This book provides a proficient guide on the relationship between AI and healthcare and how AI is changing all aspects of the health care industry. It also covers how deep learning will help in diagnosis and prediction of disease spread.
This book provides a proficient guide on the relationship between AI and healthcare and how AI is changing all aspects of the health care industry. It also covers how deep learning will help in diagnosis and prediction of disease spread.
Dr. M. A. JABBAR is a Professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Dr Ajith Abraham is the Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing and a Distinguished Lecturer of IEEE Computer Society representing Europe (2011-2013). Dr. Onur Dogan is an assistant professor at Izmir Bakircay University. Dr Ana Madureira has a PhD degree in Production and Systems from University of Minho, Portugal. Dr. Sanju Tiwari is a Senior Researcher at Universidad Autonoma de Tamaulipas, Mexico.
Inhaltsangabe
1. Foundations of Deep Learning and its Applications to Health Informatics. 2. Deep Knowledge Mining of Complete HIV Genome Sequences in Selected African Cohorts. 3. Review of Machine Learning Approach for Drug development Process. 4. A Detailed Comparison of Deep Neural Networks for Diagnosis of COVID-19. 5. Deep Learning in BioMedical Applications: Detection of Lung Disease with Convolutional Neural Networks 6. Deep Learning Methods For Diagnosis Of Covid-19 using Radiology Images And Genome Sequences: Challenges And Limitations. 7. Applications of Lifetime Modeling with Competing Risks in Biomedical Sciences. 8. PeNLP Parser: An Extraction and Visualization Tool for Precise Maternal, Neonatal and Child Healthcare Geo-locations from Unstructured Data. 9. Recent Trends in Deep learning, Challenges and Opportunities
1. Foundations of Deep Learning and its Applications to Health Informatics. 2. Deep Knowledge Mining of Complete HIV Genome Sequences in Selected African Cohorts. 3. Review of Machine Learning Approach for Drug development Process. 4. A Detailed Comparison of Deep Neural Networks for Diagnosis of COVID-19. 5. Deep Learning in BioMedical Applications: Detection of Lung Disease with Convolutional Neural Networks 6. Deep Learning Methods For Diagnosis Of Covid-19 using Radiology Images And Genome Sequences: Challenges And Limitations. 7. Applications of Lifetime Modeling with Competing Risks in Biomedical Sciences. 8. PeNLP Parser: An Extraction and Visualization Tool for Precise Maternal, Neonatal and Child Healthcare Geo-locations from Unstructured Data. 9. Recent Trends in Deep learning, Challenges and Opportunities
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