Intelligent Systems and Methods to Combat Covid-19

1. Auflage 2020
ISBN: 978-981-15-6571-7
Verlag: Springer Nature Singapore

Buch, Englisch, 91 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 172 g

Reihe: SpringerBriefs in Applied Sciences and Technology

This book discusses intelligent systems and methods to prevent further spread of COVID-19, including artificial intelligence, machine learning, computer vision, signal processing, pattern recognition, and robotics. It not only explores detection/screening of COVID-19 positive cases using one type of data, such as radiological imaging data, but also examines how data analytics-based tools can help predict/project future pandemics. In addition, it highlights various challenges and opportunities, like social distancing, and addresses issues such as data collection, privacy, and security, which affect the robustness of AI-driven tools. Also investigating data-analytics-based tools for projections using time series data, pattern analysis tools for unusual pattern discovery (anomaly detection) in image data, as well as AI-enabled robotics and its possible uses, the book will appeal to a broad readership, including academics, researchers and industry professionals.

Zielgruppe

Research

Autoren/Hrsg.

Joshi, Amit

Santosh, K. C.

Dey, Nilanjan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Infektionskrankheiten
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

Chapter 1. Data Analytics: COVID-19 Prediction using Multimodal Data.- Chapter 2. COVID-19 Apps: Privacy and security concerns.- Chapter 3. Coronavirus Outbreak: Multi-objective Prediction and Optimization.- Chapter 4. AI-Enabled Framework to Prevent COVID-19 from Further Spreading.- Chapter 5. Artificial Intelligence Enabled Robotic Drones for COVID-19 Outbreak.- Chapter 6. Understanding and Analysis of Enhanced COVID-19 Chest X-Ray Images.- Chapter 7. Deep Learning-based COVID-19 Diagnosis and Trend Predictions.- Chapter 8. COVID-19: Loose Ends.- Chapter 9. Social Distancing and Artificial Intelligence- Understanding the Duality in the times of Covid-19.- Chapter 10. Post Covid-19 and Business Analytics.

Über Autor(innen)

Amit Joshi – Dr. Joshi is currently the Director of Global Knowledge Research Foundation, also an Entrepreneur & Researcher who has completed his Masters and research in the areas of cloud computing and cryptography in medical imaging. Dr. Joshi has an experience of around 10 years in academic and industry in prestigious organizations. Dr. Joshi is an active member of ACM, IEEE, CSI, AMIE, IACSIT, Singapore, IDES, ACEEE, NPA, and many other professional societies. Currently, Dr. Joshi is the International Chair of InterYIT at International Federation of Information Processing (IFIP, Austria). He has presented and published more than 50 papers in national and international journals/conferences of IEEE and ACM. Dr. Joshi has also edited more than 40 books which are published by Springer, ACM, and other reputed publishers. Dr. Joshi has also organized more than 50 national and international conferences and programs in association with ACM, Springer, and IEEE to name afew across different including India, UK, Europe, USA, Canada, Thailand, Egypt, and many more.

Dr. Nilanjan Dey is an Assistant Professor at the Department of Information Technology at Techno International New Town (Formerly known as Techno India College of Technology), Kolkata, India. He is a Visiting Fellow at the University of Reading, UK, and was an honorary Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He was awarded his Ph.D. from Jadavpur University in 2015.  He has authored/edited more than 75 books with Springer, Elsevier, Wiley, and CRC Press and published more than 300 peer-reviewed research papers. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence, IGI Global. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing, Springer Nature; Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier; and Series Editor of Computational Intelligence inEngineering Problem Solving and Intelligent Signal Processing and Data Analysis, CRC. 

Dr. KC Santosh (IEEE Senior Member) is an Associate Professor and Graduate Program Director at the Department of Computer Science at the University of South Dakota (USD). Before joining USD, Dr. Santosh worked as a Research Fellow at the US National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a Postdoctoral Research Scientist at the LORIA Research Centre, Universite de Lorraine, in direct collaboration with ITESOFT, France. He also served as a Research Scientist at the INRIA Nancy Grand Est Research Centre, France, where he has received his Ph.D. diploma in Computer Science. Dr. Santosh has published more than 55 peer-reviewed research articles, 85 conference proceedings, and 7 book chapters. He has authored 4 books, and edited 3 books, 11 journal issues, and 4 conference proceedings. He is currently Editor-In-Chief of IJSIP and anAssociate Editor for several journals, such as International Journal of Machine Learning and Cybernetics and IEEE Access. He has also chaired more than 10 international conference events. His research projects have been funded by multiple agencies, including the SDCRGP, Department of Education (DOE), and the National Science Foundation (NSF). Dr. Santosh is the proud recipient of the Presidents Research Excellence Award (USD, 2019) and an award form the Department of Health & Human Services (2014).