Mahmoud Hassaballah – författare

The common use of the Internet and cloud services in transmission of large amounts of data over open networks and insecure channels, exposes that private and secret data to serious situations. Ensuring the information transmission over the Internet is safe and secure has become crucial, consequently information security has become one of the most important issues of human communities because of increased data transmission over social networks. Digital Media Steganography: Principles, Algorithms, and Advances covers fundamental theories and algorithms for practical design, while providing a comprehensive overview of the most advanced methodologies and modern techniques in the field of steganography. The topics covered present a collection of high-quality research works written in a simple manner by world-renowned leaders in the field dealing with specific research problems. It presents the state-of-the-art as well as the most recent trends in digital media steganography. Covers fundamental theories and algorithms for practical design which form the basis of modern digital media steganography Provides new theoretical breakthroughs and a number of modern techniques in steganography Presents the latest advances in digital media steganography such as using deep learning and artificial neural network as well as Quantum Steganography

Human-Centred AI For Intelligent Healthcare: Approaches and Technologies provides state-of-the-art AI technologies and healthcare tailored to human needs, highlighting the various ways AI is reshaping patient care, diagnostics, treatment modalities, and overall healthcare systems. From the intricacies of precision medicine to interventions in mental health, this reference provides a diverse array of AI applications. It addresses ethical considerations while emphasizing the transformative potential that AI holds for the healthcare industry. This reference is an essential companion for healthcare professionals, researchers, policymakers, technologists, and enthusiasts navigating the swiftly evolving landscape of AI in healthcare. Bridging a critical gap in literature, it offers a consolidated resource that not only explores the multifaceted applications of AI in healthcare but also underscores the importance of maintaining a human-centric approach in the integration of these technologies. With a focus on forecasting future trends and implications, this reference equips readers with the knowledge to stay at the forefront of the dynamic field of healthcare technology and empowers them to anticipate changes and adapt their strategies proactively in the face of evolving healthcare landscapes.Highlights the ethical considerations and importance of human-centeredness in AI healthcare integrationProvides an extensive exploration of various AI applications in healthcare, such as speech recognition, remote monitoring, precision medicine, and mental health interventionsAccommodates a resource that not only elucidates current applications of Artificial Intelligence but also prepares for future trends and challengesDelves into the ethical implications of AI adoption in healthcare, emphasizing the importance of a patient-centric approach

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.

This book presents a collection of high-quality research by leading experts in computer vision and its applications. Each of the 16 chapters can be read independently and discusses the principles of a specific topic, reviews up-to-date techniques, presents outcomes, and highlights the challenges and future directions. As such the book explores the latest trends in fashion creative processes, facial features detection, visual odometry, transfer learning, face recognition, feature description, plankton and scene classification, video face alignment, video searching, and object segmentation. It is intended for postgraduate students, researchers, scholars and developers who are interested in computer vision and connected research disciplines, and is also suitable for senior undergraduate students who are taking advanced courses in related topics. However, it is also provides a valuable reference resource for practitioners from industry who want to keep abreast of recent developments in this dynamic, exciting and profitable research field.

This bookprovides readers with a selection of high-quality chapters that cover boththeoretical concepts and practical applications of image feature detectors anddescriptors. It serves as reference for researchers and practitioners byfeaturing survey chapters and research contributions on image feature detectorsand descriptors. Additionally, it emphasizes several keywords in boththeoretical and practical aspects of image feature extraction. The keywordsinclude acceleration of feature detection and extraction, hardwareimplantations, image segmentation, evolutionary algorithm, ordinal measures, aswell as visual speech recognition.