Conference Papers

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

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    Smart Traffic Management System Using Multithreading and Inter-process Communication
    (Institute of Electrical and Electronics Engineers Inc., 2021) Gorti, S.S.; Khalifa, A.; Thirunavukkarasan, H.; Nisha, G.; Anand Kumar, M.
    The congestion of urban traffic is one of the critical issues of the present day. Traffic jams cause an increase in fuel consumption, add transportation costs, increase carbon dioxide based air pollution and of course causes extra delay and stress for drivers. Existing Traffic management systems suffer from several shortcomings, such as Static Traffic signal controller, lack of proper mechanism to prevent further congestion on already crowded roads etc. Hence we aim to design and test an efficient Smart Traffic Management System that overcomes various limitations of existing systems in use. © 2021 IEEE.
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    Analyzing Banking Services Applicability Using Explainable Artificial Intelligence
    (Association for Computing Machinery, 2022) Sriram, A.; Gorti, S.S.; Amin, E.G.; Anand Kumar, M.A.
    Over the last few years, the banking sector has had a pivotal role to play in the global economy, comprising of about 24% of the global GDP and employing millions of people worldwide. Banks have a wide array of products and services to offer, ranging from ATMs, Tele-Banking, Credit Cards, Debit cards, Electronic Fund Transfers (EFT), Internet Banking, Mobile Banking, etc. Machine learning is a method of data analysis that automates analytical model building and can be an essential decision support tool for banks in providing services to certain customers and to help in improving customer satisfaction and experience based on collected data. In this study, we made use of several machine learning models and Artificial Neural Networks (ANN) to help banks make predictions about timely customer loan repayment and customer satisfaction. We explored different machine learning algorithms and have performed SHAP analysis, which has helped make conclusions about the significant features driving these decisions. © 2022 ACM.
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    Automated Health Insurance Management Framework with Intelligent Fraud Detection, Premium Prediction, and Risk Prediction
    (Springer Science and Business Media Deutschland GmbH, 2024) Devaguptam, D.; Gorti, S.S.; Leela Akshaya, T.; Sowmya Kamath, S.
    Private insurance is already one of the sectors with the greatest growth potential. For the majority of high-value assets today, including houses, jewelry, cars, and other valuable items, there are insurance solutions available. To maximize profits while handling client claims, insurance firms are leading have adopted cutting-edge operations, procedures, and mathematical models for estimating risks and serving customer best interests, while also maximizing profits. In this work, we aim to develop a machine learning-based automated framework that minimizes human involvement, protects insurance operations, identifies high-risk consumers, uncovers false claims, and lowers financial loss for the insurance industry. This framework consists of fraud detection followed by risk prediction and premium prediction. We trained and tested different machine learning approaches for each of the three insurance processing tasks; the observations are presented in this article. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.