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315 results for “Optimization Model”
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Research ArticleOpen accessIn the abstractFine-Tuned and Optimized Transformer-Based Model for EEG Seizure DetectionPuspanjali Mallik, Ajit Kumar Nayak, Kumar Janardan Patra et al.Epilepsy, a widely recognized neurological disorder, results from irregularities in the transmission of electrical impulses among neurons in the brain. Over the last two decades, significant efforts have been made by researchers and clinicians to develop effective methods for its early detection and management. The electroencephalogram (EEG), a non-invasive tool used to monitor brainwave activity, has become a central device … …Journal of Computer Science, Vol. 21, Issue 11 · 11 Feb 2026 · 1,424 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimized Fractal Fusion Driven Deep Neural Network: An Efficient Hybrid Methodology for Disease Detection and Classification in Plant LeavesDeepkiran Munjal, Mrinal Pandey, Laxman SinghPlant diseases and pest attacks are among the major issues in agriculture, which have a great impact on crop growth and yield. Early and correct detection of these diseases is vital for effective management and keeping plant health at the desired level. This research proposes a new method for detection and classification of plant diseases, the Optimized Fractional Fusion Deep … …Journal of Computer Science, Vol. 21, Issue 12 · 8 Jan 2026 · 2,228 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdvanced Hierarchical Deep Learning Approach for Intrusion Detection: Hyperparameter Tuning and Performance Evaluation on the CICIDS DatasetAjeet Singh, Azath M, Shahazad Niwazi Qurashi et al.Intrusion Detection Systems (IDS) are essential in the communication security and integrity of contemporary network architectures. This study introduces a novel advanced hierarchical deep learning model for network intrusion detection using the CICIDS dataset-2019. To improve the detection accuracy, the proposed model architecture builds several deep learning layers embedded with hyperparameters' optimization. Additional preprocessing steps, including over-sampling and min-max scaling, … …Journal of Computer Science, Vol. 21, Issue 11 · 26 Dec 2025 · 2,158 accessesView PDFCite
Research ArticleOpen accessIn the abstractRandom Forest and Extreme Gradient Boosting with Bayesian Hyperparameter Optimization for Landslide Susceptibility Mapping in Penang Island, MalaysiaDorothy Anak Martin Atok, Soo See Chai, Kok Luong Goh et al.Landslide susceptibility models often face challenges of overfitting and overestimation. This research focuses on improving the predictive capabilities of the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms by applying Bayesian Hyperparameter Optimization (BayesOpt). Penang Island, a region in Malaysia prone to frequent landslides, was chosen as the study area. Ten Landslide Conditioning Factors (LCFs), including elevation, slope angle, … …Journal of Computer Science, Vol. 21, Issue 10 · 16 Dec 2025 · 3,113 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdvanced Grasshopper Optimization Scheduling Algorithm (AGOSA) for Multi-Cloud EnvironmentsS. Gowri, A. SumathiThe multi-cloud scheduling problem refers to the challenging task of efficiently allocating and managing resources across multiple cloud environments, involving the optimization of task scheduling, resource allocation to minimize makespan, reduce resource costs, and handle the complexity of multi-cloud environments while making optimal scheduling decisions. To overcome this issue. The paper proposes a novel scheduling algorithm, the Advanced Grasshopper Optimization … …Journal of Computer Science, Vol. 21, Issue 10 · 24 Nov 2025 · 2,657 accessesView PDFCite
Research ArticleOpen accessIn the abstractPleural Effusion Detection Using Optimized Pretrained ModelsRehab Fathi Ibrahim, Nashwa Mohamed Elsayed et al.Pleural effusion is an abnormal accumulation of fluid in the pleural cavity, posing a significant thoracic issue with various causes. Even with a variety of diagnostic techniques, accurate diagnosis remains challenging. Delays in diagnosis and treatment heighten the risks of the disease. In this paper, we introduce a novel approach by leveraging the Grey Wolf Optimizer (GWO) to systematically optimize … …Journal of Computer Science, Vol. 21, Issue 8 · 17 Oct 2025 · 2,317 accessesView PDFCite
Research ArticleOpen accessIn the abstractCloud Privacy Preservation Using Improved Squeezenet Based Data Sanitization and Improved Lyrebird Optimization-Based Optimal Key GenerationSmita Sharma, Sanjay TyagiMaintaining privacy in the cloud is critical, which implies reliable and effective models that are adapted to the difficulties presented by cloud settings. This paper introduces a comprehensive privacy preservation model tailored specifically for cloud environments, comprising five important phases such as data acquisition, normalization, feature extraction, sanitization and restoration. It begins with the meticulous collection of data from diverse … …Journal of Computer Science, Vol. 21, Issue 7 · 20 Aug 2025 · 3,202 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimization-Based Feature Selection and Ensemble Machine Learning Algorithms for Breast Cancer ClassificationSatyabrata Patro, Jyotirmaya Mishra, Bhavani Sankar PandaBreast cancer, which originates in a woman's breast tissue, is acknowledged to be a significant study topic in the medical field. For a long time, there has been a serious concern with the classification of breast cancer. Thus, to effectively categorize the breast cancer dataset, machine learning methods are designed and implemented. In previous research, the algorithms have classification accuracy … …Journal of Computer Science, Vol. 21, Issue 7 · 17 Jul 2025 · 3,180 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Conceptual Framework for Efficient and Sustainable Pruning Techniques in Deep Learning ModelsNyalalani Smarts, Rajalakshm Selvaraj, Venumadhav KuthadiThis research paper proposes a conceptual framework and optimization algorithm for pruning techniques in deep learning models, its focus is on key challenges such as model size, computational efficiency, inference speed and sustainable technology development. The aim of the framework is to transition from large neural networks to sparse, efficient models, indicating the benefits of pruning in improving model scalability … …Journal of Computer Science, Vol. 21, Issue 5 · 26 Apr 2025 · 5,276 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Optimization to Trust Enhanced Secure Routing Optimization with IABC and CGWAA for MANETYuvaraja Panneer Selvam, Suganthi PerumalRouting in Mobile Ad Hoc Networks (MANET) presents significant challenges due to the dynamic nature of network topology. Optimal route selection and efficient route discovery are matched by effective routing in MANET. This study proposes a Hybrid Optimization to Trust-Enhanced Secure Routing Optimization (HO-TESRO) designed to address these challenges in IoT environments by integrating metrics such as link stability, bandwidth, … …Journal of Computer Science, Vol. 21, Issue 4 · 19 Mar 2025 · 4,990 accessesView PDFCite