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9781119904885 |
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(print) |
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9781119905233 |
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(electronic bk. : oBook) |
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9781119905226 |
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(electronic bk.) |
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1119905222 |
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(electronic bk.) |
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1119904889 |
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10.1002/9781119905233 |
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(OCoLC)1370189360 |
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(OCoLC)1368356307 |
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(OCoLC)1368358588 |
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9781119904885 |
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O'Reilly Media |
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DG1 |
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eng |
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rda |
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DG1 |
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ORMDA |
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eng |
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QA76.585 |
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004.67/82 |
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23/eng/20230215 |
| 245 00 - TITLE STATEMENT |
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| Title |
Convergence of cloud with AI for big data analytics : |
| Remainder of title |
foundations and innovation / |
| Statement of responsibility, etc |
edited by Danda B. Rawat, Lalit K. Awasthi, Valentina Emilia Balas, Mohit Kumar and Jitendra Kumar Samriya. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Hoboken, NJ : |
| Name of publisher, distributor, etc |
Wiley ; |
| Place of publication, distribution, etc |
Beverly, MA : |
| Name of publisher, distributor, etc |
Scrivener Publishing, |
| Date of publication, distribution, etc |
2023. |
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1 online resource. |
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computer |
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online resource |
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Advances in learning analytics for intelligent Cloud-IoT Systems |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - CONTENTS |
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| Formatted contents note |
Table of Contents<br/>Preface xv<br/><br/>1 Integration of Artificial Intelligence, Big Data, and Cloud Computing with Internet of Things 1<br/>Jaydip Kumar<br/><br/>1.1 Introduction 2<br/><br/>1.2 Roll of Artificial Intelligence, Big Data and Cloud Computing in IoT 3<br/><br/>1.3 Integration of Artificial Intelligence with the Internet of Things Devices 4<br/><br/>1.4 Integration of Big Data with the Internet of Things 6<br/><br/>1.5 Integration of Cloud Computing with the Internet of Things 6<br/><br/>1.6 Security of Internet of Things 8<br/><br/>1.7 Conclusion 10<br/><br/>References 10<br/><br/>2 Cloud Computing and Virtualization 13<br/>Sudheer Mangalampalli, Pokkuluri Kiran Sree, Sangram K. Swain and Ganesh Reddy Karri<br/><br/>2.1 Introduction to Cloud Computing 14<br/><br/>2.1.1 Need of Cloud Computing 14<br/><br/>2.1.2 History of Cloud Computing 14<br/><br/>2.1.3 Definition of Cloud Computing 15<br/><br/>2.1.4 Different Architectures of Cloud Computing 16<br/><br/>2.1.4.1 Generic Architecture of Cloud Computing 16<br/><br/>2.1.4.2 Market Oriented Architecture of Cloud Computing 17<br/><br/>2.1.5 Applications of Cloud Computing in Different Domains 18<br/><br/>2.1.5.1 Cloud Computing in Healthcare 18<br/><br/>2.5.1.2 Cloud Computing in Education 19<br/><br/>2.5.1.3 Cloud Computing in Entertainment Services 19<br/><br/>2.5.1.4 Cloud Computing in Government Services 19<br/><br/>2.1.6 Service Models in Cloud Computing 19<br/><br/>2.1.7 Deployment Models in Cloud Computing 21<br/><br/>2.2 Virtualization 22<br/><br/>2.2.1 Need of Virtualization in Cloud Computing 22<br/><br/>2.2.2 Architecture of a Virtual Machine 23<br/><br/>2.2.3 Advantages of Virtualization 24<br/><br/>2.2.4 Different Implementation Levels of Virtualization 25<br/><br/>2.2.4.1 Instruction Set Architecture Level 25<br/><br/>2.2.4.2 Hardware Level 26<br/><br/>2.2.4.3 Operating System Level 26<br/><br/>2.2.4.4 Library Level 26<br/><br/>2.2.4.5 Application Level 26<br/><br/>2.2.5 Server Consolidation Using Virtualization 26<br/><br/>2.2.6 Task Scheduling in Cloud Computing 27<br/><br/>2.2.7 Proposed System Architecture 31<br/><br/>2.2.8 Mathematical Modeling of Proposed Task Scheduling Algorithm 31<br/><br/>2.2.9 Multi Objective Optimization 34<br/><br/>2.2.10 Chaotic Social Spider Algorithm 34<br/><br/>2.2.11 Proposed Task Scheduling Algorithm 35<br/><br/>2.2.12 Simulation and Results 36<br/><br/>2.2.12.1 Calculation of Makespan 36<br/><br/>2.2.12.2 Calculation of Energy Consumption 37<br/><br/>2.3 Conclusion 37<br/><br/>References 38<br/><br/>3 Time and Cost-Effective Multi-Objective Scheduling Technique for Cloud Computing Environment 41<br/>Aida A. Nasr, Kalka Dubey, Nirmeen El-Bahnasawy, Gamal Attiya and Ayman El-Sayed<br/><br/>3.1 Introduction 42<br/><br/>3.2 Literature Survey 44<br/><br/>3.3 Cloud Computing and Cloudlet Scheduling Problem 46<br/><br/>3.4 Problem Formulation 47<br/><br/>3.5 Cloudlet Scheduling Techniques 49<br/><br/>3.5.1 Heuristic Methods 50<br/><br/>3.5.2 Meta-Heuristic Methods 51<br/><br/>3.6 Cloudlet Scheduling Approach (CSA) 52<br/><br/>3.6.1 Proposed CSA 52<br/><br/>3.6.2 Time Complexity 53<br/><br/>3.6.3 Case Study 54<br/><br/>3.7 Simulation Results 56<br/><br/>3.7.1 Simulation Environment 56<br/><br/>3.7.2 Evaluation Metrics 56<br/><br/>3.7.2.1 Performance Evaluation with Small Number of Cloudlets 57<br/><br/>3.7.2.2 Performance Evaluation with Large Number of Cloudlets 57<br/><br/>3.8 Conclusion 64<br/><br/>References 64<br/><br/>4 Cloud-Based Architecture for Effective Surveillance and Diagnosis of COVID- 19 69<br/>Shweta Singh, Aditya Bhardwaj, Ishan Budhiraja, Umesh Gupta and Indrajeet Gupta<br/><br/>4.1 Introduction 70<br/><br/>4.2 Related Work 71<br/><br/>4.2.1 Proposed Cloud-Based Network for Management of COVID- 19 73<br/><br/>4.3 Research Methodology 75<br/><br/>4.3.1 Sample Size and Target 76<br/><br/>4.3.1.1 Sampling Procedures 77<br/><br/>4.3.1.2 Response Rate 77<br/><br/>4.3.1.3 Instrument and Measures 77<br/><br/>4.3.2 Reliability and Validity Test 78<br/><br/>4.3.3 Exploratory Factor Analysis 78<br/><br/>4.4 Survey Findings 80<br/><br/>4.4.1 Outcomes of the Proposed Scenario 82<br/><br/>4.4.1.1 Online Monitoring 82<br/><br/>4.4.1.2 Location Tracking 82<br/><br/>4.4.1.3 Alarm Linkage 82<br/><br/>4.4.1.4 Command and Control 82<br/><br/>4.4.1.5 Plan Management 82<br/><br/>4.4.1.6 Security Privacy 83<br/><br/>4.4.1.7 Remote Maintenance 83<br/><br/>4.4.1.8 Online Upgrade 83<br/><br/>4.4.1.9 Command Management 83<br/><br/>4.4.1.10 Statistical Decision 83<br/><br/>4.4.2 Experimental Setup 83<br/><br/>4.5 Conclusion and Future Scope 85<br/><br/>References 86<br/><br/>5 Smart Agriculture Applications Using Cloud and IoT 89<br/>Keshav Kaushik<br/><br/>5.1 Role of IoT and Cloud in Smart Agriculture 89<br/><br/>5.2 Applications of IoT and Cloud in Smart Agriculture 94<br/><br/>5.3 Security Challenges in Smart Agriculture 97<br/><br/>5.4 Open Research Challenges for IoT and Cloud in Smart Agriculture 100<br/><br/>5.5 Conclusion 103<br/><br/>References 103<br/><br/>6 Applications of Federated Learning in Computing Technologies 107<br/>Sambit Kumar Mishra, Kotipalli Sindhu, Mogaparthi Surya Teja, Vutukuri Akhil, Ravella Hari Krishna, Pakalapati Praveen and Tapas Kumar Mishra<br/><br/>6.1 Introduction 108<br/><br/>6.1.1 Federated Learning in Cloud Computing 108<br/><br/>6.1.1.1 Cloud-Mobile Edge Computing 109<br/><br/>6.1.1.2 Cloud Edge Computing 111<br/><br/>6.1.2 Federated Learning in Edge Computing 112<br/><br/>6.1.2.1 Vehicular Edge Computing 113<br/><br/>6.1.2.2 Intelligent Recommendation 113<br/><br/>6.1.3 Federated Learning in IoT (Internet of Things) 114<br/><br/>6.1.3.1 Federated Learning for Wireless Edge Intelligence 114<br/><br/>6.1.3.2 Federated Learning for Privacy Protected Information 115<br/><br/>6.1.4 Federated Learning in Medical Computing Field 116<br/><br/>6.1.4.1 Federated Learning in Medical Healthcare 117<br/><br/>6.1.4.2 Data Privacy in Healthcare 117<br/><br/>6.1.5 Federated Learning in Blockchain 118<br/><br/>6.1.5.1 Blockchain-Based Federated Learning Against End-Point Adversarial Data 118<br/><br/>6.2 Advantages of Federated Learning 119<br/><br/>6.3 Conclusion 119<br/><br/>References 119<br/><br/>7 Analyzing the Application of Edge Computing in Smart Healthcare 121<br/>Parul Verma and Umesh Kumar<br/><br/>7.1 Internet of Things (IoT) 122<br/><br/>7.1.1 IoT Communication Models 122<br/><br/>7.1.2 IoT Architecture 124<br/><br/>7.1.3 Protocols for IoT 125<br/><br/>7.1.3.1 Physical/Data Link Layer Protocols 125<br/><br/>7.1.3.2 Network Layer Protocols 127<br/><br/>7.1.3.3 Transport Layer Protocols 128<br/><br/>7.1.3.4 Application Layer Protocols 129<br/><br/>7.1.4 IoT Applications 130<br/><br/>7.1.5 IoT Challenges 132<br/><br/>7.2 Edge Computing 133<br/><br/>7.2.1 Cloud vs. Fog vs. Edge 134<br/><br/>7.2.2 Existing Edge Computing Reference Architecture 135<br/><br/>7.2.2.1 FAR-EDGE Reference Architecture 135<br/><br/>7.2.2.2 Intel-SAP Joint Reference Architecture (RA) 135<br/><br/>7.2.3 Integrated Architecture for IoT and Edge 136<br/><br/>7.2.4 Benefits of Edge Computing Based IoT Architecture 138<br/><br/>7.3 Edge Computing and Real Time Analytics in Healthcare 140<br/><br/>7.4 Edge Computing Use Cases in Healthcare 148<br/><br/>7.5 Future of Healthcare and Edge Computing 151<br/><br/>7.6 Conclusion 151<br/><br/>References 152<br/><br/>8 Fog-IoT Assistance-Based Smart Agriculture Application 157<br/>Pawan Whig, Arun Velu and Rahul Reddy Nadikattu<br/><br/>8.1 Introduction 158<br/><br/>8.1.1 Difference Between Fog and Edge Computing 159<br/><br/>8.1.1.1 Bandwidth 163<br/><br/>8.1.1.2 Confidence 164<br/><br/>8.1.1.3 Agility 164<br/><br/>8.1.2 Relation of Fog with IoT 165<br/><br/>8.1.3 Fog Computing in Agriculture 167<br/><br/>8.1.4 Fog Computing in Smart Cities 169<br/><br/>8.1.5 Fog Computing in Education 170<br/><br/>8.1.6 Case Study 171<br/><br/>Conclusion and Future Scope 173<br/><br/>References 173<br/><br/>9 Internet of Things in the Global Impacts of COVID-19: A Systematic Study 177<br/>Shalini Sharma Goel, Anubhav Goel, Mohit Kumar and Sachin Sharma<br/><br/>9.1 Introduction 178<br/><br/>9.2 COVID-19 – Misconceptions 181<br/><br/>9.3 Global Impacts of COVID-19 and Significant Contributions of IoT in Respective Domains to Counter the Pandemic 183<br/><br/>9.3.1 Impact on Healthcare and Major Contributions of IoT 183<br/><br/>9.3.2 Social Impacts of COVID-19 and Role of IoT 187<br/><br/>9.3.3 Financial and Economic Impact and How IoT Can Help to Shape Businesses 188<br/><br/>9.3.4 Impact on Education and Part Played by IoT 191<br/><br/>9.3.5 Impact on Climate and Environment and Indoor Air Quality Monitoring Using IoT 194<br/><br/>9.3.6 Impact on Travel and Tourism and Aviation Industry and How IoT is Shaping its Future 197<br/><br/>9.4 Conclusions 198<br/><br/>References 198<br/><br/>10 An Efficient Solar Energy Management Using IoT-Enabled Arduino-Based MPPT Techniques 205<br/>Rita Banik and Ankur Biswas<br/><br/>List of Symbols 206<br/><br/>10.1 Introduction 206<br/><br/>10.2 Impact of Irradiance on PV Efficiency 210<br/><br/>10.2.1 PV Reliability and Irradiance Optimization 211<br/><br/>10.2.1.1 PV System Level Reliability 211<br/><br/>10.2.1.2 PV Output with Varying Irradiance 211<br/><br/>10.2.1.3 PV Output with Varying Tilt 212<br/><br/>10.3 Design and Implementation 212<br/><br/>10.3.1 The DC to DC Buck Converter 215<br/><br/>10.3.2 The Arduino Microcontroller 217<br/><br/>10.3.3 Dynamic Response 219<br/><br/>10.4 Result and Discussions 220<br/><br/>10.5 Conclusions 223<br/><br/>References 224<br/><br/>11 Axiomatic Analysis of Pre-Processing Methodologies Using Machine Learning in Text Mining: A Social Media Perspective in Internet of Things 229<br/>Tajinder Singh, Madhu Kumari, Daya Sagar Gupta and Nikolai Siniak<br/><br/>11.1 Introduction 230<br/><br/>11.2 Text Pre-Processing – Role and Characteristics 232<br/><br/>11.3 Modern Pre-Processing Methodologies and Their Scope 234<br/><br/>11.4 Text Stream and Role of Clustering in Social Text Stream 241<br/><br/>11.5 Social Text Stream Event Analysis 242<br/><br/>11.6 Embedding 244<br/><br/>11.6.1 Type of Embeddings 244<br/><br/>11.7 Description of Twitter Text Stream 250<br/><br/>11.8 Experiment and Result 251<br/><br/>11.9 Applications of Machine Learning in IoT (Internet of Things) 251<br/><br/>11.10 Conclusion 252<br/><br/>References 252<br/><br/>12 APP-Based Agriculture Information System for Rural Farmers in India 257<br/>Ashwini Kumar, Dilip Kumar Choubey, Manish Kumar and Santosh Kumar<br/><br/>12.1 Introduction 258<br/><br/>12.2 Motivation 259<br/><br/>12.3 Related Work 260<br/><br/>12.4 Proposed Methodology and Experimental Results Discussion 262<br/><br/>12.4.1 Mobile Cloud Computing 266<br/><br/>12.4.2 XML Parsing and Computation Offloading 266<br/><br/>12.4.3 Energy Analysis for Computation Offloading 267<br/><br/>12.4.4 Virtual Database 269<br/><br/>12.4.5 App Engine 270<br/><br/>12.4.6 User Interface 272<br/><br/>12.4.7 Securing Data 273<br/><br/>12.5 Conclusion and Future Work 274<br/><br/>References 274<br/><br/>13 SSAMH – A Systematic Survey on AI-Enabled Cyber Physical Systems in Healthcare 277<br/>Kamalpreet Kaur, Renu Dhir and Mariya Ouaissa<br/><br/>13.1 Introduction 278<br/><br/>13.2 The Architecture of Medical Cyber-Physical Systems 278<br/><br/>13.3 Artificial Intelligence-Driven Medical Devices 282<br/><br/>13.3.1 Monitoring Devices 282<br/><br/>13.3.2 Delivery Devices 283<br/><br/>13.3.3 Network Medical Device Systems 283<br/><br/>13.3.4 IT-Based Medical Device Systems 284<br/><br/>13.3.5 Wireless Sensor Network-Based Medical Driven Systems 285<br/><br/>13.4 Certification and Regulation Issues 285<br/><br/>13.5 Big Data Platform for Medical Cyber-Physical Systems 286<br/><br/>13.6 The Emergence of New Trends in Medical Cyber-Physical Systems 288<br/><br/>13.7 Eminence Attributes and Challenges 289<br/><br/>13.8 High-Confidence Expansion of a Medical Cyber-Physical Expansion 290<br/><br/>13.9 Role of the Software Platform in the Interoperability of Medical Devices 291<br/><br/>13.10 Clinical Acceptable Decision Support Systems 291<br/><br/>13.11 Prevalent Attacks in the Medical Cyber-Physical Systems 292<br/><br/>13.12 A Suggested Framework for Medical Cyber-Physical System 294<br/><br/>13.13 Conclusion 295<br/><br/>References 296<br/><br/>14 ANN-Aware Methanol Detection Approach with CuO-Doped SnO 2 in Gas Sensor 299<br/>Jitendra K. Srivastava, Deepak Kumar Verma, Bholey Nath Prasad and Chayan Kumar Mishra<br/><br/>14.1 Introduction 300<br/><br/>14.1.1 Basic ANN Model 300<br/><br/>14.1.2 ANN Data Pre- and Post-Processing 303<br/><br/>14.1.2.1 Activation Function 304<br/><br/>14.2 Network Architectures 305<br/><br/>14.2.1 Feed Forward ANNs 305<br/><br/>14.2.2 Recurrent ANNs Topologies 307<br/><br/>14.2.3 Learning Processes 308<br/><br/>14.2.3.1 Supervised Learning 308<br/><br/>14.2.3.2 Unsupervised Learning 308<br/><br/>14.2.4 ANN Methodology 309<br/><br/>14.2.5 1%CuO–Doped SnO 2 Sensor for Methanol 309<br/><br/>14.2.6 Experimental Result 311<br/><br/>References 327<br/><br/>15 Detecting Heart Arrhythmias Using Deep Learning Algorithms 331<br/>Dilip Kumar Choubey, Chandan Kumar Jha, Niraj Kumar, Neha Kumari and Vaibhav Soni<br/><br/>15.1 Introduction 332<br/><br/>15.1.1 Deep Learning 333<br/><br/>15.2 Motivation 334<br/><br/>15.3 Literature Review 334<br/><br/>15.4 Proposed Approach 366<br/><br/>15.4.1 Dataset Descriptions 367<br/><br/>15.4.2 Algorithms Description 369<br/><br/>15.4.2.1 Dense Neural Network 369<br/><br/>15.4.2.2 Convolutional Neural Network 370<br/><br/>15.4.2.3 Long Short-Term Memory 372<br/><br/>15.5 Experimental Results of Proposed Approach 376<br/><br/>15.6 Conclusion and Future Scope 379<br/><br/>References 380<br/><br/>16 Artificial Intelligence Approach for Signature Detection 387<br/>Amar Shukla, Rajeev Tiwari, Saurav Raghuvanshi, Shivam Sharma and Shridhar Avinash<br/><br/>16.1 Introduction 387<br/><br/>16.2 Literature Review 390<br/><br/>16.3 Problem Definition 392<br/><br/>16.4 Methodology 392<br/><br/>16.4.1 Data Flow Process 394<br/><br/>16.4.2 Algorithm 395<br/><br/>16.5 Result Analysis 397<br/><br/>16.6 Conclusion 399<br/><br/>References 399<br/><br/>17 Comparison of Various Classification Models Using Machine Learning to Predict Mobile Phones Price Range 401<br/>Chinu Singla and Chirag Jindal<br/><br/>17.1 Introduction 402<br/><br/>17.2 Materials and Methods 403<br/><br/>17.2.1 Dataset 403<br/><br/>17.2.2 Decision Tree 403<br/><br/>17.2.2.1 Basic Algorithm 404<br/><br/>17.2.3 Gaussian Naive Bayes (GNB) 404<br/><br/>17.2.3.1 Basic Algorithm 405<br/><br/>17.2.4 Support Vector Machine 405<br/><br/>17.2.4.1 Basic Algorithm 406<br/><br/>17.2.5 Logistic Regression (LR) 407<br/><br/>17.2.5.1 Basic Algorithm 407<br/><br/>17.2.6 K-Nearest Neighbor 408<br/><br/>17.2.6.1 Basic Algorithm 409<br/><br/>17.2.7 Evaluation Metrics 409<br/><br/>17.3 Application of the Model 410<br/><br/>17.3.1 Decision Tree (DT) 411<br/><br/>17.3.2 Gaussian Naive Bayes 411<br/><br/>17.3.3 Support Vector Machine 412<br/><br/>17.3.4 Logistic Regression 412<br/><br/>17.3.5 K Nearest Neighbor 413<br/><br/>17.4 Results and Comparison 413<br/><br/>17.5 Conclusion and Future Scope 418<br/><br/>References 418<br/><br/>Index 421 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
CONVERGENCE of CLOUD with AI for BIG DATA ANALYTICS This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services. The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework. Audience Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals. |
| 545 0# - BIOGRAPHICAL OR HISTORICAL DATA |
|---|
| Biographical or historical note |
About the Author<br/>Danda B Rawat, PhD, is a Full Professor in the Department of Electrical Engineering & Computer Science (EECS), Founder and Director of the Howard University Data Science and Cybersecurity Center, Director of DoD Center of Excellence in Artificial Intelligence & Machine Learning, Director of Cyber-security and Wireless Networking Innovations Research Lab, Graduate Program Director of Howard CS Graduate Programs, and Director of Graduate Cybersecurity Certificate Program at Howard University, Washington, DC, USA. Dr. Rawat has published more than 250 scientific/technical articles and 11 books.<br/><br/>Lalit K Awasthi, PhD, is the Director of Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India). He received his PhD degree from the Indian Institute of Technology Roorkee in computer science and engineering. He has published more than 150 research papers in various journals and conferences of international repute and guided many PhDs in these areas.<br/><br/>Valentina E Ballas, PhD, is a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. Dr. Ballas is the author of more than 280 research papers in refereed journals and international conferences. She is the Editor-in-Chief of International Journal of Advanced Intelligence Paradigms and International Journal of Computational Systems Engineering.<br/><br/>Mohit Kumar, PhD, is an assistant professor in the Department of Information Technology at Dr. B R Ambedkar National Institute of Technology, Jalandhar, India. He received his PhD degree from the Indian Institute of Technology Roorkee in the field of cloud computing in 2018. His research topics cover the areas of cloud computing, fog computing, edge computing, Internet of Things, soft computing, and blockchain. He has published more than 25 research articles in international journals and conferences.<br/><br/>Jitendra Kumar Samriya, PhD, has a faculty position in the Department of Information Technology, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar. His research interest is cloud computing, artificial intelligence, and multi-objective evolutionary optimization techniques. He has published 15 research articles in international journals and has published five Indian and international patents. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Cloud computing. |
| Authority record control number |
https://id.loc.gov/authorities/subjects/sh2008004883. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence. |
| Authority record control number |
https://id.loc.gov/authorities/subjects/sh85008180. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Big data. |
| Authority record control number |
https://id.loc.gov/authorities/subjects/sh2012003227. |
| 655 #4 - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Rawat, Danda B., |
| Dates associated with a name |
1977- |
| Authority record control number |
https://id.loc.gov/authorities/names/n2013041943 |
| Relator term |
editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Awasthi, Lalit Kumar, |
| Authority record control number |
https://id.loc.gov/authorities/names/n2021053620 |
| Relator term |
editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Balas, Valentina Emilia, |
| Authority record control number |
https://id.loc.gov/authorities/names/no2009108627 |
| Relator term |
editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kumar, Mohit, |
| Relator term |
editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Samriya, Jitendra Kumar, |
| Relator term |
editor. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
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| Uniform title |
Advances in learning analytics for intelligent Cloud-IoT Systems |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119905233 |
| Link text |
Full text is available at Wiley Online Library Click here to view |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
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| Item type |
EBOOK |