The greatest method to anticipate the future is to look at what has happened in the past. We shall present important election behavioral predictions in this paper. This study article will focus on the data offered by Present agewise voting statistics, voter demographics, votes cast, and spatial correlation among surrounding states in order to validate that a place's exit poll data. The major goals of our paper are to first encourage voting among different age groups based on projected circumstances, and then to understand the influence of a state's neighbours. Conclusively studying the entire voting scenario of previous years, which will aid in the forecast of citizens' voting behavior in the approaching years, as well as recognizing the root cause of the weaker portions and improving upon the flaws for a better future. Our main goal is to use some current voting data from a region to train and determine the major voting population in the various states of the United States based on their geographical influence. This will aid in the analysis of the current situation as well as assisting the government in creating awareness in places where it is missing.
The use and dependence on software in various fields has been the reason why researchers for past decades have spent their efforts on finding better methods to predict software quality and reliability. Soft computing methods have been used to bring efficient improvement in prediction of software reliability. This study proposed a novel method called Fuzzy Greedy Recurrent Neural Network (FGRNN) to assess software reliability by detecting the faults in the software. A deep learning model based on the Recurrent Neural Network (RNN) has been used to predict the number of faults in software. The proposed model consists of four modules. The first module, attribute selection pre-processing, selects the relevant attributes and improves generalization that improves the prediction on unknown data. Second module called, Fuzzy conversion using membership function, smoothly collects the linear sub-models, joined together to provide results. Next, Greedy selection deals with the attribute subset selection problem. Finally, RNN technique is used to predict software failure using previously recorded failure data. To attest the performance of the software, the popular NASA Metric Data Program datasets are used. Experimental results show that the proposed FGRNN model has better performance in reliability prediction compared with existing other parameter based and NN based models.
It has long been difficult to create a safe electronic voting system that provides the transparency and flexibility provided by electronic systems, while maintaining the fairness and privacy of present voting methods. Voting, especially during elections, is a technique where participants do not trust one another since the system might be attacked not just by an outsider but also by participants themselves (voters and organizers). The traditional methods of voting systems find it challenging to maintain the characteristics of an ideal voting system since there is a chance of tampering with results and disturbing the process itself. As a result, the effectiveness of the voting system is increased by translating the characteristics of an ideal voting system into digital space. It greatly lowers the expense of the elections and the work of the inspectors. In this essay, we'll use the open-source Blockchain technology to suggest a new electronic voting system's architecture. New chances to create new kinds of digital services are being provided by Blockchain. Numerous elements of our life have been altered by Blockchain technology, including the ability to save digital transactions via the Internet, confirm their legitimacy, license them, and provide the greatest level of security and encryption. This system offers a distributed architecture for storing the data, which distributes the data among many servers. In addition to maintaining voter identity outside of the vote count, this technology makes the voting process transparent.
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