Faculty Publications

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

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    Early detection of depression using BERT and DeBERTa
    (CEUR-WS, 2022) Devaguptam, S.; Kogatam, T.; Kotian, N.; Anand Kumar, A.M.
    In today’s world, social media usage has become one of the most fundamental human activities. On the report of Oberlo, at present, 3.2 billion people are on social media, which comprises 42% of the World’s population. People usually post about their daily life style, special occasions, views about on-going issues and their networks on the social media platforms. People also share things on social media which otherwise would not have shared with other people. Social media helps us to stay connected, keep informed, mobilise on social issues. Due to the surge of suicide attempts, social media can act as a life saver in detecting and tracing users who are on the verge of depression and self-harm. Natural language processing methods with the help of deep learning are aiding in solving language/text related real world problems like sentiment analysis, translation of text into different languages, depression detection. Many transformer based models like BERT (Bidirectional Encoders Representations from Transformers) are put to use to solve NLP problems, which voluntarily learns to attend to different features differently (Weighing). In this paper, a supervised machine learning algorithm with transfer learning approach is used to detect self-harm tendency in the social media users at the earliest. © 2022 Copyright for this paper by its authors.
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    Automated Health Insurance Processing Framework with Intelligent Fraud Detection, Risk Classification and Premium Prediction
    (Springer, 2024) Devaguptam, S.; Gorti, S.S.; Akshaya, T.L.; Kamath S․, S.
    Private insurance represents one of the sectors poised for significant growth. There are insurance solutions available for most high-value assets such as homes, jewelry, vehicles, and other valuable items. To optimize profitability while managing client claims, insurance companies have embraced advanced operations, procedures, and mathematical models to assess risks and prioritize customer satisfaction, all while maximizing profits. This article introduces a machine learning-driven automated framework designed to reduce human intervention, safeguard insurance operations, identify high-risk clients, detect fraudulent claims, and mitigate financial losses within the insurance sector. Initially, the framework focuses on fraud detection to determine the legitimacy of claims. Genuine claims leverage the patient’s medical history to calculate associated risk factors and premiums. Various machine learning-based classification models and ensemble techniques were employed and evaluated for each of the three insurance processing tasks. Performance assessments using relevant metrics are presented and thoroughly discussed. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.