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Image encryption is highly required in the big data healthcare cloud to improve the security of the medical image for remote access. Data hiding method is the process of storing the medical information of the patient in the medical image in the hidden format. Many existing data hiding methods are based on wavelet and chaotic map due to its effectiveness. Wavelet based methods have limitations of lack of phase information, poor directionality, and shift sensitivity. Chaotic map is applied to improve the security of the medical image and chaotic map has the limitation of low sensitive to control parameters and initial conditions. In this research, the Improved Chaos Encryption (ICE) is applied to improve the security based on randomness. The average energy is calculated in the images and compared with adaptive threshold to segment the Lorenz 96 model applied in the chaos encryption algorithm to improve the model security. Lorenz 96 increased the randomness of the chaos encryption method due to its high sensitivity. Medial images were used to test the performance of the ICE in the image encryption and image hiding. The proposed ICE model evaluated the quality of the recovered and decrypted image in the various embedding rate. The result shows that the proposed ICE model has the PSNR value of 104.7 dB compared to the LSB-ROI method which has 97.61 dB PSNR.

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Purpose Coronavirus disease 2019 is one of the novel diseases formed by a dreadful virus called Severe Acute Respiratory Syndrome Coronavirus 2. Various countries are affected by this viral disease, and many countries declare a lockdown with several rules and conditions. To prevent this rapid viral transmission, various researchers have introduced different mobile applications. This paper aims to study issues like viral transmission, mortality rates, vaccination rates, etc. and also provides suitable solutions based on the statistical analysis with the assistance of the Six-Sigma Define-Measure-Analyse-Improve-Control (DMAIC) concept. Design/methodology/approach Statistical analysis is done for different countries, and the required solutions are provided by using the DMAIC procedure. This application has the ability to represent the current risk status of the user and notify them to secure themselves. Findings The proposed work suggests the Aarogya Setu application to prevent large viral transmission by affording many preventive measures. This application also issues the current risk status of each individual user. Hence, it gives improved results in avoiding high viral transmission. Originality/value The proposed six-sigma DMAIC concept also affords the control measures to prevent viral transmission. Hence, the suggested application has the highest chance of avoiding the rapid viral transmission.