Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item 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.Item NBA MVP Prediction and Historical Analysis Using Cross-Era Comparison Approaches(Institute of Electrical and Electronics Engineers Inc., 2024) Godbole, I.; Murali, S.S.; Sowmya Kamath, S.In order to understand the crucial player statistics that decide the Most Valuable Player (MVP) Trophy, this research study dives into a substantial 32-year dataset of the National Basketball Association (NBA). We build a predictive framework trained on historical player statistics and MVP voting results using a sophisticated ensemble of machine learning models, including Support Vector Machines (SVM), ElasticNet, AdaBoost, Random Forest and Back-propagation Neural Network (BP). We determine the key elements influencing this renowned award by evaluating connections between player stats and MVP picks. Our research provides insights into the MVP selection process by utilising the models' ability to capture complex patterns and nonlinear interactions, providing stakeholders with a reliable tool for assessing player performances.This work advances the discourse surrounding the NBA MVP Trophy and enriches our comprehension of player value assessment. Also, the prediction models are used to conduct various historical analysis experiments, by finding an objective method to compare performances of players from different eras. © 2024 IEEE.Item 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.