Intelligent Systems: Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data (ICMIB 2023) (Lecture Notes in Networks and Systems, 728)

This book features best selected research papers presented at the Third International Conference on Machine Learning, Internet of Things and Big Data (ICMIB 2023) held at Indira Gandhi Institute of Technology, Sarang, India, during March 10–12, 2023. It comprises high-quality research work by academicians and industrial experts in the field of machine learning, mobile computing, natural language processing, fuzzy computing, green computing, human–computer interaction, information retrieval, intelligent control, data mining and knowledge discovery, evolutionary computing, IoT and applications in smart environments, smart health, smart city, wireless networks, big data, cloud computing, business intelligence, Internet security, pattern recognition, predictive analytics applications in health care, sensor networks and social sensing, and statistical analysis of search techniques. Preface Organization Contents Effect of the Longitudinal Strain of PM Fiber on the Signal Group Velocity 1 Introduction and Motivation 2 Physical Principles 3 DGD Measurement 4 Experimental Results 4.1 Measurement on Proof Tester 4.2 Straining Fiber on Connectors 4.3 Usage of Fiber Clamps 5 Funding 6 Conclusion References Machine Learning Algorithms Aided Disease Diagnosis and Prediction of Grape Leaf 1 Introduction 2 Literature Review 3 Diagnosis of Grape Leaf Diseases 4 Data Analysis and Augmentation 5 Simulation Result 6 Conclusion References Optimized Fuzzy PI Regulator for Frequency Regulation of Distributed Power System 1 Introducion 2 System Model and Controller Structure 2.1 Objective Function 3 Overview of Fusion PSO and PS Calculation 4 Results and Discussions 5 Conclusion References Detecting Depression Using Quality-of-Life Attributes with Machine Learning Techniques 1 Introduction 2 Related Work 3 Proposed Methodology 3.1 Data Pre-processing 3.2 K-means Clustering 3.3 Classification 4 Results and Discussion 4.1 Parameters for Evaluation 4.2 Performance of Random Forest 4.3 Performance of Support Vector Machine 4.4 Performance of Logistic Regression 5 Conclusion References Patient Satisfaction Through Interpretable Machine Learning Approach 1 Introduction 2 Related Work 3 Proposed System 3.1 Dataset Description 3.2 Data Pre-processing 3.3 Balancing Dataset with SMOTE 3.4 Feature Selection 4 Models 5 Results and Discussion 6 Conclusion and Future Works References Predicting the Thyroid Disease Using Machine Learning Techniques 1 Introduction 2 Related Work 3 Proposed System 3.1 Dataset Description 3.2 Data Pre-processing 3.3 Models 4 Results and Discussion 5 Conclusion and Future Works References An Automatic Traffic Sign Recognition and Classification Model Using Neural Networks 1 Introduction 2 Literature Review 3 System Model 3.1 Image Preprocessing 3.2 Deep Learning Model 3.3 Feature Extraction 3.4 Classification 3.5 Training and Testing of Data 4 Data Set Analysis 5 Results and Discussions 6 Conclusion References An Artificial Intelligence Enabled Model to Minimize Corona Virus Variant Infection Spreading 1 Introduction 1.1 Brief History of Corona 1.2 Pandemic Challenges 2 Background 3 Prediction Using Deep Neural Network Model 4 Result and Performance Analysis 5 Conclusion References SoundMind: A Machine Learning and Web-Based Application for Depression Detection and Cure 1 Introduction 2 Literature Survey 3 Methodology 3.1 Model-1: Positive and Negative Text Classification 3.2 Model-2: Depression Detection Based on Patient Reports 3.3 Web Application 4 Result and Discussion 5 Conclusion and Future Scope References Japanese Encephalitis Symptom Prediction Using Machine Learning Algorithm 1 Introduction 2 Related Work 3 Proposed Work 4 Regression Methods Used in Study 5 Result and Analysis 6 Conclusion References Smart Skin-Proto: A Mobile Skin Disorders Recognizer Model 1 Introduction 2 Literature Review 3 Proposed M-health Prototype for Skin Cancer 4 Result and Analysis 5 Conclusion References Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks 1 Introduction 2 Literature Survey 3 Proposed Methodology 4 Experimentation 5 Conclusion References Real Time Air-Writing and Recognition of Tamil Alphabets Using Deep Learning 1 Introduction 2 Literature Review 3 Methodology 3.1 Dataset Description 3.2 Block Diagram 3.3 Trajectory Mapping 3.4 Pre-Processing 3.5 Dense – Net 121 4 Results and Discussion 4.1 Testing and Training 4.2 Confusion Matrix 4.3 Output 5 Conclusion References A Fuzzy Logic Based Trust Evaluation Model for IoT 1 Introduction 2 Related Work 3 Proposed Fuzzy Logic Based Trust Evaluation Model 3.1 Proposed Fuzzy Inference System 4 Results of Simulation 4.1 Simulation Domain 4.2 Simulation Results and Analysis 5 Conclusion and Future Scope References Supervised Learning Approaches on the Prediction of Diabetic Disease in Healthcare 1 Introduction 2 Literature Review 3 Classification Algorithms 4 Performance Measures 5 Experimental Analysis 6 Conclusion References Solar Powered Smart Home Automation and Smart Health Monitoring with IoT 1 Introduction 2 System Archıtecture 2.1 The Design Architecture 2.2 Descreiption of the Components Used 3 Methodology 4 Implementıonal Detaıls 4.1 Smart Solar Energy Management – The Power Requirement of the Proposed Smart Home is 4.2 Sensor Networking 5 Results and Dıscussıon 6 Conclusion References Seasonal-Wise Occupational Accident Analysis Using Deep Learning Paradigms 1 Introduction 2 Related Work 3 Basics of Deep Neural Network 3.1 Initial Data Description 3.2 Initial Processing Using SMOTE Technique 3.3 Final Processing Using DNN 4 Experimental Analysis Using DNN 5 Comparative Analysis Using Existing Models with the Proposed Model 6 Conclusion and Future Scope References MLFP: Machine Learning Approaches for Flood Prediction in Odisha State 1 Introduction 2 Related Works 3 Motivation and Objective 4 Proposed Model 4.1 Data Preparation 4.2 Odisha Rainfall Dataset 4.3 Data Preprocessing 4.4 Data Visualization 4.5 Outlier Detection Process 4.6 Dataset Splitting 4.7 Model Selection 5 Algorithms and Techniques 5.1 K-Nearest Neighbor (KNN) 5.2 Support Vector Machine (SVM) 5.3 Decision Tree (DT) 5.4 Random Forest (RF) 5.5 Logistic Regression (LR) 5.6 Naïve Bayes (NB) 6 Performance Metrics Analysis 6.1 Accuracy 6.2 Precision 6.3 Recall 6.4 F-Score 7 Results and Discussion 8 Conclusion and Future Work References Vision-Based Cyclist Travel Lane and Helmet Detection 1 Introduction 2 Literature Survey 3 Methodology 3.1 Dataset and Preprocessing 3.2 Feature Extraction and Dimensionality Reduction 3.3 Classification and Analysis 4 Performance Evaluation 4.1 Cyclist Travel Lane Detector 4.2 Cyclist’s Helmet Detector 5 Conclusion and Future Scope References Design and Experimental Analysis of Spur Gear–A Multi-objective Approach 1 Introduction 2 Formulation Details 2.1 Design Variable and Constraints 2.2 Particle Swarm Optimization (PSO) 3 Optimization Results 4 CAD Modeling and Manufacturing of Gear Set 5 Experimental Analysis 5.1 Efficiency and Frictional Power Loss 5.2 Oil Degradation and Surface Temperature 6 Conclusion References Chest X-Ray Image Classification for COVID-19 Detection Using Various Feature Extraction Techniques 1 Introduction 2 Feature Extraction Techniques 2.1 Histogram Oriented Gradient (HOG) 2.2 Local Binary Pattern (LBP) 2.3 Fractal Descriptors (FD) 3 Classification Techniques 3.1 K-Nearest Neighbour Algorithm (KNN) 4 Performance Indicators 5 Dataset Description 6 Results and Discussion 7 Conclusion 8 Future Scope References Computer Vision and Image Segmentation: LBW Automation Technique 1 Introduction 2 Background Study 2.1 Cricket - The Game 2.2 Cricket Pitch 2.3 Rules of LBW 2.4 Overstep No Ball 3 Related Work 4 Proposed Methodology 4.1 For No Ball Detection 4.2 Algorithm 5 Results 6 Conclusion and Future Scope References A Mixed Collaborative Recommender System Using Singular Value Decomposition and Item Similarity 1 Introduction 2 Literature Review 3 Problem Definition 4 Proposed Method 5 Experiment Details and Dataset 5.1 Datasets 5.2 Compared Methods 6 Performance Measures and Result Analysis 6.1 Result Analysis 7 Conclusion and Future Scope References Hybrid Clustering-Based Fast Support Vector Machine Model for Heart Disease Prediction 1 Introduction 2 Literature Review 3 Hybrid Clustering-Based Fast Support Vector Machine Model 3.1 Dataset 3.2 Data Preparation 3.3 Outlier Analysis 3.4 Feature Extraction and Selection 3.5 Principal Component Analysis (PCA) 3.6 Predictive Modeling 4 Performance Metrics and Result Analysis 5 Conclusion References Forecasting and Analysing Time Series Data Using Deep Learning 1 Introduction 2 Preliminaries and Background Study 3 Dataset Description 4 Methodology 4.1 Dense Neural-Network Model 4.2 Convolutional 1D Model 4.3 LSTM Model 4.4 GRU Model 5 Performance Evaluator Metrics 6 Experimental Setup and Result 6.1 Model 1: Dense Model 6.2 Model 2: Conv 1d Model 6.3 Model 3: LSTM (LONG SHORT-TERM MEMORY) MODEL 6.4 Model 4: GRU (Gated Recurrent Unit) Model 7 Comprehensive Performance Evaluation 8 Conclusion References Intelligent Blockchain: Use of Blockchain and Machine Learning Algorithm for Smart Contract and Smart Bidding 1 Introduction 2 Related Works 3 Briefs About the Technology Used 4 Methods Adopted InBidBlock 5 The Algorithm Involved in Blockchain 6 Algorithms used in Machine Learning (Crop Quality Determination and Bid Visualisation) 6.1 Crop Quality Determination 6.2 Bid Visualisation 7 Conclusion and Future Scope References Weed Detection in Cotton Production Systems Using Novel YOLOv7-X Object Detector 1 Introduction 2 Literature Review 3 Proposed Methodology 3.1 Methodology 3.2 CottonWeedID15 3.3 Metrics for Performance Assessment 4 Results and Discussion 4.1 Results of YOLOv7-X Model 4.2 Analysis of the Confusion Matrix and F1 Score 4.3 Precision, Recall and PR Curves 5 Conclusion References Smart Healthcare System Management Using IoT and Machine Learning Techniques 1 Introduction 2 Data Collection and Analysis 2.1 Problem Statement 2.2 Work Scope 3 Algorithms 3.1 SVM 3.2 Bayes 3.3 J48 3.4 CNN2D an Enhanced LSTM 3.5 Framework/Architecture 4 Methods and Outcomes 4.1 Information-Gathering 4.2 Measurements 5 Conclusion Bibliography Automatic Code Clone Detection Technique Using SDG 1 Introduction 2 Basic Concepts of Code Clones 2.1 Types of Code Clones 2.2 Code Clone Detection Process 3 Literature Survey 3.1 Text-Based Detection Techniques 3.2 Token-Based Techniques 3.3 Tree-Based Techniques 3.4 Program Dependency Graph-Based Techniques 3.5 Metrics-Based Techniques 4 System Dependence Graph (SDG) Based Code Clone Technique 4.1 Proposed Algorithm 4.2 Working of Proposed Clone Detection Algorithm to Show Detection 4.3 Working of Proposed Clone Detection Algorithm to Show Non-detection 5 Conclusion References Simulated Design of an Autonomous Multi-terrain Modular Agri-bot 1 Introduction 2 Methodology 2.1 Operation Block Diagram of the System 2.2 Working Flowchart 3 Structural Design 3.1 Main Body Design of the Robot 3.2 Versatile Delta Track 3.3 Plough 3.4 Cutter 3.5 Thresher 3.6 3-Dimentional Prototype of the Agri-Bot 3.7 Autonomous Movement of the Robot 4 Simulation and Result 4.1 Proteus Implementation Results 4.2 WEBOT Simulation and Result 5 Conclusion References Customer Segmentation Analysis Using Clustering Algorithms 1 Introduction 2 Related Work 3 Data Preparatıon and Vısualızatıon 3.1 Data Exploration 3.2 Visualization of Attributes 4 The Proposed Method 4.1 K-means Clustering Algorithm 4.2 Cluster Optimization 5 Results and Discussion 5.1 Elbow Method 5.2 Average Silhouette Method 5.3 Gap Statistic Method 5.4 Visualization and Analysis of the Clustering Results 6 Conclusion and Future Work References SP: Shell-Based Perturbation Approach to Localize Principal Eigen Vector of a Network Adjacency Matrix 1 Introduction 2 Related Works 3 Basic Concepts and Problem Statement 3.1 Basic Concepts 3.2 Problem Statement 4 Methodology 4.1 Overall Workflow 4.2 Existing Random Perturbation (RP) Approach and Scope of Improvement 4.3 Proposed Shell-Based Perturbation (SP) Approach 4.4 Time Complexity Analysis of the SP Approach 5 Results and Discussion 6 Conclusions and Future Scope References Development of a Robust Dataset for Printed Tamil Character Recognition 1 Introduction 2 Materials and Methods 2.1 UJTDchar Dataset 2.2 Mepco Tamil Character Dataset 2.3 Feature Extraction Methods and Classification 3 Results and Discussion 4 Conclusion References An Efficient CNN-based Method for Classification of Red Meat Based on its Freshness 1 Introduction 2 Literature Review 3 Proposed Methodology 3.1 Classification Algorithm 3.2 Image Pre-processing 3.3 Freshness Level and classes 3.4 HarNet Training 4 Result Analysis 5 Conclusion and Future Work References Multi-class Pathogenic Microbes Classification by Stochastic Gradient Descent and Discriminative Fine-Tuning on Different CNN Architectures 1 Introduction 2 Method 2.1 Dataset 2.2 Data Preprocessing and Scaling 2.3 Network Archtectures 2.4 Formulas 2.5 Stochastic Gradient Descent with Warm Restarts (SDGR) 2.6 Model Evaluation 2.7 Results and Analysis 3 Conclusion References Early Prediction of Thoracic Diseases Using Rough Set Theory and Machine Learning 1 Introduction 2 Literature Review 2.1 Background 2.2 Fundamental of RST 2.3 Various RST Algorithm 2.4 Fundamental of SVM 3 Initial Steps for Data Analysis 3.1 Application Linear Regression to Group the Dataset 3.2 Finding Reduct and Core 4 Thoracic Diseases 4.1 Background for Data Analysis 4.2 Application of SVM System 4.3 Prediction and Analysis Using SVM Systems 5 Conclusion and Future Work References Predicting Liver Disease from MRI with Machine Learning-Based Feature Extraction and Classification Algorithms 1 Introduction 2 Related Work 3 Methodology 3.1 Data Collection 3.2 Data Pre-Processing 3.3 Feature Extraction 3.4 Classification 3.5 Evaluation Parameters 4 Results 5 Conclusion References An Improved Genetic Algorithm Based on Chi-Square Crossover for Text Categorization 1 Introduction 2 Related Work 3 Proposed Method 3.1 Data Preprocessing 3.2 Vectorization 3.3 Proposed CSEGA Approach 4 Experimental Setup and Result Analysis 4.1 Dataset 4.2 Result Analysis 5 Conclusion References Tuna Optimization Algorithm-Based Data Placement and Scheduling in Edge Computing Environments 1 Introduction 2 Related Work 3 Proposed Tuna Model 3.1 Problem Formulation 3.2 Inspiration 4 Results and Discussion 5 Conclusion References Frequency Control of Single Area Hybrid Power System with DG 1 Introduction 2 System Description 2.1 Structure of the Controller 2.2 Objective Function 3 DE Algorithm 4 Sımulatıon Results 5 Conclusion References Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models 1 Introduction 2 Related Work 3 Methodology 3.1 Dataset Description 3.2 Data Preprocessing 3.3 Prediction Model 4 Result and Discussion 5 Conclusion References Verifiable Secret Image Sharing with Cheater Identification 1 Introduction 2 Literature Survey 3 Preliminaries 3.1 Generating Share Matrix 3.2 Image Fingerprinting 3.3 One Way Hashing 3.4 Chaotic Logistic Map 4 Proposed Work 4.1 Initialization Phase 4.2 Registration Phase 4.3 Share Generation Phase 4.4 Share Distribution Phase 4.5 Verification and Reconstruction Phase 4.6 Final Verification Phase 4.7 Registration 4.8 Share Generation 4.9 Share Distribution 4.10 Verification and Reconstruction 4.11 Reconstructed Image Verification 4.12 Structural Similarity Index Matrix (SSIM) 4.13 Histogram Analysis 5 Conclusion and Future Work References An ECC-Based Lightweight CPABE Scheme with Attribute Revocation 1 Introduction 2 Related Work 3 Proposed Scheme Model 4 Performance Analysis 5 Conclusions References Prediction of Schizophrenia in Patients Using Fuzzy AHP and TOPSIS Methods 1 Introduction 2 Literature Review 3 Methodology 3.1 AHP 3.2 Fuzzy AHP 3.3 TOPSIS 4 Results 5 TOPSIS 6 Conclusion References Sports Activity Recognition - Shot Put, Discus, Hammer and Javelin Throw 1 Introduction 2 Literature Review 3 Methodology 3.1 Creation of Dataset 3.2 BRISK Feature Descriptor 3.3 Training of the Model 3.4 Testing of the Model 4 Results and Discussions 5 Conclusion References User Acceptance of Contact Tracing Apps: A Study During the Covid-19 Pandemic 1 Introduction 2 Related Work 2.1 Governmental Approaches in Different Societies 2.2 Smittestopp 3 Method 3.1 Measures 4 Results 4.1 Demographics 4.2 Correlation Between Variables 4.3 Evaluation 4.4 Smittestopp 5 Discussion 5.1 Performance Expectancy 5.2 Effort Expectancy 6 Conclusion References Digital Watermark Techniques and Its Embedded and Extraction Process 1 Introduction 2 Image-to-Matrix Transformation 2.1 Discrete Wavelet Transform 2.2 Discrete Cosine Transform 2.3 Singular Value Decomposition (SVD) 3 Embedding and Extraction Process 4 Results 5 Conclusion References Galvanic Skin Response-Based Mental Stress Identification Using Machine Learning 1 Introduction 2 Background 3 Methodology 3.1 Data Collection Framework 3.2 Working Principle 3.3 Analysis Through Machine Learning Algorithms 4 Result Analysis 5 Conclusions and Future Scope References A Federated Learning Based Connected Vehicular Framework for Smart Health Care 1 Introduction 2 Literature Study 2.1 Computational Capability of RSU 2.2 Various Applications of Federated Learning 3 Proposed Model 4 Experimental Results 5 Conclusion References ELECTRE I-based Zone Head Selection in WSN-Enabled Internet of Things 1 Introduction 1.1 Motivation 1.2 Objectives 2 Related Work 3 Proposed ELECTRE I-based Zone Head Selection (EZHS) Scheme 3.1 Construction of Topology 3.2 Selection of ZHs 3.3 Adoption of ELECTRE I for ZH Selection 4 Results and Discussion 5 Conclusion References Fabrication of Metal Oxide Based Thick Film pH Sensor and Its Application for Sweat pH Measurement 1 Introduction 2 Experimental 2.1 Sensor Fabrication 2.2 EIS Analysis of the Sensor 2.3 Testing Thick Film pH Sensor Using Test Solutions 3 Conclusion References Reliable Data Delivery in Wireless Sensor Networks with Multiple Sinks and Optimal Routing 1 Introduction 2 Literature Survey 3 System Model 3.1 Assumptions 3.2 Network Model 3.3 Energy Model 3.4 CASS 3.5 Alternate Path Model 3.6 Reliability Model 3.7 Adjustable Communication Range 4 Proposed Method 5 Experimental Results 6 Conclusions and Future Scope References