Marion O. Adebiyi
Durban University of Technology

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A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels Marion O. Adebiyi; Micheal O. Arowolo; Oludayo Olugbara
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2769

Abstract

Malaria larvae embrace unpredictable variable life periods as they spread across many stratospheres of the mosquito vectors. There are transcriptomes of a thousand distinct species. Ribonucleic acid sequencing (RNA-seq) is a ubiquitous gene expression strategy that contributes to the improvement of genetic survey recognition. RNA-seq measures gene expression transcripts data, including methodological enhancements to machine learning procedures. Scientists have suggested many addressed learning for the study of biological evidence. An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification using SVM-Kernel algorithms. The efficacy of this assay is tested, and the outcome of the experiment obtained an accuracy metric of 93% and 96% respectively.
A meta-analysis of channel switching approaches for reducing zapping delay in internet protocol television Timothy T. Adeliyi; Ropo E. Ogunsakin; Marion O. Adebiyi; Oludayo O. Olugbara
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1476-1484

Abstract

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.