Connected Intelligence Transforming Education And Energy With Big Data Cloud Connectors And Artificial Intelligence
Download Connected Intelligence Transforming Education And Energy With Big Data Cloud Connectors And Artificial Intelligence PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Connected Intelligence Transforming Education And Energy With Big Data Cloud Connectors And Artificial Intelligence book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Connected Intelligence: Transforming Education and Energy with Big Data, Cloud Connectors, and Artificial Intelligence
Author: Venkata Narasareddy Annapareddy
language: en
Publisher: Deep Science Publishing
Release Date: 2025-04-13
In an increasingly interconnected world, the fusion of Big Data, Cloud Computing, and Artificial Intelligence (AI) is reshaping how we learn, teach, and power our societies. Connected Intelligence: Transforming Education and Energy with Big Data, Cloud Connectors, and AI explores the transformative impact of these technologies on two of the most critical sectors of our time—education and energy. The digital revolution has already laid the groundwork, but it is the intelligent connection of systems, platforms, and data streams that is enabling smarter, faster, and more adaptive decision-making. In education, AI-driven analytics and cloud-connected platforms are personalizing learning experiences, enabling remote instruction at scale, and providing educators with real-time insights into student progress. In the energy sector, intelligent grids, predictive maintenance, and data-driven optimization are increasing efficiency, reducing waste, and accelerating the shift toward sustainable, renewable sources. This book provides a comprehensive overview of how connected intelligence is driving innovation across these domains. We examine real-world case studies, emerging technologies, and the evolving ecosystems that support this transformation. Our goal is to bridge the gap between technical innovation and practical application—highlighting both the opportunities and the challenges that come with rapid digital integration. Whether you're a policymaker shaping the future of smart infrastructure, a technologist building the next generation of intelligent systems, or a leader in education or energy looking to adapt and thrive, this book offers actionable insights and forward-thinking perspectives. At the core of connected intelligence lies the promise of a more efficient, equitable, and sustainable future—one where data doesn't just inform decisions but enables meaningful change. As we stand on the brink of this next digital evolution, Connected Intelligence invites you to explore how technology, when thoughtfully applied, can power progress across every classroom and every grid.
Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing
Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries. Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning. Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.
Engineering Analytics
Engineering analytics is becoming a necessary skill for every engineer. Areas such as Operations Research, Simulation, and Machine Learning can be totally transformed through massive volumes of data. This book is intended to be an introduction to Engineering Analytics that can be used to improve performance tracking, customer segmentation for resource optimization, patterns and classification strategies, and logistics control towers. Basic methods in the areas of visual, descriptive, predictive, and prescriptive analytics and Big Data are introduced. Industrial case studies and example problem demonstrations are used throughout the book to reinforce the concepts and applications. The book goes on to cover visual analytics and its relationships, simulation from the respective dimensions and Machine Learning and Artificial Intelligence from different paradigms viewpoints. The book is intended for professionals wanting to work on analytical problems, for Engineering students, Researchers, Chief-Technology Officers, and Directors that work within the areas and fields of Industrial Engineering, Computer Science, Statistics, Electrical Engineering Operations Research, and Big Data.