Artificial intelligence (AI) and machine learning (ML) have transformed many standard and conventional methods in undertaking health and well-being issues of humans.  AL/ML-based systems and tools play a critical role in this digital and big data era to address a variety of medical and healthcare problems, improving treatments and quality of care for patients. 

This edition on AI and ML for healthcare consists of two volumes.  The first presents selected AI and ML studies on medical imaging and healthcare data analytics, while the second unveils emerging...

In line with advances in digital and computing systems, artificial intelligence (AI) and machine learning (ML) technologies have transformed many aspects of medical and healthcare services, delivering tangible benefits to patents and the general public. This book is a sequel of the edition on “Artificial Intelligence and Machine Learning for Healthcare”. The first volume is focused on utilization of AI and ML for image and data analytics in the medical and healthcare domains. In this second volume, emerging methodologies and future trends in AI and ML for advancing medical treatments...

This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas.

The book is suitable for students and academics aiming to build up their background...

The book gives all interested in computer science, a deep review of relevant aspects of logic. In its scope are classical and non-classical logics. The content will be valid as well for those interested in linguistic, philosophy and many other areas of research both in humane and technical branches of science as logic permeates all genuine realms of science. The book contains a substantial part of classical results in logic like those by Gödel, Tarski, Church and Rosser as well as later developments like many-valued logics, logics for knowledge engineering, first-order logics plus...

This book aims to provide a refreshing, friendly and exciting manner artificial intelligence (AI) theoretical concepts and practical methods applied in obstetrics and gynaecology. The book follows the nine and a half months journey from preconception till birth alongside with AI. It discusses topics such as the poignant role of AI in improving the change of women getting pregnant; AI methods for detecting congenital anomalies in first and second trimester foetal sonography; how AI aids physicians in determining what type of birth should be deployed (vaginal versus caesarean); how AI can...

This book introduces a variety of well-proven and newly developed nature-inspired optimization algorithms solving a wide range of real-life biomedical and healthcare problems. Few solo and hybrid approaches are demonstrated in a lucid manner for the effective integration and finding solution for a large-scale complex healthcare problem. In the present bigdata-based computing scenario, nature-inspired optimization techniques present adaptive mechanisms that permit the understanding of complex data and altering environments. This book is a voluminous collection for the confront faced by the...

This book contains contributions from several international authors to topics of current interest, such as AI, intelligent systems, and logic applications in different branches of knowledge. Foundational aspects of the various techniques are also covered, notably non-classical formalisms. The tome is intended for researchers, undergraduate and graduate students, and lay readers.

The book is dedicated to researcher Seiki Akama on his sixtieth birthday. Akama is one of the critical scientists who dedicated himself to understanding the use of alternative logic in the various issues of...

This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest...