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A decision tree based on spatial relationships for predicting hotspots in peatlands
Published 2014“…This work applied a spatial decision tree algorithm on spatial data of forest fires. The algorithm is the improvement of the conventional decision tree algorithm in which the distance and topological relationships are included to grow up spatial decision trees. …”
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Laptop price prediction using decision tree algorithm / Nurnazifah Abd Mokti
Published 2024“…This research project focuses on developing a laptop price prediction model using the decision tree algorithm based on laptop specifications. …”
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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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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…Here, we introduce a measure of similarity based on the circular distance and obtain a cluster tree using the single linkage clustering algorithm. …”
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Prospects for basal stem rot disease based on soil apparent electrical conductivity in oil palm plantation
Published 2020“…Furthermore, the software was developed by using MATLAB to produce a predicted BSR map in oil palm plantation based on the developed algorithm. …”
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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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Quantum Processing Framework And Hybrid Algorithms For Routing Problems
Published 2010“…Another quantum algorithm for a minimum weight spanning tree in the graph was also designed. …”
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Determination of tree stem volume : A case study of Cinnamomum
Published 2013“…Modelling of trees has attracted scientific research in various fields and disciplines since trees and forests play very important roles in the global system. …”
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Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination
Published 2025“…In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis
Published 2017“…The predictive model was developed using Induction Decision Tree (ID3) algorithm, a popular data mining technique an exploratory tool for knowledge discovery. …”
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Exploring students' performance in mathematics in Portugal using data analytics techniques: a data science use-case
Published 2024“…The purpose is to identify the key factors influencing academic performance, providing insights for targeted interventions and support systems. Machine learning algorithms, specifically Random Forest Regression and Decision Trees, are utilized to analyze the dataset and determine the most significant factor impacting student performance. …”
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Machine learning models for predicting the compressive strength of concrete with shredded pet bottles and m sand as fine aggregate
Published 2025“…Machine Learning (ML) and Artificial Intelligence (AI) are closely intertwined and represent the latest cutting-edge technologies that facilitate the development of intelligent prototypes. Machine learning is a critical subset of AI that deliberates the development of self-trained algorithms that use previous databases and analysis for result predictions. …”
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Price prediction model of green building based on machine learning algorithms / Nur Syafiqah Jamil
Published 2021“…The experiment involved five (5) common algorithms: Linear Regressor, Decision Tree Regressor, Random Forest Regressor, Ridge Regressor and Lasso Regressor. …”
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Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration
Published 2014“…This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. …”
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Do CEO and chairman characteristics affect green innovation? evidence from a comparative analysis of machine learning models
Published 2024“…Using the extreme gradient boosting (XGBoost) algorithm, which is at the forefront of machine learning algorithms, this study comprehensively examines the impact of CEO and chairman characteristics on corporate green innovation. …”
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Customer behavior analysis based on purchasing history and reviews using automated decision-making systems
Published 2025“…Techniques: Then, the decision-making process is used to analyze the customer’s data using the regression algorithm. Result: This results in the proposed model, the relationship of the many business models, the formation of the purchases that affect consumer needs, and the strategies that can be developed. …”
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A quality of experience approach in smartphone video selection framework for energy efficiency
Published 2021“…The last process is to analyze the relationship of VCS results by comparing the DVAP to other recent video streaming applications available. …”
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A framework for classification software security using common vulnerabilities and exposures
Published 2018“…Formal-based classification framework with notation may increase accuracy and vi-sualization compared with hierarchy-tree only,but the conclusion remains tentative because of methodological limitation in the studies.…”
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Characterization and pathogenicity of Rhizoctonia spp isolated from various crop species in different agroecosystems in Malaysia
Published 2017“…Phylogenetic analysis using different algorithms separated Rhizoctonia spp. to the distinct clades. …”
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