Artificial Intelligence for business optimization: research and applications
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| Sprache: | English |
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Boca Raton
CRC Press
2021
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | xxxiv, 287 Seiten Illustrationen |
| ISBN: | 9780367638368 9781032028866 |
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Contents Foreword by Andy Lyman Preface Readers Figures Acknowledgments Authors 1 Artificial intelligence and machine learning: Opportunities for digital business xix XXI ХХШ xxvii xxix xxxi 1 Artificial intelligence in the context of business 1 Artificial intelligence (AI) and machine learning (ML) as enablers of business optimization (BO) 2 Subjective elements in BO 2 Agility in BO 3 Collaboration in BO 4 Granularity in BO 4 The technical-business continuum 4 Strategic approach to business optimization 6 BO as a redesign of business 6 Developing a BO strategy 6 Capabilities in BO 8 AI, Big Data, and statistics 8 Data, science, and analytics 8 What and why of ML? 9 Machine learning for Big Data 10 Automation with ML 10 Applying ML in practice for BO 11 Business intelligence 11 ML types in BO 12 Supervised learning 13 VII
viii Contents Unsupervised learning 13 Reinforced learning 14 Deep learning 14 Feature engineering 15 Digital business automation and optimization 15 Value extraction from data 15 Intelligent optimization 17 Increasingly complex business situations 17 Comparing automation and optimization 18 Intelligent humanization 20 Challenges in AI-based business optimization 20 Application challenges 21 Business challenges 21 Organizational culture challenges 21 Knowledge management challenges 23 Visualization and reporting 23 User experience challenges 23 Cybersecurity challenges 24 Collaboration challenges 25 COVID-19 pandemic and digital business 25 Consolidation workshop 26 Notes 27 2 Data to decisions: Evolving interrelationships Think data 31 Think data: Handset, dataset, toolset, mindset 31 Various aspects of think data 33 Data characteristics 33 Data as enabler of optimization 36 Data to decisions pyramid 36 Layer 1: Data is a record of observations 37 Layer 2: Information makes data understandable 38 Layer 3: Analytics and services (collaborations) 38 Layer 4: Knowledge and insights 39 Layer 5: Decisions 39 Big Data types and their characteristics for analytics 40 The 3+l+L (5) Vs of Big Data 40 Sourcing of data 42 Alternative data 43 Data security and storage 45 Data analytics in business process optimization 45 31
Contents ix Data analytics 45 Business process optimization 46 Establishing the data context 47 Tools and techniques for BO 47 Data analytics design for BO 48 Granularity of analytics in BO 49 User experience analysis and BO 49 Self-serve analytics in BO 50 Data clusters and segmentation 50 Horizontal and vertical clustering 51 Segmentation 51 Clusters and segments in practice 51 Data-driven decisions 52 Nature and types of decisions 52 Automation 52 Prediction 53 Experience 53 Intuition 54 Data analytics for business agility 54 Consolidation workshop 56 Notes 57 3 Digital leadership: Strategies for AI adoption Strategizing for business optimization 59 Envisioning digital business strategy for Al 60 Digital strategies are holistic 61 Customer value is the goal 61 Addressing the business goal or problem 62 Business agility in decision-making 62 Strategic planning for BO 63 “Think data” in strategies 64 Strategic Al considerations 65 People 66 Process 67 Technology 67 Money 68 Strategic planning for BO 68 Strategies - tactics - operations 68 ML types in BO strategies 70 Leadership in business optimization 71 Automation strategies 72 59
x Contents Optimization strategies 73 Humanization strategies 74 Osers and culture changes 74 Business optimization initiatives 75 Developing a business case for Al in business optimization 76 Business stakeholders in strategy 78 Strategy considerations beyond AI technologies 79 Strategies to incorporate natural intelligence (N1) 79 Strategies for formulating the problem 79 Strategies for improving quality of decisions 80 AI and business disruptions 81 Disruptions due to Al as part of strategic planning 81 Incorporhting AI to handle externally imposed disruptions to business 81 Business disruption prediction framework (BDPF) 82 Consolidation workshop 85 Notes 86 4 Machine learning types: Statistical understanding in the business context Machine learning overview 89 Applying ML 89 Machine learning steps 90 ML terminology 92 Model 92 Parametric 93 Nonparametric 93 Model parameters 93 Hyperparameters 94 Training 94 Validation 94 Testing 94 Loss function 95 Confusion matrix 95 Precision 95 Recall 95 Overfitting 96 Underfitting 96 Data: The fuel for ML 96 Data preprocessing 96 Data cleaning 97 89
Contents xi Messy data 97 Incomplete data 98 Complex data 98 Feature selection 98 Wrapper 99 Filter 99 Evolutionary algorithms 99 Supervised learning 99 Linear regression 100 Simple linear regression 100 Multiple regression 103 Neural networks 106 Classifying California housing prices using NN 108 Unsupervised learning 109 k-means 111 Density-based spatial clustering of applications with noise (DBSCAN) 112 Semi-supervised learning 113 Self-training 114 Co-training 114 Tri-training 115 Reinforcement learning 115 Q learning 118 Financial applications of RL 118 Portfolio optimization 118 Optimal trading 118 Recommendation systems 119 Consolidation workshop 119 Notes 119 5 Dynamicky in learning: Smart selection of learning techniques Dynamicity in ML 121 Static learning 122 Dynamic learning 122 Data and algorithm selections 123 Input-output pairs 123 Absence of output variable 123 Few input-output pairs 123 Absence of state-action-reward tuples 125 Data collection by interacting with environment 125 121
xii Contents Game tree and State explosion 125 Data augmentation 127 Image data augmentation 127 Numerical data augmentation 128 Text data augmentation 129 Word-level text data augmentation 129 Sentence-level text data augmentation 129 Synthetic dataset 130 Dynamic learning framework 130 Online data repository 131 Automatic collection 131 Preprocessing 132 Expert system engine 132 Knowledge acquisition 132 Knowledge representation 133 Inference 133 ML modes in dynamic learning 134 Shallow learning 134 Deep learning 135 Transfer learning 137 ML automation and optimization 138 Neuro-evolution 140 Optimization problem formulation 140 Genetic algorithm 140 Recommendation systems 144 Popularity-based method 144 Collaborative filtering 145 Deep learning for recommendation systems 146 Data for fuelling recommendation systems 147 Consolidation workshop 147 Notes 147 6 Intelligent business processes with embedded analytics Introduction 151 Business process modeling 154 Business process modeling (BPM) in BO 154 Change management processes 155 Composite agile method and strategy (CAMS) 155 Business process agility 156 Lean-agile processes 156 151
Contents xiii Visibility and transparency 156 Change management 157 Integration solutions 157 Quality through continuous testing and showcasing 157 Data analytics and business agility 157 Decentralized decision-making 158 Finer granularity in business response 158 Elimination of redundancies 158 Enhancing sustainability in operations 158 Risks, compliance and audit requirements 158 Disaster recovery (DR) 159 Business analysis requirements modeling 159 Critical thinking in BPM 159 Art of questioning 159 Machine learning to frame questions 160 Mind mapping 161 Comparison of processes for gaps 162 Managing business system changes 163 Embedding analytics in business processes 165 Preparing the data 165 Data analytic types and relevance in BO 165 Descriptive analytics 167 Predictive analytics 167 Prescriptive analytics 167 Collaborative digital business processes 167 Collaboration advantage in a digital world 168 Collaborative digital business 169 Complexities of collaborative digital business 169 Optimized collaborations 170 Visualization and business processes 171 Device and performance consideration in visualization 173 Consolidation workshop 174 Notes 175 7 Adopting data-driven culture: Leadership and change management for business optimization Leadership and culture change in BO 177 Change of mindset 178 Managing the people risk 179 Managing human behaviors 181 177
xiv Contents Human resource (HR) management 182 HR process changes 182 Organizational process changes 183 Virtual and collaborative teams 184 Training business people 184 Educating the customer 18S Adopting ÁI for an agile culture 186 Consolidation workshop 187 Notes 188 8 Quality and risks: Assurance and control in BO Introduction 189 Direct and indirect impact of bad quality 191 Risks and governance policies 191 General data protection regulation (GDPR) 192 Quality and ethics 192 Big Data-specific challenges to quality and testing 193 Quality of “data to decisions” 194 Quality of data 195 Quality of information 196 Quality of analytics and services (collaborations) 197 Quality of knowledge and insights 198 Quality of decisions 198 Quality environment in Al and ML 199 Assuring ML quality 199 Assuring quality of business processes 200 Developing the quality environment 201 Assurance activities 201 Developing the testing environment 202 Additional quality considerations 203 Nonfunctional testing 204 Quality of metadata 205 Quality of alternative data 205 Sifting value from noise in Big Data 206 Quality in retiring data 206 Velocity testing 207 Governance-risk-compliance and data quality 207 Business compliance and quality 208 Quality of service 209 Consolidation workshop 210 Notes 210 189
Contents 9 Cybersecurity in BO: Significance and challenges for digital business XV 213 Cybersecurity aspects in BO 213 Cybersecurity functions 214 Cybersecurity as a business decision 214 Cybersecurity and penalties 215 Cybersecurity challenges during BO 215 Cybersecurity vulnerabilities and impact 216 Cyber attacker’s psyche 217 Securing the optimized business 218 Types of cyber threats 218 Malware threats 219 Phishing threats 219 Eavesdropping threats 220 Denial-of-service threats 220 Insider threats 220 Developing cybersecurity strategies 220 Organizing cybersecurity data and functions 221 Cybersecurity data analytics 222 Physical security for cyber assets 224 Cybersecurity analysis using business analysis capabilities 224 Cybersecurity standards and frameworks 225 Cybersecurity intelligence (Cl) 225 Cybersecurity metrics and measurements in Cl 226 Levensthein distance as a measure in Cl 227 Base rate fallacy in cybersecurity measure and the validity of positives and negatives in Cl 227 Filtering algorithms for email phishing for Cl 228 Tools for cybersecurity intelligence 229 Consolidation workshop 229 Notes 230 10 Natural intelligence and social aspects of AI-based decisions The “artificai” in AI 233 Subjective customer thinking 234 AI compliments N1 235 Known-unknown matrix for AI vs N1 236 Automation: Hard, mono-dimensional data 236 Experience: Soft, inter-disciplinary 237 Prediction: Fuzzy, multidimensional data 238 233
xvi Contents Intuition 238 Additional challenges in decision-making 238 Deep learning (DL) challenges 239 Ethical challenges of AI-based decisions 239 Legal issues in unexplained AI 240 Interfacing with humans 241 Superimposing NI on AI 241 Agile iterations enhance values 242 Critical thinking and problem-solving with AI 242 Decision- action-decision-feedback cycle 244 Consolidation workshop 245 Notes 245 11 Investing in the future technology of self-driving vehicles: Case study Introduction 247 Public awareness of autonomous driving technology 248 SAE levels of autonomous driving 249 Level 0: No automation 249 Level 1: Driver assistance 249 Level 2: Partial driving automation 250 Level 3: Conditional driving automation 250 Level 4: High driving automation 250 Level 5: Full driving “optimized” automation 250 Benefits of autonomous driving 251 Safety 251 Congestion 252 Pollution 252 Parking space 253 Passenger quality of life 253 Cost benefits 253 Unintended consequences of automated cars technology 254 Loss of jobs 254 Blow to the auto industry 255 Blow to the auto insurance industry 255 AVengineering 256 Analysis of the human driving cycle 256 Foreground conscious cycle 256 Background unconscious cycle 257 AV driving cycle 258 Perception 258 247
Contents xvii Ultrasonic sensors 259 Visual camera 259 Radar 259 Lidar 259 Global positioning system 260 Scene generation 260 Planning 260 Action 260 Humans vs .AVs driving 260 The state-of-art ofAVs engineering 262 Brief history of self-driving cars 262 The future of self-driving cars 262 Technology maturity 263 Cybersecurity 264 Sensor attacks 264 Hardware attacks 265 Software attacks 265 Infrastructure and network attacks 265 AVs impact on economy 266 Consolidation workshop 268 Notes 269 Appendix Ճ: Frameworks and libraries for ML Appendix B: Datasets for ML and predictive analytics Appendix C: AI and BO research areas Index 273 277 281 283 |
| adam_txt |
Contents Foreword by Andy Lyman Preface Readers Figures Acknowledgments Authors 1 Artificial intelligence and machine learning: Opportunities for digital business xix XXI ХХШ xxvii xxix xxxi 1 Artificial intelligence in the context of business 1 Artificial intelligence (AI) and machine learning (ML) as enablers of business optimization (BO) 2 Subjective elements in BO 2 Agility in BO 3 Collaboration in BO 4 Granularity in BO 4 The technical-business continuum 4 Strategic approach to business optimization 6 BO as a redesign of business 6 Developing a BO strategy 6 Capabilities in BO 8 AI, Big Data, and statistics 8 Data, science, and analytics 8 What and why of ML? 9 Machine learning for Big Data 10 Automation with ML 10 Applying ML in practice for BO 11 Business intelligence 11 ML types in BO 12 Supervised learning 13 VII
viii Contents Unsupervised learning 13 Reinforced learning 14 Deep learning 14 Feature engineering 15 Digital business automation and optimization 15 Value extraction from data 15 Intelligent optimization 17 Increasingly complex business situations 17 Comparing automation and optimization 18 Intelligent humanization 20 Challenges in AI-based business optimization 20 Application challenges 21 Business challenges 21 Organizational culture challenges 21 Knowledge management challenges 23 Visualization and reporting 23 User experience challenges 23 Cybersecurity challenges 24 Collaboration challenges 25 COVID-19 pandemic and digital business 25 Consolidation workshop 26 Notes 27 2 Data to decisions: Evolving interrelationships Think data 31 Think data: Handset, dataset, toolset, mindset 31 Various aspects of think data 33 Data characteristics 33 Data as enabler of optimization 36 Data to decisions pyramid 36 Layer 1: Data is a record of observations 37 Layer 2: Information makes data understandable 38 Layer 3: Analytics and services (collaborations) 38 Layer 4: Knowledge and insights 39 Layer 5: Decisions 39 Big Data types and their characteristics for analytics 40 The 3+l+L (5) Vs of Big Data 40 Sourcing of data 42 Alternative data 43 Data security and storage 45 Data analytics in business process optimization 45 31
Contents ix Data analytics 45 Business process optimization 46 Establishing the data context 47 Tools and techniques for BO 47 Data analytics design for BO 48 Granularity of analytics in BO 49 User experience analysis and BO 49 Self-serve analytics in BO 50 Data clusters and segmentation 50 Horizontal and vertical clustering 51 Segmentation 51 Clusters and segments in practice 51 Data-driven decisions 52 Nature and types of decisions 52 Automation 52 Prediction 53 Experience 53 Intuition 54 Data analytics for business agility 54 Consolidation workshop 56 Notes 57 3 Digital leadership: Strategies for AI adoption Strategizing for business optimization 59 Envisioning digital business strategy for Al 60 Digital strategies are holistic 61 Customer value is the goal 61 Addressing the business goal or problem 62 Business agility in decision-making 62 Strategic planning for BO 63 “Think data” in strategies 64 Strategic Al considerations 65 People 66 Process 67 Technology 67 Money 68 Strategic planning for BO 68 Strategies - tactics - operations 68 ML types in BO strategies 70 Leadership in business optimization 71 Automation strategies 72 59
x Contents Optimization strategies 73 Humanization strategies 74 Osers and culture changes 74 Business optimization initiatives 75 Developing a business case for Al in business optimization 76 Business stakeholders in strategy 78 Strategy considerations beyond AI technologies 79 Strategies to incorporate natural intelligence (N1) 79 Strategies for formulating the problem 79 Strategies for improving quality of decisions 80 AI and business disruptions 81 Disruptions due to Al as part of strategic planning 81 Incorporhting AI to handle externally imposed disruptions to business 81 Business disruption prediction framework (BDPF) 82 Consolidation workshop 85 Notes 86 4 Machine learning types: Statistical understanding in the business context Machine learning overview 89 Applying ML 89 Machine learning steps 90 ML terminology 92 Model 92 Parametric 93 Nonparametric 93 Model parameters 93 Hyperparameters 94 Training 94 Validation 94 Testing 94 Loss function 95 Confusion matrix 95 Precision 95 Recall 95 Overfitting 96 Underfitting 96 Data: The fuel for ML 96 Data preprocessing 96 Data cleaning 97 89
Contents xi Messy data 97 Incomplete data 98 Complex data 98 Feature selection 98 Wrapper 99 Filter 99 Evolutionary algorithms 99 Supervised learning 99 Linear regression 100 Simple linear regression 100 Multiple regression 103 Neural networks 106 Classifying California housing prices using NN 108 Unsupervised learning 109 k-means 111 Density-based spatial clustering of applications with noise (DBSCAN) 112 Semi-supervised learning 113 Self-training 114 Co-training 114 Tri-training 115 Reinforcement learning 115 Q learning 118 Financial applications of RL 118 Portfolio optimization 118 Optimal trading 118 Recommendation systems 119 Consolidation workshop 119 Notes 119 5 Dynamicky in learning: Smart selection of learning techniques Dynamicity in ML 121 Static learning 122 Dynamic learning 122 Data and algorithm selections 123 Input-output pairs 123 Absence of output variable 123 Few input-output pairs 123 Absence of state-action-reward tuples 125 Data collection by interacting with environment 125 121
xii Contents Game tree and State explosion 125 Data augmentation 127 Image data augmentation 127 Numerical data augmentation 128 Text data augmentation 129 Word-level text data augmentation 129 Sentence-level text data augmentation 129 Synthetic dataset 130 Dynamic learning framework 130 Online data repository 131 Automatic collection 131 Preprocessing 132 Expert system engine 132 Knowledge acquisition 132 Knowledge representation 133 Inference 133 ML modes in dynamic learning 134 Shallow learning 134 Deep learning 135 Transfer learning 137 ML automation and optimization 138 Neuro-evolution 140 Optimization problem formulation 140 Genetic algorithm 140 Recommendation systems 144 Popularity-based method 144 Collaborative filtering 145 Deep learning for recommendation systems 146 Data for fuelling recommendation systems 147 Consolidation workshop 147 Notes 147 6 Intelligent business processes with embedded analytics Introduction 151 Business process modeling 154 Business process modeling (BPM) in BO 154 Change management processes 155 Composite agile method and strategy (CAMS) 155 Business process agility 156 Lean-agile processes 156 151
Contents xiii Visibility and transparency 156 Change management 157 Integration solutions 157 Quality through continuous testing and showcasing 157 Data analytics and business agility 157 Decentralized decision-making 158 Finer granularity in business response 158 Elimination of redundancies 158 Enhancing sustainability in operations 158 Risks, compliance and audit requirements 158 Disaster recovery (DR) 159 Business analysis requirements modeling 159 Critical thinking in BPM 159 Art of questioning 159 Machine learning to frame questions 160 Mind mapping 161 Comparison of processes for gaps 162 Managing business system changes 163 Embedding analytics in business processes 165 Preparing the data 165 Data analytic types and relevance in BO 165 Descriptive analytics 167 Predictive analytics 167 Prescriptive analytics 167 Collaborative digital business processes 167 Collaboration advantage in a digital world 168 Collaborative digital business 169 Complexities of collaborative digital business 169 Optimized collaborations 170 Visualization and business processes 171 Device and performance consideration in visualization 173 Consolidation workshop 174 Notes 175 7 Adopting data-driven culture: Leadership and change management for business optimization Leadership and culture change in BO 177 Change of mindset 178 Managing the people risk 179 Managing human behaviors 181 177
xiv Contents Human resource (HR) management 182 HR process changes 182 Organizational process changes 183 Virtual and collaborative teams 184 Training business people 184 Educating the customer 18S Adopting ÁI for an agile culture 186 Consolidation workshop 187 Notes 188 8 Quality and risks: Assurance and control in BO Introduction 189 Direct and indirect impact of bad quality 191 Risks and governance policies 191 General data protection regulation (GDPR) 192 Quality and ethics 192 Big Data-specific challenges to quality and testing 193 Quality of “data to decisions” 194 Quality of data 195 Quality of information 196 Quality of analytics and services (collaborations) 197 Quality of knowledge and insights 198 Quality of decisions 198 Quality environment in Al and ML 199 Assuring ML quality 199 Assuring quality of business processes 200 Developing the quality environment 201 Assurance activities 201 Developing the testing environment 202 Additional quality considerations 203 Nonfunctional testing 204 Quality of metadata 205 Quality of alternative data 205 Sifting value from noise in Big Data 206 Quality in retiring data 206 Velocity testing 207 Governance-risk-compliance and data quality 207 Business compliance and quality 208 Quality of service 209 Consolidation workshop 210 Notes 210 189
Contents 9 Cybersecurity in BO: Significance and challenges for digital business XV 213 Cybersecurity aspects in BO 213 Cybersecurity functions 214 Cybersecurity as a business decision 214 Cybersecurity and penalties 215 Cybersecurity challenges during BO 215 Cybersecurity vulnerabilities and impact 216 Cyber attacker’s psyche 217 Securing the optimized business 218 Types of cyber threats 218 Malware threats 219 Phishing threats 219 Eavesdropping threats 220 Denial-of-service threats 220 Insider threats 220 Developing cybersecurity strategies 220 Organizing cybersecurity data and functions 221 Cybersecurity data analytics 222 Physical security for cyber assets 224 Cybersecurity analysis using business analysis capabilities 224 Cybersecurity standards and frameworks 225 Cybersecurity intelligence (Cl) 225 Cybersecurity metrics and measurements in Cl 226 Levensthein distance as a measure in Cl 227 Base rate fallacy in cybersecurity measure and the validity of positives and negatives in Cl 227 Filtering algorithms for email phishing for Cl 228 Tools for cybersecurity intelligence 229 Consolidation workshop 229 Notes 230 10 Natural intelligence and social aspects of AI-based decisions The “artificai” in AI 233 Subjective customer thinking 234 AI compliments N1 235 Known-unknown matrix for AI vs N1 236 Automation: Hard, mono-dimensional data 236 Experience: Soft, inter-disciplinary 237 Prediction: Fuzzy, multidimensional data 238 233
xvi Contents Intuition 238 Additional challenges in decision-making 238 Deep learning (DL) challenges 239 Ethical challenges of AI-based decisions 239 Legal issues in unexplained AI 240 Interfacing with humans 241 Superimposing NI on AI 241 Agile iterations enhance values 242 Critical thinking and problem-solving with AI 242 Decision- action-decision-feedback cycle 244 Consolidation workshop 245 Notes 245 11 Investing in the future technology of self-driving vehicles: Case study Introduction 247 Public awareness of autonomous driving technology 248 SAE levels of autonomous driving 249 Level 0: No automation 249 Level 1: Driver assistance 249 Level 2: Partial driving automation 250 Level 3: Conditional driving automation 250 Level 4: High driving automation 250 Level 5: Full driving “optimized” automation 250 Benefits of autonomous driving 251 Safety 251 Congestion 252 Pollution 252 Parking space 253 Passenger quality of life 253 Cost benefits 253 Unintended consequences of automated cars technology 254 Loss of jobs 254 Blow to the auto industry 255 Blow to the auto insurance industry 255 AVengineering 256 Analysis of the human driving cycle 256 Foreground conscious cycle 256 Background unconscious cycle 257 AV driving cycle 258 Perception 258 247
Contents xvii Ultrasonic sensors 259 Visual camera 259 Radar 259 Lidar 259 Global positioning system 260 Scene generation 260 Planning 260 Action 260 Humans vs .AVs driving 260 The state-of-art ofAVs engineering 262 Brief history of self-driving cars 262 The future of self-driving cars 262 Technology maturity 263 Cybersecurity 264 Sensor attacks 264 Hardware attacks 265 Software attacks 265 Infrastructure and network attacks 265 AVs impact on economy 266 Consolidation workshop 268 Notes 269 Appendix Ճ: Frameworks and libraries for ML Appendix B: Datasets for ML and predictive analytics Appendix C: AI and BO research areas Index 273 277 281 283 |
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| id | DE-604.BV047482305 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:12:59Z |
| indexdate | 2024-11-15T13:00:34Z |
| institution | BVB |
| isbn | 9780367638368 9781032028866 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032883774 |
| oclc_num | 1256558033 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-739 DE-11 DE-473 DE-BY-UBG DE-706 |
| owner_facet | DE-355 DE-BY-UBR DE-739 DE-11 DE-473 DE-BY-UBG DE-706 |
| physical | xxxiv, 287 Seiten Illustrationen |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Unhelkar, Bhuvan 1960- Verfasser (DE-588)1072529882 aut Artificial Intelligence for business optimization research and applications Bhuvan Unhelkar, Tad Gonsalves Boca Raton CRC Press 2021 xxxiv, 287 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Description based on publisher supplied metadata and other sources Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Prozessmanagement (DE-588)4353072-2 gnd rswk-swf Organisation (DE-588)4043774-7 gnd rswk-swf Organisation (DE-588)4043774-7 s Prozessmanagement (DE-588)4353072-2 s Künstliche Intelligenz (DE-588)4033447-8 s b DE-604 Gonsalves, Tad Verfasser aut Erscheint auch als Online-Ausgabe 978-1-00-040947-5 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032883774&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Unhelkar, Bhuvan 1960- Gonsalves, Tad Artificial Intelligence for business optimization research and applications Künstliche Intelligenz (DE-588)4033447-8 gnd Prozessmanagement (DE-588)4353072-2 gnd Organisation (DE-588)4043774-7 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4353072-2 (DE-588)4043774-7 |
| title | Artificial Intelligence for business optimization research and applications |
| title_auth | Artificial Intelligence for business optimization research and applications |
| title_exact_search | Artificial Intelligence for business optimization research and applications |
| title_exact_search_txtP | Artificial intelligence for business optimization research and applications |
| title_full | Artificial Intelligence for business optimization research and applications Bhuvan Unhelkar, Tad Gonsalves |
| title_fullStr | Artificial Intelligence for business optimization research and applications Bhuvan Unhelkar, Tad Gonsalves |
| title_full_unstemmed | Artificial Intelligence for business optimization research and applications Bhuvan Unhelkar, Tad Gonsalves |
| title_short | Artificial Intelligence for business optimization |
| title_sort | artificial intelligence for business optimization research and applications |
| title_sub | research and applications |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Prozessmanagement (DE-588)4353072-2 gnd Organisation (DE-588)4043774-7 gnd |
| topic_facet | Künstliche Intelligenz Prozessmanagement Organisation |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032883774&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT unhelkarbhuvan artificialintelligenceforbusinessoptimizationresearchandapplications AT gonsalvestad artificialintelligenceforbusinessoptimizationresearchandapplications |