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Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…One of the most powerful machine learning methods to handle classification problems is the decision tree. There are various decision tree algorithms, but the most commonly used are Iterative Dichotomiser 3 (ID3), CART, and C4.5. …”
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Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition
Published 2021“…FID3 algorithm combined the fuzzy system and decision tree techniques with ID3 algorithm as the decision tree learning. …”
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Decision Tree: Customer churn analysis for a loyalty program using data mining algorithm
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Intersection Features For Android Botnet Classification
Published 2019“…The Chi Square was used to select the most significant permissions, then the classification algorithms like Naïve Bayes and Decision Tree were used to classify the Android apps as botnet or benign apps. …”
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Rule extraction from multi-layer perceptron neural network using decision tree for currency exchange rates forecasting
Published 2015“…Thus, the aim of this study was to extract valuable information (rule) from trained multi-layer perceptron (MLP) neural networks using decision tree. The main process in extracting rules from MLP using decision tree for currency exchange rate forecasting can be divided into two stages. …”
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Delineating mangrove forest zone using spectral reflectance
Published 2020“…Species identification with spectral library derived from in-situ measurements using SID algorithm and derived from Landsat 8 using SAM algorithm was done. …”
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Optimizing B-tree search performance of big data sets / Mohsen Marjani
Published 2017“…Every time search process is running against indexed data using B-tree technique, the process traverses all left child nodes of a node to find lowers values or traverses the right side child nodes for finding bigger values. …”
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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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Improvement of horizontal streak on disparity map thru parameter optimization for stereo vision algorithm
Published 2024“…Next, the cost aggregation uses minimum spanning tree (MST) segmentation, which equipped with edge preserving properties and noise filtering. …”
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An Illustration of Generalised ARMA (GARMA) Time Series Modelling of Forest Area in Malaysia.
Published 2012“…The estimation of the model was done using Hannan-Rissanen Algorithm, Whittle's Estimation and Maximum Likelihood Estimation. …”
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The impact of fuzzy discretization�s output on classification accuracy of random forest classifier
Published 2020“…Random Forest is known as among the widely used classification algorithms by researchers and machine learning enthusiast in solving classification problems. …”
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Developing an app for streamlined inventory tracking with barcode scanning and load planning optimization
Published 2025“…The application was implemented using React Native for mobile development and Firebase Firestore as the backend database to enable real-time data synchronization, while a binary tree bin packing algorithm was applied to generate efficient cargo loading arrangements. …”
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Investigating photovoltaic solar power output forecasting using machine learning algorithms
Published 2023Article -
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Car dealership web application
Published 2022“…Tree SHAP, which was implemented by third-party SHAP Python library, were used in model monitoring and made the models interpretable. …”
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Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.]
Published 2021“…This paper provides an empirical study report, that building price predictions are based on green building and other general determinants. This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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Power lines vegetation enchroachment monitoring based on satellite stereo images using stereo matching
Published 2023“…In this paper,a new method based estimating 3D height/depth of vegetation or trees near transmission lines using satellite stereo vision is proposed. …”
Conference Paper -
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An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting
Published 2024“…This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. …”
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Artificial intelligence for spectral classification to identify the basal stem rot disease in oil palm using dielectric spectroscopy measurements
Published 2021“…This study investigated the feasibility of applying genetic algorithm (GA) as a feature selection algorithm to select the most significant frequencies of dielectric spectral data for identifying BSR disease in oil palms. …”
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