Conference Papers

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

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Author: search.filters.author.Gowhar, S.

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    Advancing Human-Like Summarization: Approaches to Text Summarization
    (CEUR-WS, 2023) Gowhar, S.; Sharma, B.; Gupta, A.K.; Anand Kumar, A.K.
    Text summarization, a well-explored domain within Natural Language Processing, has witnessed significant progress. The ILSUM shared task, encompassing various languages, such as English, Hindi, Gujarati, and Bengali, concentrates on text summarization. The proposed research focuses on leveraging pretrained sequence-to-sequence models for abstractive summarization specifically in the context of the English language. This paper provides an extensive exposition of our model and approach. Notably, we achieved the top ranking in the English Language subtask. Furthermore, this paper dives into an analysis of various techniques for extractive summarization, presenting their outcomes and drawing comparisons with abstractive summarization. © 2023 Copyright for this paper by its authors.
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    Binarization in DeepFake Audio Detection: A Comparative Study and Performance Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2025) Gowhar, S.; Pandey, A.; Rudra, B.
    DeepFake audio, generated through advanced AI techniques, poses significant risks such as fraud, misinformation, and identity theft. As the quality of synthetic audio improves, detecting such fakes has become increasingly challenging. Traditional detection methods struggle to keep pace as AI-generated voices replicate speech patterns, tone, and pitch convincingly. While computationally intensive large-scale models can help detect DeepFakes generated by AI, their resource requirements make them impractical for deployment on mobile devices as well as on resource-constrained devices. This paper proposes a lightweight yet effective approach using binarized neural networks (BNNs) and further enhancements using additional dense layers and stacked modeling to overcome these challenges. We conduct a comprehensive performance analysis of the network and compare it with various machine learning and neural network methods to evaluate the tradeoff between detection accuracy and computational efficiency as an effect of binarization and precision loss in feature embeddings. © 2025 IEEE.
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    A Comprehensive Analysis of Classification Techniques for Effective Multi-class Research Article Categorization on an Imbalanced Dataset
    (Springer Science and Business Media Deutschland GmbH, 2025) Gowhar, S.; Kempaiah, P.; Sowmya Kamath, S.; Sugumaran, V.
    Categorizing scientific articles into specific research fields is a challenging problem, affected by the volume and variety of literature published. However, existing classification systems often suffer from limitations regarding taxonomy or the models used for classification. This article explores a comprehensive analysis of approaches built on Sentence Transformer embeddings combined with Machine Learning algorithms, Neural Networks, and Transformers to classify articles into 123 predefined classes, with the dataset being heavily imbalanced. The effectiveness of Large Language Models (LLMs) for generating synthetic data is also experimented with, along with synonym augmentation SMOTE and employing 1D CNNs for text classification. The best-performing model is a hierarchical classification model trained on MP-Net sentence embeddings that achieved an accuracy of 78%, outperforming all other models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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    Imbalanced Multi-Class Research Article Classification using Sentence Transformers and Machine Learning Algorithms
    (Association for Computing Machinery, Inc, 2025) Gowhar, S.; Kempaiah, P.; Kamath, S.S.; Sugumaran, V.
    Categorizing scientific articles into specific research fields is a challenging problem, considering the volume and variety of published literature. However, existing classification systems often suffer from limitations regarding taxonomy or the models used for classification. This article explores approaches built on Sentence Transformer embeddings combined with Machine Learning algorithms to classify articles into 123 predefined classes, with the dataset being heavily imbalanced in nature. The effectiveness of Large Language Models (LLMs) for generating synthetic data is also experimented with, along with synonym augmentation and SMOTE. The best-performing model, the One vs Rest classifier trained on MP-Net sentence embeddings with SMOTE, achieved an accuracy of 77%, and outperformed all the other models. © 2024 Copyright held by the owner/author(s).