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1
State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm
Published 2023Conference Paper -
2
Fuzzy Systems and Bat Algorithm for Exergy Modeling in a Gas Turbine Generator
Published 2011“…The fuzzy models are trained applying locally linear model tree algorithm followed by a meta-heuristic nature inspired algorithm called bat algorithm. …”
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3
Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
Published 2021“…The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. …”
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4
Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm
Published 2024“…Hence, a differential search algorithm (DSA) is employed to search for the optimal values of trees and leaves in the RFR algorithm. …”
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5
Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
Published 2023“…Model 2 of CNN has maximum performance of validation in terms of accuracy, precision, sensitivity and specificity but the calibration model requires more optimization. …”
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6
Modeling of static and dynamic components of bio-nanorobotic systems
Published 2012“…In addition, a graph algorithm based on greedy methods is employed to compute a new set of optimal weighted electronic properties of the fullerenes via computing their Minimum Weight Spanning Trees (MWSTs). …”
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7
Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease
Published 2024“…The optimized LightGBM model was trained and evaluated using metrics such as accuracy, precision, and AUC-ROC on the test set, with cross-validation to ensure robustness and generalizability. …”
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A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
Published 2014“…Then, the inventory was randomly split into two datasets, 70 % was used for training the models, and the remaining 30 % was used for validation purpose. …”
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Enhanced computational methods for detection and interpretation of heart disease based on ensemble learning and autoencoder framework / Abdallah Osama Hamdan Abdellatif
Published 2024“…This approach integrates a conditional variational autoencoder (CVAE) to effectively balance the dataset and a stack predictor (SPFHD) that utilizes tree-based ensemble learning algorithms. The base models' predictions are integrated using a support vector machine, significantly enhancing detection accuracy. …”
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10
Edge assisted crime prediction and evaluation framework for machine learning algorithms
Published 2022“…Criminal risk is predicted using classification models for a particular time interval and place. …”
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Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data
Published 2016“…Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. …”
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Electricity distribution network for low and medium voltages based on evolutionary approach optimization
Published 2015“…This thesis proposes an algorithm to find the optimum distribution substation placement and sizing by utilizing the PSO algorithm and optimum feeder routing using modified Minimum Spanning Tree (MST). …”
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13
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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14
Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well?
Published 2022“…The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. …”
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15
Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization
Published 2022“…Finally, the optimal values are (P = 400 bar, T = 3.38 K 102, Y = 1.064 10ˉ³ mol fraction) using this model.…”
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16
Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…It is organized into three phases: preliminary investigation, implementation and analysis, and validation. 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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17
A Data Mining Approach to Enhancing Birth and Death Registration Processes
Published 2025“…The optimal number of clusters of clusters for birth and death data is determined as three using elbow and silhouette validation methods. …”
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18
Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration
Published 2014“…LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. …”
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19
Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and...
Published 2024“…We employed LightGBM, a gradient-boosting framework for loan approval classification, optimized via Random Search hyperparameter tuning and validated using 10-fold cross-validation. …”
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20
Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well?
Published 2022“…The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. …”
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