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Full Description
Artificial Intelligence and Data Science in Healthcare Applications provides a thorough in-depth examination of how AI and data science are transforming predictive analytics and outlier detection in every industry. With in-depth examinations of machine learning, neural networks, NLP, and ethics of AI, this book prepares readers with both theoretical principles and practical tools to create smart, scalable systems. Real-world healthcare, cybersecurity, and finance case studies exemplify real world applications, and tutorials with leading libraries serve as a starting point for implementation.
Features:
Offers a broad overview of both foundational and advanced topics in artificial intelligence and data science, focusing particularly on their applications in prediction and detection.
Addresses the ethical implications and social impact of artificial intelligence and data science, discussing topics such as algorithmic bias, privacy, and the ethical use of predictive technologies.
Discusses enhanced deep learning model to detect lung diseases from Chest-Xray.
Highlights lung cancer prediction using variational autoencoders and early stopping for neural network clustering and optimal tuning.
Evaluates mental well-being through wearable sensors utilizing machine learning.
It will serve as an ideal text for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer engineering, information technology, and biomedical engineering.
Contents
1. Computer-Aided ADHD Diagnosis using EEG Analysis. 2. Classification of Brain Tumors Using Convolutional Neural Network. 3. Predicting the Need for Mental Treatment Across Various Age Groups Using Machine Learning Algorithms. 4. Revolutionizing Disease Forecasting: The Role of Machine Learning in Predictive Healthcare Analytics. 5. Exploring the Role of AI in Neurology: Advancements in Brain Imaging and Mental Health Care. 6. Machine Learning Perspectives for Detection of Parkinson's Disease. 7. Revolutionizing Chronic Disease Care With AI-Driven Wearable Technology. 8. Digital Twin and Digital Triplet Technology in Healthcare: Benefits and Security Considerations. 9. Employing Deep Learning Paradigms for Fire and Smoke Detection. 10. Emerging Trends and Future Directions in Edge-Driven Intelligence. 11. Advancements in Early Detection of Lung Cancer: A Comprehensive Review of AI-Based Techniques. 12. Fitness and Nutrition Planner Driven by AI. 13. Introduction to AI and Data Science in Healthcare Applications. 14. AI-Driven Prediction of Drug-Target Interactions and Binding Affinity for Drug Discovery in Healthcare. 15. Enhanced Detection of Epileptic Seizure Using Supervised and Unsupervised Machine Learning Algorithms. 16. Network Setup model Over a Private Blockchain Network Dealing with Security Issues
