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Keyword: Explainable AI
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))
Identifying and Addressing Supply Chain Data Gaps: Impacts on Predictive Quality and CTQ Parameter Management in EV Battery Production
by Sundararaman Ganapathiraman, Sumedha Vijayaram Kumar, Akshaya Bharadhwaj, Suresh N, Ramitha Sundar and Phani Kumar Pullela
Journal of Engineering Research and Sciences, Volume 5, Issue 8, Page # 28-35, 2026; DOI: 10.55708/js0508002
Abstract: The supply chains for electric vehicles (EVs) have data gaps that hinder traceability, predictive quality control, and management of Critical-to-Quality (CTQ) parameters such as energy capacity, electrode uniformity, thermal stability, state of charge/health, and recyclability. This systematic literature review collates research on raw-material sourcing, manufacturing, in-use monitoring, secondlife applications, and recycling. Following a documented PRISMA-style… Read More
(This article belongs to the Special Issue on Special Issue on Digital and Engineering Transformations in Science and Technology (SI-DETST-26) and the Section Manufacturing Engineering (MNE))