Conference Papers

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

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Author: search.filters.author.Vijayasenan, D.Subject: search.filters.subject.deep learning

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    Leveraging Deep Learning for Fever Temperature Analysis and Pattern Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2024) Prabhakaran, A.; Sumam David, S.; Vijayasenan, D.; Mahabala, C.; Dakappa, P.
    Tympanic temperature is one of the most fundamental indicators for the diagnosis of diseases. Due to its importance, using patients' temperature data to aid in the diagnostic process would be beneficial. This work uses temperature data collected from various patients to classify diseases. We consider dengue, tuberculosis, and non-infectious and non-tubercular bacterial diseases. Extracting essential features from the temperature data is necessary so that the downstream layers only have to consider important features, not miscellaneous information. This feature extraction is done using two methods - Convolution Neural Networks and Autoencoders. We introduce three models for Explainable Temperature Analysis - ExTemp-Conv-SM, ExTemp-Conv-LG and ExTemp-Auto. We achieve a classification accuracy of 70% over these four disease classes. We also use explainable AI tools, like GradCAM, to identify distinguishing patterns in temperature fluctuations that can characterize diseases. We generate such patterns for all four diseases under consideration. We note that the patterns generated for dengue and tuberculosis match the findings in biological observation studies. We hope that the methods in this paper can be leveraged for other diseases and used to aid the diagnostic process. © 2024 IEEE.
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    Language Detection in Overlapping Multilingual Speech: A Focus on Indian Languages
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kolsur, A.A.; Prajwal, K.; Vijayasenan, D.
    The growing demand for technology capable of recognizing spoken languages and extracting information from real-world audio, especially in scenarios with overlapping speech, has become a significant focus of research due to its essential role in improving global connectivity and accessibility. In our paper, we focus on identifying languages present in audio files that consist of overlapping speech. We have focused our research particularly on Indian languages, as there is limited research on identifying low-resource languages in overlapping speech. In this paper, we have synthesized a custom dataset from the VoxLingua107 dataset due to the lack of overlapping Indian speech data. Further, we have developed a novel solution that first separates the overlapped audio using a speaker separation model and then uses a language recognition model to detect the languages present in the separated audio. We have compared the results obtained through our method with the current state-of-the-art model, Whisper, and concluded that our solution significantly outperforms the Whisper model. The results highlight the potential for significant improvements in multilingual communication systems and speech processing applications, paving the way for more inclusive and accurate language recognition technologies. © 2025 IEEE.