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All Journal Bulletin of Electrical Engineering and Informatics Journal of Information Systems and Informatics
Oludayo Olugbara
Durban University of Technology
Computer Science & IT Electrical & Electronics Engineering

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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.
Bibliometric Analysis of Deep Learning for Social Media Hate Speech Detection Raymond Tapiwa Mutanga; Oludayo Olugbara; Nalindren Naicker
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.549

Abstract

Social media has become an important web technology for creating and sharing information plus enhancing business reputations worldwide. However, the anonymity accorded by social media platforms has been cryptically vituperated to spread horrendous content such as hate speech. Recently, researchers have been progressively gravitating towards the use of deep learning techniques to address the problem of social media hate speech detection. This study provides bibliometric analysis and mapping of the existing literature on hate speech detection using deep learning algorithms. The study used articles published between 2016 and 2022 from the Scopus database, while Vos Viewer, Biblioshiny, and Panda’s software tools were employed for the bibliometric analysis. The research explored the yearly trajectory of recent publications, dominant countries, collaborative institutions, sources of primary studies that have employed deep learning for hate speech detection, and the intellectual and social structures of the research constituents. It has been observed that the literature on hate speech detection is rapidly growing, but research output and collaborations from the developing countries of the world are still limited. The findings of this study provide insights into the intellectual structure and advancements in deep learning applications for hate speech detection while identifying research gaps for future work.
Co-Authors Marion O. Adebiyi Micheal O. Arowolo Nalindren Naicker Raymond Tapiwa Mutanga