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Artificial Intelligence for Business Optimization: Research and Applications [Kietas viršelis]

, (University of South Florida)
  • Formatas: Hardback, 288 pages, aukštis x plotis: 234x156 mm, weight: 580 g, 21 Tables, black and white; 60 Line drawings, black and white; 60 Illustrations, black and white
  • Išleidimo metai: 10-Aug-2021
  • Leidėjas: CRC Press
  • ISBN-10: 0367638363
  • ISBN-13: 9780367638368
Kitos knygos pagal šią temą:
Artificial Intelligence for Business Optimization: Research and ApplicationsDidesnis paveikslėlis
  • Formatas: Hardback, 288 pages, aukštis x plotis: 234x156 mm, weight: 580 g, 21 Tables, black and white; 60 Line drawings, black and white; 60 Illustrations, black and white
  • Išleidimo metai: 10-Aug-2021
  • Leidėjas: CRC Press
  • ISBN-10: 0367638363
  • ISBN-13: 9780367638368
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780367638368.html
Raktažodžiai:

Artificial Intelligence for Business Optimization: Research and Applications is primarily a business book that discusses the research and associated practical application of Artificial Intelligence (AI) and Machine Learning (ML) in order to achieve Business Optimization (BO).



Artificial Intelligence for Business Optimization: Research and Applications is primarily a business book that discusses the research and associated practical application of artificial intelligence (AI) and machine learning (ML) in order to achieve business optimization (BO). AI comprises a wide range of technologies, databases algorithms, and devices. This book aims for a holistic approach to AI by focusing on developing business strategies that will not only automate but also optimize business functions, processes, and people’s behaviors.

This book explores AI and ML from a business viewpoint with the key purpose of enhancing customer value. It applies research methods and fundamentals from a practitioner’s viewpoint and incorporates discussions around risks and changes associated with the utilization of AI in business. Furthermore, governance risks, privacy, and security are also addressed in this book to ensure compliance with AI/ML applications. Readers should find direct and practical applications of the discussions in this book quite useful in their work environment. Researchers will find many ideas to further explore the applications of AI to business.

Foreword xix
Andy Lyman
Preface xxi
Readers xxiii
Figures xxvii
Acknowledgments xxix
Authors xxxi
1 Artificial intelligence and machine learning: Opportunities for digital business 1(30)
Artificial intelligence in the context of business
1(5)
Artificial intelligence (AI) and machine learning
1(1)
(ML) as enablers of business optimization (BO)
2(1)
Subjective elements in BO
2(1)
Agility in BO
3(1)
Collaboration in BO
4(1)
Granularity in BO
4(1)
The technical-business continuum
4(2)
Strategic approach to business optimization
6(2)
BO as a redesign of business
6(1)
Developing a BO strategy
6(2)
Capabilities in BO
8(1)
AI, Big Data, and statistics
8(4)
Data, science, and analytics
8(1)
What and why of ML?
9(1)
Machine learning for Big Data
10(1)
Automation with ML
10(1)
Applying ML in practice for BO
11(1)
Business intelligence
11(1)
ML types in BO
12(3)
Supervised learning
13(1)
Unsupervised learning
13(1)
Reinforced learning
14(1)
Deep learning
14(1)
Feature engineering
15(1)
Digital business automation and optimization
15(5)
Value extraction from data
15(2)
Intelligent optimization
17(1)
Increasingly complex business situations
17(1)
Comparing automation and optimization
18(2)
Intelligent humanization
20(1)
Challenges in AI-based business optimization
20(6)
Application challenges
21(1)
Business challenges
21(1)
Organizational culture challenges
21(2)
Knowledge management challenges
23(1)
Visualization and reporting
23(1)
User experience challenges
23(1)
Cybersecurity challenges
24(1)
Collaboration challenges
25(1)
COVID-19 pandemic and digital business
25(1)
Consolidation workshop
26(1)
Notes
27(4)
2 Data to decisions: Evolving interrelationships 31(28)
Think data
31(5)
Think data: Handset, dataset, toolset, mindset
31(2)
Various aspects of think data
33(1)
Data characteristics
33(3)
Data as enabler of optimization
36(1)
Data to decisions pyramid
36(4)
Layer 1: Data is a record of observations
37(1)
Layer 2: Information makes data understandable
38(1)
Layer 3: Analytics and services (collaborations)
38(1)
Layer 4: Knowledge and insights
39(1)
Layer 5: Decisions
39(1)
Big Data types and their characteristics for analytics
40(2)
The 3+1+1 (5) Vs of Big Data
40(2)
Sourcing of data
42(3)
Alternative data
43(2)
Data security and storage
45(1)
Data analytics in business process optimization
45(5)
Data analytics
45(1)
Business process optimization
46(1)
Establishing the data context
47(1)
Tools and techniques for BO
47(1)
Data analytics design for BO
48(1)
Granularity of analytics in BO
49(1)
User experience analysis and BO
49(1)
Self-serve analytics in BO
50(1)
Data clusters and segmentation
50(2)
Horizontal and vertical clustering
51(1)
Segmentation
51(1)
Clusters and segments in practice
51(1)
Data-driven decisions
52(1)
Nature and types of decisions
52(2)
Automation
52(1)
Prediction
53(1)
Experience
53(1)
Intuition
54(1)
Data analytics for business agility
54(2)
Consolidation workshop
56(1)
Notes
57(2)
3 Digital leadership: Strategies for AI adoption 59(30)
Strategizing for business optimization
59(4)
Envisioning digital business strategy for AI
60(1)
Digital strategies are holistic
61(1)
Customer value is the goal
61(1)
Addressing the business goal or problem
62(1)
Business agility in decision-making
62(1)
Strategic planning for BO
63(5)
"Think data" in strategies
64(1)
Strategic Al considerations
65(3)
People
66(1)
Process
67(1)
Technology
67(1)
Money
68(1)
Strategic planning for BO
68(3)
Strategies - tactics - operations
68(2)
ML types in BO strategies
70(1)
Leadership in business optimization
71(4)
Automation strategies
72(1)
Optimization strategies
73(1)
Humanization strategies
74(1)
Users and culture changes
74(1)
Business optimization initiatives
75(4)
Developing a business case for AI in business optimization
76(2)
Business stakeholders in strategy
78(1)
Strategy considerations beyond AI technologies
79(2)
Strategies to incorporate natural intelligence (NI)
79(1)
Strategies for formulating the problem
79(1)
Strategies for improving quality of decisions
80(1)
AI and business disruptions
81(4)
Disruptions due to Al as part of strategic planning
81(1)
Incorporating AI to handle externally imposed disruptions to business
81(1)
Business disruption prediction framework (BDPF)
82(3)
Consolidation workshop
85(1)
Notes
86(3)
4 Machine learning types: Statistical understanding in the business context 89(32)
Machine learning overview
89(3)
Applying ML
89(1)
Machine learning steps
90(2)
ML terminology
92(4)
Model
92(1)
Parametric
93(1)
Nonparametric
93(1)
Model parameters
93(1)
Hyper parameters
94(1)
Training
94(1)
Validation
94(1)
Testing
94(1)
Loss function
95(1)
Confusion matrix
95(1)
Precision
95(1)
Recall
95(1)
Over fitting
96(1)
Under fitting
96(1)
Data: The fuel for ML
96(3)
Data preprocessing
96(1)
Data cleaning
97(1)
Messy data
97(1)
Incomplete data
98(1)
Complex data
98(1)
Feature selection
98(1)
Wrapper
99(1)
Filter
99(1)
Evolutionary algorithms
99(1)
Supervised learning
99(10)
Linear regression
100(1)
Simple linear regression
100(3)
Multiple regression
103(3)
Neural networks
106(3)
Classifying California housing prices using NN
108(1)
Unsupervised learning
109(6)
k-means
111(1)
Density-based spatial clustering of applications with noise (DBSCAN)
112(1)
Semi-supervised learning
113(1)
Self-training
114(1)
Co-training
114(1)
Tri-training
115(1)
Reinforcement learning
115(4)
Q learning
118(1)
Financial applications of RL
118(3)
Portfolio optimization
118(1)
Optimal trading
118(1)
Recommendation systems
119(1)
Consolidation workshop
119(1)
Notes
119(2)
5 Dynamicity in learning: Smart selection of learning techniques 121(30)
Dynamicity in ML
121(2)
Static learning
122(1)
Dynamic learning
122(1)
Data and algorithm selections
123(2)
Input-output pairs
123(1)
Absence of output variable
123(1)
Few input-output pairs
123(2)
Absence of state-action-reward tuples
125(1)
Data collection by interacting with environment
125(1)
Game tree and state explosion
125(2)
Data augmentation
127(3)
Image data augmentation
127(1)
Numerical data augmentation
128(1)
Text data augmentation
129(1)
Word-level text data augmentation
129(1)
Sentence-level text data augmentation
129(1)
Synthetic dataset
130(1)
Dynamic learning framework
130(4)
Online data repository
131(1)
Automatic collection
131(1)
Preprocessing
132(1)
Expert system engine
132(1)
Knowledge acquisition
132(1)
Knowledge representation
133(1)
Inference
133(1)
ML modes in dynamic learning
134(4)
Shallow learning
134(1)
Deep learning
135(2)
Transfer learning
137(1)
ML automation and optimization
138(9)
Neuro-evolution
140(4)
Optimization problem formulation
140(1)
Genetic algorithm
140(4)
Recommendation systems
144(1)
Popularity-based method
144(1)
Collaborative filtering
145(1)
Deep learning for recommendation systems
146(1)
Data for fuelling recommendation systems
147(1)
Consolidation workshop
147(1)
Notes
147(4)
6 Intelligent business processes with embedded analytics 151(26)
Introduction
151(3)
Business process modeling
154(2)
Business process modeling (BPM) in BO
154(1)
Change management processes
155(1)
Composite agile method and strategy (CAMS)
155(1)
Business process agility
156(1)
Lean-agile processes
156(1)
Visibility and transparency
156(1)
Change management
157(1)
Integration solutions
157(1)
Quality through continuous testing and showcasing
157(1)
Data analytics and business agility
157(2)
Decentralized decision-making
158(1)
Finer granularity in business response
158(1)
Elimination of redundancies
158(1)
Enhancing sustainability in operations
158(1)
Risks, compliance and audit requirements
158(1)
Disaster recovery (DR)
159(1)
Business analysis er requirements modeling
159(6)
Critical thinking in BPM
159(1)
Art of questioning
159(1)
Machine learning to frame questions
160(1)
Mind mapping
161(1)
Comparison of processes for gaps
162(1)
Managing business system changes
163(2)
Embedding analytics in business processes
165(2)
Preparing the data
165(1)
Data analytic types and relevance in BO
165(2)
Descriptive analytics
167(1)
Predictive analytics
167(1)
Prescriptive analytics
167(1)
Collaborative digital business processes
167(4)
Collaboration advantage in a digital world
168(1)
Collaborative digital business
169(1)
Complexities of collaborative digital business
169(1)
Optimized collaborations
170(1)
Visualization and business processes
171(3)
Device and performance consideration in visualization
173(1)
Consolidation workshop
174(1)
Notes
175(2)
7 Adopting data-driven culture: Leadership and change management for business optimization 177(12)
Leadership and culture change in BO
177(5)
Change of mindset
178(1)
Managing the people risk
179(2)
Managing human behaviors
181(1)
Human resource (HR) management
182(4)
HR process changes
182(1)
Organizational process changes
183(1)
Virtual and collaborative teams
184(1)
Training business people
184(1)
Educating the customer
185(1)
Adopting Al for an agile culture
186(1)
Consolidation workshop
187(1)
Notes
188(1)
8 Quality and risks: Assurance and control in BO 189(24)
Introduction
189(5)
Direct and indirect impact of bad quality
191(1)
Risks and governance policies
191(1)
General data protection regulation (GDPR)
192(1)
Quality and ethics
192(1)
Big Data-specific challenges to quality and testing
193(1)
Quality of "data to decisions"
194(5)
Quality of data
195(1)
Quality of information
196(1)
Quality of analytics and services (collaborations)
197(1)
Quality of knowledge and insights
198(1)
Quality of decisions
198(1)
Quality environment in AI and ML
199(4)
Assuring ML quality
199(1)
Assuring quality of business processes
200(1)
Developing the quality environment
201(1)
Assurance activities
201(1)
Developing the testing environment
202(1)
Additional quality considerations
203(4)
Nonfunctional testing
204(1)
Quality of metadata
205(1)
Quality of alternative data
205(1)
Sifting value from noise in Big Data
206(1)
Quality in retiring data
206(1)
Velocity testing
207(1)
Governance-risk-compliance and data quality
207(3)
Business compliance and quality
208(1)
Quality of service
209(1)
Consolidation workshop
210(1)
Notes
210(3)
9 Cybersecurity in BO: Significance and challenges for digital business 213(20)
Cybersecurity aspects in BO
213(5)
Cybersecurity functions
214(1)
Cybersecurity as a business decision
214(1)
Cybersecurity and penalties
215(1)
Cybersecurity challenges during BO
215(1)
Cybersecurity vulnerabilities and impact
216(1)
Cyber attacker's psyche
217(1)
Securing the optimized business
218(2)
Types of cyber threats
218(1)
Malware threats
219(1)
Phishing threats
219(1)
Eavesdropping threats
220(1)
Denial-of-service threats
220(1)
Insider threats
220(1)
Developing cybersecurity strategies
220(5)
Organizing cybersecurity data and functions
221(1)
Cybersecurity data analytics
222(2)
Physical security for cyber assets
224(1)
Cybersecurity analysis using business analysis capabilities
224(1)
Cybersecurity standards and frameworks
225(1)
Cybersecurity intelligence (CI)
225(4)
Cybersecurity metrics and measurements in CI
226(1)
Levensthein distance as a measure in CI
227(1)
Base rate fallacy in cybersecurity measure and the validity of positives and negatives in CI
227(1)
Filtering algorithms for email phishing for CI
228(1)
Tools for cybersecurity intelligence
229(1)
Consolidation workshop
229(1)
Notes
230(3)
10 Natural intelligence and social aspects of AI-based decisions 233(14)
The "artifical" in AI
233(3)
Subjective customer thinking
234(1)
AI compliments NI
235(1)
Known-unknown matrix for AI vs NI
236(2)
Automation: Hard, mono-dimensional data
236(1)
Experience: Soft, inter-disciplinary
237(1)
Prediction: Fuzzy, multidimensional data
238(1)
Intuition
238(1)
Additional challenges in decision-making
238(3)
Deep learning (DL) challenges
239(1)
Ethical challenges of AI-based decisions
239(1)
Legal issues in unexplained Al
240(1)
Interfacing with humans
241(1)
Superimposing NI on AI
241(1)
Agile iterations enhance values
242(3)
Critical thinking and problem-solving with AI
242(2)
Decision- action-decision-feedback cycle
244(1)
Consolidation workshop
245(1)
Notes
245(2)
11 Investing in the future technology of self-driving vehicles: Case study 247(26)
Introduction
247(1)
Public awareness of autonomous driving technology
248(1)
SAE levels of autonomous driving
249(7)
Level 0: No automation
249(1)
Level 1: Driver assistance
249(1)
Level 2: Partial driving automation
250(1)
Level 3: Conditional driving automation
250(1)
Level 4: High driving automation
250(1)
Level 5: Full driving "optimized" automation
250(1)
Benefits of autonomous driving
251(1)
Safety
251(3)
Congestion
252(1)
Pollution
252(1)
Parking space
253(1)
Passenger quality of life
253(1)
Cost benefits
253(1)
Unintended consequences of automated cars technology
254(2)
Loss of jobs
254(1)
Blow to the auto industry
255(1)
Blow to the auto insurance industry
255(1)
AV engineering
256(6)
Analysis of the human driving cycle
256(2)
Foreground conscious cycle
256(1)
Background unconscious cycle
257(1)
AV driving cycle
258(2)
Perception
258(1)
Ultrasonic sensors
259(1)
Visual camera
259(1)
Radar
259(1)
Lidar
259(1)
Global positioning system
260(1)
Scene generation
260(1)
Planning
260(1)
Action
260(1)
Humans vs AVs driving
260(2)
The state-of-art of AVs engineering
262(4)
Brief history of self-driving cars
262(1)
The future of self-driving cars
262(2)
Technology maturity
263(1)
Cybersecurity
264(2)
Sensor attacks
264(1)
Hardware attacks
265(1)
Software attacks
265(1)
Infrastructure and network attacks
265(1)
AVs impact on economy
266(2)
Consolidation workshop
268(1)
Notes
269(4)
Appendix A: Frameworks and libraries for ML 273(4)
Appendix B: Datasets for ML and predictive analytics 277(4)
Appendix C: AI and BO research areas 281(2)
Index 283
Dr Bhuvan Unhelkar (BE, MDBA, MSc, PhD, FACS) has extensive strategic and hands- on professional experience in the Information and Communication Technologies (ICT) industry. He is a full Professor and lead faculty of IT at the University of South Florida Sarasota-Manatee (USFSM), and is the founder and Consultant at MethodScience and PlatiFi. He is also an adjunct Professor at Western Sydney University, Australia and an honorary Professor at Amity University, India . His current industrial research interests include AI and ML in Business Optimization, Big Data and business value and Business Analysis in the context of Agile. Dr. Unhelkar holds a Certificate-IV in TAA and TAE, Professional Scrum Master - I, SAFe (Scaled Agile Framework for Enterprise) Leader and is a Certified Business Analysis Professional® (CBAP of the IIBA). Tad Gonsalves is full Professor in the Department of Information & Communication Sciences, Sophia University, Tokyo, Japan. Dr. Gonsalves' research areas include Bio-inspired Optimization techniques and application of Deep Learning techniques to diverse problems like autonomous driving, drones, digital art and computational linguistics. He holds a BS in theoretical Physics and MS in Astrophysics and earned his PhD in Information Systems from Sophia University, Tokyo, Japan. His research lab in Tokyo specializes in multi-GPU computing. Dr. Gonsalves is the author of Introduction to AI: A Non-Technical Introduction, (Sophia Univ. Press, 2017) which serves as a standard AI textbook for the university curriculum.