Search Results - (( weight distribution svm algorithm ) OR ( parameter estimation max algorithm ))

Improved Location And Positioning Utilizing Single MIMO Base Station In IMT-Advanced System by Azmi, Awang Md Isa, Mustafa H., Othman, Zahariah, Manap, Mohd Sa'ari, Mohamad Isa, Mohd Shahril Izuan, Mohd Zin, Mohd Shakir, Md Saat, Zahriladha, Zakaria

“…This algorithm based on the angle of arrival (AOA) and angle of departure (AOD) measurement parameter completed the new SMBS algorithm with virtual base station (SMVirBS). …”
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Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

“…These results can be seen as further explanation on the success of Truncated Newton method in TR-KLR and TR Iteratively Re-weighted Least Square (TR-IRLS) algorithm respectively, because of the equivalence of iterative method used by these algorithms. …”
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Kernel and multi-class classifiers for multi-floor wlan localisation by Abd Rahman, Mohd Amiruddin

“…The multi-class classification strategy is used to ensure quick estimation of the multi-class NN algorithms. All of the algorithms are later combined to provide device location estimation for multi-floor environment. …”
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Classification of imbalanced travel mode choice to work data using adjustable svm model by Qian, Y., Aghaabbasi, M., Ali, M., Alqurashi, M., Salah, B., Zainol, R., Moeinaddini, M., Hussein, E.E.

“…For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution. …”
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Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Ahmad Salihin, Samsudin, Amir Izzani, Mohamed, Mohd Mawardi, Saari

“…This study presents a hybrid approach combining the CatBoost algorithm with metaheuristic optimization techniques to enhance SoC estimation accuracy and robustness. …”
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Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection by Alsmadi, Issa Mohammad Ibrahim

“…In the second stage, grey wolf optimization (GWO) algorithm, a new heuristic search algorithm, uses the SVM accuracy as a fitness function to find the optimal subset feature.…”
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Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach by Marpaung, Faridawaty, Ramadhani, Fanny, Dinata, Dewan

“…The best model was created using the grid search cross-validation, while the best prediction results were created using the RF algorithm, with the following parameters: n-estimator = 50, max depth = 10, min samples split = 2, and min samples leaf = 1. …”
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New correlation for oil formation volume factor by Sulaimon, A.A., Ramli, N., Adeyemi, B.J., Saaid, I.M.

“…It is a family of inductive algorithms which executes computer-based mathematical modeling of multi-parametric data sets. …”
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Comparative analysis of spatio/spectro-temporal data modelling techniques by Abdullah, Mohd Hafizul Afifi, Othman, Muhaini, Kasim, Shahreen

“…Section 2 overviews two different inference-based techniques for SSTD modelling which includes global modelling, local modelling, and personalized modelling; and data modelling for SSTD classifier including, support vector machines (SVM), Evolving Classification Function (ECF), k-Nearest Neighbor (kNN), weighted k-Nearest Neighbor (wkNN), and weighted-weighted k-Nearest Neighbor (wwkNN). …”
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Optimization of hydropower reservoir system using genetic algorithm for various climatic scenarios by Tayebiyan, Aida

“…The first step, ANN was calibrated and validated by using daily observed evapotranspiration, rainfall, and stream flow (2003-2012). In order to estimate daily evapotranspiration, daily observed Min and Max temperature was used in the estimation based on Hargreaves-Samani equation. …”
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Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration by Chia, Min Yan

“…The Bayesian model averaging (BMA) enhanced the estimation of the ensembles of the base MLP, SVM and ANFIS. This was done through the Bayesian weight assignments to combine the favourable traits of the individual models. …”
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Development of a hybrid machine learning model for rockfall source and hazard assessment using laser scanning data and GIS by Fanos, Ali Mutar

“…Different machine learning algorithms (Artificial Neural Network [ANN], K Nearest Neighbor [KNN] and Support Vector Machine [SVM]) were tested individually and with various ensemble models (bagging, voting, and boosting) to detect the probability of the landslide and rockfall occurrences. …”
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