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

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

With Artificial Intelligence Integration in Energy and Other Use Cases

1st Edition - June 1, 2022

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  • Authors: Bahman Zohuri, Farhang Mossavar Rahmani, Farahnaz Behgounia
  • Paperback ISBN: 9780323951128

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Description

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.

Key Features

  • 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

Readership

Energy engineers; electrical engineers; data scientists; environmental engineers; alternative energy researchers

Table of Contents

  • 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
    Appendix
    A: Pendulum Problem
    B: Fluorescence Microscopy
    C: Factors Contributed to the Financial Crisis 2008 - 2009
    D: Factors contributing to the financial crisis of 2008
    E: Forecasting the Future by the OECD
    F: The 2025 Global Landscape
    G: The World in 2050
    H: Risk

    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
    Appendix
    A: Fission Nuclear Energy Research and Development Roadmap
    B: Thermonuclear Fusion Reaction Driving Electrical Power Generation

    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
    Appendix
    A: The Weibull Distribution
    B: The Logarithm Transformation
    C: Geometric Random Walk Model
    D: Random Walk Model
    E: Examples of Forecasting Driven by Artificial Intelligence and Machine Learning
    F: Examples of Python Programming Driving Artificial Intelligence and Machine Learning

    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
    Appendix
    A: Artificial Intelligence and Human Intelligence
    B: Deep Learning, Machine Learning Limitations and Flaws
    C: Machine Learning Driven an E-Commerce
    D: From Business Intelligence to Artificial Intelligence

Product details

  • No. of pages: 800
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: June 1, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323951128

About the Authors

Bahman Zohuri

Dr. Bahman Zohuri is currently an Adjunct Professor at Artificial Intelligence Scientist at Golden Gate University, San Francisco California, while he also is Research Associate Professor at Electrical Engineering and Computer Science at University of New Mexico at Albuquerque. He is also consulting through his own consulting company that he stared himself in 1991. He has also been a consultant at Sandia National Laboratory after leaving the United States Navy. Dr. Zohuri earned his Bachelor’s and Master’s degrees in Physics from the University of Illinois and his second Master degree in Mechanical Engineering as well as his Doctorate in Nuclear Engineering from University of New Mexico. He has been awarded three patents and has published more than 40 textbooks and numerous other journal publications.

Affiliations and Expertise

Adjunct Professor, Artificial Intelligence Scientist, Golden Gate University, San Francisco, CA; Research Associate Professor, Electrical Engineering and Computer Science, University if New Mexico, Albuquerque, New Mexico, USA

Farhang Mossavar Rahmani

Farahnaz Behgounia

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.

Affiliations and Expertise

Graduate Student, Golden Gate University at San Francisco, California, USA

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