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Keyword: Leakage
CFD Analysis of Data Center Hall Cooling Performance under Normal and Failure Modes with Control Strategies and Airflow Leakages
by Sushil Ashok Surwase, Suribabu Badde and R. Balakrishnan
Journal of Engineering Research and Sciences, Volume 5, Issue 1, Page # 9-28, 2026; DOI: 10.55708/js0501002
Abstract: Data centers have become the backbone of an increasingly digitized world, supporting the rapid growth of cloud computing, big data, IoT, 5G, and other emerging IT technologies, with rising demand and innovations in AI and ML reinforcing their significance. Data centers are energy intensive, with data processing and storage accounting for 3 to 4% of… Read More
(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2025 and the Section Mechanical Engineering (MEE))
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))
Model Uncertainty Quantification: A Post Hoc Calibration Approach for Heart Disease Prediction
by Peter Adebayo Odesola, Adewale Alex Adegoke and Idris Babalola
Journal of Engineering Research and Sciences, Volume 4, Issue 12, Page # 25-54, 2025; DOI: 10.55708/js0412003
Abstract: We investigated whether post-hoc calibration improves the trustworthiness of heart-disease risk predictions beyond discrimination metrics. Using a Kaggle heart-disease dataset (n = 1,025), we created a stratified 70/30 train-test split and evaluated six classifiers, Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Naive Bayes, Random Forest, and XGBoost. Discrimination was quantified by stratified 5-fold cross-validation with… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))
CAPEF: Context-Aware Policy Enforcement Framework for Android Applications
by Saad Inshi, Mahdi Elarbi, Rasel Chowdhury, Hakima Ould-Slimane and Chamseddine Talhi
Journal of Engineering Research and Sciences, Volume 2, Issue 1, Page # 13-23, 2023; DOI: 10.55708/js0201002
Abstract: The notion of Context-Awareness of mobile applications is drawing more attention, where many applications need to adapt to physical environments of users and devices, such as location, time, connectivity, resources, etc. While these adaptive features can facilitate better communication and help users to access their information anywhere at any time, this however bring risks caused… Read More
(This article belongs to the Special Issue on Special Issue on Computing, Engineering and Sciences 2023-23 and the Section Electrical Engineering (ELE))