LEADER 00804nam0-22002771i-450- 001 990004916380403321 005 19990530 035 $a000491638 035 $aFED01000491638 035 $a(Aleph)000491638FED01 035 $a000491638 100 $a19990530d1953----km-y0itay50------ba 101 0 $aita 105 $aaf------00--- 200 1 $aLaclos$ePar lui-mOme$fimages et textes prèsentès par Roger Vailland 210 $aParis$cÉditionditions du Seuil$h(stampa 215 $a190 p., [1] tav.$cill.$d18 cm 225 1 $aÉcrivains de toujours$v16 700 1$aVailland,$bRoger$f<1907-1965>$0197130 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004916380403321 952 $aSH 381$bFil. Mod. 5933$fFLFBC 959 $aFLFBC 996 $aLaclos$9525442 997 $aUNINA LEADER 05447nam 22007455 450 001 9910522955603321 005 20251113193234.0 010 $a3-030-79753-8 024 7 $a10.1007/978-3-030-79753-9 035 $a(MiAaPQ)EBC6826372 035 $a(Au-PeEL)EBL6826372 035 $a(CKB)20133876600041 035 $a(OCoLC)1288632838 035 $a(PPN)259389501 035 $a(DE-He213)978-3-030-79753-9 035 $a(EXLCZ)9920133876600041 100 $a20211213d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAssessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis /$fedited by Subhendu Kumar Pani, Sujata Dash, Wellington P. dos Santos, Syed Ahmad Chan Bukhari, Francesco Flammini 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (416 pages) 225 1 $aComputer Science Series 311 08$aPrint version: Pani, Subhendu Kumar Assessing COVID-19 and Other Pandemics and Epidemics Using Computational Modelling and Data Analysis Cham : Springer International Publishing AG,c2022 9783030797522 327 $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. 330 $aThis book comprehensively covers the topic of COVID-19 and other pandemics and epidemics data analytics using computational modelling. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care. The new era of pandemics and epidemics bring tremendous opportunities and challenges due to the plentiful and easily available medical data allowing for further analysis. The aim of pandemics and epidemics research is to ensure high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant medical, and healthcare data including patient?s data, electronic health records (EHRs) and lifestyle. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis will play a vital role in improving human life in response to pandemics and epidemics. The state-of-the-art approaches for data mining-based medical and health related applications will be of great value to researchers and practitioners working in biomedical, health informatics, and artificial intelligence. 410 0$aComputer Science Series 606 $aArtificial intelligence 606 $aQuantitative research 606 $aCooperating objects (Computer systems) 606 $aInternet of things 606 $aPublic health 606 $aDiseases$xAnimal models 606 $aArtificial Intelligence 606 $aData Analysis and Big Data 606 $aCyber-Physical Systems 606 $aInternet of Things 606 $aPublic Health 606 $aDisease Models 615 0$aArtificial intelligence. 615 0$aQuantitative research. 615 0$aCooperating objects (Computer systems) 615 0$aInternet of things. 615 0$aPublic health. 615 0$aDiseases$xAnimal models. 615 14$aArtificial Intelligence. 615 24$aData Analysis and Big Data. 615 24$aCyber-Physical Systems. 615 24$aInternet of Things. 615 24$aPublic Health. 615 24$aDisease Models. 676 $a006.3 700 $aHooper$b Jane$f1981-$01254115 702 $aKumar Pani$b Subhendu 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910522955603321 996 $aAssessing COVID-19 and other pandemics and epidemics using computational modelling and data analysis$92908042 997 $aUNINA