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E-grāmata: Applied Artificial Intelligence in Business: Concepts and Cases

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This book offers students an introduction to the concepts of big data and artificial intelligence (AI) and their applications in the business world. It answers questions such as what are the main concepts of artificial intelligence and big data? What applications for artificial intelligence and big data analytics are used in the business field? It offers application-oriented overviews and cases from different sectors and fields to help readers discover and gain useful insights. Each chapter features discussion questions and summaries. To assist professors in teaching, the book supplementary materials will include answers to questions, and presentation slides.

Part I Artificial Intelligence Concepts
1 Artificial Intelligence for Business
3(10)
1.1 Introduction
3(1)
1.2 AI Origin and Commercialization
4(1)
1.3 Big Data Fueling Artificial Intelligence
5(1)
1.4 Technology Landscape of AI in Business
6(1)
1.5 Business Perspectives on Artificial Intelligence
7(6)
References
10(3)
2 Big Data Powering Business Intelligence
13(16)
2.1 Introduction
13(1)
2.2 Business Process and Big Data
14(3)
2.2.1 Data from Business Operations
15(1)
2.2.2 Social Media Data
15(1)
2.2.3 Types of Business Data
16(1)
2.2.4 Big Data in Business
17(1)
2.3 Big Data Analytics
17(3)
2.4 Business Analytics
20(1)
2.5 Business Intelligence
21(4)
2.5.1 Data Mining
22(1)
2.5.2 Data Warehousing
22(3)
2.6 Cloud Technology and Big Data Analytics
25(4)
References
27(2)
3 Artificial Intelligence Technologies for Business Applications
29(16)
3.1 Introduction
29(1)
3.2 Expert Systems
30(1)
3.3 Robotic Process Automation
31(1)
3.4 Fuzzy Logic
32(1)
3.5 Interactive Decision Support Systems
33(1)
3.6 Time Series Forecasting
34(1)
3.7 Case-Based Reasoning
35(1)
3.8 Procedural Content Generation
36(1)
3.9 Voice Chatbots
36(2)
3.10 Genetic Algorithm-Radial Basis Function (GA-RBF)
38(1)
3.11 Hybrid AI Systems
39(6)
References
43(2)
4 Machine Learning for Business Applications
45(20)
4.1 Introduction
45(1)
4.2 Three Types of Machine Learning
46(1)
4.2.1 Supervised Learning
46(1)
4.2.2 Unsupervised Learning
47(1)
4.2.3 Reinforcement Learning
47(1)
4.3 Machine Learning Algorithms
47(18)
4.3.1 Linear and Multiple Regression
48(1)
4.3.2 Polynomial and Logistic Regression
49(1)
4.3.3 Decision Tree
49(1)
4.3.4 Neural Networks
50(2)
4.3.5 Deep Learning
52(2)
4.3.6 Genetic Algorithms
54(2)
4.3.7 Support Vector Machine
56(2)
4.3.8 Naive Bayes Algorithm
58(1)
4.3.9 Bayesian Network
59(2)
References
61(4)
Part II Artificial Intelligence for Core Business Functions
5 Artificial Intelligence in Marketing and Sales
65(18)
5.1 Introduction
65(2)
5.2 The Development of AI Technologies in Marketing
67(1)
5.3 AI Technologies for Marketing
68(4)
5.3.1 Deep Learning
68(1)
5.3.2 Artificial Neural Networks (ANNs)
69(1)
5.3.3 Naive Bayes Classifier
69(1)
5.3.4 Decision Tree
70(1)
5.3.5 Anomaly Detection
71(1)
5.3.6 Genetic Algorithms
71(1)
5.3.7 Rule-Based System
72(1)
5.4 Application Areas of AI in Marketing
72(8)
5.4.1 Market Segmentation and Targeting
73(1)
5.4.2 Sales and Product Pricing
74(1)
5.4.3 Market Research and Forecasting
75(1)
5.4.4 Advertising
76(1)
5.4.5 Brand Positioning
76(4)
5.5 Key Takeaways
80(1)
5.6 Conclusion
80(3)
References
81(2)
6 Artificial Intelligence for Customer Service
83(18)
6.1 Introduction
83(1)
6.2 The Development of AI in Customer Service
84(1)
6.3 AI Technologies for Customer Service
85(4)
6.3.1 Deep Learning
85(1)
6.3.2 Support Vector Machines
86(1)
6.3.3 Naive Bayesian Classification
87(1)
6.3.4 Natural Language Processing
87(1)
6.3.5 Hybrid AI Systems
88(1)
6.4 Features of AI Applications in Customer Service
89(8)
6.4.1 Collaborative Filtering
90(1)
6.4.2 Customer Churn Analysis
91(1)
6.4.3 Social Media Analytics
92(1)
6.4.4 Customer Loyalty Programs
93(4)
6.5 Key Takeaways
97(1)
6.6 Conclusion
98(3)
References
99(2)
7 Artificial Intelligence in Finance
101(18)
7.1 Introduction
101(1)
7.2 Development of AI in Finance
102(2)
7.3 AI Technologies in Finance and Banking
104(4)
7.3.1 Financial Expert Systems
104(1)
7.3.2 Machine Learning
105(1)
7.3.3 Artificial Neural Network in Finance
105(1)
7.3.4 Decision Analytics Network
106(1)
7.3.5 AI Robo-Advisors
107(1)
7.4 Features of AI Applications in Financial Services
108(8)
7.4.1 Investment Banking
108(1)
7.4.2 Personalized Finance
109(1)
7.4.3 Credit Management
110(1)
7.4.4 Loans and Lending
110(1)
7.4.5 Asset Management
110(1)
7.4.6 High-Frequency Trading
111(1)
7.4.7 Fraud Detection and Security
111(1)
7.4.8 The "FinTech and RegTech" Paradigm
112(4)
7.5 Key Takeaways
116(1)
7.6 Conclusions
117(2)
References
117(2)
8 Artificial Intelligence in Accounting and Auditing
119(20)
8.1 Introduction
119(1)
8.2 Development of AI in Accounting
120(1)
8.3 Enabling Technologies for AI in Accounting
121(3)
8.4 Features of AI Applications in Accounting
124(10)
8.4.1 General Accounting
124(1)
8.4.2 Accounts Payable
125(1)
8.4.3 Purchasing
125(1)
8.4.4 Accounts Receivable
126(1)
8.4.5 Payment Processing
126(1)
8.4.6 Billing and Invoicing
127(1)
8.4.7 Debt Collection
128(1)
8.4.8 Financial Reporting
129(1)
8.4.9 Auditing
129(1)
8.4.10 Financial Fraud Detection
130(1)
8.4.11 Financial Risk Management
130(4)
8.5 Key Takeaways
134(2)
8.6 Conclusion
136(3)
References
136(3)
9 Artificial Intelligence in Human Resources
139(18)
9.1 Introduction
139(1)
9.2 Development of AI in HRM
140(1)
9.3 AI Technologies in HR
141(2)
9.4 AI Applications for HR Functions
143(8)
9.4.1 Employee Recruitment
144(1)
9.4.2 Employee Scheduling Management
145(1)
9.4.3 Employee Training Management
146(1)
9.4.4 Employee Turnover and Retention
147(1)
9.4.5 Performance and Engagement Management
148(3)
9.5 Key Takeaways
151(2)
9.6 Conclusion
153(4)
References
153(4)
10 AI in Supply Chain and Logistics
157(16)
10.1 Introduction
157(1)
10.2 Development of AI Technology in Supply Chain
158(1)
10.3 Enabling Artificial Intelligence Technologies for SCM
159(4)
10.4 Application Areas of AI in SCM
163(8)
10.5 Conclusion
171(2)
References
171(2)
11 Artificial Intelligence in Manufacturing
173(16)
11.1 Introduction
173(1)
11.2 Development of Artificial Intelligence in Manufacturing
174(1)
11.3 Application Areas of AI in Manufacturing
175(1)
11.4 AI Technologies in Manufacturing
176(6)
11.4.1 Semantic Web of Things for Industry 4.0 (SWEeTI) Platform
176(1)
11.4.2 Interoperative STEP-NC Computer-Aided Manufacturing and Intelligent Agent Systems
177(1)
11.4.3 Fuzzy Interference, Relational Databases, and Rule-Based Decision-Making Systems
177(1)
11.4.4 Time-Series Forecasting and Recurrent Neural Networks
177(1)
11.4.5 Other AI Technologies and Applications
178(4)
11.5 Key Takeaways
182(1)
11.6 Conclusion
183(6)
References
184(5)
Part III Artificial Intelligence for Industrial Applications 12 Artificial Intelligence in Insurance
189(174)
12.1 Introduction
189(1)
12.2 The Development of Insurance Technology
190(1)
12.3 Enabling Technologies of AI for Insurtech
191(2)
12.3.1 Chatbot and Natural Language Processing
191(1)
12.3.2 Robotic Process Automation
192(1)
12.3.3 Computer Vision
192(1)
12.3.4 Telematics
192(1)
12.3.5 Predictive Analytics
193(1)
12.4 AI Applications in the Insurance Industry
193(4)
12.4.1 Claims Process
193(1)
12.4.2 Fraud Detection
194(1)
12.4.3 Personalized Policies
194(3)
12.5 Key Takeaways
197(1)
12.6 Conclusion
198(3)
References
199(2)
13 Artificial Intelligence in Credit, Lending, and Mortgage
201(12)
13.1 Introduction
201(1)
13.2 Technology Development
202(1)
13.3 AI Applications in Various Areas
203(6)
13.4 Key Takeaways
209(1)
13.5 Conclusion
210(3)
References
210(3)
14 Artificial Intelligence in Tourism and Hospitality
213(18)
14.1 Introduction
213(1)
14.2 Development of AI in Tourism
214(2)
14.3 Enabling Technology for AI in Tourism
216(4)
14.3.1 Expert System
216(1)
14.3.2 Chatbots
217(1)
14.3.3 Artificial Neural Network
218(1)
14.3.4 Belief Network
218(1)
14.3.5 Sentiment Analysis
219(1)
14.3.6 Fuzzy Logic Systems
219(1)
14.3.7 Virtual Reality
220(1)
14.4 Applications of AI in Tourism
220(8)
14.4.1 Smart Tourism
220(1)
14.4.2 Demand Forecasting
221(1)
14.4.3 Customer Data Analytics
222(6)
14.5 Conclusion
228(3)
References
228(3)
15 Artificial Intelligence in Transportation
231(18)
15.1 Introduction
231(1)
15.2 Development of Autonomous Vehicles
232(2)
15.3 AI Technology in Autonomous Vehicles
234(3)
15.4 Applications of AI in the Transportation Industry
237(8)
15.5 Future Trends
245(1)
15.6 Conclusion
246(3)
References
246(3)
16 Artificial Intelligence in Real Estate
249(16)
16.1 Introduction
249(1)
16.2 AI Technologies for Real Estate
250(3)
16.3 Al-Supported Real Estate Platforms
253(8)
16.3.1 Houzen Real Estate Platform
254(1)
16.3.2 Finding a Home Through NeighborhoodScout
255(1)
16.3.3 HomesnapApp
255(6)
16.4 Conclusion
261(4)
References
262(3)
17 Artificial Intelligence in Education
265(14)
17.1 Introduction
265(1)
17.2 Evolution of AI in Education
266(2)
17.3 Applications of AI in Learning Platforms
268(4)
17.4 Features of AI in Education
272(3)
17.4.1 Learning Personalization
272(1)
17.4.2 Teaching Customization
272(1)
17.4.3 Effectiveness
273(1)
17.4.4 Smart Contents
273(1)
17.4.5 Big Data Driven
273(2)
17.5 Key Takeaways
275(2)
17.5.1 Impacts on Learning Style
275(1)
17.5.2 Impacts on Teachers
276(1)
17.5.3 Impact on Business
276(1)
17.6 Conclusion
277(2)
References
278(1)
18 Artificial Intelligence in Healthcare
279(14)
18.1 Introduction
279(1)
18.2 Evolution of AI in Healthcare
280(1)
18.3 Current AI Technologies in Healthcare
281(1)
18.4 Major Categories of AI in Healthcare
282(6)
18.5 Key Takeaways
288(1)
18.6 Conclusion
289(4)
References
290(3)
19 Artificial Intelligence in Energy
293(12)
19.1 Introduction
293(1)
19.2 Evolution of AI in Energy
294(2)
19.3 Features of AI Applications in Energy
296(7)
19.3.1 Smart Grid
296(1)
19.3.2 Smart Homes
297(1)
19.3.3 Renewable and Nonrenewable Resources
298(5)
19.4 Conclusion
303(2)
References
303(2)
20 AI in Media and Entertainment
305(20)
20.1 Introduction
305(1)
20.2 AI for Traditional Media Services
306(5)
20.2.1 AI for Television Broadcasting
307(2)
20.2.2 AI for Radiobroadcasting
309(1)
20.2.3 AI in Journalism and Print Media
310(1)
20.2.4 AI in Cinema and Films
310(1)
20.3 AI for New Media Streaming Services
311(2)
20.4 AI for Social Media and Web Analytics
313(1)
20.5 AI for Music Industry
314(6)
20.5.1 Music Research
314(1)
20.5.2 Music Psychology
315(5)
20.6 Key Takeaways and Outlook
320(3)
20.7 Conclusion
323(2)
References
323(2)
21 Artificial Intelligence in Fashion
325(10)
21.1 Introduction
325(1)
21.2 Current AI Applications
326(3)
21.3 AI Applications for Fashion
329(3)
21.4 Conclusion
332(3)
References
333(2)
22 Artificial Intelligence in Video Games and eSports
335(18)
22.1 Introduction
335(1)
22.2 Evolution of AI in Video Games and eSports
336(1)
22.3 Enabling Technologies for AI in Gaming
337(3)
22.3.1 Big Data in Gaming
338(1)
22.3.2 Virtual Reality and AI in Gaming
339(1)
22.3.3 Graphics Processing Units and AI Chips
339(1)
22.3.4 Online Gaming and Cloud Platforms
340(1)
22.4 AI Applications in Video Games and eSports
340(10)
22.4.1 AI Opponents
341(1)
22.4.2 AI Hirelings, Followers, and Non-Player Characters
342(1)
22.4.3 Procedural Content Generation
343(1)
22.4.4 Player Experience Modeling
344(1)
22.4.5 Antisocial Behavior Detection and Governance in Multiplayer Gaming
344(1)
22.4.6 Win Prediction
345(1)
22.4.7 Intelligent Tutoring and Training
345(1)
22.4.8 Player Telemetry Sign-Up, Engagement, and Retention Analytics
346(4)
22.5 Key Takeaways
350(2)
22.6 Conclusion
352(1)
References
352(1)
23 Artificial Intelligence in Sports
353(10)
23.1 Introduction
353(1)
23.2 AI for Sports Management
354(2)
23.3 AI Applications for Basketball
356(1)
23.4 AI Applications for Baseball
357(2)
23.5 AI Applications for Golf
359(2)
23.6 Conclusion
361(2)
References
361(2)
Index 363
Leong Chan is an Assistant Professor in the School of Business at Pacific Lutheran University (Washington, USA). His research focuses on the application of emerging technologies in various business sectors. He is an Associate Editor of the Engineering Management Journal and serves as editorial board member for several journals and conferences.





Liliya Hogaboam is an author, consultant and a lecturer in the fields of economics and management, healthcare assessment, decision-making and AI. She has over 20 years of experience in analytics and research. She also has 10 years of entrepreneurial experience running a software services consulting company Nascentia Corp (Oregon, USA). Liliya has a number of publications in peer-reviewed journals. She co-authored the book Healthcare Technology Innovation Adoption (Springer, 2016).





Renzhi Cao is an Assistant Professor at Pacific Lutheran University (Washington, USA). His research interest is mainly focused on developing and applying machine learning and data mining techniques to solve biomedical problems. In addition, he is interested in promoting early engagement of undergraduate students (especially for women and underrepresented students) in machine learning, and the data science field by interdisciplinary studies, and inspiring students to pursue advanced STEM education/research careers.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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