Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Jayanth, P.

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    Multimodal Meme Troll and Domain Classification Using Contrastive Learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Phadatare, A.; Jayanth, P.; Anand Kumar, M.A.
    This paper presents a holistic approach to meme trolling detection and domain classification, focusing on Telugu and Kannada languages. Leveraging a spectrum of methodologies ranging from basic machine learning models such as Support Vector Machines (SVM), Random Forest, Naive Bayes, to image-based models like Convolutional Neural Networks (CNN), ResNet-50, and state-of-the-art models such as CLIP, multilingual BERT, XLM-BERT, and Vision Transformers, we explore diverse modalities including image classification, extracted text classification, and combined text-caption classification. Our system integrates multiple models to achieve two primary goals: accurately detecting trolling behavior and classifying memes into thematic domains like politics, movies, sports.. By training on multilingual data and considering linguistic diversity, our approach ensures robust performance across different linguistic contexts, providing valuable insights into meme culture and trolling behavior in Telugu and Kannada-speaking communities. © 2024 IEEE.
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    Large Language Models for Indian Legal Text Summarisation
    (Institute of Electrical and Electronics Engineers Inc., 2024) Hemanth Kumar, M.; Jayanth, P.; Anand Kumar, M.
    Summarizing legal case judgments is a complex task in Legal Natural Language Processing (NLP), with a gap in understanding how various summarization models, including extractive and abstractive approaches and analysing the perform within the domain of legal documents. Since there are around 4 crore pending cases in the Indian court system, this study addresses the challenge of laborious task of manually summarizing legal documents. It introduces both supervised and unsupervised models for both extractive and abstractive summarization, showcasing their effective performance through evaluations using ROUGE metrics and BERT score. BART, T5, PEGASUS, ROBERTA, Legal-PEGASUS, Legal-BERT models are used for abstractive summarisation. TextRank, LexRank, LSA, Summarizer BERT, KL-Summ are used in case of extractive summarisation. Longformer, Bert - Legal Pegasus are also considered for the task of Summarisation. In the domain of legal document summarization, we used GPT-4 and LLAMA-2, employing prompt engineering with both Zero-shot and Oneshot prompts to extract summaries. As far of our knowledge, this is the first paper that used Large Language Models like GPT-4 and LLama-2 for the task of Legal Text summarisation. Along with that a user-friendly chatbot has been developed utilizing the Llama model and specifically designed to respond for queries related to legal texts. Additionally, a web application has been created, allowing users to upload legal documents for summarization. An option is given to users to select from various languages including Telugu, Tamil, Kannada, Malayalam, and Hindi. As a result the summarised text is converted into respective language. © 2024 IEEE.
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    Predicting Air Quality Index with Recurrent Neural Networks and Meta-heuristic Algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2024) Jayanth, P.; Sowmya Kamath, S.
    Millions of people worldwide suffer from the impacts of air pollution, a significant health risk. The metric Air Quality Index (AQI) serves as a crucial tool, providing valuable insights into current air quality conditions and potential health risks. This study utilizes two datasets: one from Wuhan City and the other from Shanghai. The features utilized for forecasting the AQI include PM2.5, PM10, SO2, NO2, O3, CO, l-temp, h-temp, temp, wet, wind, Hecto-pascal Pressure Unit (hpa), visibility, precipitation, and cloud content. This work focuses on developing models to predict AQI for a given data by comparing Long Short Term Memory (LSTM) and its variants, including Bidirectional LSTM (BiLSTM), Stacked LSTM, and Gated Recurrent Unit (GRU) models. Additionally, Particle Swarm Optimization is utilized as an evolutionary feature selection method. © 2024 IEEE.