Assess Reliability Parameters of an Electronic Voting Machine using a Neural Network Technique
C. M. Batra1, Ritu Gupta2, Ekata3
1C. M. Batra, Department of Applied Sciences, KIET Group of Institutions, Ghaziabad, India.
2Ritu Gupta, Department of Applied Sciences, KIET Group of Institutions, Ghaziabad, India.
3Ekata*, Department of Applied Sciences, KIET Group of Institutions, Ghaziabad, India.
Manuscript received on July 02, 2020. | Revised Manuscript received on July 10, 2020. | Manuscript published on August 30, 2020. | PP: 269-275 | Volume-9 Issue-6, August 2020. | Retrieval Number: E1056069520/2020©BEIESP | DOI: 10.35940/ijeat.E1056.089620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Back Propagation algorithm, EVM, Gradient descent method, Human failure, Neural Network approach, Neural weights, Profit function, Reliability, unreliability.