The potentials of deep learning techniques for the classification of SARS-CoV-2 variants based on genomic sequence information

Genetic mutations give rise to a quasi-species of drug/vaccine-resistant and virulent organisms. These organisms are classified as strains or variants depending on the extent of their phenotypic manifestation. Thus, there is a thin dichotomy between SARS-CoV-2 strains and their associated variants. This paper sought to comprehensively review the successes achieved in the classification of SARS-CoV-2 strains based on genomic sequences (GSs) using deep learning architectures, thereby stimulating further research on the variants identified recently. Selective screening and analysis of research articles centered on deep learning architectures employed for SARS-CoV-2 detection based on GS information were carried out. This incorporated the use of relevant key/search terms and logical/Boolean operators to scan through the Scopus repository. To provide a foundation for future investigations on the classification of SARS-CoV-2 strains, meticulous analysis of the three key aspects, such as abstract, methodology, and conclusion, was implemented. Despite the high level of intra-species similarity, this article presents new studies that use deep learning technology to detect SARS-CoV-2 strains on the premise of the primary sequence of nucleotides in their genome. Manually searching through specific genes for mutations to identify variants after sequencing can be very laborious. This is where the use of computational acumen comes into play. Deep learning, an offshoot of machine learning, has been utilized in various literature to tackle such problems. Rapid identification of SARS-CoV-2 variant after sequencing aids quick response by clinicians to administer relevant drugs and save lives. Also, governments utilize this information to map out strategies for the timely containment of the spread of an identified variant with elevated virulence. The deep learning models reported in this paper show the remarkable predictive results achieved in identifying SARS-CoV-2 strains. However, no work has been done on the identification of recent variants reported globally

A meta-analysis of channel switching approaches for reducing zapping delay in internet protocol television

Channel zapping delays are inconveniences that are often experienced by the subscribers of Internet protocol television (IPTV). It is a major bottleneck in the IPTV channels switching system that affect the quality of experience of users. Consequently, numerous channels switching approaches to minimize zapping delay in IPTV have been suggested. However, there is little knowledge reported in the literature on the determination of the strength of the evidence presented on the approaches of reducing zapping delay in IPTV, which is the prime purpose of this study. The extraction of the relevant articles was designed following the technique of preferred reporting items for systematic reviews and meta-analyses (PRISMA). All the included research articles were searched from the widely used databases of Google Scholar, and Web of Science. All statistical analyses were performed with the aid of the random-effects model implementation in Stata version 15. The overall pooled estimated delay component was presented in forest plots. Overall, thirteen studies were included in the meta-analysis and the overall pooled estimate was 10% (95% CI: 7%, 30%)). Experimental studies have shown that virtual elimination of IPTV zapping delay is possible for a relevant chunk of channel switching requests