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Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigm - Bahman Zohuri, Farhang Mossavar Rahmani, Farahnaz Behgounia

Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigm

With Artificial Intelligence Integration in Energy and Other Use Cases
Buch | Softcover
998 Seiten
2022
Academic Press Inc (Verlag)
978-0-323-95112-8 (ISBN)
CHF 269,95 inkl. MwSt
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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.

Dr. Bahman Zohuri is currently an Adjunct Professor in Artificial Intelligence Science at Golden Gate University, San Francisco, California, who runs his own consulting company and was previously a consultant at Sandia National Laboratory. Dr. Zohuri earned his bachelor’s and master’s degrees in physics from the University of Illinois. He earned his second master’s degree in mechanical engineering, and also his doctorate in nuclear engineering from the University of New Mexico. He owns three patents and has published more than 40 textbooks and numerous journal publications. Farahnaz Behgounia is presently a graduate student at Golden Gate University at San Francisco, California and in the process of obtaining her Master of Science degree from the school of Business Analytics. She has obtained her Bachlor Degreee (BS) in pure mathematics and have taught the subject at various schools as an instructor. Ms. Behgounia’s present interest is in Artificial Intelligence (AL) and its application in industry along with its sub-component such as Machine Learning (ML) and Deep Leaning (DL). Her recent interest in the subject of AI has directed her into more innovative research in AI and writing various algorithim by utilizing python language for various applications such as E-Commerce,the medical field and others.

Part I: Infrastructure Concepts 1. Knowledge is Power 2. A General Approach to Business Resilience System (BRS) 3. Data Warehousing, Data Mining, Data Modeling, and Data Analytics 4. Structured and Unstructured Data Processing 5. Mathematical Modeling Driven Predication 6. Fuzzy Logics: A New Method of Predictions 7. Neural Network Concept 8. Population - Human Growth Driving Ecology 9. Economic Factors 10. Risk Management, Risk Assessment, and Risk Analysis 11. Today’s Fast-Paced Technology

Part II: The Impact of Energy on Tomorrow’s World 12. Understanding of Energy 13. Economic Impact of Energy 14. Renewable Energy 15. Non-Renewable Energy 16. Nuclear Energy as Non-Renewable Energy Source 17. Energy Storage Technologies and their Role in Renewable Integration

Part III: The Mathematical Approach and Modeling 18. Predictive Analytics 19. Engineering Statistics 20. Data and Data Collection Driven Information 21. Statistical Forecasting - Regression and Time Series Analysis 22. Introduction to Forecasting: The Simplest Models 23. Notes on Linear Regression Analysis 24. Principles and Risks of Forecasting 25. Artificial Intelligence Driving Predictive and Forecasting Paradigm

Part IV: Python Programming Driven Artificial Intelligence 26. Python Programming Driven Artificial Intelligence 27. Artificial Intelligence, Machine Learning and Deep Learning Driving Big Data 28. Artificial Intelligence, Machine Learning and Deep Learning Use Cases

Erscheinungsdatum
Verlagsort Oxford
Sprache englisch
Maße 191 x 235 mm
Gewicht 450 g
Themenwelt Technik Elektrotechnik / Energietechnik
Technik Maschinenbau
ISBN-10 0-323-95112-0 / 0323951120
ISBN-13 978-0-323-95112-8 / 9780323951128
Zustand Neuware
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