Recent Advances on Soft Computing and Data Mining

Name: Recent Advances on Soft Computing and Data Mining | Proceedings of the Sixth International Conference on Soft Computing and Data Mining (SCDM 2024), August 21-22, 2024
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Proceedings of the Sixth International Conference on Soft Computing and Data Mining (SCDM 2024), August 21-22, 2024

Rozaida Ghazali Nazri Mohd Nawi Mustafa Mat Deris Jemal H. Abawajy Nureize Arbaiy(Editor)

Springer (Publisher)

1st Edition

Published on 29. July 2024

XVII, 435 pages

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978-3-031-66965-1 (ISBN)

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Content

Intro
Preface
Conference Organization
Contents
Prediction of OPEC Carbon Dioxide Emissions Using K-Means Clustering and Ensemble Algorithm
1 Introduction
2 Related Works
3 Fuzzy Nearest Neighbor
4 Sequential Minimal Optimization
5 Logistic Regression
6 K-Means Clustering
7 Proposed Methodology
8 Experimental Setup and Analysis
8.1 Assessment Measures
8.2 Dataset Description
8.3 Simulation Results
9 Conclusion
References
Detection of Phishing Websites from URLs Using Hybrid Ensemble-Based Machine Learning Technique
1 Introduction
2 Related Work
3 Methodology
3.1 Description of the Machine Learning Classifiers
3.2 Dataset Description
3.3 Model Construction
4 Performance Evaluation Metrics
5 Result
6 Conclusion
7 Future Work
References
Minimal Data for Maximum Impact: An Indonesian Part-of-Speech Tagging Case Study
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Semi-supervised Learning Preprocessing
3.3 Feature Selection
3.4 Classification
3.5 Evaluation
4 Result and Discussion
5 Conclusion
5.1 Summary
5.2 Future Work
References
Alleviating Sparsity to Enhance Group Recommendation with Cross-Linked Domain Model
1 Introduction
2 Literature Review
2.1 Group Recommender System
2.2 Cross-Domain Recommender System
2.3 Linked Open Data
3 Methodology
3.1 Experiment Setup
4 Result and Discussion
5 Conclusion and Recommendation
References
Evaluating Deep Transfer Learning Models for Detecting Various Face Mask Wearings
1 Introduction
2 Literature Review
2.1 Deep Learning
2.2 Transfer Learning
2.3 Existing Works
3 Methodology
4 Results and Discussion
5 Conclusions
References
Classification of Stunting Events: Case Study in West Java, Indonesia
1 Introduction
2 Related Work
3 Methods
3.1 Dataset Collection
3.2 Data Pre-processing
3.3 Model Comparison
3.4 Model Implementation
3.5 Model Evaluation
4 Results and Discussion
5 Conclusion
References
The Effects of Data Reduction Using Rough Set Theory on Logistic Regression Model
1 Introduction
2 The Basic Theories and Methodology
2.1 Rough Set Theory (RST)
2.2 Logistic Regression Model
3 Implementation Hybrid Classification Approach with LR Analysis and RST
3.1 Implementation Hybrid Model on Anemia Data Set
3.2 Implementation Hybrid Model on Diabetes Data Set
3.3 Discussion
4 Conclusion
References
Robust Heart Disease Prognosis: Integrating Extended Isolation Forest Outlier Detection with Advanced Prediction Models
1 Introduction
2 Methodology
2.1 Summary of Dataset
2.2 Data Preprocessing
2.3 Machine Learning Techniques
2.4 Deep Learning Algorithm
2.5 Evaluation Parameters
3 Results Evaluation
3.1 Implementing the First Strategy, Which Involves Neither Feature Selection nor Outlier Detection
3.2 Implementing the 2nd Strategy: Feature Selection Without Outlier Detection
3.3 Employing the 3rd Strategy (Feature Selection and Detection of Outliers)
4 Conclusion
References
Overlapping Granular Clustering: Application in Fuzzy Rule-Based Classification
1 Introduction
2 GrC-Fuzzy Logic Models
2.1 Granular Clustering
2.2 Formation of Fuzzy Logic Rule Base
3 Overlapping GrC
3.1 R-Value
3.2 A New Overlapping Measure During the Iterative Data Granulation
4 Case Study and Simulation Results
5 Interpretability Index
6 Conclusion
References
Improved Rough-Multiple Regression for Unemployment Rate Model in Indonesia
1 Introduction
2 Variable Framework and Methods
2.1 Multiple Linear Regression
2.2 Rough Sets Theory
3 Results and Discussion
3.1 Descriptive Statistics for Unemployment Rate and Its Variables
3.2 Multiple Linear Regression Model for Unemployment Rate
3.3 Rough-Multiple Regression Model for Unemployment Rate
3.4 Comparison Multiple Regression and Rough-Multiple Regression
4 Conclusion
References
Utilizing Machine Learning for Gene Expression Data: Incorporating Gene Sequencing, K-Mer Counting and Asymmetric N-Grams Features
1 Introduction
2 Materials and Methods
2.1 Data Pre-processing
2.2 Classification Model
2.3 Performance Metrics
3 Result and Discussion
4 Conclusion and Future Work
References
Text Sentiment Analysis on VIX's Impact on Market Sentiment Dynamics
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Sentiment Analysis of SnowNLP
3.3 Sentiment Index
3.4 Pearson's Correlation
3.5 Linear Correlation
3.6 Granger Causality Test
4 Empirical Result and Analysis
4.1 Data Description and Cleaning
4.2 Text Sentiment Index
4.3 Pearson's Correlation
4.4 Linear Correlation
4.5 Granger Causality Test
4.6 Test for Chinese Market
5 Conclusion
References
Multilevel Monte Carlo Simulation Model for Air Pollution Index Prediction of a Smart Network
1 Introduction
2 Related Work
3 Methods
3.1 Monte Carlo Simulation
3.2 Multilevel Monte Carlo Simulation
3.3 Air Pollution Dataset
3.4 Performance Evaluation Metrics
4 Results and Discussion
4.1 Correlation Analysis
5 Conclusion
References
An In-Depth Strategy using Deep Generative Adversarial Networks for Addressing the Cold Start in Movie Recommendation Systems
1 Introduction
2 Related Works
3 Research Methodology
3.1 Data Preparation
3.2 Collaborative Filtering with Singular Value Decomposition (CF-SVD)
3.3 Generative Adversarial Networks (GANs)
3.4 Collaborative Filtering (CF) with SVD and GANs
3.5 Content Based Filtering (CB)
4 Results
5 Conclusion
References
Predicting Undergraduate Academic Success with Machine Learning Approaches
1 Introduction
2 Related Work
3 Research Design and Methodology
3.1 Dataset Source
3.2 Exploratory Data Analysis
3.3 Data Preprocessing
3.4 Classification Algorithms
4 Results and Discussion
4.1 Evaluations of Classifiers Using Default Parameters
4.2 Model Parameter Optimization by Hyperparameter Tuning
5 Conclusion
References
Comparative Assessment of Facial Expression Recognition Models for Unraveling Emotional Signals with Convolutional Neural Networks
1 Introduction
2 Related Work
3 Dataset Description
4 Methodology
4.1 Pre-processing
4.2 Feature Extraction
4.3 CNN Architecture
5 Results
6 Discussions and Future Work
References
Evaluating Path-Finding Algorithms for Real-Time Route Recommendation System Built using FreeRTOS
1 Introduction
2 Related Work
3 Research Design and Methodology
3.1 Adjacency Matrix
3.2 Path Finding Algorithms
3.3 Real-Time Operating System (RTOS)
3.4 Functional Diagram of the Simulated System using FreeRTOS
4 Results and Discussion
4.1 Validate the accuracy of the recommended route
4.2 Performance Evaluation
5 Conclusion
References
Machine Learning-Based Phishing Website Detection: A Comparative Analysis and Web Application Development
1 Introduction
2 Literature Review
3 Research Design
3.1 Dataset Overview
3.2 Feature Selection
3.3 Detection Techniques Implementation
3.4 Performance Evaluation and Comparison
3.5 Web Application Development
4 Results and Discussion
5 Conclusion
References
Comparative Performance of Multi-level Pre-trained Embeddings on CNN, LSTM and CNN-LSTM for Hate Speech and Offensive Language Detection
1 Introduction
2 The Architecture of HSOLC Detection Model
2.1 Text Embedding Layer
2.2 Representation Layer
2.3 Output Layer
3 Experimental Setup
4 Dataset and Results
4.1 Results and Discussion
5 Conclusion
References
Improved Classifier Chain Method Based on Particle Swarm Optimization and Genetic Algorithm for Multilabel Classification Problem
1 Introduction
1.1 Motivation
1.2 Random Label Sequence Ordering (RLSO)
2 Related Work
3 Method
3.1 Dataset
3.2 Data Preprocessing
3.3 Classification (Proposed Model)
3.4 Performance Measures
4 Results and Discussion
5 Conclusion
References
Sentiment Analysis on Umrah Packages Review in Malaysia
1 Introduction
2 Sentiment Analysis on Social Media
2.1 Related Works of Similar
2.2 Mobile Phone Reviews from Amazon Using Support Vector Machine
2.3 Sentiment Classification of Online Consumer Reviews Using Word Vector Representation
2.4 Online Reviews of Hospitality Services Using Naïve Bayes
2.5 Customer Satisfaction Towards Umrah Travel Agencies in Malaysia
3 Methodology
3.1 Preliminary Study
3.2 Data Analysis
3.3 Interface and Architecture Design
3.4 System Development
4 Analysis and Discussions
4.1 Naïve Bayes - Gaussian
4.2 Naïve Bayes - Multinomial
4.3 Support Vector Machine
4.4 Random Forest
4.5 Analysis
5 Conclusion and Recommendations
References
Opinion Mining System for Influence Detection Using Machine Learning to Secure Business Reputation
1 Introduction
2 Related Works
2.1 Sentiment Analysis
2.2 Supervised Machine Learning Approach
3 Methodology
3.1 Data Preprocessing and Feature Extraction
3.2 Modelling
3.3 Performance Evaluation
3.4 Interface Development
4 Results and Analysis
5 Conclusion
References
A Presentation Mining Framework: From Text Mining to to Mind Mapping
1 Introduction
2 Materials and Methods
2.1 Pre-processing Stage
2.2 Presentation Mining Stage
2.3 Visualization Stage
2.4 Evaluation Strategies
3 Results and Discussion
3.1 Ontology-Based Evaluation
3.2 Dictionary-Based Evaluation
3.3 Expert Evaluation
4 Conclusions
References
Enhancing Network Intrusion Detection Systems Through Dimensionality Reduction
1 Introduction
2 Related Works
3 NSLKDD Dataset Preprocessing
4 Attack Prediction Using SVM
5 Performance Evaluation
6 Conclusions and Future Work
References
Performance Evaluation of Whale and Harris Hawks Optimization Algorithms with Intrusion Prevention Systems
1 Introduction
2 Related Work
3 NSL KDD Dataset
4 HHO and WOA Optimizers
4.1 HHO Optimizer
4.2 WOA Optimizer
5 HHO and WOA Comparative Framework
5.1 Data Preprocessing
5.2 Feature Selection Using WOA and HHO Optimizers
5.3 Using KNN for Attack Classification
6 Performance Evaluation
7 Conclusion
References
Domestic Solid Waste Prediction with an Enhanced LSTM with SigmoReLU and RAdam Optimizer
1 Introduction
2 Literature Review
2.1 Overview of Malaysian Domestic Solid Waste
2.2 Long Short-Term Memory (LSTM)
3 The Proposed e-LSTM (Method)
3.1 The Architecture of e-LSTM
3.2 RAdam Optimizer and Dropout Mechanism
3.3 Hybrid Activation Function (SigmoReLU)
4 Research Method and Materials
4.1 Summary of the Dataset
4.2 Model Training and Optimization Strategies
4.3 Performance Evaluation Measures
4.4 Future Prediction with e-LSTM
5 Results and Discussions
5.1 Comparing e-LSTM with Alternative Machine Learning Models
5.2 The e-LSTM Model Prediction
6 Conclusion
References
Sounds Prediction Instruments Based Using K-Means and Bat Algorithm
1 Introduction
2 Related Work
2.1 K-Means Classifier as Discretization Method
2.2 Bat Algorithm
3 Methodology
4 Conclusion
References
A Comparative Study on Ant-Colony Algorithm and Genetic Algorithm for Mobile Robot Planning
1 Introduction
2 Related Works
2.1 Comparison Study of Previous Researcher Work on Genetic Algorithms
2.2 Comparison Study of Previous Researcher Work on Ant-Colony Optimization
3 Methodology
3.1 Research Framework
3.2 Research Activities
3.3 Algorithm Implementation
3.4 Dataset
3.5 Evaluation Metrics
4 Results and Discussion
5 Conclusion
References
Enhanced Air Quality Index Prediction Using a Hybrid Convolutional Network
1 Introduction
2 Related Works
3 Experimental Method
3.1 The GCN Model
3.2 The TCN Model
4 Experimental Result and Discussion
5 Conclusion
References
Filter Method Feature Selection Techniques for Solid Waste Prediction Based on GRU Deep Learning Model
1 Introduction
2 Literature Review
2.1 Deep Learning Models for Waste Generation Prediction
2.2 Filter Method Based Feature Selection Techniques in Waste Prediction
3 Methodology
3.1 Data Collection
3.2 Application of Feature Selection Techniques
3.3 Model Development
3.4 Performance Evaluation Criteria
4 Results
5 Conclusion
References
Spiking Neural Network for Microseismic Events Detection Using Distributed Acoustic Sensing Data
1 Introduction
2 Literature Review
3 Methods
3.1 Data Collection, Cleaning and Exploration
3.2 Model Training and Validation
3.3 Model Evaluation
3.4 Proposed Approach: SNN Architecture
4 Results and Discussion
5 Conclusions
References
Battery Electric Vehicle Charging Load Forecasting Using LSTM on STL Trend, Seasonality, and Residual Decomposition
1 Introduction
2 Objectives
3 Methodology
3.1 Feature Engineering
3.2 STL Decomposition
3.3 Recurrent Neural Network Model
3.4 One-Step Ahead Forecasting
4 Results and Discussion
5 Conclusions
References
Convolutional Neural Network Using Regularized Conditional Entropy Loss (CNNRCoE) for MNIST Handwritten Digits Classification
1 Introduction
2 Literature Review
2.1 CNN Architectures for MNIST Handwritten Digit Classification
2.2 Regularization Techniques
2.3 Conditional Entropy Loss
3 Proposed CNNRCoE
4 Experimental Design and Results
4.1 Result Analysis
5 Conclusion and Future Works
References
Optimizing Team Formation for Welfare Activities: A Study Using Four Metaheuristic Optimization Algorithms
1 Introduction
2 Existing Algorithms
2.1 Sine-Cosine Algorithm (SCA)
2.2 Jaya Algorithm (JA)
2.3 Firefly Algorithm (FA)
2.4 Particle Swarm Optimization (PSO)
3 Related Works
3.1 Data Synthesis
3.2 Development
3.3 Data Design
3.4 Time Complexity
3.5 Testing
4 Results and Discussions
5 Conclusion
References
Detection of Paddy Plant Diseases Using Google Teachable Machine
1 Introduction
2 Literature Review
3 Methodology
3.1 Image Preprocessing
3.2 Model Development
3.3 Model Evaluation
4 Results and Discussion
5 Conclusion
References
Comparative Analysis of ResNet Models for Skin Cancer Diagnosis: Performance Evaluation and Insights
1 Introduction
2 Related Works
3 Methodology
3.1 Datasets
3.2 Preprocessing
3.3 Learning Model
3.4 ResNet Models
3.5 Evaluation
4 Results and Discussion
5 Conclusion
References
The Predictive Modelling of Student Academic Performance Using Machine Learning Approaches
1 Introduction
2 Methodology
2.1 Preparing and Preprocessing Dataset
2.2 Predictive Modelling
3 Results and Discussion
4 Conclusion
References
Predictive Modeling of Gold Prices: Integrating Technical Indicators for Enhanced Accuracy
1 Introduction
2 Data Exploration and Experimental Setup
2.1 Data Overview
2.2 Data Pre-Processing
2.3 Data Selection
2.4 Data Construction
2.5 Data Preparation
2.6 Modeling
3 Results
3.1 Technical Indicators' Influence
3.2 Overall Model Performance
4 Conclusion and Future Improvement
References
Portfolio Optimization with Percentage Error-Based Fuzzy Random Data for Industrial Production
1 Introduction
2 Literature Review
2.1 Mean-Variance Model
2.2 Fuzzy Set
2.3 Fuzzy Random Variables
3 Portfolio Selection Model Using Fuzzy Random Data Based on Percentage Error on Industrial Production Index
3.1 Fuzzy Random Data Pre-processing
3.2 Fuzzy Random Based Portfolio Selection Model for Industrial Production Index
4 Numerical Experiment
5 Result and Discussion
6 Conclusions
References
The Football Matches Outcome Prediction for English Premier League (EPL): A Comparative Analysis of Multi-class Models
1 Introduction
2 Literature Review
3 Research Methodology
3.1 Dataset
3.2 Algorithms
3.3 Evaluation Metrics
4 Result and Discussion
5 Conclusion and Future Work
References
An Automated Quasi-Identification (QID) for Re-identification
1 Introduction
2 Related Work
3 Methodology
3.1 Selection of QID
4 Experiment Evaluation
5 Result
6 Conclusion
References
Correction to: Predicting Undergraduate Academic Success with Machine Learning Approaches
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Recent Advances on Soft Computing and Data Mining

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