
Smart Data Intelligence
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This book presents high-quality research papers presented at 4th International Conference on Smart Data Intelligence (ICSMDI 2024) organized by Kongunadu College of Engineering and Technology at Trichy, Tamil Nadu, India, during February 2024. This book brings out the new advances and research results in the fields of algorithmic design, data analysis, and implementation on various real-time applications. It discusses many emerging related fields like big data, data science, artificial intelligence, machine learning, and deep learning which have deployed a paradigm shift in various data-driven approaches that tends to evolve new data-driven research opportunities in various influential domains like social networks, health care, information, and communication applications.
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Persons
R. Asokan received his B.E. degree in electronics and communication from Bharathiar University and M.S. degree in electronics and control from Birla Institute of Technology. He obtained M.Tech. degree in electronics and communication from Pondicherry Engineering College, with distinction. He obtained Ph.D. in information and communication engineering from Anna University, Chennai. He is currently the principal, Kongunadu College of Engineering and Technology, Thottiyam, Tamil Nadu, India. He has published more than 65 papers in national and international journals and conferences. He has over 25 years of teaching experience. He is a member of various scientific and professional societies. His areas of interest include wireless networks, network security, and image processing.
Diego P. Ruiz received the M.S. and Ph.D. degrees in Physics from the University of Granada, Spain, in 1991 and 1995, respectively. He was a teacher in the University of Malaga in 1994-95, and he is currently an associate professor at the Faculty of Sciences of the University of Granada. He is also a coordinator of courses in the open training classroom of the University of Granada. He is interested and participates in several commissions and projects for the assessment of teaching quality and teaching innovation and collaborates in science dissemination activities through the Scientific Culture Unit of the University of Granada. His current research interests are environmental pollution and its modeling, especially acoustic and electromagnetic pollution, signal processing, and energy efficiency in buildings. His main contributions have been in the development of signal processing algorithms for the automatic classification of radar objects, analysis, and modeling of data coming from physical agents (noise and vibrations), development of indoor air quality indices, and new technologies for pollution control.
Selwyn Piramuthu is Professor of Information Systems at the University of Florida, where he has taught since Fall 1991. Trained in machine learning, his research interests also include cryptography with applications related to IoT/RFID, privacy/security, supply chain management, among others. His (co-authored with Wei Zhou) book titled, "RFID and Sensor Network Automation in the Food Industry" was published by Wiley in 2016. He received his B.Tech., M.S., and Ph.D. respectively from IIT Madras, University of Arizona, and University of Illinois at Urbana-Champaign.
Content
- Intro
- Preface
- Contents
- About the Editors
- 1 Lung Cancer Prediction using Combination of Oversampling with Standard Random Forest Algorithm for Imbalanced Dataset
- 1 Introduction
- 2 Related Works
- 3 Methodology
- 3.1 Data Collection
- 3.2 Data Preprocessing
- 3.3 Class Balancing
- 3.4 Algorithms
- 4 Results and Discussion
- 4.1 Evaluation Measures
- 5 Conclusion
- References
- 2 Driver Drowsiness Detection System Based on Yawn Detection
- 1 Introduction
- 2 Literature Survey
- 3 Methodology
- 3.1 Proposed System
- 3.2 Yawning Detectıon
- 3.3 Drowsiness Detection System
- 3.4 Driving Pattern Detection
- 4 Results and Discussion
- 5 Conclusion
- 6 Future Scope
- References
- 3 Learning Human Anatomy Using Augmented Reality
- 1 Introduction
- 1.1 Augmented Reality
- 1.2 Drawbacks of 2D Learning of Anatomy
- 1.3 AR and 3D Learning
- 1.4 Benefits of AR
- 1.5 Advantages of 3D Learning
- 2 Literature Review
- 3 Methodology
- 4 Results
- 5 Conclusion
- References
- 4 Predictive Modeling of COVID-19 Patient Recovery Using Complete Blood Count Data
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 3.1 Dataset Description
- 3.2 Logistics Regression
- 4 Results and Analysis
- 5 Discussion
- 6 Conclusion and Future Work
- References
- 5 Survey of Land Using Augmented Reality
- 1 Introduction
- 1.1 Augmented Reality
- 1.2 Application of AR
- 1.3 Types of AR
- 2 Literature Survey
- 3 Methodology
- 3.1 Creating the Default Plane
- 3.2 Creating the Points
- 3.3 Creating the Line
- 3.4 Augmented Reality Camera (AR Camera)
- 4 Results
- 5 Conclusion
- References
- 6 Adaptive Lotus Effect Optimization with DKN for Fake News Detection on Social Media with Tamil Language
- 1 Introduction
- 2 Related Work
- 2.1 Major Gaps
- 3 Proposed ALEO-DKN to Detect the Fake News in Tamil Language
- 3.1 Input Data Acquisition
- 3.2 Tokenization Process
- 3.3 Feature Extraction
- 3.4 Fake News Detection
- 4 Result Analysis
- 4.1 Precision
- 4.2 Recall
- 4.3 F-measure
- 4.4 Rouge
- 5 Conclusion
- References
- 7 Ensemble Classification of Hydrogen Storage Materials Using Its Properties
- 1 Introduction
- 2 Related Works
- 3 Proposed Ensemble Classıfıcatıon
- 4 Result and Analysıs
- 5 Conclusion
- References
- 8 Scalable and Intelligent Big Data Analytics Framework (SIBDAF) for Cloud Environments
- 1 Introduction
- 2 Literature Review
- 2.1 Cloud Computing for Big Data Analytics
- 2.2 Big Data Processing Frameworks
- 2.3 Scalable Resource Management in Clouds
- 2.4 Intelligent Analytics in Cloud Environments
- 2.5 Bridging the Gap: Cloud-Based Intelligent Analytics
- 3 Problem Statement
- 4 Implementation Details
- 4.1 Data Ingestion and Storage
- 4.2 Scalable Data Processing Engines
- 4.3 Intelligent Analytics Modules
- 4.4 Resource Management and Scaling
- 4.5 Interaction and Workflow
- 5 Evaluation and Results
- 5.1 Evaluation Methodology
- 5.2 Performance Metrics
- 6 Conclusion
- References
- 9 Multilingual Subtitle Generator Using Machine Learning
- 1 Introduction
- 2 Literature Survey
- 3 Existing System
- 3.1 Limited Language Support
- 3.2 Accuracy and Consistency
- 3.3 Synchronization Issues
- 3.4 Translation Limitations
- 3.5 Resource Intensiveness
- 4 Proposed System
- 4.1 Multilingual Support
- 4.2 High Accuracy and Quality
- 4.3 Performance
- 4.4 Precise Synchronization
- 4.5 Translation and Localization
- 4.6 Optimized Resource Utilization
- 4.7 User-Friendly Experience
- 4.8 Enhance Transparency
- 4.9 Automatic Speech Recognition Model
- 4.10 Testing and Validation
- 5 Methodology
- 5.1 Data Collection and Preprocessing
- 5.2 File Conversion and Standardization:
- 5.3 Speech Recognition
- 5.4 Synchronization of Text with Audio
- 5.5 Multilingual Subtitle Generation
- 5.6 Evaluation and Validation
- 6 Figures and Tables
- 7 Result
- 8 Conclusion
- References
- 10 Question-Based Answering Using ML: A Survey
- 1 Introduction
- 2 Literature Review
- 3 Methodologies and Techniques
- 3.1 Supervised Learning Approaches
- 3.2 Transfer Learning
- 3.3 Reinforcement Learning
- 3.4 Unsupervised Learning Approaches
- 3.5 Transfer Learning from Pre-trained Language Models
- 3.6 Memory-Augmented Neural Networks
- 4 Performance Evaluation Metrics
- 4.1 Answer Plausibility
- 4.2 User Engagement Metrics
- 5 Challenges and Future Directions
- 5.1 Handling Multi-modal Data
- 5.2 Privacy and Bias Concerns
- 6 Conclusion
- 6.1 Comprehensive Overview
- 6.2 Promising Results
- 6.3 Real-World Applications
- 6.4 Ethical Considerations
- 6.5 User-Centric Evaluations
- 6.6 Future Directions
- References
- 11 Deepening Sustainability: Waste Segregation Through Advanced Deep Learning Techniques
- 1 Introduction
- 2 Related Work
- 3 Proposed Work
- 3.1 Data Collection
- 3.2 Architecture
- 3.3 Data Augmentation
- 3.4 Classification
- 4 Result Analysis
- 5 Conclusion
- References
- 12 Deep Learning-Based Facial Deepfake Detection Using MobileNetV2 and VGG16
- 1 Introduction
- 1.1 Face Forgery Detection
- 1.2 Fake Face Identification
- 1.3 VGG-16 Model
- 2 Related Work
- 3 Proposed Work
- 3.1 Load Data
- 3.2 Data Pre-processing
- 3.3 Feature Extraction
- 3.4 Training and Testing
- 3.5 Fake Face Detection by MobileNetV2 and VGG16
- 4 Result Analysis
- 5 Conclusion
- References
- 13 Implementation of Machine Learning Algorithms in Diabetes Prediction
- 1 Introduction
- 2 Literature Review
- 3 Proposed System
- 3.1 Dataset Collection
- 3.2 Data Preprocessing
- 3.3 Training Data and Test Data
- 3.4 Methodology
- 3.5 Build Model for Proposed Methodology
- 3.6 Performance Evaluation
- 4 Result
- 5 Conclusion and Future Work
- References
- 14 A Machine Learning Perspective for Early Alzheimer's Diagnosis
- 1 Introduction
- 1.1 Overview
- 1.2 Machine Learning
- 1.3 Alzheimer's Disease
- 2 Literature Review
- 3 Methodology
- 3.1 Objective of the System
- 3.2 Support Vector Machine (SVM)
- 3.3 Random Forest
- 3.4 Decision Tree
- 3.5 Logistic Regression
- 4 Result Analysis
- 4.1 Evaluation Metrics
- 5 Results
- 6 Conclusion and Future Enhancements
- References
- 15 Unlocking the Potential of Decentralized Video Sharing with Blockchain Technology
- 1 Introduction
- 2 Theoretical Background
- 2.1 Blockchain
- 2.2 Smart Contracts
- 2.3 Blockchain-Based Video-Sharing Platforms
- 3 Types of Attacks
- 3.1 Distributed Denial of Service (DDoS) Attack
- 3.2 Cross-Site Scripting (XSS) Attack
- 3.3 SQL Injection Attack
- 3.4 Man-in-the-Middle (MITM) Attack
- 3.5 Social Engineering Attack
- 3.6 Malware Attack
- 3.7 Insider Attack
- 4 Methodology
- 4.1 Distributed Denial of Service (DDoS) Attacks
- 4.2 Cross-Site Scripting (XSS) Attack
- 4.3 SQL Injection Attack
- 4.4 Man-in-the-Middle (MITM) Attack
- 4.5 Social Engineering Attack
- 4.6 Malware Attack
- 4.7 Insider Attack
- 5 Result
- 6 Conclusion and Future Scope
- References
- 16 An Analysis of the Machine Learning Algorithm for Early-Stage Prediction of Lung Cancer
- 1 Introduction
- 2 Literature Review
- 3 Methodology
- 3.1 Data Description
- 3.2 Data Preprocessing
- 3.3 Classification Techniques
- 4 Data Visualization
- 5 Result and Analysis
- 6 Conclusion
- References
- 17 Identification and Prevention of Brute Force Attacks
- 1 Introduction
- 2 Literature Survey
- 3 Existing Method
- 4 Proposed System
- 5 Results and Discussion
- 6 Conclusion
- References
- 18 Fairness in Predicting Recidivism Score
- 1 Introduction
- 2 Literature Work
- 3 COMPAS and ML Models
- 3.1 COMPAS
- 3.2 SVM Algorithm
- 3.3 Decision Tree Algorithm
- 3.4 Ensemble Learning
- 4 Methodology
- 4.1 Dataset
- 4.2 Results and Discussion
- 5 Conclusion and Future Work
- References
- 19 System Analysis of Adaptive Genetic Algorithms for Water Dıstribution Optimizing in Water Scarcity Conditions
- 1 Introduction
- 2 Related Work
- 3 Proposed Work
- 4 Result Analysis
- 4.1 Adaptation of the Population Volume
- 4.2 Adaptive Change of Crossing-Over and Mutation Parameters
- 4.3 Adaptation Based on Fuzzy Controllers
- 5 Conclusion
- References
- 20 Informed Choices: Identifying Deceptive in Online Reviews
- 1 Introduction
- 2 Literature Survey
- 3 Overview of Existing System
- 4 Proposed System
- 5 Working Model
- 6 Results and Discussion
- 7 Conclusion and Future Scope
- References
- 21 Spell Checker Using Norvig Algorithm for Gujarati Language
- 1 Introduction
- 2 Peter Norvig Algorithm of Spell Checker
- 2.1 Norvig's Algorithm Spelling Corrector
- 2.2 Analysis [7]
- 2.3 Dataset
- 2.4 Norving Algorithm Workflow
- 3 Result and Discussion
- 4 Future Work
- 5 Conclusion
- References
- 22 Decentralized File Storage System Using IPFS and Blockchain
- 1 Introduction
- 2 Problem Statement
- 2.1 Motivation for Decentralized File Storage
- 3 Project Requirements
- 3.1 IPFS Network Requirements
- 3.2 Blockchain Network Requirements
- 3.3 Crypto-payment Module Requirements
- 4 Design Approach
- 4.1 Blockchain Module
- 4.2 IPFS Module
- 4.3 Payment Module
- 4.4 Login/Signup Module
- 5 System Architecture
- 5.1 Components of EtherSync
- 5.2 System Interactions
- 5.3 Interaction Between Modules
- 5.4 Overall System Flow
- 6 Implementation Details
- 6.1 IPFS Integration
- 6.2 Smart Contracts on Ethereum
- 6.3 Crypto-payment Module
- 7 Results and Discussion
- 7.1 Data Retrieval
- 7.2 Security and Transparency
- 7.3 Reliability
- 7.4 Results
- 7.5 Future Considerations
- 8 Conclusion
- References
- 23 Prediction of Heart Problems in Diabetic Patients Using Machine Learning Algorithm
- 1 Introduction
- 2 Methodology
- 2.1 Data Collection
- 2.2 Data Preprocessing
- 2.3 Feature Selection/Engineering
- 2.4 Splitting Data
- 2.5 Model Selection
- 2.6 Training of Model
- 2.7 Model Evaluation
- 2.8 Hyperparameter Tuning
- 2.9 Model Interpretation
- 2.10 Deployment
- 2.11 Monitoring and Maintenance
- 2.12 Interpretability
- 3 Experimental Result and Discussion
- 4 Conclusion
- References
- 24 House Price Prediction Using Machine Learning Algorithm
- 1 Introduction
- 2 Literature Review
- 3 Methodology
- 3.1 Dataset Exploration
- 3.2 Data Preprocessing
- 3.3 Model Selection
- 3.4 Model Training and Validation
- 3.5 Evaluation Metrics
- 3.6 Feature Importance Analysis
- 3.7 Testing and Training
- 4 Result
- 5 Conclusion
- References
- 25 A Recommender System for the Optimal Combo Offers with Cost Benefit Analysis
- 1 Introduction
- 2 Literature Survey
- 2.1 Item-Based Collaborative Filtering
- 2.2 Cost Benefit Analysis
- 3 Implementation
- 3.1 Data Cleaning
- 3.2 K-Nearest Neighbor
- 3.3 Cost Benefit Analysis
- 4 System Design
- 4.1 Data
- 4.2 Setting Flask Environment
- 5 System Operation
- 6 Investigative Analysis
- 7 Conclusion
- References
- 26 Deep Learning-Based Invasion Detection System Enhancing Wireless Sensor Network Security
- 1 Introduction
- 1.1 Preface
- 1.2 Origin of the Problem
- 1.3 Background and Key Definitions
- 1.4 Problem Statement with Objectives and Consequences
- 1.5 Applications of Proposed Work
- 2 Related Work
- 2.1 Introduction
- 2.2 Summary of Literature Review
- 3 Proposed Work
- 3.1 Introduction
- 3.2 Design Methodology
- 3.3 Flow Diagram
- 3.4 Summary
- 4 Results analysis
- 4.1 Introduction
- 4.2 Stepwise Description of Results
- 4.3 Test Case Results/Result Analysis
- 4.4 Observations from the Work
- 5 Conclusion and Future Scope
- 5.1 Conclusion
- 5.2 Future Study
- References
- 27 Detection and Mitigation of DDOS Attack Using CART-SVC Approach in SDN
- 1 Introduction
- 2 Related Works
- 3 Proposed Methodology
- 3.1 Dataset Generation and Preprocessing
- 3.2 Feature Selection
- 3.3 Detection
- 3.4 Classification
- 3.5 Mitigation
- 4 Result and Analysis
- 5 Conclusion and Future Works
- References
- 28 Time-Weighted Dynamic Time Warping Classification Algorithm for Land Cover Mapping by Using SAR Imagery
- 1 Introduction
- 2 Materials and Methodology
- 2.1 Area of Interest
- 2.2 Satellite Data Acquisition and Image Rectification of Sentinel-1A Products
- 2.3 Agricultural Field Boundary Delineation and Training Samples for TWDTW
- 2.4 Temporal Patterns Development in TWDTW Algorithm
- 2.5 Implementation of TWDTW Algorithm
- 3 Results and Discussions
- 4 Conclusion
- References
- 29 Optimizing Urban Traffic Flow Prediction: Integrating Spatial-Temporal Analysis with a Hybrid GNN and Gated-Attention GRU Model
- 1 Introduction
- 2 Related Works
- 3 Proposed Methodology
- 3.1 Graph Neural Networks (GNNs) for Spatial Dependencies
- 3.2 Gated-Attention Gated Recurrent Units (GRUs) for Temporal Dynamics
- 3.3 Fusing Spatial and Temporal Insights
- 4 Experiment and Result Analysis
- 5 Conclusion
- References
- 30 Efficient Farming with Solar-Powered Multipurpose Agribots and Smart Field Monitoring
- 1 Introduction
- 2 Design and Functionality of Multipurpose Agricultural Robots
- 3 Development and Testing of Solar-Powered Agribots
- 4 Experiment Methodology
- 5 Results and Discussion
- 6 Conclusion
- References
- 31 Transforming Text into Art: Exploring DALL-E's Text-to-Image Generation Capabilities
- 1 Introduction
- 2 Literature Survey
- 3 Proposed Methodology
- 3.1 Dataset Selection and Preprocessing
- 3.2 CLIP Training
- 3.3 Glide Training and Diffusion Model
- 3.4 Semantic Image Generation Pipeline: From Text Input to Visual Manifestation
- 4 Result
- 5 Conclusion
- References
- 32 Automatic Content Checker Using Unstructured Text Analysis
- 1 Introduction
- 2 Literature Survey
- 3 Proposed System
- 3.1 Authentication
- 3.2 User and Admin
- 3.3 System Deployment
- 3.4 System Architecture
- 4 Pre-processing
- 4.1 Text Filtering
- 4.2 Stemming
- 4.3 Elimination of Stop Words
- 5 Results
- 6 Testing
- 7 Conclusion
- References
- 33 Generative AI Pipeline for Modeling Digital Humans for Interactive Learning
- 1 Introduction
- 2 Related Work
- 3 Proposed Work
- 3.1 CQA Modeling
- 3.2 Speech Synthesis
- 3.3 Video Generation
- 3.4 Metrics and Modeling
- 4 Results and Analysis
- 5 Conclusion and Future Work
- References
- 34 Blockchain-Enhanced Multi-factor Authentication for Secure Electronic Voting
- 1 Introduction
- 2 Literature Review
- 3 Methodology
- 3.1 Facial Recognition System
- 3.2 Email One-Time Password Verification Process
- 4 Modules
- 5 Experimental Results
- 6 Comparative Analysis
- 7 Conclusion
- References
- 35 Smart Traffic Signal Control for Emergency Vehicles
- 1 Introduction
- 1.1 Problem Statement
- 2 Related Works
- 2.1 Edge Detection
- 3 Results
- 3.1 Time Algorithm
- 4 Conclusion
- References
- 36 A Real-Time Hand Gesture Recognition System Using Image Processing
- 1 Introduction
- 2 Related Work
- 2.1 Different Recognition Approach
- 2.2 Different Sign Languages Used Across the World
- 3 Proposed Work
- 3.1 Constraints
- 3.2 Software
- 4 The Dependencies of Hand Gesture Detection and Recognition are as follows:
- 4.1 Detection
- 5 Flow of Hand Gesture Recognition
- 5.1 Displaying Gesture Name or Meaning
- 5.2 Detected Text Converted to Speech
- 5.3 Hand Gesture Detection
- 6 Results
- 7 Conclusion
- 8 Future Scope
- References
- 37 Prediction of Lung Diseases Using Deep Learning Models
- 1 Introduction
- 2 Related Works
- 3 Proposed Method
- 3.1 System Flow
- 4 Result Analysis
- 5 Conclusion
- References
- 38 AI-Driven Personalized Learning Paths: Enhancing Education Through Adaptive Systems
- 1 Introduction
- 2 Review of Related Literature
- 2.1 Overview of AI-Powered Customized Learning Paths
- 2.2 Personalization and Data Privacy in Education
- 2.3 Learning Analytics to Provide Insightful Feedback
- 2.4 Adaptive Curriculum Design Implementation to Address Diverse Learning Styles
- 2.5 Challenges and Future Prospects for AI-Powered Education
- 3 Methodology
- 4 Discussion
- 4.1 Use of Networked Learning
- 4.2 EDUBOT Program
- 4.3 AI Characters
- 5 Conclusion and Recommendation
- References
- 39 Cloud-Enabled Predictive Modeling of Cancer Progression in Digital Twins: A LightGBM Classification Approach
- 1 Introduction
- 1.1 Digital Twin (DT)
- 1.2 Cloud Platform
- 1.3 LightGBM
- 2 Literature Review
- 3 Problem Statement
- 4 Proposed Framework for Digital Twins
- 5 Scope of the Framework
- 5.1 Execution Time Period
- 5.2 Suitability for the Application
- 5.3 Reasoning
- 6 Results
- 7 Conclusion
- References
- 40 Smart Refrigeration Management System Using Internet of Things (IoT)
- 1 Introduction
- 2 Related Works
- 3 Methodology
- 4 Result Analysis
- 5 Conclusion
- References
- 41 Empowering Mobility with Voice-Controlled Smart Wheelchairs for Elderly People Using IoT
- 1 Introduction
- 2 Related Works
- 3 Proposed System
- 3.1 Components of Wheelchair
- 3.2 Role of IOT
- 3.3 Voice Recognition Technology
- 3.4 Internet of Things (IoT) Integration
- 4 Implementation
- 4.1 Requirements
- 4.2 Working
- 5 Implementations Details
- 6 Prototype Model of Voice-Controlled Smart Wheelchair
- 6.1 Implementations Details
- 6.2 Technical Specifications
- 6.3 User-Centric Interaction and Comfort
- 6.4 Iterative Testing and User-Centric Feedback
- 6.5 Future Prospects and Continuous Improvement
- 7 Future Scope
- 8 Results and Discussions
- 9 Conclusion
- References
- 42 Adaptation of Blockchain Technologies into Monetary Transmission in Banking Operations
- 1 Introduction
- 1.1 Blockchain Technology and Distributed Databases
- 2 Related Work
- 2.1 Anticipated Risk
- 2.2 Behavioural Intention
- 2.3 Cost Reduction
- 2.4 Effort Expectancy
- 2.5 Regulatory Terms and Conditions
- 2.6 Digital Money from Central Banks (CBDC) as a Medium of Exchange
- 3 Methods of Research
- 3.1 Data Collection and Sampling
- 3.2 Measurement of the Constructs and Research Design
- 4 Result Analysis
- 4.1 Model Evaluation
- 4.2 Result Interpretation
- 4.3 Limitations of the Study
- 5 Conclusion
- References
- 43 Detection and Grading of Diabetic Retinopathy Using Deep Learning
- 1 Introduction
- 2 Literature Survey
- 3 Dataset Description
- 4 Description of CNN Architectures Used
- 4.1 LeNet
- 4.2 ResNet
- 4.3 ZFNet
- 4.4 AlexNet
- 4.5 MobileNet
- 5 Research Objectives
- 6 Performance Analysis
- 6.1 Overall Analysis
- 7 Conclusion
- 8 Future Work
- References
- 44 A Comprehensive Analysis of Machine Learning Algorithms for Email Spam Detection
- 1 Introduction
- 2 Methodology
- 2.1 Data Collection and Preparation
- 2.2 Feature Extraction
- 2.3 Algorithm Selection
- 2.4 Model Training
- 2.5 Initialize the Model
- 2.6 Final Model Selection
- 2.7 Deployment and Testing
- 2.8 Logistic Regression
- 2.9 Random Forest
- 2.10 Naïve Bayes
- 2.11 Support Vector Classifier
- 2.12 AdaBoost
- 3 Results and Discussion
- 3.1 Strengths and Weaknesses
- 4 Conclusion
- References
- 45 Email Guard: Enhancing Security Through Spam Detection
- 1 Introduction
- 2 Methodology
- 2.1 Collecting Data for Email Spam Detection Typically Involves the Following Steps
- 2.2 Algorithms
- 3 Experimental Results
- 4 Conclusion
- References
- 46 Generative Adversarial Networks for Synthetic Training Data Replacement in Phishing Email Detection Using Natural Language Processing
- 1 Introduction
- 2 Related Works
- 2.1 ML Classifiers
- 3 Methods
- 3.1 Text Mining via Term Frequency-Inverse Document Frequency
- 3.2 Generative Adversarial Networks
- 4 Experimental Setup
- 5 Experimental Outcomes
- 6 Conclusion
- References
- 47 An Extensive Study of Alzheimer's Disease Detection Using Deep Learning
- 1 Introduction
- 2 Background of AD
- 3 Search Strategy
- 3.1 Databases and Keywords of Search
- 4 Available Datasets for AD
- 4.1 DementiaBank Dataset
- 4.2 HABS Dataset
- 4.3 MCSA Dataset
- 4.4 ADNI
- 4.5 Other Datasets
- 5 Technical Background for AD Classification
- 5.1 Pre-processing of Data
- 5.2 Deep Learning Systems
- 6 Related Study on Recent Publications
- 7 Challenges and Discussion
- 7.1 Class Imbalance
- 7.2 Data Leakage
- 7.3 Overfitting
- 7.4 Data Quality
- 7.5 Interpretability and Transparency
- 7.6 Reproducibility
- 8 Limitations
- 9 Summary and Future Works
- References
- 48 A Survey on Cluster-Based Hybrid Nature-Inspired Routing Algorithm in Wireless Sensor Network
- 1 Introduction
- 1.1 Advantage of Using Nature-Inspired Algorithm
- 2 Cluster-Based Hybrid Nature-Inspired Algorithm
- 2.1 Exponential Hybridization of Optimized Lion Whale and Ant Algorithm [10]
- 2.2 Hybridization of Frog Leaping and Firefly Algorithm (MOSFA) [14]
- 2.3 Hybridization of Optimized Swarm in Differing Cluster Size (CSO-UCRA) [6]
- 2.4 Fuzzy Logic Implemented Cuckoo Search Optimization-EMEER [15]
- 2.5 Hybridization of Optimized Swarm and Optimized Colonization of Ant FSACO [13]
- 2.6 Hybridization of Homogenous Cuckoo Krill (HOCK) and Heterogeneous Cuckoo Krill (HECK) [7]
- 2.7 Optimized Harris and Hawk with Cross-Layer-CL-HHO [8]
- 2.8 Hybridization of Optimized Particle Swarm and Genetic Algorithm-PSO-GA [16]
- 2.9 Hybridization of Optimized Particle Swarm and Optimized Gray Wolf-PSOGWO [17]
- 3 Study on Simulation Assumptions and Parameters Considered
- 4 Conclusion
- References
- 49 Ethical Limitations of AI Algorithms: Insights from the Altug Scenario
- 1 Introduction
- 2 Material and Methods
- 3 Altug Scenario and Others Scenarios in the Mater
- 3.1 Presentation of Altug Scenario
- 3.2 Applying the PRISMA Methodology
- 3.3 Dialog Analysis with ChatGPT
- 4 Discussion About Altug Scenario
- 5 Conclusion
- References
- Author Index
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