Journal of Innovations in Data Science and Big Data Management

JWT-Based Authentication and Role-Based Access Control for Securing a Healthcare Management System REST API: An Implementation Case Study

Prajakta V. Yadav — 2026-10-26

Healthcare management systems store sensitive patient and staff data and are increasingly built as REST APIs that are used by separate client applications. Protecting such an API needs an authentication and authorization mechanism that does not depend on server-side sessions. This study presents an implementation case study of a security layer for a Healthcare Management System developed with Spring Boot 3, Spring Security 6, Spring Data JPA, and a Microsoft SQL Server database. The layer issues a signed JSON Web Token (JWT) at login, validates it in a custom filter placed in the Spring Security filter chain, runs with stateless sessions, and restricts the administrator endpoints through a role claim. Functional tests were executed with Postman: an administrator login returned a token, and a protected endpoint returned HTTP 401 when the token was missing, altered, or expired and HTTP 200 when a valid token was supplied. The study also states the limitations of the current version, including unhashed password storage and the role-mismatch and cross-origin cases that are yet to be tested, and outlines the planned next steps, which include an Angular front end and a later move to microservices. The study is intended as a practical and reproducible reference for developers who secure small healthcare REST APIs with JWT.

E-Waste Management: Environmental Impacts, Health Risks, Regulations and Sustainable Management Strategies

Suraj R. Nalawade — 2026-10-19

E-waste, or waste electrical and electronic equipment, is one of the fastest-growing waste streams globally. Discarded electrical and electronic products contain valuable materials such as copper, aluminium, iron and precious metals, but may also contain substances that require controlled handling. When e-waste is dumped, openly burned or dismantled using unsafe methods, contaminants may be released into the surrounding environment and may expose workers and communities to health risks. This review examines the major environmental and health impacts of e-waste, life cycle assessment (LCA), Extended Producer Responsibility (EPR), reuse and repair, recycling strategies, regulatory approaches, and selected international practices, with particular emphasis on India. The study is based on secondary research and a review of published literature and authoritative regulatory and health sources. The review indicates that sustainable e-waste management should follow a life-cycle approach that prioritizes prevention, durable and repairable product design, reuse, refurbishment, formal collection, environmentally sound recycling and safe management of residual waste. In India, the E-Waste (Management) Rules, 2022 provide the current EPR-based regulatory framework for covered electrical and electronic equipment. Strengthening collection systems, enforcement, consumer awareness, recycling capacity, data quality, and the safe integration of informal workers is necessary for effective implementation.

An Intelligent Machine Learning Framework for Early Cardiovascular Disease Risk Prediction

Gagana G — 2026-09-09

Heart disease continues to be a major health problem across the world, and identifying it at an early stage can help improve treatment and reduce serious complications. In recent years, machine learning has become a useful approach for studying patient health records and predicting the possibility of heart disease. This survey reviews and compares different machine learning techniques such as Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, K-Nearest Neighbors, Gradient Boosting, and XGBoost. It also looks at common data preparation methods, including data cleaning, feature selection, handling imbalanced datasets using SMOTE, and model tuning to improve prediction results. From the studies reviewed, ensemble-based methods like XGBoost and Gradient Boosting generally provide more consistent and accurate predictions than many traditional algorithms. This also discusses the advantages, limitations, and research opportunities in this area. Overall, the survey shows that machine learning can support doctors by providing faster and more reliable predictions, making early diagnosis of heart disease more effective.

AI Methods and Deployment Challenges from Prediction to Trusted Operation in Renewable-Rich Smart Grids: A Review

Ganesh Kumar Gautam — 2026-09-28

The inclusion of renewable energy sources, distributed energy resources, and electric vehicles in the power grid has transformed conventional power grids into complex, multi-directional cyber-physical systems, for which traditional model-based tools are inadequate and/or linear. Artificial Intelligence (AI) and Machine Learning (ML) have therefore become key enablers of next-generation smart grids, with AI-driven data-based solutions helping forecast energy loads and renewable generation, diagnose faults and protect against them, optimise microgrids and electric-vehicle charging, and support cybersecurity and explainable decision-making. This work summarizes the current AI/ML applications for smart grid optimization and renewable-energy integration, based mainly on literature published from 2024 to 2026, including peer-reviewed studies and recent preprints. It presents recent breakthroughs in deep learning-based forecasting, reinforcement learning-based microgrid energy management, and digital-twin-based grid monitoring and AI-based cyber defense, and places these advances in the context of the fast-digitalizing power system in India as an illustrative example from emerging economies. There are persistent data quality issues, model interpretability concerns, real-time scalability issues, and AI-specific security concerns, which are identified, and directions for future research are proposed. The review aims to serve as a single reference to help researchers, utility engineers, and policymakers design "smart" power systems that are able to be powered by renewables.

Predictive Modeling in Healthcare using Deep Learning: A Comprehensive Review

Chandrasekhar Koppireddy — 2026-10-06

Predictive modelling is essential to the healthcare sector for enhancing clinical outcomes and to maximize scarce resources in the healthcare system. The increasing digitization of healthcare systems creates a vast amount of data, including electronic health records (EHRs), medical imaging, genome data, and data from wearable devices. Traditional analytical techniques frequently cannot be applied to the analysis of this high-dimensional and heterogeneous data. Many of these challenges are addressed by a subfield of artificial intelligence (AI) known as deep learning, which constructs hierarchical representations directly from raw data, without relying on much feature engineering. This study offers a systematic review of predictive modeling in healthcare with deep learning, which includes popular architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Transformer-based systems, and hybrid multi-modal systems. In addition to summarizing previous research, this review critically compares these architectures in terms of their strengths and weaknesses, data needs, and application to specific prediction tasks in healthcare. Applications are explained for disease prediction, medical imaging, patient monitoring in ICUs, personalized medicine, and prediction of health in populations. The study also provides a detailed discussion on key open problems such as interpretability, data quality, algorithmic fairness, regulatory compliance, and clinical integration, as well as new trends, such as federated learning, explainable AI, foundation models, Vision Transformers, and Retrieval-Augmented Generation (RAG) in healthcare. In conclusion, the evidence suggests that deep learning has significant promise of changing the landscape of healthcare delivery, but achieving this will involve continued collaboration among many disciplines to build trust, transparency, and safe clinical deployment.