Proceedings of International Conference on Data Science and Applications: ICDSA 2022, Volume 2

Description

This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing. Preface Contents Editors and Contributors Improving River Streamflow Forecasting Utilizing Multilayer Perceptron-Based Butterfly Optimization Algorithm 1 Introduction 2 Study Area 3 Methodology 3.1 MLP 3.2 BOA 4 Results and Discussions 5 Conclusion References COVID-19 Contact Tracing Using Low Calibrated Transmission Power from BLE—Approach and Algorithm Experimentation 1 Introduction 2 Overcoming RSSI Shortcomings 2.1 Internal Factors 2.2 External Factors 3 Solution Approach—Experimentation and Results 3.1 Experimentation, Hardware and Software Set-Up 3.2 Experimentation Part One—Proof of Concept 3.3 Experimentation Part Two—Algorithm Test 3.4 Discussion 4 Conclusion References Monitoring Loud Commercials in Television Broadcast 1 Introduction 2 Materials and Methods 3 Results and Discussion 4 Conclusion References Potential Customers Prediction in Bank Telemarketing 1 Introduction 2 Dataset and Preprocessing 2.1 Data Description 2.2 Data Correlation 2.3 Category Data Encoding 3 Experimental and Result 3.1 Data Mining Models 3.2 Result 4 Conclusion References Analysis and Implementation of Normalisation Techniques on KDD’99 Data Set for IDS and IPS 1 Introduction 2 Intrusion Detection System 2.1 Taxonomy of IDS 2.2 Intrusion Detection Methodologies 2.3 IDS and Their Functions 2.4 Data Sets for IDS 3 An Approaches of Machine Learning 3.1 Decision Tree 3.2 Naive Bayes 3.3 K-nearest Neighbour 3.4 Artificial Neural Network 3.5 Support Vector Machines 3.6 Fuzzy Logic 4 Proposed Research Work 5 Normalisation 5.1 Quick Overview of Normalisation Techniques 6 Literature Review 7 Proposed Work 8 Research Methodology 8.1 Evaluation Metrics 9 Result and Discussion 9.1 Comparition of Normalisation Techniques 10 Z-Score Implementation on KDD CUP ’99 11 Conclusion References Deep Neural Networks Predicting Student Performance 1 Introduction 2 Methodology 2.1 Dataset and Data Processing 2.2 Deep Neural Network 3 Results and Discussions 4 Conclusion References An Efficient Group Signature Scheme Based on ECDLP 1 Introduction 2 Preliminaries 2.1 Background of Elliptic Curve Group 2.2 ECDLP Assumption 3 Proposed GS Scheme 3.1 KGC 3.2 Extract 3.3 GroupSign 3.4 GroupVerif 4 Security Analysis 5 Application 6 Conclusion References Sentiment Analysis of COVID-19 Tweets Using TextBlob and Machine Learning Classifiers 1 Introduction 2 Related Works 3 Design and Analysis 3.1 Data Extraction 3.2 Data Preprocessing 3.3 Visualization 3.4 Model Training 3.5 Support Vector Machine (SVM) 3.6 Classifier and Performance Measure 4 Finding and Discussion 5 Conclusion and Future Work References Reflection of Star Ratings on Online Customer Reviews; Its Influence on Consumer Decision-Making 1 Introduction to Star Ratings and Online Customer Reviews 2 Literature Review 3 Methodology 4 Discussion 4.1 Sentiment Analysis 4.2 Star Rating and Sentiment Analysis 4.3 Emotion Analysis 4.4 Discrepancy of Star Rating with Sentiments and Emotions of the Customer Reviews 5 Major Findings and Recommendations 5.1 Recommendations 5.2 Scope for Further Study 5.3 Optimization Process to Assess Star Rating and Reviews 6 Conclusion Appendix References An Integrated Machine Learning Approach Predicting Stock Values Using Order Book Details 1 Introduction 2 Review of Literature 3 Proposed Methodology 3.1 Multiple Linear Regression (MLR) 3.2 Long Short Term Memory (LSTM) 3.3 K-means Clustering 3.4 Bayesian Correlation 4 Sample Data Analysis 5 Conclusion References Hybrid Genetic-Bees Algorithm in Multi-layer Perceptron Optimization 1 Introduction 2 Methodology 2.1 Overview of Bees Algorithm (BA) 2.2 Improving Global Search Phase of BA by Employing GA 2.3 Our Proposed Method: HGBA for Training MLP 3 Experiments Settings 4 Results and Analysis 4.1 Means Squared Error 4.2 Shrink Factor (sf) 4.3 The Number of Scout Bees 4.4 MSE and Accuracy 5 Conclusion References Intellectual Identification Method of the Egg Development State Based on Deep Neural Nets 1 Introduction 2 The Ovoscoping Model on the Basis of the Convolution Neural Net of 2D Generalized Lenet 3 The Ovoscoping Model on the Basis of the Visual Transformer of One-Block Vit 4 Choice of Quality Criterion of the Method for Identification of the State of Egg Development 5 Determination of the Identification Method Structure for the State of the Egg 6 Numerical Research 7 Conclusions References Predicting Order Processing Times in E-Pharmacy Supply Chains During COVID Pandemic Using Machine learning—A Real-World Study 1 Introduction 2 Literature Review 3 Problem Statement 4 Materials and Methods 5 Feature Engineering 6 Model Construction and Training 7 ML Regressors for Order Processing Time Prediction Problem 8 ML Classifiers for Shipment Time Prediction Problem 9 Results 10 Discussion 11 Conclusion and Future Work 11.1 Disclosure Statement 11.2 Funding References Cognitive Science: An Insightful Approach 1 Introduction 2 Background and Related Work 2.1 Artificial Intelligence 2.2 Neuroscience 2.3 Artificial Neural Network 2.4 Robotics 3 Information Processing Approach 3.1 Recent Advances 3.2 Implementation Approach 4 Conclusion References Predicting the Dynamic Viscosity of Biodiesels at 313 K Using Empirical Models 1 Introduction 2 Material and Methods 2.1 Data 2.2 Machine Learning Models (MLMs) 2.3 QM and MLR 3 Results and Discussion 3.1 MLMs 3.2 QM and MLR 3.3 Performance Evaluation of Machine Learning Models, MLR and QM 4 Conclusions References Artificial Neural Networks, Quadratic Regression, and Multiple Linear Regression in Modeling Cetane Number of Biodiesels 1 Introduction 2 Material and Methods 2.1 Data 2.2 Machine Learning Models 2.3 QM and MLR Models 3 Results and Discussion 3.1 Machine Learning Models 3.2 Mathematical Models 3.3 Performance Evaluation of Machine Learning Models, MLR, and QM 4 Conclusions References AI-Based Automated Approach for Trend Data Generation and Competitor Benchmark to Enhance Voice AI Services 1 Introduction 2 Related Work 3 Proposed Framework 4 Trending Data Generation 4.1 Generation of Unstructured Utterances 4.2 Generation of Structured Utterances 5 AI-Based Voice Solutions Benchmarking 5.1 Model Evaluation Concepts on BERT 5.2 ASR End-to-End Evaluation Method 5.3 Model Based E2E Evaluation of Trend Data 5.4 Model-Based NLU and E2E Evaluation for Native Apps 6 Results and Impact 6.1 Assessment of Effectiveness Result 7 Conclusion and Future Scope References Identification of ADHD Disorder in Children Using EEG Based on Visual Attention Task by Ensemble Deep Learning 1 Introduction 2 Methods 2.1 Subjects 2.2 Preprocessing 2.3 Classification Models 2.4 Experimentation Setup 3 Results 3.1 Results of Independent Architecture 3.2 Result of Ensemble Framework 3.3 Comparison of Classification Results 4 Conclusion References Machine Learning as a Service (MLaaS)—An Enterprise Perspective 1 Introduction 2 Machine Learning Applications 2.1 Health care 2.2 Education 2.3 Economy and Finance 2.4 Social Networks 2.5 Complementary Applications 3 Companies that Develop Machine Learning Techniques 4 Data Protection Privacy in Machine Learning 5 Trends in Machine Learning Jobs 6 GPUs Evolution 7 Conclusions References Very Low Illumination Image Enhancement via Lightness Mapping 1 Introduction 2 The Proposed Method 3 Image Quality Metrics 4 Experimental Results 5 Conclusion 6 Future Work References Clustering High Dimensional Transcriptomic Data with Spectral Clustering for Patient Subtyping 1 Introduction 2 Proposed Methodology 2.1 t-distributed Stochastic Neighborhood Embedding (t-SNE) 2.2 Spectral Clustering 3 Results and Discussion 4 Conclusion References 3D CNN-Based Classification of Severity in COVID-19 Using CT Images 1 Introduction 2 Materials and Methods 2.1 Introduction to 3D CNN 2.2 Dataset Description 2.3 Data Pre-processing 2.4 Model Design 2.5 Results and Discussion 3 Conclusions and Future Scope References A Hybrid Architecture for Action Recognition in Videos Using Deep Learning 1 Introduction 2 Literature Survey 2.1 Data Set 3 Proposed Architecture for Activity Identification 3.1 Architecture Diagram 3.2 Implementation 4 Results and Discussion 5 Conclusion References Data Envelopment Analysis: A Tool for Performance Evaluation of Undergraduate Engineering Programs 1 Introduction 1.1 Data Envelopment Analysis 1.2 DEA Versus Conventional Efficiency Approaches 1.3 Review of Literature 2 Problem Definition and Requirement Analysis 3 Data Collection and Validation 4 Solution Design 4.1 Data Normalizing 4.2 Importing Data into R and Installing Packages 4.3 Designing the DEA Model on R 4.4 Verification on Banxia’s Frontier Analyst 5 Analysis of Results 5.1 Results Obtained in R 5.2 Results Obtained on Banxia’s Frontier Analyst 5.3 Summary of Results 6 Conclusion and Future Enhancements Appendix 1: Program Code for DEA of Departments Using R References The Role of Big Data in Color Trend Forecasting: Scope and Challenges-A Systematic Literature Review 1 Introduction 2 Methodology 3 Applications of Big Data in Color Forecasting 3.1 Big Data and Artificial Intelligence 3.2 Curating Datasets 3.3 Color Forecasting Process 4 Benefits and Challenges of Integrating Big Data in Color Forecasting 5 Discussions 6 Conclusions References Forensic Facial Recognition: Review and Challenges 1 Introduction 2 Facial Recognition Systems 3 Challenges of Forensic Facial Recognition 3.1 Facial Aging 3.2 Sketch Recognition 3.3 Facial Artifacts 3.4 Degraded/Uncontrolled Conditions 3.5 Pose Variations 3.6 3D Reconstructions 3.7 Access Control 3.8 Low Quality Images 3.9 Imperfect Facial Data 3.10 Privacy Preservations 3.11 Plastic Surgery 4 Proposed System 4.1 Input Image 4.2 Pre-processing 4.3 Face Detection 4.4 Feature Extraction 4.5 Classification 5 Implementation 5.1 Parameter Selection 5.2 Experimental Setup 6 Result Analysis 7 Conclusion References Spatio-Temporal Analysis of Urbanization by Using Supervised Image Classification with Correlation of Land Surface Temperature and Topography 1 Introduction 2 Study Area 3 Materials and Methods 3.1 Data Collection and Workflow 3.2 Land Use and Land Cover Classification 3.3 Calculation of NDVI 3.4 Calculation of NDWI 3.5 Land Surface Temperature (LST) 3.6 Urban Heat Island Retrieval 3.7 Correlation Analysis 4 Results and Discussions 4.1 Land Use and Land Cover Analysis 4.2 Normalized Difference Vegetation Index Distribution Analysis (NDVI) 4.3 Normalized Difference Water Index Distribution Analysis 4.4 Land Surface Temperature Distribution (LST) 4.5 Correlation Analysis Between LST and the Factors Influencing LST 5 Conclusions and Suggestions References COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images 1 Introduction 2 Related Works 2.1 Deep Learning in Computer Vision 2.2 Detection of COVID-19 3 Methodology 3.1 Data Generation 3.2 Preprocessing 3.3 Model Architecture 3.4 Model Implementation 3.5 Prediction and Heatmap Generation 4 Results and Discussion 4.1 Main Results 4.2 Detailed Results 4.3 Patient-Wise Cross-Validation 4.4 Different Initial Weights 4.5 Comparison with Standard Computer Vision Models 4.6 Qualitative Analysis 5 Limitations and Future Works 6 Conclusion References Pneumonia Chest X-ray Classification Using Support Vector Machine 1 Introduction 2 Methodology 2.1 Dataset 2.2 Image Segmentation 2.3 Feature Extraction 2.4 Classification 3 Results 4 Conclusions References Linking Social Media Data and Clinical Methods to Detect Depression Using Artificial Intelligence: A Review 1 Introduction 2 Modules 2.1 Identification 2.2 Screening 2.3 Eligibility 2.4 Included 3 Analysis of Papers on Social Media Data 3.1 Limitations Found in the Use of Social Media Data 4 Analysis of Papers Using Medical Data 4.1 Unimodal 4.2 Multimodal 5 Reviewed Databases for Medical Data 6 Discussions Based on the Above-Mentioned Databases 7 Preprocessing 8 Feature Extraction 9 Result 9.1 Textual Data 9.2 Acoustic Data 9.3 Limitations 10 Conclusion References Triplet Multi-task Learning Strategy for Person Re-identification Using Deep Learning 1 Introduction 2 Related Work 3 Triplet Multi-task Learning Strategy 3.1 Region Aligned Pooling 3.2 Semantic Segmentation 3.3 Triplet Prediction (Triplet Loss) 3.4 Triplet Multi-loss Training 4 Experiment 4.1 Dataset 4.2 Implementation Details 4.3 Results and Discussion 4.4 Ablation Study 5 Conclusion References K-Means Algorithm to Form Dynamic Cluster Formation to Counter the Static Property of K-Means 1 Introduction 2 Literature Survey 3 K-Means Explored 3.1 Data Redundancy 3.2 Load Balancing 3.3 High Availability 3.4 Monitoring and Automation 4 Research Methodology 4.1 Pseudo Code for the Algorithm 4.2 Distance Formula 4.3 Proposed Methodology for Cluster Count 4.4 Cluster Formation 4.5 Cluster Count Iteration for 300 Dataset Point 5 Result and Analysis 5.1 Cluster Count Iteration for 500 Dataset Point 5.2 Cluster Count Iteration for 2000 Dataset Point 5.3 Cluster Count Iteration for 3000 Dataset Point 5.4 Time Comparison for Formed Clusters 6 Conclusion References Analysis of an Efficient Elite Group-Based Routing Protocol for Wireless Sensor Networks 1 Introduction 2 Related Work 3 Proposed Work 3.1 Proposed Network Model 3.2 Cluster Head Selection Scheme 3.3 Association of Nodes 3.4 Selection of Elite Group 4 Result and Analysis 5 Conclusion References A Systematic Study of Fake News Detection Systems Using Machine Learning Algorithms 1 Introduction 2 Literature Review 3 A Framework of Fake News Detection (FND) System 4 Various Fake News Classification Methods 4.1 Logistic Regression (LR) Classifier 4.2 KNN Classifier 4.3 Decision Tree (DT) Classifier 4.4 Random Forest (RF) Classifier 4.5 Naïve Bayes (NB) Classifier 4.6 Support Vector Machine (SVM) Classifier 4.7 Long Short-Term Memory (LSTM) Classifier 5 Existing Result Analysis 6 Conclusion and Further Work References Training Logistic Regression Model by Hybridized Multi-verse Optimizer for Spam Email Classification 1 Introduction 2 Preliminaries and Related Works 3 Proposed Method 3.1 Basic MVO Algorithm 3.2 MVO Hybridized with ABC Metaheuristics 4 Experimental Setup, Findings and Comparative Analysis 5 Conclusion References Optimization of Spatial Pyramid Pooling Module Placement for Micro-expression Recognition 1 Introduction 2 Recent Works 2.1 Traditional Feature-Based Approach 2.2 Convolutional Neural Network Feature-Based Approach 3 Methodology 3.1 Dataset 3.2 Convolutional Neural Network Model 3.3 Spatial Pyramid Pooling 4 Result and Discussion 5 Conclusion References Image Colorization: A Convolutional Network Approach 1 Introduction 2 State of Art 3 Methodology 3.1 Hyperparameter 3.2 Data Pre-processing 3.3 Network Architecture 4 Experimental Results and Discussion 4.1 About Dataset 4.2 System Configuration 4.3 Result and Discussion 5 Conclusion and Future Work References Prediction of Particulate Matter (PM2.5) Across India Using Machine Learning Methods 1 Introduction 2 Methodology 2.1 Data Collection 2.2 Preparation of Data Set 2.3 Machine Learning Algorithms Used for Building Models 2.4 Procedure of Designing Prediction Models 3 Results and Discussion 4 Conclusion and Future Work References Convolutional Neural Network for COVID-19 Detection 1 Introduction 2 Literature Overview 2.1 Existing Work 2.2 Drawback of Existing Work 2.3 Our Contribution 3 Proposed Work 4 Results and Discussion 4.1 Supported by X-ray Model 4.2 Supported by CT Scan Model 5 Conclusion References Posit Extended RISC-V Processor and Its Enhancement Using Data Type Casting 1 Introduction 2 Background 2.1 Posit Format 2.2 RISC-V ISA 3 Approaches to Enhance RISC-V ISA with Posit Arithmetic 3.1 Posit Integration as a Tightly Coupled Unit by Replacing the F-Extension 3.2 Posit Integration as an Accelerator by Utilizing the Custom Opcode Space of the RISC-V ISA 3.3 Posit Integration as a Tightly Coupled Unit Using Custom Opcode Space 4 Implementation of Data Type Casting in RV32IMF_XPosit 4.1 MOT: Mixed Operand Type Block 4.2 DTC: Data Type Converter Block 5 Implementation Results 6 Conclusion References Securing Microservice-Driven Applications Based on API Access Graphs Using Supervised Machine Learning Techniques 1 Introduction 2 Related Work 3 Proposed System 4 Experiment 4.1 Dataset 4.2 The Graph 4.3 node2vec 4.4 Classification Algorithms 5 Results and Discussion 6 Future Research Directions 7 Conclusion References Scaling and Cutout Data Augmentation for Cardiac Segmentation 1 Introduction 2 Related Work 3 Methods 3.1 Overview 3.2 CNN Network Structure 3.3 Data Augmentation 4 Experiment Result and Discussion 4.1 Dataset 4.2 Experimental Setup 5 Conclusion References 43 An Improved Method to Recognize Bengali Handwritten Characters Using CNN Abstract 1 Introduction 2 Proposed Methodology 2.1 Image Dataset Collection and Preprocessing 3 Experimental Analysis and Discussion 3.1 Training and Validation Accuracy and Loss 3.2 Performance Accuracy 3.3 Testing Result 4 Conclusion and Future Work References Dynamic Pricing for Electric Vehicle Charging at a Commercial Charging Station in Presence of Uncertainty: A Multi-armed Bandit Reinforcement Learning Approach 1 Introduction 2 Model and Problem Formulation 3 Solution Techniques 4 Simulations and Results 5 Conclusion References Photo Restoration: A Sequential Pipeline Approach Involving Denoising and Deblurring 1 Introduction 2 Related Works 3 Methodology 3.1 Pipeline Overview 3.2 Dataset Consolidation 3.3 Metrics Used 4 Implementation Details 4.1 Spatial Filters 4.2 Deblurring 4.3 Denoising 5 Experiments and Results 5.1 Noise Generation 5.2 Pipeline Formation 5.3 Experimental Analysis 5.4 Performance Evaluation 6 Conclusion and Future Work References Development of a Linear-Scaling Consensus Mechanism of the Distributed Data Ledger Technology 1 Introduction 2 Analysis of the Problem of Scalability and Security of Distributed Ledger Systems 3 Statement of the Main Research Material 4 Basic Probabilistic Models for Exploring the Blockchain Scalability Problem 5 Conclusion References A Comparative Study on Distracted Driver Detection Using CNN and ML Algorithms 1 Introduction 2 Related Work 3 Proposed Approach 3.1 Proposed CNN Model 3.2 ResNet50 Model 3.3 VGG16 Model 3.4 Logistic Regression 4 Experimental Design 4.1 Dataset Description 4.2 Preprocessing the Dataset 4.3 Result and Discussion 5 Conclusion References Exploring Word2vec Embedding for Sentiment Analysis of Bangla Raw and Romanized Text 1 Introduction 2 Related Works 3 Proposed Methodology 3.1 Pre-processing Data 3.2 Word Embedding 3.3 Training Deep Recurrent Neural Network Model for Sentiment Analysis 3.4 Classification 3.5 Post-processing 4 Experimental Evaluation 4.1 Experimental Setup 4.2 Dataset 4.3 Experimental Results 5 Conclusion and Future Work References Anomaly Based Network Intrusion Detection System for IoT 1 Introduction 2 Related Theory 2.1 Network Security 2.2 Machine Learning 2.3 Deep Learning 3 Related Work 4 Experimental Design 4.1 Dataset Description 4.2 Preprocessing the Dataset 4.3 Model Architecture 5 Result and Discussions 6 Conclusion References CoviIS: A Real-Time Covid Help Information System Using Digital Media 1 Introduction 2 Related Work 2.1 Covid Informatics Applications 2.2 Covid Advisory Bots 2.3 Covid Sentiment Analysis 3 Design Methodology 3.1 Statistical Analysis 3.2 Geographical Analysis 3.3 News Analysis 3.4 Social Media Analysis 3.5 Sentiment Analysis 3.6 Health Safety 4 Development 5 Results and Discussion 6 Conclusion and Future Work References Distributed Denial of Service Attack Detection Using Optimized Hybrid Neuro-Fuzzy Classifiers 1 Introduction 2 Related Work 3 Proposed System 3.1 Pre-processing 3.2 Extracting Features 3.3 Optimized Hybrid Neuro-Fuzzy Classifier 3.4 Grasshopper Optimization Algorithm (GOA) 4 Results and Discussion 5 Conclusion References An Efficient Framework for Forecasting of Crime Trend Using Machine Learning Technique 1 Introduction 1.1 Author's Contributions 1.2 Organization 2 Related Works 3 Experimental Methodologies 3.1 Naive Method 3.2 Simple Average Method 3.3 Simple Moving Average Method 3.4 Simple Exponential Smoothing 3.5 Holt's Method with Trend 3.6 Holt-Winters' Additive Method with Trend and Seasonality 3.7 Holt-Winter's Multiplicative Method with Trend and Seasonality 4 Proposed Model 5 Experimental Outcomes and Discussion 6 Conclusion and Future Work References Performance Evaluation of a Novel Thermogram Dataset for Diabetic Foot Complications 1 Introduction 2 Methodology 2.1 Data Acquisition and Preprocessing 2.2 Feature Extraction 2.3 Feature Selection and Classification 3 Result and Discussion 4 Conclusion References Improving Indoor Well-Being Through IoT: A Methodology for User Safety in Confined Spaces 1 Introduction 2 Updates from Recent Literature 3 Methodology 3.1 Methodological Path 4 Expected Impact 5 Conclusions References General Natural Language Processing Translation Strategy and Simulation Modelling Application Example 1 Introduction 2 Epistemic Knowledge Orgiton Model 3 Natural Language Processing Translation Strategy or Algorithm 4 Application Example for the Simulation of Movement as a Translation Form Natural Language into Computer Processed Language 4.1 Minimalistic Example Set 4.2 Translation into a Computational Representation 4.3 Search- and Find Process or Search-Find-o 5 Results and Recommendation 6 Conclusion and Outlook References Artificial Intelligence in Disaster Management: A Survey 1 Introduction 2 Literature Survey 2.1 Disaster Management 2.2 Recent Development in NIA and Their Applications in Disaster Management 3 Conclusion References A Survey on Plant Leaf Disease Detection Using Image Processing 1 Introduction 2 Literature Review 3 Methodology 3.1 Image Preprocessing 3.2 Detection of Model 3.3 Feature Extraction and Training 3.4 Classification of the Object 3.5 Identification 4 Conclusion References Feature Importance in Explainable AI for Expounding Black Box Models 1 Introduction 2 Literature Survey 2.1 Surrogate Explainability 2.2 Local Perturbation Based Explainability 2.3 Propagation-Based Explainability 2.4 Metadata-Based Explainability 3 Implementation Details 3.1 ELI5 4 Challenges 5 Conclusion References Sign Language Recognition System Using Customized Convolution Neural Network 1 Introduction 2 Literature Survey 3 System Architecture 3.1 OpenCV 3.2 Convolutional Neural Network 4 Methodology 4.1 Set Histogram 4.2 Creating Dataset 4.3 Image Processing 4.4 Model Creation 4.5 Displaying Gestures 4.6 Real-Time Classification 5 Results and Discussions 5.1 Dataset 5.2 Software Used 5.3 Specifications 5.4 Confusion Matrix 6 Conclusion References Space Fractionalized Lattice Boltzmann Model-Based Image Denoising 1 Introduction 2 Statement of Problem 3 Formulation of Problem 4 Numerical Computations 5 Results and Discussion 6 Conclusion and Future Direction References A Review About Analysis and Design Methodology of Two-Stage Operational Transconductance Amplifier (OTA) 1 Introduction 2 Literature Survey 3 Design Methodology 4 Mathematical Modeling 4.1 Comparision Table 5 Conclusion References Design and Optimization of Wideband RF Energy Harvesting Antenna for Low-Power Wireless Sensor Applications 1 Introduction 2 Methodology 3 Antenna Design 4 Results and Discussions 5 Conclusion References Forecasting of Novel Corona Cases in India Using LSTM-Based Recurrent Neural Networks 1 Introduction 2 Methodology 2.1 Data Collection 2.2 Data Cleaning 2.3 Recurrent Neural Network Model 3 Performance Evaluation 4 Software Information 5 Results and Discussion 6 Conclusion References Algorithms for Syllogistic Using RMMR 1 Background and Historical Preliminaries 2 Retooled Method of Minimal Representation 2.1 Preface of RMMR 2.2 Propositions in RMMR 2.3 Examining Syllogisms in RMMR 3 Functioning of the RMMR 4 Algorithms for RMMR 5 Summary and Conclusion References Machine Learning-Based Approach for Airfare Forecasting 1 Introduction 2 Literature Survey 3 Proposed System 3.1 Data Preparation 3.2 Feature Selection 3.3 Data Analysis 4 Results and Discussion 4.1 Experimental Results 4.2 Comparative Analysis 5 Conclusion References Author Index