Volume 5, Issue 3 - 4 Articles
This issue presents a collection of research addressing intelligent data management, fluid mechanics, enterprise information systems, and healthcare analytics. The published papers examine AI-driven optimization of cloud-based data lakes through the integration of quality monitoring and intelligent physical design decisions, analytical solutions to modified Stokes’ flow problems involving pressure-dependent viscosity, dynamic error management frameworks for SAP enterprise environments, and predictive denial pattern recognition in healthcare claims using machine learning and real-time Power BI analytics. Collectively, these contributions highlight the growing role of artificial intelligence, advanced mathematical modeling, enterprise automation, predictive analytics, and decision-support systems in improving operational efficiency, system reliability, and data-driven decision-making across industrial, scientific, and healthcare domains.
Front Cover
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # i–i, 2026
Editorial Board
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # ii–vii, 2026
Editorial
by Jinhua Xiao
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # viii–ix, 2026
Table of Contents
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # x–x, 2026
AI-Driven Data Lake Optimization: Integrating Quality Monitoring with Intelligent Physical Design Decisions
by Sowjanya Deva and Surya Narayana Reddy Chintacunta
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # 1-13, 2026; DOI: 10.55708/js0503001
Abstract: Cloud data lakes require continuous optimization across multiple dimensions: physical design (partitioning, compression), query execution, and data quality assurance. This paper presents AIDALOS (AI-Driven Autonomous Data Lake Optimization System), a framework that integrates quality monitoring with physical optimization decisions. The system uses reinforcement learning to adapt monitoring intensity and trigger physical design changes based on… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))
A Note on Modified Stokes’ Problems for Fluids with Power-Law Dependence of Viscosity on Pressure with 3/2 index
by Constantin Fetecau
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # 14-20, 2026; DOI: 10.55708/js0503002
Abstract: The modified Stokes’ problems for incompressible Newtonian fluids with power-law dependence of viscosity on the pressure of 3/2 index are analytically investigated. The influence of the gravitational acceleration is taken into account. Exact expressions are derived for permanent dimensionless velocity and shear stress fields in terms of standard Bessel functions. They satisfy the governing equations… Read More
(This article belongs to the Section Fluids and Plasma Physics (FPP))
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))
An Analytical Examination of Predictive Denial Pattern Recognition in Healthcare Claims Utilizing Real-Time Power BI Analytics for Revenue Enhancement
by Nida Fatima and Amir Ghazanfer
Journal of Engineering Research and Sciences, Volume 5, Issue 3, Page # 27-32, 2026; DOI: 10.55708/js0503004
Abstract: This article looks at the growing problems in the healthcare revenue cycle, especially the big money losses that come from claim rejections. It emphasizes the need for predictive, real-time analytics to diminish avoidable rejections and improve overall operational efficiency. The novelty of this study lies in the operational integration of a machine-learning–based denial prediction model… Read More
(This article belongs to the Section Health Care Sciences and Services (HCS))