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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Marion Olubunmi Adebiyi
Landmark University
Computer Science & IT

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A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree Micheal Olaolu Arowolo; Marion Olubunmi Adebiyi; Ayodele Ariyo Adebiyi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.16381

Abstract

Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively.
Advancements in accurate speech emotion recognition through the integration of CNN-AM model Marion Olubunmi Adebiyi; Timothy T. Adeliyi; Deborah Olaniyan; Julius Olaniyan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 3: June 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i3.25708

Abstract

In this study, we introduce an innovative approach that combines convolutional neural networks (CNN) with an attention mechanism (AM) to achieve precise emotion detection from speech data within the context of e-learning. Our primary objective is to leverage the strengths of deep learning through CNN and harness the focus-enhancing abilities of attention mechanisms. This fusion enables our model to pinpoint crucial features within the speech signal, significantly enhancing emotion classification performance. Our experimental results validate the efficacy of our approach, with the model achieving an impressive 90% accuracy rate in emotion recognition. In conclusion, our research introduces a cutting-edge method for emotion detection by synergizing CNN and an AM, with the potential to revolutionize various sectors.
Co-Authors Ayodele Ariyo Adebiyi Deborah Olaniyan Julius Olaniyan Micheal Olaolu Arowolo Timothy T. Adeliyi