A Vendor-Agnostic Multi-Cloud Integration Framework Using Boomi and SAP BTP
by Padmanabhan Venkiteela
Journal of Engineering Research and Sciences, Volume 4, Issue 12, Page # 1-14, 2025; DOI: 10.55708/js0412001
Abstract: The shift toward multi-cloud strategies has made a vendor-agnostic integration framework indispensable for seamlessly orchestrating workflows across heterogeneous platforms. Modern enterprises increasingly rely on a mix of cloud ecosystems leveraging Amazon Web Services (AWS) for elasticity, Google Cloud Platform (GCP) for advanced AI/ML capabilities, Azure Cloud and Oracle Cloud Infrastructure (OCI) for critical enterprise workloads… Read More
(This article belongs to the Section Information Systems – Computer Science (ISC))
Experimental Study of the Short-Circuit Current Performance of \(10\,\mathrm{kA_{R.M.S}}\) and \(20\,\mathrm{kA_{R.M.S}}\) Polymer Surge Arrester
by Cristian-Eugeniu Sălceanu, Daniela Iovan and Daniel-Constantin Ocoleanu
Journal of Engineering Research and Sciences, Volume 4, Issue 12, Page # 15-24, 2025; DOI: 10.55708/js0412002
Abstract: To study the behavior of metal oxide surge arresters at short-circuit current, this paper presents an experimental study on four pieces of 36 kV, \(10\,\mathrm{kA_{R.M.S}}\) and \(20\,\mathrm{kA_{R.M.S}}\) surge arresters at different values of short-circuit current. Prior to the experiments, each surge arrester was electrically pre-faulted with a power frequency overvoltage without any physical modification. The tests… Read More
(This article belongs to the Special Issue on SP7 (Special Issue on Multidisciplinary Sciences and Advanced Technology (SI-MSAT 2025)) and the Section Electrical Engineering (ELE))
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))