Search Results - regression ((((bees algorithm) OR (((path algorithm) OR (means algorithm))))) OR (based algorithm))
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Investigating the reliability of machine learning algorithms as an advanced tool for ozone concentration prediction
Published 2023“…The hybrid technique has been developed by using deep learning algorithms with the structure of multiple layers (with several neurons) of CNN and LSTM. …”
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A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm
Published 2024“…The developed framework, grounded in fault-based testing theory, comprises three key components: inputs, prioritization factors, and a prioritization algorithm. …”
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Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score
Published 2021“…A novel methodology was used to compute simple heart risk scores called non-laboratory based heart risk score (NLHRS). The methodology is proposed as stacking ensemble ML and the best ML algorithms are used as a base learner to compute relative feature weights. …”
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Automatic clustering of generalized regression neural network by similarity index based fuzzy c-means clustering
Published 2004“…Experimental results show that the accuracy of the algorithm is quite acceptable. …”
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The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model
Published 2017“…In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. …”
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Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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Comparative study of clustering-based outliers detection methods in circularcircular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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Kernel and multi-class classifiers for multi-floor wlan localisation
Published 2016“…For fingerprint database optimisation, novel access point (AP) selection algorithms which are based on variant AP selection are investigated to improve computational accuracy compared to existing AP selection algorithms such as Max-Mean and InfoGain. …”
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Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms
Published 2021“…Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. …”
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TOPSIS-based Regression Algorithms Evaluation
Published 2022“…The results showed that different preferences led to varying algorithm rankings, but top-ranked algorithms were distinguished using a specific dataset. …”
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Logistic regression methods for classification of imbalanced data sets
Published 2012“…Kernel Logistic Regression Newton-Raphson (KLR-NR) and Regularized Logistic Regression NR (RLR-NR) which are Logistic Regression (LR)based methods. …”
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Hub Angle Control for A Single Link Flexible Manipulator Based on Cuckoo Search Algorithm
Published 2021“…System identification was implemented via swarm intelligence algorithm known as cuckoo search algorithms based on auto regressive with exogenous model structure. …”
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Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm
Published 2023“…Backpropagation; Backpropagation algorithms; Charging (batteries); Electric batteries; Electric vehicles; Errors; Ions; Learning algorithms; Learning systems; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Radial basis function networks; Secondary batteries; Torsional stress; Back propagation neural networks; Backtracking search algorithms; Battery residual capacity; Extreme learning machine; Generalized Regression Neural Network(GRNN); Mean absolute percentage error; Radial basis function neural networks; State of charge; Battery management systems…”
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Energy band gap modeling of doped bismuth ferrite multifunctional material using gravitational search algorithm optimized support vector regression
Published 2021“…The proposed hybrid gravitational search based support vector regression HGS-SVR model was evaluated by its mean squared error (MSE), correlation coefficient (CC), and root mean square error (RMSE). …”
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Modeling and prediction of the specific heat capacity of Al₂O₃/water nanofluids using hybrid genetic algorithm/support vector regression model
Published 2019“…The proposed (genetic algorithm/support vector regression) GA/SVR model was formulated using volume fractions and specific heat capacities of the alumina nanoparticles. …”
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Robust multivariate least angle regression
Published 2017“…The least angle regression selection (LARS) algorithms that use the classical sample means, variances, and correlations between the original variables are very sensitive to the presence of outliers and other contamination. …”
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Real time long range (LoRa) based indoor positioning system using Deep Gaussian Process (DGP) algorithm / Ng Tarng Jian
Published 2025“…Through experimental evaluation and comparison of various machine learning algorithms, including Deep Gaussian Process (DGP) regression, the research demonstrates the effectiveness of DGPs in achieving precise single-point estimation, by keeping the mean absolute error to below 5 meters. …”
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Response surface analysis, clustering, and random forest regression of pressure in suddenly expanded high-speed aerodynamic flows
Published 2020“…Regression of both the pressures using a random forest classification algorithm is carried out. …”
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Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm
Published 2019“…This extension of Autometrics for model selection was also developed for multiple equations by integrating it with seemingly unrelated regressions equations (SURE) and estimated using feasible generalized least squares (FGLS), known as SURE-Autometrics algorithm. …”
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