{"id":{"repo_id":"bradford","oai_identifier":"oai:bradscholars.brad.ac.uk:10454/19906"},"canonical_url":"https://search.dev.ndltd.org/etd/bradford/oai:bradscholars.brad.ac.uk:10454/19906","repository":{"repo_id":"bradford","name":"University of Bradford","base_url":"https://bradscholars.brad.ac.uk/oai/request"},"display":{"title":"A State-of-the-Art Artificial intelligence model for Infectious Disease Outbreak Prediction. Infectious disease outbreak have been predicted in England and Wales using Artificial Intelligence, Machine learning, and Fast Fourier Transform for COVID-19.","abstract":"The pandemic produced by the COVID-19 virus has resulted in an estimated 6.4 million deaths worldwide and a rise in unemployment rates, notably in the UK. Healthcare monitoring systems encounter several obstacles when regulating and anticipating epidemics. The study aims to present the AF-HIDOP model, an artificial neural network Fast Fourier Transform hybrid technique, for the early identification and prediction of the risk of Covid-19 spreading within a specific time and region. The model consists of the following five stages: 1) Data collection and preprocessing from reliable sources; 2) Optimal machine learning algorithm selection, with the Random Forest tree (RF) classifier achieving 94.4% accuracy; 3) Dimensionality reduction utilising principal components analysis (PCA) to optimise the impact of the data volume; 4) Predicting case numbers utilising an artificial neural network model, with 52% accuracy; 5) Enhancing accuracy by incorporating Fast Fourier Transform (FFT) feature extraction and ANN, resulting in 91% accuracy for multi-level spread risk classification. The AF-HIDOP model provides prediction accuracy ranging from moderate to high, addressing issues in healthcare-based datasets and costs of computing, and may have potential uses in monitoring and managing infectious disease epidemics.","abstract_html":"The pandemic produced by the COVID-19 virus has resulted in an estimated 6.4 million deaths worldwide and a rise in unemployment rates, notably in the UK. Healthcare monitoring systems encounter several obstacles when regulating and anticipating epidemics. The study aims to present the AF-HIDOP model, an artificial neural network Fast Fourier Transform hybrid technique, for the early identification and prediction of the risk of Covid-19 spreading within a specific time and region. The model consists of the following five stages: 1) Data collection and preprocessing from reliable sources; 2) Optimal machine learning algorithm selection, with the Random Forest tree (RF) classifier achieving 94.4% accuracy; 3) Dimensionality reduction utilising principal components analysis (PCA) to optimise the impact of the data volume; 4) Predicting case numbers utilising an artificial neural network model, with 52% accuracy; 5) Enhancing accuracy by incorporating Fast Fourier Transform (FFT) feature extraction and ANN, resulting in 91% accuracy for multi-level spread risk classification. The AF-HIDOP model provides prediction accuracy ranging from moderate to high, addressing issues in healthcare-based datasets and costs of computing, and may have potential uses in monitoring and managing infectious disease epidemics.","abstract_has_math":false,"creators":["Fayad, Moataz B.M."],"institution":"University of Bradford","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Youseffi, Mansour","Li, Jian-Ping"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T01:15:21Z","subjects":["Covid-19 outbreak","Prediction","Artificial Intelligence (AI)","Machine learning","Fast Fourier Transform","Computational cost","Infectious disease outbreak","Epidemics"],"languages":["en"],"rights":["<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/3.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png\" /></a><br />The University of Bradford theses are licenced under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/3.0/\">Creative Commons Licence</a>."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10454/19906","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Youseffi, Mansour","Li, Jian-Ping"]},{"key":"dc:creator","label":"Author","values":["Fayad, Moataz B.M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-06-12T14:01:35Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-06-12T14:01:35Z"]},{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Biomedical and Electronics Engineering. 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Healthcare monitoring systems encounter several obstacles when regulating and anticipating epidemics. The study aims to present the AF-HIDOP model, an artificial neural network Fast Fourier Transform hybrid technique, for the early identification and prediction of the risk of Covid-19 spreading within a specific time and region. The model consists of the following five stages: 1) Data collection and preprocessing from reliable sources; 2) Optimal machine learning algorithm selection, with the Random Forest tree (RF) classifier achieving 94.4% accuracy; 3) Dimensionality reduction utilising principal components analysis (PCA) to optimise the impact of the data volume; 4) Predicting case numbers utilising an artificial neural network model, with 52% accuracy; 5) Enhancing accuracy by incorporating Fast Fourier Transform (FFT) feature extraction and ANN, resulting in 91% accuracy for multi-level spread risk classification. The AF-HIDOP model provides prediction accuracy ranging from moderate to high, addressing issues in healthcare-based datasets and costs of computing, and may have potential uses in monitoring and managing infectious disease epidemics."]},{"key":"dc:title","label":"Title","values":["A State-of-the-Art Artificial intelligence model for Infectious Disease Outbreak Prediction. Infectious disease outbreak have been predicted in England and Wales using Artificial Intelligence, Machine learning, and Fast Fourier Transform for COVID-19."]}]}],"canonical_facts":{"dc:contributor.advisor":["Youseffi, Mansour","Li, Jian-Ping"],"dc:creator":["Fayad, Moataz B.M."],"dc:date.accessioned":["2024-06-12T14:01:35Z"],"dc:date.available":["2024-06-12T14:01:35Z"],"dc:date.issued":["2023"],"dc:description.abstract":["The pandemic produced by the COVID-19 virus has resulted in an estimated 6.4 million deaths worldwide and a rise in unemployment rates, notably in the UK. 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