We conducted a theoretical study on the potential use of amorphous hydrogenated silicon (a-Si:H) as the high-index material in quarter-wave-stack Bragg mirrors for cavity quantum electrodynamics applications. Compared to conventionally employed T a 2

The aim of this work is to design an algorithm which combines between steganography andcryptography that can hide a text in an image in a way that prevents, as much as possible, anysuspicion of the hidden textThe proposed system depends upon preparing the image data for the next step (DCT Quantization)through steganographic process and using two levels of security: the RSA algorithm and the digitalsignature, then storing the image in a JPEG format. In this case, the secret message will be looked asplaintext with digital signature while the cover is a coloured image. Then, the results of the algorithmare submitted to many criteria in order to be evaluated that prove the sufficiency of the algorithm andits activity. Thus, the proposed algorit

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

Because of vulnerable threats and attacks against database during transmission from sender to receiver, which is one of the most global security concerns of network users, a lightweight cryptosystem using Rivest Cipher 4 (RC4) algorithm is proposed. This cryptosystem maintains data privacy by performing encryption of data in cipher form and transfers it over the network and again performing decryption to original data. Hens, ciphers represent encapsulating system for database tables

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet