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Case Slicing Technique for Feature Selection
Published 2004“…This technique with k = 10 has been used in this thesis to evaluate the proposed approach. CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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Thesis -
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Evaluation of data mining classification and clustering techniques for diabetes / Tuba Pala and Ali Yilmaz Camurcu
Published 2014“…Multilayer Perceptron algorithm has been the best algorithm with the highest success percentage in both of the programs; Decision Trees has been the algorithm which has the lowest success percentage again in both of the programs. …”
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Article -
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Artificial Intelligence (AI) to predict dental student academic performance based on pre-university results
Published 2022“…Logistic Regression (LR) is the most effective algorithm for forecasting student success in Year 1 with accuracy 0.88 and Decision Tree (DT) in Year 3 with accuracy 0.9. …”
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Proceeding Paper -
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Feature selection methods application towards a new dataset based on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
Published 2023“…This study will perform Analysis of Variance Test (ANOVA), Chisquared Test, Recursive Feature Elimination (RFE) and Extra Tree algorithm (ET) as feature selection methods to pre-process the proposed dataset that is considered raw data. …”
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A study on component-based technology for development of complex bioinformatics software
Published 2004“…From the enriched GO tree, the BTreeBicluster algorithm is applied during the clustering process. …”
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Monograph -
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The comparison of interactive 3D visualization between static and animated approaches for learning binary tree topic / Mohd Zulhisam Yaakub
Published 2016“…However, the research has not consistently considered instructional approaches for learning algorithm lesson, and some researches indicated that utilized methods might not be enough. …”
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Thesis -
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Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan
Published 2020“…After evaluating the results of these algorithms, a hybrid Artificial Neural Network-based Imperial Competitive Algorithm (ANN-ICA) was presented in the deployment step of the proposed methodology to identify the structural damage of illustrative structures. …”
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9
Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
Published 2026journal::journal article -
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Factors with retirement behaviour among retirees and pre-retirees identified with a machine learning method / Muhammad Aizat Zainal Alam
Published 2023“…This study uses 3,067 responses which are then be coupled with a machine learning methodology (ranging from Naïve Bayesian, Generalised Linear Model, Logistic Regression, Artificial Neural Network, Decision Tree, Random Forest, and Gradient Boosted Trees) via RapidMiner Studio to expand the understanding of how categories of wealth and expenditures can affect retirement behaviour, given the increasingly important role of machine learning algorithms within the context of behavioural economics where it has been demonstrated to describe patterns and relationships in behavioural data better than standard statistical analysis. …”
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Thesis -
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Prediction of Fetal Health Status Using Machine Learning
Published 2024“…We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. …”
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Article -
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An Automated System For Classifying Conference Papers
Published 2021“…A randomised stratified 5- fold cross validation was then applied on several data mining algorithms and evaluated using the F-measure as a metric. …”
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Final Year Project / Dissertation / Thesis -
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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Identifying and predicting Muslim’s community funeral funding protocols
Published 2024“…Selected Machine Learning algorithms such as Decision Tree, Random Forest, and Naïve Bayes were used to classify the people that will go through funeral poverty based on a selected dataset and a survey conducted. …”
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Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning
Published 2024“…Linear Discriminant Analysis (LDA), Decision Trees (DTs), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) algorithms have been employed to achieve better accuracy in quantifying the muscle spasticity level. …”
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Automated Segmentation And Classification Technique For Brain Stroke
Published 2019“…The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). …”
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Leveraging mechanomyography signal for quantitative muscle spasticity assessment of upper limb in neurological disorders using machine learning
Published 2024“…Linear Discriminant Analysis (LDA), Decision Trees (DTs), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) algorithms have been employed to achieve better accuracy in quantifying the muscle spasticity level. …”
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…The last phase involves developing a new fusion algorithm using SVM and Fuzzy K-Means Clustering (FKM) algorithms for Sentinel 2 data to enhance LCM accuracy. …”
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Thesis -
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Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset
Published 2020“…This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using several well-known machine learning algorithm such as Decision Tree, Support Vector Machine and Naïve Bayers. …”
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Article -
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A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures
Published 2023“…The dataset is pre-processed with Pearson correlation feature selection algorithm to discover the features which influence the students� academic performance. …”
Conference Paper
