Web and Mobile Platforms for Managing Elections based on IoT And Machine Learning Algorithms. (arXiv:2303.09045v1 [cs.LG])

The global pandemic situation has severely affected all countries. As a
result, almost all countries had to adjust to online technologies to continue
their processes. In addition, Sri Lanka is yearly spending ten billion on
elections. We have examined a proper way of minimizing the cost of hosting
these events online. To solve the existing problems and increase the time
potency and cost reduction we have used IoT and ML-based technologies.
IoT-based data will identify, register, and be used to secure from fraud, while
ML algorithms manipulate the election data and produce winning predictions,
weather-based voters attendance, and election violence. All the data will be
saved in cloud computing and a standard database to store and access the data.
This study mainly focuses on four aspects of an E-voting system. The most
frequent problems across the world in E-voting are the security, accuracy, and
reliability of the systems. E-government systems must be secured against
various cyber-attacks and ensure that only authorized users can access
valuable, and sometimes sensitive information. Being able to access a system
without passwords but using biometric details has been there for a while now,
however, our proposed system has a different approach to taking the
credentials, processing, and combining the images, reformatting and producing
the output, and tracking. In addition, we ensure to enhance e-voting safety.
While ML-based algorithms use different data sets and provide predictions in
advance.