1. Ramesh, G., Gorantla, V. A. K., & Gude, V. (2023). A hybrid methodology with learning based approach for protecting systems from DDoS attacks. Journal of Discrete Mathematical Sciences and Cryptography, 26(5), 1317-1325.
2. Logeshwaran, J., Gorantla, V. A. K., Gude, V., & Gorantla, B. (2023, September). The Smart Performance Analysis of Cyber Security Issues in Crypto Currency Using Blockchain. In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 6, pp. 2235-2241). IEEE.
3. Komatireddy, S. R., Meghana, K., Gude, V., & Ramesh, G. (2023, December). Facial Shape Analysis and Accessory Recommendation: A Human-Centric AI Approach. In 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 182-191). IEEE.
4. Sriramulugari, S. K., Gorantla, V. A. K., Gude, V., Gupta, K., & Yuvaraj, N. (2024, March). Exploring mobility and scalability of cloud computing servers using logical regression framework. In 2024 2nd International Conference on Disruptive Technologies (ICDT) (pp. 488-493). IEEE.
5. Gorantla, V. A. K., Gude, V., Sriramulugari, S. K., Yuvaraj, N., & Yadav, P. (2024, March). Utilizing hybrid cloud strategies to enhance data storage and security in e-commerce applications. In 2024 2nd International Conference on Disruptive Technologies (ICDT) (pp. 494-499). IEEE.
6. Bharathi, G. P., Chandra, I., Sanagana, D. P. R., Tummalachervu, C. K., Rao, V. S., & Neelima, S. (2024). AI-driven adaptive learning for enhancing business intelligence simulation games. Entertainment Computing, 50, 100699.
7. Sanagana, D. P. R., & Tummalachervu, C. K. (2024, May). Securing Cloud Computing Environment via Optimal Deep Learning-based Intrusion Detection Systems. In 2024 Second International Conference on Data Science and Information System (ICDSIS) (pp. 1-6). IEEE.
8. Sivaramkumar, V., Thansekhar, M. R., Saravanan, R., & Miruna Joe Amali, S. (2017). Multi-objective vehicle routing problem with time windows: Improving customer satisfaction by considering gap time. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(7), 1248-1263.
9. Reka, R., R. Karthick, R. Saravana Ram, and Gurkirpal Singh. “Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET.” Computers & Security 136 (2024): 103526.
10. Meenalochini, P., R. Karthick, and E. Sakthivel. “An Efficient Control Strategy for an Extended Switched Coupled Inductor Quasi-Z-Source Inverter for 3 Φ Grid Connected System.” Journal of Circuits, Systems and Computers 32.11 (2023): 2450011.
11. Karthick, R., et al. “An optimal partitioning and floor planning for VLSI circuit design based on a hybrid bio-inspired whale optimization and adaptive bird swarm optimization (WO-ABSO) algorithm.” Journal of Circuits, Systems and Computers 32.08 (2023): 2350273.
12. Rajagopal RK, Karthick R, Meenalochini P, Kalaichelvi T. Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images. Biomedical Signal Processing and Control. 2023 Jan 1;79:104197.
13. Karthick, R., and P. Meenalochini. “Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA).” Journal of the National Science Foundation of Sri Lanka 48.4 (2020).
14. Karthick, R., A. Senthilselvi, P. Meenalochini, and S. Senthil Pandi. “Design and analysis of linear phase finite impulse response filter using water strider optimization algorithm in FPGA.” Circuits, Systems, and Signal Processing 41, no. 9 (2022): 5254-5282.
15. Karthick, R., and M. Sundararajan. “SPIDER-based out-of-order execution scheme for HtMPSOC.” International Journal of Advanced Intelligence paradigms 19.1 (2021): 28-41.
16. Karthick, R., Dawood, M.S. & Meenalochini, P. Analysis of vital signs using remote photoplethysmography (RPPG). J Ambient Intell Human Comput 14, 16729–16736 (2023). https://doi.org/10.1007/s12652-023-04683-w
17. Kiran, A., Kalpana, V., Madanan, M., Ramesh, J. V. N., Alfurhood, B. S., & Mubeen, S. (2023). Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization. Optical and Quantum Electronics, 55(13), 1193.
18. Lalithambigai, M., Kalpana, V., Kumar, A. S., Uthayakumar, J., Santhosh, J., & Mahaveerakannan, R. (2023, February). Dimensionality reduction with DLMNN technique for handling secure medical data in healthcare-IoT model. In 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 111-117). IEEE.
19. Kalpana, V., Mishra, D. K., Chanthirasekaran, K., Haldorai, A., Nath, S. S., & Saraswat, B. K. (2022). On reducing energy cost consumption in heterogeneous cellular networks using optimal time constraint algorithm. Optik, 270, 170008.
20. Kalpana, V., & Karthik, S. (2020). Route availability with QoE and QoS metrics for data analysis of video stream over a mobile ad hoc networks. Wireless Personal Communications, 114(3), 2591-2612.
21. Kalpana, V., & Karthik, S. (2018, February). Bandwidth Constrained Priority Based Routing Algorithm for Improving the Quality of Service in Mobile Ad hoc Networks. In 2018 International Conference on Soft-computing and Network Security (ICSNS) (pp. 1-8). IEEE.
Page No: 395 - 400
Abstract : Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. The study's findings demonstrate that the optimized model significantly improves detection rates while reducing false positives, making it a viable solution for real-world implementation. Future research will focus on integrating this system with autonomous vehicles and exploring the potential for real-time intervention strategies, such as alert mechanisms and automated driving assistance, to prevent accidents caused by driver fatigue.
Keyword Driver Drowsiness Detection, Machine Learning, Optimization, Real-Time Data Analysis, Intelligent Transportation Systems
Reference: