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• Chapte
• r1: A Systematic Review on Supervised & Unsupervised Machine Learning Algorithms for Data Science.- Chapte
• r2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints.- Chapte
• r3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout.- Chapte
• r4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling.- Chapte
• r5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application.- Chapte
• r6: Semantic Unsupervised Learning for Word Sense Disambiguation.- Chapte
• r7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network.- Chapte
• r8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering.
• (source: Nielsen Book Data)

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Part I Data Mining
• An Improved Particle Swarm Optimization via Velocity-Based Reinitialization for Feature Selection
• 1 Introduction
• 2 Related Works
• 3 The Proposed PSO_ImVBR Method
• 4 Experimental Setup
• 5 Results and Discussion
• 6 Conclusion
• References
• Classifying Forum Questions Using PCA and Machine Learning for Improving Online CQA
• 1 Introduction
• 2 Experiment
• 2.1 CQA Dataset and Motivation
• 2.2 Classification Algorithms
• 2.3 Factor Analysis
• 2.4 Classification Model Performance
• 3 Conclusions

• 3.3 Missing Values
• 3.4 Typing Errors
• 3.5 Redundant Data
• 3.6 Incorrect Data Values
• 4 Findings and Discussion
• 4.1 Enhancement of Data Cleansing Process
• 4.2 Data Type Mismatch
• 4.3 Specification Mismatch
• 4.4 Missing Values
• 4.5 Typing Error
• 4.6 Redundant Data
• 4.7 Incorrect Data Values
• 5 Conclusion
• References
• Reviewing Classification Approaches in Sentiment Analysis
• 1 Introduction
• 2 Related Works
• 3 Sentiment Classification Approaches
• 3.1 Lexicon-Based Approach
• 3.2 Machine Learning Approach
• 4 Comparison of Sentiment Classification Approaches

• 5 Conclusion
• References
• Comparisons of ADABOOST, KNN, SVM and Logistic Regression in Classification of Imbalanced Dataset
• 1 Introduction
• 2 Literature Review
• 2.1 Machine Learning Techniques
• 2.2 Oversampling and Undersampling
• 3 Method
• 3.1 Data Set
• 3.2 Methods
• 4 Results and Discussions
• 5 Conclusions
• References
• Finding Significant Factors on World Ranking of e-Governments by Feature Selection Methods over KPIs
• 1 Introduction
• 2 Empirical Data Analysis
• 2.1 The Dataset
• 2.2 Feature Selection Algorithm
• 2.3 The Experimental Results

• 2.4 Comparison with Other Methods
• 3 Conclusions
• References
• Part II Fuzzy Computing
• Possibility Vague Soft Expert Set Theory and Its Application in Decision Making
• 1 Introduction
• 2 Preliminaries
• 3 Possibility Vague Soft Expert Sets
• 4 Basic Operations on Possibility Vague Soft Expert Sets
• 5 Application of PVSESs in a Decision Making Problem
• 6 Conclusion
• References
• An Iterative Method for Solving Fuzzy Fractional Differential Equations
• 1 Introduction
• 2 BasicConcepts
• 3 Solution Method
• 4 NumericalExperiment
• 5 Conclusion
• References

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2015, held in Putrajaya, Malaysia, in September 2015. The 25 revised full papers presented were carefully reviewed and selected from 69 submissions. The papers are organized in topical sections on data mining; fuzzy computing; evolutionary computing and optimization; pattern recognition; human machine interface; hybrid methods.

• Artificial Intelligence Techniques and Applications
• Computing and Optimization
• Data Analytics and Technologies
• Data Mining and Image Processing
• Mathematical and Statistical Learning.

This book constitutes the refereed proceedings of the 7th International Conference on Soft Computing in Data Science, SCDS 2023, which was held virtually in January 2023. The 21 revised full papers presented were carefully reviewed and selected from 61 submissions. The papers are organized in topical sections on artificial intelligence techniques and applications; computing and optimization; data analytics and technologies; data mining and image processing; mathematical and statistical learning.

• Part I: Artificial Intelligence
• Extractive Text Summarization Using Syntactic Sub- Graph Models
• Analysis of Big Five Personality Factors to determine the Appropriate Type of Career using the C4.5 Algorithm
• Predicting Disaster Type from Social Media Imagery via Deep Neural Networks Directed by Visual Attention
• Dissemination Management for Official Statistics Using Artificial Intelligence-Based Media Monitoring
• Part II: Computational Vision
• A Naive but Effective Post-Processing Approach for Dark Channel Prior (DCP)
• COVID-19 Face Mask Classification Using Deep Learning
• Gender Classification Using Transfer Learning and Fine-Tuning
• Multi-Language Recognition Translator by Using the Convolutional Neural Network (CNN) Algorithm and Optical Character Recognition (OCR)
• Autonomous Driving Through Road Segmentation Based on Computer Vision Techniques
• Part III: Cybersecurity
• Phishing Attack Types and Mitigation: A Survey
• A Review of Privacy Protection Methods for Smart Homes Against Wireless Snooping Attack
• Development of Graph-Based Knowledge on Ransom-Ware Attacks Using Twitter Data
• Part IV: Big Data Analytics
• BigMDHealth: Supporting Multidimensional Big Data Management and Analytics over Big Healthcare Data via Effective and Efficient Multidimensional Aggregate Queries over Key-Value Stores (Prof Alredo's paper)
• Design and Implementation of Data Warehouse Solution at Kumpulan Wang Persaraan (KWAP)
• Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics
• Big Data Application on Prediction of HDD Manufacturing Process Performance
• Visualising Economic Situation Through Malaysia Economic Recovery Dashboard (MERD)
• Part V: Machine/Deep Learning
• Lung Nodules Classification Using Convolutional Neural Network with Transfer Learning
• Plant Growth Phase Classification Using Deep Neural Network
• The Implementation of Genetic Algorithm-Ensemble Learning on QSAR Study of Diacylglycerol Acyltransferase-1(DGAT1) Inhibitors as Anti-Diabetes
• Classification of Exercise Game Data for Rehabilitation Using Machine Learning Algorithms
• SDDLA: A New Architecture for Secured Decentralized Distributed Learning
• Gated Memory Unit: A Novel Recurrent Neural Network Architecture for Sequential Analysis
• Multi-Class Classification for Breast Cancer with High Dimensional Microarray Data Using Machine Learning Classifier
• Predicting Risks of Late Delivery to Online Shopping Customers Using Machine Learning Techniques
• Quora Insincere Questions Classification Using Attention Based Model
• Suicide Ideation Detection: A Comparative Study of Sequential and Transformer Hybrid Algorithms
• Well Log Data Preparation and Effective Utilization of Drilling Parameters Using Data Science Based Approaches
• Deep Learning-Based Approach for Classifying the Severity of Metal Corrosion Using SEM Images
• Insurance Risk Prediction Using Machine Learning
• Loan Default Forecasting Using StackNet
• Part VI: Statistical Learning
• Neural Network Autoregressive Model for Forecasting Malaysia Under-5 Mortality
• Robustness of Support Vector Regression and Random Forest Models: A Simulation Study
• The Impact of Restrictions Community Activities on COVID-19 Transmission: A Case Study in Sumatra Island, Indonesia
• Predicting Internet Usage for Digital Finance Services: Multitarget Classification Using Vector Generalized Additive Model with SMOTE-NC
• Part VII: Text Mining and Classification
• Identifying Topic Modeling Technique in Evaluating Textual Datasets
• Y-X-Y Encoding for Identifying Types of Sentence Similarity
• Evaluation of Extractive and Abstract Methods in Text Summarization.

The book presents selected papers from International Conference on Data Science and Emerging Technologies (DaSET 2022), held online at UNITAR International University, Malaysia, during December 2021, 2022. This book aims to present current research and applications of data science and emerging technologies. The deployment of data science and emerging technology contributes to the achievement of the Sustainable Development Goals for social inclusion, environmental sustainability, and economic prosperity. Data science and emerging technologies such as artificial intelligence and blockchain are useful for various domains such as marketing, health care, finance, banking, environmental, and agriculture. An important grand challenge in data science is to determine how developments in computational and social-behavioral sciences can be combined to improve well-being, emergency response, sustainability, and civic engagement in a well-informed, data-driven society. The topics of this book include, but not limited to: artificial intelligence, big data technology, machine and deep learning, data mining, optimization algorithms, blockchain, Internet of Things (IoT), cloud computing, computer vision, cybersecurity, augmented and virtual reality, cryptography, and statistical learning.

• AI Techniques and applications.- Data Analytics and Technologies.- Data Mining and Image Processing.- Machine & Statistical Learning. .
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 6th International Conference on Soft Computing in Data Science, SCDS 2021, which was held virtually in November 2021. The 31 revised full papers presented were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections on AI techniques and applications; data analytics and technologies; data mining and image processing; machine & statistical learning. .
(source: Nielsen Book Data)