Search Results - (( java implementation clustering algorithm ) OR ( using missing learning algorithm ))
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Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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Thesis -
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Image clustering comparison of two color segmentation techniques
Published 2010“…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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Systematic review on missing data imputation techniques with machine learning algorithms for healthcare
Published 2022“…Many machine learning algorithms have been applied to impute missing data with plausible values. …”
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A Toolkit for Simulation of Desktop Grid Environment
Published 2014“…A simulator for desktop grid environment has been developed using Java as the implementation language due to its wide popularity. …”
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Final Year Project -
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ExtraImpute: a novel machine learning method for missing data imputation
Published 2022“…In this paper, we propose a new imputation approach using Extremely Randomized Trees (Extra Trees) of machine learning ensemble learning methods named (ExtraImpute) to tackle numerical missing values in healthcare context. …”
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New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Missing values lead to the difficulty of extracting useful information from that data set. …”
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A web-based implementation of k-means algorithms
Published 2022“…Firstly, k-luster could incorporate additional clustering algorithms, or even classification algorithms in the future. …”
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Final Year Project / Dissertation / Thesis -
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Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
Published 2020“…This problem inspires the idea to develop a prediction algorithm to predict the missing values in the dataset, where Support vector regression (SVR) has been proposed as a prediction method to predict missing values in several academic types of researches. …”
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Deep-learning-based detection of missing road lane markings using YOLOv5 algorithm
Published 2021“…In this work, preliminary study of the implementation of one of the latest deep learning algorithms, i.e. YOLOv5, has been carried out in the detection and classification of missing road lane markings. …”
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Proceeding Paper -
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Imputation Analysis of Time-Series Data Using a Random Forest Algorithm
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Conference or Workshop Item -
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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform
Published 2009“…The development of this architecture is based on several programming language as it involves algorithm implementation on C, parallelization using Parallel Virtual Machine (PVM) and Java for web services development. …”
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Conference or Workshop Item -
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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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Tangible interaction learning model to enhance learning activity processes among children with dyslexia
Published 2024“…To find optimum variables, Machine Learning approach needs to be utilized. In this research, an imputation approach using Extremely Randomized Trees (Extra Trees) of ensemble machine learning methods named (ImputeX) is proposed. …”
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Thesis -
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Intelligent imputation method for mix data-type missing values to improve data quality
Published 2024“…To find optimum variables, Machine Learning approach needs to be utilized. In this research, an imputation approach using Extremely Randomized Trees (Extra Trees) of ensemble machine learning methods named (ImputeX) is proposed. …”
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Extreme learning machine classification of file clusters for evaluating content-based feature vectors
Published 2018“…Consequently, an Extreme Learning Machine (ELM) neural network algorithm is used to evaluate the performance of the three methods in which it classifies the class label of the feature vectors to JPEG and Non-JPEG images for files in different file formats. …”
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