Research ArticleOpen accessIn the abstractPerformance Assessment of CPU Scheduling Algorithms: A Scenario-Based Approach with FCFS, RR, and SJFOlaa Hajjar, Escelle Mekhallalati, Nada Annwty et al.This study presents an extensive examination of CPU scheduling algorithms, focusing on the First-Come, First-Served (FCFS), Round-Robin (RR), and Shortest-Job-First (SJF) strategies through a carefully designed scenario-based approach. By deploying a Java-based simulation to dynamically generate random process arrival and burst times, this study simulates a variety of operational conditions to test these scheduling algorithms’ adaptability and performance in environments … …Journal of Computer Science, Vol. 20, Issue 9 · 16 Jun 2024 · 9,114 accessesView PDFCite
Research ArticleOpen accessIn the abstractDesign and Evaluation of a Blockchain-Integrated Secure Cloud Storage SystemWalde Rajesh Baliram, Bashir Alam, M. N. DojaCloud storage is a critical part of modern computer systems that has a centralized structure, which opens sensitive data to security risks, including unauthorized access, insider threats, and data tampering. In order to mitigate these issues, this paper proposed a novel cloud storage system with built-in blockchain, AES-256 encryption, access control using smart contracts, and a dual-hash integrity check system. … …Journal of Computer Science, Vol. 22, Issue 7 · 22 Jul 2026 · 917 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhanced Lifetime Maximization Using Metaheuristics-Based Unequal Clustering With Routing Protocol on Wireless Sensor NetworksN. E. Kanaghadhara, R. SubanThe Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors that monitor environmental or physical factors such as sound, pressure, or temperature, and pass the information directly through the network to a sink or central location. Clustering and routing are essential in WSNs to optimize their longevity, scalability, and efficiency. Clustering includes grouping Sensor Nodes (SN) into clusters, each … …Journal of Computer Science, Vol. 22, Issue 7 · 21 Jul 2026 · 686 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Intrusion Detection Framework for MEC-Enabled IoT NetworksRofaida Tawfik, Abdelfattah Hegazy, Hesham Dahshan, Ahmed Gaber AbuabdallahMobile Edge Computing (MEC) has emerged as a promising paradigm for supporting latency-sensitive Internet of Things (IoT) applications by bringing computational resources closer to data sources. However, the distributed nature of MEC environments increases exposure to network-based security threats, particularly network intrusions that can impact both system reliability and task offloading efficiency. This paper proposes a security-aware framework that integrates … …Journal of Computer Science, Vol. 22, Issue 4 · 1 Apr 2026 · 1,927 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredictive Mathematical Modeling and Classification of Retail Sales Orders Using AI Machine Learning TechniquesMohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Hamzeh ZureigatThis study presents a systematic mathematical model known as a low-code approach to classifying retail sales orders by size using AI machine learning techniques within the Orange Data Mining platform. Leveraging a real-world sales dataset sourced from Kaggle, we implemented and evaluated ten classification models, including ensemble learners (AdaBoost, Gradient Boosting), probabilistic classifiers (Naïve Bayes), distance-based methods (kNN), and interpretable … …Journal of Computer Science, Vol. 22, Issue 3 · 10 Mar 2026 · 1,573 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comparative Study of Resampling, Cost-Sensitive, and Ensemble Techniques for Handling Class Imbalance in Indonesian Financial DataGunawan Kurnia, Ditdit Nugeraha UtamaHandling class imbalance is a critical challenge in machine learning applications, particularly in financial domains where minority instances often represent significant anomalies such as fraud or audit risks. Various oversampling and undersampling methods were tested, alongside cost-sensitive adjustments and ensemble models including Random Forest, AdaBoost, Gradient Boosting, and XGBoost. The evaluation, based on 10-fold stratified cross-validation and performance metrics such … …Journal of Computer Science, Vol. 22, Issue 2 · 27 Feb 2026 · 1,882 accessesView PDFCite
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 abstractA Novel Deep Learning Approach for Tomato Leaf Disease Detection Using Optimized CNN ArchitectureSunil K. Vithalani, Vipul K. DabhiEarly disease detection in plants is essential for sustaining agricultural yield and guaranteeing food security. A comparative analysis of transformer-based and convolutional based deep learning models for classifying tomato leaf diseases is presented. Specifically, it examines the performance of a Vision Transformer (ViT), tested both in a form of scratch training setup and through transfer learning, against well-known CNN architectures … …Journal of Computer Science, Vol. 22, Issue 1 · 7 Feb 2026 · 3,924 accessesView PDFCite
Research ArticleOpen accessIn the abstractCommunity Aware Recommendation System with Explicit and Implicit Link PredictionMuhammed E Abd Alkhalec Tharwat, Mohd Farhan Md Fudzee, Shahreen Kasim et al.Recommendation systems are essential tools that help users discover content they may be interested in, amidst the vast amount of information available online. However, current methods, such as using historical user-item interactions and collaborative filtering, have limitations in accurately predicting user preferences. Our research aims to address these challenges and improve the performance of recommendation systems. In this article, we … …Journal of Computer Science, Vol. 19, Issue 8 · 19 Jul 2023 · 5,866 accessesView PDFCite