Assessing Covid-19 and Other Pandemics and Epidemics Using Computational Modelling and Data Analysis

ISBN-13: 9783030797522 / Angielski / Twarda / 2021 / 292 str.

Subhendu Kumar Pani; Sujata Dash; Wellington Dos Santos

Recenzje

"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)

Spis treści

Chapter 1: Artificial Intelligence (AI) and Big Data Analytics for COVID-19 Pandemic

Pramit Pandit , K. N. Krishnamurthy and Bishvajit Bakshi

Chapter 2: COVID-19 TravelCover: Post-lockdown Smart Transportation Management System for COVID-19

Sandeep Tiwari, Hari Mohan Rai, Barnini Goswami , Shreya Majumdar, Kajal Gupta

Chapter 3:  Diverse techniques applied for effective diagnosis of COVID 19

 Charles Oluwaseun Adetunji, Olugbemi Tope Olaniyan, Olulope Olufemi Ajayi, Osikemekha Anthony Anani,  Ruth Ebunoluwa Bodunrinde,  Abel Inobeme

Chapter 4: A Review on Detection of Covid-19 Patients using Deep Learning Techniques

 Babita Majhi , Rahul Thangeda , Ritanjali Majhi

Chapter 5: Internet of Health Things (IoHT) for COVID 19

Charles Oluwaseun Adetunji, Olugbemi Tope Olaniyan, Olulope Olufemi Ajayi, Osikemekha Anthony Anani,  Ruth Ebunoluwa Bodunrinde,  Abel Inobeme

Chapter 6: Diagnosis for COVID-19

Ashish Tripathi , Anand Bhushan Pandey , Arun Kumar Singh , K. K. Mishra , Prem Chand Vashist

Chapter 7: IoT in Combating Covid 19 Pandemics: Lessons for Developing Countries

Oyekola Peter, Suchismita Swain, Kamalakanta Muduli, Adimuthu Ramasamy

Chapter 8: Machine learning approaches for COVID 19 pandemic

Charles Oluwaseun Adetunji, Olugbemi Tope Olaniyan, Olulope Olufemi Ajayi, Osikemekha Anthony Anani,  Ruth Ebunoluwa Bodunrinde,  Abel Inobeme

Chapter 9: Smart sensing for COVID 19 Pandemic

Charles Oluwaseun Adetunji, Olugbemi Tope Olaniyan, Olulope Olufemi Ajayi, Osikemekha Anthony Anani,  Ruth Ebunoluwa Bodunrinde,  Abel Inobeme

Chapter 10: eHealth, mHealth and Telemedicine for COVID-19 pandemic

Charles Oluwaseun Adetunji, Olugbemi Tope Olaniyan, Olulope Olufemi Ajayi, Osikemekha Anthony Anani,  Ruth Ebunoluwa Bodunrinde,  Abel Inobeme

 

Chapter 11:  Prediction of care for patients in a Covid-19 pandemic situation based on haematological parameters

 Arianne Sarmento Torcate , Flávio Secco Fonseca , Antônio Ravely. T. Lima , Flaviano Palmeira Santos , Tássia D. Muniz S. Oliveira , Maíra Araújo de Santana , Juliana Carneiro Gomes , Clarisse Lins de Lima , Valter Augusto de Freitas Barbosa , Ricardo Emmanuel de Souza , Wellington Pinheiro dos Santos

Chapter 12: Bioinformatics in Diagnosis of Covid-19

Sanjana Sharma, Saanya Aroura, Archana Gupta, Anjali Priyadarshini

Chapter 13: Predicting the Covid-19 Morbidity Outspread and Mortality Using Deep Learning Techniques

Bhimavarapu Usharani

Chapter 14: LSTM -CNN Deep learning Based Hybrid system for real time COVID-19 data analysis and prediction using Twitter data

Sitanath Biswas, Sujata Dash

Chapter 15: An intelligent tool to support diagnosis of Covid-19 by texture analysis of computerized tomography x-ray images and machine learning

Maíra Araújo de Santana , Juliana Carneiro Gomes , Valter Augusto de Freitas Barbosa , Clarisse Lins de Lima , Jonathan Bandeira , Mêuser Jorge Silva Valença , Ricardo Emmanuel de Souza, Aras Ismael Masood , Wellington Pinheiro dos Santos

Chapter 16: Analysis of Blockchain Backed Covid19 Data

Tadepalli Sarada Kiranmayee, Ruppa K. Thulasiram

Chapter 17: Intelligent systems for dengue, chikungunya and zika temporal and spatio-temporal forecasting: a contribution and a brief review

Clarisse Lins de Lima , Ana Clara Gomes da Silva , Cecilia Cordeiro da Silva , Giselle Machado Magalhães Moreno , Abel Guilhermino da Silva Filho , Anwar Musah , Aisha Aldosery , Livia Dutra , Tercio Ambrizzi , Iuri Valério Graciano Borges , Merve Tunali , Selma Basibuyuk , Orhan Yenigün , Tiago Lima Massoni , Kate Jones , Luiza Campos , Patty Kostkova , Wellington Pinheiro dos Santos

Chapter 18: Machine learning approaches for temporal and spatio-temporal Covid-19 forecasting: a brief review and a contribution

Ana Clara Gomes da Silva , Clarisse Lins de Lima , Cecilia Cordeiro da Silva , Giselle Machado Magalhães Moreno , Eduardo Luiz Silva , Gabriel Souza Marques , Lucas Job Brito de Araújo , Luiz Antônio Albuquerque Júnior , Samuel Barbosa Jatobá de Souza , Maíra Araújo de Santana , Juliana Carneiro Gomes , Valter Augusto de Freitas Barbosa , Anwar Musah , Patty Kostkova , Abel Guilhermino da Silva Filho , Wellington Pinheiro dos Santos

Chapter 19: Image Reconstruction for COVID-19 using Multi-frequency Electrical Impedance Tomography

Julia Grasiela Busarello Wolff, David William Cordeiro Marcondes, Wellington Pinheiro dos Santos, Pedro Bertemes-Filho

O autorze

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.

Blurb

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 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.