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Coronaviruses are responsible on respiratory diseases in animal and human. The combination of numerical encoding techniques and digital signal processing methods are becoming increasingly important in handling large genomic data. In this paper, we propose to analyze the SARS-CoV-2 genomic signature using the combination of different nucleotide representations and signal processing tools in the aim to identify its genetic origin. The sequence of SARS-CoV-2 was compared with 21 relevant sequences including Bat, Yak and Pangolin coronavirus sequences. In addition, we developed a new algorithm to locate the nucleotide modifications. The results show that the Bat and Pangolin coronaviruses were the most related to SARS-CoV-2 with 96% and 86% of identity all along the genome. Within the S gene sequence, the Pangolin sequence presents local highest nucleotide identity. Those findings suggest genesis of SARS-Cov-2 through evolution from Bat and Pangolin strains. This study offers new ways to automatically characterize viruses.

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This paper proposes to apply optimized

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This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twentyone features based on temporal and amplitude distances between detected fiducial points and 10 morphological descriptors are extracted from each heartbeat. Then, support vector machine (SVM) is used as a classifier. A comparative study between two kernels, Gaussian and polynomial, was made in order to determine the best kernel and the appropriate values of hyperparameters that improve the recognition performance. The algorithm is evaluated using two databases, namely MIT-BIH Arrhythmia and MIT-BIH Normal Sinus Rhythm. Analysis of the results shows that the combination of all features allows improvement of our system efficiency with regard to healthy human subjects and those with arrhythmia.

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