Ibrahim Yasser

Chaos-based encryption algorithms offer many advantages over conventional cryptographic algorithms, such as speed, high security, affordable overheads for computation, and procedure power. In this paper, we propose a novel perturbation algorithm for data encryption based on double chaotic systems. A new image encryption algorithm based on the proposed chaotic maps is introduced. The proposed chaotification method is a hybrid technique that parallels and combines the chaotic maps. It is based on combination between Discrete Wavelet Transform to decompose the original image into sub-bands and both permutation and diffusion properties are attained using the chaotic states and parameters of the proposed maps, which are then concerned in shuffling of pixel and operations of substitution, respectively. Security, statistical test analyses, and comparison with other techniques indicate that the proposed algorithm has promising effect and it can resist several common attacks. Namely, the average values for UACI and NPCR metrics were 33.6248% and 99.6472%, respectively. Additionally, unscrambling quality can fulfill security and execution prerequisites as evidenced by PSNR and entropy values. In sum, the proposed method has enough ability to achieve low residual intelligibility with high quality recovered data, high sensitivity, and high security performance compared to some other recent literature approaches.

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. Technological developments like edge computing, fog computing, Internet of Things (IoT), and Big Data have gained importance due to their robustness and ability to provide diverse response characteristics based on target application. In this paper, we present a novel Health-Fog framework universal system to automatically assist the early diagnosis, treatment, and preventive of people with COVID-19 in an efficient manner. Achieving an empirical of the proposed framework which mix between deep learning and Neutrosophic classifiers in the task of classifying COVID-19. There are some proposed applications based on the proposed COVID-X framework such as smart mask, smart medical suit, safe spacer, and Medical Mobile Learning (MML) will be presented. Computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates