Computer-aided Learning and Analysis for COVID-19 Disease
Published on 28. February 2022
136 pages
978-1-80382-768-1 (ISBN)
Description
Computer-aided Learning and Analysis for COVID-19 Disease
More details
Content
- Cover
- Special issue (part 1) on computer-aided learning and analysis for COVID-19 disease
- COVID-19: risk prediction through nature inspired algorithm
- E-biomedical: a positive prospect to monitor human healthcare system using blockchain technology
- Pattern analysis: predicting COVID-19 pandemic in India using AutoML
- Predicting future diseases based on existing health status using link prediction
- Detection of COVID-19 cases through X-ray images using hybrid deep neural network
- Time series analysis of COVID-19 cases
- Development of a classifier with analysis of feature selectionmethods for COVID-19 diagnosis
- Online learning in COVID-19 pandemic: an empirical study of Indian and Turkish higher education institutions
- Role of digital technologies to combat COVID-19 pandemic
- Implementation of the QoS framework using fog computing to predict COVID-19 disease at early stage
- Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network
- Association of vaccine medication for the efficacious COVID-19 treatment
- Queries related to COVID-19: a more effective retrieval through finetuned ALBERT with BM25L question answering system
- Cyberlaw and cyberspace vis-a-vis impact of internet during COVID-19 pandemic
- Voice activity detection using optimal window overlapping especially over health-care infrastructure
- Sentiment analysis and sarcasm detection fromsocial network to train health-careprofessionals
