001472210 000__ 04254cam\\22006497i\4500
001472210 001__ 1472210
001472210 003__ OCoLC
001472210 005__ 20230908003401.0
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001472210 008__ 230802s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001472210 020__ $$a9783031382048$$q(electronic bk.)
001472210 020__ $$a3031382048$$q(electronic bk.)
001472210 020__ $$z9783031382031
001472210 0247_ $$a10.1007/978-3-031-38204-8$$2doi
001472210 035__ $$aSP(OCoLC)1391989914
001472210 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP
001472210 049__ $$aISEA
001472210 050_4 $$aR858.A2
001472210 08204 $$a610.285$$223/eng/20230802
001472210 1112_ $$aInternational Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities$$n(3rd :$$d2022 :$$cBraga, Portugal)
001472210 24510 $$aAI-assisted solutions for COVID-19 and biomedical applications in smart cities :$$bThird EAI International Conference, AISCOVID-19 2022, Braga, Portugal, November 16-18, 2022, Proceedings /$$cJosé Manuel Machado, Hugo Peixoto, editors.
001472210 2463_ $$aAISCOVID-19 2022
001472210 264_1 $$aCham :$$bSpringer,$$c2023.
001472210 300__ $$a1 online resource (ix, 101 pages) :$$billustrations (some color).
001472210 336__ $$atext$$btxt$$2rdacontent
001472210 337__ $$acomputer$$bc$$2rdamedia
001472210 338__ $$aonline resource$$bcr$$2rdacarrier
001472210 4901_ $$aLecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering,$$x1867-822X ;$$v485
001472210 500__ $$aIncludes author index.
001472210 5050_ $$aCOVID-19 Global Impact -- Not Necessarily Relaxed: How Work Interruptions affect Users' Perception of Stress in Remote Work Situations -- COVID-19 cases and their impact on global air traffic -- The Impact of contingency measures on the COVID-19 reproduction rate -- AI applied to COVID-19 -- Business Intelligence Platform for COVID-19 Monitoring: A Case Study -- First Clustering Analysis of COVID in Portugal -- Multichannel services for patient home-based care during COVID-19 -- Machine Learning In Healthcare -- Steps Towards Intelligent Diabetic Foot Ulcer Follow-up based on Deep Learning -- Recommendation of Medical Exams to Support Clinical Diagnosis based on Patient's Symptoms.
001472210 506__ $$aAccess limited to authorized users.
001472210 520__ $$aThis book constitutes the refereed post-conference proceedings of the Third International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022, held in November 2022 in Braga, Portugal. The 8 full papers of AISCOVID-19 2022 were carefully selected from 21 submissions and present a comprehensive and up-to-date look at the intersection of COVID-19, big data, machine learning, deep learning, and healthcare. The theme of AISCOVID-19 2022 was Healthcare effective and efficient Solutions for COVID-19 that can be achieved using Artificial Intelligence and Computer-Assisted paradigms.
001472210 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 1, 2023).
001472210 650_0 $$aMedical informatics$$vCongresses.
001472210 650_0 $$aBiomedical engineering$$vCongresses.
001472210 650_0 $$aArtificial intelligence$$xMedical applications$$vCongresses.
001472210 650_0 $$aSmart cities$$vCongresses.
001472210 650_0 $$aCOVID-19 (Disease)$$vCongresses.
001472210 655_0 $$aElectronic books.
001472210 7001_ $$aMachado, José Manuel,$$eeditor.
001472210 7001_ $$aPeixoto, Hugo,$$d1985-$$eeditor.
001472210 830_0 $$aLecture notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering ;$$v485.$$x1867-822X
001472210 852__ $$bebk
001472210 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-38204-8$$zOnline Access$$91397441.1
001472210 909CO $$ooai:library.usi.edu:1472210$$pGLOBAL_SET
001472210 980__ $$aBIB
001472210 980__ $$aEBOOK
001472210 982__ $$aEbook
001472210 983__ $$aOnline
001472210 994__ $$a92$$bISE