Large groups can make decisions via techniques like voting, referendums, and elections. During the realization and evaluation of these events, time cost, count honesty, and voting reliability are crucial activities. Users can create their own polls, votes, and surveys using the interfaces created in this study. On these designed election processes, they can cast an electronic ballot. The CNN machine learning system evaluates the votes, or the counting process, with great accuracy, preventing manipulation and bias errors. Blockchain and zero-knowledge proof-based infrastructures support evaluation processes concurrently, enhancing voting privacy, data security, and transparency procedures. Users can create their own polls, votes, and surveys using the interfaces created in this study. On these designed election processes, they can cast an electronic ballot. The CNN machine learning system evaluates the votes, or the counting process, with great accuracy, preventing manipulation and bias errors. Blockchain and zero-knowledge proof-based infrastructures support evaluation processes concurrently, enhancing voting privacy, data security, and transparency procedures. The values obtained from the results of the obtained CNN algorithm and data privacy criteria are quite satisfactory.

When looking at the daily life flow and working sectors, it is seen that almost all work and transactions are carried out electronically. It performs many data streams in the electronic transactions performed. The importance of information security is exactly at this point. To ensure the security of the data, the journey of the data between the sender and the receiver is encrypted. In this study, a hybrid application that creates encrypted text using genetic algorithm and particle swarm algorithm has been developed. In the first step of the study, two separate keys were generated to encode the message using the genetic algorithm and particle swarm algorithm. Shannon Entropy method was used as a fitness function in both algorithms. The message was encrypted with the genetic algorithm method by choosing the key that obtained the best result from the compliance function. The encrypted message was decoded by applying a reverse genetic algorithm to the recipient. The encryptions made using the generated key were measured and the results of the AES algorithm were compared. In the proposed model, successful performances were obtained as the maximum switching space and encryption time for encryption. As a result, the proposed application offers an alternative method of data encryption and decryption that can be used for message transmission.