Work place: Department of Biomedical Engineering, P. S. R. Engineering College, Sivakasi-626140, Tamil Nadu, India
E-mail: jbtnec@gmail.com
Website: https://orcid.org/0000-0003-1134-0599
Research Interests:
Biography
J.Babitha Thangamalar is an Associate Professor in the Department of Biomedical Engineering at P.S.R. Engineering College, Tamil Nadu. She has over twenty-one years of teaching and research experience in reputed engineering institutions. She completed her Ph.D. in Information and Communication Engineering from Anna University, Chennai, in 2020. She obtained her M.E. in Applied Electronics from Noorul Islam College of Engineering in 2007. She completed her B.E. in Electronics and Instrumentation Engineering from National Engineering College, Kovilpatti, in 2002 with first-class. She has served as Associate Professor, Assistant Professor (Senior Grade), and Lecturer during her career. Her areas of expertise include Digital Electronics, Embedded Systems, Microprocessors, IoT, and Instrumentation Systems. She has published more than 10 papers in international journals. She has presented over 20 papers in national and international conferences. She has 1 patent granted and 7 patents published in various technological domains. She developed two funded products: Biometric-Based Water Dispenser and Smart Quality Water Dispenser. These projects were supported by NSTEDB under the NewGen IEDC scheme. She has organized more than 20 workshops and training programs sponsored by DRDO, AICTE, and CSIR. She has attended over 60 FDPs and workshops in areas like VLSI, IoT, and Embedded Systems. Her research interests include Embedded Systems, IoT, Signal Processing, and Biomedical Instrumentation, and she has earned NPTEL Elite Silver certifications.
By S.Gopalakrishnan J.Babitha Thangamalar M. Sahaya Sheela M. Mohammed Mustafa Bindu Babu N.Senthil Madasamy
DOI: https://doi.org/10.5815/ijem.2026.04.12, Pub. Date: 8 Aug. 2026
Fake news detection focuses on identifying and preventing the spread of misleading or false information. It is crucial for maintaining the integrity of public discourse and protecting individuals from the harmful effects of misinformation. By ensuring the correctness and reliability of the information, the fake news detection hinders the loss of trust in the media, institutions, and public communication channels. The fake news detection system suggested is in the process of data acquisition where news stories are either manually or automatically retrieved from the net via web crawlers. The collected data later filters the information so it will use only credible sources. Phase two consists of the pre-processing phase using BERT, wherein the data will be tokenized and mapped into contextual embeddings that reflect the semantic meaning of words. Phase three is about engineering features using methods like TF-IDF and Word2vec to
fine-tune the embeddings and label the important textual features. The final Phase of Classification occurs using the engineered features such that BERT-generated outputs are fine-tuned and passed through softmax functions to ascertain whether the news is fake or real. This holistic and all-encompassing approach integrates advanced natural language processing with feature engineering for an effective system concerning detection of fake news accurately. The model achieved remarkable results over various phases. Training accuracy went from 75% up to those above 95% whereas test accuracy tips above 90%, soaring from below 70%. The model's performance was validated with a balanced confusion matrix and a high ROC AUC of 0.94. Throughout different phases, accuracy, precision, recall, and F1-score increased, reaching 97.0%, 96.7%, 96.8%, and 96.9%, respectively, in the final classification phase, demonstrating robust and reliable detection capabilities.
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