A Hybrid Grey Wolf-Whale Optimization Algorithm for Classification of Corona Virus Genome Sequences using Deep Learning

Genome sequence data is widely accepted as complex data and is still growing in an exponential rate. Classification of genome sequences plays a crucial role as it finds its applications in the area of biology, medical and forensics etc. For classification, Genome sequences can be represented in terms of features. More number of less significant features leads to lower accuracy in classification task. Feature selection addresses this issue by selecting the most important features which aids to improve the accuracy and lessens the computational complexity. In this research, Hybrid Grey Wolf-Whale Optimization Algorithm (HGWWOA) is proposed for Genome sequence classification. The proposed algorithm is evaluated using 23 benchmark objective functions along with Convolutional Neural Network classifier and its efficiency is verified using a novel metric namely "Feature Reduction Rate”. The proposed optimization algorithm can be applied for any optimization problems. In this research work, the proposed algorithm is used for classification of Corona Virus genome sequences. Performance comparison of the proposed and existing algorithms was carried out and it is evident that the performance of proposed algorithm exceeds the previous algorithms with an accuracy of 98.2%. © 2023, Zarka Private University. All rights reserved.

Satisfaction of Para Surfers on Classification: A Survey Analysis

A new evidence-informed classification structure following the Paralympic classification code was recent ly developed and implemented at two para surfing competitions. The purpose of this study was to under-stand the agreement and satisfaction levels of this novel classification structure among para surfers. Pre-and post-surveys were conducted in September 2019 and March 2020 at two international competitions before the COVID-19 pandemic. Surfers (n=131) met inclusion criteria of being older than 18 years old, completed classification, and surfed at least twice in their sport class. Pre-surveys (n=79) were conducted after classification and before surfing and post-surveys (n=98) were conducted after surfing twice in their sport class. Agreement and satisfaction levels were measured using a 4-point Likert scales. Results demonstrated high agreement and high levels of satisfaction (95%-100%) with this para surfing classification. No significant difference was found between the pre-and post-survey scores except in one sport class, Para Surfing Stand 2, that showed a significant difference in the requirement to be classified. Findings suggest para surfers widely accepted this new classification structure immediately after classification and after competing at least twice in their sport class.

A Novel Frequency Based Feature Extraction Technique for Classification of Corona Virus Genome and Discovery of COVID-19 Repeat Pattern

Genome sequence regulates the life of all living organisms on earth. Genetic diseases cause genomic disorders and therefore early prediction of severe genetic diseases is quite possible by Genomesequence analysis. Genomic disorders refer to the mutation that is rearrangement of bases in the Genomeof an organism. Genome sequence analysis and mutation identification can help to classify the diseasedgenome which can be accomplished using Machine Learning techniques. Feature Extraction plays a crucialrole in classification as it is used to convert the Genome sequences into a set of quantitative values. In thisarticle, we propose a novel feature extraction technique called Frequency based Feature ExtractionTechnique which extracts 120 features from genome sequences for classification. In the current scenario,COVID-19 is the pandemic disease and Corona virus is the source of this disease. So, in this research work,we tested the proposed feature extraction technique with 1000 samples of Genome sequences of Coronavirus affected patients across the world. The extracted features were classified using both Machine Learningand Deep Learning techniques. From the results, it is evident that the proposed feature extraction techniqueperforms well with Convolutional Neural Network classifier giving an accuracy of 97.96%. The proposedtechnique also helps to find the most repeat patterns in the genome sequences. It is discovered that thepattern “TTGTT” is the most repeat pattern in COVID-19 genome © 2021 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY NC) license (https://creativecommons.org/licenses/by-nc/4.0/)