Explainable artificial intelligence (XAI): concepts, enabling tools, technologies and applications
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| Format: | Book |
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| Language: | English |
| Published: |
Stevenage
Institution of Engineering and Technology
2023
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| Edition: | First published |
| Series: | IET computing series
62 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxiv, 504 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9781839536953 1839536950 |
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| 245 | 1 | 0 | |a Explainable artificial intelligence (XAI) |b concepts, enabling tools, technologies and applications |c edited by Pethuru Raj, Utku Köse, Usha Sakthivel, Susila Nagarajan, Vijanth Sagayan Asirvadam |
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Contents About the editors Preface 1 2 An overview of past and present progressions in XAI xix xxiii 1 Shweta Shankar Shete, M.S. Rachana, A.P. Jyothi and S. Usha 1.1 Introduction 1.2 Background study 1.2.1 Key-related ideas of XAI 1.3 Overview of XAI 1.4 History of XAI 1.5 Top AI patterns 1.6 Conclusion References 1 3 5 6 8 10 13 14 Demystifying explainable artificial intelligence (EAI) 17 B. Narendra Kumar Rao and Sailaja 2.1 Introduction 2.1.1 An overview of artificial intelligence 2.1.2 Introduction to explainable AI 2.2 Concept of XAI 2.3 Explainable AI (EAI) architecture 2.4 Learning techniques 2.5 Demystifying EAI methods 2.5.1 Clever Hans 2.5.2 Different users and goals in EAI 2.5.3 EAI as quality assurance 2.6 Implementation: how to create explainable solutions 2.6.1 Method taxonomy 2.6.2 Rules - intrinsic local explanations 2.6.3 Prototypes 2.6.4 Learned representation 2.6.5 Partial dependence plot - global post-hoc explanations 2.6.6 Feature attribution (importance) 2.7 Applications 2.8 Conclusion References 17 17 18 18 19 21 21 22 22 23 24 24 24 25 25 26 26 27 28 29
vi XAI: concepts, enabling tools, technologies and applications Illustrating the significance of explainable artificial intelligence (XAI) Contents 5 31 Pethuru Raj and Chellammal Surianarayanan 3.1 3.2 3.3 3.4 3.5 3.6 Introduction The growing power of AI The challenges and concerns of AI About the need for AI explainability The importance of XAI The importance of model interpretation 3.6.1 Model transparency 3.6.2 Start with interpretable algorithms 3.6.3 Standard techniques for model interpretation 3.6.4 ROC curve 3.6.5 Focus on feature importance 3.6.6 Partial dependence plots (PDPs) 3.6.7 Global surrogate models 3.6.8 Criteria for ML model interpretation methods 3.7 Briefing feature importance scoring methods 3.8 Local interpretable model-agnostic explanations (LIMEs) 3.9 SHAP explainability algorithm 3.9.1 AI trust with symbolic AI 3.10 The growing scope of XAI for the oil and gas industry 3.10.1 XAI for the oil and gas industry 3.11 Conclusion Bibliography Inclusion of XAI in artificial intelligence and deep learning technologies Μ. SureshKumar, S.I. Vishwa Raviraaj and R. Sukhresswarun 4.1 Introduction 4.2 What is XAI? 4.3 Why is XAI important? 4.4 How does XAI work? 4.5 Role of XAI in machine learning and deep learning algorithm 4.6 Applications of XAI in machine learning in deep learning 4.7 Difference between XAI and AI 4.8 Challenges in XAI 4.9 Advantages of XAI 4.10 Disadvantages of XAI 4.11 Future scope of XAI 4.12 Conclusion References 31 32 32 33 35 37 37 38 38 • 38 39 39 40 40 42 43 45 46 47 48 48 49 51 51 52 53 54 55 57 58 58 60 61 62 63 64 6 Explainable
artificial intelligence: tools, platforms, and new taxonomies Ghulam E. Mustafa Abro, Parteek Kumar, Vijanth Sagayan Asirvadam, Nirbhay Mathur and Fawad Salam Khan 5.1 Introduction 5.2 ML-based systems and awareness 5.3 Challenges of the time 5.3.1 Requirement of explainability 5.3.2 Impact of high-stake decisions 5.3.3 Concerns of society 5.3.4 Regulations and interpretability issue 5.4 State-of-the-art approaches 5.5 Assessment approaches 5.6 Drivers for XAI 5.6.1 Tools and frameworks 5.7 Discussion 5.7.1 For researchers outside of computer science: taxonomies 5.7.2 Taxonomies and reviews focusing on specific aspects 5.7.3 Fresh perspectives on taxonomy 5.7.4 Taxonomy levels at new levels 5.8 Conclusion References An overview of AI platforms, frameworks, libraries, and processes Sruthi Anand, Ram Gurusamy Raja and T. Sheela 6.1 Introduction to AI 6.2 Role of AI in the 21st century 6.2.1 The 2000s 6.2.2 The 2010s 6.2.3 The future 6.3 How AI transformed the world 6.3.1 Transportation 6.3.2 Finance 6.3.3 Healthcare 6.3.4 Intelligent cities 6.3.5 Security 6.4 AI process 6.5 TensorFlow 6.5.1 Installation 6.5.2 TensorFlow basics 6.6 Scikit learn 6.6.1 Features 6.6.2 Installation 6.6.3 Scikit modeling 6.6.4 Data representation in scikit vii 65 65 67 69 70 70 70 71 71 75 75 77 78 79 79 80 80 81 82 93 93 94 94 94 94 95 95 95 95 95 96 96 97 97 97 99 99 100 100 102
viii Contents XAI: concepts, enabling tools, technologies and applications 6.7 Keras 6.7.1 Features 6.7.2 Building a model in Keras 6.7.3 Applications of Keras 6.8 Open NN 6.8.1 Application 6.8.2 RNN 6.9 Theano 6.9.1 Anoverview 6.10 Why go for Theano Python library? 6.10.1 PROS 6.10.2 CONS 6.11 Basics of Theano 6.11.1 Subtracting two scalars 6.11.2 Adding two scalars 6.11.3 Adding two matrices 6.11.4 Logistic function References Quality framework for explainable artificial intelligence (XAI) and machine learning applications Muthu Ramachandran 7.1 Introduction 7.2 Background 7.3 Integrated framework for AI applications development 7.4 AI systems characteristics vs. SE best practices 7.4.1 Explainable AI characteristics 7.5 ML lifecycle (model, data-oriented, and data analytics-oriented lifecycle) 7.6 AI/ML requirements engineering 7.7 Software effort estimation for AMD, RL, and NLP systems 7.7.1 Modified COCOMO model for AI, ML, and NLP applications and apps 7.8 Software engineering framework for AI and ML (SEF4 AI and ML) applications 7.9 Reference Architecture for AI ML 7.10 Evaluation of Reference Architecture (REF) for AI ML: explainable Chatbot case study 7.11 Conclusions and further research References Methods for explainable artificial intelligence Sajid AH 8.1 Preliminarily study 8.2 Importance of XAI for human-interpretable models 104 104 106 106 106 107 108 110 110 111 111 111 112 112 112 112 113 113 115 115 117 120 123 124 126 128 129 129 131 132 133 136 136 139 140 141 9 ix 8.3 8.4 Overview of XAI techniques Taxonomy of popular XAI methods 8.4.1 Backpropagation-
based methods 8.4.2 Perturbation methods 8.4.3 Influence methods 8.4.4 Knowledge extraction 8.4.5 Concept methods 8.4.6 Visualization methods 8.4.7 Example-based explanation 8.5 Conclusion References 142 145 145 150 152 153 154 154 156 157 157 Knowledge representation and reasoning (KRR) Syed Muzamil Basha and Naif K. Al-Shammari 9.1 Introduction 9.2 Methodology 9.2.1 Reference model 9.2.2 Ontologies 9.2.3 Knowledge graphs 9.2.4 Semantic web technologies 9.2.5 ML 9.2.6 Tools and techniques 9.3 Results and discussion 9.3.1 Case study: using different techniques for representing medical knowledge 9.3.2 Case study: using different techniques for representing academic knowledge 9.3.3 Case study: using different techniques for representing farmer knowledge 9.3.4 Case study: social media knowledge representation techniques 9.3.5 Case study: using different techniques for representing cyber security knowledge 9.4 Conclusion and future work References 163 10 Knowledge visualization: AI integration with 360-degree dashboards K. Sasikala Rani and Chandrasekar Nagarajan 10.1 Introduction 10.2 Information visualization vs. knowledge visualization 10.3 Knowledge visualization in design thinking 10.4 Visualization in transferring knowledge 10.5 The knowledge visualization model 10.5.1 Knowledge visualization framework 163 164 164 165 165 166 166 167 171 171 172 173 175 176 177 177 179 179 180 180 181 182 182
X Contents XAI: concepts, enabling tools, technologies and applications 10.6 Formats and examples of knowledge visualization 10.6.1 Conceptual diagrams 10.6.2 Visual metaphors 10.6.3 Knowledge animation 10.6.4 Knowledge maps 10.6.5 Knowledge domain visualization 10.7 Types and usage of knowledge visualization tools 10.8 Knowledge visualization templates 10.8.1 Mind maps 10.8.2 Swimlane diagrams 10.8.3 Matrix diagrams 10.8.4 Flowcharts 10.8.5 Concept maps 10.8.6 Funnel charts or diagrams 10.9 Visualization in machine learning 10.9.1 Decision trees 10.9.2 Decision graph 10.10 Conclusion References 11 Empowering machine learning with knowledge graphs for the semantic era Pethuru Raj and G. Jaspher Willsie Kathrine 11.1 Introduction 11.2 Tending towards digitally transformed enterprises 11.3 The emergence of KGs 11.4 Briefing the concept of KGs 11.5 Formalizing KGs 11.6 Creating custom KGs 11.7 Characterizing KGs 11.8 Use cases of KGs 11.9 ML and KGs 11.10 KGs for explainable and responsible AI 11.11 Stardog enterprise KG platform 11.12 What CANNOT be considered a KG? 11.13 Conclusion Bibliography 12 Enterprise knowledge graphs using ensemble learning and data management Janson Luke Ong Wai Kit, Vijanth Sagayan Asirvadam and Mohd. Fadzil B. Hassan 12.1 Introduction 12.2 Current ensemble model learning 12.2.1 Bagging 183 183 184 185 185 186 187 188 188 190 195 195 1,96 196 197 197 199 201 201 203 203 204 205 206 207 211 214 217 218 221 222 223 224 224 227 227 228 228 12.2.2 Boosting 12.2.3 Random Forest 12.3 Related work and literature review 12.4 Methodology 12.4.1 Enhanced
ensemble model framework 12.4.2 Training and testing datasets 12.4.3 Enhanced ensemble model and algorithm 12.5 Experimental setup and enterprise dataset 12.5.1 Ensemble models performance evaluation using enterprise knowledge graph 12.5.2 Tree classification as knowledge graph 12.6 Result and discussion 12.7 Conclusion References 13 Illustrating graph neural networks (GNNs) and the distinct applications Pethuru Raj, N. Susila and S. Usha 13.1 Introduction 13.2 Briefing the distinctions of graphs 13.3 The challenges 13.4 ML algorithms 13.5 DL algorithms 13.6 The emergence of GNNs 13.7 Demystifying DNNs on graph data 13.8 GNNs: the applications 13.9 The challenges for GNNs 13.10 Conclusion Bibliography 14 AI applications—computer vision and natural language processing J. Granty Regina Elwin and Vijayalakshmi Karunakaran 14.1 Object recognition 14.2 AI-powered video analytics 14.3 Contactless payments 14.4 Foot tracking 14.5 Animal detection 14.6 Airport facial recognition 14.7 Autonomous driving 14.8 Video surveillance 14.9 Healthcare medical detection 14.10 Computer vision in agriculture 14.10.1 Drone-based crop monitoring 14.10.2 Yield analysis xi 228 229 229 230 231 231 232 233 233 234 234 236 236 239 239 239 243 245 248 251 252 261 263 264 265 267 267 269 269 270 271 271 271 272 272 273 274 274
xii Contents XA/: concepts, enabling tools, technologies and applications 14.10.3 Smart systems for crop grading and sorting 14.10.4 Automated pesticide spraying 14.10.5 Phenotyping 14.10.6 Forest information 14.11 Computer vision in transportation 14.11.1 Safety and driver assistance 14.11.2 Traffic control 14.11.3 Driving autonomous vehicles 14.12 Computer vision in healthcare 14.13 Benefits of computer vision in healthcare 14.13.1 Reliable image analysis 14.13.2 Contemporary operating rooms 14.13.3 Improved patient identification 14.13.4 Faster medical research 14.14 Computer vision in manufacturing 14.15 Predictive maintenance 14.16 Reading text and barcodes 14.17 Inventory management 14.18 Defects reduction 14.19 3D vision inspection 14.20 Computer vision in retail 14.21 Virtual mirrors and recommendation engines 14.22 Self-checkout 14.23 Inventory management 14.24 Retail theft prevention 14.25 In-store advertisement 14.26 Computer vision in sports 14.27 Detection of rule violations using computer vision in sports 14.28 Object detection and ball tracking 14.29 Predicting the winner of a sporting event 14.30 Injury detection with computer vision 14.31 AI batting and pitching performance 14.32 Computer vision in education sector 14.33 Applications of computer vision in the education sector 14.33.1 AI for security at schools 14.33.2 Conducting online exams 14.33.3 Automated attendance monitoring 14.33.4 Facial emotion analysis 14.33.5 Reduce fraud instances 14.34 Natural language processing 14.35 E-mail filtering 14.36 Advantages of e-mail filtering using NLP 14.37
Disadvantages of e-mail filtering using NLP 14.38 Different types of machine translation in NLP 274 274 274 275 275 275 275 276 276 277 277 277 . 277 ' 277 277 277 278 278 278 278 279 279 279 279 279 280 280 280 280 280 281 281 281 281 281 281 282 282 282 282 283 283 284 284 Computer vision in construction Construction equipment detection and tracking Asset management and maintenance Dangerous goods sign recognition Automated inspection with AI vision process optimization and tracking Safety and security monitoring Computer vision in aviation Aircraft inspection and maintenance Intelligent baggage handling AI vision security at airports Face recognition at airports Computer vision in insurance AI technology trends for insurers Sensing the physical world Key technology trends in Insurtech Top applications of computer vision in insurance 14.55.1 Risk assessment with computer vision 14.56 AI in insurance for underwriting process automation 14.57 AI to understand new and complex risks References 14.39 14.40 14.41 14.42 14.43 14.44 14.45 14.46 14.47 14.48 14.49 14.50 14.51 14.52 14.53 14.54 14.55 15 Machine learning and computer vision - beyond modeling, training, and algorithms R. Ranjana, B. Narendra Kumar Rao, J. Raja, Nagendra Panini Challa and K. Reddy Madhavi 15.1 Introduction to machine learning 15.2 Classification of machine learning algorithms 15.2.1 Logistic regression 15.2.2 Support vector machines 15.2.3 К-nearest neighbors 15.2.4 Kernel SVM 15.2.5 Naive Bayes 15.2.6 Decision tree 15.2.7 Random forest 15.3 Applications where machine learning enhances efficiency
15.3.1 Video surveillance 15.3.2 Social media services 15.3.3 Health care 15.3.4 Online fraud detection 15.4 Evolution of machine learning algorithms 15.5 Techniques and framework 15.5.1 Scikit-learn 15.5.2 Amazon machine learning 15.5.3 MLib (Spark) xiii 285 285 285 286 286 286 287 287 287 288 288 289 289 289 289 290 290 290 290 291 291 293 293 294 294 294 295 295 295 295 295 295 295 295 296 296 296 296 296 296 296
xiv Contents XAI: concepts, enabling tools, technologies and applications 15.5.4 Azure ML Studio 15.5.5 Tensorflow 15.5.6 Veles 15.5.7 Caffe 15.5.8 Torch 15.5.9 H2O 15.5.10 Theano 15.6 Challenges in implementing machine learning 15.7 Emerging tools for ML 15.7.1 Scikit 15.8 Computer vision and relevant technologies 15.9 Scope for ML in computer vision - use of CNN and DL algorithms 15.9.1 Uses 15.10 Applications of advanced machine learning algorithms and computer vision 15.11 Integrated ML and CV solutions 15.12 Data models and data set for CV 15.13 Frameworks for integrating CV and ML 15.13.1 Keras 15.13.2 Tensorflow 15.13.3 Shogun 15.13.4 Caffe 15.13.5 OpenCV 15.14 CNN architectures 15.14.1 Architectures 15.14.2 Network-in-network 15.14.3 ZefNet 15.14.4 GoogLeNet 15.14.5 Highway network 15.14.6 ResNet 15.14.7 DenseNet 15.14.8 WideResNet 15.14.9 Residual attention neural network 15.14.10 Convolutional block attention module 15.14.11 CapsuleNet 15.14.12 High-resolution network (HRNet) 15.14.13 Architectures 15.15 Conclusion References 16 Assistive image caption and tweet development using deep learning Parth Birthare, R. Maheswari, R. Ganesan and P. Vijaya 16.1 Introduction 297 297 297 297 297 297 297 297 298 298 298 298 299 299 300 300 302 302 302 302 302 302 303 303 303 303 303 304 304 304 304 304 305 305 305 306 306 306 309 309 16.1.1 Deep learning models 16.1.2 Additional technology used 16.2 Literature review 16.3 Proposed system 16.4 AICT architecture 16.4.1 Import relevant packages 16.4.2 Acquire data and perform data cleaning 16.4.3 Extract feature vector from
images 16.4.4 Load dataset for training model 16.4.5 Tokenize vocabulary 16.4.6 Create a data generator 16.4.7 Define a CNN-RNN model 16.4.8 Train model 16.4.9 Test model 16.4.10 Test interface 16.5 Implementation and interface 16.6 Trust in system 16.7 Inference and future work 16.8 Conclusions References xn 310 311 311 312 313 315 315 315 315 315 316 316 316 316 318 318 322 324 325 325 17 Explainable renegotiation for SLA in cloud-based system Irving V. Paputungan 17.1 Introduction 17.2 Explainable artificial intelligence and cloud computing 17.3 SLA renegotiation 17.4 SLA violation 17.5 Weightage for SLOs 17.6 Manifold learning for SLO violation detection 17.7 Support vector regression for SLO violation prediction 17.8 Renegotiation simulation 17.9 Service violation detection and estimation 17.10 Simulation and result 17.11 Analysis and conclusion References 329 18 Explainable AI for stock price prediction in stock market Pattabiraman Venkatasubbu, Sharath Kumar Jagannathan, Anusooya Govindarajan and Maheswari Raja 18.1 Introduction 18.1.1 Explainable AI 18.2 Literature review 18.2.1 State of the art 18.3 Architecture 18.3.1 Pre-processing 347 329 330 331 332 333 334 334 335 336 337 343 343 347 349 350 351 354 355
xvi Contents XAI: concepts, enabling tools, technologies and applications 18.4 Model implementation 18.4.1 Data used in this analysis 18.4.2 Applying the moving average algorithm 18.4.3 Applying linear regression 18.4.4 KNN 18.4.5 LSTM 18.5 User interface website for stock prediction 18.6 Result and discussion 18.7 Conclusion References 19 Advancements of XAI in healthcare sector Gudi Varaprasad and J. Sheela 19.1 XAI in medical image analysis based on deep learning (DL) techniques 19.1.1 Enabling tools and technologies 19.1.2 Use cases and applications 19.2 XAI in clinical decision support systems using machine learning (ML) approaches 19.2.1 Enabling tools and technologies 19.2.2 Use cases and applications 19.3 XAI in healthcare 19.3.1 Enabling tools and technologies 19.3.2 Use cases and applications 19.4 Healthcare framework based on XAI and global digital surveillance to prevent pandemic 19.4.1 Enabling tools and technologies 19.4.2 Use cases and applications 19.5 Significantly enhance trust in health care with XAI 19.6 Skillful assessments of XAI techniques in the healthcare domain 19.6.1 Enabling tools and technologies 19.7 An alternative viewpoint on healthcare and XAI 19.7.1 Enabling tools and technologies 19.7.2 Use cases and applications 19.8 XAI techniques helpful to allergy diagnosis 19.8.1 Enabling tools and technologies 19.8.2 Use cases and applications 19.9 Conclusion References 20 Adequate lung cancer prognosis system using data mining algorithms Loshiga Mohan, S. Usha and N. Susila 20.1 Introduction 356 356 356 357 357 357 358 358 362 363 367 367 368 370
370 371 373 373 374 375 376 377 380 381 382 383 386 387 388 388 390 392 393 393 397 397 20.1.1 Motivations 20.1.2 Scope of data mining 20.1.3 Objectives 20.1.4 Research questions 20.2 Related work 20.3 Methodology 20.3.1 Performance measures 20.4 System design 20.4.1 Statistical features 20.4.2 Data mining 20.4.3 Data mining tasks 20.4.4 Data mining functionalities 20.4.5 Classification 20.5 Implementation 20.5.1 Naive Bayes algorithm 20.5.2 Decision tree algorithm 20.5.3 KNN algorithm 20.5.4 Random forest algorithm 20.5.5 WEKA 20.6 Testing and evaluation 20.6.1 Results 20.7 Conclusion and future work 20.7.1 Conclusion 20.7.2 Future work References 21 Comparison of artificial intelligence models for prognosis of breast cancer Anish Samantaray, C. Saravanan, Meghana Bollam, R. Maheswari and P. Vijaya 21.1 Introduction 21.2 Related work 21.3 Proposed work 21.3.1 Dataset 21.3.2 Data pre-processing 21.3.3 Algorithms used 21.3.4 Hyperparameters used 21.3.5 Performance metrics 21.3.6 Summary of the proposed work 21.4 Results and discussion 21.5 Conclusion References xvii 397 398 398 398 399 405 406 407 407 408 409 409 410 410 410 411 414 417 420 422 422 426 426 426 426 429 429 431 433 433 433 436 437 438 439 439 443 444
xviii XAI: concepts, enabling tools, technologies and applications 22 AI-powered virtual therapist: for enhanced human-machine interaction 447 Kondhare Mugdha Rajabhau, Ganachari Sai Yathin, T. Harsha Vardhan, R. Maheswari and Sharath Kumar Jagannathan 22.1 Introduction 447 22.2 Related work 449 22.3 Current existing systems 451 22.4 Proposed system 452 22.4.1 Emotion detection model 453 22.4.2 Voice-assisted bot 457 22.4.3 Integration 459 22.4.4 Applications 460 22.4.5 Limitations 460 22.5 Results and discussion 461 22.6 Future scope 462 22.7 Conclusion 463 References 463 23 Conclusion: an insight into the recent developments and future trends in XAI 467 Srihari Raghavendra Rao, Nibedita Panigrahi, A.P. Jyothi and S. Usha 23.1 Introduction 467 23.2 Need for XAI 468 23.3 An insight into operationalworking of XAI 472 23.4 Challenges of XAI 474 23.5 Benefits of XAI 475 23.6 Recent developmentsin XAI 477 23.6.1 Generative AI for content creation and chatbots 477 23.6.2 Man-made intelligence driven QA and investigation 478 23.6.3 Game-changing AI leaps forward in medical services 478 23.6.4 No-code AI stages in something like three regions 478 23.6.5 Diversity in AI 479 23.7 Future trends of XAI 479 23.8 Conclusion 482 References 482 Index 485 |
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| id | DE-604.BV049724788 |
| illustrated | Illustrated |
| indexdate | 2024-12-13T13:01:57Z |
| institution | BVB |
| isbn | 9781839536953 1839536950 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035067092 |
| oclc_num | 1454750769 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | xxiv, 504 Seiten Illustrationen, Diagramme 24 cm |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Institution of Engineering and Technology |
| record_format | marc |
| series | IET computing series |
| series2 | IET computing series |
| spelling | Explainable artificial intelligence (XAI) concepts, enabling tools, technologies and applications edited by Pethuru Raj, Utku Köse, Usha Sakthivel, Susila Nagarajan, Vijanth Sagayan Asirvadam First published Stevenage Institution of Engineering and Technology 2023 xxiv, 504 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier IET computing series 62 Erklärbare künstliche Intelligenz (DE-588)1263068472 gnd rswk-swf Artificial intelligence Intelligence artificielle artificial intelligence Erklärbare künstliche Intelligenz (DE-588)1263068472 s DE-604 Chelliah, Pethuru Raj ca. 20./21. Jahrhundert Sonstige (DE-588)1088794912 oth Köse, Utku 1985- Sonstige (DE-588)1189595796 oth Sakthivel, Usha ca. 20./21. Jh. Sonstige (DE-588)1337735701 oth Nagarajan, Susila ca. 20./21. Jh. Sonstige (DE-588)1337736139 oth Asirvadam, Vijanth S. ca. 20./21. Jh. Sonstige (DE-588)1337736554 oth ebook version 9781839536960 IET computing series 62 (DE-604)BV049807506 62 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035067092&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Explainable artificial intelligence (XAI) concepts, enabling tools, technologies and applications IET computing series Erklärbare künstliche Intelligenz (DE-588)1263068472 gnd |
| subject_GND | (DE-588)1263068472 |
| title | Explainable artificial intelligence (XAI) concepts, enabling tools, technologies and applications |
| title_auth | Explainable artificial intelligence (XAI) concepts, enabling tools, technologies and applications |
| title_exact_search | Explainable artificial intelligence (XAI) concepts, enabling tools, technologies and applications |
| title_full | Explainable artificial intelligence (XAI) concepts, enabling tools, technologies and applications edited by Pethuru Raj, Utku Köse, Usha Sakthivel, Susila Nagarajan, Vijanth Sagayan Asirvadam |
| title_fullStr | Explainable artificial intelligence (XAI) concepts, enabling tools, technologies and applications edited by Pethuru Raj, Utku Köse, Usha Sakthivel, Susila Nagarajan, Vijanth Sagayan Asirvadam |
| title_full_unstemmed | Explainable artificial intelligence (XAI) concepts, enabling tools, technologies and applications edited by Pethuru Raj, Utku Köse, Usha Sakthivel, Susila Nagarajan, Vijanth Sagayan Asirvadam |
| title_short | Explainable artificial intelligence (XAI) |
| title_sort | explainable artificial intelligence xai concepts enabling tools technologies and applications |
| title_sub | concepts, enabling tools, technologies and applications |
| topic | Erklärbare künstliche Intelligenz (DE-588)1263068472 gnd |
| topic_facet | Erklärbare künstliche Intelligenz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035067092&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV049807506 |
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