Note: Intro -- Preface -- Contents -- Contributors -- Forensic Analysis in Cloud Computing -- 1 Cloud Computing Forensic Analysis: Trends and Challenges -- Abstract -- 1 Introduction -- 2 Cloud Computing Environment -- 2.1 The Cloud Models -- 2.2 The Cloud Structure and Services -- 3 Digital Forensics -- 3.1 Digital Forensics Common Phases -- 3.1.1 Identification -- 3.1.2 Acquisition and Preservation -- 3.1.3 Analysis -- 3.1.4 Presentation -- 3.2 Digital Evidence -- 3.3 Digital Forensic Analysis -- 4 Forensic Analysis in the Cloud Environment -- 4.1 Isolation of Crime Scene in Cloud -- 4.2 Pros and Cons of Forensic Analysis in Cloud -- 4.3 Challenges of Analysis and Examination -- 4.4 Conclusions and Open Research Issues -- References -- Data Storage Security Service in Cloud Computing: Challenges and Solutions -- 1 Introduction -- 2 Data Storage Auditing Methods -- 2.1 Public Auditing Versus Private Auditing -- 2.2 Auditing Single-Copy Versus Multiple-Copy Data -- 2.3 Static Versus Dynamic Data Auditing -- 3 Access Control Methods -- 3.1 Traditional Encryption -- 3.2 Broadcast Encryption -- 3.3 Identity-Based Encryption -- 3.4 Hierarchical Identity-Based Encryption -- 3.5 Attribute-Based Encryption -- 3.6 Hierarchical Attribute-Based Encryption -- 3.7 Role-Based Access Control -- 4 Comparative Analysis of Security Methods on Cloud Data Storage -- 4.1 Performance Analysis of Data Storage Auditing Methods -- 4.2 Performance Analysis of Access Control Methods -- 5 Discussions and Concluding Remarks -- 6 Conclusion -- References -- Homomorphic Cryptosystems for Securing Data in Public Cloud Computing -- 1 Introduction -- 2 Cloud Computing -- 3 Security Issues in Cloud Computing -- 4 Why Homomorphic Encryption (HE)? -- 5 Homomorphic Encryption (HE) -- 6 Different Encryption Cryptosystem -- 6.1 Somewhat Homomorphic Encryption (SHE). , 6.2 Fully Homomorphic Encryption (FHE) -- 7 Result and Discussion -- 8 Conclusion -- References -- 4 An Enhanced Cloud Based View Materialization Approach for Peer-to-Peer Architecture -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Solutions and Recommendations -- 4 The Experimental Results -- 5 Future Research Directions -- 6 Conclusion -- Appendix -- References -- 5 Distributed Database System (DSS) Design Over a Cloud Environment -- Abstract -- 1 Introduction -- 2 Background -- 3 Research Issues -- 4 Solutions and Recommendations -- 4.1 Application Client Layer -- 4.2 Distributed Database System Manager Layer -- 4.3 Distributed Database Clusters Layer -- 5 Enhanced Allocation and Replication Technique -- 6 Experimental Results -- 7 Clustering Distributed Database Sites -- 7.1 Clustering Distributed Database Sites Based on Region Fields -- 7.2 Clustering Distributed Database Sites Based on Communication Costs Between Network Sites -- 8 Future Research Directions -- 9 Conclusions -- References -- Additional Reading -- 6 A New Stemming Algorithm for Efficient Information Retrieval Systems and Web Search Engines -- Abstract -- 1 Introduction -- 2 Background -- 3 The System Methodology -- 3.1 The System Architecture -- 3.2 The Enhance Porter Stemming Algorithm (EPSA) -- 3.3 The Ranking Algorithm -- 4 Performance Studies -- 4.1 Evaluation of the EPSA Algorithm -- 4.2 Experimental Results -- 5 Conclusion -- References -- Forensics Multimedia and Watermarking Techniques -- 7 Face Recognition via Taxonomy of Illumination Normalization -- Abstract -- 1 Introduction -- 2 Illumination Normalization Face Recognition -- 2.1 Illumination Normalization -- 2.1.1 Wavelet-Based (WA) -- 2.1.2 Steerable Filtering-Based -- 2.1.3 Non-local Means Based -- 2.1.4 Adoptive Non-local Mean -- 2.2 Illumination Modeling -- 2.2.1 Single Scale Retinex (SSR). , 2.2.2 Multi Scale Retinex (MSR) -- 2.2.3 Adaptive Single Scale Retinex (ASR) -- 2.3 Illumination Invariant Feature Extraction -- 2.3.1 Homomorphic Filtering-Based Normalization (HOMO) -- 2.3.2 Discrete Cosine Transformation (DCT) -- 2.3.3 Isotropic Diffusion-Based Normalization -- 2.3.4 Modified Anisotropic Diffusion (MAD) -- 2.3.5 Difference of Gaussian (DoG) -- 3 Current Trends -- 4 Comparisons -- 5 Discussion -- 6 Conclusion -- References -- 8 Detecting Significant Changes in Image Sequences -- Abstract -- 1 Introduction -- 2 Background Feature Analysis -- 3 Problem Statement and Legacy Techniques for Solution -- 4 Image Content Interpretation and Comparison -- 4.1 Construction of Voronoi Diagrams on Salient Image Points -- 4.2 Similarity Metrics for Voronoi Diagram Comparison -- 4.3 Procedure of Image Change Detection -- 5 Test Collections, Processing Evaluation and Development Trends -- 6 Conclusion -- References -- 9 VW16E: A Robust Video Watermarking Technique Using Simulated Blocks -- Abstract -- 1 Introduction -- 2 Block Simulation -- 3 Test the Imperceptibility -- 3.1 Test Results of VW16E Technique for the Video Sample 1 -- 3.2 Test Results of VW16E Technique for the Video Sample 2 -- 3.3 Test Results of VW16E Technique for the Video Sample 3 -- 3.4 Test Results of VW16E Technique for the Video Sample 4 -- 3.5 Test Results of VW16E Technique for the Video Sample 5 -- 3.6 Test Results of VW16E Technique for the Video Sample 6 -- 3.7 Test Results of VW16E Technique for the Video Sample 7 -- 3.8 Test Results of VW16E Technique for the Video Sample 8 -- 3.9 Test Results of VW16E Technique for the Video Sample 9 -- 3.10 Test Results of VW16E Technique for the Video Sample 10 -- 3.11 Test Results of VW16E Technique for the Video Sample 11 -- 3.12 Test Results of VW16E Technique for the Video Sample 12. , 3.13 Test Results of VW16E Technique for the Video Sample 13 -- 3.14 Test Results of VW16E Technique for the Video Sample 14 -- 4 Discussion and Analysis on Test and Evaluation -- 4.1 Attacks on Video Sample No 1 -- 4.1.1 Crop Attack on Video Sample No 1 -- 4.1.2 Frame Deletion Attack on Video Sample No 1 -- 4.1.3 Frame Exchange Attack on Video Sample No 1 -- 4.1.4 Frame Insert Attack on Video Sample No 1 -- 4.1.5 Rotate Attack on Video Sample No 1 -- 4.1.6 Reverse Rotate Attack on Video Sample No 1 -- 4.1.7 Salt and Pepper Attack on Video Sample No 1 -- 4.1.8 Shift Frames Attack on Video Sample No 1 -- 4.1.9 Superimpose Attack on Video Sample No 1 -- 5 Conclusion -- References -- 10 A Robust and Computationally Efficient Digital Watermarking Technique Using Inter Block Pixel Differencing -- Abstract -- 1 Introduction -- 2 Related Literature -- 3 Proposed Watermarking Algorithm -- 3.1 Block Pre-processing -- 3.2 Watermark Embedding -- 3.3 Watermark Extraction -- 4 Experimental Results and Discussions -- 4.1 Imperceptivity Analysis -- 4.2 Payload Analysis -- 4.3 Robustness Analysis -- 4.3.1 Salt and Pepper Noise -- 4.3.2 Addition of Gaussian Noise -- 4.3.3 Sharpening -- 4.3.4 Cropping -- 4.3.5 Rotation Attack -- 4.3.6 JPEG Compression -- 4.3.7 Rotation + Cropping -- 4.3.8 Salt and Pepper Noise + Gaussian Noise -- 4.3.9 Salt and Pepper Noise + Gaussian Noise + Sharpening -- 4.4 Computational Complexity Analysis -- 5 Conclusion -- Acknowledgments -- References -- 11 JPEG2000 Compatible Layered Block Cipher -- Abstract -- 1 Introduction -- 1.1 Background -- 2 Literature Review -- 2.1 Applications -- 2.2 Approaches -- 3 Proposed Approach -- 4 Performance Analysis -- 5 Discussion and Conclusions -- References -- Digital Forensic Applications -- 12 Data Streams Processing Techniques -- Abstract -- 1 Introduction -- 2 Background -- 2.1 Data Streams. , 2.2 Data Steams Processing -- 2.3 Sliding Windows -- 2.4 Continuous Query Optimization -- 3 Continuous Queries Processing -- 3.1 Continuous Queries Processing Based on Multiple Query Plans -- 3.2 Continuous Queries with Probabilistic Guarantees -- 3.3 Continuous Bounded Queries Processing -- 3.4 Continuous Queries Processing Over Sliding Window Join -- 3.5 Continuous Pattern Mining -- 3.6 Continuous Top-K Queries Processing -- 3.7 Continuous Nearest Neighbor Queries Processing -- 3.8 Continuous Queries Processing Based Tree Structure -- 3.9 Continuous Skyline Queries Processing -- 3.10 Multi Continuous Queries Processing -- 4 Data Streams Execution Environments -- 4.1 Parallel and Distributed Stream Processing -- 4.1.1 Data Streams Processing Based on Map Reduce Framework -- 4.1.2 Data Streams Processing Based on the Query Mega Graph (QMG) -- 4.1.3 Data Streams Processing Based on Task Graph Structure -- 4.1.4 Data Streams Processing in Traffic Distributed Networks -- 4.1.5 Controlling Streams Processing on Overload Conditions -- 4.2 Data Streams Processing Based on Cloud Environment -- 5 Research Issues -- 6 Solutions and Recommendations -- 7 Future Research Directions -- 8 Conclusion -- References -- Evidence Evaluation of Gait Biometrics for Forensic Investigation -- 1 Introduction -- 2 Gait Biometrics -- 2.1 Gait Datasets -- 2.2 Gait Recognition Methods -- 3 Gait Analysis for Forensics -- 3.1 Descriptive-Based Methods -- 3.2 Metric-Based Methods -- 4 Evidence Evaluation and Challenges -- 5 Conclusions -- References -- 14 Formal Acceptability of Digital Evidence -- Abstract -- 1 Introduction -- 2 Research Goals -- 2.1 Research methodology -- 3 Data Collection and Sample Characteristic -- 4 Research Results -- 5 Defining the Formal Acceptability of Digital Evidence -- 6 Implementing Rules Through DEMF -- 7 Conclusion -- References. , A Comprehensive Android Evidence Acquisition Framework.

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: Intro -- Preface -- Contents -- Part IIntelligent Systems and Informatics (I) -- 1 Automatic Rules Generation Approach for Data Cleaning in Medical Applications -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Data Quality in Medical Applications -- 4 Proposed Approach -- 5 Experimental Study -- 5.1 Experimental Setting -- 5.2 Experimental Results -- 6 Conclusions -- References -- 2 Action Classification Using Weighted Directional Wavelet LBP Histograms -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Proposed Action Representation and Description -- 3.1 Weighted Directional Wavelet Local Binary Pattern Histogram -- 3.2 Global Description Using Invariant Moments -- 4 Experimental Results -- 4.1 Human Action Dataset -- 4.2 Experiments and Results -- 5 Conclusions -- References -- 3 Markerless Tracking for Augmented Reality Using Different Classifiers -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Theoretical Background -- 4 Methodology -- 4.1 Camera Calibration -- 4.2 Camera Pose Estimation -- 4.3 System Framework -- 4.4 Implementation Details -- 5 Experimental Results -- 6 Conclusion -- References -- 4 An Experimental Comparison Between Seven Classification Algorithms for Activity Recognition -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Classification Methods -- 3.1 Naïve Bayes Classification -- 3.2 K-Nearest Neighbor Classification -- 3.3 Neural Network -- 3.4 Support Vector Machine -- 3.5 Dynamic Time Warping -- 3.6 1 Gesture Recognition -- 3.7 D1 Recognition -- 4 Experimental Methodology and Results -- 5 Discussion and Conclusion -- References -- Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning -- 1 Introduction -- 2 Core Concepts -- 2.1 Support Vector Machines (SVMs) -- 2.2 Random Forests (RF) -- 3 The Proposed Classification System -- 3.1 Pre-processing Phase. , 3.2 Feature Extraction Phase -- 3.3 Classification Phase -- 4 Experimental Results -- 5 Conclusions and Future Work -- References -- 6 Detection of Dead Stained Microscopic Cells Based on Color Intensity and Contrast -- Abstract -- 1 Introduction -- 2 Literature Survey -- 3 Proposed System -- 4 Result and Discussion -- 5 Conclusion -- Acknowledgment -- References -- Digital Opportunities for 1st Year University Students' Educational Support and Motivational Enhancement -- 1 Introduction -- 2 Testbed -- 3 Getting of the Raw Data -- 4 Results of Feedback -- 5 Numerical Results of Student Activities -- 6 Conclusions -- References -- 8 Building Numbers with Rods: Lesson Plans with Cuisenaire Method -- Abstract -- 1 Introduction -- 2 Lesson Plans -- 2.1 Worksheet 1 -- 2.2 Worksheet 2 -- 3 Application in the Field of ICT -- 4 Conclusion -- References -- Part IIMultimedia Computingand Social Networks -- Telepresence Robot Using Microsoft Kinect Sensor and Video Glasses -- 1 Introduction -- 2 The Proposed System -- 2.1 Human--Robot Interaction Using Microsoft Kinect Sensor -- 2.2 Compositing User's Video and Robot Scene's Video -- 2.3 Telepresence Using Video Glasses -- 3 Experiment Results -- 4 Conclusions -- References -- Video Flash Matting: Video Foreground Object Extraction Using an Intermittent Flash -- 1 Introduction -- 2 Video Flash Matting -- 3 Experiments and Discussions -- 4 Conclusions and Future Work -- References -- Enhanced Region Growing Segmentation for CT Liver Images -- 1 Introduction -- 2 Morphological Operations -- 3 Filtering Techniques: Preliminaries -- 4 Region Growing Segmentation -- 5 CT Liver Segmentation Proposed Approach -- 5.1 Preprocessing Phase: Applying Filters and Ribs Boundary Detection -- 5.2 Region Growing Phase -- 6 Experimental Results and Discussion -- 7 Conclusion and Future Work -- References. , A Multi-Objective Genetic Algorithm for Community Detection in Multidimensional Social Network -- 1 Introduction -- 2 Related Work -- 3 Community Detection in M-D Networks Problem -- 4 Algorithm Description -- 5 Experimental Results -- 5.1 Synthetic Network Dataset -- 5.2 Real World Social Network Data -- 5.3 Further Analysis -- 6 Conclusion and Future Work -- References -- 13 Creativity in the Era of Social Networking: A Case Study at Tertiary Education in the Greek Context -- Abstract -- 1 Introduction -- 2 Literature Review on Creativity and Social Network Tools -- 3 Research Methodology -- 4 Data Analysis and Discussion -- 5 Conclusions -- References -- OCR System for Poor Quality Images Using Chain-Code Representation -- 1 Introduction -- 2 The Proposed System -- 2.1 Characters Extraction -- 2.2 Characters Recognition -- 2.3 Estimated Text Correction -- 3 Experiment Results -- 3.1 Datasets -- 3.2 Results of Chain-Code Representation -- 3.3 Results of the Proposed System -- 4 Conclusions -- References -- Human Thermal Face Extraction Based on SuperPixel Technique -- 1 Introduction -- 2 Theoretical Background -- 2.1 Quick-Shift Method -- 2.2 Otsu's Thresholding Method -- 3 Proposed Thermal Face Extraction Model -- 4 Experimental Results and Discussion -- 5 Conclusion -- References -- Unsupervised Brain MRI Tumor Segmentation with Deformation-Based Feature -- 1 Background -- 2 Methods -- 2.1 Segmentation of Lateral Ventricles -- 2.2 3-Dimensional Alignment -- 2.3 Transforming Lateral Ventricular Deformation to Feature -- 3 Experiments -- 3.1 Experimental Settings and Evaluation Methods -- 3.2 Results and Discussion -- 4 Conclusion -- References -- Face Sketch Synthesis and Recognition Based on Linear Regression Transformation and Multi-Classifier Technique -- 1 Introduction -- 2 Preliminaries -- 2.1 Gabor Features. , 2.2 Linear Discriminant Analysis (LDA) -- 2.3 Classifier Fusion -- 3 Proposed Model -- 3.1 Photo to Sketch Transformation -- 3.2 Training Phase -- 3.3 Testing Phase -- 4 Experimental Results and Discussion -- 4.1 Experimental Setup -- 4.2 First Experiment (Photo to Pseudo-Sketch Transformation) -- 4.3 Second Experiment (Face Sketch Recognition) -- 4.4 Discussion -- 5 Conclusions and Future Work -- References -- Part IIISwarms Optimization and Applications -- A New Learning Strategy for Complex-Valued Neural Networks Using Particle Swarm Optimization -- 1 Introduction -- 2 Complex-Valued Neural Networks -- 3 Particle Swarm Optimization -- 4 Problem Formulation -- 5 Data Description and Processing -- 6 Experimental Results -- 7 Conclusions -- References -- A XOR-Based ABC Algorithm for Solving Set Covering Problems -- 1 Introduction -- 2 Problem Description -- 3 Artificial Bee Colony Algorithm -- 4 Improving Solution Quality and Solving Speed -- 5 Experiments and Results -- 6 Conclusions -- References -- 20 Abdominal CT Liver Parenchyma Segmentation Based on Particle Swarm Optimization -- Abstract -- 1 Introduction -- 2 Preliminaries -- 3 The Proposed Segmentation Approach -- 3.1 Preprocessing Phase -- 3.2 CT Image Clustering Based on PSO Phase -- 3.3 Post-Processing Phase: Morphological Operators -- 3.4 Region of Interest Extraction Phase -- 3.5 Evaluation Phase -- 4 Experimental Results and Discussion -- 5 Conclusion and Future Works -- References -- Grey Wolf Optimizer and Case-Based Reasoning Model for Water Quality Assessment -- 1 Introduction -- 2 The Proposed GWO-CBR Hybrid Classification Model -- 2.1 Case Representation Phase -- 2.2 Retrieve Phase -- 2.3 Reuse/Adapt Phase -- 2.4 Revise Phase -- 2.5 Retrain Phase -- 3 Experimental Analysis and Discussion -- 4 Conclusions and Future Work -- References. , New Rough Set Attribute Reduction Algorithm Based on Grey Wolf Optimization -- 1 Introduction -- 2 Preliminaries -- 2.1 Rough Set Theory -- 2.2 Grey Wolf Optimization -- 3 Rough Set Based on Gray Wolf for Attribute Reduction (GWORSAR) -- 4 Experimental Results and Discussions -- 5 Conclusion -- References -- Solving Manufacturing Cell Design Problems Using a Shuffled Frog Leaping Algorithm -- 1 Introduction -- 2 Related Work -- 3 Manufacturing Cell Design Problem -- 3.1 Similarity Matrices -- 4 Shuffled Frog Leaping Algorithm -- 4.1 Discretization of Decision Variables -- 5 Experimental Result -- 6 Conclusion and Future Work -- References -- A Hybrid Bat-Regularized Kaczmarz Algorithm to Solve Ill-Posed Geomagnetic Inverse Problem -- 1 Introduction -- 2 The Proposed Regularized Kaczmarz Model -- 3 Experimental Results and Discussion -- 4 Conclusion and Future Work -- References -- A Fish Detection Approach Based on BAT Algorithm -- 1 Introduction -- 2 The Bat Algorithm: An Overview -- 3 The Proposed Approach -- 4 Experimental Results and Discussion -- 5 Conclusions and Future Directions -- References -- Part IVHybrid Intelligent Systems -- Hybrid Differential Evolution and Simulated Annealing Algorithm for Minimizing Molecular Potential Energy Function -- 1 Introduction -- 2 The Problem of Minimizing Molecular Potential Energy Function -- 3 Hybrid Differential Evolution Algorithm for Minimizing Molecular Potential Energy Function -- 3.1 The Proposed HDESA Algorithm -- 4 Numerical Experiments -- 4.1 Parameter Setting -- 4.2 The General Performance of the Proposed HDESA with the Minimization of Molecular Potential Energy Function Problem -- 4.3 Comparison Between the Proposed HDESA Algorithm and Other Algorithms -- 5 Conclusion -- References -- A Hybrid Classification Model for EMG Signals Using Grey Wolf Optimizer and SVMs -- 1 Introduction. , 2 Related Work.

Note: Intro -- Preface -- Contents -- Sentiment Analysis -- 1 Using Deep Neural Networks for Extracting Sentiment Targets in Arabic Tweets -- Abstract -- 1 Introduction -- 2 Background -- 3 Data Collection and Annotation -- 4 Building Word Embeddings -- 5 The Implemented Models -- 5.1 The Baseline Model -- 5.2 The Deep Neural Network Model -- 5.2.1 Bidirectional Long Short-Term Memory Networks (BI-LSTMs) -- 5.2.2 The Conditional Random Fields (CRF) Tagging Model -- 5.2.3 BI-LSTM-CRF -- 6 Performance Evaluation -- 7 Conclusion -- Acknowledgements -- References -- 2 Evaluation and Enrichment of Arabic Sentiment Analysis -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Challenges Arabic Sentiment Analysis -- 3.1 Encoding -- 3.2 Sentiment Analysis Impacted Due to Unavailability of Punctuations -- 3.3 Excess Resources Required -- 3.4 Sarcastic Tamper -- 3.5 One Word Represents Two Polarities -- 3.6 Indifferent Writing Style -- 3.7 Free Writing Style -- 3.8 Word Short Forms -- 3.9 Same Word Usage for Both Polarities -- 4 Data Collection -- 5 Implementation of Arabic Sentiment Analysis -- 6 Evaluation and Results -- 7 Conclusion -- References -- 3 Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Arabic Sentiment Analysis -- 4 Hotel Arabic Reviews Dataset (HARD) -- 4.1 Collection -- 4.2 Properties -- 5 Sentiment Analysis -- 5.1 Text Pre-processing -- 5.2 Feature Extraction -- 5.3 Classifiers -- 6 Experimental Results -- 6.1 Bag of Words -- 6.2 Lexicon-Based Classification -- 7 Conclusions -- References -- 4 Using Twitter to Monitor Political Sentiment for Arabic Slang -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Corpus Collection and Preparation -- 3.2 Pre-processing -- 3.3 Text Classification -- 4 Results and Evaluation. , 5 Conclusion and Future Work -- References -- Estimating Time to Event of Future Events Based on Linguistic Cues on Twitter -- 1 Introduction -- 2 Related Research -- 3 Time-to-Event Estimation Method -- 4 Experimental Set-Up -- 4.1 Data Sets -- 4.2 Features -- 4.3 Training and Test Regimes -- 4.4 Evaluation and Baselines -- 4.5 Hyperparameter Optimization -- 5 Test Results -- 6 Discussion -- 7 Conclusions and Future Work -- References -- Machine Translation -- Automatic Machine Translation for Arabic Tweets -- 1 Introduction -- 2 Arabic Language Challenges Within NLP and Social Media -- 2.1 Modern Standard Arabic Challenges for MT -- 2.2 Arabic Language in Microblogs -- 3 Overview of Statistical Machine Translation -- 3.1 Language Model -- 3.2 Word Alignment -- 3.3 Translation Model -- 3.4 Decoding -- 4 Translating Arabic Tweets -- 4.1 Data Collection -- 4.2 Data Collection -- 4.3 Experiments -- 4.4 Results -- 4.5 Discussion -- 5 Conclusion and Future Work -- References -- Developing a Transfer-Based System for Arabic Dialects Translation -- 1 Introduction -- 2 Related Studies -- 3 Arabic Language Variation -- 4 Machine Translation Paradigms -- 4.1 Rule Based MT -- 4.2 Statistical MT -- 4.3 Hybrid MT -- 5 (ALMoFseH) Arabic Dialects Machine Translation -- 6 Methodology -- 6.1 Building a Lexical Database -- 6.2 The Transfer System -- 6.3 Naive Bayesian Classifier (NB) -- 6.4 Rewrite Rules for Dialectal Normalization -- 6.5 Functional Model -- 7 Evaluation of the System -- 8 Results -- 9 Conclusion -- References -- 8 The Key Challenges for Arabic Machine Translation -- Abstract -- 1 Introduction -- 2 Challenges for Arabic Translation -- 2.1 Classical Arabic -- 2.2 Modern Standard Arabic -- 2.3 Dialect Arabic -- 3 Machine Translation in Natural Language Processing -- 3.1 Metaphor Translation -- 3.2 Metaphor in Holy Quran. , 3.3 Metaphor in Modern Standard Arabic -- 3.4 Metaphor in Dialect Arabic -- 4 Named Entity Recognition Translation -- 5 Word Sense Disambiguation Translation -- 6 Conclusion -- References -- Information Extraction -- Graph-Based Keyword Extraction -- 1 Introduction -- 2 Related Work -- 3 Research Methodology -- 3.1 Overview -- 3.2 The Proposed Methodology -- 3.3 Dataset -- 3.4 Data Processing -- 3.5 Learn Classifier and TF/IDF -- 3.6 Performance Evaluation -- 4 Discussion -- 5 Conclusion and Future Prospects -- References -- 10 CasANER: Arabic Named Entity Recognition Tool -- Abstract -- 1 Introduction -- 2 State of the Art on NER Systems -- 2.1 Previous NE Categorization -- 2.2 NER Approaches and Systems -- 3 ANE Identification and Categorization -- 3.1 Identification of the ANE Forms and Categories -- 3.2 Relationship Between ANEs -- 4 Proposed Method -- 4.1 Analysis Transducer Establishment -- 4.2 Synthesis Transducer Establishment -- 5 Implementation and Evaluation -- 6 Conclusion -- References -- 11 Semantic Relations Extraction and Ontology Learning from Arabic Texts-A Survey -- Abstract -- 1 Introduction -- 2 Arabic Semantic Relation Extraction and Ontology Learning -- 3 Arabic Semantic Relation Extraction -- 3.1 Semantic Relation Extraction Between Arabic Named Entities -- 3.1.1 Rule-Based Approach -- 3.1.2 Machine Learning Approach -- 3.1.3 Hybrid Approach -- 3.2 Semantic Relation Extraction Between Arabic Ontological Concepts -- 4 Arabic Ontology Learning -- 4.1 Upper Ontology -- 4.1.1 Arabic WordNet Ontology -- 4.1.2 Formal Arabic Ontology -- 4.2 Domain Ontology -- 4.2.1 General Domains -- Manual Approach -- Statistical Approach -- Linguistic Approach -- Hybrid Approach -- Uncategorized -- 4.2.2 Islamic Domain -- Quran Ontology -- Hadith Ontology -- 5 Conclusion -- Information Retrieval and Question Answering. , 12 A New Semantic Distance Measure for the VSM-Based Information Retrieval Systems -- Abstract -- 1 Introduction -- 2 The Proposed Approach -- 2.1 A Novel Indexing Approach -- 2.2 The Significance Level of a Concept (SLC) -- 2.3 Semantic Distance Between Query and CS -- 3 System Architecture -- 4 Experimental Analysis -- 4.1 Experimental Setup -- 4.2 Experiments, Results, and Evaluation -- 4.2.1 The Conceptualization Levels -- 4.2.2 The Retrieval Capability -- 4.2.3 The Ranking Accuracy -- 5 Conclusion and Future Work -- Appendix: The Implementation Algorithms -- References -- An Empirical Study of Documents Information Retrieval Using DWT -- 1 Introduction -- 2 Background -- 2.1 Term Signal -- 2.2 Weighting Scheme -- 2.3 Document Segmentation -- 2.4 Wavelet Transform Algorithm -- 3 Design Issues and Implementation of Information Retrieval Using DWT -- 3.1 Problems and Design Issues -- 3.2 Implementation of the Suggested Model -- 3.3 Document Segmentation -- 3.4 Term Weighting -- 4 Experiments and Results -- 5 Conclusion -- References -- 14 A Review of the State of the Art in Hindi Question Answering Systems -- Abstract -- 1 Introduction -- 2 A Typical Pipeline Architecture of a Question Answering System -- 2.1 Question Processing -- 2.1.1 Question Classification -- 2.2 Answer Type Determination -- 2.3 Keyword Extraction -- 2.4 Query Expansion -- 2.5 Document Processing -- 2.5.1 Passage Retrieval -- 2.6 Answer Extraction -- 2.6.1 Named Entity Recognition -- 2.6.2 Answer Scoring and Ranking -- 2.6.3 Answer Presentation -- 3 Developments in Hindi Question Answering System -- 3.1 Developments in Tasks of Question Answering Systems -- 4 Introduction to Hindi Language and Its Challenges for QASs -- 5 Tools and Resources for Hindi Question Answering -- 6 Future Scopes -- References -- Text Classification. , 15 Machine Learning Implementations in Arabic Text Classification -- Abstract -- 1 Introduction -- 2 Problem Definition -- 2.1 Problem Scope, Input and Output -- 2.2 Problem Formalization -- 3 Text Classification Steps -- 3.1 Data Selection and Preparation -- 3.2 Text Preprocessing -- 3.3 Document Indexing and Term Weighting Methods -- 3.4 Feature Reduction -- 4 Classification Algorithms -- 5 Arabic Text Classification -- 6 Directions for Further Research -- 7 Conclusion -- References -- Authorship and Time Attribution of Arabic Texts Using JGAAP -- 1 Introduction -- 2 Background -- 2.1 Authorship Attribution and NLP -- 2.2 Authorship Attribution in Arabic -- 3 Data -- 3.1 Corpus Description -- 3.2 Selection of Texts -- 4 Methods -- 4.1 JGAAP -- 4.2 Canonicizers -- 4.3 Event Drivers -- 4.4 Analysis Methods -- 5 Results -- 5.1 Character n-grams -- 5.2 Word n-grams -- 5.3 Word Length -- 5.4 Rare Words -- 5.5 Most Common Words -- 6 Analysis of Errors -- 7 Future Work and Conclusions -- References -- 17 Automatic Text Classification Using Neural Network and Statistical Approaches -- Abstract -- 1 Reviewing the Previous Work -- 2 Preprocessing Procedures for the Two Proposed Classifiers -- 2.1 Word Extraction -- 2.2 Stop Words Removal -- 2.3 Word Stemming -- 2.4 Improvements -- 2.5 Reuters 21,758 Test Collection for Text Categorization -- 2.6 Term Weighting Techniques -- 3 The Proposed Statistical Classifier -- 3.1 Converting the Text Documents into a Database -- 3.2 The Resulting Database Model -- 3.3 Weighting Techniques -- 3.4 Improvements for Weighting -- 3.5 Experimental Details -- 4 Neural Network Based Classifier -- 4.1 Dimensionality Reduction for Text Categorization -- 4.2 The Proposed Neural Network Based Text Classifier -- 4.3 Experimental Details -- 5 Comparison Between Proposed Statistical Classifier and the Neural Network Based Classifier. , 6 Conclusions.

Note: Intro -- Preface -- Contents -- Artificial Intelligence Technology Against COVID-19 -- Digital Transformation and Emerging Technologies for Tackling COVID-19 Pandemic -- 1 Introduction -- 2 Artificial Intelligence -- 2.1 Solutions Based on Artificial Intelligence -- 2.2 Solutions Based on Deep Learning -- 3 Internet of Things -- 3.1 Solution Based on the Internet of Things -- 3.2 Solution Based on Robots -- 3.3 Solutions Based on Unmanned Aerial Vehicles (Drones) -- 4 Big Data Analytics -- 5 Blockchain Technology -- 6 Cloud and Fog Computing -- 7 The Fourth Industrial Revolution -- 8 Conclusion and Future Aspects -- References -- The Role of Emerging Technologies for Combating COVID-19 Pandemic -- 1 Introduction -- 2 Literature Reviews -- 2.1 Blockchain Technology -- 2.2 Artificial Intelligence -- 2.3 Internet of Things Technology -- 2.4 Big Data Technology -- 3 COVID-19 Datasets -- 4 Applications of Emerging Technologies in COVID-19 -- 4.1 Application of AI Against COVID-19 -- 4.2 Application of IoT Against COVID-19 -- 4.3 Application of Big Data Against COVID-19 -- 4.4 Blockchain Applications Against COVID-19 -- 5 Challenges -- 5.1 Shortage of Standard Datasets -- 5.2 Regulation Reflection -- 5.3 Security -- 5.4 Privacy Preservation -- 6 Future Directions -- 6.1 Covid19 Datasets -- 6.2 AI Against Covid19 -- 6.3 Blockchain Against COVID-19 -- 6.4 Big Data Against COVID-19 -- 6.5 Merge with Other Emerging Technologies -- 7 Conclusion -- References -- An Optimized Classification Model for COVID-19 Pandemic Based on Convolutional Neural Networks and Particle Swarm Optimization Algorithm -- 1 Introduction -- 2 Related Work -- 3 Basics and Background -- 3.1 Convolutional Part -- 3.2 Classifier Part -- 3.3 Training a CNN Network -- 4 The Proposed COVID-19 Classification Optimized Model -- 4.1 Data Preprocessing Phase -- 5 Conclusion and Future Work. , References -- Automatic Scoring and Grading of COVID-19 Lung Infection Approach -- 1 Introduction -- 2 Materials and Methods -- 2.1 CT COVID-19 Image Segmentation Methods -- 2.2 Particle Swarm Optimization (PSO) Algorithm -- 3 The Proposed Scoring of COVID-19 Lung Infection Grading Approach -- 3.1 PSO-Based FCM Algorithm -- 3.2 PSO-Based K-Means Algorithm -- 3.3 PSO-Based Thresholding Algorithm -- 4 Infection Rate Estimation -- 5 Results and Discussion -- 5.1 Data Description -- 5.2 Experimental Results for Segmentation Algorithms -- 5.3 Experimental Results for Infection Rate -- 6 Conclusions -- References -- Artificial Intelligence Strategy in the Age of Covid-19: Opportunities and Challenges -- 1 Introduction -- 2 Artificial Intelligence Strategy to Understand, Track and Monitor COVID-19 -- 2.1 Machine and Deep Learning Techniques -- 2.2 Artificial Intelligence Solutions to Fight Against COVID-19 -- 3 Application: AI for the Diagnosis of COVID 19 -- 4 Problems, Challenges and Future Perspectives -- 4.1 Problems and Challenges -- 4.2 Future Perspectives -- 5 Concluding Remarks -- References -- SAKHA: An Artificial Intelligence Enabled VisualBOT for Health and Mental Wellbeing During COVID'19 Pandemic -- 1 Introduction -- 2 Brief History of Chatbots and Emergence of Visual-Bots -- 2.1 Evolution of Visual Bots -- 2.2 Facial Emotion Recognition (FER) Classification -- 3 Proposed Architecture and Framework for 'Smart and Adaptive Knowledge-driven Human Assistant' (SAKHA) -- 4 Future Work -- References -- Classification of COVID19 X-ray Images Based on Transfer Learning InceptionV3 Deep Learning Model -- 1 Introduction and Related Work -- 2 Basics and Background -- 2.1 COVID-19 Pandemic -- 2.2 InceptionV3 Algorithm -- 3 The Proposed Classification Model -- 3.1 Dataset Description -- 3.2 Experimental Implementation. , 3.3 Results, Discussion, and Analysis -- 3.4 The Validity of the Proposed Model -- 4 Conclusion -- References -- Artificial Intelligence Approach to Predict the COVID-19 Patient's Recovery -- 1 Introduction -- 2 COVID-19: Overview -- 3 Some Machine Learning Classifiers for COVID-19 -- 3.1 Artificial Neural Networks (ANNs) -- 3.2 Support Vector Machine (SVM) -- 3.3 Data Sets Characteristics and Analysis -- 3.4 Methodology and Results -- 4 Conclusion and Future Work -- References -- Deep Learning Technology for Tackling COVID-19 Pandemic -- 1 Introduction -- 2 SARS-CoV-2(COVID-19: Strategies and Evidence -- 2.1 Transmission -- 2.2 Diagnosis -- 2.3 Evaluation and Management -- 3 Deep Learning: Basic Terminologies -- 3.1 Deep Learning History -- 3.2 Deep Learning: How It Works -- 3.3 Deep Learning Models -- 4 Deep Learning for COVID-19: Approaches and Future Directions -- 4.1 Prediction Based Approaches -- 4.2 Medical Image Diagnosis Based Approaches -- 4.3 Genome Based Approaches -- 5 Conclusion -- References -- Digital and  Emerging Technologies for Monitoring and Controlling COVID-19 -- Monitoring COVID-19 Disease Using Big Data and Artificial Intelligence-Driven Tools -- 1 Introduction -- 2 Recent Studies in Monitoring COVID-19 Using Big Data -- 3 COVID-19 Artificial Intelligence-Driven Tools -- 4 Case Study: The Role of Big Data and Geographic Information System Technologies to Curb the COVID-19 Outbreak in Germany -- 5 Problems, Challenges and Future Trends -- 6 Conclusion -- References -- Drones Combat COVID-19 Epidemic: Innovating and Monitoring Approach -- 1 Introduction -- 2 The Societal Impact of Drones in the Age of COVID-19 -- 2.1 IRS Camera System and Thermal Imaging -- 2.2 Drone-Based COVID-19 Monitoring and Control Room -- 2.3 Drone-Network and Data Systems -- 2.4 Smart Healthcare System. , 3 The COVID-19 Proposed Monitoring Framework Based on Drones -- 4 Problems and Challenges -- 5 Security Issues -- 6 Scheduling Drones Charging Problem -- 7 Big Data Governance -- 8 Trajectory Prediction -- 9 Routing and Direction Planning -- 10 Conclusions and Future Perspective -- References -- 3D Printing Supports COVID-19 Pandemic Control -- 1 Introduction -- 2 Basics and Background -- 2.1 3D Printing Technology -- 2.2 Methods -- 2.3 Material -- 2.4 3D Printing Applications -- 2.5 3D Printing of Medical Devices -- 2.6 3D Printing Fights COVID-19 -- 3 Integration of 3D Printing with COVID-19 Dosage Forms -- 4 Problems and Challenges -- 5 Conclusion -- References -- The Role of Social Robotics to Combat COVID-19 Pandemic -- 1 Introduction -- 2 Basics and Background -- 2.1 The COVID-19 Pandemic -- 2.2 Reconfiguring Factories at the Peak of the Pandemic -- 3 The Role of Social Robotics in Fighting COVID-19 -- 3.1 Robots -- 3.2 Social Distancing -- 3.3 Enablers for Reconfiguring Factories Through Robots -- 4 Social Robotics Requirements to Fight COVID-19 -- 4.1 Modularization -- 4.2 Standardized and Rapid to Deploy Hardware -- 4.3 Software Templates -- 4.4 Digital Twins Digital Twin (DT) -- 5 Conclusion -- References -- The 4th Industrial Revolution in Coronavirus Pandemic Era -- 1 Introduction -- 2 The Fourth Industrial Revolution -- 2.1 Phases of Combating Coronavirus -- 2.2 Economic Rescue from Pandemic Consequences -- 3 International Endeavors of Tracing and Restraining the Coronavirus Pandemic -- 3.1 Case Study: The Fourth Industrial Revolution Eradicates the Pandemic Outbreak in China -- 4 Problems and Challenges -- 5 Conclusion -- References -- Social and Smart Networking and Cyber Security for Tracking COVID-19 -- The Future Scope of Internet of Things for Monitoring and Prediction of COVID-19 Patients -- 1 Introduction -- 2 Literature Overview. , 3 Basics and Background -- 3.1 Sensor Networks in Healthcare Applications -- 3.2 Internet of Things Overview -- 3.3 Internet of Things for Healthcare Applications -- 3.4 The Use of Internet of Things Against Fighting COVID-19 -- 4 Problems, Challenges, and Future Trends -- 5 Conclusion -- References -- The Truth About 5G and COVID-19: Basics, Analysis, and Opportunities -- 1 Introduction -- 2 Basics and Background -- 2.1 Wireless Data Transmission Evolution -- 2.2 Business and Cell Network Generations -- 3 The Role of 5G Against Fighting COVID-19 -- 4 Concluding Remarks -- References -- Blockchain Use Cases for COVID-19: Management, Surveillance, Tracking and Security -- 1 Introduction -- 2 Blockchain Technology: An Overview -- 3 Blockchain Use Cases for COVID-19 Virus -- 3.1 Recoding Management: Blockchain is a Single Source of Truth -- 3.2 Blockchain Healthcare Surveillance System -- 3.3 Tracking COVID-19- Infection Spread Using Blockchain -- 3.4 Securing Medical Supply-Cain Using Blockchain -- 3.5 Tracking Zoonotic Diseases Using Blockchain -- 4 Blockchain Platforms for Managing Epidemic Diseases -- 4.1 HashLog -- 4.2 XMED Chain -- 5 Conclusion -- References -- Cyber Security in the Age of COVID-19 -- 1 Introduction -- 2 Impact of Covid-19 Related Fake News or Misinformation -- 3 Remote Working from Home and Cyber security Issues -- 4 Contact Tracing and Privacy Concerns -- 4.1 Epic -- 4.2 TraceTogether -- 4.3 Reichert's MPC Based Solution -- 4.4 CAUDHT -- 4.5 Berke et al.'s Location-Based System -- 4.6 DP-3T -- 5 Covid-19 Medical Imaging: Security Issues -- 6 General Covid-19 Related Security and Privacy Issues -- 6.1 Sensitive Information Exposure -- 6.2 Data Injection Attacks -- 6.3 Solutions and Mitigation Strategies -- 6.4 Trust and Reputation Models for Enhancing the Adoption of IT in the Battle Against COVID-19 -- 7 Conclusion -- References. , Tracking of COVID-19 Geographical Infections on Real-Time Tweets.

Note: Intro -- Preface -- Contents -- Machine Learning and Deep Learning Applications in Medical Diagnosis -- A Deep Learning Based Cockroach Swarm Optimization Approach for Segmenting Brain MRI Images -- 1 Introduction -- 2 Preliminaries -- 2.1 Cockroach Swarm Optimization -- 2.2 Deep Learning -- 3 The Proposed Approach -- 4 Experimental Results and Discussion -- 5 Conclusion and Future Directions -- References -- Efficient Classification Model for Melanoma Based on Convolutional Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Imbalanced Data Handling -- 3.2 Deep Learning Approach -- 4 The Proposed Melanoma Classification Model -- 4.1 Description of Dataset -- 4.2 Data Preprocessing -- 4.3 Data Preprocessing -- 5 Experimental Results -- 6 Conclusion -- References -- Machine Learning-Supported MRI Analysis of Brain Asymmetry for Early Diagnosis of Dementia -- 1 Introduction -- 2 Literature Review -- 2.1 Brain Asymmetry for the Diagnosis of Brain-Related Disorders -- 2.2 Classification of Alzheimer's Disease and Early Mild Cognitive Impairment Using the ADNI Database -- 3 The Proposed Approach -- 3.1 Image Preprocessing and Segmentation of Image Asymmetry -- 3.2 Feature Engineering and Analysis -- 4 Experiments and Results -- 5 Discussion and Conclusion -- References -- Feature Selection Based Coral Reefs Optimization for Breast Cancer Classification -- 1 Introduction -- 2 Related Work -- 3 Methods and Materials -- 3.1 The CRO Algorithm (Coral Reef Optimization) -- 3.2 CRO as a Feature Selection Technique -- 3.3 Utilized Classification Algorithms -- 3.4 Proposed Method -- 4 Results and Discussion -- 4.1 Dataset Description -- 4.2 Experiments and Results -- 4.3 Statistical Results Analysis -- 5 Conclusion and Future Work -- References. , A Comprehensive Review on Brain Disease Mapping-The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals -- 1 Introduction -- 2 Raw EEG Signals -- 3 Signal Processing -- 4 Feature Extraction Method -- 5 Classification -- 6 Case Studies Consolidation and Their Analysis -- 7 Conclusion -- References -- Recognition of Ocular Disease Based Optimized VGG-Net Models -- 1 Introduction -- 2 Related Work -- 3 Methodologies -- 3.1 The Proposed Model -- 3.2 Ocular Disease Recognition Dataset -- 3.3 DCNN Classification Models -- 4 Experimental Results and Discussion -- 4.1 Performance Evaluation -- 4.2 Confusion Matrix -- 5 Conclusion -- References -- Artificial Intelligence in COVID-19 Diagnosis -- Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life -- 1 Introduction -- 2 Applications of AI and IoT in COVID-19 -- 2.1 Diagnosis of COVID-19 -- 2.2 Drug and Vaccine Development -- 2.3 Distribution of COVID-19 Vaccine Using AI -- 2.4 Monitoring Vaccine Temperate Using IoT -- 2.5 Management of Vaccine (Co-WIN) -- 2.6 Application of AI in Predicting COVID-19 Outcome -- 2.7 Sentiment Analysis and Fake News Detection Regarding Covid-19 -- 3 Impact of COVID-19 Vaccine on Social Life -- 4 The Plans of Government and Hospitals to Reduce the Impact of Corona Especially in Saudi Arabia -- 5 Acceptance and Demand for COVID-19 Vaccines -- 6 Conclusion -- References -- COVID-19 Forecasting Based on an Improved Interior Search Algorithm and Multilayer Feed-Forward Neural Network -- 1 Introduction -- 2 Preliminaries -- 2.1 Multilayer Feed-Forward Neural Network -- 2.2 Interior Search Algorithm -- 2.3 The Motivation of This Work -- 3 Proposed ISACL Algorithm -- 3.1 Composition-Based Experience Strategy -- 3.2 Chaotic Learning Based-Local Searching Mechanism -- 3.3 Framework of ISACL. , 4 Experiment and Results -- 4.1 Description of the COVID-19 Dataset -- 4.2 Parameter Settings -- 4.3 Indices for Performance Assessment -- 4.4 Results and Discussions -- 5 Conclusions -- References -- Development of Disease Diagnosis Model for CXR Images and Reports-A Deep Learning Approach -- 1 Introduction -- 2 Background Study -- 2.1 CXR Images and Radiology Reports -- 2.2 Convolutional Neural Networks (CNNs) -- 2.3 Pre-trained Convolutional Neural Networks (CNNs) -- 2.4 Radiology Report -- 2.5 Natural Language Processing in Producing the Radiology Report -- 3 Literature Review -- 4 Methodology -- 4.1 Dataset Description -- 4.2 Exploratory Data Analysis -- 4.3 Building Models -- 5 Model Evaluation and Results Analysis -- 6 Conclusion -- References -- Intelligent Drug Descriptors Analysis: Toward COVID-19 Drug Repurposing -- 1 Introduction -- 2 Discussion -- 2.1 Physicochemical Properties and Descriptors -- 2.2 Drug Approved Analysis -- 3 Theoretical Background of Support Vector Machine (SVM) -- 4 Intelligent Drugs Descriptors Analysis Approach -- 4.1 Preprocessing Phase -- 4.2 Support Vector Regression Phase -- 4.3 Kernel Selection -- 4.4 Descriptors Analysis -- 5 Results and Discussion -- 6 Conclusion and Future Work -- 7 Appendix -- References -- Emerging Technologies and Applications -- Analysis of Aortic Valve Using a Finite Element Model -- 1 Introduction -- 2 Materials and Methods -- 3 Results -- 3.1 Aortic Root and Leaflet Deformation -- 3.2 Von-Mises Stress in the Leaflets -- 4 Conclusion and Future Work -- References -- Fundus Images Enhancement Using Gravitational Force and Lateral Inhibition Network for Blood Vessel Detection -- 1 Introduction -- 2 Material and Methods -- 2.1 Description of Dataset -- 2.2 Background of Model -- 3 GF and LIN Pattern to Improve the Image Contrast of a Vessel Image. , 4 Metrics for Evaluating the Improved Image -- 5 Results and Discussion -- 6 Conclusion -- References -- Multivariate Fuzzy Logic Based Smart Healthcare Monitoring for Risk Evaluation of Cardiac Patients -- 1 Introduction -- 2 Related Work -- 3 General Layout of Fuzzy Logic Theory -- 4 Proposed Fuzzy-Based Cardiac Patient Monitoring Model -- 4.1 Simulation Environment Setting -- 5 Simulation Results and Analysis -- 6 Conclusions -- References -- Ensemble Machine Learning Model for Mortality Prediction Inside Intensive Care Unit -- 1 Introduction -- 2 Related Work -- 2.1 Scoring System -- 2.2 Machine Learning Systems -- 3 Data Set -- 3.1 Dataset Description -- 3.2 Table Selection -- 4 Proposed Framework -- 4.1 Data Preprocessing -- 5 Resuls and Discussion -- 5.1 Single Classifier Results -- 5.2 Ensemble Classifier Results -- 6 Conclusion and Future Work -- References.

Note: Intro -- Preface -- Organization -- Honorary Chair -- General Chair -- International Advisory Board -- Program Chairs -- Publicity Chairs -- Technical Program Committee -- Local Arrangement Chairs -- Contents -- Swarm Intelligence and Optimization -- A Hybrid Grey Wolf-Bat Algorithm for Global Optimization -- 1 Introduction -- 2 Preliminaries -- 2.1 GWO -- 2.2 BA -- 3 Proposed Hybrid Grey Wolf-Bat Optimization Algorithm -- 4 Experimental Results and Discussion -- 4.1 CEC2017 Benchmark Suite -- 4.2 Results and Parameter Settings -- 5 Conclusion and Future Work -- References -- Fractional Order Sliding Mode PID Controller/Observer for Continuous Nonlinear Switched Systems with PSO Parameter Tuning -- 1 Introduction -- 2 Problem Formulation -- 3 Observer Design -- 4 Fractional Order Sliding Mode PID Controller Design -- 4.1 Sliding Mode Controller Design -- 4.2 Fractional Order PID Controller Design -- 4.3 Parameter Tuning by Particle Swarm Optimization -- 5 Numerical Example and Simulation Results -- 6 Discussion -- 7 Conclusion -- References -- Modified Optimal Foraging Algorithm for Parameters Optimization of Support Vector Machine -- 1 Introduction -- 2 Optimal Foraging Algorithm -- 3 The Proposed OFA for SVM Parameter Optimization Algorithm -- 3.1 Parameters Initialization -- 3.2 Fitness Function, Positions Updates and Termination Criteria -- 4 Experimental Results and Discussion -- 4.1 Datasets Description -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Pareto Based Bat Algorithm for Multi Objectives Multiple Constraints Optimization in GMPLS Networks -- Abstract -- 1 Introduction -- 2 MPLS/GMPLS Networks -- 3 Problem Evaluation -- 3.1 Total Routing Costs Objective Function -- 3.2 Total Load Balancing Costs Objective Function -- 4 Proposed Algorithm -- 4.1 Pareto Based Bat Algorithm (PBA) -- 5 Experimental Setup. , 6 Result Analysis -- 7 Conclusion -- References -- Fish Image Segmentation Using Salp Swarm Algorithm -- 1 Introduction -- 2 Preliminaries -- 2.1 Salp Swarm Algorithm (SSA) -- 2.2 SLIC Method -- 3 Proposed Fish Segmentation Model -- 4 Experimental Results and Discussion -- 5 Conclusion -- References -- Swarming Behaviors of Chicken for Predicting Posts on Facebook Branding Pages -- 1 Introduction -- 2 Preliminaries -- 2.1 Adaptive Neuro-Fuzzy Inference System -- 2.2 Chicken Swarm Optimization Algorithm -- 3 The Proposed Predicting Posts of Facebook Branding Pages -- 4 Experiments and Discussion -- 5 Conclusion and Future Work -- References -- Enhancing AGDE Algorithm Using Population Size Reduction for Global Numerical Optimization -- Abstract -- 1 Introduction -- 2 Adaptive Guided Differential Evolution with Non-linear Population Reduction -- 2.1 Novel Mutation Strategy -- 2.2 New Adaptive Crossover -- 2.3 Setting the Initial Population Size -- 2.4 AGDE with Non-linear Population Size Reduction -- 3 Evaluation of EAGDE on CEC'2013 Benchmark Functions -- 3.1 Parameters Setup -- 3.2 Comparison Against EAGDE on CEC'2013 Benchmark Functions -- 4 Conclusion and Future Work -- References -- Simulated Annealing Based Quantum Inspired Automatic Clustering Technique -- 1 Introduction -- 2 Preliminaries -- 2.1 Simulated Annealing -- 2.2 Quantum Computing -- 3 Clustering Validation Techniques and Fitness Function -- 4 The Proposed Automatic Clustering Technique -- 5 Experimental Results and Analysis -- 6 Concluding Remarks -- References -- Hybrid Grasshopper Optimization Algorithm and Support Vector Machines for Automatic Seizure Detection in EEG Signals -- 1 Introduction -- 2 Materials and Methods -- 2.1 EEG Data Acquisition -- 2.2 Discrete Wavelet Transforms (DWT) -- 2.3 Grasshopper Optimization Algorithm (GOA) -- 2.4 Support Vector Machine (SVM). , 3 The Proposed Classification GOA-SVM Approach -- 3.1 Pre-processing and Feature Extraction Using DWT -- 3.2 Features Selection and Parameters Optimization Using GOA -- 3.3 Fitness Function -- 4 Experimental Results and Discussion -- 4.1 Performance Evaluation Measurements -- 4.2 Experimental Results -- 5 Conclusion -- References -- A Novel Genetic Algorithm Based k-means Algorithm for Cluster Analysis -- Abstract -- 1 Introduction -- 2 Clustering -- 3 The Presented Methodology -- 4 Experimental Results -- 5 Conclusion -- References -- Pairwise Global Sequence Alignment Using Sine-Cosine Optimization Algorithm -- Abstract -- 1 Introduction -- 2 Pairwise Global Alignment -- 2.1 Global Versus Local Alignment -- 2.2 Needle-Wunch Global Alignment Algorithm -- 3 Sine-Cosine Optimization Algorithm (SCA) -- 4 Global Alignment Using SCA -- 5 Experimental Results -- 6 Conclusions and Future Work -- References -- An Automated Fish Species Identification System Based on Crow Search Algorithm -- 1 Introduction -- 2 Crow Search Algorithm (CSA) -- 2.1 Inspiration Analysis -- 2.2 Mathematical Model of CSA -- 3 Fish Species Identification System -- 3.1 Pre-processing Phase -- 3.2 Fish Segmentation Phase -- 3.3 Feature Extraction Phase -- 3.4 Feature Selection Phase -- 3.5 Fish Classification Phase -- 4 Dataset Description -- 5 Experimental Results and Discussion -- 5.1 Preprocessing and Fish Segmentation Phases Results -- 5.2 DT vs. SVM Classifier -- 5.3 BCSA vs. Other Feature Selection Algorithms -- 6 Conclusions and Future Work -- References -- Fuzzy Logic and Applications -- Design and Implementation of Fuzzy PID Controller into Multi Agent Smart Library System Prototype -- Abstract -- 1 Introduction -- 2 Robot Motion Planning by Differential Drive Kinematic -- 3 Controller Design -- 3.1 Proposed Multi Position Controller -- 3.2 Proposed PID Controller. , 3.3 Proposed Fuzzy Controller -- 3.4 Proposed Fuzzy-PID Controller -- 4 Result and Discussion -- 5 Conclusion -- References -- Fuzzy Logic Controller with Color Vision System Tracking for Mobile Manipulator Robot -- Abstract -- 1 Introduction -- 2 Mathematical Model -- 2.1 Kinematic Model of a Nonholonomic Mobile Robot -- 2.2 Mathematical Modeling of a DC Motor -- 3 Controller Design -- 4 Results and Discussion -- 4.1 Cart Robot Controller's Results -- 5 Conclusion -- References -- Interactive Fuzzy Cellular Automata for Fast Person Re-Identification -- 1 Introduction -- 2 Background -- 3 Proposed Approach -- 3.1 Fuzzy Dominant Color Descriptor -- 3.2 Fuzzy Cellular Automata Descriptor -- 4 Experimental Results -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusions -- References -- Decision Support System for Determination of Forces Applied in Orthodontic Based on Fuzzy Logic -- 1 Introduction -- 2 The Proposed Orthodontic System -- 2.1 Fuzzification -- 2.2 Inputs for Fuzzy Logic System -- 2.3 Outputs Variables of Fuzzy Logic System -- 2.4 Rules for Fuzzy Logic system -- 2.5 Fuzzy Inference Engine -- 2.6 Defuzzification -- 3 Results and Discussion -- 4 Conclusions -- References -- Advanced Machine Learning and Applications -- Design and Implementation of IoT Platform for Real Time Systems -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Message Queue Telemetry Transport (MQTT) -- 4 Proposed IoT Platform Implementation -- 4.1 Proposed IoT Nodes -- 4.2 IoT Server -- 4.3 Proposed Communication Protocol -- 4.4 Frame Format -- 5 Experiment Setup -- 6 Experimental Results -- 7 Conclusion -- References -- Machine Learning: A Convergence of Emerging Technologies in Computing -- Abstract -- 1 Introduction -- 2 IoT and Fog Computing -- 3 Machine Learning (Core) and Fog Computing (Business Acumen) -- 4 Optimization in Fog Computing for IoT. , 5 Roadmap to Machine Learning Proficiency -- 5.1 Fundamental Theories -- 5.2 Probability and Statistic -- 5.3 Machine Learning and Deep Learning -- 5.4 Software Tools and Programming Languages -- 5.5 Fog Computing and IoT -- 5.6 Validation and Application -- 6 Conclusion -- References -- Discrimination of Satellite Signals from Opencast Mining of Mineral Ores of Hematite and Uranium Using Digital Image Processing and Geostatistical Algorithms -- Abstract -- 1 Introduction -- 2 Study Area -- 3 Methodology -- 4 Results -- 5 Discussion and Future Scope -- Acknowledgement -- References -- Detecting Cross-Site Scripting Attacks Using Machine Learning -- 1 Introduction -- 2 Background -- 2.1 JavaScript -- 2.2 Obfuscation -- 3 Related Work -- 4 Methodology -- 4.1 Datasets -- 4.2 Selecting Features -- 4.3 Classifiers -- 5 Results -- 5.1 Experiments -- 5.2 Discussion -- 6 Conclusion -- References -- Text Mining Approach to Extract Associations Between Obesity and Arabic Herbal Plants -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Research Approach -- 3.1 Text Gathering -- 3.2 Domain Understanding -- 3.3 Text Analysis -- 3.4 Text Representation -- 3.5 Modelling -- 3.6 Validation and Discussion -- 4 Conclusions and Future Work -- References -- A Reinforcement Learning-Based Adaptive Learning System -- Abstract -- 1 Introduction -- 2 Reinforcement Learning -- 3 Proposed Approach -- 3.1 Main Components -- 3.2 State-Action-Reward -- 4 Evaluations -- 5 Conclusions and Future Work -- References -- Performance Evaluation of SVM-Based Amazighe Named Entity Recognition -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Named Entity Recognition Approaches and Methods -- 2.2 Previous Amazighe NER Studies -- 3 Amazighe Language -- 4 Amazighe Named Entity Recognition Challenges -- 5 Experiments and Results -- 5.1 System Architecture. , 5.2 Experiment Data Sets.

Note: Intro -- Preface -- Contents -- Artificial Intelligence in Climate Change Applications -- Artificial Intelligence for Predicting Floods: A Climatic Change Phenomenon -- 1 Introduction -- 2 Artificial Intelligence Approaches and Flood Prediction -- 2.1 How Does AI-Models Work to Forecast Floods -- 2.2 Machine and Deep Learning for Flood Forecasting -- 2.3 AI Systems for Flood Forecasting -- 3 Proposed Machine Learning-Based Flood Prediction Model -- 3.1 The Pre-processing Phase -- 3.2 Feature Selection Based on the PIO Phase -- 3.3 Flood Prediction Based on Machine Learning Phase -- 4 Experimental Results -- 4.1 Flood Prediction Results Using PIO and GB Algorithms -- 4.2 Flood Prediction Results Validation Using Random Forest (RF) Algorithm -- 5 Conclusion -- References -- Prediction of Climate Change Impact Based on Air Flight CO2 Emissions Using Machine Learning: Towards Green Air Flights -- 1 Introduction -- 2 Preliminary -- 3 Methodology -- 3.1 Proposed Model -- 3.2 Dataset Characteristics and Pre-processing -- 3.3 Feature Selection -- 4 Results and Performance Evaluation -- 5 Conclusion -- References -- The Impact of Artificial Intelligence on Waste Management for Climate Change -- 1 Introduction -- 2 Waste Management and Artificial Intelligence Application -- 3 The Application of Artificial Intelligence to Estimate Greenhouse Gas (GHG) -- 4 Challenges in Waste Management and Artificial Intelligence -- 5 Conclusion -- References -- A Machine Learning-Based Model for Predicting Temperature Under the Effects of Climate Change -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Linear Regression -- 3.2 Decision Tree Regressor -- 3.3 Random Forest Regressor -- 3.4 K-Nearest Neighbor Regressor -- 3.5 Support Vector Regressor -- 4 Cat Boost Regressor -- 5 Evaluation Results and Discussion -- 5.1 Dataset Description. , 5.2 Evaluation Results -- 6 Conclusion and Perspectives -- References -- Emerging Technologies in Industry and Energy Sector -- Prediction of COSubscript 22 Emission in Cars Using Machine Learning Algorithms -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contribution -- 2 The Proposed Carbon Dioxide Emission Prediction Model -- 3 Simulation Results and Discussions -- 3.1 Performance Evaluation Measurements -- 3.2 Dataset Description -- 3.3 Experimental Results -- 4 Conclusions -- References -- Climate Change: The Challenge of Tunisia and Previsions for Renewable Energy Production -- 1 Introduction -- 2 Study Area -- 3 The Energy Situation in Tunisia -- 4 Energy and Climate Change -- 5 Renewable Energies in Tunisia -- 5.1 Current Situation -- 5.2 Self-Production of Photovoltaic Solar Energy -- 6 Decision Support Framework for Photovoltaic Energy Prediction -- 6.1 Data Collection -- 6.2 The Proposed Data Warehouse -- 7 Conclusions and Recommendations -- References -- Clean Energy Management Based on Internet of Things and Sensor Networks for Climate Change Problems -- 1 Introduction -- 2 Greenhouse Monitoring -- 3 Challenges Related to Energy Use -- 4 The Internet of Things for Sustainable Energy -- 4.1 Coal-Plant Sensors -- 4.2 Oxygen Sensing -- 4.3 Carbon Monoxide Sensors -- 4.4 Flame Detection -- 4.5 Sensing Coal Flow -- 4.6 Sensing Airflow -- 4.7 Ash Carbon Sensing -- 4.8 Temperature-Sensing Gases -- 5 Sensors for the Transmission System -- 5.1 Methods for Sensing at Substations -- 5.2 Sensing of Overhead Lines -- 6 Meters with Internet-Enabled Functions -- 7 Sensing of the Solar and Wind Fields -- 8 Conclusion -- References -- Digital Twin Technology for Energy Management Systems to Tackle Climate Change Challenges -- 1 Introduction -- 2 Basics and Background -- 2.1 Digital Twins Overview -- 2.2 Digital Twins Origin, Concept, and Scenario. , 2.3 Digital Twin's Characteristics -- 2.4 Internet of Things -- 2.5 Sensor Networks -- 3 Digital Twins in Energy Management Applications -- 4 A Proposed Framework of Digital Twins in Climate Change Adaptation -- 5 Collaborative Digital Twins -- 5.1 The Need for Collaborative Digital Twins -- 5.2 Use Case of Collaborative Digital Twins in Healthcare -- 6 Challenges and Potential Solutions -- 6.1 Problems and Challenges -- 6.2 The Potential Solutions -- 7 Concluding Remarks -- References -- The Role of Internet of Things in Mitigating the Effect of Climate Change: Case Study: An Ozone Prediction Model -- 1 Introduction -- 2 Preliminaries -- 2.1 Internet of Things -- 2.2 The Role of the Internet of Things on Climate Change -- 3 Dataset Description -- 4 The Proposed Internet of Things Model-Based Machine Learning Techniques for Ozone Prediction -- 4.1 Data Preparing and Preprocessing -- 4.2 Training and Testing Phase -- 4.3 The Evaluation Measures -- 5 Experiments, Results, and Discussion -- 5.1 Experiment I -- 5.2 Experiment II -- 6 Conclusion -- References -- Emerging Climate Change Technology in Agriculture Sector -- Optimized Multi-Kernel Predictive Model for the Crop Prediction with Climate Factors and Soil Properties -- 1 Introduction -- 2 Related Work -- 3 The Proposed Optimized Crop Prediction Model -- 4 Results, Discussion, and Analysis -- 4.1 Dataset Description -- 4.2 Experimental Results and Analysis -- 5 Conclusion and Future Work -- References -- An Intelligent Crop Recommendation Model for the Three Strategic Crops in Egypt Based on Climate Change Data -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions -- 2 Background and Literature Review -- 3 Climate Change Scenarios -- 3.1 The Representative Concentration Pathway -- 3.2 The Special Report on Emissions Scenarios. , 4 The Proposed Automated Crop Recommendation Model Using Deep Learning -- 4.1 The Optimized Conventional Neural Network Using the Grey Wolf Optimization Algorithm (GWO) -- 5 The Experimental Results -- 5.1 The Dataset Used in the Experiments -- 5.2 Performance Evaluation Measures -- 5.3 Experiment 1: The Projected Impacts on the Three Strategic Crops Using Different Climate Scenarios -- 5.4 Experiment 2: The Accuracy Values for the Proposed ACRM Compared to Different Models -- 5.5 Experiment 3: The Most Significant Climate Change Factors that Affected Crop Recommendation -- 6 Conclusion and Discussion -- References -- Cost Effective Decision Support System for Smart Water Management System -- 1 Introduction -- 2 Urbanization Trends -- 3 Smart Cities Concept -- 4 The Proposed DSS for the Smart Water Management System -- 4.1 Decision Support System -- 4.2 Cloud-Native Systems -- 4.3 The Proposed DSS for Smart Water Management System -- 5 Conclusion -- References -- The Role of Artificial Intelligence in Water Management in Agriculture for Climate Change Impacts -- 1 Impact of Artificial Intelligence on Environmental Issues -- 1.1 Global Warming and Artificial Intelligence -- 1.2 Impacts of Climate Change on Production -- 1.3 Effect of Climate Change on Water Management -- 2 Automation and Traditional Farming for Water Management -- 3 Artificial Intelligence and Soilless Culture for Facing Climate Change -- 3.1 Necessity of Hydroponics -- 3.2 Benefits of Hydroponics Systems -- 3.3 Problems of Hydroponics -- 3.4 Classification of Soilless Culture Systems -- 3.5 Coupling Between Artificial Intelligence and Soilless Culture -- 3.6 Interaction Between Water Management and Soilless Culture -- 4 Conclusion -- References -- Emerging Climate Change Technologies in Healthcare Sector. , The Influence of Climate Change on the Re-emergence of Malaria Using Artificial Intelligence -- 1 Introduction -- 2 Related Work -- 3 Basics and Background for Malaria Disease -- 4 Machine Learning for Medical Applications -- 4.1 The Importance of Machine Learning for Detecting Malaria Disease -- 4.2 Problems and Challenges for Malaria Detection Using Machine Learning -- 5 Conclusion -- References -- Index.

Note: Intro -- Preface -- Contents -- Challenges of Smart Healthcare Applications -- Big Data Challenges and Opportunities in Healthcare Informatics and Smart Hospitals -- 1 Introduction -- 2 Big Data -- 2.1 Big Data Definition -- 2.2 Big Data Attributes -- 3 Cloud Computing -- 3.1 Advantages of Cloud Computing -- 3.2 Disadvantages of Cloud Computing -- 4 Big Data Analytics -- 4.1 Apache Hadoop -- 4.2 Spark -- 5 Big Data and Data Mining in Biomedical and Health Informatics -- 5.1 Health Informatics Era -- 5.2 Ambient Assisted Living (AAL) Models -- 6 Case Study -- 6.1 Datasets -- 6.2 Tools and Techniques -- 6.3 Results and Discussions -- 7 Conclusion -- References -- Healthcare Analysis in Smart Big Data Analytics: Reviews, Challenges and Recommendations -- 1 Introduction -- 2 The Healthcare System Challenges -- 2.1 High-Dimensionality -- 2.2 Irregularity -- 2.3 Corrupted and Missing Data -- 2.4 Noise -- 2.5 Bias -- 3 Data Analysis -- 3.1 Data Annotation -- 3.2 Data Cleansing -- 3.3 The Data Integration -- 3.4 The Data Visualization -- 4 Healthcare Systems Techniques -- 4.1 Clustering -- 4.2 Phenotyping -- 4.3 The Disease Progression Modelling (DPM) -- 4.4 The Statistical Regression Methods -- 4.5 The Machine Learning Method -- 4.6 The Deep Learning Method -- 4.7 The Image Data Analysis -- 4.8 Mobile Healthcare -- 4.9 The Environment Monitoring -- 4.10 Disease Detection -- 5 The Proposed System -- 5.1 Data Collection -- 5.2 The Healthcare Knowledge Based System -- 6 Conclusion and Future Work -- References -- Realization of an Adaptive Data Hiding System for Electronic Patient Record, Embedding in Medical Images -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 EPR Embedding -- 3.2 EPR Extraction -- 4 Experimental Results -- 4.1 Imperceptibility Analysis -- 4.2 Robustness Analysis -- 4.3 A Brief Discussion About the Results -- 5 Conclusion. , References -- A Swarm Intelligence Model for Enhancing Health Care Services in Smart Cities Applications -- 1 Introduction -- 2 Related Work -- 3 Basics and Background -- 4 The Proposed Intelligent Model of Cloud Computing for HCS -- 5 The Proposed PSO and PPSO of Cloud Computing for HCS -- 6 The Proposed PSO for Cloud Computing -- 6.1 The Proposed PPSO for Cloud Computing -- 7 Experimental Results -- 7.1 Implementation of PSO and PPSO -- 7.2 Implementation of PPSO Algorithm on CloudSim -- 8 Conclusion and Future Work -- References -- A Machine Learning Model for Predicting of Chronic Kidney Disease Based Internet of Things and Cloud Computing in Smart Cities -- 1 Introduction -- 2 Related Work -- 3 Basics and Background -- 4 The Proposed Architecture of Cloud-IOT for CKD -- 5 The Proposed Model for Predicting of CKD -- 6 Experimental Results -- 6.1 Implementation of LR Model in Windows Azure -- 6.2 Implementation of NN Model in Windows Azure -- 6.3 Case Study -- 7 Conclusion and Future Work -- References -- Secure Models for Smart Cities Applications -- Data Security and Challenges in Smart Cities -- 1 Introduction -- 2 Background -- 3 Related Work -- 4 Mathematical Model of AES and RLE -- 5 WIFI Security System -- 6 Experimental Results and Discussion -- 7 Performance Evaluation -- 8 Conclusion -- References -- Engineering Large Complex Critical Infrastructures of Future Smart Cities as Self-adaptive Systems -- 1 Introduction -- 2 NOSHAPE Decisions -- 3 The Development Methodology -- 3.1 Analyses Phase -- 3.2 Design Phase -- 3.3 Implementation Phase -- 4 Performance Evaluation -- 5 Conclusions -- References -- Security Challenges in IoT Cyber World -- 1 An IoT Overview -- 1.1 An IoT Applications -- 1.2 An IoT Architecture -- 2 Cyber Security : An Overview -- 2.1 Security Parameters and Attack Terminologies -- References. , Blockchains for Smart Cities: A Survey -- 1 Introduction -- 2 Related Work -- 3 Contributions Summary -- 4 Evaluation Criteria for Selected Researches -- 5 Proposed Architecture for Using Hyperledger Indy for Identity Management in Smart Cities -- 6 Challenges, Limitations, and Open Problems -- 7 Conclusion and Future Work -- References -- Cryptanalysis of 4-Bit Crypto S-Boxes in Smart Applications -- 1 Introduction -- 2 Literature Study -- 2.1 Cryptography and Cryptology -- 2.2 Some Old and Recent References on Linear Cryptanalysis -- 2.3 Some Old and Recent References on Differential Cryptanalysis -- 2.4 Linear Cryptanalysis and Differential Cryptanalysis of Stream Ciphers -- 2.5 Strict Avalanche Criterion (SAC) of S-Boxes -- 2.6 Polynomials -- 3 A Brief Review of Differential Cryptanalysis of 4-Bit Crypto S-Boxes and a New Technique with Boolean Functions for Differential Cryptanalysis of 4-Bit S-Boxes -- 3.1 4-Bit Crypto S-Boxes -- 3.2 Relation Between 4-Bit S-Boxes and 4-Bit Boolean Functions (4-Bit BFs) -- 3.3 Review of Differential Cryptanalysis of 4-Bit Crypto S-Boxes [12, 123] -- 3.4 Differential Cryptanalysis of 4-Bit Crypto S-Boxes Using 4-Bit BFs -- 4 A Brief Review of Linear Cryptanalysis of 4-Bit Crypto S-Boxes and a New Technique with Boolean Functions for Linear Cryptanalysis of 4-Bit Crypto S-Boxes or Linear Approximation Analysis -- 4.1 A Review of Boolean Functions (BF) and Its Algebraic Normal Form (ANF) -- 4.2 A Review on Linear Cryptanalysis of 4-Bit Crypto S-Boxes [HH96][HH02] -- 4.3 Linear Approximation Analysis -- 5 Result and Analysis and Security Criterion for All Four Algorithms of 4-Bit Crypto S-Boxes -- 5.1 The Analysis Criterion of Differential Cryptanalysis of 4-Bit Crypto S-Boxes -- 5.2 The Analysis Criterion of Differential Cryptanalysis with 4-Bit BFs. , 5.3 The Analysis Criterion of Linear Cryptanalysis of 4-Bit Crypto S-Boxes -- 5.4 The Analysis Criterion of Linear Approximation Analysis of 4-Bit Crypto S-Boxes -- 5.5 The Result and Analysis of Results of Four Algorithms on 32 DES 4-Bit S-Boxes -- 6 Conclusions -- References -- Secure Network Communication in Smart Cities Applications -- A Robust Watermarking Scheme Using Machine Learning Transmitted Over High-Speed Network for Smart Cities -- 1 Introduction -- 2 Transmission Over LTE Network -- 2.1 LTE-An Overview -- 3 Overview of Data Hiding Scheme -- 3.1 Spatial Domain Watermarking Techniques -- 3.2 Frequency Domain Watermarking Techniques -- 4 Proposed Watermarking Model -- 4.1 Classification Stage -- 4.2 DWT-SVD Watermarking Stage -- 5 Experimental Results and Discussion -- 5.1 Watermarking -- 5.2 Transmission -- 6 Conclusion -- References -- 4, 8, 32, 64 Bit Substitution Box Generation Using Irreducible or Reducible Polynomials Over Galois Field GF(Pq) for Smart Applications -- 1 Introduction -- 2 Polynomials Over Galois Field GF(Pq) and log2q + 1 Bit S-Boxes -- 2.1 Polynomials Over Galois Field GF(Pq) -- 2.2 4-Bit Crypto S-Boxes -- 2.3 Relation Between 4-Bit S-Boxes and Polynomials Over Galois Field GF(215) -- 3 3 and 8 Bit S-Box Generation by Respective BCNs Over Binary Galois Field GF(2q) Where Q €15 and 255 Respectively -- 3.1 Generation of 4-Bit Identity Crypto S-Box from Four Polynomials Over Binary Galois Field GF(215) -- 3.2 Generation of 8-Bit Identity Crypto S-Box from Eight Polynomials Over Binary Galois Field GF(2255) -- 3.3 Algorithm to Generate S-Box from Polynomials Over Galois Field GF(215) or GF(2255) -- 4 4 and 8 Bit S-Box Generation by Respective BCNs Over Non Binary Galois Field GF(1615) and Galois Field GF(256255) Respectively -- 5 Conclusion -- References. , Local Binary Patterns Based Facial Expression Recognition for Efficient Smart Applications -- 1 Introduction -- 2 Local Binary Pattern (LBP) -- 3 Variants of LBP -- 3.1 Rotation Invariant LBP (LBP_RI) -- 3.2 Uniform LBP (LBP_U2) -- 3.3 Multiscale LBP -- 3.4 Center Symmetric LBP (CSLBP) -- 3.5 Local Ternary Pattern (LTP) -- 4 Properties of LBP and Its Variants -- 4.1 Gray Scale Invariance -- 4.2 Rotation Invariance -- 4.3 Multiresolution Analysis -- 5 Facial Expression Recognition Framework -- 5.1 Preprocessing -- 5.2 Feature Extraction -- 5.3 Dimensionality Reduction -- 5.4 Classification -- 6 Experiments and Results -- 6.1 Japanese Female Facial Expression (JAFFE) Dataset Results -- 6.2 Extended Cohn-Kanade (CK+) Dataset Results -- 6.3 Yale Dataset Results -- 7 Conclusions and Future Work -- References -- Efficient Generation of Association Rules from Numeric Data Using Genetic Algorithm for Smart Cities -- 1 Introduction -- 2 Basic Terminology -- 2.1 Numeric Association Rule Mining -- 2.2 Genetic Algorithm -- 3 Literature Survey -- 3.1 Chromosome Representation -- 3.2 Objective Functions -- 3.3 Evolutionary Operators -- 4 Proposed Approach -- 4.1 Technical Details of Implementation -- 4.2 Flow Chart for Implementation -- 4.3 Pseudo Code for Implementation -- 5 Experimental Setup -- 5.1 Datasets -- 6 Results of Proposed Algorithm -- 6.1 Comparison of Proposed Approach with Existing Approach -- 7 Conclusion and Future Scope -- References -- Online User Authentication System Based on Finger Knuckle Print Using Smartphones -- 1 Introduction -- 1.1 Smartphone -- 1.2 Biometrics -- 1.3 Finger Knuckle Print Biometric -- 2 Related Work -- 2.1 ROI Extraction of FKP -- 2.2 Feature Extraction of FKP -- 2.3 Matching of FKP -- 3 The Proposed Workflow -- 3.1 The Enrollment Phase -- 3.2 The Authentication Phase -- 4 Feature Extraction. , 4.1 Histogram of Oriented Gradients Descriptor.

Note: Intro -- Preface -- Organization -- Honorary Chair -- General Chairs -- Co-chairs -- International Advisory Board -- Publication Chair -- Program Chairs -- Publicity Chairs -- Technical Program Committee -- Local Arrangement Chairs -- Contents -- Deep Learning and Applications -- Plant Leaf Diseases Detection and Identification Using Deep Learning Model -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 4 Experimental Results -- 5 Conclusions -- References -- Reinforcement Learning for Developing an Intelligent Warehouse Environment -- 1 Introduction -- 2 Machine Learning Techniques -- 3 Results and Discussion -- 4 Conclusion and Future Research -- References -- A Low-Cost Multi-sensor Deep Learning System for Pavement Distress Detection and Severity Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Overall System Architecture -- 3.2 Deep Learning Distress Detection -- 3.3 Dataset and Training Information -- 3.4 Projection onto the Depth 3D Point Cloud and ROI Filtering -- 4 Case Study: Pothole Severity Classification -- 5 Experimental Results -- 5.1 Results for the Distress Detection -- 5.2 Results for Pothole Severity Classification -- 6 Conclusion -- References -- An Intrusion Detection Model Based on Deep Learning and Multi-layer Perceptron in the Internet of Things (IoT) Network -- 1 Introduction -- 2 Related Work -- 2.1 Multi Agent Systems for IDS -- 2.2 Fuzzy Systems for IDS -- 2.3 Game Theory Models for IDS -- 3 Architecture of the Proposed Intrusion Detection System -- 3.1 Pre-processing and Feature Engineering -- 3.2 Deep Learning Layer -- 3.3 Evaluation Layer -- 4 The Experimental Results -- 5 Comparison Between Proposed Models and the Others -- 6 Conclusion -- References -- Transfer Learning and Recurrent Neural Networks for Automatic Arabic Sign Language Recognition -- 1 Introduction. , 2 Related Work -- 3 Arabic Sign Language Dataset -- 4 Methodology -- 4.1 Prepare the Dataset -- 4.2 Extract the Spatial Features -- 4.3 Extract the Temporal Features -- 4.4 Video Augmentation -- 5 Experimental and Results -- 5.1 Experiment Settings -- 5.2 Models Results -- 6 Conclusion and Future Works -- References -- Robust Face Mask Detection Using Local Binary Pattern and Deep Learning -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 4 Experimental Results -- 5 Conclusion -- References -- Steganography Adaptation Model for Data Security Enhancement in Ad-Hoc Cloud Based V-BOINC Through Deep Learning -- 1 Introduction -- 1.1 Ad-Hoc Cloud Computing -- 1.2 Deep Steganography -- 1.3 Contribution -- 1.4 Paper Organization -- 2 Literature Review -- 3 Proposed Solution -- 4 Experiment -- 5 Discussion and Analysis -- 6 Conclusion -- References -- Performance of Different Deep Learning Models for COVID-19 Detection -- 1 Introduction -- 2 Deep Learning (DL) -- 2.1 The DL-Algorithms Steps in COVID-19 Diagnosis -- 2.2 DL-Models for COVID-19 Detection -- 3 Discussion -- 4 Conclusion -- References -- Deep Learning-Based Apple Leaves Disease Identification Approach with Imbalanced Data -- 1 Introduction -- 2 Basics and Background -- 2.1 Data Imbalance -- 2.2 Convolutional Neural Networks -- 2.3 Transfer Learning -- 3 The Proposed Approach -- 3.1 Dataset Description -- 3.2 Data Preprocessing Phase -- 3.3 Training Phase -- 3.4 Evaluation Phase -- 4 Experimental Results and Analysis -- 4.1 Data Imbalance Problem -- 4.2 Data Augmentation -- 4.3 Setup of the Experiment -- 4.4 Evaluation of the Model -- 5 Conclusion and Future Work -- References -- Commodity Image Retrieval Based on Image and Text Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Image and Text Feature Fusion -- 3.2 Target Function -- 4 Experiment -- 4.1 Evaluation Metrics. , 4.2 Datasets -- 4.3 Experimental Details -- 4.4 Experimental Results and Analysis -- 5 Conclusion -- References -- Machine Learning Technologies -- Artificial Intelligence Based Solutions to Smart Warehouse Development: A Conceptual Framework -- 1 Introduction -- 2 SWOT Analysis -- 2.1 Strengths -- 2.2 Weaknesses -- 2.3 Opportunities -- 2.4 Threats -- 3 Proposed Solutions and Current Approaches -- 3.1 WO Strategy (Improve): Testbed as a Trial for Investment Decision -- 3.2 WO Strategy (Improve): AI-Powered Solutions -- 3.3 SO Strategy (Attack): AI Resource Development -- 4 Conclusions and Future Research -- References -- Long-Short Term Memory Model with Univariate Input for Forecasting Individual Household Electricity Consumption -- 1 Introduction -- 2 Related Works -- 3 Deep Learning Models for Load Forecasting -- 3.1 LSTM and LSTM-ED Neural Networks -- 3.2 CNN-LSTM Neural Networks -- 3.3 GRU Neural Networks -- 3.4 BiLSTM Neural Networks -- 3.5 ConvLSTM Neural Networks -- 4 Results and Discussion -- 4.1 Dataset Description -- 4.2 Evaluation Metrics -- 4.3 Prediction Results of ConvLSTM -- 4.4 Discussion of the Forecasting Models -- 5 Conclusion and Future Work -- References -- DNA-Binding-Proteins Identification Based on Hybrid Features Extraction from Hidden Markov Model -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Encoding -- 2.3 Framing -- 2.4 Hybrid Visual HMM Structure -- 2.5 Features Extraction -- 2.6 Classifier -- 3 Results and Discussions -- 4 Conclusions -- References -- Machine Learning Based Mobile Applications for Cardiovascular Diseases (CVDs) -- 1 Introduction -- 2 ML Based m-Health for CVDs -- 3 Characteristics of the Commercially Available CVDs Mobile Applications -- 4 Future Requirements -- 5 Conclusion -- References -- Regression Analysis for Remaining Useful Life Prediction of Aircraft Engines. , 1 Introduction -- 2 Related Work -- 3 Aircraft Engine System -- 4 Proposed Model for Predicting the RUL -- 5 Experimental Results and Discussion -- 6 Conclusion and Future Work -- References -- Applying Machine Learning Technology to Perform Automatic Provisioning of the Optical Transport Network -- 1 Introduction -- 2 The Challenges in the Current Model of the Supervision of the OTN -- 3 Proposed Model for the Automatic Provision of the OTN -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Robo-Nurse Healthcare Complete System Using Artificial Intelligence -- 1 Introduction -- 1.1 Related Work -- 2 Research Method -- 2.1 Software Implementation -- 2.2 Hardware Implementation -- 2.3 External Design Implementation -- 3 Results and Discussions -- 4 Conclusion -- References -- Resolving Context Inconsistency Approach Based on Random Forest Tree -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 IoT Data Collection Phase -- 3.2 Context Inconsistency Validator -- 3.3 Best Resolution Selection -- 3.4 Random Forest Tree -- 4 Experimental Results and Evaluations -- 5 Conclusion and Future Directions -- References -- Arduino Line Follower Using Fuzzy Logic Control -- 1 Introduction -- 2 Methodology -- 2.1 Lab Simulation -- 2.2 The ATmega328p Microcontroller -- 2.3 Voltage Regulator -- 2.4 Circuit Diagram Explanation -- 2.5 Microcontroller-Motor Driver IC Interface -- 2.6 Microcontroller-IR Sensor Module Interface -- 2.7 Microcontroller-Variable Resistor Interface -- 2.8 Arduino IDE Interface with Microcontroller -- 3 Summary of Methodology -- 4 Physical Modeling -- 4.1 Block Diagram -- 4.2 Flow Chart -- 4.3 Working Principle -- 5 Result and Analysis -- 6 Conclusion -- References -- Evaluating Adaptive Facade Performance in Early Building Design Stage: An Integrated Daylighting Simulation and Machine Learning. , 1 Introduction -- 2 Related Works -- 3 Building as a Machine and Machine Learning in Architecture -- 4 Adaptive Facade -- 5 Methodology -- 5.1 Data Collection: Available Forms of Kinetic Façade Systems -- 5.2 Data Preparation: Applying System Possibility Scores -- 5.3 Data Exploration and Case Study Setup -- 5.4 Prediction Stage: Applying the KNN Algorithm as a Selective Filter -- 6 Systems Modeling and Simulation -- 7 Results and Discussion -- 8 Conclusion -- References -- LTE Downlink Scheduling with Soft Policy Gradient Learning -- 1 Introduction -- 2 Downlink Resource Allocation in LTE -- 3 Related Work -- 4 DSPG Scheduler: The Proposed Scheduling Algorithm -- 4.1 Problem Statement -- 4.2 Model Design -- 5 Simulation Implementation and Results -- 6 Conclusions -- References -- Predicting the Road Accidents Severity Using Artificial Neural Network -- 1 Introduction -- 2 Literature Review -- 3 Dataset -- 4 The Proposed Methodology -- 5 Results and Discussions -- 5.1 Attributes vs Accident Severity -- 5.2 Accident Severity Prediction Results -- 6 Conclusion -- References -- Predicting the Intention to Use Audi and Video Teaching Styles: An Empirical Study with PLS-SEM and Machine Learning Models -- 1 Introduction -- 2 Theoretical Framework -- 2.1 Technology Acceptance Model (TAM) -- 2.2 Flow Theory -- 2.3 Virtual Reality Attributes -- 3 Research Methodology -- 3.1 Data Collection -- 3.2 Personal/Demographic Information -- 3.3 Study Instrument -- 3.4 Survey Structure -- 4 Findings and Discussion -- 4.1 Data Analysis -- 4.2 Convergent Validity -- 4.3 Discriminant Validity -- 4.4 Hypotheses Testing Using PLS-SEM -- 4.5 Hypothesis Testing Using Machine Learning Algorithms -- 5 Discussion of Results -- References -- Intellgenet Systems and Applications. , Immunity of Signals Transmission Using Secured Unequal Error Protection Scheme with Various Packet Format.