S. Poonkuntran - Böcker

Emotional Artificial Intelligence: Practical Challenges and Applications explores the intersection of artificial intelligence and human emotions, challenging the myth that AI will replace humans. Instead, it emphasizes how emotional AI can enhance collaboration, improving efficiency across fields like healthcare, transportation, and space exploration. The book explores foundational theories such as Goleman’s emotional model and Ekman’s emotion theory, discussing methodologies for understanding human emotions through natural language processing and the ethical implications of AI in decision-making. It showcases real-world applications, from virtual nurses to emotion-aware driving systems, illustrating how these technologies can improve daily interactions and outcomes.This essential resource equips researchers, practitioners, and policymakers with practical insights for integrating emotional AI into diverse domains. By addressing the complexities of human emotion recognition and ethical frameworks, it provides a forward-looking perspective on enhancing collaboration between humans and AI, ultimately driving improved productivity and efficiency.Provides a thorough examination of emotional AI, integrating foundational theories and methodologies to help readers understand human emotions in the context of artificial intelligenceShowcases practical applications of emotional AI across various fields, including healthcare and transportation, demonstrating how these technologies improve daily interactions and outcomesAddresses the ethical implications of deploying emotional AIOffers a forward-looking perspective on emerging trends, equipping readers with insights to navigate the evolving landscape of human-centered artificial intelligence

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval.The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.Features:A structured overview of deep learning in object detectionA diversified collection of applications of object detection using deep neural networksEmphasize agriculture and remote sensing domainsExclusive discussion on moving object detection

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval.The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.Features:A structured overview of deep learning in object detectionA diversified collection of applications of object detection using deep neural networksEmphasize agriculture and remote sensing domainsExclusive discussion on moving object detection

Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.

The book explores the critical challenge of securing sensitive medical data in the face of rising cyber threats. It examines how artificial intelligence can be leveraged to detect and mitigate cyber threats in healthcare environments. It integrates advanced technologies such as AI security applications, blockchain techniques, cryptanalysis, and 5G security to strengthen the protection of healthcare systems. By offering insights into the latest vulnerability assessment technologies and effective protection strategies, this book serves as an essential resource for professionals and researchers dedicated to enhancing cyber security in the healthcare industry.