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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 abstractAn Edge Efficient and Lightweight Hybrid CNN Vision Transformer Network With CBAM for IoT Based Plant Disease DetectionNyabeye Pangop Doris-Khöler, Kengni Ngangmo OlgaDetecting crop disease early enough to act on it remains a bottleneck for smallholder and precision farming alike, particularly when diagnosis depends on manual inspection or lab analysis that cannot keep pace with an outbreak. We present AgriViTNet, a compact CNN-Vision Transformer hybrid built for exactly this constraint: It extracts local texture cues through convolution, relates them across the whole … …Journal of Computer Science, Vol. 22, Issue 9 · 20 Sep 2026 · 578 accessesView PDFCite
Research ArticleOpen accessIn the abstractMorphological Database and Quantification of Liberica Coffee Beans Using a Hybrid CNN ModelDioneces O. Alimoren, Francis G. Balazon, Jonnah R. Melo et al.Traditional evaluation of Liberica coffee (Kapeng Barako) in Batangas is commonly performed through manual visual inspection and sensory judgment, which are slow, subjective, and difficult to reproduce across farms, buyers, and grading personnel. These limitations can lead to inconsistent quality classification, weak traceability, and reduced bargaining power for local producers. This study develops and validates a web-based morphological analysis system … …Journal of Computer Science, Vol. 22, Issue 8 · 24 Aug 2026 · 1,337 accessesView PDFCite
Research ArticleOpen accessIn the abstractAutoencoder-Based Intrusion Detection for ARP Poisoning in SDN ControllersKago Seemela, Banyatsang Mphago, Thabo Semong, Phephisa MethulaAddress Resolution Protocol (ARP) poisoning is still a critical control plane attack in Software Defined Networks (SDNs), and spoofing of ARP mappings can trick SDNs controller and accomplish man in the middle, eavesdropping and denial of service attacks. Current SDN intrusion detection systems of ARP attacks rely on supervised learning and labelled attack data which is rather expensive and not … …Journal of Computer Science, Vol. 22, Issue 7 · 21 Jul 2026 · 869 accessesView PDFCite
Research ArticleOpen accessIn the abstractUncertainty-Aware Ensemble Models for Improved Defect Detection in Noisy DataMadhavi Perla, Gadi Lava Raju, A Radha Krishna et al.Software defect prediction plays a crucial role in ensuring software quality and reliability, especially as modern systems become more complex and data rich. This study introduces an uncertainty-aware ensemble learning framework aimed at improving defect classification performance in noisy and imbalanced datasets, particularly those from the PROMISE and NASA KC1 repositories. The proposed model integrates multiple classifiers in a multi-learner … …Journal of Computer Science, Vol. 22, Issue 4 · 27 Apr 2026 · 1,542 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 · 1,567 accessesView PDFCite
Research ArticleOpen accessIn the abstractSenior Expertise and Peer Consensus: A Comparative Analysis of AI and Clinician Measurements in Multi-Curve Scoliosis AssessmentFrank Ibañez Elijorde, Joselito F. Villaruz, Ma Beth S. Concepcion, Mylo N. SoriasoGiven the scarcity in the literature, this study explored the use of AI for multi-curve scoliosis assessment. Its performance was analyzed through comparison against a group of clinicians composed of one senior and five non-senior orthopedic surgeons. The analysis focused on Cobb angle measurement and identification of vertebral endplates across three curve regions, namely Main Thoracic (MT), Proximal Thoracic (PT), … …Journal of Computer Science, Vol. 22, Issue 3 · 9 Mar 2026 · 1,390 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdvancing Stroke Lesion Segmentation With U-Net Variants: Classical, Transfer Learning, and MRI Sequence-Specific Customized ApproachesSonia Flora Panesar, Amit P. GanatraIschemic stroke, caused by restricted cerebral blood flow, is a leading cause of death and disability worldwide. Accurate segmentation of stroke lesions in MRI is essential for timely diagnosis but remains labor-intensive when done manually. This study presents a comparative evaluation of three U-Net variants for automated ischemic stroke lesion segmentation using the ISLES 2022 dataset: (1) Classical U-Net with … …Journal of Computer Science, Vol. 22, Issue 2 · 27 Feb 2026 · 1,791 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 · 3,917 accessesView PDFCite
Research ArticleOpen accessIn the abstractPreparation of a Measure of Kinetic Analysis using Artificial Intelligence Methods for Volleyball CoachesMarwa Ahmed FadlCoaches need tool codified, modern and fast determine their ability to quantitative analysis using modern techniques to improve the performance and scalability while conducting education and training through the use of analytical skills ByumKaniki modern techniques with coaches some sports selected, as the researcher used the descriptive style screening for suitability and nature of the research, random sample was selected … …American Journal of Biochemistry and Biotechnology, Vol. 16, Issue 2 · 15 May 2020 · 6,835 accessesView PDFCite