Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis introduces the latest emerging trends and applications of deep learning in biomedical data analysis. The book delves into various use cases where deep learning is applied in industrial, social, and personal contexts within the biomedical domain. By gaining a comprehensive understanding of deep learning in biomedical data analysis, readers will develop the skills to critically evaluate research papers, methodologies, and emerging trends. In 14 chapters this book provides both insights into the fundamentals as the latest research trends in the applications of deep learning in biosciences. With several case studies and use cases it familiarizes the reader with a comprehensive understanding of deep learning algorithms, architectures, and methodologies specifically applicable to biomedical data analysis. This title is an ideal reference for researchers across the biomedical sciences.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. ix 7. About the editors - p. xi 8. Chapter 1. Deep learning, artificial intelligence, and bioinformatics promises innovations and imminent forecasts in SARS-COVID-19 genome data analysis - p. 1 9. Chapter 2. Integration of IoT and AI for potato leaf disease detection: enhancing agricultural efficiently and sustainability - p. 25 10. Chapter 3. A hybridized long–short-term memory networks-based deep learning model using reptile search optimization for COVID-19 prediction - p. 49 11. Chapter 4. Improving coronavirus classification accuracy with transfer learning and chest radiograph analysis - p. 73 12. Chapter 5. A hybrid deep neural network using the Levenberg–Marquart algorithm applied to the nonlinear magnetohydrodynamic Jeffery–Hamel blood flow problem - p. 91 13. Chapter 6. An image segmentation method using intuitionistic fuzzy k-means and convolutional neural networks in multiclass image classification - p. 111 14. Chapter 7. Deep learning for wearable sensor data analysis - p. 131 15. Chapter 8. Unveiling emotions in real-time: a novel approach to face emotion recognition - p. 153 16. Chapter 9. Unleashing the power of convolutional neural networks for diabetic retinopathy detection in ophthalmology - p. 169 17. Chapter 10. Case studies and use cases of deep learning for biomedical applications - p. 179 18. Chapter 11. A convolutional neural network-based deep ensemble method for computed tomography scan image-based lung cancer diagnosis - p. 193 19. Index - p. 211
Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis introduces the latest emerging trends and applications of deep learning in biomedical data analysis. The book delves into various use cases where deep learning is applied in industrial, social, and personal contexts within the biomedical domain. By gaining a comprehensive understanding of deep learning in biomedical data analysis, readers will develop the skills to critically evaluate research papers, methodologies, and emerging trends. In 14 chapters this book provides both insights into the fundamentals as the latest research trends in the applications of deep learning in biosciences. With several case studies and use cases it familiarizes the reader with a comprehensive understanding of deep learning algorithms, architectures, and methodologies specifically applicable to biomedical data analysis. This title is an ideal reference for researchers across the biomedical sciences.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. ix 7. About the editors - p. xi 8. Chapter 1. Deep learning, artificial intelligence, and bioinformatics promises innovations and imminent forecasts in SARS-COVID-19 genome data analysis - p. 1 9. Chapter 2. Integration of IoT and AI for potato leaf disease detection: enhancing agricultural efficiently and sustainability - p. 25 10. Chapter 3. A hybridized long–short-term memory networks-based deep learning model using reptile search optimization for COVID-19 prediction - p. 49 11. Chapter 4. Improving coronavirus classification accuracy with transfer learning and chest radiograph analysis - p. 73 12. Chapter 5. A hybrid deep neural network using the Levenberg–Marquart algorithm applied to the nonlinear magnetohydrodynamic Jeffery–Hamel blood flow problem - p. 91 13. Chapter 6. An image segmentation method using intuitionistic fuzzy k-means and convolutional neural networks in multiclass image classification - p. 111 14. Chapter 7. Deep learning for wearable sensor data analysis - p. 131 15. Chapter 8. Unveiling emotions in real-time: a novel approach to face emotion recognition - p. 153 16. Chapter 9. Unleashing the power of convolutional neural networks for diabetic retinopathy detection in ophthalmology - p. 169 17. Chapter 10. Case studies and use cases of deep learning for biomedical applications - p. 179 18. Chapter 11. A convolutional neural network-based deep ensemble method for computed tomography scan image-based lung cancer diagnosis - p. 193 19. Index - p. 211
