This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.

Pandemic influenza is a re-emerging pathogen with serious public health consequences. The A(H1N1) pandemic in 2009/10 and the continuing threat to humans from avian influenza A(H5N1) and other novel influenza viruses have both underlined the importance of preparedness at local, national and international levels. With a strong emphasis on practicality, this book offers comprehensive coverage of the science and operational application of influenza epidemiology, virology and immunology, vaccinology, pharmaceutical and public health measures, biomathematical modelling, policy issues and ethics involved in preparing for and responding to pandemic influenza. Each chapter raises key questions and answers them in clear and concise sections, detailing relevant modelling studies and further reading. Comprehensively updated to incorporate major lessons from the 2009/10 pandemic, this second edition includes new contributions on surveillance, International Health Regulations, clinical management and local health service responses, illustrated with vibrant international case studies. Written in an easily accessible style by global experts, this is an essential text for students of public health and those involved in local, national and international pandemic preparedness and response.

This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.

Pandemic influenza is a re-emerging pathogen with serious public health consequences. The A(H1N1) pandemic in 2009/10 and the continuing threat to humans from avian influenza A(H5N1) and other novel influenza viruses have both underlined the importance of preparedness at local, national and international levels. With a strong emphasis on practicality, this book offers comprehensive coverage of the science and operational application of influenza epidemiology, virology and immunology, vaccinology, pharmaceutical and public health measures, biomathematical modelling, policy issues and ethics involved in preparing for and responding to pandemic influenza. Each chapter raises key questions and answers them in clear and concise sections, detailing relevant modelling studies and further reading. Comprehensively updated to incorporate major lessons from the 2009/10 pandemic, this second edition includes new contributions on surveillance, International Health Regulations, clinical management and local health service responses, illustrated with vibrant international case studies. Written in an easily accessible style by global experts, this is an essential text for students of public health and those involved in local, national and international pandemic preparedness and response.