Conference Papers

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Author: search.filters.author.Kelkar, S.Has files: No

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    LSTM-Attention Architecture for Online Bilingual Sexism Detection
    (CEUR-WS, 2023) Ravi, S.; Kelkar, S.; Anand Kumar, M.
    The paper describes the results submitted by ‘Team-SMS’ at EXIST 2023. A dataset of 6920 tweets for training, 1038 for validation, and 2076 tweets for testing was provided by the task organizers to train and test our models. Our models include LSTM models coupled with attention layers and without attention. For calculation of soft scores according to the task we tried to mimic human performance by taking an average of different machine learning model predictions using Multinomial Naive Bayes, Linear Support Vector Classifier, Multi Layer Perceptron, XGBoost, LSTM using GloVe embeddings, and LSTM using fastText embeddings. We discuss our approach to remove the ambiguity in the labeling process and detailed description of our work. © 2023 Copyright for this paper by its authors.
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    Comparative analysis of Software Reliability using Grey Wolf Optimisation and Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kelkar, S.; Vishvasrao, S.P.; Agarwal, A.; Rajput, C.; Mohan, B.R.; Das, M.
    Software reliability is a crucial aspect of software quality. In this paper, we aim to explore the application of Gray Wolf Optimization (GWO) for feature selection and classification on various software dataset, such as KC1, JM1, and PC5. We compare the performance of Machine Learning models (Random Forest, Decision Tree, Support Vector Machine, XGBoost and Neural Networks) with and without GWO-based feature selection. Our results demonstrate the effectiveness of GWO in enhancing the accuracy of software reliability analysis. Or Math in Paper Title or Abstract. © 2024 IEEE.
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    Multimodal Propaganda Detection in Memes with Tolerance-Based Soft Computing Method
    (Springer Science and Business Media Deutschland GmbH, 2024) Kelkar, S.; Ravi, S.; Ramanna, S.; Anand Kumar, M.
    This paper presents a tolerance-based near sets-based classifier applied to multimodal propaganda detection task using text and image data originating from Memes. Memes on the internet consist of an image superimposed with text and are very popular in social media. They are often used as a part of disinformation campaign whereby social media users are influenced via a number of rhetorical and psychological techniques known as persuasion techniques. The focus of this paper is on a subtask of the SemEval-2024 Multilingual Detection of Persuasion Techniques Competition in Memes to detect the presence or absence of a persuasion technique. We introduce a multimodal Tolerance Near Sets Classifier (MTNSC) trained on a combination of word embeddings (RoBERTa) and pre-trained image features (ResNet and ResNet-Memes) using the competition data. This work extends our earlier work in the Natural Language Processing domain where a tolerance-based near sets-based sentiment classifier was introduced. The proposed MTNSC achieves a macro F1 score of 70.15% and micro-F1 score of 75.33% on the test dataset demonstrating satisfactory performance of TNS-based classifiers in a multimodal setting. Our findings point to the model’s effectiveness when compared to a few leading submissions based on deep learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.