International Journal of Engineering Research and Sustainable Technologies (IJERST)

Editorial Message

Dr.V.RameshBabu — Tue, 25 Mar 2025 00:00:00 +0000

International Journal of Engineering Research and Sustainable Technologies (IJERST)

Volume 3, No.1 Online ISSN: 2584-1394

Message from Editorial Desk 25th March 2025

Dear Readers, Researchers, and Contributors,

It is with immense pride that we present the latest edition of the International Journal of Engineering Research and Sustainable Technologies (IJERST). This issue marks a significant milestone in our journey, as IJERST has officially received DOI approval from CrossRef and has become a registered member of this esteemed organization. This achievement will greatly enhance the indexing, visibility, and accessibility of our published research, ensuring that scholarly contributions reach a broader global audience.

Additionally, we are pleased to announce that IJERST has been assigned an Archival Resource Key (NAAN), further solidifying our commitment to long-term digital preservation and accessibility. This development will facilitate seamless citation, retrieval, and archival of our articles, reinforcing the credibility and impact of the research we publish.

In this edition, we continue to uphold our mission of promoting pioneering research and technological advancements in engineering and science. The featured articles showcase innovative approaches, sustainable solutions, and groundbreaking methodologies that address contemporary challenges in various engineering disciplines.

Our dedication to academic excellence and research integrity remains unwavering. As we move forward, IJERST will persist in expanding its reach by forging strategic partnerships with academic databases, research institutions, and indexing platforms. These efforts aim to further elevate the journal’s influence and ensure that our authors' contributions gain the recognition they deserve.

We extend our sincere appreciation to our esteemed authors, diligent reviewers, and dedicated editorial board members. Your support and contributions have been instrumental in making IJERST a trusted platform for high-quality research dissemination.

We encourage scholars and researchers to submit their innovative work to IJERST and become part of our growing academic community. With the advancements in indexing and digital archiving, we are committed to providing a robust platform for knowledge exchange and impactful research.

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

COMPARATIVE ANALYSIS OF TRANSFORMER MODELS FOR SENTIMENT CLASSIFICATION IN CODE- MIXED INDIC LANGUAGES

Mohana Priya K.T, Shrinithi G, Nithish P, Panesh A C — Tue, 25 Mar 2025 00:00:00 +0000

Multiple language usage in a single message, or code-mixed text, has increased dramatically as a result of increased social media engagement. Because of this, activities involving Natural Language Processing (NLP), such as sentiment analysis and cyberbullying identification. Models that can effectively manage linguistic variability while retaining high accuracy are needed to address these issues. We investigate transformer-based designs that improve classification performance by utilizing knowledge transfer strategies. RoBERTa, GPT-2, XLM-RoBERTa, and IndicBERT are used in our method, which enhances classification accuracy by the transfer of sharing-private information across code-mixed and monolingual tasks. Results from experiments show that our multi-task framework surpasses single-task models with high accuracy on all datasets with:IndicBERT achieved 96.86% for Hinglish, XLM-RoBERTa achieved 96.95% for Punglish, and IndicBERT obtained 97.55% for Tanglish. In order to advance reliable NLP applications in multilingual environments, this project highlights the transformers' multi-task learning capabilities in enhancing performance on low-resource and code-mixed languages.

TWO-STAGE DC-DC ISOLATED CONVERTER FOR EV BATTERY CHARGING WITH AI CONTROLLER

M Amuthanantham, M Bharath, Sai kumar, S Mahaboob Basha — Tue, 25 Mar 2025 00:00:00 +0000

The Electric vehicles (EVs) require efficient and reliable charging systems to optimize battery performance and lifespan. Traditional converters, however, face limitations in meeting these demands, especially under dynamic charging conditions. This project presents a novel approach to EV battery charging using an AI-controlled two-stage DC-DC isolated converter, enhanced with an artificial neural network (ANN) for real-time optimization. The proposed system utilizes a two-stage converter architecture, where the first stage boosts the input voltage, and the second isolated stage provides galvanic separation, ensuring safe and efficient power transfer to the EV battery. An ANN- based controller dynamically adjusts the converter parameters based on input voltage, battery state-of-charge (SOC), and output current. This intelligent control minimizes energy losses, improves voltage regulation, and reduces thermal stress on the converter components, leading to enhanced system efficiency. Simulation results indicate that the ANN controller adapts effectively to varying input conditions, maintaining optimal charging rates while safeguarding battery health. This AI-driven approach offers a promising solution for fast, efficient, and adaptive EV battery charging, aligning with the increasing demand for smart and sustainable transportation technologies.