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Research ArticleOpen accessIn the abstractExplainability Aware Heterogeneous Temporal Graph Transformer for Automated Genetic Disorder Detection from Genomic DataPuvaneswari Subramaniapillai, Indirani Govindasamy, Revathy GurumurthyGenetic disorders affect millions worldwide, posing major challenges for accurate diagnosis and effective treatments. New developments in genomic technologies, especially Next-Generation Sequencing (NGS), have enhanced the classification of genetic modification and improved diagnostic abilities. Moreover, the Machine Learning (ML) and Deep Learning (DL) methods emerged as efficient tools for evaluating the intricate genomic data and determining the patterns, which can … …Journal of Computer Science, Vol. 22, Issue 11 · 14 Oct 2026 · 581 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Kidney Stone Detection Accuracy Using Image Processing and a Hybrid Convolutional Deep Belief NetworkGeetha M. N, Barkathulla A, S. Nandhini Devi et al.Automated CKD is a rapidly expanding non-communicable ailment that significantly contributes to global mortality and morbidity. CKD impacts more than 10% of the worldwide population, leading to millions of deaths annually. This paper proposes an enhanced deep learning model for renal disease detection that integrates convolutional and involution layers to enhance classification accuracy. CKD is a worldwide health issue characterised … …Journal of Computer Science, Vol. 22, Issue 10 · 9 Oct 2026 · 774 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing SQL Injection Detection: A Machine Learning Approach Using Network Flow DataP. Vinoth, K. Muthamil Sudar, S. MuthukumarBackground: SQL injection is one of the cyberattacks which occurs in web application vulnerabilities. It targets the poor input validation and enables the attackers to inject malicious queries into the database fields in any application. It will grant access to unauthorized users and data modification, or they will take control over the complete database. To prevent this, there are several … …Journal of Computer Science, Vol. 22, Issue 8 · 18 Aug 2026 · 1,059 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Explainable AI (XAI) Framework for Intrusion Detection and Identification to Secure IoT NetworksVibhor Harit, Rajeev Dahiya, Umang Garg, Anil KumarThe Internet of Things (IoT) encompasses a wide range of applications from wearable devices to smart homes, as well as industrial automation and smart cities. As IoT networks and devices continue to grow rapidly, security has become crucial. There are many techniques utilized to provide security aspects in IoT networks. These security aspects focus on mitigating risks and fixing vulnerabilities … …Journal of Computer Science, Vol. 22, Issue 7 · 23 Jul 2026 · 1,014 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Misuse and Anomaly Intrusion Detection System Using Hybrid Supervised and Unsupervised Data Mining ApproachesHoma Molavi, Mohammad KhanbabaeiIntrusion Detection Systems (IDSs) play a crucial role in monitoring computer network security. Data mining and machine learning techniques facilitate the identification of intrusion patterns within large volumes of data. Hybrid data mining models have become increasingly popular in IDSs due to their enhanced effectiveness. This study presents a novel hybrid IDS that combines misuse and anomaly detection by integrating … …Journal of Computer Science, Vol. 22, Issue 6 · 6 Jul 2026 · 1,011 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntegrated Monitoring of Endocrine-Disrupting Chemicals: Analytical Chemistry and Biological Assessment ApproachesChangjin Li, Jing Huo, Zijun Dou et al.Endocrine-Disrupting Chemicals (EDCs) in aquatic environments pose significant threats to ecosystems and human health. These compounds interfere with hormonal regulation at low concentrations and have non-monotonic dose-response relationships, posing challenges to conventional risk assessment. This review systematically compares current EDC monitoring methods, including instrumental chemical analysis and receptor-based biological detection systems, to evaluate sensitivity thresholds, sample throughput, and mechanistic selectivity … …American Journal of Biochemistry and Biotechnology, Vol. 22, Issue 2 · 3 Jul 2026 · 1,089 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnsemble Learning-Based Hybrid Explainable Intrusion Detection System With SMOTE for Enhanced Detection and Interpretability in CybersecurityMohammad Subhi Al-Batah, Taqieddin AldrousWith cyberattacks becoming increasingly sophisticated, there is a growing need for Intrusion Detection Systems (IDS) that are both accurate and interpretable. This study introduces a hybrid IDS framework that integrates Random Forest (RF) and Light Gradient Boosting Machine (LGBM) classifiers with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. To enhance transparency and trust, the system also incorporates … …Journal of Computer Science, Vol. 22, Issue 5 · 19 May 2026 · 1,284 accessesView PDFCite
Research ArticleOpen accessIn the abstractImplementation and Performance Evaluation of YOLOv8 for Wheat Fungal Disease DetectionShivani Sood, Shallu Duggal, Monika Sethi et al.Among the many difficulties in producing wheat, a vital food source for the world, there are biotic variables like fungal diseases, which drastically lower crop output. Around the world, biotrophic fungi particularly, leaf rust, powdery mildew, and yellow rusts-have become a constant threat to wheat production. Traditional methods of identification and mitigation are labor-intensive, slow, and imprecise, limiting therefore early … …Journal of Computer Science, Vol. 22, Issue 4 · 2 May 2026 · 1,609 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning and Image Analytics for Forest Land Management: Classification, and Object DetectionDevam Dixit, Aditya Bhavsar, Shreyas Jani, Nilesh YadavRapid urbanization and industrialization have contributed to deforestation which has led to a severe degradation of the environment and biodiversity loss. Traditional field survey methods are not applicable in the monitoring of large forest areas due to the fact that such methods are manual, costly and time consuming as well as subject to human error. The current methods of remote … …Journal of Computer Science, Vol. 22, Issue 3 · 26 Mar 2026 · 1,602 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Brain MR Image Quality Using CNN With Best Denoising Modality for Improved Diagnosis of Abnormality: An AppraisalKavery Verma, Subodh Srivastava, Ritesh Kumar MishraDigital medical images acquired from the brain are highly susceptible to noise, which causes significant challenges for radiologists to identify abnormalities in a precise manner. Noise interference hampers both diagnostic accuracy and the interpretation of underlying abnormalities, potentially leading to flaw conclusions. Magnetic Resonance (MR) imaging is the most preferred digital imaging technique for brain abnormality detection. To achieve precise … …Journal of Computer Science, Vol. 22, Issue 3 · 26 Mar 2026 · 1,470 accessesView PDFCite