Faculty Publications

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Author: search.filters.author.Mahit Nandan, A.D.Has files: No

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    Classifying Emotional States Through EEG-Derived Spectrograms
    (Institute of Electrical and Electronics Engineers Inc., 2024) Mahit Nandan, A.D.; Dhiraj Choudhary, D.; Godbole, I.; Anand Kumar, M.
    Identifying emotional state of a person plays an important role in a multitude of applications such as affective computing, human-computer interaction and most importantly healthcare. Understanding and correctly identifying human emotions can improve mental health assessments, making it possible to obtain personalized treatment plans and increase the user experience across a variety of digital applications. Our work explores the feasibility of emotion classification using EEG signals recorded from multiple users while experiencing various emotions. Working with time series data of the brain activity in various participants, who are experiencing particular emotions over an interval of 15 seconds. To train and evaluate the classification model several kinds of machine learning and deep learning models such as CNN, RNN and LSTM, are employed. We compare general sequential models with image processing models in the task of classifying signal data. Observing the model's ability to generalize across the population. © 2024 IEEE.
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    Comparative Analysis of Religious Texts: NLP Approaches to the Bible, Quran, and Bhagavad Gita
    (Association for Computational Linguistics (ACL), 2025) Mahit Nandan, A.D.; Godbole, I.; Kapparad, P.; Bhattacharjee, S.
    Religious texts have long influenced cultural, moral, and ethical systems, and have shaped societies for generations. Scriptures like the Bible, the Quran, and the Bhagavad Gita offer insights into fundamental human values and societal norms. Analyzing these texts with advanced methods can help improve our understanding of their significance and the similarities or differences between them. This study uses Natural Language Processing (NLP) techniques to examine these religious texts. Latent Dirichlet allocation (LDA) is used for topic modeling to explore key themes, while GloVe embeddings and Sentence transformers are used to comapre topics between the texts. Sentiment analysis using Valence Aware Dictionary and sEntiment Reasoner (VADER) assesses the emotional tone of the verses, and corpus distance measurement is done to analyze semantic similarities and differences. The findings reveal unique and shared themes and sentiment patterns across the Bible, the Quran, and the Bhagavad Gita, offering new perspectives in computational religious studies. © 2025 Association for Computational Linguistics.