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Keyword: Anomaly DetectionEarly Warning for Maritime Storm Formation Using Temporal Autoencoder-Based Anomaly Detection
by Snehashish Srivastava, Haiping Xu, Donghui Yan and Ramprasad Balasubramanian
Journal of Engineering Research and Sciences, Volume 5, Issue 5, Page # 19-39, 2026; DOI: 10.55708/js0505003
Abstract: Storms remain a serious hazard at sea, exposing vessels to rapidly changing conditions that endanger human life and result in substantial economic losses. Satellite-based detection methods are widely used but require significant computational resources and depend on land-to-sea communication links, which may become unreliable during severe weather. Machine learning approaches offer strong potential for early… Read More
(This article belongs to the Special Issue on SP9 (Special Issue on Multidisciplinary Sciences & Advanced Technology (SI-MSAT 2026)) and the Section Artificial Intelligence – Computer Science (AIC))
From ITIL to AIOps in Public Sector: A Systematic Literature Review
by Catherine Ganduri
Journal of Engineering Research and Sciences, Volume 5, Issue 6, Page # 56-66, 2026; DOI: 10.55708/js0506005
Abstract: Public-sector agencies rely on complex and highly regulated digital systems to deliver essential services. ITIL-based change and release management supports operational control, but many agencies still depend on manual approvals, fragmented operational data, and reactive monitoring. Artificial Intelligence for IT Operations (AIOps) and Machine Learning Operations (MLOps) can improve anomaly detection, failure prediction, release validation,… Read More
(This article belongs to the Section Interdisciplinary Applications – Computer Science (IAC))
Identification of Walking Balance using Acceleration Sensors
by Junyu Chen, Michiyuki Hirokane and Yukio Horiguchi
Journal of Engineering Research and Sciences, Volume 5, Issue 5, Page # 1-11, 2026; DOI: 10.55708/js0505001
Abstract: The risk of falling increases with age, affecting approximately one in three individuals over 65 and one in two over 80 annually. In Japan, the fall rate among older adults ranges from 8.5% to 25.3%, and falls are a major cause of fractures and long-term care needs. Balance impairment is one of the key factors… Read More
(This article belongs to the Special Issue on SP8 (Special Issue on Digital and Engineering Transformations in Science and Technology (SI-DETST-26)) and the Section Medical Informatics (MDI))
Explainable AI for SSD Failure Prediction: Using LIME and SHAP for Transparency
by Saurav Kant Kumar
Journal of Engineering Research and Sciences, Volume 5, Issue 4, Page # 1-16, 2026; DOI: 10.55708/js0504001
Abstract: Artificial Intelligence (AI) has become increasingly crucial for modern data centers for automating tasks ranging from anomaly detection to predictive maintenance. Nevertheless, a significant limitation of underlying machine learning (ML) models is their “black box” nature. This lack of transparency limits trust among stakeholders who require visibility into model decisions. We address this lack of… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))
Dynamic Error Management in SAP: A Comprehensive Analysis
by Vinayak Kalabhavi
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # 21-26, 2026; DOI: 10.55708/js0503003
Abstract: Enterprise Resource Planning (ERP) systems, particularly SAP, face increasing demands for real-time operations and minimal downtime, necessitating sophisticated error management approaches. This paper examines the evolution from reactive to dynamic error management in SAP environments, analyzing theoretical frameworks and practical implementations. Through comprehensive literature review spanning 2000-2025, we explore hybrid error detection frameworks combining rule-based… Read More
(This article belongs to the Section Software Engineering – Computer Science (SEC))