06 Oct Home>Information Systems homew
Complete the following assignment in one MS word document:
Chapter 5 –discussion question #1-4 & exercise 6.
When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.
All work must be original (not copied from any source).
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Week 6 Assignment
Varunbhai Patel University of the Cumberlands
Business Intelligence
Chapter 5 Assignment
Discussion questions 1. What is an artificial neural network and for what types of problems can be used? An artificial neural network (ANN) is the piece
of a computing system that is designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human statistical standards. 2. Compare artificial and biological neural networks. What aspect of bio-
logical networks are mimicked by artificial ones? What aspects are similar? Artificial Neural network has a faster processing speed whereas biological neural network has a slow processing speed. In artificial neural network, allocation for storage to a new process is strictly irreplaceable as the old location is saved for the previ-
ous process whereas in biological neural network, the allocation for storage to a new process is easy as it is added just by adjusting the interconnection strength. In ar- tificial neural network, if any information is corrupted in the memory, it cannot be retrieved. In BNN, information is distributed into the network throughout into sub- nodes even if it gets corrupted it can be retrieved. Biological Neural Networks is a structure that is made up of synapse, dendrites, cell body and axon. In the
neural network, the processing is done by neurons. Dendrites receive signals from other neurons. 3. What are the most common ANN architectures? For what
types of problems can they be used? Single layer fee forward network. Multilayer feed forward network, Single node with its own feedback
Single layer recurrent network
Multilayer recurrent network. They are used for solving a number of business problems such as sales forecasting, customer research, data validation, and risk
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management. 4. ANN can be used for both supervised and unsupervised learning. explain how they learn in a supervised mode and in an unsupervised mode. In
supervised learning, ANN uses backpropagation to improve the fitting by decreasing the error in the predictions. A neural net is said to learn supervised if the de-
sired output is already known. In unsupervised learning, the learning process is independent, the input vectors of similar types are combines to form clusters.
When a new input pattern is applied, the neural network gives as output response indicating the class to which pattern belongs.
Exercise 6 Go to google scholar. Conduct a research to find two papers written in the last five years that compare and contrast multiple machine learning meth-
ods for a given problem domain. Observe commonalities and differences among their findings and prepare a report to summarize your understanding. The two arti- cles analyzed are Machine learning and deep learning methods for cybersecurity and, Using machine-learning methods for musical style modeling. In the first article, the authors find out that, with development of the internet, there are rapid changes in the cyber-attacks and the cyber security situation is not optimistic. Dubnov, (2019) notes that datasets for network intrusion detection are very important for training and testing systems. Machine learning and DL methods do not work without representative data and obtaining such a dataset is difficult and time consuming. In the second article, Xin, (2018) have found out that, the ability to construct a musi- cal theory from examples presents a great intellectual challenge that which if it was successfully met, could foster a new creative applications. The authors applied ma- chine learning methods on the problem of musical style modelling. In this article, machine learning is found to be composed of deriving a mathematical model, such as a set of stochastic rules, from a set of musical examples.
References
McKinsey. (2017, July 9). Ask the AI experts: What advice would you give to executives about AI? YouTube. https://www.youtube.com/watch?
v=JPLYc6cull0&ab_channel=McKinsey%26Company. Healthcare AI Solutions & Services: Nuance. Nuance Communications. (n.d.).
https://www.nuance.com/healthcare.html.
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Varunbhai Patel University of the Cumberlands
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University of Cumberlands
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Chapter 5 Assignment
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Week 5 Assignment
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Discussion questions 1.
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Questions for Discussion (Chapter 6) 1
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What is an artificial neural network and for what types of problems can be used?
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What is an artificial neural network and for what types of problems can it be used
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An artificial neural network (ANN) is the piece of a computing system that is designed to simulate the way the human brain analyzes and processes information. It is the founda- tion of artificial intelligence (AI) and solves problems that would prove impossible or diffi- cult by human statistical standards.
Original source
An artificial neural network (ANN) is the piece of a computing system designed to simu- late the way the human brain analyzes and processes information It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards
1
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Compare artificial and biological neural networks. What aspect of biological networks are mimicked by artificial ones? What aspects are similar?
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Compare artificial and biological neural networks What aspects of biological networks are not mimicked by artificial ones What aspects are similar
6/13/2021 Originality Report
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In artificial neural network, allocation for storage to a new process is strictly irreplaceable as the old location is saved for the previous process whereas in biological neural network, the allocation for storage to a new process is easy as it is added just by adjusting the in- terconnection strength.
Original source
In artificial neural networks, allocation for storage to a new process is prohibited as the old location is saved for the previous process, while in biological neural networks, it is much simpler as it is added by just adjusting the interconnection strengths (Marijana Zek- ić-Sušac, Sanja Pfeifer, Nataša Šarlija, 2014)
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Biological Neural Networks is a structure that is made up of synapse, dendrites, cell body and axon. In the neural network, the processing is done by neurons.
Original source
Biological Neural Network (BNN) is a structure that consists of Synapse, dendrites, cell body, and axon In this neural network, the processing is carried out by neurons
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What are the most common ANN architectures? For what types of problems can they be used?
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What are the most common ANN architectures For what types of problems can they be used
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Single layer fee forward network.
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Single-layer feed-forward network
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Multilayer feed forward network, Single node with its own feedback
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Single node with its own feedback
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Single layer recurrent network Multilayer recurrent network.
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Single-layer recurrent network Multilayer recurrent network
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They are used for solving a number of business problems such as sales forecasting, cus- tomer research, data validation, and risk management.
Original source
Today, neural networks are used for solving many business problems such as sales fore- casting, customer research, data validation, and risk management
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ANN can be used for both supervised and unsupervised learning. explain how they learn in a supervised mode and in an unsupervised mode.
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ANN can be used for both supervised and unsupervised learning Explain how they learn in a supervised mode and in an unsupervised mode
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A neural net is said to learn supervised if the desired output is already known.
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A neural network is said to learn supervised if the desired output is already known
6/13/2021 Originality Report
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In unsupervised learning, the learning process is independent, the input vectors of similar types are combines to form clusters.
Original source
In unsupervised learning, when ANNs are trained, the input vectors of similar types com- bine to form clusters
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When a new input pattern is applied, the neural network gives as output response indicat- ing the class to which pattern belongs.
Original source
When a new input pattern is applied, the neural network gives an output response indi- cating the class to which input pattern belongs
1
Student paper
Exercise 6 Go to google scholar. Conduct a research to find two papers written in the last five years that compare and contrast multiple machine learning methods for a given problem domain. Observe commonalities and differences among their findings and pre- pare a report to summarize your understanding.
Original source
Exercise 6 Go to Google Scholar (scholar.google.com) Conduct a search to find two papers written in the last five years that compare and contrast multiple machine-learning meth- ods for a given problem domain Observe commonalities and differences among their findings and prepare a report to summarize your understanding
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Ask the AI experts: What advice would you give to executives about AI?
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Ask the AI Experts What Advice Would You Give to Executives About AI
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https://www.nuance.com/healthcare.html.
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https://www.nuance.com/index.html
,
Analytics, Data Science, and Artificial Intelligence, 11th Edition.pdf
ANALYTICS, DATA SCIENCE, & ARTIFICIAL INTELLIGENCE
SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda Oklahoma State University
Dursun Delen Oklahoma State University
Efraim Turban University of Hawaii
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Library of Congress Cataloging-in-Publication Data
Library of Congress Cataloging in Publication Control Number: 2018051774
iii
Preface xxv
About the Authors xxxiv
PART I Introduction to Analytics and AI 1 Chapter 1 Overview of Business Intelligence, Analytics,
Data Science, and Artificial Intelligence: Systems for Decision Support 2
Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 73
Chapter 3 Nature of Data, Statistical Modeling, and Visualization 117
PART II Predictive Analytics/Machine Learning 193 Chapter 4 Data Mining Process, Methods, and Algorithms 194
Chapter 5 Machine-Learning Techniques for Predictive Analytics 251
Chapter 6 Deep Learning and Cognitive Computing 315
Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics 388
PART III Prescriptive Analytics and Big Data 459 Chapter 8 Prescriptive Analytics: Optimization and
Simulation 460
Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 509
PART IV Robotics, Social Networks, AI and IoT 579 Chapter 10 Robotics: Industrial and Consumer Applications 580
Chapter 11 Group Decision Making, Collaborative Systems, and AI Support 610
Chapter 12 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors 648
Chapter 13 The Internet of Things as a Platform for Intelligent Applications 687
PART V Caveats of Analytics and AI 725 Chapter 14 Implementation Issues: From Ethics and Privacy to
Organizational and Societal Impacts 726
Glossary 770
Index 785
BRIEF CONTENTS
iv
CONTENTS
Preface xxv
About the Authors xxxiv
PART I Introduction to Analytics and AI 1
Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support 2 1.1 Opening Vignette: How Intelligent Systems Work for
KONE Elevators and Escalators Company 3
1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics 5
Decision-Making Process 6
The Influence of the External and Internal Environments on the Process 6
Data and Its Analysis in Decision Making 7
Technologies for Data Analysis and Decision Support 7
1.3 Decision-Making Processes and Computerized Decision Support Framework 9
Simon’s Process: Intelligence, Design, and Choice 9
The Intelligence Phase: Problem (or Opportunity) Identification 10 0 APPLICATION CASE 1.1 Making Elevators Go Faster! 11
The Design Phase 12
The Choice Phase 13
The Implementation Phase 13
The Classical Decision Support System Framework 14
A DSS Application 16
Components of a Decision Support System 18
The Data Management Subsystem 18
The Model Management Subsystem 19 0 APPLICATION CASE 1.2 SNAP DSS Helps OneNet Make
Telecommunications Rate Decisions 20
The User Interface Subsystem 20
The Knowledge-Based Management Subsystem 21
1.4 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science 22
A Framework for Business Intelligence 25
The Architecture of BI 25
The Origins and Drivers of BI 26
Data Warehouse as a Foundation for Business Intelligence 27
Transaction Processing versus Analytic Processing 27
A Multimedia Exercise in Business Intelligence 28
Contents v
1.5 Analytics Overview 30
Descriptive Analytics 32 0 APPLICATION CASE 1.3 Silvaris Increases Business with Visual
Analysis and Real-Time Reporting Capabilities 32 0 APPLICATION CASE 1.4 Siemens Reduces Cost with the Use of Data
Visualization 33
Predictive Analytics 33 0 APPLICATION CASE 1.5 Analyzing Athletic Injuries 34
Prescriptive Analytics 34 0 APPLICATION CASE 1.6 A Specialty Steel Bar Company Uses Analytics
to Determine Available-to-Promise Dates 35
1.6 Analytics Examples in Selected Domains 38
Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics 38
Analytics Applications in Healthcare—Humana Examples 43 0 APPLICATION CASE 1.7 Image Analysis Helps Estimate Plant Cover 50
1.7 Artificial Intelligence Overview 52
What Is Artificial Intelligence? 52
The Major Benefits of AI 52
The Landscape of AI 52 0 APPLICATION CASE 1.8 AI Increases Passengers’ Comfort and
Security in Airports and Borders 54
The Three Flavors of AI Decisions 55
Autonomous AI 55
Societal Impacts 56 0 APPLICATION CASE 1.9 Robots Took the Job of Camel-Racing Jockeys
for Societal Benefits 58
1.8 Convergence of Analytics and AI 59
Major Differences between Analytics and AI 59
Why Combine Intelligent Systems? 60
How Convergence Can Help? 60
Big Data Is Empowering AI Technologies 60
The Convergence of AI and the IoT 61
The Convergence with Blockchain and Other Technologies 62 0 APPLICATION CASE 1.10 Amazon Go Is Open for Business 62
IBM and Microsoft Support for Intelligent Systems Convergence 63
1.9 Overview of the Analytics Ecosystem 63
1.10 Plan of the Book 65
1.11 Resources, Links, and the Teradata University Network Connection 66
Resources and Links 66
Vendors, Products, and Demos 66
Periodicals 67
The Teradata University Network Connection 67
vi Contents
The Book’s Web Site 67 Chapter Highlights 67 • Key Terms 68
Questions for Discussion 68 • Exercises 69
References 70
Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 73 2.1 Opening Vignette: INRIX Solves Transportation
Problems 74
2.2 Introduction to Artificial Intelligence 76
Definitions 76
Major Characteristics of AI Machines 77
Major Elements of AI 77
AI Applications 78
Major Goals of AI 78
Drivers of AI 79
Benefits of AI 79
Some Limitations of AI Machines 81
Three Flavors of AI Decisions 81
Artificial Brain 82
2.3 Human and Computer Intelligence 83
What Is Intelligence? 83
How Intelligent Is AI? 84
Measuring AI 85 0 APPLICATION CASE 2.1 How Smart Can a Vacuum Cleaner Be? 86
2.4 Major AI Technologies and Some Derivatives 87
Intelligent Agents 87
Machine Learning 88 0 APPLICATION CASE 2.2 How Machine Learning Is Improving Work
in Business 89
Machine and Computer Vision 90
Robotic Systems 91
Natural Language Processing 92
Knowledge and Expert Systems and Recommenders 93
Chatbots 94
Emerging AI Technologies 94
2.5 AI Support for Decision Making 95
Some Issues and Factors in Using AI in Decision Making 96
AI Support of the Decision-Making Process 96
Automated Decision Making 97 0 APPLICATION CASE 2.3 How Companies Solve Real-World Problems
Using Google’s Machine-Learning Tools 97
Conclusion 98
Contents vii
2.6 AI Applications in Accounting 99
AI in Accounting: An Overview 99
AI in Big Accounting Companies 100
Accounting Applications in Small Firms 100 0 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are Using AI 100
Job of Accountants 101
2.7 AI Applications in Financial Services 101
AI Activities in Financial Services 101
AI in Banking: An Overview 101
Illustrative AI Applications in Banking 102
Insurance Services 103 0 APPLICATION CASE 2.5 US Bank Customer Recognition and
Services 104
2.8 AI in Human Resource Management (HRM) 105
AI in HRM: An Overview 105
AI in Onboarding 105 0 APPLICATION CASE 2.6 How Alexander Mann Solutions (AMS) Is
Using AI to Support the Recruiting Process 106
Introducing AI to HRM Operations 106
2.9 AI in Marketing, Advertising, and CRM 107
Overview of Major Applications 107
AI Marketing Assistants in Action 108
Customer Experiences and CRM 108 0 APPLICATION CASE 2.7 Kraft Foods Uses AI for Marketing
and CRM 109
Other Uses of AI in Marketing 110
2.10 AI Applications in Production-Operation Management (POM) 110
AI in Manufacturing 110
Implementation Model 111
Intelligent Factories 111
Logistics and Transportation 112 Chapter Highlights 112 • Key Terms 113
Questions for Discussion 113 • Exercises 114
References 114
Chapter 3 Nature of Data, Statistical Modeling, and Visualization 117 3.1 Opening Vignette: SiriusXM Attracts and Engages a
New Generation of Radio Consumers with Data-Driven Marketing 118
3.2 Nature of Data 121
3.3 Simple Taxonomy of Data 125 0 APPLICATION CASE 3.1 Verizon Answers the Call for Innovation: The
Nation’s Largest Network Provider uses Advanced Analytics to Bring the Future to its Customers 127
viii Contents
3.4 Art and Science of Data Preprocessing 129 0 APPLICATION CASE 3.2 Improving Student Retention with
Data-Driven Analytics 133
3.5 Statistical Modeling for Business Analytics 139
Descriptive Statistics for Descriptive Analytics 140
Measures of Centrality Tendency (Also Called Measures of Location or Centrality) 140
Arithmetic Mean 140
Median 141
Mode 141
Measures of Dispersion (Also Called Measures of Spread or Decentrality) 142
Range 142
Variance 142
Standard Deviation 143
Mean Absolute Deviation 143
Quartiles and Interquartile Range 143
Box-and-Whiskers Plot 143
Shape of a Distribution 145 0 APPLICATION CASE 3.3 Town of Cary Uses Analytics to Analyze Data
from Sensors, Assess Demand, and Detect Problems 150
3.6 Regression Modeling for Inferential Statistics 151
How Do We Develop the Linear Regression Model? 152
How Do We Know If the Model Is Good Enough? 153
What Are the Most Important Assumptions in Linear Regression? 154
Logistic Regression 155
Time-Series Forecasting 156 0 APPLICATION CASE 3.4 Predicting NCAA Bowl Game Outcomes 157
3.7 Business Reporting 163 0 APPLICATION CASE 3.5 Flood of Paper Ends at FEMA 165
3.8 Data Visualization 166
Brief History of Data Visualization 167 0 APPLICATION CASE 3.6 Macfarlan Smith Improves Operational
Performance Insight with Tableau Online 169
3.9 Different Types of Charts and Graphs 171
Basic Charts and Graphs 171
Specialized Charts and Graphs 172
Which Chart or Graph Should You Use? 174
3.10 Emergence of Visual Analytics 176
Visual Analytics 178
High-Powered Visual Analytics Environments 180
3.11 Information Dashboards 182
Contents ix
0 APPLICATION CASE 3.7 Dallas Cowboys Score Big with Tableau and Teknion 184
Dashboard Design 184 0 APPLICATION CASE 3.8 Visual Analytics Helps Energy Supplier Make
Better Connections 185
What to Look for in a Dashboard 186
Best Practices in Dashboard Design 187
Benchmark Key Performance Indicators with Industry Standards 187
Wrap the Dashboard Metrics with Contextual Metadata 187
Validate the Dashboard Design by a Usability Specialist 187
Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 188
Enrich the Dashboard with Business-User Comments 188
Present Information in Three Different Levels 188
Pick the Right Visual Construct Using Dashboard Design Principles 188
Provide for Guided Analytics 188 Chapter Highlights 188 • Key Terms 189
Questions for Discussion 190 • Exercises 190
References 192
PART II Predictive Analytics/Machine Learning 193
Chapter 4 Data Mining Process, Methods, and Algorithms 194 4.1 Opening Vignette: Miami-Dade Police Department Is Using
Predictive Analytics to Foresee and Fight Crime 195
4.2 Data Mining Concepts 198 0 APPLICATION CASE 4.1 Visa Is Enhancing the Customer
Experience while Reducing Fraud with Predictive Analytics and Data Mining 199
Definitions, Characteristics, and Benefits 201
How Data Mining Works 202 0 APPLICATION CASE 4.2 American Honda Uses Advanced Analytics to
Improve Warranty Claims 203
Data Mining Versus Statistics 208
4.3 Data Mining Applications 208 0 APPLICATION CASE 4.3 Predictive Analytic and Data Mining Help
Stop Terrorist Funding 210
4.4 Data Mining Process 211
Step 1: Business Understanding 212
Step 2: Data Understanding 212
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