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Problem The lately emerged SARS-CoV-2 infection, which has put the whole world in an aberrant demanding situation, has generated an urgent need for developing effective responses through artificial intelligence (AI). Aim This paper aims to overview the recent applications of machine learning techniques contributing to prevention, diagnosis, monitoring, and treatment of coronavirus disease (SARS-CoV-2). Methods A progressive investigation of the recent publications up to November 2020, related to AI approaches towards managing the challenges of COVID-19 infection was made. Results For patient diagnosis and screening, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are broadly applied for classification purposes. Moreover, Deep Neural Network (DNN) and homology modeling are the most used SARS-CoV-2 drug repurposing models. Conclusion While the fields of diagnosis of the SARS-CoV-2 infection by medical image processing and its dissemination pattern through machine learning have been sufficiently studied, some areas such as treatment outcome in patients and drug development need to be further investigated using AI approaches.

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A category of techniques for secret data communication called steganography hides data in multimedia mediums. It involves embedding secret data into a cover-medium by means of small perceptible and statistical degradation. In this paper, a new adaptive steganography method based on contourlet transform is presented that provides large embedding capacity. We called the proposed method ContSteg. In contourlet decomposition of an image, edges are represented by the coefficients with large magnitudes. In ContSteg, these coefficients are considered for data embedding because human eyes are less sensitive in edgy and non-smooth regions of images. For embedding the secret data, contourlet subbands are divided into 4×4 blocks. Each bit of secret data is hidden by exchanging the value of two coefficients in a block of contourlet coefficients. According to the experimental results, the proposed method is capable of providing a larger embedding capacity without causing noticeable distortions of stego-images in comparison with a similar wavelet-based steganography approach. The result of examining the proposed method with two of the most powerful steganalysis algorithms show that we could successfully embed data in cover-images with the average embedding capacity of 0.05 bits per pixel.