It was known to us from decades it is been a factual challenge for us to transfer the privileged data specially images to transfer over a unsecured channel. In this paper we propose a novel image encryption technique using DNA interwearing based Hybridization along with chaotic maps to transfer image data over a unsecured channel. In the early stages we apply Duffing map(chaotic map) on the original image, the resultant image we obtain will be the scrambled original image, where pixel position will be scattered over the image plain. And in the next phase we apply the technique of modified DNA hybridization based on the interwearing on the resultant image. Finally encrypted using a modified hill cipher.By doing so we attain chaotic behavior(Butterfly effect with small change in initial condition leads to big change in resultant outcome) by using Duffing map and we achieve highely security with less processing by using DNA Hybridization based image encryption. The security analysis of proposed techniques has achieved satisfactory outcome and results were presented.

Abstract. An Edge of an image is a sudden change in the intensity of an image. Edge detection is process of finding the edges of an image. Edge detection is one of the image preprocessing techniques which significantly reduces the amount of data and eliminates the useless information by processing the important structural properties in an image. There are many traditional algorithms used to detect the edges of an image. Some of the important algorithms are Sobel, Prewitt, Canny, Roberts etc. A Hybrid approach for Image edge detection using Neural Networks and Particle swarm optimization is a novel algorithm to find the edges of image. The training of neural networks follows back propagation approach with particle swarm optimization as a weight updating function. 16 visual patterns of four bit length are used to train the neural network. The optimized weights generated from neural network training are used in the testing process in order to get the edges of an image.

No abstract

The entire world is suffering from a novel disease called covid-19 caused by a coronavirus since 2019. The main reason for the seriousness of the disease is the lack of efficient legitimate medication or vaccine. The World Health Organization (WHO) suggested several precautions to regulate the spread of disease and to reduce the contamination thereby reducing deaths. In this paper, we analysed the covid-19 dataset available in Kaggle. The previous contributions from several authors of similar work focused on covid-19 datasets having a limited number of samples. Our paper used the dataset updated till November 15th 2020. Three different aspects are considered mainly in this paper, namely the number of confirmed cases, number of recovered cases, and number of death cases. All the aspects are analysed in a daily and weekly manner. We applied linear regression, polynomial regression, and holt’s method to predict the future number of confirmed, recovered, and death cases. This analysis is useful for the health sectors and frontline workers to help reduce the contamination caused by this disease.