Review ArticleOpen accessIn the abstractA Systematic Review of Artificial Intelligence and Deep Learning Approaches for Cocoa Pod Disease DetectionHenry Techie-Menson, Michael Asante, Yaw Marfo Missah et al.Cocoa pod diseases destroy an estimated 700,000 metric tons of cocoa annually, threatening 40–50 million smallholder farmers across tropical regions. Accurate field diagnosis remains constrained by symptom similarity, labour-intensive scouting, and limited specialist access in remote communities. This paper presents a PRISMA-guided systematic review of 25 peer-reviewed studies on automated cocoa pod disease detection and classification (2016–2026), addressing architectural approaches, … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 733 accessesView PDFCite
Research ArticleOpen accessIn the abstractLightweight Deep Learning Model for TFT-LCD Pixel Defect Detection Using MobileNetV2J. Sheikshabjan, K. Dhanakodi, A.T. Rajamanickam et al.The proposed methodology aims to achieve high classification accuracy while maintaining computational efficiency and minimizing inference latency, making it highly suitable for real-time industrial inspection scenarios. This study evaluated five traditional transfer learning models (VGG16, ResNet50, MobileNetV2, Xception, and InceptionV3) for comparison in pixel defect detection. MobileNetV2-based implementation demonstrated a competitive average test accuracy of 0.9916, outperforming several baselines while … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 673 accessesView PDFCite
Research ArticleOpen accessIn the abstractCascaded MNetV3UNet: A Lightweight Two-Stage Architecture for High-Precision Brain Tumor Segmentation in MRIMayuri Popat, Sanskruti PatelPrecise brain tumor segmentation is an essential but quite challenging process in MRI images because of their irregular shapes, heterogeneous appearance, and low contrast with surrounding tissues. While U-Net-based architectures have achieved significant success, their high computational complexity limits deployment on resource-constrained systems. In this study, a Novel two-stage cascaded architecture, MNetV3UNet, is introduced, which employs the light-weight MobileNetV3-Large as … …Journal of Computer Science, Vol. 22, Issue 2 · 23 Feb 2026 · 1,788 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Vision-Based Approach to Real-Time Trust Evaluation within Multiagent SystemsAlan Kruger, Sun YiAs robotic autonomy increases, single-agent systems are evolving into more complex multiagent systems that require more factors for successful implementation. In exchange for this complexity, applications such as autonomous delivery services and reconnaissance swarms become possible when they are infeasible with only one agent. By incorporating Trust into the multiagent system, the complexity of the agent-to-agent relationships can be managed … …Journal of Mechatronics and Robotics, Vol. 9, Issue 1 · 21 Mar 2025 · 7,937 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 abstractParameter-Expanded Data Augmentation for Analyzing Nominal Data With Missing Values Using Multinomial Probit ModelsSuwash Silwal, Xiao ZhangNominal data occur in many scientific fields, such as health-related studies, transportation, and econometrics. Due to the ubiquitousness of missing data, it is inevitable to analyze nominal data with missing values. As is well known, statistical methods for analyzing nominal variables are quite limited. The Multinomial Probit (MNP) model has been an essential tool for analyzing nominal categorical data, but … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 23 May 2026 · 1,804 accessesView PDFCite
Research ArticleOpen accessIn the abstractIoT Anomaly Detection Using Picture Fuzzy Clustering ApproachFehmin Nadira Laskar, Vijo Arul Selvi M., Fokrul Alom Mazarbhuiya et al.Improving the effectiveness of security systems without slowing them down is a major challenge in cybersecurity. Many methods have been explored for detecting anomalous behaviour in network data, with fuzzy set-based approaches standing out for their potential. The Internet of Things (IoT) consists of many connected devices that constantly generate large amounts of data and perform tasks in real time. … …Journal of Computer Science, Vol. 22, Issue 3 · 6 Mar 2026 · 1,515 accessesView PDFCite
Research ArticleOpen accessIn the abstractLT-LBP-Based Spatial Texture Feature Extraction with Deep Learning for X-Ray ImagesPankaja Lakshmi P., Sivagami M.The novel logarithmic transformation of Local Binary Pattern (LT-LBP) for texture feature extraction is used with Deep Learning (DL) models. The Computational complexity in the DL models asymptotically increases with the data sesssst and the depth of the layers in Convolutional Neural Networks (CNN). Pre-processing the images before training the DL model holds a strong impact on the operation speed … …Journal of Computer Science, Vol. 20, Issue 1 · 18 Dec 2023 · 7,190 accessesView PDFCite