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008			230125s2022 sz | s |||| 0|eng d
020			_a9783030797539
024	7		_a10.1007/978-3-030-79753-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRA644.C67 _b2022 EB
245	0	0	_aAssessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis _cedited by Subhendu Kumar Pani, Sujata Dash, Wellington P. dos Santos, Syed Ahmad Chan Bukhari, Francesco Flammini
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXVI, 405 páginas) _b164 ilustraciones
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aChapter 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			_aThis 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.
988			_aSpringer_Computer_2022
650		7	_2embne _9683668 _aCOVID-19
650		7	_2embne _9421154 _aInformática médica
650		7	_2embne _9670612 _aEpidemias
776	0	8	_iPrinted edition: _z9783030797522
776	0	8	_iPrinted edition: _z9783030797546
776	0	8	_iPrinted edition: _z9783030797553
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi-org.ezproxy.universidadeuropea.es/10.1007/978-3-030-79753-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eIG _zSI