This 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 patients data, electronic health records (EHRs) and lifestyle. In the past, it was a common requirement to have domain experts for developing models for biomedical or healthcare. However, recent advances in representation learning algorithms allow us to automatically learn the pattern and representation of the given data for the development of such models. Medical Image Mining, a novel research area (due to its large amount of medical images) are increasingly generated and stored digitally. These images are mainly in the form of: computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new and useful information that can be helpful for scientists and biomedical practitioners.
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..
Recenzijos
The book, nonetheless, is written clearly and easy to follow. Rather than deep into the methodology, it leans toward analytical applications with several real data analyses and case studies which should facilitate engaging a broader audience and sparking interest. this book provides a good introduction and overview of the computational approaches in COVID- 19-related research which may be of particular value to those interested in applying ML/AI solutions to public health and medicine. (Yen-Chen Anne Feng, Biometrics, Vol. 78 (4), December, 2022)
Chapter 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.
Subhendu Pani is Professor and Principal at Krupajal Computer Academy, Odisha, India. His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He has been published in more than 150 international publications, five authored books, fifteen edited and forthcoming books, and twenty book chapters. He is a fellow in SSARSC and life member in IE, ISTE, ISCA, OBA, OMS, SMIACSIT, SMUACEE, and CSI. Sujata Dash is Associate Professor of Computer Science at North Orissa University in the Department of Computer Application, Baripada, India. She is a recipient of Titular Fellowship from Association of Commonwealth Universities, UK. She has worked as a visiting professor of Computer Science Department of University of Manitoba, Canada. She has published more than 170 technical papers.
Wellington P. dos Santos is Associate Professor, Department of Biomedical Engineering, Federal University of Pernambuco (UFPE), Recife, Brazil. PhD in Electrical Engineering from the Federal University of Campina Grande (UFCG), Campina Grande, Master in Electrical Engineering and Graduated in Electronic Electrical Engineering from UFPE, Recife, Brazil. His main research interests are: diagnostic support systems, digital epidemiology, applied neuroscience, serious games in health, and artificial intelligence applied to health.
Syed Ahmad Chan Bukhari is Assistant Professor and Director of Healthcare Informatics at St. John's University, New York. He received his Ph.D. in Computer Science from the University of New Brunswick, Canada, and then went on to complete his postdoctoral fellowship at Yale School of Medicine, where he worked with Stanford University, Centre of Expanded Data Annotation and Retrieval (CEDAR) to develop data submission pipelines to improve scientific experimental reproducibility.
Francesco Flammini is Professor of Computer Science at Mälardalen University, Sweden. He has been an Associate Professor leading the Cyber-Physical Systems environment at Linnaeus University, Sweden. He has worked for fifteen years in private and public companies, including Ansaldo STS (now Hitachi Rail) and IPZS (Italian State Mint and Polygraphic Institute), leading international projects addressing intelligent transportation and infrastructure security.
