Conference Papers
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Item Stress Detection Using Deep Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2023) Angalakuditi, H.; Bhowmik, B.Stress has become a prevalent issue in modern society, with various negative impacts on mental and physical health. Stress in people is a physiological and psychological reaction to an imagined threat or difficulty. Several things, including employment, relationships, income, health problems, and significant life transitions, can cause stress. Depending on the person and the circumstance, stress symptoms can vary. They frequently include emotions of worry, irritation, and restlessness, as well as physical symptoms like headaches and muscle strain. Early stress detection is crucial for effective intervention and prevention of stress-related health issues. Detecting stress in real-time can be valuable in various domains such as healthcare, mental health, human-computer interaction, and workplace performance. This paper proposes a method for detecting stress using deep learning. A set of pre-trained models are employed for stress detection. The proposed technique is evaluated with publicly available datasets. Experimented results showed that the proposed stress detection method achieves accuracy in the range of 85.71-97.50% and the loss ranging from 0.4061 to 1.8144. © 2023 IEEE.Item Feature Selection for Peer-to-Peer Lending Default Risk Using Boruta and mRMR Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Anusha Hegde, H.; Bhowmik, B.Peer-to-peer (P2P) lending in the Financial Technology (FinTech) sector is increasingly gaining attention from people where the online platform enables lenders to offer loans to borrowers. The platform as a much needed mechanism targets to reduce the risk of default and increase profitability for lenders and the platform. Each loan record maintains a variety of attributes, including details about the loan, the borrower, their credit history, their finances, and public data. If all the features are considered, the performance of the lending platform may decline. Finding the necessary characteristics more helpful in forecasting loan default is a concern. This paper investigates essential features of the P2P lending mechanism with adequate performance in lending money to individuals or businesses. We employ two algorithms to find the pertinent features: Boruta and Max-Relevance and Min-Redundancy (mRMR). Further, we use two classifiers-decision tree and XGBoost that exercise the selected elements to predict the loan defaults. © 2023 IEEE.