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Special Issue on Multidisciplinary Sciences and Advanced Technology (SI-MSAT 2026)
Guest Editors: Prof. Paul Andrew
Deadline: 31 December 2026

Special Issue on Artificial Intelligence for Energy Transition and Decarbonization (SI-AIETD26)
Guest Editors: Dr. Elkhatib Kamal, Dr. Reza Ghorbani, Dr. Ahmed Ragab, Prof. Mohamed Kouki
Deadline: 31 December 2026

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Keyword: Anomaly detection
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Open AccessArticle
21 Pages, 1,371 KB Download PDF
Early 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))

Open AccessArticle
16 Pages, 1,702 KB Download PDF
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))

Open AccessReview
6 Pages, 1,779 KB Download PDF
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))

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