Blockchain and Machine Learning Combined Secured Voting System
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Abstract
This paper proposes a secure, transparent, and tamper resistant electronic voting system that combines blockchain technology with machine learning (ML) for anomaly detection and voter-behaviour validation. The primary contribution is an end-to-end system architecture that uses a permissioned blockchain for immutable ballot storage and smart contracts for vote verification and tallying, while ML modules run off-chain to detect fraudulent patterns, ensure voter eligibility, and flag suspicious activity in real time. A prototype implementation (Ethereum/Hyperledger-compatible) and simulation show improved security, auditability, and resistance to common attack vectors compared to traditional electronic voting methods. The approach emphasizes voter privacy, scalability through sharding and off-chain storage for ballot payloads, and explainability of ML alerts.
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1. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
2. Sakthivel, T. S., Ragupathy, P., & Chinnadurai, N. (2025). Solar system integrated smart grid utilizing hybrid coot-genetic algorithm optimized ANN controller. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 1–24.
3. Ananth, S., Radha, D. K., Prema, D. S., & Nirajan, K. (2019). Fake news detection using convolution neural network in deep learning. International Journal of Innovative Research in Computer and Communication Engineering, 7(1), 49–63.
4. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E. (2025, September). Cloud-based extreme learning machines for mining waste detoxification efficiency. In 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 1348–1353). IEEE.
5. Kumar, A. S., Saravanan, M., Joshna, N., & Seshadri, G. (2019). Contingency analysis of fault and minimization of power system outage using fuzzy controller. International Journal of Innovative Technology and Exploring Engineering, 9(1), 4111–4115.
6. Madheswaran, M., Dhanalakshmi, R., Ramasubramanian, G., Aghalya, S., Raju, S., & Thirumaraiselvan, P. (2024, April). Advancements in immunization management for personalized vaccine scheduling with IoT and machine learning. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1566–1570). IEEE.
7. Sammy, F., Chettier, T., Boyina, V., Shingne, H., Saluja, K., Mali, M., ... & Shobana, A. (2025). Deep learning-driven visual analytics framework for next-generation environmental monitoring. Journal of Applied Science and Technology Trends, 114–122.
8. Dharnasi, P. (2025). A multi-domain AI framework for enterprise agility integrating retail analytics with SAP modernization and secure financial intelligence. International Journal of Humanities and Information Technology, 7(4), 61–66.
9. Saravanan, M., & Sivakumaran, T. S. (2016). Three phase dual input direct matrix converter for integration of two AC sources from wind turbines. Circuits and Systems, 7, 3807–3817.
10. Prasanna, D., & Manishvarma, R. (2025, February). Skin cancer detection using image classification in deep learning. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–8). IEEE.
11. Inbavalli, M., & Arasu, T. (2015). Efficient analysis of frequent item set association rule mining methods. International Journal of Scientific & Engineering Research, 6(4).
12. Vani, S., Malathi, P., Ramya, V. J., Sriman, B., Saravanan, M., & Srivel, R. (2024). An efficient black widow optimization-based faster R-CNN for classification of COVID-19 from CT images. Multimedia Systems, 30(2), 108.
13. Aashiq Banu, S., Sucharita, M. S., Soundarya, Y. L., Nithya, L., Dhivya, R., & Rengarajan, A. (2020). Robust image encryption in transform domain using duo chaotic maps—A secure communication. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 271–281). Springer.
14. Sugumar, R. (2025). Explainable AI-driven secure multi-modal analytics for financial fraud detection and cyber-enabled pharmaceutical network analysis. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(6), 13239–13249.
15. David, A. (2020). Air pollution control monitoring & delivery rate escalated by efficient use of Markov process in MANET networks: To measure quality of service parameters. Test Engineering & Management.
16. Lakshmi, A. J., Dasari, R., Chilukuri, M., Tirumani, Y., Praveena, H. D., & Kumar, A. P. (2023, May). Design and implementation of a smart electric fence built on solar with an automatic irrigation system. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 1553–1558). IEEE.
17. Saravanan, M., Kumar, A. S., Devasaran, R., Seshadri, G., & Sivaganesan, S. (2019). Performance analysis of very sparse matrix converter using indirect space vector modulation. International Journal of Innovative Technology and Exploring Engineering, 9(1), 4756–4762.
18. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder–decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
19. Yashwanth, K., Adithya, N., Sivaraman, R., Janakiraman, S., & Rengarajan, A. (2021, July). Design and development of pipelined computational unit for high-speed processors. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1–5). IEEE.
20. Prasanna, D., Ahamed, N. A., Abinesh, S., Karthikeyan, G., & Inbatamilan, R. (2024, November). Cloud-based automatically human document authentication processes for secured system. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1–7). IEEE.
21. Karthikeyan, K., Umasankar, P., Parathraju, P., Prabha, M., & Pulivarthy, P. Integration and analysis of solar vertical axis wind hybrid energy system using modified zeta converter.
22. Ananth, S., Radha, D. K., Prema, D. S., & Nirajan, K. (2019). Fake news detection using convolution neural network in deep learning. International Journal of Innovative Research in Computer and Communication Engineering, 7(1), 49–63.
23. Sammy, F., Chettier, T., Boyina, V., Shingne, H., Saluja, K., Mali, M., ... & Shobana, A. (2025). Deep learning-driven visual analytics framework for next-generation environmental monitoring. Journal of Applied Science and Technology Trends, 114–122.