03745nam 22006495 450 991090719600332120241108115757.09789819772513981977251610.1007/978-981-97-7251-3(CKB)36517536800041(DE-He213)978-981-97-7251-3(MiAaPQ)EBC31850616(Au-PeEL)EBL31850616(OCoLC)1467928358(EXLCZ)993651753680004120241108d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierHydraulic Structure and Hydrodynamics /edited by Weiqiang Wang, Chengzhi Wang, Yang Lu1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (X, 490 p. 258 illus., 185 illus. in color.) Lecture Notes in Civil Engineering,2366-2565 ;6089789819772506 9819772508 Structural safety and testing of dams -- Study of hydraulic soil stability and seepage effects -- Hydrodynamics and rheology.This open access book delves into discussions central to hydraulic structures and research in the realm of hydrodynamics. Hydraulic structures stand as pivotal components within civil engineering and construction, playing a safeguarding role for structures vital to human development. Examples encompass the Hoover Dam in the USA, the Three Gorges Dam in China and the Almendra Dam in Salamanca, Spain. Monitoring the safety and ensuring the structural stability of hydraulic structures has long remained a focal point within hydraulic engineering. Factors affecting the safety of hydraulic structures, water pressure, and loading demand meticulous attention. The stability of structures and materials experiences degradation due to hydraulic impact and long-term corrosion, compromising the safety of hydraulic structures. The inability to adequately support and release water during flood season or flooding can result in irreversible damage. The book aims to furnish global civil engineers with cutting-edge research and engineering examples pertaining to the safety and hydrodynamics of hydraulic structures, with a particular emphasis on dam safety and inspection. It endeavors to inspire novel insights and research avenues for the readers and provide some experiences and results for disciplinary research in this field. The topics of this book include but are not limited to the following: 1. Structural safety and testing of dams 2. Study of hydraulic soil stability and seepage effects 3. Hydrodynamics and rheology.Lecture Notes in Civil Engineering,2366-2565 ;608Hydraulic engineeringCivil engineeringFoundationsEngineering geologyHydraulic EngineeringCivil EngineeringFoundation EngineeringHydraulic engineering.Civil engineering.Foundations.Engineering geology.Hydraulic Engineering.Civil Engineering.Foundation Engineering.627Wang Weiqiangedthttp://id.loc.gov/vocabulary/relators/edtWang Chengzhiedthttp://id.loc.gov/vocabulary/relators/edtLu Yangedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910907196003321Hydraulic Structure and Hydrodynamics4287884UNINA11332nam 22006133 450 991100482810332120231110221744.01-83724-485-51-5231-5346-61-83953-534-2(MiAaPQ)EBC30084091(Au-PeEL)EBL30084091(CKB)24846060300041(NjHacI)9924846060300041(BIP)084502323(OCoLC)1345584915(EXLCZ)992484606030004120220920d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierIntelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computing1st ed.Stevenage :Institution of Engineering & Technology,2022.©2022.1 online resource (372 pages)Computing and Networks 1-83953-533-4 Includes bibliographical references and index.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: 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.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.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 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.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.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 -- 13.1.1 What is an image? -- 13.2 Image processing.13.2.1 Equivalent image processing.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.Computing and Networks Internet of thingsCloud computingInternet of things.Cloud computing.004.6782Kumar Sunil868762Mapp Glenford1823790Cengiz Korhan1359531MiAaPQMiAaPQMiAaPQBOOK9911004828103321Intelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computing4390740UNINA