Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis
portes grátis
Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis
Dash, Sujata; Pani, Subhendu Kumar; Chan Bukhari, Syed Ahmad; Flammini, Francesco; dos Santos, Wellington P.
Springer Nature Switzerland AG
12/2022
405
Mole
Inglês
9783030797553
15 a 20 dias
658
Descrição não disponível.
Chapter 1 Artificial Intelligence (AI) and Big Data Analytics for COVID-19 Pandemic.- Chapter 2 COVID-19 TravelCover Post-lockdown Smart Transportation Management System for COVID-19.- Chapter 3 Diverse techniques applied for effective diagnosis of COVID 19.- Chapter 4 A Review on Detection of Covid-19 Patients using Deep Learning Techniques.-Chapter 5 Internet of Health Things (IoHT) for COVID 19.- Chapter 6 Diagnosis for COVID-19.- Chapter 7 IoT in Combating Covid 19 Pandemics Lessons for Developing Countries.- Chapter 8 Machine learning approaches for COVID 19 pandemic.- Chapter 9 Smart sensing for COVID 19 Pandemic.- Chapter 10 eHealth, mHealth and Telemedicine for COVID-19 pandemic.- Chapter 11 Prediction of care for patients in a Covid-19 pandemic situation based on haematological parameters.- Chapter 12 Bioinformatics in Diagnosis of Covid-19.- Chapter 13 Predicting the Covid-19 Morbidity Outspread and Mortality Using Deep Learning Techniques.- Chapter 14 LSTM -CNN Deep learning Based Hybrid system for real time COVID-19 data analysis and prediction using Twitter data.- Chapter 15 An intelligent tool to support diagnosis of Covid-19 by texture analysis of computerized tomography x-ray images and machine learning.- Chapter 16 Analysis of Blockchain Backed Covid19 Data.- Chapter 17 Intelligent systems for dengue, chikungunya and zika temporal and spatio-temporal forecasting a contribution and a brief review.- Chapter 18 Machine learning approaches for temporal and spatio-temporal Covid-19 forecasting a brief review and a contribution.- Chapter 19 Image Reconstruction for COVID-19 using Multi-frequency Electrical Impedance Tomography.
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COVID-19;computational modelling;computational Intelligence;pandemics;Smart sensing;Big data analytics;Internet of Health Things;Cognition computing;Predictive Modeling
Chapter 1 Artificial Intelligence (AI) and Big Data Analytics for COVID-19 Pandemic.- Chapter 2 COVID-19 TravelCover Post-lockdown Smart Transportation Management System for COVID-19.- Chapter 3 Diverse techniques applied for effective diagnosis of COVID 19.- Chapter 4 A Review on Detection of Covid-19 Patients using Deep Learning Techniques.-Chapter 5 Internet of Health Things (IoHT) for COVID 19.- Chapter 6 Diagnosis for COVID-19.- Chapter 7 IoT in Combating Covid 19 Pandemics Lessons for Developing Countries.- Chapter 8 Machine learning approaches for COVID 19 pandemic.- Chapter 9 Smart sensing for COVID 19 Pandemic.- Chapter 10 eHealth, mHealth and Telemedicine for COVID-19 pandemic.- Chapter 11 Prediction of care for patients in a Covid-19 pandemic situation based on haematological parameters.- Chapter 12 Bioinformatics in Diagnosis of Covid-19.- Chapter 13 Predicting the Covid-19 Morbidity Outspread and Mortality Using Deep Learning Techniques.- Chapter 14 LSTM -CNN Deep learning Based Hybrid system for real time COVID-19 data analysis and prediction using Twitter data.- Chapter 15 An intelligent tool to support diagnosis of Covid-19 by texture analysis of computerized tomography x-ray images and machine learning.- Chapter 16 Analysis of Blockchain Backed Covid19 Data.- Chapter 17 Intelligent systems for dengue, chikungunya and zika temporal and spatio-temporal forecasting a contribution and a brief review.- Chapter 18 Machine learning approaches for temporal and spatio-temporal Covid-19 forecasting a brief review and a contribution.- Chapter 19 Image Reconstruction for COVID-19 using Multi-frequency Electrical Impedance Tomography.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
