Focusing on the broader areas of Industry 4.0 as it applies to small and medium-sized enterprises (SMEs), this book offers a smooth adoption of techniques and technologies and presents advances, challenges, and opportunities for implementation. It will also enhance the role of academia by training new engineers on Industry 4.0 and digital transformation. Industry 4.0 in Small and Medium-Sized Enterprises (SMEs): Opportunities, Challenges, and Solutions presents concepts of predictive maintenance, digital factory, digital twin, additive manufacturing, and machining for sustainable development. It discusses the challenges faced by adopting Industry 4.0 including new security and privacy measures in the whole smart manufacturing setup while also explaining the impact of Industry 4.0 on Lean production systems. Implementation recommendations in the form of case studies, research studies, and the role academia can play are also provided. Practitioners, research scholars, academicians, and those studying or working in the Industry 4.0 sector will find this book of interest.

This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24-25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike.