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.

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.

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.

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.

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.

Note: Intro -- Preface -- Contents -- 1 Artificial Intelligence Powered Automation for Industry 4.0 -- 1 Introduction -- 2 Automation for Industry 4.0 -- 2.1 What is Industry 4.0? -- 2.2 Merging the Virtual and Real World -- 2.3 The Role of Automation in Industry 4.0 -- 2.4 Automation Challenges in Industry 4.0 -- 2.5 Example of the Industry 4.0 Revolution -- 2.6 Challenges and Issues of Implementing Industry 4.0 -- 2.7 Advantages of Automation in Industry 4.0 -- 2.8 The Future of Industry 4.0 -- 3 AI in Automation for Industry 4.0 -- 3.1 Role of AI in Industry 4.0 -- 3.2 Impact of AI in Industry 4.0 -- 3.3 Industrial Artificial Intelligence -- 3.4 Applications of AI in Smart Industry -- 3.5 AI Algorithms Used in Smart Industry -- 4 Results and Discussion -- 5 Conclusion and Future Scope -- References -- 2 Business Sustainability and Growth in Journey of Industry 4.0-A Case Study -- 1 Introduction -- 2 Literature Review -- 3 Revolution of Industry 4.0 -- 3.1 Characteristics of Industry 4.0 -- 3.2 Why Industry 4.0 is Important? -- 3.3 Implementation of Industry 4.0 in Indian Industry -- 4 Proposed Work -- 4.1 Methodology -- 4.2 Discussion -- 5 Conclusion -- References -- 3 Foundation Concepts for Industry 4.0 -- 1 Introduction -- 1.1 History and Evolution of Industrial Revolutions -- 2 Industry 4.0 -- 3 Significance of Industry 4.0 in Global Business -- 4 India Readiness for Industry 4.0 -- 5 Conceptual Model of Industry 4.0 for Sustainable Development -- 6 Conclusion -- References -- 4 Challenges and Opportunities for Mutual Fund Investment and the Role of Industry 4.0 to Recommend the Individual for Speculation -- 1 Introduction -- 2 Literature Review -- 3 Industry 4.0 Bridges with Mutual Fund -- 4 Introduction to Mutual Fund -- 4.1 Types of Mutual Funds -- 4.2 Benefits and Disadvantages of Mutual Fund -- 4.3 Making Investment in Mutual Funds. , 5 Mutual Fund Parameters -- 5.1 Alpha -- 5.2 Beta -- 5.3 R-Squared -- 5.4 Standard Deviation -- 5.5 The Sharpe Ratio -- 6 Industry 4.0 Technologies -- 7 Tools for Analyzing Mutual Funds -- 7.1 Artificial Intelligence -- 7.2 Introduction to RPA -- 7.3 Chabot -- 7.4 Machine Learning -- 7.5 Neural Networks -- 8 Case Studies-Three Mutual Funds -- 8.1 Tata Infrastructure Fund -- 8.2 Aditya Birla Sun Life Infrastructure Fund -- 8.3 LIC MF Infrastructure Fund -- 9 Finding and Results -- 10 Conclusion and Future Scope -- References -- 5 Implementing Digital Age Experience Marketing to Make Customer Relations More Sustainable -- 1 Introduction -- 2 Experiential Marketing-The Incentive of Sustainable Development -- 3 Experiential Marketing-Application and Standing in Industry 4.0 -- 4 Benefits of Experiential Marketing in Industry 4.0 -- 5 Results -- 6 Discussions -- 7 Related Strategies -- 8 Research Implication and Future Research Directions -- Appendix -- References -- 6 Prospective Application of Blockchain in Mutual Fund Industry -- 1 Introduction of Blockchain Towards Industry 4.0 -- 2 Organization Overview -- 3 Industry 4.0-Introduction -- 3.1 Importance of Industry 4.0 in Asset Management Industry -- 4 Role and Impact of Blockchain Technology on Mutual Fund Industry -- 4.1 Need of Blockchain on Mutual Fund Industry -- 4.2 Boom of Blockchain Technology in Mutual Fund Industry -- 5 Blockchain Exchange Traded Fund -- 6 Challenges of Blockchain in Mutual Fund Industry -- 7 Conclusion and Further Scope -- References -- 7 Industry 4.0 Internet of Medical Things Enabled Cost Effective Secure Smart Patient Care Medicine Pouch -- 1 Introduction -- 2 Literature Review -- 3 Overview of Industry 4.0 and IoMT Devices -- 3.1 Internet of Medical Things in Industry 4.0. , 4 Impact, Challenges and Applications of IoMT Enabled Cost Effective Secure Smart Patient Care Medicine Pouch -- 4.1 Impacts of Proposed Smart Patient Care Medicine Pouch -- 4.2 Challenges in Designing the Proposed Smart Patient Care Medicine Pouch -- 4.3 Applications of Proposed Smart Patient Care Medicine Pouch -- 5 Industry 4.0 IoT Enabled Cost Effective Secure Smart Patient Care Medicine Pouch: Architecture and Implementation -- 5.1 System Architecture -- 5.2 Implementation of IoMT Enabled Smart Medical Patient Medicine Pouch -- 6 Performance Analysis of Proposed System -- 6.1 Alzheimer Patients as Potential Users of Proposed System: A Case Study -- 7 Conclusion and Future Scope -- References -- 8 Design and Automation of Hybrid Quadruped Mobile Robot for Industry 4.0 Implementation -- 1 Introduction -- 2 Structural Design -- 3 Robot Automation -- 4 Experimental Results -- 5 Conclusion and Future Scope -- References -- 9 To Trust or Not to Trust Cybots: Ethical Dilemmas in the Posthuman Organization -- 1 Introduction -- 2 Industry 4.0 as Posthuman Organization -- 3 Digital Trust and Posthuman Industry 4.0 -- 4 Digital Distrust and the Posthuman -- 5 The Need for Curiosity: The Determinism in Industry 4.0 -- 6 Toward an Uncritical Curiosity: Michel Foucault's Ethics of Curiosity -- 7 Two Foucauldian Scenarios: Resolving Dilemmas of Trust in Industry 4.0 -- 7.1 The Centrality of Care: The 'Bad' Scenario in the Posthuman Organization -- 7.2 Not-Yet-Post Humanized Trust: The 'Good' Scenario in Industry 4.0 -- 8 Conclusion and Future Scope -- References -- 10 Hydrogel Based on Alginate as an Ink in Additive Manufacturing Technology-Processing Methods and Printability Enhancement -- 1 Introduction -- 1.1 Overview of Additive Manufacturing Technologies Using Hydrogel Inks -- 1.2 Application of Extrusion Techniques in the Biomedical Field. , 2 Improvement of Properties of Alginate-Based Hydrogel Ink -- 2.1 Additives Improving Printability -- 2.2 Methods Using the Support-Bath -- 2.3 Sacrificial Material as an Outline -- 3 Influence of Process Parameters on Mechanical Properties of Hydrogel Structure -- 4 Conclusion and Future Scope -- References -- 11 Coal Fly Ash Utilization in India -- 1 Introduction -- 1.1 Characterization of Fly Ash -- 2 Fly Ash Utilization in India -- 2.1 Cement and Concrete Industries -- 2.2 Mine Filling and Agriculture -- 2.3 Construction of Roads/Embankments/Flyovers and Raising of Ash Dykes -- 2.4 Reclamation of Low Laying Area and Building Materials -- 2.5 Geopolymer Materials -- 2.6 Additive Manufacturing for Sustainable Materials -- 3 Conclusion and Future Scope -- References -- 12 3D Printing Pathways for Sustainable Manufacturing -- 1 Introduction -- 2 Literature Review -- 2.1 Sustainable Manufacturing -- 2.2 3D Printing -- 2.3 Related Studies -- 3 Additive Manufacturing Over Traditional Manufacturing -- 4 Materials Used in 3D Printing -- 4.1 Metallic Materials -- 4.2 Ceramics Materials -- 4.3 Polymers Materials -- 4.4 Composites Materials -- 4.5 Smart Materials -- 5 Types of 3D Printing -- 5.1 Fuse Deposition Modelling -- 5.2 Direct Energy Deposition -- 5.3 Selective Laser Sintering -- 5.4 Digital Light Processing -- 5.5 Stereolithography -- 6 Applications of 3D Printing -- 6.1 Aerospace Industry -- 6.2 Automotive Industries -- 6.3 Medical Sector -- 7 Contribution of 3D Printing Towards Sustainable Manufacturing -- 8 Limitation of 3D Printing -- 9 Conclusion and Future Scope -- References -- 13 Role of 3D Printing in Pharmaceutical Industry -- 1 Introduction -- 2 Procedure for 3D Printing -- 3 Advantages and Limitations of 3D Printing -- 4 Fabrication of 3D Medical Product -- 4.1 Fused Deposition Modeling or Material Extrusion. , 4.2 Vat Photo-Polymerization (V.P.P.) -- 4.3 Selective Laser or Powder Bed Fusion -- 4.4 Binder Jetting -- 4.5 Material Jetting -- 4.6 Direct Energy Deposition -- 5 Challenges of 3D Printing -- 6 Application of 3D Printing in the Pharmaceutical Field -- 6.1 Customized Implant and Prostheses -- 6.2 Bio-printing Tissues and Organs -- 6.3 Tablets -- 6.4 Microneedles -- 6.5 Transdermal Patch -- 6.6 Wound Dressing -- 6.7 Buccal Film -- 6.8 Abuse-Deterrent or Discouragement Dosage Forms -- 7 Advantages of 3D Printing in the Pharma Industry -- 8 Conclusion and Future Scope -- References -- 14 3D Printing: A Game Changer for Indian MSME Sector in Industry 4.0 -- 1 Introduction -- 1.1 3D Printing Terminology -- 2 Background of Additive Manufacturing -- 2.1 History of Printing in General and 3D Printing in Specific: (Timeline of Growth of Additive Manufacturing) [7, 8] -- 2.2 Types of 3D Printing -- 2.3 Materials Used in Manufacturing Industry for 3D Printing -- 2.4 Product Cycle: Traditional Manufacturing Process Versus Additive Manufacturing Process -- 3 Application of Additive Manufacturing in Various Sectors [19, 20] -- 3.1 Education Sector -- 3.2 Aeronautics and Aerospace Industries -- 3.3 Medical and Dentistry -- 3.4 Construction Sector -- 3.5 Jewellery Sector -- 3.6 Cultural Heritage -- 4 Recent Developments and Key Players -- 4.1 Global Scenario [26] -- 4.2 Indian Scenario -- 5 Additive Manufacturing: Game Changer for Indian MSME Sector -- 5.1 Overall View of MSME Sector in India -- 5.2 Various Government Initiatives for Adoption of AM [31] -- 5.3 Adoption Model of AM by MSMEs and Role Enrichment of Facilitating Agencies with Revitalisation Government Concern -- 5.4 Development of Additive Manufacturing Centre -- 5.5 Engineering Technique-A Case Study -- 6 Contribution of AM During COVID-19 Pandemic. , 7 Perspective Contribution and Operational Constrains.