This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence

This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence

This book presents real-world problems and pioneering research in computational statistics, mathematical modeling, artificial intelligence and software engineering in the context of intelligent systems. It gathers the peer-reviewed proceedings of the 2nd Computational Methods in Systems and Software 2018 (CoMeSySo 2018), a conference that broke down traditional barriers by being held online. The goal of the event was to provide an international forum for discussing the latest high-quality research results.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

Artificial Intelligence-Based System Models in Healthcare provides a comprehensive and insightful guide to the transformative applications of AI in the healthcare system. This book is a groundbreaking exploration of the synergies between artificial intelligence and healthcare innovation. In an era where technological advancements are reshaping the landscape of medical practices, this book provides a comprehensive and insightful guide to the transformative applications of AI in healthcare systems. From conceptual foundations to practical implementations, the book serves as a roadmap for understanding the intricate relationships between AI-based system models and the evolution of healthcare delivery. The first section delves into the fundamental role of technology in reshaping the healthcare landscape. With a focus on daily life activities, decision support systems, vision-based management, and semantic frameworks, this section lays the groundwork for understanding the pivotal role of AI in revolutionizing traditional healthcare approaches. Each chapter offers a unique perspective, emphasizing the intricate integration of technology into healthcare ecosystems. The second section takes a deep dive into specific applications of AI, ranging from predictive analysis and machine learning to deep learning, image analysis, and biomedical text processing. With a focus on decision-making support systems, this section aims to demystify the complex world of AI algorithms in healthcare, offering valuable insights into their practical implications and potential impact on patient outcomes. The final section addresses the modernization of healthcare practices and envisions the future landscape of AI applications. From medical imaging and diagnostics to predicting ventilation needs in intensive care units, modernizing health record maintenance, natural language processing, chatbots for medical inquiries, secured health insurance management, and glimpses into the future, the book concludes by exploring the frontiers of AI-driven healthcare innovations. Audience This book is intended for researchers and postgraduate students in artificial intelligence and the biomedical and healthcare sectors. Medical administrators, policymakers and regulatory specialists will also have an interest.

Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

This book examines the latest developments in the area of soft computing with engineering applications. It explores topics such as fuzzy sets, intuitionistic fuzzy sets, unmanned aerial vehicles, soft sets, neutrosophic sets, fractional calculus, big data analytics, and the mathematical foundations of convolutional neural network (CNNs). Soft Computing: Engineering Applications offers readers a comprehensive and in-depth understanding of various cutting-edge technologies that are transforming industries worldwide. The book explores soft computing techniques in a very systematic manner. It elucidates the concepts, theories, and applications of fuzzy sets, enabling readers to grasp the fundamentals and explore their applications in various fields. It provides new insight into unmanned aerial vehicle applications to fuzzy soft set based decision making. It then discusses new fixed point results in orthogonal neutrosophic generalized metric spaces and explores statistical convergence of triple sequences in a credibility space. The authors then provide readers with a solid grasp of the mathematical underpinnings of CNNs, enabling them to design, train, and optimize neural networks for image recognition, object detection, and other computer vision tasks. The authors also present new studies in fractional calculus and explores advanced visualization algorithms and techniques for big data analytics. Soft Computing will be useful for beginners and advanced researchers in engineering, applied sciences and healthcare professionals working in soft computing applications.