# Secure Voting Website Using Ethereum and Smart Contracts
## 5 Analysis and Discussion
To calculate the efficiency of a traditional voting system, we considered several factors, such as the time taken for vote counting, the number of errors made during the process, the accuracy of the results, and the level of transparency and trust in the system. A traditional voting system takes an average of 2 hours to count the votes, has a 1% error rate, and results in a 90% accuracy rate; the efficiency can be calculated using the following formula:  
\[\text{Efficiency}=(\text{Total time taken}/\text{Number of eligible voters})/(1-\text{Error rate})\times\text{Accuracy rate}\]  
Therefore, the efficiency of the traditional voting system would be 0.0018. Comparing this to the efficiency of the blockchain-based voting system (0.0078), we can see that the blockchain-based system is more efficient in terms of time and accuracy. However, it is important to note that this is just one factor to consider, and other factors, such as security and trust, should also be evaluated before deciding on a voting system. However, there are also some potential drawbacks to consider. One issue is the risk of centralization, where a small group of entities control most of the blockchain's computing power and therefore have a disproportionate influence over the voting process. Additionally, the immutability of blockchain can make it difficult to correct errors or address disputes once the voting process has ended. Another potential issue is the need for voter privacy. While blockchain provides transparency and accountability, it can also compromise the anonymity of voters if their identities are linked to their votes. To address this, some blockchain-based voting systems use zero-knowledge proofs, which allow voters to prove that they cast a valid vote without revealing their actual vote [28]. The main drawback of an online voting system where anyone can cast a vote instead of the intended user is the issue of voter authentication and identification. In a traditional paper-based voting system, voters must physically present themselves at the polling station and verify their identity through government-issued identification, such as a passport or driver's license. However, in an online voting system, it is challenging to ascertain the identity of the voter, and there is a higher risk of fraud or impersonation [29; 40; 41]. The proposed system is integrated with AIML-based face recognition to tackle this issue. AIML-based face recognition is a modern technique that uses computer algorithms to detect and recognize human faces. In the context of online voting systems, this technology can combat the issue of unauthorized voting by verifying the identity of the person casting the vote. The system can ensure that only authorized users are casting votes by requiring users to present their faces in front of a camera during the voting process. The face recognition system uses various computer vision algorithms to detect and extract facial features such as the eyes, nose, and mouth from images. These features are then compared to a database of known faces to identify the person in the picture. The algorithm can also check for anomalies, such as masks or other facial coverings, that could be used to deceive the system. AIML-based face recognition can significantly enhance the security of online voting systems by reducing the risk of unauthorized voting. This technology provides a reliable and efficient way to verify the voter's identity in real-time, significantly reducing the risk of fraud and manipulation. In addition to enhancing security, face recognition technology can also improve the user experience for voters. By streamlining the verification process, voters can quickly and easily cast their votes without requiring lengthy identity verification procedures.