Summary of Revitalizing Electoral Trust: Enhancing Transparency and Efficiency Through Automated Voter Counting with Machine Learning, by Mir Faris et al.
Revitalizing Electoral Trust: Enhancing Transparency and Efficiency through Automated Voter Counting with Machine Learning
by Mir Faris, Syeda Aynul Karim, Md. Juniadul Islam
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates the potential of using advanced image processing techniques for automated voter counting in election procedures, aiming to increase effectiveness and openness. Utilizing OpenCV, CVZone, and the MOG2 algorithm, researchers explored how automated systems can enhance voting processes and rebuild public confidence in election outcomes. The empirical findings demonstrate improved accuracy compared to manual counting methods, with rigorous metrics like the F1 score used for systematic comparison. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores the use of advanced image processing techniques for automated voter counting during elections. Researchers aim to increase effectiveness and openness by utilizing OpenCV, CVZone, and the MOG2 algorithm. The findings show that automated systems can improve accuracy compared to manual counting methods. |
Keywords
* Artificial intelligence * F1 score
