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Titlebook: Computational Intelligence for COVID-19 and Future Pandemics; Emerging Application Utku Kose,Junzo Watada,Jose Antonio Marmolejo Sauc Book

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發(fā)表于 2025-3-21 18:40:12 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Intelligence for COVID-19 and Future Pandemics
副標(biāo)題Emerging Application
編輯Utku Kose,Junzo Watada,Jose Antonio Marmolejo Sauc
視頻videohttp://file.papertrans.cn/233/232447/232447.mp4
概述Includes research works to fight against COVID-19.Comprises of innovative data-oriented and problem-learning models to predict COVID-19-related issues.Focuses on success stories and details for elimin
叢書名稱Disruptive Technologies and Digital Transformations for Society 5.0
圖書封面Titlebook: Computational Intelligence for COVID-19 and Future Pandemics; Emerging Application Utku Kose,Junzo Watada,Jose Antonio Marmolejo Sauc Book
描述.The book covers a wide topic collection starting from essentials of Computational Intelligence to advance, and possible application types against COVID-19 as well as its effects on the field of medical, social, and different data-oriented research scopes. Among these topics, the book also covers very recently, vital topics in terms of fighting against COVID-19 and solutions for future pandemics. The book includes the use of computational intelligence for especially medical diagnosis and treatment, and also data-oriented tracking-predictive solutions, which are key components currently for fighting against COVID-19. In this way, the book will be a key reference work for understanding how computational intelligence and the most recent technologies (i.e. Internet of Healthcare Thing, big data, and data science techniques) can be employed in solution phases and how they change the way of future solutions...The book also covers research works with negative results so that possibledisadvantages of using computational intelligence solutions and/or experienced side-effects can be known widely for better future of medical solutions and use of intelligent systems against COVID-19 and pandem
出版日期Book 2022
關(guān)鍵詞COVID-19; Computational Intelligence; Pandemic; Machine Learning; Medical Diagnosis; Intelligent Systems
版次1
doihttps://doi.org/10.1007/978-981-16-3783-4
isbn_softcover978-981-16-3785-8
isbn_ebook978-981-16-3783-4Series ISSN 2730-9061 Series E-ISSN 2730-907X
issn_series 2730-9061
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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R. Engenhart-Cabillic,M. Wannenmacher public and health care professionals due to its rapid spread around the world. Health care professionals and researchers at present days are targeting various approaches for the detection and prevention of viral growth. One of the best approaches to tackle this pandemic is the implementation of the
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G. Gademann,G. Kraft,M. Wannenmachern detail both the traditional methods for pandemics forecasting, namely, the SIR, SEIR, SEIRS, SIRD, and ARIMA and novel data-driven approaches that presented promising results, the LSTM, AE, MAE, CNN, and Random Forest. The article aims to give a comprehensive understanding of the main advantages a
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Nasenh?hle und Nasennebenh?hlenth the count rising to 6,48,315 (confirmed cases) as of July 4, 2020. The study aims to find the spatial relationship between the confirmed cases of the different months and to classify it into three sets. The findings show that the Monte-Carlo simulations that generated probability distribution map
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