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Elsevier

Leveraging Artificial Intelligence in Global Epidemics

  • 1 Edición - 28 de julio de 2021
  • Última edición
  • Editores: Le Gruenwald, Sarika Jain, Sven Groppe
  • Idioma: Inglés

Leveraging Artificial Intelligence in Global Epidemics provides readers with a detailed technical description of the role Artificial Intelligence plays in various stages of a dise… Leer más

Descripción

Leveraging Artificial Intelligence in Global Epidemics provides readers with a detailed technical description of the role Artificial Intelligence plays in various stages of a disease outbreak, using COVID-19 as a case study. In the fight against epidemics, medical staff are on the front line; but behind the lines the battle is fought by researchers, and data scientists. Artificial Intelligence has been helping researchers with computer modeling and simulation for predictions about disease progression, the overall economic situation, tax incomes and population development. In the same manner, AI can prepare researchers for any emergency situation by backing the medical science. Artificial Intelligence plays a key and cutting-edge role in the preparedness for and dealing with the outbreak of global epidemics. It can help researchers analyze global data about known viruses to predict the patterns of the next pandemic and the impacts it will have. Not only prediction, AI plays an increasingly important role in assessing readiness, early detection, identification of patients, generating recommendations, situation awareness and more. It is up to the right input and the innovative ways by humans to leverage what AI can do. As COVID-19 has grabbed the world and its economy today, an analysis of the COVID-19 outbreak and the global responses and analytics will pay a long way in preparing humanity for such future situations.

Puntos claves

  • Provides readers with understanding of how Artificial Intelligence can be applied to the prediction, forecasting, detection, and testing of global epidemics, using COVID-19 and other recent epidemics such as Ebola, Corona viruses, Zika, influenza, Dengue, Chikungaya, and malaria as case studies
  • Includes background material regarding readiness for coping with epidemics, including Machine Learning models for prediction of epidemic outbreaks based on existing data
  • Includes technical coverage of key topics such as generating recommendations to combat outbreaks, genome sequencing, AI-assisted testing, AI-assisted contact tracing, situation awareness and combating disinformation, and the role of Artificial Intelligence and Machine Learning in drug discovery, vaccine development, and drug re-purposing

De interès para

Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science, Biology, and healthcare practitioners. The audience includes researchers and practitioners in any field that deals with epidemics research. Courses in Artificial Intelligence, Machine Learning, and Data Mining

Índice

1. An overview of global epidemics and the challenges faced

2. Leveraging artificial intelligence and digital tech to help citizens, societies, and economies survive and strive during pandemics

3. Towards an alternative to lockdown: Pandemic management leveraging digital technologies and artificial intelligence

4. Exploratory study of existing approaches for analyzing epidemics

5. A data science perspective of real-world COVID-19 databases

6. Preparing with predictions: forecasting epidemics with artificial intelligence

7. The worldwide methods of artificial intelligence for detection and diagnosis of COVID-19

8. The role of AI in digital contact tracing

9. Covid-19 accelerating the dynamics of Artificial Intelligence disruption

10. Use of artificial intelligence in pharmacovigilance for social media network

11. System-level knowledge representation for artificial intelligence during pandemics

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 6 de agosto de 2021
  • Idioma: Inglés

Sobre los editores

LG

Le Gruenwald

Dr. Le Gruenwald is a Professor, Dr. David W. Franke Professor, and Samuel Roberts Noble Foundation Presidential Professor in the School of Computer Science at The University of Oklahoma. She received her Ph.D. in Computer Science from Southern Methodist University. She worked for National Science Foundation (NSF) as a Cluster Lead and Program Director of the Information Integration and Informatics program and a Program Director of the Cyber Trust program; for NEC America, Advanced Switching Laboratory as a Member of the Technical Staff in the Database Management Group; for Southern Methodist University as a Lecturer in the Computer Science and Engineering Department; and for WRT as a Software Engineer.
Afiliaciones y experiencia
Professor, School of Computer Science, The University of Oklahoma, Norman, Oklahoma, USA

SJ

Sarika Jain

Dr. Sarika Jain graduated from Jawaharlal Nehru University (India) in 2001. Her doctorate is in the field of Knowledge Representation in Artificial Intelligence which was awarded in 2011. She has served in the field of education for over 19 years and is currently working at the National Institute of Technology Kurukshetra (Institute of National Importance), India. Dr. Jain has authored / co-authored over 100 publications including books. Her current research interests include Knowledge Management and Analytics; Semantic Web; Ontological Engineering; and Intelligent Systems. Dr. Jain has supervised two doctoral scholars (5 ongoing) who are now pursuing their post doctorates, one in Spain and the other in Germany. Currently, she is guiding 15 students for their Master’s and Doctoral research work in the area of Knowledge Representation. She is serving as a reviewer for Journals of IEEE, Elsevier, and Springer. She has been involved as a program and steering committee member in many prestigious conferences in India and abroad. She has two research-funded projects: one ongoing project is funded by CRIS TEQUIP-III worth Rs 2.58 lakhs, and the other completed project is funded by DRDO, India worth Rs 40 lakhs. She has also applied for a patent in Nov 2019. Dr. Jain has held various administrative positions at department as well as at institute level in her career like HOD, Hostel Warden, Faculty Incharge of technical and cultural fests, member of Research Degree Committee, and Center Incharge Examinations. Dr. Jain has visited the United Kingdom and Singapore for presenting her research work. She has constantly been supervising DAAD interns from different German universities and many interns from India every summer. She works in collaboration with various researchers across the globe including Germany, Austria, Australia, Malaysia, the United States, Romania and many others. She has organized various challenges, conferences and workshops including NITC, GIAN by MHRD, ISIC, ICSCC, ICACCT, ICECCS, and EWAD. She is a member of IEEE and ACM and a Life Member of Computer Society of India (CSI), International Association of Engineers (IAENG), and the International Association of Computer Science and Information Technology (IACSIT). Dr. Jain is highly interested in world-wide collaborations and seeking scholars and interns in her research group.
Afiliaciones y experiencia
National Institute of Technology, Kurukshetra, Haryana, India

SG

Sven Groppe

Sven Groppe is a Professor at the University of Lübeck, Germany. He was the project leader of the DFG project LUPOSDATE with a focus on Semantic Web Database techniques and was the project leader of two research projects on FPGA acceleration of relational and Semantic Web databases. He is the project coordinator of the BMBF-funded QC4DB project about database optimizations accelerated by quantum computing. Furthermore, he is the principal investigator of three DFG projects, one dealing with GPU acceleration of database indices, one in the area of Semantic Internet of Things and one about high-quality COVID-19 knowledge graphs. Over 100 program committee memberships in international conferences and workshops, reviewer activities in over 40 internationally recognized journals, editorship in 4 journals and chairing of Semantic Big Data, Big Data in Emergent Distributed Environments, Very Large Internet of Things and Quantum Data Science and Management workshops at the world-class ACM SIGMOD and VLDB conferences, and General Chair of the International Semantic Intelligence Conference, International Conference on Applied Machine Learning and Data Analytics and International Healthcare Informatics Conference as well as co-authorship with 191 scientists from 28 countries on 6 continents demonstrate a strong integration into the scientific community of Sven Groppe.
Afiliaciones y experiencia
University of Lübeck, Lübeck, Schleswig-Holstein, Germany

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