 · 26 Oct 2011 · 7,339 accessesView PDFCite
Research ArticleOpen accessIn the keywordsMemory Convergence and Optimization with Fuzzy PSO and ACSSubhash C. Pandey, Dr. P. MisraAssociative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this study, we introduce fuzzy swarm particle optimization technique for convergence of associative neural memories based on fuzzy set theory. A Fuzzy … …Journal of Computer Science, Vol. 4, Issue 2 · 28 Feb 2008 · 7,169 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAnt Colony Based Node Disjoint Hybrid Multi-path Routing for Mobile Ad Hoc NetworkB. Kalaavathi, K. DuraiswamyMobile Ad hoc Networks are characterized by multi-hop wireless links, without any infrastructure and frequent node mobility. A class of ant colony based routing protocols has recently gained attention because of their adaptability to the network changes. AntHocNet is ant colony based hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. Multi-path routing represents a … …Journal of Computer Science, Vol. 4, Issue 2 · 28 Feb 2008 · 7,043 accessesView PDFCite
Research ArticleOpen accessIn the keywordsHEURISTIC DISCRETIZATION METHOD FOR BAYESIAN NETWORKSMariana D.C. Lima, Silvia M. Nassar, Pedro Ivo R.B.G. Rodrigues et al.Bayesian Network (BN) is a classification technique widely used in Artificial Intelligence. Its structure is a Direct Acyclic Graph (DAG) used to model the association of categorical variables. However, in cases where the variables are numerical, a previous discretization is necessary. Discretization methods are usually based on a statistical approach using the data distribution, such as division by quartiles. In … …Journal of Computer Science, Vol. 10, Issue 5 · 18 Jan 2014 · 7,054 accessesView PDFCite
Research ArticleOpen accessIn the keywordsOPTIMIZING LOCALIZATION ROUTE USING PARTICLE SWARM-A GENETIC APPROACHL. Lakshmanan, D. C. TomarOne of the most key problems in wireless sensor networks is finding optimal algorithms for sending packets from source node to destination node. Several algorithms exist in literature, since some are in vital role other may not. Since WSN focus on low power consumption during packet transmission and receiving, finally we adopt by merging swarm particle based algorithm with genetic … …American Journal of Applied Sciences, Vol. 11, Issue 3 · 20 Jan 2014 · 7,381 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAdaptive Resonance Theory Training Parameters: Pretty Good SetsTaisir Mohammad Eldos, Abdulaziz Suleiman AlmazyadProblem statement: ART1 artificial neural networks offer good tools for test clustering, where no expert is needed if the system is well trained. However, having no output reference for the input patterns makes it hard to judge the quality of the training. Moreover, the performance depends to a great extent on a set of training parameters. Designers follow some recommendations … …Journal of Computer Science, Vol. 6, Issue 12 · 6 Nov 2010 · 7,155 accessesView PDFCite
Research ArticleOpen accessIn the abstractLeveraging Modified Cascaded Fully Convolutional Neural Networks and Optimized Swin U-Net for Liver Lesion PredictionSugandha Chakraverti, Tejaswi Khanna, Vijay ShuklaLiver lesions are abnormal growths that can develop for various reasons, some of which are noncancerous (benign) while others are cancerous. Machine learning and, deep learning techniques are employed for liver health assessment and lesion detection, but the quality and, diversity of training data can hinder the performance, leading to overfitting or underfitting. These models often need extensive pre-processing and … …Journal of Computer Science, Vol. 22, Issue 10 · 8 Oct 2026 · 372 accessesView PDFCite
Research ArticleOpen accessIn the abstractTAGN-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 accessIn the abstractDynamic Weighting Technique Based Arctic Puffin Optimization for Energy Efficient Data Transmission in Wireless Sensor NetworksM. Bharathraj Kumar, S. Avinash, Prakash G. Gani, Prathapchandra ShettyWireless Sensor Networks (WSNs) are designed using various sensor nodes, which are employed for communication in diverse applications that include healthcare, industrial areas, and smart cities. Specifically, WSNs demand energy-efficient clustering and routing mechanisms that assist in extending network lifetime due to the limited battery capacity of sensor nodes. However, many existing meta-heuristic-based approaches incorporate fixed objective weights that struggle … …Journal of Computer Science, Vol. 22, Issue 9 · 10 Sep 2026 · 767 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Crow Search Optimization Based Feature Selection With Optimal DNN for Big Data ClassificationC. Mahesh, J. Ruby Elizabeth, S. Gnana Selvan et al.Big data analytics has become popular due to its applicability in various real-time applications. To attain better performance, big data can be analyzed using the machine learning and deep learning models at recent times, various domains have started to deal with big dataset which involves numerous sets of features. Repetitive and unwanted features, which reduces the classification performance can be … …Journal of Computer Science, Vol. 22, Issue 6 · 30 Jun 2026 · 1,244 accessesView PDFCite