Novel coronavirus 2019 (COVID-2019) initially started at Wuhan, China and spread all over the world and announced as pandemic by World Health Organization in March 2020. It makes use of all the available resources to reduce the disastrous effect of such Black Swan event. This virus causes pneumonia in human being and changes the respiratory pattern (different from common cold and flu). Compared to the reversetranscription polymerase chain reaction (RT-PCR) chest X-ray and computed tomography imaging may be reliable and quick to diagnose the COVID-19 patients in the epidemic regions. Above mentioned imaging modalities along with the machine learning techniques can be helpful for accurate diagnosis of the disease and may be assistive in the absence of specialized physicians. Further, ML can improve throughput by accurate figuration of contagious X-ray and CT images for disease diagnosis, tracking and prognosis. In this research, potential of artificial intelligence has been investigated to develop a deep neural network model for rapid, accurate and effective COVID-19 detection from the CT and Xray images. The proposed method provides robust deep learning technique for binary (COVID vs. NON-COVID) and multi-class (COVID vs. NON-COVID vs. Pneumonia) classification from Xray and CT images. A 24-layer CNN network has been proposed for the classification. It attains an accuracy of 99.68% and 71.81% on X-ray and CT images, respectively. For both the datasets Sgdm optimizer has been used with a learning rate 0.001.

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In this paper, a robust and highly imperceptible audio watermarking technique is presented based on discrete cosine transform (DCT) and singular value decomposition (SVD). The low-frequency components of the audio signal have been selectively embedded with watermark image data making the watermarked audio highly imperceptible and robust. The imperceptibility of proposed methods is evaluated by computing signal-to-noise ratio and by conducting subjective listening tests. The robustness of proposed technique is evaluated by computing bit error rate and average information loss in retrieved watermark image subjected to MP3 compression, AWGN, re-sampling, re-quantization, amplitude scaling, low-pass filtering, and high-pass filtering attacks with high data payload of 6 kbps. The information-theoretic approach is used to model the proposed watermarking technique as discrete memoryless channel. The Shannon's entropy concept is used to highlight the robustness of proposed technique by computing the information loss in retrieved watermarked image.