Serkan Kiranyaz – författare

This is a pioneering book that delves into the intersection of artificial intelligence (AI) and healthcare, specifically focusing on the detection and prevention of infectious diseases. Authored by leading experts in the field, this book offers a comprehensive overview of the latest advancements, challenges, and applications of AI in combating infectious diseases. With a unique emphasis on big data, wearable data, and computer vision, the book presents original research works that showcase innovative approaches to leveraging AI for disease surveillance, screening, and severity stratification.

Through a combination of review chapters summarizing the current state of the field and novel applications of AI technology, readers gain valuable insights into the potential of AI in addressing major life-threatening infectious diseases identified by the UN Sustainable Development Goal 3. The book''s structure ensures a balanced blend of theoretical foundations and practical applications, making it accessible to both researchers and healthcare professionals. By exploring cutting-edge methodologies and case studies, the book equips readers with the knowledge and tools needed to harness the power of AI in the fight against infectious diseases, ultimately contributing to global efforts to improve public health outcomes.

With its interdisciplinary approach and focus on AI-driven solutions, " Surveillance, prevention, and control of infectious diseases: An AI perspective" serves as an invaluable resource for researchers, practitioners, and policymakers seeking to stay abreast of the latest developments in this rapidly evolving field. Whether exploring the role of wearable devices in disease monitoring or examining the potential of computer vision for early detection, this book offers a comprehensive overview of AI applications that have the potential to revolutionize infectious disease management and prevention strategies.

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.

After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterizedby strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets.

The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.