Abstract
The structure of Electronic Voting Machine (EVM) is an interconnected network of discrete components that record and count the votes of voters. The EVM system consists of four main subsystems which are Mother board of computer, Voting keys, Database storage system, power supply (AC and DC) along with various conditions of functioning as well as deficiency. The deficiency or failure of system is due to its components (hardware), software and human mismanagement. It is essential to reduce complexity of interconnected components and increase system reliability. Reliability analysis helps to identify technical situations that may affect the system and to predict the life of the system in future. The aim of this research paper is to analyze the reliability parameters of an EVM system using one of the approaches of computational intelligence, the neural network (NN). The probabilistic equations of system states and other reliability parameters are established for the proposed EVM model using neural network approach. It is useful for predicting various reliability parameters and improves the accuracy and consistency of parameters. To guarantee the reliability of the system, Back Propagation Neural Network (BPNN) architecture is used to learn a mechanism that can update the weights which produce optimal parameters values. Numerical examples are considered to authenticate the results of reliability, unreliability and profit function. To minimize the error and optimize the output in the form of reliability using gradient descent method, authors iterate repeatedly till the precision of 0.0001 error using MATLAB code. These parameters are of immense help in real time applications of Electronic Voting Machine during elections.
Highlights
A neural network involves simple processing elements, neurons that are well connected by weighted synaptic links and form network architecture (Figure 1)
We propose an Neural network (NN) based reliability growth model based on Electronic Voting Machine (EVM) system
The results shown in numerical example includes initial values and optimized values of the reliability, unreliability, and profit function
Summary
To guarantee the privilege of the right to vote of the citizens, the electoral procedure with security and integrity is a fundamental condition of any nation. Authors proposed architecture of neural network comprises three layers, which incorporate the following: Input layer that takes input in context of problem; Hidden layer particular to update the weights in between input and output layers and Output layer manifests the optimized results to the supervisor. The whole complex system can be in a total failure state due to failure of unit LED / Rc/ Re or any key in subsystem B / PC/ SCAN and due to human error
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