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  • 1
    Online Resource
    Online Resource
    Cham :Springer International Publishing AG,
    Keywords: Internet of things. ; Drone aircraft-Automatic control. ; Aerial surveillance-Automatic control. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (275 pages)
    Edition: 1st ed.
    ISBN: 9783030633394
    Series Statement: Studies in Systems, Decision and Control Series ; v.332
    DDC: 629.1333902854678
    Language: English
    Note: Intro -- Preface -- Contents -- About the Editors -- The Swarm Is More Than the Sum of Its Drones -- 1 Introduction -- 1.1 From Drones to Swarms -- 1.2 The Challenge of Deploying a Swarm Indoors -- 1.3 Contributions of This Chapter -- 1.4 Situating the Chapter in the Context of the Literature -- 1.5 The Intended Audience for this Chapter -- 2 Background -- 2.1 An Internet of Drones -- 2.2 Application Areas for Drones, and Swarms of Drones -- 2.3 Swarming Behaviour in Natural Sciences -- 2.4 Summary -- 3 Modelling Indoor WSN Deployment -- 3.1 The Model for the Drones -- 3.2 The Model for the Application -- 3.3 The Model for the Scenario/Environments -- 3.4 Performance Measures -- 3.5 Defining Characteristics of the Approach -- 4 A Decentralised and Self-organising Approach -- 4.1 Inspiration from Nature -- 4.2 A Voronoi Tessellation-Based Approach -- 4.3 Defining Characteristics -- 4.4 The BISON Algorithm Family -- 4.5 Materials and Methods -- 4.6 Results and Discussion -- 5 The World Is a Messy Place (Full of Uncertainty and Noise) -- 5.1 The Impact of Noise -- 5.2 Materials and Methods -- 5.3 Results and Discussion -- 6 Swarm Behaviour: Drift and Diffusion -- 6.1 Measuring Group Behaviour in Biology: Drift and Diffusion -- 6.2 Materials and Methods -- 6.3 Results and Discussion -- 7 Conclusion -- 7.1 Towards an InterNET of Drones: Swarming Behaviour -- 7.2 A Word of Caution -- 8 Abbreviations and Notations -- References -- Underwater Drones for Acoustic Sensor Network -- 1 Introduction -- 2 Dynamics of Drone Modeling -- 3 Underwater Acoustic Architecture -- 3.1 Static Two-Dimensional UW-ANs -- 3.2 Static Three-Dimensional UW-ANs -- 3.3 Three-Dimensional Hybrid UW-ANs -- 4 Control and Navigation of Drone Vehicle -- 4.1 Control Law Subsystem -- 4.2 Control Allocation Subsystem -- 4.3 Navigation of AUV's -- 5 Design Issues of Acoustic Drones. , 6 Underwater Network Challenges -- 7 Emerging Applications of Underwater Drones -- 8 Conclusion and Discussion -- References -- Smart Agriculture Using IoD: Insights, Trends and Road Ahead -- 1 Introduction -- 2 Remote Sensing and Digital Image Processing for Environment Monitoring by Drones -- 2.1 Remote Sensing -- 2.2 Remote Sensing Platforms in Agriculture -- 2.3 Digital Image Processing -- 3 Drone Technology and Developments -- 3.1 Drone Systems -- 3.2 Components of an Agricultural Internet of Drone -- 3.3 Imaging Cameras Used by Agricultural Drones -- 4 Applications of Drones in Agriculture -- 4.1 Soil Properties Analysis -- 4.2 Planting and Weed Control -- 4.3 Crop Spraying -- 4.4 Crop Monitoring -- 4.5 Irrigation -- 4.6 Health Assessment -- 5 Case Study -- 6 Challenges in IoD -- 7 Conclusion -- 8 Future Work -- References -- Towards a Smarter Surveillance Solution: The Convergence of Smart City and Energy Efficient Unmanned Aerial Vehicle Technologies -- 1 Introduction -- 2 Related Work -- 2.1 Smart City and Challenges in Surveillance Use Case -- 2.2 Unmanned Aerial Vehicles -- 2.3 Internet of Drones -- 2.4 Energy Conservation in Drones -- 3 Energy Management Proposed Framework for UAVs -- 3.1 Tilt-Rotor Drone-Based Energy Conservation Framework for UAVs in Smart City Surveillance -- 4 Case Study on IoD Based Surveillance -- 4.1 Case Study on Singapore: An Energy-Efficient Smart City -- 4.2 Case Study on India: Smart City Surveillance in India -- 5 Potential Implications and Suggestion for Future Research in Smart City Surveillance -- 6 Conclusion -- References -- Efficient Design of Airfoil Employing XFLR for Smooth Aerodynamics of Drone -- 1 Introduction -- 1.1 Airfoil: Heart of Aircraft -- 2 Motivation -- 3 Science-Behind Drone Flight -- 4 Understanding the Heart of Aircraft-Airfoil -- 5 Results -- 6 Future Scope -- 7 Conclusion. , References -- Drone-Based Monitoring and Redirecting System -- 1 Introduction -- 2 Related Work -- 3 Case Studies -- 4 Conclusion -- References -- Drone Application in Smart Cities: The General Overview of Security Vulnerabilities and Countermeasures for Data Communication -- 1 Introduction -- 2 Background -- 2.1 Smart City -- 2.2 Drones -- 2.3 Challenges for Drones in Smart Cities -- 2.4 Data Communication Methods -- 3 Cybersecurity -- 3.1 Threats and Attacks -- 4 Security Countermeasures -- 4.1 Detection Measures -- 4.2 Defense Mechanisms -- 5 Conclusion -- References -- Cloud-Based Drone Management System in Smart Cities -- 1 Introduction -- 2 Related Works -- 3 Cloud-Based Drone Management System -- 3.1 Physical Layer -- 3.2 Cloud Layer -- 3.3 Control Layer -- 4 Components of Drone -- 4.1 Software -- 4.2 Hardware -- 4.3 Communication Method -- 5 Experimental Study and Evaluation -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 5.3 Discussion and Future Research Directions -- 6 Conclusion -- References -- Smart Agriculture: IoD Based Disease Diagnosis Using WPCA and ANN -- 1 Introduction -- 2 Literature Review -- 3 Proposed System Design -- 3.1 The IoD Based System Architecture -- 4 Result and Discussion -- 5 Performance Analysis -- 6 Conclusion -- References -- DroneMap: An IoT Network Security in Internet of Drones -- 1 Introduction -- 2 Overview of IoD and UAV -- 2.1 Layers of UAV Drone -- 2.2 UAV Sensor Technologies in 5G Networks -- 3 Security Threats and Attacks of IoDT (Internet of Drones Things) -- 4 Security Issues Associated by IoD -- 5 Evaluation of IoD Control Sensors -- 6 Conclusions -- References.
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  • 2
    Online Resource
    Online Resource
    Cham :Springer International Publishing AG,
    Keywords: Artificial intelligence. ; Computational intelligence. ; Communications engineering, networks. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (244 pages)
    ISBN: 9783030828004
    DDC: 006.3
    Language: English
    Note: Intro -- Preface -- Contents -- About the Authors -- Applications of Internet of Things (IoT) in Green Computing -- 1 Introduction -- 2 Internet of Things (IoT) -- 3 Green Computing and Green IoT -- 3.1 Principles of Green IoT -- 4 IoT in Green Computing -- 4.1 Applications of IoT Green Computing Based on Policies -- 4.1.1 IoT-Based Green Campus -- 4.1.2 Green Agriculture and Green Health Care -- 4.1.3 Intelligent Automobiles -- 4.1.4 Intelligent Houses -- 4.1.5 Intelligent Protection/Security -- 4.2 IoTGC Applications Based on Software -- 4.2.1 Cloud Computing -- 4.2.2 Edge Computing -- 4.2.3 Fog Computing -- 4.2.4 Telecommuting -- 4.2.5 Based on Data Center -- 4.2.6 Virtualization Based -- 4.3 Hardware-based IoTGC Applications -- 4.3.1 RFID Based -- 4.3.2 Based on Integrated Circuit -- 4.3.3 Based on Processor -- 4.3.4 Based on Sensor -- 4.4 Applications of IoT Green Computing Based on Awareness -- 4.4.1 Usefulness of Ambient Notification -- 4.4.2 Proper Visualization of Information in Public Sectors -- 4.5 IoTGC Applications Based on Recycling -- 5 A Case Study on the Influence of Smartphones on the Environment -- 5.1 Minimization of the Drastic Change in the Environment -- 5.1.1 Hazardous Materials of Smartphone -- 5.1.2 Recycling -- 5.1.3 Selection of Right Design of Repair and Disassembly -- 5.1.4 Development of Smartphones using Green Materials -- 5.1.5 Energy-Saving Smartphone Batteries -- 5.1.6 Reduce Packaging and Accessories Requirements -- 5.2 Assessment of Smartphones using Life Cycle Assessment (LCA) -- 5.3 Selling and Emission Rate of the Smartphone -- 6 Discussion on Case Study -- 7 Conclusion -- References -- Vehicular Intelligence System: Time-Based Vehicle Next Location Prediction in Software-Defined Internet of Vehicles (SDN-IOV) ... -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology. , 3.1 Proposed (ERS-SDN-IOV) Routing Protocol -- 3.2 Prediction Neural Network -- 4 Experimental Results -- 4.1 Simulation Parameters -- 4.2 Simulation Scenario -- 4.3 Simulation Results -- 5 Conclusion -- References -- An Enhanced Cloud-IoMT-based and Machine Learning for Effective COVID-19 Diagnosis System -- 1 Introduction -- 2 Application of Cloud Computing and IoMT to Combat COVID-19 Pandemic -- 3 Related Works -- 4 The Proposed Cloud-IoMT-Based Architecture for Early Diagnosis of COVID-19 Outbreak -- 4.1 Data Capture and Collection of Symptoms Layer -- 4.2 Isolation/Quarantine Layer -- 4.3 Computers and AI Data Analysis Center -- 4.4 Healthcare Experts -- 4.5 Cloud-IoT-based Storage Database -- 4.6 Gateway -- 5 Applicability of the Proposed Model -- 5.1 Preprocessing -- 5.2 Diagnosis Models, Parameters, and Metrics -- 6 Results and Discussion -- 6.1 Confusion Matrix -- 6.2 ROC Curves -- 7 Conclusion -- References -- AIIoT for Development of Test Standards for Agricultural Technology -- 1 Introduction -- 1.1 Artificial Intelligence-Internet of Things (AIIoT) -- 1.1.1 Internet of Things (IoT) -- Architecture of IoT -- Applications of IoT -- 1.1.2 Artificial Intelligence (AI) -- 1.1.3 Machine Learning (ML) -- 2 Internet of Things Using Artificial Intelligence -- 2.1 Advantages of AIIoT -- 2.2 Applications of AIIoT -- 3 Role of AI and IoT in Agriculture -- 3.1 Role of IoT in Agriculture -- 3.1.1 Applications of IoT in Agriculture -- 3.2 Role of AI in Agriculture -- 3.2.1 Applications of AI in Agriculture -- 3.3 Future Aspects of IoT and AI in Agriculture -- 4 Testing -- 4.1 Software Testing in Agriculture -- 5 Test Standards in Agriculture -- 6 The International Organization for Standardization (ISO) -- 6.1 Case Study -- 7 Conclusion -- References. , Study and Analysis of 5G Enabling Technologies, Their Feasibility and the Development of the Internet of Things -- 1 Introduction -- 2 Background -- 3 Definition of the Problem -- 4 Objectives of the Research Problem -- 4.1 General Objective -- 4.2 Specific Objectives -- 5 Hypothesis -- 6 Research Methodology -- 7 Evolution of Mobile Networks from 0G to 5G: Basic Mobile Broadband Concept -- 8 Mobile Broadband Evolution: First Generations -- 9 Digital Generation -- 10 Multiple Accesses by Code Division -- 11 Third Generation -- 12 Long Term Evolution (LTE) -- 13 Essential Technologies for 5G Network Deployment -- 13.1 Millimeter Waves -- 14 Heterogeneous Networks -- 15 Massive MIMO -- 16 Beam Forming -- 17 Device to Device Communication (D2D) -- 18 NOMA Access Technology -- 19 Full Duplex -- 20 5G Architecture -- 21 NG-RAN -- 22 Internet of Things, Basic Principles, and Architecture -- 23 Internet of Everything -- 24 Industrial Internet of Things -- 25 Infrastructure and IoT Technologies -- 26 Convergence of the Physical and Virtual World -- 27 General Structure of the IoT -- 28 IoT Protocols -- 29 IoT Networks -- 30 Edge Computing -- 31 5G Security -- 32 IoT Use Cases -- 33 Public IoT -- 34 IoT in Industry -- 35 IoT at Home -- 36 Conclusions and Suggestions -- 37 Recommendations -- References -- Automated Methods for the Detection of Green Land in Satellite Images -- 1 Introduction -- 2 Literature Survey -- 3 The U-NET Method -- 4 Skip-U-NET Model -- 4.1 System Model -- 4.2 Architecture and Working -- 5 Experimentation and Analysis -- 5.1 Experimentation Platform -- 5.2 DSTL Dataset -- 5.3 Data Transformation -- 5.4 Data Augmentation -- 5.5 Performance Metrics -- 5.5.1 Jaccard Index -- 5.5.2 Loss Function -- 5.6 Segmentation Results -- 5.7 Analysis -- 6 Conclusion -- References. , Artificial Cyber Espionage Based Protection of Technological Enabled Automated Cities Infrastructure by Dark Web Cyber Offender -- 1 Introduction -- 1.1 Using AI to Prevent Cybercrime Targeting Technological Enabled Colonies on the DW -- 1.1.1 How Technological Enabled Colonies Are Using Artificial Cyber Espionage -- 1.1.2 What AI and ML can do for Automated City? -- 2 WEBNET Can Be Hacked Through Automated Light Bulb -- 2.1 Automated City Surveillance Issues -- 2.2 Seclusion and Surveillance -- 2.3 Information Bias -- 3 Solving Urban Issues with AI and ML -- 3.1 Technological Enabled Colonies Peril -- 3.1.1 Perpetrator Eavesdrop Threat -- 3.1.2 Dossier and Identity Thievery by Gadget Hijacking -- 3.1.3 Network Information Flooding Threat -- Securing Technological Enabled Colonies -- Firmware Integrity and Impregnable Boot -- Mutual Testament -- Surveillance Contemplating and Analysis -- Surveillance Lifecycle Governance -- Root of Credible -- Protocol Engines -- Provisioning and Key Governance -- 4 IOT Architectural View -- 4.1 Automated Gadget/Sensing Chips Tiers -- 4.2 Gateways and Webnet -- 4.3 Governance Liturgy Tiers -- 4.4 IOT Conceptual View -- 4.4.1 Equate Tiers -- 4.4.2 Access Tiers -- 4.4.3 Abstraction Tiers -- 4.4.4 Service Tiers -- 5 Conclusion and Future Scope -- References -- Role of Artificial Intelligence and IoT in Next Generation Education System -- 1 Introduction -- 2 Boons of AI in the Education System -- 3 AI-Based Technologies for Education -- 4 AI-Based Educational Solutions: Current Scenario -- 5 Development of AI-Enabled Online Learning Platforms -- 6 IoT Applications in Education -- 6.1 Smart Infrastructure -- 6.1.1 Computer Resource Allocation in Schools -- 6.1.2 Multimedia Classrooms -- 6.1.3 Digital Resource for Schools -- 6.2 Students Attendance Management -- 6.3 Safety Measures to the Institutional Infrastructure. , 6.4 Technological Advancement as a Learning Privilege to the Disabled Students -- 6.4.1 Mobility Assistance -- 6.4.2 Reading Surroundings -- 6.4.3 Improvement in Autonomy -- 7 Potential Use of AI and IoT in Education Sector -- 8 Conclusion -- 8.1 Future Scope -- References -- Social Media Data Analysis: Rough Set Theory Based Innovative Approach -- 1 Introduction -- 1.1 Terminologies -- 1.2 Social Data Analysis-Mathematical Perspective -- 1.2.1 Structure of the Chapter -- 2 Rough Sets: Foundations -- 2.1 Terminologies of Rough Sets -- 3 Rough Sets: Data Analysis Techniques -- 3.1 Rule Discovery on Rough Set Theory -- 4 Rough Graph -- 4.1 Discernibility Matrix for Rough Graph -- 4.2 Reduct Calculation Using Graph Approximation -- 5 Experimental Analysis -- 6 Conclusions and Future Work -- References -- Index.
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  • 3
    Keywords: Swarm intelligence. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (169 pages)
    ISBN: 9781000726794
    DDC: 006.3
    Language: English
    Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- Contributors -- Abbreviations -- CHAPTER 1 . Swarm Intelligence and Evolutionary Algorithms in Disease Diagnosis-Introductory Aspects -- 1.1 Introduction -- 1.2 Terminologies -- 1.2.1 Swarm Intelligence -- 1.2.1.1 Merits of Swarm Intelligence -- 1.2.1.2 Classifications and Terminology -- 1.2.2 Evolutionary Computation -- 1.2.3 Evolutionary Computation Paradigms -- 1.3 Importance of Swarm Intelligence in Disease Diagnosis -- 1.4 Importance of Evolutionary Algorithms in Disease Diagnosis -- 1.5 Conclusion -- CHAPTER 2 . Swarm Intelligence and Evolutionary Algorithms for Cancer Diagnosis -- 2.1 Introduction -- 2.2 Classification of Cancer -- 2.3 Challenges in Cancer Diagnosis -- 2.3.1 Methods of Cancer Detection -- 2.3.2 Issues and Challenges Faced While Cancer Detection Process -- 2.4 Applying Swarm Intelligence Algorithm for Cancer Diagnosis -- 2.4.1 SI Algorithms for Detection of Lung Cancer -- 2.4.2 Swarm Intelligence for Breast Cancer -- 2.4.3 Swarm Intelligence for Ovarian Cancer -- 2.4.4 SI Algorithm for Early Detection of Gastro Cancer -- 2.4.5 Swarm Intelligence for Treating Nano-Robots -- 2.5 Applying Evolutionary Algorithm for Cancer Detection -- 2.6 Conclusion -- CHAPTER 3 . Brain Tumour Diagnosis -- 3.1 Introduction -- 3.2 Applying Evolutionary Algorithms for Brain Tumor diagnosis -- 3.2.1 Evolutionary Algorithm -- 3.2.2 Conceptual Framework 1: Applying Evolutionary Algorithm for Brain Tumor Diagnosis. -- 3.3 Applying Swarm Intelligence Algorithms for Brain Tumor Diagnosis -- 3.3.1 Swarm Intelligence (SI) - Based Algorithms -- 3.3.2 Self-Organization: -- 3.3.3 Division of Labor: -- 3.3.4 Particle Swarm Optimization -- 3.3.5 Particle Swarm Optimization Algorithm. , 3.3.6 Conceptual Framework 2: Applying Swarm Intelligence Based Algorithm for Brain Tumor Diagnosis -- 3.4 Applying Swarm Intelligence and Evolutionary Algorithms Together for Diagnosis of Brain Tumor -- 3.5 Applying Swarm Intelligence, Evolutionary Algorithm and Incorporating Topological Data Analysis (TDA) for Brain Tumor Diagnosis -- 3.5.1 Topological Data Analysis -- 3.6 Conclusion -- CHAPTER 4 . Swarm Intelligence and Evolutionary Algorithms for Diabetic Retinopathy Detection -- 4.1 Introduction -- 4.1.1 Classification of Diabetic Retinopathy -- 4.1.2 Swarm Optimization and EvolutionaryAlgorithms -- 4.1.3 Objectives and Contributions -- 4.2 Feature of Diabetic Retinopathy -- 4.2.1 Microaneurysms -- 4.2.2 Haemorrhages -- 4.2.3 Hard Exudates -- 4.2.4 Soft Exudates -- 4.2.5 Neo-Vascularization -- 4.2.6 Macular Edema -- 4.3 Detection of Diabetic Retinopathy by Applying Swarm Intelligence and Evolutionary Algorithms -- 4.3.1 Genetic Algorithm -- 4.3.2 Particle Swarm Optimization -- 4.3.3 Ant Colony Optimization -- 4.3.4 Cuckoo Search -- 4.3.5 Bee Colony Optimization -- 4.4 Conclusion -- CHAPTER 5 . Swarm Intelligence and Evolutionary Algorithms for Heart Disease Diagnosis -- 5.1 Introduction -- 5.2 Prediction and Classification of Heart Disease Using Machine Learning/Swarm Intelligence -- 5.2.1 Decision Support System -- 5.2.2 Clinical Decision Support System -- 5.2.3 Heart Disease Datasets -- 5.3 Predicting Heart Attacks in Patients Using Artificial Intelligence Methods (Fuzzy Logic) -- 5.3.1 Fuzzy Logic Approach for Heart Disease Diagnosis -- 5.3.2 Fuzzy Rule Base -- 5.3.3 Fuzzy Inference Engine -- 5.3.4 Defuzzification -- 5.4 Predicting Heart Disease using Genetic Algorithms -- 5.5 Swarm Intelligence based Optimization Problem for Heart Disease Diagnosis -- 5.5.1 Ant Colony Optimization -- 5.5.2 Particle Swarm Optimization. , 5.6 Heart Disease Prediction Using Data Mining Techniques -- 5.7 Performance Metrics -- 5.8 Conclusion -- CHAPTER 6 . Swarm Intelligence and Evolutionary Algorithms for Drug Design and Development -- 6.1 Introduction -- 6.2 Drug Design and Development: Past, Present and Future -- 6.3 Role of Swarm Intelligence in Drug Design and Development -- 6.4 Role of Evolutionary Algorithms in Drug Design and Development -- 6.5 QSAR Modelling Using Swarm Intelligence and Evolutionary Algorithms -- 6.6 Prediction of Molecule Activity Swarm Intelligence and Evolutionary Algorithms -- 6.6.1 Particle Swarm Optimization -- 6.7 Conclusion -- Index.
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  • 4
    Online Resource
    Online Resource
    Milton :CRC Press LLC,
    Keywords: Swarm intelligence. ; Computer algorithms. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (313 pages)
    Edition: 1st ed.
    ISBN: 9780429820151
    DDC: 006.3824
    Language: English
    Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1. Evolutionary Computation: Theory and Algorithms -- 1.1 History of Evolutionary Computation -- 1.2 Motivation via Biological Evidence -- 1.3 Why Evolutionary Computing? -- 1.4 Concept of Evolutionary Algorithms -- 1.5 Components of Evolutionary Algorithms -- 1.6 Working of Evolutionary Algorithms -- 1.7 Evolutionary Computation Techniques and Paradigms -- 1.8 Applications of Evolutionary Computing -- 1.9 Conclusion -- References -- 2. Genetic Algorithms -- 2.1 Overview of Genetic Algorithms -- 2.2 Genetic Optimization -- 2.3 Derivation of Simple Genetic Algorithm -- 2.4 Genetic Algorithms vs. Other Optimization Techniques -- 2.5 Pros and Cons of Genetic Algorithms -- 2.6 Hybrid Genetic Algorithms -- 2.7 Possible Applications of Computer Science via Genetic Algorithms -- 2.8 Conclusion -- References -- 3. Introduction to Swarm Intelligence -- 3.1 Biological Foundations of Swarm Intelligence -- 3.2 Metaheuristics -- 3.3 Concept of Swarm -- 3.4 Collective Intelligence of Natural Animals -- 3.5 Concept of Self-Organization in Social Insects -- 3.6 Adaptability and Diversity in Swarm Intelligence -- 3.7 Issues Concerning Swarm Intelligence -- 3.8 Future Swarm Intelligence in Robotics - Swarm Robotics -- 3.9 Conclusion -- References -- 4. Ant Colony Optimization -- 4.1 Introduction -- 4.2 Concept of Artificial Ants -- 4.3 Foraging Behaviour of Ants and Estimating Effective Paths -- 4.4 ACO Metaheuristics -- 4.5 ACO Applied Toward Travelling Salesperson Problem -- 4.6 ACO Framework -- 4.7 The Ant Algorithm -- 4.8 Comparison of Ant Colony Optimization Algorithms -- 4.9 ACO for NP Hard Problems -- 4.10 Current Trends in ACO -- 4.11 Application of ACO in Different Fields -- 4.12 Conclusion -- References -- 5. Particle Swarm Optimization. , 5.1 Particle Swarm Optimization - Basic Concepts -- 5.2 PSO Variants -- 5.3 Particle Swarm Optimization (PSO) - Advanced Concepts -- 5.4 Applications of PSO in Various Engineering Domains -- 5.5 Conclusion -- References -- 6. Artificial Bee Colony, Firefly Swarm Optimization, and Bat Algorithms -- 6.1 Introduction -- 6.2 The Artificial Bee Colony Algorithm -- 6.3 The Firefly Algorithm -- 6.4 The Bat Algorithm -- 6.5 Conclusion -- References -- 7. Cuckoo Search Algorithm, Glowworm Algorithm, WASP, and Fish Swarm Optimization -- 7.1 Introduction to Optimization -- 7.2 Cuckoo Search -- 7.3 Glowworm Algorithm -- 7.4 Wasp Swarm Optimization -- 7.5 Fish Swarm Optimization -- 7.6 Conclusion -- References -- 8. Misc. Swarm Intelligence Techniques -- 8.1 Introduction -- 8.2 Termite Hill Algorithm -- 8.3 Cockroach Swarm Optimization -- 8.4 Bumblebee Algorithm -- 8.5 Social Spider Optimization Algorithm -- 8.6 Cat Swarm Optimization -- 8.7 Monkey Search Algorithm -- 8.8 Intelligent Water Drop -- 8.9 Dolphin Echolocation -- 8.10 Biogeography-Based Optimization -- 8.11 Paddy Field Algorithm -- 8.12 Weightless Swarm Algorithm -- 8.13 Eagle Strategy -- 8.14 Conclusion -- References -- 9. Swarm Intelligence Techniques for Optimizing Problems -- 9.1 Introduction -- 9.2 Swarm Intelligence for Communication Networks -- 9.3 Swarm Intelligence in Robotics -- 9.4 Swarm Intelligence in Data Mining -- 9.5 Swarm Intelligence and Big Data -- 9.6 Swarm Intelligence in Artificial Intelligence (AI) -- 9.7 Swarm Intelligence and the Internet of Things (IoT) -- 9.8 Conclusion -- References -- Index.
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  • 5
    Keywords: Artificial intelligence-Congresses. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (301 pages)
    Edition: 1st ed.
    ISBN: 9783030823221
    Series Statement: Communications in Computer and Information Science Series ; v.1434
    DDC: 307.760285
    Language: English
    Note: Intro -- Preface -- Organization -- Contents -- Sentimental and Emotions Analysis for Smart Cities -- Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Death of an Indian Actor -- 1 Introduction -- 1.1 Classifiers -- 2 Methodology -- 2.1 Data Gathering -- 2.2 Data Cleaning -- 2.3 Exploratory Data Analysis (EDA) -- 2.4 Topic Modelling -- 3 Results and Discussion -- 3.1 Naïve Bayes Results -- 3.2 SVM Results -- 3.3 Random Forest Results -- 3.4 Neural Network Results -- 4 Comparison with Previous Methods -- 5 Conclusion -- References -- Emotion Recognition Using Portable EEG Device -- 1 Introduction -- 2 Related Work -- 3 Method Overview -- 4 Experimental Results -- 5 Conclusion -- References -- Classification of Extraversion and Introversion Personality Trait Using Electroencephalogram Signals -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Feature Extraction Technique -- 3.3 Classification Algorithms -- 4 Experimental Results -- 5 Conclusions -- References -- Smart Specialization Strategies for Smart Cities -- The Productivity Forecasting in the Sector of Non-market Services (Education and Health Care Sectors): A Case Study of Ukraine -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Findings and Discussion -- 5 Conclusions -- References -- An Emergent Role of Knowledge Graph and Summarization Methodology to Simplify Recruitment for the Indian IT Industry -- 1 Introduction -- 2 Objectives of the Study -- 3 Literature Review -- 4 Research Methodology -- 4.1 Summarization Approach -- 4.2 Knowledge Graph Visualization -- 5 Data Analysis -- 5.1 Population and Sample of the Study -- 5.2 Candidates' Profile Summarization -- 5.3 Knowledge Graph Generation for Candidates' Profiles -- 6 Conclusion -- 7 Discussion and Future Research Direction -- References. , Knowledge and Innovation Management for Transforming the Field of Renewable Energy -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Knowledge Economy as a Factor of the RE Development -- 5 Transformational Processes in Knowledge Management at RE Enterprises -- 6 Conclusions -- References -- Security in Smart Cities -- A Research Perspective on Security in Fog Computing Through Blockchain Technology -- 1 Introduction -- 2 Related Works -- 3 Security Issues in Fog Computing -- 3.1 Authentication -- 3.2 Access Control and Data Protection in Fog Computing -- 4 Blockchain Technology -- 4.1 Advantages of Blockchain Technology -- 5 Blockchain in Fog Computing Environment -- 6 Conclusion -- References -- ZKPAUTH: An Authentication Scheme Based Zero-Knowledge Proof for Software Defined Network -- 1 Introduction -- 2 Related Work -- 3 ZKPAUTH the Proposed Scheme -- 3.1 Key-Generation Phase -- 3.2 Registration Phase -- 3.3 Login and Identification Phase -- 3.4 Verification Phase -- 4 Implementation and Formal Security Verification -- 4.1 The Specification and Simulation Result -- 5 Security Analysis -- 5.1 DoS Attack -- 5.2 MITM Attack -- 5.3 Host Impersonation Attack -- 5.4 Dictionary Attack -- 5.5 Brute Force Attack -- 5.6 Mutual Authentication -- 6 Performance Evaluation -- 6.1 Security Features -- 6.2 Computational Cost Communication Cost and Storage Overhead -- 7 Conclusion and Future Direction -- References -- Efficiency Evaluation of Handover Management Techniques in LTE Heterogeneous Networks -- 1 Introduction -- 1.1 Handover Technique -- 2 Literature Review -- 3 Problem Statement -- 4 Performance Metrics -- 4.1 Reference Signal Received Power (RSRP) -- 4.2 Time to Trigger -- 4.3 Failure Ratio in Case of Handover -- 4.4 Ping-Pong Handover Ratio -- 4.5 Call Dropping Ratio -- 5 Simulation Result Evaluation -- 6 Conclusion -- References. , Advances Applications for Future Smart Cities -- IoT Based Design of an Intelligent Light System Using CoAP -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology of Proposed Light Control System -- 4 Contiki-Cooja -- 5 Simulation Environment -- 6 Implementation -- 6.1 Connection of Border Router -- 6.2 Response from a Light Sensor (Above Threshold Value) -- 6.3 LED State is OFF -- 6.4 Response from Light Sensor (Below Threshold Value) -- 6.5 LED State is ON -- 7 Conclusion -- 8 Future Work -- References -- Implementation of Touch-Less Input Recognition Using Convex Hull Segmentation and Bitwise AND Approach -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Hardware and Software Specifications -- 3.2 Proposed Modules -- 4 Experimental Analysis and Results -- 4.1 Functionalities of the Proposed Methodology -- 5 Results and Discussion -- 6 Conclusion and Future Enhancement -- References -- Virtually Interactive User Manual for Command and Control Systems Using Rule-Based Chatbot -- 1 Introduction -- 2 Literature Review -- 2.1 Existing Technology -- 3 Proposed System -- 3.1 An Overview -- 3.2 Methodology -- 4 Experiment -- 5 Results and Discussion -- 6 Conclusion and Future Work -- References -- Healthcare in Smart Cities -- Wrapper-Based Best Feature Selection Approach for Lung Cancer Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Haralick Features -- 3.2 Sequential Forward Selection -- 3.3 Sequential Backward Selection -- 3.4 Exhaustive Feature Selection -- 3.5 Hybrid Sequential Exhaustive Feature Selection (HSEFS) -- 4 Result and Discussion -- 5 Conclusion -- References -- Application of Ensemble Techniques Based Sentiment Analysis to Assess the Adoption Rate of E-Learning During Covid-19 Among the Spectrum of Learners -- 1 Introduction -- 2 Related Work -- 3 Methodology. , 3.1 Data Collection -- 3.2 Classification -- 3.3 Sentiment Analysis -- 4 Experimental Results and Discussions -- 4.1 Questionnaire Method for Data Collection -- 5 Conclusion -- References -- A Hybrid Mathematical Model Using DWT and SVM for Epileptic Seizure Classification -- 1 Introduction -- 2 Literature Review -- 3 Background -- 3.1 Discrete Wavelet Transforms -- 3.2 Discrete Wavelet Transforms -- 3.3 Hjorth Parameters -- 3.4 Higuchi's Fractal Dimension (HFD) -- 3.5 Skewness and Kurtosis -- 3.6 Support Vector Machines (SVMs) -- 4 Materials and Methods -- 4.1 Datasets -- 4.2 Epileptic Seizure Detection -- 4.3 Performance Evaluation -- 5 Results and Discussion -- 6 Conclusion -- References -- Machine Learning Applications in Smart Cities -- Multimodal Cyberbullying Detection Using Ensemble Learning -- 1 Introduction -- 2 Related Work -- 2.1 Text Based Cyberbullying Detection -- 2.2 Text and Image Based Detection -- 3 Methodology -- 3.1 Text Processing -- 3.2 Image Processing -- 3.3 Multimodal LSTM for Cyberbullying Detection -- 3.4 Multimodal Concatenation Model -- 3.5 Ensemble Approach -- 4 Dataset -- 5 Experiment Setting -- 6 Results -- 6.1 Comparative Analysis -- 6.2 Conclusion and Future Work -- References -- Frequent Route Pattern Mining Technique for Route Prediction in Transportation Network -- 1 Introduction -- 2 Related Work -- 3 Proposed Pattern Mining Algorithm -- 4 Experimental Results -- 5 Conclusion and Future Scope -- References -- Student Clickstreams Activity Based Performance of Online Course -- 1 Introduction -- 2 Related Work -- 3 Objectives -- 4 Methodology -- 4.1 Data Pre-processing and Cleaning -- 4.2 Feature Extraction -- 4.3 Dataset Split and Evaluation -- 5 Experiment and Analysis -- 5.1 Setup -- 5.2 Summary of the Dataset -- 5.3 Evaluation Metrics -- 5.4 Results and Discussion -- 6 Conclusion -- References. , Feature Selection with Random Forests Predicting Metagenome-Based Disease -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Feature Selection Using Random Forest -- 3.2 Predict Using Random Forest on RF -- 3.3 Predict Using Support Vector Machine on RF -- 4 The Experiments -- 4.1 Dataset Description -- 4.2 Data Division and Scoring Metrics -- 4.3 Prediction Result with ACC Metrics -- 5 Conclusion -- References -- Predicting the Default Borrowers in P2P Platform Using Machine Learning Models -- 1 Introduction -- 2 Background Studies -- 2.1 Overview of P2P Lending -- 2.2 Random Forest (RF) -- 2.3 Logistic Regression (LR) -- 2.4 K-Nearest Neighbor (KNN) -- 2.5 Multi-Layer Perceptron (MLP) -- 2.6 Recursive Feature Elimination (RFE) -- 2.7 Previous Research in P2P Lending -- 3 Methodology -- 3.1 Dataset and Pre-processing -- 3.2 Feature Selection -- 3.3 Building Machine Learning Models -- 3.4 Training and Testing Data -- 4 Experiment and Result Analysis -- 5 Conclusion and Future Scope -- References -- Human Activity Recognition for Multi-label Classification in Smart Homes Using Ensemble Methods -- 1 Introduction -- 1.1 Ensemble Methods and Base Classifiers -- 1.2 The Motivation and Contributions of This Paper -- 2 Related Research Works -- 3 Methodology -- 3.1 Ensemble Method -- 3.2 Data Preprocessing and Feature Selection -- 4 Experimental Results and Discussions -- 4.1 Experimental Dataset -- 4.2 Performance Evaluations -- 4.3 Results and Discussions -- 5 Conclusion and Future Work -- References -- Correction to: Application of Ensemble Techniques Based Sentiment Analysis to Assess the Adoption Rate of E-Learning During Covid-19 Among the Spectrum of Learners. , Correction to: Chapter "Application of Ensemble Techniques Based Sentiment Analysis to Assess the Adoption Rate of E-Learning During Covid-19 Among the Spectrum of Learners" in: A. Solanki et al. (Eds.): Artificial Intelligence and Sustainable Computing for Smart City, CCIS 1434, https://doi.org/10.1007/978-3-030-82322-1_14.
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  • 6
    Keywords: Machine learning. ; Signal processing. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (389 pages)
    Edition: 1st ed.
    ISBN: 9781000487817
    DDC: 006.31
    Language: English
    Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1. Introduction to Signal Processing and Machine Learning -- 1.1 Introduction -- 1.2 Basic Terminologies -- 1.2.1 Signal Processing -- 1.2.1.1 Continuous and Discrete Signals -- 1.2.1.2 Sampling and Quantization -- 1.2.1.3 Change of Basis -- 1.2.1.4 Importance of Time Domain and Frequency Domain Analyses -- 1.2.2 Machine Learning -- 1.3 Distance-Based Signal Classification, Nearest Neighbor Classifier, and Hilbert Space -- 1.3.1 Distance-Based Signal Classification -- 1.3.1.1 Metric Space -- 1.3.1.2 Normed Linear Space -- 1.3.1.3 Inner Product Space -- 1.3.2 Nearest Neighbor Classification -- 1.3.3 Hilbert Space -- 1.4 Fusion of Machine Learning in Signal Processing -- 1.5 Benefits of Adopting Machine Learning in Signal Processing -- 1.6 Conclusion -- References -- 2. Learning Theory (Supervised/Unsupervised) for Signal Processing -- 2.1 Introduction -- 2.1.1 Signal Processing -- 2.2 Machine Learning -- 2.2.1 Why Do We Need ML for Signal Processing? -- 2.2.2 Speaker ID - A Utilization of ML Calculations in Sign Handling -- 2.2.3 Discourse and Audio Processing -- 2.2.4 Discourse Recognition -- 2.2.5 Listening Devices -- 2.2.6 Independent Driving -- 2.2.7 Picture Processing and Analysis -- 2.2.8 Wearables -- 2.2.9 Information Science -- 2.2.10 Wireless Systems and Networks -- 2.3 Machine Learning Algorithms -- 2.4 Supervised Learning -- 2.5 Unsupervised Learning -- 2.6 Semi-Supervised Learning -- 2.7 Reinforcement Learning -- 2.8 Use Case of Signal Processing Using Supervised and Unsupervised Learning -- 2.8.1 Features and Classifiers -- 2.8.2 Linear Classifiers -- 2.8.3 Decision Hyperplanes -- 2.8.4 Least Squares Methods -- 2.8.5 Mean Square Estimation -- 2.8.6 Support Vector machines -- 2.8.7 Non-Linear Regression. , 2.8.8 Non-Linearity of Activation Functions -- 2.8.8.1 Sigmoid Function -- 2.8.8.2 Rectified Linear Unit (ReLU) -- 2.8.9 Classification -- 2.8.9.1 Linear Classification -- 2.8.9.2 Two-Class Classification -- 2.8.9.3 Geometrical Interpretation of Derivatives -- 2.8.9.4 Multiclass Classification: Loss Function -- 2.8.10 Mean Squared Error -- 2.8.11 Multilabel Classification -- 2.8.12 Gradient Descent -- 2.8.12.1 Learning Rate -- 2.8.13 Hyperparameter Tuning -- 2.8.13.1 Validation -- 2.8.14 Regularization -- 2.8.14.1 How Does Regularization Work? -- 2.8.15 Regularization Techniques -- 2.8.15.1 Ridge Regression (L2 Regularization) -- 2.8.16 Lasso Regression (L1 Regularization) -- 2.8.17 K-Means Clustering -- 2.8.18 The KNN Algorithm -- 2.8.19 Clustering -- 2.8.20 Clustering Methods -- 2.9 Deep Learning for Signal Data -- 2.9.1 Traditional Time Series Analysis -- 2.9.2 Recurrence Domain Analysis -- 2.9.3 Long Short-Term Memory Models for Human Activity Recognition -- 2.9.4 External Device HAR -- 2.9.5 Signal Processing on GPUs -- 2.9.6 Signal Processing on FPGAs -- 2.9.7 Signal Processing is coming to the Forefront of Data Analysis -- 2.10 Conclusion -- References -- 3. Supervised and Unsupervised Learning Theory for Signal Processing -- 3.1 Introduction -- 3.1.1 Supervised Learning -- 3.1.2 Unsupervised Learning -- 3.1.3 Reinforcement Learning -- 3.1.4 Semi-Supervised Learning -- 3.2 Supervised Learning Method -- 3.2.1 Classicfiation Problems -- 3.2.2 Regression Problems -- 3.2.3 Examples of Supervised Learning -- 3.3 Unsupervised Learning Method -- 3.3.1 Illustrations of Unsupervised Learning -- 3.4 Semi-Supervised Learning Method -- 3.5 Binary Classification -- 3.5.1 Different Classes -- 3.5.2 Classification in Preparation -- 3.5.2.1 Logistic Regression Model -- 3.5.2.2 Odds Ratio -- 3.5.2.3 Logit Function -- 3.5.2.4 The Sigmoid Function. , 3.5.2.5 Support Vector Machines -- 3.5.2.6 Maximum Margin Lines -- 3.6 Conclusion -- References -- 4. Applications of Signal Processing -- 4.1 Introduction -- 4.2 Audio Signal Processing -- 4.2.1 Machine Learning in Audio Signal Processing -- 4.2.1.1 Spectrum and Cepstrum -- 4.2.1.2 Mel Frequency Cepstral Coefficients -- 4.2.1.3 Gammatone Frequency Cepstral Coefficients -- 4.2.1.4 Building the Classifier -- 4.3 Audio Compression -- 4.3.1 Modeling and Coding -- 4.3.2 Lossless Compression -- 4.3.3 Lossy Compression -- 4.3.4 Compressed Audio with Machine Learning Applications -- 4.4 Digital Image Processing -- 4.4.1 Fields Overlapping with Image Processing -- 4.4.2 Digital Image Processing System -- 4.4.3 Machine Learning with Digital Image Processing -- 4.4.3.1 Image Classification -- 4.4.3.2 Data Labelling -- 4.4.3.3 Location Detection -- 4.5 Video Compression -- 4.5.1 Video Compression Model -- 4.5.2 Machine Learning in Video Compression -- 4.5.2.1 Development Savings -- 4.5.2.2 Improving Encoder Density -- 4.6 Digital Communications -- 4.6.1 Machine Learning in Digital Communications -- 4.6.1.1 Communication Networks -- 4.6.1.2 Wireless Communication -- 4.6.1.3 Smart Infrastructure and IoT -- 4.6.1.4 Security and Privacy -- 4.6.1.5 Multimedia Communication -- 4.6.2 Healthcare -- 4.6.2.1 Personalized Medical Treatment -- 4.6.2.2 Clinical Research and Trial -- 4.6.2.3 Diagnosis of Disease -- 4.6.2.4 Smart Health Records -- 4.6.2.5 Medical Imaging -- 4.6.2.6 Drug Discovery -- 4.6.2.7 Outbreak Prediction -- 4.6.3 Seismology -- 4.6.3.1 Interpreting Seismic Observations -- 4.6.3.2 Machine Learning in Seismology -- 4.6.4 Speech Recognition -- 4.6.5 Computer Vision -- 4.6.6 Economic Forecasting -- 4.7 Conclusion -- References -- 5. Dive in Deep Learning: Computer Vision, Natural Language Processing, and Signal Processing -- 5.1 Deep Learning: Introduction. , 5.2 Past, Present, and Future of Deep- Learning -- 5.3 Natural Language Processing -- 5.3.1 Word Embeddings -- 5.3.1.1 Word2vec -- 5.3.2 Global Vectors for Word Representation -- 5.3.3 Convolutional Neural Networks -- 5.3.4 Feature Selection and Preprocessing -- 5.3.4.1 Tokenization -- 5.3.4.2 Stop Word Removal -- 5.3.4.3 Stemming -- 5.3.4.4 Lemmatization -- 5.3.5 Named Entity Recognition -- 5.4 Image Processing -- 5.4.1 Introduction to Image Processing and Computer Vision -- 5.4.1.1 Scene Understanding -- 5.4.2 Localization -- 5.4.3 Smart Cities and Surveillance -- 5.4.4 Medical Imaging -- 5.4.5 Object Representation -- 5.4.6 Object Detection -- 5.5 Audio Processing and Deep Learning -- 5.5.1 Audio Data Handling Using Python -- 5.5.2 Spectrogram -- 5.5.3 Wavelet- Based Feature Extraction -- 5.5.4 Current Methods -- 5.5.4.1 Audio Classification -- 5.5.4.2 Audio Fingerprinting -- 5.5.4.3 Feature Extraction -- 5.5.4.4 Speech Classification -- 5.5.4.5 Music Processing -- 5.5.4.6 Natural Sound Processing -- 5.5.4.7 Technological Tools -- 5.6 Conclusion -- References -- 6. Brain-Computer Interfacing -- 6.1 Introduction to BCI and Its Components -- 6.1.1 BCI Components -- 6.2 Framework/Architecture of BCI -- 6.3 Functions of BCI -- 6.3.1 Correspondence and Control -- 6.3.2 Client State Checking -- 6.4 Applications of BCI -- 6.4.1 Healthcare -- 6.4.1.1 Prevention -- 6.4.1.2 Detection and Diagnosis -- 6.4.1.3 Rehabilitation and Restoration -- 6.4.2 Neuroergonomics and Smart Environment -- 6.4.3 Neuromarketing and Advertisement -- 6.4.4 Pedagogical and Self-Regulating Oneself -- 6.4.5 Games and Entertainment -- 6.4.6 Security and Authentication -- 6.5 Signal Acquisition -- 6.5.1 Invasive Techniques -- 6.5.1.1 Intracortical -- 6.5.1.2 ECoG and Cortical Surface -- 6.5.2 Noninvasive Techniques -- 6.5.2.1 Magneto-encephalography (MEG). , 6.5.2.2 fMRI (functional Magnetic Resonance Imaging) -- 6.5.2.3 fNIRS (functional Near-Infrared Spectroscopy) -- 6.5.2.4 EEG (Electroencephalogram) -- 6.6 Electrical Signal of BCI -- 6.6.1 Evoked Potential (EP) or Evoked Response -- 6.6.2 Event-Related Desynchronization and Synchronization -- 6.7 Challenges of BCI and Proposed Solutions -- 6.7.1 Challenges of Usability -- 6.7.2 Technical Issues -- 6.7.3 Proposed Solutions -- 6.7.3.1 Noise Removal -- 6.7.3.2 Disconnectedness of Multiple Classes -- 6.8 Conclusion -- References -- 7. Adaptive Filters and Neural Net -- 7.1 Introduction -- 7.1.1 Adaptive Filtering Problem -- 7.2 Linear Adaptive Filter Implementation -- 7.2.1 Stochastic Gradient Approach -- 7.2.2 Least Square Estimation -- 7.3 Nonlinear Adaptive Filters -- 7.3.1 Volterra-Based Nonlinear Adaptive Filter -- 7.4 Applications of Adaptive Filter -- 7.4.1 Biomedical Applications -- 7.4.1.1 ECG Power-Line Interference Removal -- 7.4.1.2 Maternal-Fetal ECG Separation -- 7.4.2 Speech Processing -- 7.4.2.1 Noise Cancelation -- 7.4.3 Communication Systems -- 7.4.3.1 Channel Equalization in Data Transmission Systems -- 7.4.3.2 Multiple Access Interference Mitigation in CDMA -- 7.4.4 Adaptive Feedback Cancellation in Hearing Aids -- 7.5 Neural Network -- 7.5.1 Learning Techniques in ANN -- 7.6 Single and Multilayer Neural Net -- 7.6.1 Single-Layer Neural Networks -- 7.6.2 Multilayer Neural Net -- 7.7 Applications of Neural Networks -- 7.7.1 ECG Classicafition -- 7.7.1.1 Methodology -- 7.7.2 Speech Recognition -- 7.7.2.1 Methodology -- 7.7.3 Communication Systems -- 7.7.3.1 Mobile Station Location Identification Using ANN -- 7.7.3.2 ANN-Based Call Handoff Management Scheme for Mobile Cellular Network -- 7.7.3.3 A Hybrid Path Loss Prediction Model based on Artificial  Neural Networks -- 7.7.3.4 Classification of Primary Radio Signals. , 7.7.3.5 Channel Capacity Estimation Using ANN.
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  • 7
    Keywords: Robotics and Automation ; Robotics ; Automation ; User interfaces (Computer systems) ; Big data ; Industrial organization ; Industrial engineering ; Production engineering ; Sustainable development
    Description / Table of Contents: Industry: A sustainable, intelligent, innovative, internet-of-things Industry -- Development of Industry 4.0 -- Sustainability development in Industry 4.0 -- Big Data and Analytics in Industry 4.0 -- Ubiquitous Computing for Industry 4.0 -- Cloud Computing in Industry 4.0 -- Modelling and Simulation for Industry 4.0 -- Augmented Reality and Industry 4.0 -- Robotics and Industry 4.0 -- Additive Manufacturing: Concept and Technologies -- Challenges within the Industry 4.0 Setup
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (XIV, 205 p. 137 illus)
    Edition: 1st ed. 2020
    ISBN: 9783030145446
    Series Statement: Advances in Science, Technology & Innovation, IEREK Interdisciplinary Series for Sustainable Development
    Language: English
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  • 8
    Keywords: Robotics. ; Automation. ; Computer engineering. ; Internet of things. ; Embedded computer systems. ; Technology.
    Description / Table of Contents: IoT Aided Robotics development and Applications with AI -- Convergence of IoT and CPS in Robotics -- IoT, IIoT and Cyber Physical Systems Integration -- Event and Activity Recognition in Video Surveillance for Cyber Physical Systems -- An IoT Based Autonomous Robot System for MAIZE Precision Agriculture Operations in Sub-Saharan Africa -- A Concept of Internet of Robotic Things for Smart Automation -- IoT in Smart Automation and Robotics with Streaming Analytical Challenges -- Managing IoT and Cloud-based Healthcare Record System using Unique Identification Number to promote Integrated Healthcare Delivery System: A Perspective from India -- Internet of Robotic Things: Domain, Methodologies and Applications -- Applications of GPUs for Signal Processing Algorithms: A Case Study on Design Choices for Cyber Physical Systems -- The Role of IoT and Narrow Band (NB)-IoT for Several Use Cases -- Robust and Secure routing protocols for MANETs based Internet of Things Systems- A Survey -- IoT for Smart Automation and Robot -- Application of Internet of Things and Cyber Physical Systems in Industry 4.0 Smart Manufacturing.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource(VIII, 217 p. 136 illus., 111 illus. in color.)
    Edition: 1st ed. 2021.
    ISBN: 9783030662226
    Series Statement: Advances in Science, Technology & Innovation, IEREK Interdisciplinary Series for Sustainable Development
    Language: English
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  • 9
    Online Resource
    Online Resource
    Singapore : Springer Nature Singapore | Singapore : Imprint: Springer
    Keywords: Sustainability. ; Education ; People with disabilities ; Science
    Description / Table of Contents: Roles and Responsibilities of a Virtual Teacher -- Hybrid Learning System: Analysis, Opportunities, Challenges, and Prospects -- COVID-19 Pandemic and changing dynamics in Teaching and Learning Strategies: A study of student-centric blended learning approach -- Blended Learning in COVID-19 Era and Way-Forward -- Blended learning in COVID-19 Era: Pre and Post COVID times, Lessons learned and way forward -- An investigative study of students’ and faculty perspective towards transition to online teaching during COVID-19 pandemic -- Survey of Blended Learning Approaches, Frameworks, Tools and Techniques for Science and Management Students -- Blended Learning and STEM Education for students with special needs and learning disabilities -- Designing Integrative and Collaborative Learning for Students with Special Needs and Learning Disabilities in an Inclusive Classroom -- Maintaining Performance and QoS of Software Tools for Remote-Teaching Environment -- Students’ Learning Outcomes and Emerging Practices of Blended learning: A case study -- Collaborative and Sustainable Blended Learning in UTAS Salalah -- Integration of Blended Mode of Technologies in Teaching and Learning of Engineering Content at Higher Educational Institutions -- Exploring the Scope of Learning Analytics in Blended Learning Environments.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource(XVI, 340 p. 76 illus., 67 illus. in color.)
    Edition: 1st ed. 2023.
    ISBN: 9789819934973
    Series Statement: Contributions to Environmental Sciences & Innovative Business Technology
    Language: English
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  • 10
    Online Resource
    Online Resource
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Keywords: Sustainability. ; Cooperating objects (Computer systems). ; Cloud Computing. ; Internet of things. ; Blockchains (Databases).
    Description / Table of Contents: Foundation Concepts for Industry 4.0 -- PROSPECTIVE APPLICATION OF BLOCKCHAIN IN MUTUAL FUND INDUSTRY -- Waste Management 4.0- An Industry 4.0 Approach to the Future Waste Management System -- Artificial Intelligence Powered Automation for Industry 4.0 -- To Trust or not to Trust Cybots: Ethical Dilemmas in the Posthuman Organization -- Business Sustainability and Growth in Journey of Industry 4.0- A Case Study -- Challenges and opportunities for mutual fund investment and the role of industry 4.0 to recommend the individual for speculation -- Blockchain Based Secure Manufacturing Network Management for Industry 4.0 -- AIC Algorithm for Using Intention of Online Food Delivery Services in Industry 4.0: Evidence from Vietnam -- Design and Automation of Hybrid Quadruped Mobile Robot for Industry 4.0 Implementation -- Hydrogel based on alginate as an ink in Additive Manufacturing technology - processing methods and printability enhancement -- Industry 4.0 Internet of Medical Things Enabled Cost Effective Secure Smart Patient Care Medicine Pouch -- 3D PRINTING PATHWAYS FOR SUSTAINABLE MANUFACTURING -- IMPLEMENTING DIGITAL AGE EXPERIENCE MARKETING TO MAKE CUSTOMER RELATIONS MORE SUSTAINABLE -- 3D Printing: A Game Changer for Indian MSME Sector in Industry 4.0 -- ROLE OF 3D PRINTING IN THE PHARMACEUTICAL INDUSTRY.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource(X, 329 p. 108 illus., 84 illus. in color.)
    Edition: 1st ed. 2023.
    ISBN: 9783031204432
    Series Statement: Contributions to Environmental Sciences & Innovative Business Technology
    Language: English
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