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[null,"1.","Record Nr.",null,"UNINA9911004828103321"]
[null,null,"Autore",null,"Kumar Sunil"]
[null,null,"Titolo",null,"Intelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computing"]
[null,null,"Pubbl/distr/stampa",null,"Stevenage : , : Institution of Engineering & Technology, , 2022"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-83724-485-5"]
[{"$nulls":4},"1-5231-5346-6"]
[{"$nulls":4},"1-83953-534-2"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (372 pages)"]
[null,null,"Collana",null,"Computing and Networks"]
[null,null,"Disciplina",null,"004.6782"]
[null,null,"Soggetti",null,"Internet of things"]
[{"$nulls":4},"Cloud computing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Halftitle Page -- Series Page -- Title Page -- Copyright -- Contents -- About the Editors -- 1 Introduction to intelligent network design driven by big data analytics, IoT, AI and cloud computing -- Preface -- Chapter 2: Role of automation, Big Data, AI, ML IBN, and cloud computing in intelligent networks -- Chapter 3: An intelligent verification management approach for efficient VLSI computing system -- Chapter 4: Evaluation of machine learning algorithms on academic big dataset by using feature selection techniques -- Chapter 5: Accurate management and progression of Big Data analysis -- Chapter 6: Cram on data recovery and backup cloud computing techniques -- Chapter 7: An adaptive software defined networking (SDN) for load balancing in cloud computing -- Chapter 8: Emerging security challenges in cloud computing: An insight -- Chapter 9: Factors responsible and phases of speaker recognition system -- Chapter 10: IoT-based water quality assessment using fuzzy logic controller -- Chapter 11: Design and analysis of wireless sensor network for intelligent transportation and industry automation -- Chapter 12: A review of edge computing in healthcare Internet of Things: theories, practices, and challenges -- Chapter 13: Image processing for medical images on the basis of intelligence and bio computing -- Chapter 14: IoT-based architecture for smart health-care systems -- Chapter 15: IoT-based heart disease prediction system -- Chapter 16: DIAIF: "]
[{"$nulls":4},"detection of interest flooding using artificial intelligence-based framework in NDN android -- Chapter 17: Intelligent and cost-effective mechanism for monitoring road quality using machine learning -- References -- 2 Role of automation, Big Data, AI, ML IBN, and cloud computing in intelligent networks -- 2.1 Evolution of networks: everything is connected -- 2.1.1 Intelligent devices."]
[{"$nulls":4},"2.1.2 Intelligent devices connection with networks -- 2.2 Huge volume of data generation by intelligent devices -- 2.2.1 Issues and challenges of Big Data Analytics -- 2.2.2 Storage of Big Data -- 2.3 Need of data analysis by business -- 2.3.1 Sources of information -- 2.3.2 Data visualization -- 2.3.3 Analyzing Big Data for effective use of business -- 2.3.4 Intelligent devices thinking intelligently -- 2.4 Artificial intelligence and machine learning in networking -- 2.4.1 Role of ML in networks -- 2.5 Intent-based networking -- 2.6 Role of programming -- 2.6.1 Basic programming using Blockly -- 2.6.2 Blockly games -- 2.7 Role of technology to design a model -- 2.7.1 Electronic toolkits -- 2.7.2 Programming resources -- 2.8 Relation of AI, ML, and IBN -- 2.9 Business challenges and opportunities -- 2.9.1 The evolving job market -- 2.10 Security -- 2.10.1 Challenges to secure device and networks -- 2.11 Summary -- References -- 3 An intelligent verification management approach for efficient VLSI computing system -- 3.1 Introduction -- 3.2 Literature study -- 3.3 Verification management approach: Case Study 1 -- 3.3.1 The pseudo random number generator in a verification environment -- 3.3.2 Implementation of PRNG in higher abstraction language and usage of DPI -- 3.4 Verification management approach: Case Study 2 -- 3.5 Challenges and research direction -- 3.5.1 Challenges in intelligent systems -- 3.6 Conclusion -- References -- 4 Evaluation of machine learning algorithms on academic big dataset by using feature selection techniques -- 4.1 Introduction -- 4.1.1 EDM -- 4.1.2 EDM process -- 4.1.3 Methods and techniques -- 4.1.4 Application areas of data mining -- 4.2 Literature survey -- 4.3 Materials and methods -- 4.3.1 Dataset description -- 4.3.2 Classification algorithms -- 4.3.3 FS algorithms -- 4.3.4 Data preprocessing phase."]
[{"$nulls":4},"4.4 Implementation of the proposed algorithms -- 4.4.1 Model construction for the standard classifier -- 4.4.2 Implementation after attribute selection using ranker method -- 4.5 Result analysis and discussion -- 4.6 Conclusion -- References -- 5 Accurate management and progression of Big Data Analysis -- 5.1 Introduction -- 5.1.1 Examples of Big Data -- 5.2 Big Data Analysis -- 5.2.1 Life cycle of Big Data -- 5.2.2 Classification of the Big Data -- 5.2.3 Working of Big Data Analysis -- 5.2.4 Common flaws that undermine Big Data Analysis -- 5.2.5 Advantages of Big Data Analysis -- 5.3 Processing techniques -- 5.3.1 Traditional method -- 5.3.2 MapReduce -- 5.3.3 Advantages of MapReduce -- 5.4 Cyber crime -- 5.4.1 Different strategies in Big Data to help in various circumstances -- 5.4.2 Big Data Analytics and cybercrime -- 5.4.3 Security issues associated with Big Data -- 5.4.4 Big Data Analytics in digital forensics -- 5.5 Real-time edge analytics for Big Data in IoT -- 5.6 Conclusion -- References -- 6 Cram on data recovery and backup cloud computing techniques -- 6.1 Introduction -- 6.1.1 Origin of cloud -- 6.1.2 Sole features of cloud computing -- 6.1.3 Advantages of cloud computing -- 6.1.4 Disadvantages of cloud computing -- 6.2 Classification of data recovery and backup -- 6.2.1 Recovery -- 6.2.2 Backup -- 6.3 Study on data recovery and backup cloud computing techniques -- 6.3.1 Backup of real-time data and recovery using cloud computing -- 6.3.2 Data recovery and security in cloud -- 6.3.3 Amoeba: An autonomous backup and recovery solid-state drives for ransomware attack defense -- 6.3.4 A cloud-based "]
[{"$nulls":4},"automatic recovery and backup system for video compression -- 6.3.5 Efficient and reliable data recovery techniques in cloud computing -- 6.3.6 Cost-efficient remote backup services for enterprise cloud."]
[{"$nulls":4},"6.3.7 DR-cloud: Multi-cloud-based disaster recovery service -- 6.4 Conclusion -- References -- 7 An adaptive software-defined networking (SDN) for load balancing in cloud computing -- 7.1 Introduction -- 7.2 Related works -- 7.3 Architecture overview of SDN -- 7.4 Load-balancing framework in SDN -- 7.4.1 Classification of SDN controller architectures -- 7.5 Problem statement -- 7.5.1 Selection strategy of controller head -- 7.5.2 Network setup -- 7.6 Illustration -- 7.7 Results and discussion -- 7.7.1 Comparison of throughput -- 7.7.2 Comparison of PTR -- 7.7.3 Comparison of number of packet loss -- 7.8 Conclusion -- References -- 8 Emerging security challenges in cloud computing: an insight -- 8.1 Introduction -- 8.1.1 An introduction to cloud computing and its security -- 8.2 The security issues in different cloud models and threat management techniques -- 8.2.1 Five most indispensable characteristics -- 8.2.2 The security issues in cloud service model -- 8.2.3 Security issues in cloud deployment models -- 8.2.4 Security challenges in the cloud environment -- 8.2.5 The threat management techniques -- 8.3 Review on existing proposed models -- 8.3.1 SeDaSC -- 8.3.2 The 'SecCloud' protocol -- 8.3.3 Data accountability and auditing for secure cloud data storage -- 8.4 Conclusion and future prospectives -- References -- 9 Factors responsible and phases of speaker recognition system -- 9.1 Study of related research -- 9.2 Phases of speaker recognition system -- 9.2.1 Speaker database collection -- 9.2.2 Feature extraction -- 9.2.3 Feature mapping -- 9.3 Basics of speech signals -- 9.3.1 Speech production system -- 9.3.2 Speech perception -- 9.3.3 Speech signals -- 9.3.4 Properties of the sinusoids -- 9.3.5 Windowing signals -- 9.3.6 Zero-crossing rate -- 9.3.7 Autocorrelation -- 9.4 Features of speech signals -- 9.4.1 Physical features."]
[{"$nulls":4},"9.4.2 Perceptual features -- 9.4.3 Signal features -- 9.5 Localization of speaker -- 9.6 Conclusion -- References -- 10 IoT-based water quality assessment using fuzzy logic controller -- 10.1 Introduction -- 10.2 Experimental procedures -- 10.3 Working -- 10.4 Results and discussions -- 10.5 Conclusion -- References -- 11 Design and analysis of wireless sensor network for intelligent transportation and industry automation -- 11.1 Introduction -- 11.2 Wireless sensor network -- 11.3 WSN application -- 11.4 Limitations of WSN -- 11.5 Literature survey -- 11.6 Related work -- 11.7 Methodology -- 11.7.1 Throughput -- 11.7.2 Delay -- 11.7.3 Packet delivery ratio -- 11.7.4 Design of WiMAX-based WSN system -- 11.8 Related results -- 11.9 Conclusion -- 11.10 Future scope -- References -- 12 A review of edge computing in healthcare Internet of things: theories, practices and challenges -- 12.1 Introduction -- 12.2 Cloud computing in healthcare and its limitations -- 12.2.1 Public cloud -- 12.2.2 Private cloud -- 12.2.3 Hybrid cloud -- 12.2.4 Community cloud -- 12.3 Edge computing and its advantages over cloud computing -- 12.3.1 Advantages of edge/fog computing -- 12.3.2 Disadvantages of edge/fog computing -- 12.4 IoT in healthcare -- 12.5 Edge computing in healthcare -- 12.6 Machine learning in healthcare -- 12.7 Integrated role of IOT, ML and edge computing in healthcare -- 12.7.1 Patient care during surgical procedure -- 12.7.2 Patient care at home -- 12.7.3 Patient care in ambulance -- 12.8 Modelling and simulation tools for edge/fog computing -- 12.9 Edge computing in Covid-19 pandemic era -- 12.10 Challenges of edge computing -- 12.11 Conclusion -- References -- 13 Image Processing for medical images on the basis of intelligence and biocomputing -- 13.1 Introduction -- "]
[{"$nulls":4},"13.1.1 What is an image? -- 13.2 Image processing."]
[{"$nulls":4},"13.2.1 Equivalent image processing."]
[null,null,null,"Sommario/riassunto","This book shows how innovations in network analytics, IoTs and cloud computing platforms are being used to ingest, analyse and correlate a myriad of big data across the entire network stack in order to increase quality of service and quality of experience (QoS/QoE) and to improve network performance."]