Innovations In Machine And Deep Learning
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Innovations in Machine and Deep Learning
In recent years, significant progress has been made in achieving artificial intelligence (AI) with an impact on students, managers, scientists, health personnel, technical roles, investors, teachers, and leaders. This book presents numerous successful applications of AI in various contexts. The innovative implications covered fall under the general field of machine learning (ML), including deep learning, decision-making, forecasting, pattern recognition, information retrieval, and interpretable AI. Decision-makers and entrepreneurs will find numerous successful applications in health care, sustainability, risk management, human activity recognition, logistics, and Industry 4.0. This book is an essential resource for anyone interested in challenges, opportunities, and the latest developments and real-world applications of ML. Whether you are a student, researcher, practitioner, or simply curious about AI, this book provides valuable insights and inspiration for your work and learning.
Advances in Machine Learning/Deep Learning-based Technologies
Author: George A. Tsihrintzis
language: en
Publisher: Springer Nature
Release Date: 2021-08-05
As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.
Innovations in Power Systems and Applications
The transformation of power systems is reshaping how energy is generated, distributed, and utilized, driven by the growing demand for cleaner, more efficient, and resilient solutions. Innovations in renewable energy, smart grids, energy storage, and power electronics are at the forefront of this evolution, addressing critical challenges like sustainability and energy security. The integration of advanced technologies into power systems is enabling smarter, more adaptive energy infrastructure. These advancements not only redefine the future of energy systems but also have profound societal and environmental implications, promoting sustainable development and global energy equity. Innovations in Power Systems and Applications provides a comprehensive and up-to-date resource that captures the latest advancements and trends in the field of power systems. It bridges the gap between academic research and practical applications, offering insights that are both theoretically robust and pragmatically relevant. Covering topics such as adsorption technologies, energy optimization, and smart grid efficiency, this book is an excellent resource for academicians, researchers, industry professionals, policymakers, regulatory bodies, students, educators, and more.