Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigm: With Artificial Intelligence Integration in Energy and Other Use Cases

Front Cover
KNOWLEDGE IS POWER IN FOUR DIMENSIONS: MODELS TO FORECAST FUTURE PARADIGMS
KNOWLEDGE IS POWER IN FOUR DIMENSIONS: MODELS TO FORECAST FUTURE PARADIGMS
Copyright
Dedication
Short Contents
About the authors
Preface
Acknowledgment
I - The general infrastructure
	1 - Knowledge is power
		1.1 Introduction
		1.2 History of knowledge
		1.3 Definition of knowledge
		1.4 Types of knowledge
		1.5 What value there is in knowing
		1.6 Limitation of knowing
		1.7 Measurability of knowledge
		1.8 Creation of knowledge
		1.9 Knowledge resources and power
		1.10 Knowledge and information as goods
			1.10.1 M.K. Buckland “information as thing”
		1.11 Reasons why knowledge is power
		1.12 Knowledge is power? Those days are long gone
		1.13 There is more to “the knowledge is power”
		1.14 Facilitates decision—making capabilities
		1.15 Stimulates cultural change and innovation
		1.16 Bottom line
			1.16.1 Issues in the private sector
			1.16.2 Issues in the private sector
			1.16.3 Issues in the public sector
			1.16.4 Ten principles for managing knowledge
			1.16.5 Particular issues in evaluating knowledge services
		1.17 Summary and conclusion
		References
	2 - A general approach to business resilience system (BRS)
		2.1 Introduction
		2.2 Resilience and stability
		2.3 Reactive to proactive safety through resilience
		2.4 Reactive to proactive safety through resilience
		2.5 Risk atom key concept
		2.6 Business resilience system features
			2.6.1 Top 10 questions to think about
		2.7 Summary of the business resilience system
		2.8 Business resilience system project plan
			2.8.1 Assumptions
		2.9 Summary and conclusion
		References
		Further reading
	3 - Data warehousing, data mining, data modeling, and data analytics
		3.1 Introduction
		3.2 Data warehousing concept
		3.3 Data mart concept
		3.4 Data mining concept
			3.4.1 Data mining and machine learning concept
		3.5 Data modeling concept
		3.6 Data analytics concept
			3.6.1 A 10-stage data science methodology
		3.7 Big data and master data management concepts
		3.8 Architecture and benefits of MDM and big data
		3.9 Summary and conclusion
		References
	4 - Structured and unstructured data processing
		4.1 Introduction
		4.2 Master data management versus big data
			4.2.1 Input: providing context to master data management
			4.2.2 Big data infrastructure demands
			4.2.3 The human side of big data
			4.2.4 Input: facilitating big data context to master data management
		4.3 Big data history and current considerations
			4.3.1 Big data's big potential
			4.3.2 Why is big data important?
			4.3.3 Who uses big data?
			4.3.4 How it works?
		4.4 Real time data processing and data mining
			4.4.1 Behind the scenes with data mining
			4.4.2 Modeling techniques
			4.4.3 Deploying models in a scalable environment
		4.5 Improving big data analytics with machine learning-as-a-service
			4.5.1 Application development
			4.5.2 Data science
			4.5.3 Cloud vitality
			4.5.4 The point of big data: the internet of things
			4.5.5 The Internet of things
				4.5.5.1 Additional benefits
				4.5.5.2 Potential drawbacks
				4.5.5.3 Internet on things transactions
				4.5.5.4 Internet on things applications retailers
				4.5.5.5 Cloud ramifications
				4.5.5.6 Data streaming
				4.5.5.7 Under the hood
				4.5.5.8 Dynamic transactions
			4.5.6 Natural language process involvement
			4.5.7 The epicenter
		4.6 The mathematics of data: graph analytics-as-a-service
			4.6.1 Deep reasoning inference analytics
			4.6.2 Data algebra
			4.6.3 Integration, data modeling, and data lake architecture
			4.6.4 Governance and self-service graph analytics
			4.6.5 Ubiquitous computing
		4.7 Cloud database
			4.7.1 What is database as a service (DBaaS)?
			4.7.2 Database as a service (DBaaS) benefits
			4.7.3 Cloud database architecture
			4.7.4 Cloud database benefits
			4.7.5 Migrating legacy databases to the cloud
			4.7.6 How does a cloud database work?
		4.8 Summary and conclusions
		References
	5 - Mathematical modeling driven predication
		5.1 Introduction
		5.2 Mathematics for data-driven modeling—the science of crystal balls
			5.2.1 The principle of mathematical modeling
			5.2.2 Some methods of mathematical modeling
				5.2.2.1 Dimensional homogeneity and consistency
				5.2.2.2 Abstraction and scaling
				5.2.2.3 Conservation and balance principles
				5.2.2.4 Constructing linear models
		5.3 Mathematics of probability
		5.4 Statistical analysis
		5.5 Bayesian methods and concepts
		5.6 Markov chain Monte Carlo methods and concepts
		5.7 Predictive modeling and analysis
			5.7.1 Predictive modeling methods
			5.7.2 Predictive modeling considerations
			5.7.3 Predictive analytic process
		5.8 Descriptive, prescriptive-analytics-driven predictive analytic
			5.8.1 Descriptive analytics concept
			5.8.2 Prescriptive analytics concept
		5.9 Predictive versus prescriptive analytics
		5.10 Big data analytics: descriptive versus predictive versus prescriptive
		5.11 Diagnostic analytics
		5.12 Analytical techniques
			5.12.1 Regression techniques
			5.12.2 Machine learning techniques
		5.13 Summary and conclusion
		References
	6 - Fuzzy logics: a new method of predictions
		6.1 Introduction
			6.1.1 Neural networks
			6.1.2 Expert systems
			6.1.3 Artificial intelligence
		6.2 What is fuzzy logic and how it works
		6.3 Fuzzy logic and fuzzy sets
		6.4 The fuzzy logic method
			6.4.1 Fuzzy perception
			6.4.2 Novices can beat the pros
			6.4.3 A milestone passed for intelligent life on earth
			6.4.4 Fuzzy logic terms found in books and articles
		6.5 The world's first fuzzy logic controller
			6.5.1 Progress in fuzzy logic
			6.5.2 Fuzzy logic control input—human and computer
			6.5.3 More about how fuzzy logic works
		6.6 The rationale for fuzzy logic
		6.7 Information processing driven by fuzzy logic
		6.8 Fuzzy logic system type-1 and type-2
			6.8.1 Definition of fuzzy set
		6.9 Adaptive neuro-fuzzy interface system
		6.10 Back Propagation Neural Network
			6.10.1 k-nearest neighbor classifiers
		6.11 Bayesian network
			6.11.1 Bayesian learning
			6.11.2 Application of Bayesian networks
		6.12 Fuzzy logic algorithms and neural networking
		6.13 Summary and conclusion
		References
	7 - Neural network concept
		7.1 Introduction
		7.2 Artificial neural network (ANN)
			7.2.1 Artificial neural network (ANN)
			7.2.2 Single-layer network
			7.2.3 Multilayer network
			7.2.4 Learning process
		7.3 Back-propagation neural networks
			7.3.1 Linear separability and the XOR problem
			7.3.2 The architecture of back-propagation networks
			7.3.3 Back-propagation processing unit
			7.3.4 Back-propagation learning algorithm
			7.3.5 Local minimum problem
			7.3.6 Generalization
		7.4 Biological background
		7.5 Biological neural networks
		7.6 Learning in a neural network
			7.6.1 The pattern associator
			7.6.2 The Hebb rule
			7.6.3 The delta rule
			7.6.4 The generalized delta rule
		7.7 Fuzzy logic and neural networks
		7.8 Summary and conclusion
		References
		Further reading
	8 - Population—human growth driving ecology
		8.1 Introduction
		8.2 Population—human growth
		8.3 What is population
		8.4 The status of population in the world
		8.5 Trends—forecasting
		8.6 Growth rates
		8.7 How many people can planet earth support?
		8.8 Criteria to be used in our model
		8.9 Population structure
		8.10 Religions
			8.10.1 Projected growth
			8.10.2 Islam
		8.11 The relationship between population and other critical factors
			8.11.1 Population and climate change
			8.11.2 What is climate change?
			8.11.3 Reasons for climate change
		8.12 The impact of climate change on human life
		8.13 Population and economic growth and development
			8.13.1 What is economic growth?
			8.13.2 What is development?
			8.13.3 GDP/GDP per capita
		8.14 Population and governance
			8.14.1 Age
			8.14.2 Population composition
			8.14.3 Consumer expenditures
			8.14.4 Poverty
			8.14.5 Earnings/minimum wages
			8.14.6 Social problems
			8.14.7 Education
		8.15 Population and immigration
		8.16 Refugees
		8.17 Urbanization
		8.18 Summary and conclusion
		References
		Further reading
	9 - Economic factors
		9.1 Introduction
		9.2 What is economics?
		9.3 Key players
		9.4 Business cycle
		9.5 Economics factors
			9.5.1 Economic indicators
				9.5.1.1 Leading indicators
					9.5.1.1.1 Retail sales
					9.5.1.1.2 Housing starts
					9.5.1.1.3 Durable goods report
					9.5.1.1.4 New orders
					9.5.1.1.5 Shipment
					9.5.1.1.6 Unfilled orders
					9.5.1.1.7 Purchasing manager index (PMI)
					9.5.1.1.8 Industrial production
					9.5.1.1.9 Jobless claims
				9.5.1.2 Coincident index
				9.5.1.3 Lagging indicators
					9.5.1.3.1 Changes in the gross domestic product (GDP)
					9.5.1.3.2 Consumer price index (inflation)
					9.5.1.3.3 Interest rates
					9.5.1.3.4 Corporate profits
					9.5.1.3.5 Balance of trade
				9.5.1.4 Gross domestic products (GDP)
					9.5.1.4.1 GDP component
						9.5.1.4.1.1 Business investment
						9.5.1.4.1.2 Government spending
						9.5.1.4.1.3 Net exports of goods and services
					9.5.1.4.2 What makes economies grow?
					9.5.1.4.3 World GDP
						9.5.1.4.3.1 Major factors that affect world GDP
				9.5.1.5 Globalization
				9.5.1.6 Trade and international trade
				9.5.1.7 Factors affecting the flow of free trade
					9.5.1.7.1 Tariffs
					9.5.1.7.2 Nontariff barriers
					9.5.1.7.3 Inflation
						9.5.1.7.3.1 Economic consequences of inflation
					9.5.1.7.4 Government policies
					9.5.1.7.5 National income
					9.5.1.7.6 Exchange rates
					9.5.1.7.7 Balance of payments
						9.5.1.7.7.1 The balance of payments components
							9.5.1.7.7.1.1 Current account
							9.5.1.7.7.1.2 Capital or financial account
				9.5.1.8 The concept of capital
					9.5.1.8.1 Capital and industrial revolution
					9.5.1.8.2 The role of interest rate
					9.5.1.8.3 Modern interpretation of the industrial revolution
					9.5.1.8.4 The concept of capital today
						9.5.1.8.4.1 Social capital
					9.5.1.8.5 The concept of capital in Islam
				9.5.1.9 Capital formation
				9.5.1.10 Investment in human capital
				9.5.1.11 Investing in education
				9.5.1.12 Gender parity and human capital
				9.5.1.13 Regulations
					9.5.1.13.1 Banking regulations trends in the United States
					9.5.1.13.2 Brief history of banking and regulations in the United States
						9.5.1.13.2.1 The crash of the stock market in 1929
						9.5.1.13.2.2 Regulations and deregulation after 1980
						9.5.1.13.2.3 Banking crisis of the 1980s
						9.5.1.13.2.4 Regulatory factor that contributed to the financial crisis of 2008
					9.5.1.13.3 The cost of regulations and deregulations
					9.5.1.13.4 The difficulty of calculating the total cost of regulations
					9.5.1.13.5 Conclusion
		9.6 Other economic issues
			9.6.1 Standard of living
			9.6.2 Poverty
				9.6.2.1 Poverty in the world
				9.6.2.2 Poverty and shared prosperity
		9.7 Summary and conclusion
		References
	10 - Risk management, risk assessment, and risk analysis
		10.1 Introduction
		10.2 Predictive risk intelligence
		10.3 Cognitive computing applications for risk management
		10.4 Harnessing product safety and recall analytics
		10.5 Managing algorithmic risks
		10.6 The future risk, new game, new rules
		10.7 Global risk management
		10.8 Risk sensing: the evolving state of the art
		10.9 Summary and conclusion
		References
	11 - Today's fast-paced technology
		11.1 Introduction
		11.2 Climate change
		11.3 Human behavior
		11.4 Technology definition
			11.4.1 Traditional technology
			11.4.2 Modern technology
			11.4.3 Our approach to technology
		11.5 The impact of modern technology on human behavior
			11.5.1 Cyberbullying or cyber harassment
				11.5.1.1 The impact of bullying in cyberspace on the victim
				11.5.1.2 The impact of technology on children
		11.6 The impact of technology on climate change
			11.6.1 What causes climate change
				11.6.1.1 Variation in the Sun's energy reaching earth
				11.6.1.2 Greenhouse gasses
				11.6.1.3 Man-made effect
				11.6.1.4 Population
		11.7 Is there any hope
			11.7.1 Negative impact of technology
			11.7.2 The positive ecological impacts of technology
				11.7.2.1 Healthcare
				11.7.2.2 Business
				11.7.2.3 Climate change
		11.8 Summary and conclusion
		References
		Further reading
II - The impact of energy on tomorrow's world
	12 - Understanding of energy
		12.1 Introduction
		12.2 What is energy?
			12.2.1 Measurement of energy
			12.2.2 Demand for energy
			12.2.3 Historical trend in world energy consumptions
		12.3 Moving from one source of energy to another
		12.4 World energy 2017
		12.5 United States trends
		12.6 Global demand growth
			12.6.1 Technology
			12.6.2 Policy
			12.6.3 Consumer preferences
		12.7 Use of energy by different sectors in the United States
			12.7.1 Use of energy explained
			12.7.2 Industry uses many energy sources
			12.7.3 Different types of energy sources (or fuels) driven transportation in the United States
			12.7.4 Energy use by type of industry
			12.7.5 Energy use for transportation
				12.7.5.1 Different types of energy sources (or fuels) driving transportation in the United States
				12.7.5.2 Energy sources are used in several major ways
				12.7.5.3 Petroleum is the main source of energy for transportation
			12.7.6 Energy use at homes
			12.7.7 Many factors affect the amount of energy a household uses
			12.7.8 Electricity and natural gas driving the most-used energy sources at homes
			12.7.9 Energy use per household has declined
		12.8 Energy use in commercial buildings
			12.8.1 Energy use by type of building
		12.9 Nature of demand for the energy in the future
			12.9.1 Energy used in the buildings sector
			12.9.2 Electricity
		12.10 Summary and conclusion
		References
	13 - Economic impact of energy
		13.1 Introduction
		13.2 Historical relationship between energy and GDP
			13.2.1 Energy and industrialization
			13.2.2 The relationship between energy consumption and GDP
		13.3 Factors influence the demand for energy
			13.3.1 Industrial undergirds global economic expansion
			13.3.2 Factor affecting demand for different types of energy
		13.4 Energy and climate change
			13.4.1 Energy and climate change
				13.4.1.1 The impact of electricity on the environment
				13.4.1.2 The effect of power plants on the landscape
				13.4.1.3 Fossil fuel, biomass, and waste burning power plants
				13.4.1.4 Power plants reduce air pollution emissions in various ways
				13.4.1.5 Some power plants also produce liquid and solid wastes
				13.4.1.6 Electric power lines and other distribution infrastructure also have a footprint
		13.5 The potential future impacts change in climate on the US energy sector
		13.6 Summary and conclusion
		References
	14 - Renewable energy
		14.1 Introduction
		14.2 Different types of energy
		14.3 Definition of the renewable energy
		14.4 Factors affecting the future renewable energy
		14.5 Types of renewable energy
			14.5.1 Wind energy
				14.5.1.1 History of wind power
				14.5.1.2 Total installed capacity
				14.5.1.3 Future of the wind energy in the world
				14.5.1.4 Offshore wind power
				14.5.1.5 Future of wind energy in the United States
				14.5.1.6 Wind energy technology
				14.5.1.7 Advantages and disadvantages of wind energy
			14.5.2 Solar energy
				14.5.2.1 History of solar energy
				14.5.2.2 Solar photovoltaics
				14.5.2.3 Total capacity
				14.5.2.4 Ways in which solar is harnessed
				14.5.2.5 Future of the solar energy
				14.5.2.6 Advantages and disadvantages of solar energy
			14.5.3 Geothermal power
				14.5.3.1 History of geothermal power
				14.5.3.2 Technology driving geothermal energy
				14.5.3.3 Type of geothermal power plant
				14.5.3.4 Total installed geothermal electric capacity in some countries
				14.5.3.5 Geothermal power in the United States
				14.5.3.6 The future of geothermal energy
				14.5.3.7 Geothermal as an inexhaustible source
				14.5.3.8 Different depths
				14.5.3.9 Advantages and disadvantages of geothermal energy
			14.5.4 Hydropower energy
				14.5.4.1 History of hydropower energy
				14.5.4.2 Hydropower relies on the water cycle
				14.5.4.3 Size of hydropower facilities
				14.5.4.4 Key findings from the 2017 hydropower market report
				14.5.4.5 Future of hydropower
				14.5.4.6 The impact of hydropower on environment
				14.5.4.7 Fish ladders help salmon reach their spawning grounds
				14.5.4.8 Advantages and disadvantages of hydropower energy
			14.5.5 Biomass
				14.5.5.1 Converting biomass to energy
				14.5.5.2 History of biomass
				14.5.5.3 How much biomass is used for fuel?
				14.5.5.4 Present status of biomass
				14.5.5.5 Future of biomass
				14.5.5.6 Advantages and disadvantages of biomass
		14.6 Summary and conclusion
		References
		Further reading
	15 - Nonrenewable energy
		15.1 Introduction
		15.2 Types of nonrenewable energy
			15.2.1 Oil
			15.2.2 Crude oil
				15.2.2.1 The difference between crude oil, petroleum products, and petroleum4
				15.2.2.2 Petroleum products and their applications
				15.2.2.3 Number of gallons of gasoline and diesel fuel made from one barrel of oil
				15.2.2.4 Type of crude oils
				15.2.2.5 Crude oil classification
				15.2.2.6 Organic oils
				15.2.2.7 Crude oil price
			15.2.3 World consumption
				15.2.3.1 Does the world have enough oil to meet our future needs?
				15.2.3.2 US oil production as of April 2019
				15.2.3.3 How much oil is consumed in the United States?
				15.2.3.4 The future of oil production in United States
				15.2.3.5 Future demand
			15.2.4 Oil and climate change
				15.2.4.1 CO2 emissions
				15.2.4.2 CO2 emission in the world
				15.2.4.3 Annual CO2 emission in the United States
			15.2.5 US electricity generation by energy source
				15.2.5.1 Electricity
			15.2.6 Advantages and disadvantages of oil
				15.2.6.1 Advantages of oil eneregy
				15.2.6.2 Disadvantages of oil energy
		15.3 Coal
			15.3.1 Introduction to coal
			15.3.2 Definition
			15.3.3 Types of coal
				15.3.3.1 Anthracite
				15.3.3.2 Bituminous
				15.3.3.3 Subbituminous
				15.3.3.4 Lignite
			15.3.4 Coal rock types
			15.3.5 World consumption
			15.3.6 Resources and reserves
			15.3.7 US consumption
			15.3.8 How large are US coal reserves?
			15.3.9 How much coal is in the United States?
			15.3.10 Coal price
				15.3.10.1 Coal transportation costs can be significant
			15.3.11 The rate driving KW/Hr of electricity from coal, natural gas, or petroleum
			15.3.12 Coal CO2 emissions by sector
			15.3.13 Emissions from burning coal
			15.3.14 Fossil CO2 emissions in China
			15.3.15 Fossil CO2 emissions in India
			15.3.16 Reducing the environmental effects of coal use
			15.3.17 Advantages and disadvantages of using coal as a source of energy
				15.3.17.1 Advantages
				15.3.17.2 Disadvantages
		15.4 Natural gas energy
			15.4.1 History of natural gas
			15.4.2 Natural gas origins
			15.4.3 Global consumption
			15.4.4 US consumption of natural gas
			15.4.5 Future demand for natural gas
			15.4.6 The United States leads production growth
			15.4.7 The major factors affecting natural gas prices
				15.4.7.1 The geopolitics of gas market
			15.4.8 Advantages and disadvantages of natural gas
			15.4.9 Summary and conclusion
		15.5 World of cautious
		References
	16 - Nuclear energy as nonrenewable energy source
		16.1 Introduction
		16.2 Nuclear fission process in a nutshell
		16.3 Nuclear fusion process in nutshell
		16.4 Why we need nuclear power plants
		16.5 Is nuclear energy renewable source of energy
		16.6 Argument for nuclear as renewable energy
		16.7 Argument against nuclear as renewable energy
		16.8 Safety
		16.9 Conclusion
		References
	17 - Energy storage technologies and their role in renewable integration
		17.1 Introduction
		17.2 The electric grid
		17.3 Power generation
		17.4 Transmission and distribution
		17.5 Load management
		17.6 Types of storage technology
			17.6.1 Kinetic energy storage or flywheels concept
			17.6.2 Superconducting magnetic energy storage
			17.6.3 Batteries
				17.6.3.1 Lead-acid batteries
				17.6.3.2 Lithium-ion batteries
			17.6.4 Other and future batteries in development
		17.7 A battery-inspired strategy for carbon fixation
		17.8 Saliva-powered battery
		17.9 Summary
		References
III - The mathematical approach and modeling
	18 - Predictive analytics
		18.1 Introduction
		18.2 Predictive analytics history and current advances
		18.3 How does predictive analytics work?
		18.4 Why is predictive analytics important?
		18.5 Predictive analytics benefits and what are they?
		18.6 Difference between predictive analytics and traditional analytics
		18.7 Predictive analytics starts driving an organization
		18.8 Predictive analytics examples and who's using it?
		18.9 Predictive modeling and how it works?
			18.9.1 Bayesian statistics
			18.9.2 Bayes' theorem
			18.9.3 Bayesian inference
			18.9.4 Statistical modeling
			18.9.5 Experiments design
			18.9.6 Statistical graphics
			18.9.7 Bayesian core topics driving machine learning
				18.9.7.1 Central problems
			18.9.8 Bayesian network
			18.9.9 Probabilistic
			18.9.10 Graphical
			18.9.11 Nodes
			18.9.12 Discrete
			18.9.13 Continuous
			18.9.14 Links
			18.9.15 Structural learning
			18.9.16 Feature selection
		18.10 Directed acyclic graph (DAG)
			18.10.1 Directed cycles
		18.11 Notation
		18.12 Probability
			18.12.1 Joint probability
			18.12.2 Conditional probability
			18.12.3 Marginal probability
		18.13 Distributions
		18.14 Parameter learning
		18.15 Online learning
		18.16 Evidence
		18.17 Instantiation
		18.18 Joint probability of a Bayesian network
		18.19 Distributive law
		18.20 Bayes theorem utilization
		18.21 Are Bayesian networks Bayesian?
		18.22 Inference
			18.22.1 Exact inference
			18.22.2 Approximate inference
			18.22.3 Algorithms
		18.23 Queries
		18.24 Analysis
		18.25 Dynamic Bayesian networks
		18.26 Decision graphs
			18.26.1 Decision nodes
			18.26.2 Utility nodes
		18.27 Decision automation approach
			18.27.1 Decision automation example
				18.27.1.1 Prescription
				18.27.1.2 Prognostics
				18.27.1.3 Decision automation versus optimization
		18.28 Bayesian statistics-driven data science
		18.29 The difference between Bayesian statistics and machine learning
		18.30 What do you need to get started using predictive analytics?
		18.31 Predictive analytics—common use cases plus case study
			18.31.1 Identification of customers likely to churn
			18.31.2 Predictive maintenance and quality fulfillment with IoT monitoring
			18.31.3 Prediction of features for product development
			18.31.4 Inventory forecasting and pricing strategy
			18.31.5 Calculation of risk
		18.32 Rise of prediction in marketing
		18.33 Benefits of predictive scoring for marketers
		18.34 The four common challenges of predictive analytics
		18.35 Summary
		References
	19 - Engineering statistics
		19.1 Introduction
		19.2 Statistical thinking driving decision-making
		19.3 Statics driving future forecast
		19.4 Definition of a linear trend
		19.5 Expected temperature changes: signal and noise
		19.6 Deriving trend statistics
		19.7 Trend uncertainties
		19.8 Confidence intervals and significance testing
			19.8.1 Confidence intervals
			19.8.2 Significance testing
			19.8.3 A complication; the effect of autocorrelation
		19.9 Comparing trends in two datasets
		19.10 Multiple atmosphere/ocean general circulation model (AOGCM) simulations
		19.11 Practical versus statistical significance
		19.12 Climate change and global warming
		References
		Further reading
	20 - Data and data collection driven information
		20.1 Introduction
		20.2 Data versus information
			20.2.1 Example of data and information
			20.2.2 Misleading data
			20.2.3 Etymology
			20.2.4 Grammar and usage
		20.3 Detrend data driving statistics
			20.3.1 Statistical noise
		20.4 Engineering data collection
			20.4.1 Retrospective study
			20.4.2 Observational study
			20.4.3 Designed experiments
		20.5 Exploratory data analysis
			20.5.1 Data traces plots
			20.5.2 Histograms plots
			20.5.3 Bihistograms plots
			20.5.4 Probability plots
			20.5.5 Lag plots
			20.5.6 Block plots
			20.5.7 Youden plots
			20.5.8 Mean plots
			20.5.9 Standard deviation plots
			20.5.10 Box plots
		20.6 Exploratory data analysis versus classical and Bayesian data analysis
			20.6.1 Models
				20.6.1.1 Classical
				20.6.1.2 Exploratory
			20.6.2 Focus
				20.6.2.1 Classical
				20.6.2.2 Exploratory
			20.6.3 Techniques
				20.6.3.1 Classical
				20.6.3.2 Exploratory
			20.6.4 Rigor
				20.6.4.1 Classical
				20.6.4.2 Exploratory
			20.6.5 Data treatment
				20.6.5.1 Classical
				20.6.5.2 Exploratory
			20.6.6 Assumptions
				20.6.6.1 Classical
				20.6.6.2 Exploratory
		20.7 Exploratory data analysis versus summary analysis
		20.8 Exploratory data analysis primary and secondary goals
		20.9 Summary and conclusion
		References
	21 - Statistical forecasting—regression and time series analysis
		21.1 Introduction
		21.2 Signal versus noise
			21.2.1 Some simple cases
		21.3 Risk of forecasting
		21.4 Introduction to ARIMA: a nonseasonal models
			21.4.1 Autoregressive integrated moving average (ARIMA) definition
			21.4.2 Other special forms
			21.4.3 Differencing
			21.4.4 Choosing the order of nonseasonal ARIMA
			21.4.5 Forecasting intervals
		References
	22 - Introduction to forecasting: the simplest models
		22.1 Introduction
		22.2 Forecasting with the mean model
		22.3 More rules of thumb for confidence intervals
		22.4 More about t
		22.5 Our example continued
		22.6 Presentation of results
		22.7 Summary
	23 - Notes on linear regression analysis
		23.1 Introduction
		23.2 Linear regression analysis description
		23.3 Linear regression and correlation
			23.3.1 Correlation
		23.4 Correlation and regression to mediocrity
		23.5 Mathematics of the simple regression model
		23.6 Introducing R-squared
		23.7 The standard errors of means and forecasts
		23.8 Linear regression methods
		23.9 The listen learned
	24 - Principles and risks of forecasting
		24.1 Introduction
		24.2 Forecasting principles and perspectives
		24.3 How to move data around
			24.3.1 Text data
			24.3.2 Binary data
			24.3.3 Counting bits and bytes
			24.3.4 Text=a universal medium of data exchange
			24.3.5 Text file formats
		24.4 Get to know your data
		24.5 Inflation adjustment (deflation)
		24.6 Seasonal adjustment
			24.6.1 Additive adjustment
			24.6.2 Additive adjustment
			24.6.3 Acronyms
		24.7 Stationarity and differencing
		24.8 Autoregressive integrated moving average (ARIMA) models with regressors
		24.9 The mathematical structure of ARIMA models
			24.9.1 The backshift operator
			24.9.2 The equivalence of pure AR and pure MA models
			24.9.3 The danger of overfitting a mixed AR/MR model: redundancy and cancellation
			24.9.4 Unit roots and the Dickey–Fuller tests
			24.9.5 Stationarity and invertibility of second order AR or MA models
		References
	25 - Artificial intelligence driving predictive and forecasting paradigm
		25.1 Introduction
		25.2 Prediction versus forecasting
			25.2.1 Prediction and forecasting
			25.2.2 Why weather forecasting and not weather prediction?
			25.2.3 Challenges of forecasting
		25.3 Artificial intelligence role
		25.4 Data analytics role
		25.5 Predictive analytics role
			25.5.1 Artificial intelligence improving predictive analytics
			25.5.2 Artificial intelligence improving predictive analytics paradigm
			25.5.3 Artificial intelligence takes predictive analytics to the next level
		25.6 Predictive analytics and machine learning
		25.7 Applications of predictive analytics and machine learning
		25.8 Conclusion
		References
		Further reading
IV - Python programming-driven artificial intelligence
	26 - Python programming–driven artificial intelligence
		26.1 Introduction
		26.2 History of knowledge
		26.3 Definition of knowledge
		26.4 Python basics
			26.4.1 Programming in IDLE
			26.4.2 Data types in python
		26.5 Python standard and add-on libraries
			26.5.1 Pandas
			26.5.2 NumPy
			26.5.3 Matplotlib
			26.5.4 SciPy
			26.5.5 Seaborn
			26.5.6 Statsmodels
			26.5.7 Scikit-lear
			26.5.8 TensorFlow
			26.5.9 Keras
		26.6 Python for artificial intelligence, machine learning, and deep learning
		References
		Further reading
	27 - Artificial intelligence, machine learning, and deep learning driving big data
		27.1 Introduction
		27.2 What is machine learning?
			27.2.1 Machine learning techniques
		27.3 What is deep learning?
			27.3.1 How deep learning works
		27.4 Short description of artificial intelligence, machine learning, and deep learning
		27.5 Big data
			27.5.1 Big data history and current considerations
			27.5.2 What are big data and big data analytics?
			27.5.3 What is big data important?
			27.5.4 What is big data important?
			27.5.5 How does big data work?
		27.6 Natural language processing (NLP)
			27.6.1 How does NLP work?
		27.7 Cognitive science and cognitive linguistics
		27.8 Neural networks concepts
			27.8.1 How neural networks work?
			27.8.2 Examples of how neural networks work?
			27.8.3 Recurrent neural networks
		27.9 Data science and data science platform
			27.9.1 How data science is transforming business
			27.9.2 How data science is conducted
			27.9.3 Tools for data science
			27.9.4 Who oversees the data science process?
			27.9.5 What is a data scientist?
			27.9.6 Challenges of implementing data science projects
			27.9.7 The data science platform delivers new capabilities
			27.9.8 The benefits of a data science platform
			27.9.9 What a data scientist needs in a platform
			27.9.10 When a data science platform is the right move
		27.10 Conclusion
		References
	28 - Artificial intelligence, machine learning, and deep learning use cases
		28.1 Introduction
		28.2 Apple stock prediction, using machine learning
			28.2.1 Executive summary
			28.2.2 Project goal and purpose
			28.2.3 Literature review and predicting stock prices with machine learning
			28.2.4 Materials and methods and summary of the data
			28.2.5 Apple Stock Closing Price
			28.2.6 Apple Stock Opening Price
			28.2.7 The difference between apple stock closing and opening price
			28.2.8 Simple moving average (SMA) with close price
			28.2.9 Feature engineering
			28.2.10 Data preparation and processing
			28.2.11 Transform dataset
			28.2.12 Predictive analysis
			28.2.13 Model selection
			28.2.14 Model building
			28.2.15 Results and analysis
			28.2.16 Conclusion
			28.2.17 Python code for project predictive model
		28.3 Boston housing market analysis
			28.3.1 Introduction
			28.3.2 Approach and methodology
				28.3.2.1 Descriptive statistics
				28.3.2.2 Box plot illustration
			28.3.3 Z-score normalization
			28.3.4 Reducing dimensions using principal component analysis (PCA)
			28.3.5 Supervised learning
			28.3.6 Conclusion
		28.4 Customer segmentation using K-means clustering a machine learning
			28.4.1 Executive summary
			28.4.2 Introduction
			28.4.3 Literature review
			28.4.4 Data collection
			28.4.5 Exploratory data analysis and data preparation
				28.4.5.1 Cleaning the data
					28.4.5.1.1 Removing null values
					28.4.5.1.2 Column formatting
				28.4.5.2 Handing outlines
					28.4.5.2.1 Categorical outliers country
					28.4.5.2.2 Quantitative outliers
						28.4.5.2.2.1 Unit price
						28.4.5.2.2.2 Quantity
			28.4.6 Summarizing exploratory data analysis (EDA)
			28.4.7 Recency, frequency, monetary (RFM) segmentation using K-mean clustering
				28.4.7.1 Recency
				28.4.7.2 Frequency
			28.4.8 Monetary value
				28.4.8.1 Segment visualization
			28.4.9 Finding and conclusion
		28.5 Credit card fraud detection
			28.5.1 Abstract
			28.5.2 Introduction
			28.5.3 Procedure (HTML file screenshots, graphics, histograms)
			28.5.4 Decision tree training
			28.5.5 Random Forest training
			28.5.6 K-nearest neighbor (KNN) training
			28.5.7 Results
			28.5.8 Future enhancements
			28.5.9 Conclusion
		References
		Further reading
A: Pendulum problem
B: Fluorescence microscopy
	References
	Further reading
C: Factors contributed to the financial crisis 2008–09
D: factors contributing to the financial crisis of 2008
E: Forecasting the future by the OECD
	Reference
F: The 2025 global landscape
G: The world in 2050
H: Risk
I: Fission nuclear energy research and development roadmap
	I.1 Introduction
	I.2 US industry opportunities for advanced nuclear technology development
	I.3 Benefits of small modular reactors
	I.4 Cooling water requirement for nuclear power reactors
	I.5 Open air brayton gas power cycle
		I.5.1 Computer code development
	I.6 Modeling the nuclear air Brayton combined cycle
	I.7 A combined cycle power conversion system for small modular LMFBR
		I.7.1 The air Brayton cycle pros and cons
		I.7.2 The feed water heater
		I.7.3 Results of modeling
	I.8 Summary
	References
	Further reading
J: Thermonuclear fusion reaction driving electrical power generation
	J.1 Introduction
	J.2 Magnetic confinement fusion (MCF)
		J.2.1 Magnetic mirrors
		J.2.2 Toroidal machines
			J.2.2.1 Z-pinch machine
			J.2.2.2 Stellarators confinement system
			J.2.2.3 Tokamaks confinement system
			J.2.2.4 Other systems
				J.2.2.4.1 Advantages
				J.2.2.4.2 Disadvantages
			J.2.2.5 Compact toroid
	J.3 Inertial confinement fusion (ICF)
		J.3.1 How inertial confinement fusion (ICF) works
		J.3.2 How fast ignition (IF) works
		J.3.3 Issues with successful achievement
		J.3.4 National ignition laser facility
	References
K: The Weibull distribution
	Reference
	Further reading
L: The logarithm transformation
	L.1 Introduction
	L.2 Change in natural log ≈ percentage change
	L.3 Linearization of exponential growth and inflation
	L.4 Trend measured in natural-log units ≈ percentage growth
	L.5 Errors measured in natural-log units ≈ percentage errors
	L.6 Coefficients in log–log regressions ≈ proportional percentage changes
		L.6.1 Example L.1: regression example, transformation of variable
		L.6.2 Example L.2: regression example, additional predictors
M: Geometric random walk model
N - Appendix N: Random walk model
O: Examples of forecasting driven by artificial intelligence and machine learning
	O.1 Comma-separated values
	O.2 How to make predictions with Keras
		O.2.1 Finalize model
		O.2.2 Classification predictions
			O.2.2.1 Class Predictions
			O.2.2.2 A note on class labels
			O.2.2.3 Probability Predictions
		O.2.3 Regression predictions
	O.3 How to make predictions with Long Short-Term Memory models in Keras
		O.3.1 What is a final LSTM model?
		O.3.2 How to finalize an LSTM model?
	O.4 How to make predictions with TensorFlow
	O.5 Conclusion
	References
P: Examples of python programming driving artificial intelligence and machine learning
Q: Artificial intelligence and human intelligence
	References
	Further reading
R: Deep learning, machine learning limitations and flaws
	References
	Further reading
S: Machine learning–driven e-commerce
	S.1 Introduction
	S.2 What are artificial intelligence, machine learning, and deep learning
	S.3 Risk atom and business resilience system (BRS)
	S.4 Machine learning: trends, perspective, and prospects in support of e-commerce
	S.5 Machine learning shaping world of e-commerce
		S.5.1 Mobility
		S.5.2 Artificial intelligence
	S.6 A good looking and feeling of an e-commerce website
	S.7 Conclusion
	S.8 Article published reference
	References
	Further reading
T: From business intelligence to artificial intelligence
	T.1 Introduction
	T.2 What is business intelligence (BI)
	T.3 What is artificial intelligence (AI)
	T.4 Artificial intelligence versus BI: difference and synergies
		T.4.1 Contributions
		T.4.2 Research areas
		T.4.3 Applications
		T.4.4 Missions
		T.4.5 Drawbacks
	T.5 Integration of business intelligence with artificial intelligence
	T.6 Artificial intelligence is the future of business intelligence
	T.7 Conclusion
	T.8 Article published reference
	References
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Y
	Z
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