Predicting Novel Coronavirus (nCoV) strains detecting the mutation process applying neural networking

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Opis bibliograficzny
Główni autorzy:	Rafi, Abdullah Hasan Sajjad, Das, Arkadeep, Das, Moumita
Kolejni autorzy:	Shakil, Arif
Format:	Praca dyplomowa
Język:	English
Wydane:	Brac University 2024
Hasła przedmiotowe:	Coronavirus Machine learning Neural network Genome sequences COVID-19 (Disease) > Data processing Coronavirus infections Neural networks (Computer science)
Dostęp online:	http://hdl.handle.net/10361/22852

id	10361-22852
record_format	dspace
spelling	10361-228522024-05-16T21:04:50Z Predicting Novel Coronavirus (nCoV) strains detecting the mutation process applying neural networking Rafi, Abdullah Hasan Sajjad Das, Arkadeep Das, Moumita Shakil, Arif Department of Computer Science and Engineering, Brac University Coronavirus Machine learning Neural network Genome sequences COVID-19 (Disease)--Data processing Coronavirus infections Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-38). As viruses undergo rapid evolution, the SARS-CoV-2 which is known as Covid- 19 has persisted in human populations for approximately three and a half years rapidly, continually exhibiting swift and unpredictable mutations. The relentless emergence of various new strains of SARS-CoV-2 has posed a significant challenge, leaving researchers grappling for effective strategies. This study employs a machine learning approach known as the Seq2Seq model to predict future new variants of the Human Coronavirus family by using the genome sequences of Human Coronaviruses in time series manner based on their first evolution. Through this methodology, the research successfully predicts and generates the future possible variants genome sequence of Human Coronavirus. This model would be a useful tool to predict genome sequences of future Human Coronaviruses and get important insights of the future variants to tackle the problem of fast evaluation of the human coronaviruses. Abdullah Hasan Sajjad Rafi Arkadeep Das Moumita Das B.Sc in Computer Science 2024-05-16T09:16:21Z 2024-05-16T09:16:21Z ©2024 2024-01 Thesis ID: 19301097 ID: 19101431 ID: 19301209 http://hdl.handle.net/10361/22852 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 48 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Coronavirus Machine learning Neural network Genome sequences COVID-19 (Disease)--Data processing Coronavirus infections Neural networks (Computer science)
spellingShingle	Coronavirus Machine learning Neural network Genome sequences COVID-19 (Disease)--Data processing Coronavirus infections Neural networks (Computer science) Rafi, Abdullah Hasan Sajjad Das, Arkadeep Das, Moumita Predicting Novel Coronavirus (nCoV) strains detecting the mutation process applying neural networking
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Shakil, Arif
author_facet	Shakil, Arif Rafi, Abdullah Hasan Sajjad Das, Arkadeep Das, Moumita
format	Thesis
author	Rafi, Abdullah Hasan Sajjad Das, Arkadeep Das, Moumita
author_sort	Rafi, Abdullah Hasan Sajjad
title	Predicting Novel Coronavirus (nCoV) strains detecting the mutation process applying neural networking
title_short	Predicting Novel Coronavirus (nCoV) strains detecting the mutation process applying neural networking
title_full	Predicting Novel Coronavirus (nCoV) strains detecting the mutation process applying neural networking
title_fullStr	Predicting Novel Coronavirus (nCoV) strains detecting the mutation process applying neural networking
title_full_unstemmed	Predicting Novel Coronavirus (nCoV) strains detecting the mutation process applying neural networking
title_sort	predicting novel coronavirus (ncov) strains detecting the mutation process applying neural networking
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22852
work_keys_str_mv	AT rafiabdullahhasansajjad predictingnovelcoronavirusncovstrainsdetectingthemutationprocessapplyingneuralnetworking AT dasarkadeep predictingnovelcoronavirusncovstrainsdetectingthemutationprocessapplyingneuralnetworking AT dasmoumita predictingnovelcoronavirusncovstrainsdetectingthemutationprocessapplyingneuralnetworking
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Predicting Novel Coronavirus (nCoV) strains detecting the mutation process applying neural networking

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