Faculty Publications
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Item Hybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems(Institute of Electrical and Electronics Engineers Inc., 2024) Goyal, G.; Sharma, K.; Anshuman; Mittal, V.; Singla, B.; Das, M.; Mohan, B.R.Software reliability is a paramount determinant of software quality. In this research paper, we delve into utilizing Genetic Algorithms (GAs) for feature selection and classification. We undertake a comprehensive evaluation and comparative analysis of Machine Learning models, specifically Random Forest and Logistic Regression, both with and without Genetic Algorithmdriven feature selection. Our findings substantiate the significant impact of Genetic Algorithms in improving the accuracy of software reliability analysis. © 2024 IEEE.Item TCP SYN Flood Attack Detection Using Logistic Regression and Multi-Agent Reinforcement Learning(Institute of Electrical and Electronics Engineers Inc., 2025) Sanjay, M.; Arun Raj Kumar, P.In the realm of cybersecurity, Distributed Denial of Service (DDoS) attacks remain a continuous threat, particularly TCP SYN flood attacks due to their stealthiness and potential for disruption. In this paper, we propose a combination of Multi-Agent Reinforcement Learning (MARL) with logistic regression for enhancing TCP SYN attack detection, leveraging Actor-Critic as the reinforcement learning algorithm. A novel approach is introduced for hyperparameter optimization using MARL, offering an alternative to traditional techniques such as GridSearchCV and RandomSearchCV. We present a comparative analysis between traditional logistic regression and MARL enhanced approaches, evaluating their performance using metrics such as accuracy, false negatives, and false positives. Results demonstrate that our proposed approach significantly improves detection accuracy and reduces false positives, underscoring its potential in bolstering cybersecurity defenses against sophisticated DDoS threats. © 2025 IEEE.Item Adoption of Crop Insurance by Smallholder Farmers: Farm-Level Evidence from India(Nan Yang Academy of Sciences Pte. Ltd, 2024) Rajesh Acharya, H.The paper aims to analyse the extent and determinants of smallholder farmers’ adoption of crop insurance. The study conducted a primary survey and collected data from farmers in a drought-prone area of Karnataka state in India using a structured questionnaire. The study has applied a binary logistic regression model to identify the determinants of crop insurance adoption. Empirical results reveal that though most farmers experienced crop loss, only a small percentage subscribed to crop insurance regularly. Lack of money to pay premiums and lack of information are the most common reasons for not subscribing to crop insurance schemes. Further, farmers feel the premium is expensive and do not receive the promised compensation due to the stringent eligibility rules. Most farmers who received compensation think the money is inadequate to cover the cultivation cost. Farmers feel each farm should be treated as a unit against the area-based insurance concept, and more crops should be brought under insurance. They also highlighted the need to further subsidise the premium. Results of the logistic regression con- firm that socially marginalised groups and farmers practising agriculture as an ancestral profession are less likely to insure their crops. © 2024 by the author(s).