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This volume provides the reader with a comprehensive and up-to-date treatise positioned at the junction of the areas of Machine Learning (ML) and Granular Computing (GrC). ML offers a wealth of architectures and learning methods. Granular Computing addresses useful aspects of abstraction and knowledge representation that are of importance in the advanced design of ML architectures. In unison, ML and GrC support advances of the fundamental learning paradigm. As built upon synergy, this unified environment focuses on a spectrum of methodological and algorithmic issues, discusses implementations and elaborates on applications. The chapters bring forward recent developments showing ways of designing synergistic and coherently structured ML-GrC environment. The book will be of interest to a broad audience including researchers and practitioners active in the area of ML or GrC and interested in following its timely trends and new pursuits.
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his textbook introduces readers to digital business from a management standpoint. It provides an overview of the foundations of digital business with basics, activities and success factors, and an analytical view on user behavior. Dedicated chapters on mobile and social media present fundamental aspects, discuss applications and address key success factors. The Internet of Things (IoT) is subsequently introduced in the context of big data, cloud computing and connecting technologies, with a focus on industry 4.0 and the industrial metaverse. In addition, areas such as smart business services, smart homes and digital consumer applications as well as artificial intelligence, quantum computing and automation based on artificial intelligence will be analysed. The book then turns to digital business models in the B2C (business-to-consumer) and B2B (business-to-business) sectors. Building on the business model concepts, the book addresses digital business strategy, discussingthe strategic digital business environment and digital business value activity systems (dVASs), as well as strategy development in the context of digital business. Special chapters explore the implications of strategy for digital marketing and digital procurement. Lastly, the book discusses the fundamentals of digital business technologies and security, and provides an outline of digital business implementation. A comprehensive case study on Google/Alphabet, explaining Google's organizational history, its integrated business model and its market environment, rounds out the book.
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This book gathers selected papers from the 13th International Conference on Green Intelligent Transportation Systems and Safety, held in Qinghuadao, China, on September 16-18, 2022. It presents cutting-edge studies on Green Intelligent Mobility Systems, with the guiding motto being to achieve "green, intelligent, and safe transportation systems". The book presented here helps promote the development of green mobility and intelligent transportation technologies to improve interconnectivity, resource sharing, flexibility, and efficiency. Given its scope, the book benefits researchers and engineers in the fields of Transportation Technology and Traffic Engineering, Automotive and Mechanical Engineering, Industrial and System Engineering, and Electrical Engineering alike. Readers will be able to learn about the advances in green intelligent transportation systems and safety.
Data is everywhere and it’s growing at an unprecedented rate. But making sense of all that data is a challenge. Data Mining is the process of discovering patterns and knowledge from large data sets, and Data Mining with Python focuses on the hands-on approach to learning Data Mining. It showcases how to use Python Packages to fulfill the Data Mining pipeline, which is to collect, integrate, manipulate, clean, process, organize, and analyze data for knowledge. The contents are organized based on the Data Mining pipeline, so readers can naturally progress step by step through the process. Topics, methods, and tools are explained in three aspects: “What it is” as a theoretical background, “why we need it” as an application orientation, and “how we do it” as a case study. This book is designed to give students, data scientists, and business analysts an understanding of Data Mining concepts in an applicable way. Through interactive tutorials that can be run, modified, and used for a more comprehensive learning experience, this book will help its readers to gain practical skills to implement Data Mining techniques in their work.
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Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.