Buch, Englisch, 101 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 184 g
Third EAI International Conference, AISCOVID-19 2022, Braga, Portugal, November 16-18, 2022, Proceedings
Buch, Englisch, 101 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 184 g
ISBN: 978-3-031-38203-1
Verlag: Springer Nature Switzerland
This 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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Informationstheorie, Kodierungstheorie
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
Weitere Infos & Material
COVID-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.
