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

• Autore: Ghazali Rozaida
• Pubblicazione: Cham : , : Springer, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (451 pages)
• Altri autori: NawiNazri Mohd DerisMustafa Mat AbawajyJemal H ArbaiyNureize
• Nota di contenuto: 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.
• Titolo autorizzato: Recent Advances on Soft Computing and Data Mining
• ISBN: 9783031669651; 9783031669644
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910878056303321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems Series