International Journal of Engineering Research and Sustainable Technologies (IJERST)

Editorial Note

2026-09-30T04:55:16+00:00

International Journal of Engineering Research and Sustainable Technologies (IJERST)

Volume 4, No.3 Online ISSN: 2584-1394

Message from Editorial Desk 25th September 2026

Dear Readers, Researchers, and Contributors,

It is a pleasure to welcome you to the September 2026 issue of the International Journal of Engineering Research and Sustainable Technologies (IJERST).

Research and innovation continue to reshape the way we understand technology, engineering, sustainability, and their role in addressing the complex challenges of contemporary society. In this rapidly evolving environment, scholarly journals have an important responsibility—not only to provide a platform for new ideas, but also to encourage rigorous inquiry, meaningful discussion, and the responsible application of knowledge.

The present issue brings together research contributions reflecting the breadth and diversity of contemporary engineering and technology studies. The articles address different areas of inquiry while sharing a common objective: to contribute knowledge that can support further research, practical innovation, and sustainable development. We hope that these contributions will stimulate discussion among researchers and encourage further investigation in their respective fields.

At IJERST, we remain committed to strengthening the quality and integrity of the peer-review and publication process. We believe that constructive peer review, transparent editorial practices, responsible authorship, and adherence to ethical standards are essential foundations of credible scholarly communication. The continued confidence of authors, reviewers, editors, and readers is therefore central to the growth of the journal.

We are particularly grateful to the researchers who continue to contribute their work to the journal and to the reviewers who generously devote their time and expertise to evaluating submitted manuscripts. Their collective efforts enable us to maintain an academic environment in which research can be communicated with clarity, responsibility, and scholarly purpose.

As engineering and technology increasingly intersect with sustainability, data, artificial intelligence, emerging technologies, and societal needs, opportunities for interdisciplinary research are expanding. We encourage researchers, academicians, professionals, and emerging scholars to explore these intersections and share research that can contribute meaningfully to both academic knowledge and real-world applications.

We look forward to continuing this journey together and to building a vibrant scholarly community committed to research excellence, innovation, sustainability, and the advancement of knowledge.

Thank you for your continued interest and support for IJERST.

Warm regards,

On behalf of Editorial Team Dr.V.RameshBabu Managing Editor

Dean-University Journal , Dr.M.G.R. Educational and Research Institute Chennai,Tamilnadu,India

dean-univ.journals@drmgrdu.ac.in / ijerst@drmgrjournals.org

MACHINE LEARNING APPROACHES FOR EARLY DIABETES PREDICTION: A COMPARATIVE STUDY USING CLINICAL DATA

2026-09-30T05:16:21+00:00

This study focuses on the accurate and early prediction of diabetes, which plays a vital role in improving clinical care and treatment planning. The research evaluates the performance of three machine learning techniques - Logistic Regression, Random Forest, and Support Vector Machine (SVM) - using data collected from 769 patients in Chennai. The models were compared based on their ability to predict diabetes effectively. The results indicate that the SVM model achieved the highest prediction accuracy of 78%, while Logistic Regression and Random Forest recorded accuracies of 77% and 76% respectively. Statistical analysis showed that the variation in performance among the models was not significant (p = 0.3484). Even though SVM demonstrated slightly better predictive capability, Logistic Regression provided clearer interpretation of risk-related factors, making it more suitable for clinical decision-making. In addition, the Random Forest model helped identify the most influential variables associated with diabetes risk. Overall, the findings suggest that machine learning approaches can support early diagnosis and assist healthcare professionals in developing personalised treatment and management strategies for diabetic patients. Incorporating a wider range of clinical attributes in future studies may further improve prediction performance and enhance medical risk assessment.

DIFFUSION AND TRANSFORMER-BASED DEEP LEARNING MODELS FOR MEDICAL IMAGE PROCESSING: A COMPREHENSIVE SURVEY

2026-09-30T05:16:00+00:00

Deep learning has played an important role in improving medical image processing tasks such as image segmentation, reconstruction, and diagnosis. In recent years, diffusion models and transformer-based methods have gained attention due to their strong performance. Diffusion models are effective in generating and restoring images by learning from noise, while transformers help in capturing global features through attention mechanisms.This survey reviews the use of diffusion and transformer models in medical imaging, focusing on their applications, advantages, and limitations. It also discusses recent developments that combine both approaches and highlights future research directions for improving efficiency and accuracy in real-world medical applications

BATTERY PLANTATIONS: A COMPREHENSIVE ANALYSIS OF BENEFITS AND DETRIMENTS IN PRESENT AND FUTURE ENERGY LANDSCAPES

2026-09-30T05:15:59+00:00

The rapid global transition toward renewable energy and electric mobility has propelled battery plantations—large-scale battery manufacturing and energy storage facilities—into the spotlight of modern industrial development. These installations promise grid stabilization, decarbonization, and energy independence, yet they simultaneously raise environmental, social, and geopolitical concerns. This paper presents a ten-section examination of battery plantations, analyzing their utility in current energy infrastructure, their projected role in future smart grids, and the harmful consequences associated with raw material extraction, manufacturing emissions, end-of-life disposal, and supply chain vulnerabilities. Through a synthesis of contemporary literature, case studies, and projected trends, this paper concludes that while battery plantations are indispensable for achieving net-zero targets, their long-term sustainability depends on circular economy practices, recycling innovations, and equitable resource governance.

AI-BASED WATER QUALITYDETECTION: USING pH STRIP, COMPUTER VISION AND CLOUD DASHBOARD

2026-09-30T05:15:58+00:00

Water quality assessment is a basic idea of environmental management. It has effects on human health, farming and industry. Traditional ways to check parameters, such as pH usually use manual color strips. These strips are highly subjective and often produce mistakes. Digital probes are expensive. Need regular calibration. This paper proposes a scalable mobile‑first Internet of Things (IoT) and Artificial Intelligence (AI) framework that automates water pH testing. The framework uses a Flutter app, computer vision with machine learning models from Scikit‑learn or TensorFlow. It maps the Red, Green and Blue (RGB) values of a standard pH strip to continuous pH numbers. The system automatically classifies water as acidic, safe or alkaline. Sends real‑time data to a Firebase‑hosted web dashboard built with HTML and Chart.js. This paper explains the system’s architecture, the specific mathematical algorithms used, optimization functions, data augmentation strategies, security measures, fuzzy logic implementation, a detailed economic feasibility study, real‑world deployment cases and the future direction of AI‑driven environmental telemetry