Search Results - (( java application mining algorithm ) OR ( changes classification tree algorithm ))
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Direct approach for mining association rules from structured XML data
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…Algorithmic level based methods however are based on introducing new optimization task to improve the minority class classification rate, without changing the data characteristics. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
Published 2021“…The supervised machine learning algorithm, SVM and Decision Tree are used for the estimation of the mangrove age into young and mature. …”
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Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation
Published 2025“…A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. …”
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Monitoring Trends of Land Use and Land Cover Changes in Rajang River Basin
Published 2020“…Supervised classification with the MaximumLikelihood-Algorithm technique was adopted for monitoring the LULC changes using Geographic Information System (GIS) and ERDAS Imagine tools. …”
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Conference or Workshop Item -
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Footwear quality evaluation using decision tree and logistic regression models
Published 2022“…The analysis showed that Decision Tree with Gini algorithm (three branches) in the first method prevails against the other methods with misclassification rate of 0.1307. …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
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Detection and classification for group moving humans
Published 2007“…However, it is extremely sensitive to dynamic changes like change of illumination. The detected foreground pixels usually contain noise, small movements like tree leaves. …”
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Delineating mangrove forest zone using spectral reflectance
Published 2020“…The use of SID and SAM may provide the most promising classification algorithm for improving mangrove species mapping. …”
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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A comparative assessment between object and pixel-based classification approaches for land-use/land-cover mapping using Spot 5 imagery
Published 2014“…Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. …”
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Analysing the performance of classification algorithms on diseases datasets
Published 2023“…The proposed classification algorithms measure the diseases using the disease datasets which estimates the accurate prediction. …”
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Automated classification of celestial objects using machine learning
Published 2025“…This study performs the classification of stars, galaxies, and quasars from SDSS (Sloan Digital Sky Survey) data using the Random Forest, XGBoost, Decision Tree, Gradient Boosting, Linear SVM, KNN, and Logistic Regression. …”
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Reverse migration prediction model based on machine learning / Azreen Anuar
Published 2024“…For this purpose, three (3) algorithms have been assessed, namely, the Random Forest, Decision Tree, and Gradient Boosted Tree. …”
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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