Knowledge Is Power In Four Dimensions Models To Forecast Future Paradigm
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Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigm
Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigms, Forecasting Energy for Tomorrow's World with Mathematical Modeling and Python Programming Driven Artificial Intelligence delivers knowledge on key infrastructure topics in both AI technology and energy. Sections lay the groundwork for tomorrow's computing functionality, starting with how to build a Business Resilience System (BRS), data warehousing, data management, and fuzzy logic. Subsequent chapters dive into the impact of energy on economic development and the environment and mathematical modeling, including energy forecasting and engineering statistics. Energy examples are included for application and learning opportunities. A final section deliver the most advanced content on artificial intelligence with the integration of machine learning and deep learning as a tool to forecast and make energy predictions. The reference covers many introductory programming tools, such as Python, Scikit, TensorFlow and Kera. - Helps users gain fundamental knowledge in technology infrastructure, including AI, machine learning and fuzzy logic - Compartmentalizes data knowledge into near-term and long-term forecasting models, with examples involving both renewable and non-renewable energy outcomes - Advances climate resiliency and helps readers build a business resiliency system for assets
A Model to Forecast Future Paradigms
In this volume, A Model to Forecast Future Paradigms, Volume 1: Introduction to Knowledge Is Power in Four Dimensions, the authors' two-fold objective is to lay out a methodology and approach that allows the reader to learn how to utilize existing technology in the form of computer software and hardware for forecasting and decision-making and to discuss factors that affect upcoming events that, in turn, shape future paradigms. The book provides an understanding of these factors that will help decision-makers be better prepared to face future challenges and will assist them coping with unexpected circumstances. This volume is divided into two parts. Part one discusses a "technological infrastructure" so that new readers can gain a greater understanding based on the knowledge of tomorrow's computing functionality. The second part goes on to discuss the key indicators in the areas of population, culture, economics, climate change, and the impacts of technology in commerce and socially--which all need to be considered when forecasting a future paradigm.
Quantum Computing and Artificial Intelligence in Logistics and Supply Chain Management
This book discusses the transformative potential of quantum computing in reshaping the landscape of supply chain management. It bridges the gap between these two dynamic fields, offering a comprehensive guide to the application of quantum principles in supply chain operations. Through detailed examples and case studies, it highlights how quantum computing can tackle industry-specific issues, such as managing global supply chain disruptions, enhancing production schedules, and enabling real-time decision-making. This book is for researchers, professionals, and technologists interested in quantum computing and supply chain practices. Features: Provides an in-depth analysis of quantum computing technologies and their capacity to solve complex optimisation problems at scales unimaginable with traditional computing Examines the impact of quantum computing on manufacturing and logistics, with a focus on sectors such as automotive and aerospace Real-world scenarios illustrate how quantum solutions can streamline operations and drive efficiency Explores quantum algorithms and their use in addressing challenges like route optimisation, inventory management, and demand forecasting, offering strategies to reduce costs and improve resilience Considers the current limitations, ethical implications, and the path to widespread adoption of quantum computing in supply chains, emphasising the need for interdisciplinary collaboration