Corn disease prediction is an essential part of agricultural productivity. This paper presents a novel 3D-dense convolutional neural network (3D-DCNN) optimized using the Ebola optimization search (EOS) algorithm to predict corn disease targeting the increased prediction accuracy than the conventional AI methods. Since the dataset samples are generally insufficient, the paper uses some preliminary pre-processing approaches to increase the sample set and improve the samples for corn disease. The Ebola optimization search (EOS) technique is used to reduce the classification errors of the 3D-CNN approach. As an outcome, the corn disease is predicted and classified accurately and more effectually. The accuracy of the proposed 3D-DCNN-EOS model is improved, and some necessary baseline tests are performed to project the efficacy of the anticipated model. The simulation is performed in the MATLAB 2020a environment, and the outcomes specify the significance of the proposed model over other approaches. The feature representation of the input data is learned effectually to trigger the model's performance. When the proposed method is compared to other existing techniques, it outperforms them in terms of precision, the area under receiver operating characteristics (AUC), f1 score, Kappa statistic error (KSE), accuracy, root mean square error value (RMSE), and recall.

Elections are one of the biggest events to take place in any democratic country. In India, which has a population of 1.2 Billion, elections are especially important as it selects the leader of about one-sevenths of the World’s Population. The usual method of voting is through the use of Electronic Voting Machines (EVM). In this modern era, where technology is being used in every sphere of life, considering the fact that Voting is one of the fundamental rights of every citizen of a democratic country, technology should be used to stop any unfair means being used in the elections. The objective of this project is to develop a system which will be suitable for elections in countries like India. In this project we use Arduino and Finger Print Scanner which will identify its voters and prevent digital malpractices. We also propose a mobile application which will enable the voters to cast their vote from anyplace they want. The proposed system is more digital, technology-based and secure.

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