Search Results - (( missing value tree algorithm ) OR ( java application optimization algorithm ))
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Improving knowledge extraction of Hadith classifier using decision tree algorithm
Published 2012“…Decision tree algorithms have the ability to deal with missing values. …”
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Proceeding Paper -
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Enhanced mechanism to handle missing data of Hadith classifier
Published 2011“…Decision tree algorithms have the ability to deal with missing values or wrong data. …”
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Proceeding Paper -
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Crown counting and mapping of missing oil palm tree using airborne imaging system
Published 2019“…Watershed segmentation function was employed to separate the connected trees and missing trees regions. Different structuring elements of morphological filtering were applied to oil palm trees and missing trees counting for the purpose of noise removal. …”
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Thesis -
4
ExtraImpute: a novel machine learning method for missing data imputation
Published 2022“…This approach imputes each missing value that exists in features by predicting its value using other observed values in the dataset. …”
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Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…In the final part of the study on the missing value problem in LFRM, the modern imputation techniques, namely the expectation-maximization (EM) algorithm and the expectation-maximization with bootstrapping (EMB) algorithm is proposed. …”
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Thesis -
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New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Missing values in that attribute were replaced by the mode value. …”
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Thesis -
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Intelligent imputation method for mix data-type missing values to improve data quality
Published 2024“…A common challenge is the occurrence of missing data during the data input process. Numerous studies have proposed methods to impute missing values for data across multiple fields. …”
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Thesis -
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Tangible interaction learning model to enhance learning activity processes among children with dyslexia
Published 2024“…A common challenge is the occurrence of missing data during the data input process. Numerous studies have proposed methods to impute missing values for data across multiple fields. …”
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Thesis -
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A novel approach for handling missing data to enhance network intrusion detection system
Published 2025“…Our approach employs the Random Missing Value (RMV) algorithm to simulate missing data, enabling thorough testing and comparison of various imputation techniques. …”
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Article -
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
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Conference or Workshop Item -
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Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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Conference or Workshop Item -
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Route Optimization System
Published 2005“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Final Year Project -
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Data Mining Analysis Of Chronic Kidney Disease (CKD) Level
Published 2022“…Data classifications are performed at a 10-fold-cross-validation mode using Naïve Bayes (NB), Support Vector Machine (SVM), and J48 Trees. The ZeroR algorithm was set as the baseline There are three levels of classification analyses: before and after handling the missing values, before and after the outliers’ treatment, and adding uncertain classes. …”
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Monograph -
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Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease
Published 2024“…The baseline LightGBM model with dropped missing values had an accuracy of 0.8333, sensitivity of 0.1081, precision of 0.3429, F1 score of 0.1644, and AUC of 0.6875. …”
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Ant colony optimization algorithm for load balancing in grid computing
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Monograph -
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Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms
Published 2025“…A comprehensive data preprocessing pipeline was implemented, including missing value treatment, outlier removal, and feature normalization using Min-Max scaling. …”
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