Powered by OpenAIRE graph
Found an issue? Give us feedback
ZENODOarrow_drop_down
ZENODO
Journal . 2025
License: CC BY
Data sources: Datacite
ZENODO
Journal . 2025
License: CC BY
Data sources: Datacite
ZENODO
Journal . 2025
License: CC BY
Data sources: Datacite
versions View all 3 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

FROM EVM TO I-VOTING 2.0: HARNESSING AI IN DATA ANALYTICS FOR SECURE AND TRANSPARENT ELECTIONS

Authors: Pramanik S.L., Matyrola S.S. & Raphel G.;

FROM EVM TO I-VOTING 2.0: HARNESSING AI IN DATA ANALYTICS FOR SECURE AND TRANSPARENT ELECTIONS

Abstract

Out of India’s total population of 1.41 billion, the voter turnout in the Lok Sabha elections was 65.79%, while in the Maharashtra Assembly Elections, with a population of 127.68 million, the voter turnout was 65.11%. While these figures reflect commendable participation, challenges like long queues, interstate voting complexities, high training costs, and voter impersonation compromise the efficiency and security of Electronic Voting Machines (EVMs). India's ongoing digital transformation creates an opportunity to introduce Internet-based voting (I-voting) enhanced by AI-powered data analytics. By leveraging AI, I-voting can offer secure, transparent, and efficient electoral processes through features such as precise voter authentication, turnout prediction, and improved accessibility for remote voters. Drawing inspiration from Estonia, integrating AI with I-voting could revolutionize India’s democratic processes, ensuring inclusivity and trustworthiness. Primary data will be collected through surveys distributed among voters to assess their knowledge, attitudes, and potential adoption of I-voting, while secondary data will be sourced from government reports, academic research, and global case studies like Estonia to analyze the integration of AI in I-voting systems.

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!