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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.
Explains the theory behind Machine Learning and highlights how Mathematics can be used in Artificial Intelligence Illustrates how to improve existing algorithms by using advanced mathematics and discusses how Machine Learning can support mathematical modeling Captures how to simulate data by means of artificial neural networks and offers cutting-edge Artificial Intelligence technologies Emphasizes the classification of algorithms, optimization methods, and statistical techniques Explores future integration between Machine Learning and complex mathematical techniques
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 eac...
The book develops the capabilities arising from the cooperation between mathematicians and statisticians working in insurance and finance fields. It gathers some of the papers presented at the conference MAF2010, held in Ravello (Amalfi coast), and successively, after a reviewing process, worked out to this aim.
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. Artificial Intelligence ...
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 analy...
These proceedings report on the conference "Math Everywhere", celebrating the 60th birthday of the mathematician Vincenzo Capasso. The conference promoted ideas Capasso has pursued and shared the open atmosphere he is known for. Topic sections include: Deterministic and Stochastic Systems. Mathematical Problems in Biology, Medicine and Ecology. Mathematical Problems in Industry and Economics. The broad spectrum of contributions to this volume demonstrates the truth of its title: Math is Everywhere, indeed.
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.
This two-volume set LNCS 11662 and 11663 constitutes the refereed proceedings of the 16th International Conference on Image Analysis and Recognition, ICIAR 2019, held in Waterloo, ON, Canada, in August 2019. The 58 full papers presented together with 24 short and 2 poster papers were carefully reviewed and selected from 142 submissions. The papers are organized in the following topical sections: Image Processing; Image Analysis; Signal Processing Techniques for Ultrasound Tissue Characterization and Imaging in Complex Biological Media; Advances in Deep Learning; Deep Learning on the Edge; Recognition; Applications; Medical Imaging and Analysis Using Deep Learning and Machine Intelligence; Image Analysis and Recognition for Automotive Industry; Adaptive Methods for Ultrasound Beamforming and Motion Estimation.