Davide La Torre – författare

Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications introduces readers to the methodology and AI/ML algorithms as well as cutting-edge applications to medicine, such as cancer, precision medicine, critical care, personalized medicine, telemedicine, drug discovery, molecular characterization, and patient mental health. Research in medicine and tailored clinical treatment are being quickly transformed by artificial intelligence (AI) and machine learning (ML). The content in this book is tailored to the reader's needs in terms of both type and fundamentals. It covers the current ethical issues and potential developments in this field.This book will be beneficial for academics, professionals in the IT industry, educators, students, and anyone else involved in the use and development of AI in the medical field.Covers the basic concepts of Artificial Intelligence and Machine Learning, methods and practices, and advanced topics and applications to clinical and precision medicinePresents readers with an understanding of how AI is revolutionizing medicine by demonstrating the applications of computational intelligence to the field, along with an awareness of how AI can improve upon traditional medical structuresProvides researchers, practitioners, and project stakeholders with a complete guide for applying AI techniques in their projects and solutions

The fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams.Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book.This book is written for researchers, practitioners, engineers, and AI consultants.

The fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams.Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book.This book is written for researchers, practitioners, engineers, and AI consultants.

Belonging to the realm of intelligent technologies, it is increasingly accepted that artificial intelligence (AI) has evolved from being merely a development standpoint in computer science. Indeed, recent reports and academic publications show that we are clearly on the path toward pervasive AI in both business and society. Organizations must adopt AI to maintain a competitive advantage and explore opportunities for unprecedented innovation. This book focuses on understanding the wide range of opportunities as well as the spectrum of challenges AI brings in different business contexts and society at large. The book highlights novel and high-quality research in data science and business analytics and examines the current and future impact of AI in business and society. The authors bridge the gap between business and technical perspectives and demonstrate the potential (and actual) impact on society. Embracing applied, qualitative, and quantitative research as well as field experiments and data analysis, the book covers a broad range of topics including but not limited to human-centered AI, product and process innovation, corporate governance, AI and ethics, organizational performance, and entrepreneurship. This comprehensive book will be a valuable resource for researchers, academics, and postgraduate students across AI, technology and innovation management, and a wide range of business disciplines.

Belonging to the realm of intelligent technologies, it is increasingly accepted that artificial intelligence (AI) has evolved from being merely a development standpoint in computer science. Indeed, recent reports and academic publications show that we are clearly on the path toward pervasive AI in both business and society. Organizations must adopt AI to maintain a competitive advantage and explore opportunities for unprecedented innovation. This book focuses on understanding the wide range of opportunities as well as the spectrum of challenges AI brings in different business contexts and society at large. The book highlights novel and high-quality research in data science and business analytics and examines the current and future impact of AI in business and society. The authors bridge the gap between business and technical perspectives and demonstrate the potential (and actual) impact on society. Embracing applied, qualitative, and quantitative research as well as field experiments and data analysis, the book covers a broad range of topics including but not limited to human-centered AI, product and process innovation, corporate governance, AI and ethics, organizational performance, and entrepreneurship. This comprehensive book will be a valuable resource for researchers, academics, and postgraduate students across AI, technology and innovation management, and a wide range of business disciplines.

This book explores the dynamic intersection of quantum computing and management strategy, offering an exploration of this cutting-edge technology's potential impact. From its inception to its current state, the book traces the evolution of quantum computing, providing readers with a contextual understanding of its development. It illuminates the transformative power of quantum computing and its implications for business and management practices. Through case studies and expert analysis, readers gain insights into how quantum computing can revolutionize data analysis, optimization, and cybersecurity. The chapters in this book equip managers and entrepreneurs with the knowledge and foresight needed to capitalize on the opportunities presented by the quantum computing era.Unlocking Quantum Information Technology will be beneficial to a mixed audience of specialists, analysts, scholars, researchers, academics and students in fields of business and management, especially those interested in quantum computing and technology, machine learning and artificial technology.The chapters in this book were originally published as a special issue of Technology Analysis & Strategic Management.

This book explores the dynamic intersection of quantum computing and management strategy, offering an exploration of this cutting-edge technology's potential impact. From its inception to its current state, the book traces the evolution of quantum computing, providing readers with a contextual understanding of its development. It illuminates the transformative power of quantum computing and its implications for business and management practices. Through case studies and expert analysis, readers gain insights into how quantum computing can revolutionize data analysis, optimization, and cybersecurity. The chapters in this book equip managers and entrepreneurs with the knowledge and foresight needed to capitalize on the opportunities presented by the quantum computing era.Unlocking Quantum Information Technology will be beneficial to a mixed audience of specialists, analysts, scholars, researchers, academics and students in fields of business and management, especially those interested in quantum computing and technology, machine learning and artificial technology.The chapters in this book were originally published as a special issue of Technology Analysis & Strategic Management.

The idea of modeling the behaviour of phenomena at multiple scales has become a useful tool in both pure and applied mathematics. Fractal-based techniques lie at the heart of this area, as fractals are inherently multiscale objects; they very often describe nonlinear phenomena better than traditional mathematical models. In many cases they have been used for solving inverse problems arising in models described by systems of differential equations and dynamical systems. "Fractal-Based Methods in Analysis" draws together, for the first time in book form, methods and results from almost twenty years of research in this topic, including new viewpoints and results in many of the chapters.  For each topic the theoretical framework is carefully explained using examples and applications. The second chapter on basic iterated function systems theory is designed to be used as the basis for a course and includes many exercises.  This chapter, along with the three background appendices on topological and metric spaces, measure theory, and basic results from set-valued analysis, make the book suitable for self-study or as a source book for a graduate course. The other chapters illustrate many extensions and applications of fractal-based methods to different areas. This book is intended for graduate students and researchers in applied mathematics, engineering and social sciences.  Herb Kunze is a professor of mathematics at the University of Guelph in Ontario.  Davide La Torre is an associate professor of mathematics in the Department of Economics, Management and Quantitative Methods of the University of Milan.   Franklin Mendivil is a professor of mathematics at Acadia University in Nova Scotia.  Edward Vrscay is a professor in the department of Applied Mathematics at the University of Waterloo inOntario.  The major focus of their research is on fractals and the applications of fractals.

The idea of modeling the behaviour of phenomena at multiple scales has become a useful tool in both pure and applied mathematics. Fractal-based techniques lie at the heart of this area, as fractals are inherently multiscale objects; they very often describe nonlinear phenomena better than traditional mathematical models. In many cases they have been used for solving inverse problems arising in models described by systems of differential equations and dynamical systems. "Fractal-Based Methods in Analysis" draws together, for the first time in book form, methods and results from almost twenty years of research in this topic, including new viewpoints and results in many of the chapters.  For each topic the theoretical framework is carefully explained using examples and applications. The second chapter on basic iterated function systems theory is designed to be used as the basis for a course and includes many exercises.  This chapter, along with the three background appendices on topological and metric spaces, measure theory, and basic results from set-valued analysis, make the book suitable for self-study or as a source book for a graduate course. The other chapters illustrate many extensions and applications of fractal-based methods to different areas. This book is intended for graduate students and researchers in applied mathematics, engineering and social sciences.  Herb Kunze is a professor of mathematics at the University of Guelph in Ontario.  Davide La Torre is an associate professor of mathematics in the Department of Economics, Management and Quantitative Methods of the University of Milan.   Franklin Mendivil is a professor of mathematics at Acadia University in Nova Scotia.  Edward Vrscay is a professor in the department of Applied Mathematics at the University of Waterloo inOntario.  The major focus of their research is on fractals and the applications of fractals.

We wrote this book to help financial experts and investors to understand the state of the art of artificial intelligence and machine learning in finance. But first, what is artificial intelligence? The foundations of artificial intelligence lie in the human desire to automate. Often this desire has had foundations in grand civilization-defining visions or economic needs, such as the Antikythera mechanism, circa 200 BCE. Considered to be the oldest known example of an analog computer, it is thought that the mechanism automated the prediction of the positions of the sun, the moon, and the planets to assist in navigation.No matter the specific industry or application, AI has become a new engine of growth. Both finance and banking have been leveraging AI technologies and algorithms, applying them to automate routine tasks, procedures and forecasting, thereby improving overall customer experience.The topics covered in this book make it an invaluable resource for academics, researchers, policymakers, and practitioners alike who want to understand how AI has affected the banking and financial industries and how it will continue to change them in the years to come.