Abstract
A digital voting system that allows people to vote sitting at their homes based on their face recognition. The traditional voting system does not allow people to vote while sitting in their homes. Considering the situation of COVID-19, everything is going digital. Questions on EVM from losing parties regarding some malfunctioning. Digital, secured, reliable, user-friendly, and a low-cost system. DDoS assaults, polling booth capturing, vote tampering and manipulation, malware attacks, and other security issues are all present in today’s voting systems. The voting process may be made more safe, transparent, immutable, and dependable by utilizing blockchain technology. Our current voting system is questioned by a variety of stakeholders, including political parties and ordinary citizens because votes are manipulated, citizens find voting a time-consuming task, and, most importantly, the current pandemic situation has digitized almost all areas of interest in our county. As a result, to resolve the issue produced by the physical voting system. The system we are creating is a blockchain-based decentralized app which uses machine learning for the identification of the user, it uses principles like face matching, checking confidence level, and then allows the user to vote based on the verification of her/his documents and eligibility to vote without compromising the right to vote anonymously.
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References
McCorry P, Shahandashti SF, Hao F (Jan 2017) A smart contract for boardroom voting with maximum voter privacy. Lecture notes in computer science, vol 10322, pp 357–375. https://eprint.iacr.org/2017/110.pdf
Chowdhury A (Jan 2016) Secure voting: a guide to secure #onlinevoting in election, WebRoots Democracy. https://webrootsdemocracy.files.wordpress.com/2019/03/secure-voting-second-%20edition.pdf
McCorry P, Shahandashti SF, Hao F (Jan 2017) A smart contract for boardroom voting with maximum voter privacy https://eprint.iacr.org/2017/110.pdf
Hjálmarsson F, Hreiðarsson G, Hamdaqa M (Nov 2020) Blockchain-based e-voting system. In: IEEE 11th international conference on cloud computing, vol 112, pp 859–874. https://doi.org/10.1016/j.future.2020.06.051
Jafar U, Juzaiddin M, Aziz A, Shukur Z (31 Aug 2021) Review and open research challenges, digital voting with the use of blockchain technology. https://www.mdpi.com/1424-8220/21/17/5874/htm
Cuimei L, Zhiliang Q, Nan J, Jianhua W (2017) Human face detection algorithm via Haar cascade classifier combined with three additional classifiers. In: 13th IEEE international conference on electronic measurement & instruments (ICEMI), pp 483–487. https://ieeexplore.ieee.org/abstract/document/8265863
Gangopadhyay I, Chatterjee A, Das I (2019) Face detection and expression recognition using Haar cascade classifier and fisherface algorithm. In: Bhattacharyya S, Pal S, Pan I, Das A (eds) Recent trends in signal and image processing. Advances in intelligent systems and computing, vol 922. Springer, Singapore. https://doi.org/10.1007/978-981-13-6783-0_1
Nofer M, Gomber P, Hinz O, Schiereck D (2017) Blockchain business & information systems engineering 59(3):183–187. https://link.springer.com/article/10.1007%2Fs12599-017-0467-3
Blockchain for government: digital identity, land records and other kinds of record keeping between government entities Dr Behara. Gopala Krishna https://www.expresscomputer.in/blockchain/why-blockchain-technology-is-the-need-of-the-hour-%20for-land-management/43907/
Sharma A, Podoplelova E, Shapovalov G, Tselykh A, Tselykh A (2021) Sustainable smart cities: convergence of artificial intelligence and blockchain. Sustainability 13(23):13076
Jafar U, Aziz MJA, Shukur Z (2021) Blockchain for electronic voting system—review and open research challenges. Sensors 21(17):5874
Wang C (2018) What’s the difference between Haar-feature classifiers and convolutional neural networks. https://towardsdatascience.com/whats-the-difference-between-haar-feature-classifiers-and-convolutional-neural-networks-ce6828343aeb#:~:text=All%20these%20observations%20were%20in,better%20fit%20than%20the%20other
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Rastogi, R., Arora, P., Dhamija, L., Shrivastava, R. (2023). Intelligent Online Voting System for Twenty-First Century and Smart Cities 5.0: An Empirical Approach through Blockchain with ML Techniques. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_15
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DOI: https://doi.org/10.1007/978-981-99-3656-4_15
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