Deepfakes by
Deepfakes is a synthetic media that leverage powerful Artificial Intelligence (AI) and machine learning (ML) techniques to generate fake visual and audio content that are extremely realistic, thus making it very hard for a human to distinguish from the original ones. Apart from technological introduction to the Deepfakes concept, the book details algorithms to detect Deepfakes, techniques for identifying manipulated content and identifying face swap, generative adversarial neural networks, media forensic techniques, deep learning architectures, forensic analysis of DeepFakes and so forth. Provides a technical introduction to DeepFakes, its benefits, and the potential harms Presents practical approaches of creation and detection of DeepFakes using Deep Learning (DL) Techniques Draws attention towards various challenging issues and societal impact of DeepFakes with their existing solutions Includes research analysis in the domain of DL fakes for assisting the creation and detection of DeepFakes applications Discusses future research directions with emergence of DeepFakes technology This book is aimed at graduate students, researchers and professionals in data science, artificial intelligence, computer vision, and machine learning.
Process Mining Techniques for Pattern Recognition by
This book focuses on the theory, practice, and concepts of process mining techniques in detail, especially pattern recognition in diverse society, science, medicine, engineering, and business. The book deliberates several perspectives on process mining techniques in the broader context of data science and big data approaches. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice provides an introduction to process mining techniques and pattern recognition. After that, it delivers the fundamentals of process modelling and mining essential to comprehend the book. The text emphasizes discovery as an important process mining task and includes case studies as well as real-life examples to guide users in successfully applying process mining techniques for pattern recognition in practice. Intended to be an introduction to process mining and pattern recognition for students, academics, and practitioners, this book is perfect for those who want to learn the basics, and also gain an understanding of the concepts on a deeper level.
Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics by
In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity. Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges. This book is useful for research scholars and students involved in critical condition analysis and computation models.
Artificial Neural Networks: Advances and Applications by
A piece of a computing system designed to simulate the way the human brain analyzes and processes information is referred to as an artificial neural network. It lays the foundation of artificial intelligence and solves problems that are impossible or difficult by human or statistical standards. Artificial neural networks have self-learning potential that enables them to produce better results as more data becomes available. They are designed like the human brain with neuron nodes interconnected to a web. Some of the major applications of artificial neural networks are text classification and categorization, named entity recognition, part-of-speech tagging, semantic parsing and question answering and paraphrase detection. The ever-growing need for advanced technology is the reason that has fueled the research in the field of artificial neural network in recent times. This book unravels the recent studies in this field. It will provide comprehensive knowledge to the readers.
Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems by
This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems.
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