Over the course of time, electronic voting has evolved as a substitute for paper ballot voting to decrease redundancies and inconsistencies. Due to the many security and privacy vulnerabilities experienced over time, the past results of e-voting in the last three decades indicate that it has not been very successful. All cryptocurrencies are actually based on block chain. Block chain is based on the principle of distribution and decentralization. It is a continuous and continuously growing ledger that holds, in a secure, chronological and immutable way, a permanent record of all the transactions that have taken place. Value in a block chain can be anything. In the case of cryptocurrencies, it takes the meaning of money or currency. While in a game it takes the form of points or score. The value can take the form of a vote or a ballot while e-voting. The blockchain can be used in this article to pass votes between two parties. In our case, the electorate is one party and the candidate who earns the vote is the other. Without having a controlling central authority body, the block chain can be implemented in a more stable manner in mass electoral voting practise. A voting system that uses a more stable, tamper-proof block chain (immutable to voting modifications either by other voters or by any third party) and is cost-effective.

In current development in science and technology, Machine learning algorithms play an essential role for prediction, classification, data analysis and data visualization. With this efficient algorithm, we can solve many real-world problems in all domains like education, healthcare, banking, geographical analysis, etc., in the current scenario; much research work is going on with the new virus's infection called the corona. This Corona virus is a comprehensive unit of virus this cause illness in humans or animals, now in East Asian countries, this virus affected more people. In India, the first case was found in January month, originated from China. The entire world is focusing on the disease, and day by day, the infection and death rate is increasing. In this, we intended to focus on the spread of this deadly disease and to demonstrate which countries are the most affected by doing statistical analysis. On December 2019, As of 10 February 2020, China reported overall of 40,235 cases 909 deaths, evoking local and foreign terror. Here we provide estimates of the major epidemiological parameters, Based on the epidemiological data available to the public for Hubei, China, 11 January to 10 February 2020. In particular, we give an estimate Fatality and case recovery rates, along with their 90 per cent confidence levels as the epidemic progresses. For this work implementation, Extreme Learning Machine algorithm used. ELM is a feed-forward network and its learning rate also fast when compare to normal neural network. No need to provide any weights and bias values. This algorithm will give a promising result with the best accuracy.

With the invent of automobile and advances in the world of machinery, there is a notable increase in the number of vehicles, two wheelers and three wheelers in the road transport. With the raise in the number of vehicles there is a lot of traffic violations that is happening in the road system. Based on the Indian traffic road system, monitoring the violation of such a incident is a huge task and also a tedious system. But however steps are taken to monitor the traffic breach that happens in the road transport through License Plate Detection mechanism for the vehicles that involves in over speeding or violating the rules. License plate location is a very important concept in vehicle license plate recognition for intelligent transport systems. Number plates can have different shapes and sizes along with different colors. The most common vehicle number plate in India have the background color as yellow or white with the font color black. Identification of number plate for vehicles in India has been discussed in this paper and the numbers have been segmented to identify them specifically. Our focus here is on two main steps: first is to find the number plate and second the segmentation of the number to identify them specifically. The main objective of this paper is to efficiently design and implement a method for License Plate Recognition (LPR) of Indian License Plates. We have manually acquired the images of various vehicles. Here for the detection point, we have used COCO-API RCNN and K-NEAREST NEIGHBOR.