東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Assessing COVID-19 and other pandemi...

Pani, Subhendu Kumar.

FindBook

Google Book

Amazon

博客來

Assessing COVID-19 and other pandemics and epidemics using computational modelling and data analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Assessing COVID-19 and other pandemics and epidemics using computational modelling and data analysis/ edited by Subhendu Kumar Pani ... [et al.].
其他作者:	Pani, Subhendu Kumar.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xxvi, 405 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	COVID-19 Pandemic, 2020- - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-79753-9
ISBN:	9783030797539

Assessing COVID-19 and other pandemics and epidemics using computational modelling and data analysis
Assessing COVID-19 and other pandemics and epidemics using computational modelling and data analysis[electronic resource] /edited by Subhendu Kumar Pani ... [et al.]. - Cham :Springer International Publishing :2022. - xxvi, 405 p. :ill., digital ;24 cm.

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.

This book comprehensively covers the topic of COVID-19 and other pandemics and epidemics data analytics using computational modelling. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care. The new era of pandemics and epidemics bring tremendous opportunities and challenges due to the plentiful and easily available medical data allowing for further analysis. The aim of pandemics and epidemics research is to ensure high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant medical, and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis will play a vital role in improving human life in response to pandemics and epidemics. The state-of-the-art approaches for data mining-based medical and health related applications will be of great value to researchers and practitioners working in biomedical, health informatics, and artificial intelligence.

ISBN: 9783030797539

Standard No.: 10.1007/978-3-030-79753-9doiSubjects--Topical Terms:

3490700
COVID-19 Pandemic, 2020-
--Data processing.

LC Class. No.: RA644.C67 / A77 2022

Dewey Class. No.: 614.592414

Assessing COVID-19 and other pandemics and epidemics using computational modelling and data analysis
LDR:03868nmm a2200325 a 4500 001 2296912
003 DE-He213
005 20211213081051.0
006 m d
007 cr nn 008maaau
008 230324s2022 sz s 0 eng d
020 $a 9783030797539 $q (electronic bk.)
020 $a 9783030797522 $q (paper)
024 7 $a 10.1007/978-3-030-79753-9 $2 doi
035 $a 978-3-030-79753-9
040 $a GP $c GP
041 0 $a eng
050 4 $a RA644.C67 $b A77 2022
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 614.592414 $2 23
090 $a RA644.C67 $b A846 2022
245 0 0 $a Assessing COVID-19 and other pandemics and epidemics using computational modelling and data analysis $h [electronic resource] / $c edited by Subhendu Kumar Pani ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xxvi, 405 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a This book comprehensively covers the topic of COVID-19 and other pandemics and epidemics data analytics using computational modelling. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care. The new era of pandemics and epidemics bring tremendous opportunities and challenges due to the plentiful and easily available medical data allowing for further analysis. The aim of pandemics and epidemics research is to ensure high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant medical, and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis will play a vital role in improving human life in response to pandemics and epidemics. The state-of-the-art approaches for data mining-based medical and health related applications will be of great value to researchers and practitioners working in biomedical, health informatics, and artificial intelligence.
650 0 $a COVID-19 Pandemic, 2020- $x Data processing. $3 3490700
650 0 $a Medical informatics. $3 661258
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Statistics, general. $3 896933
650 2 4 $a Cyber-physical systems, IoT. $3 3386699
650 2 4 $a Professional Computing. $3 3201325
650 2 4 $a Public Health. $3 624351
650 2 4 $a Developmental Biology. $3 755375
700 1 $a Pani, Subhendu Kumar. $3 3591965
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-79753-9
950 $a Computer Science (SpringerNature-11645)