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

乳汁

https://doi.org/10.1007/978-981-16-3783-4COVID-19; Computational Intelligence; Pandemic; Machine Learning; Medical Diagnosis; Intelligent Systems

Constituent

978-981-16-3785-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

變形詞

Utku Kose,Junzo Watada,Jose Antonio Marmolejo SaucIncludes 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

slow-wave-sleep

Disruptive Technologies and Digital Transformations for Society 5.0http://image.papertrans.cn/c/image/232447.jpg

責(zé)怪

BYRE

Influx

Predicting Transmission Rate of Coronavirus (COVID-19) Pandemic Using Machine Learning Techniques,s quickly transmitted to virtually all the nations of the world. This novel virus has posed a serious health challenge, and many countries are finding it difficult to preventing the spread of the deadly virus. In spite of all the money, time and energy invested in research toward finding a lasting s

外星人

deforestation

,Epidemiology Forecasting of?COVID-19 Using AI—A Survey,n 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

獨(dú)輪車

Geographical Weighted Regression Approach: A Case Study on Covid-19 in India,th 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