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Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms by Abdellatief M., Wong L.S., Din N.M., Mo K.H., Ahmed A.N., El-Shafie A.

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Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition by Al Jawarneh, Abdullah Suleiman Saleh

“…These components have been used in several studies as new predictor variables to predict the behaviour of the response variable. …”
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Characteristic wavelength optimization for partial least squares regression using improved flower pollination algorithm by Pauline Ong, Pauline Ong, Jinbao Jian, Jinbao Jian, Jianghua Yin, Jianghua Yin, Guodong Ma, Guodong Ma

“…This study proposes a new wavelength selection method, interval flower pollination algorithm (iFPA), for spectral variable selection in the partial least squares regression (PLSR) model. …”
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Penalized Quantile Regression Methods And Empirical Mode Decomposition For Improving The Accuracy Of The Model Selection by Ambark, Ali Saleh Al-Massri

“…Moreover, selecting the relevant variables when fitting the regression model is critical. …”
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Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear reg... by Balogun, A.-L., Tella, A.

“…The random forest outperformed other algorithms with a very high R2 of 0.970, low RMSE of 2.737 and MAE of 1.824, followed by linear regression, support vector regression and decision tree regression, respectively. …”
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Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear reg... by Balogun, A.-L., Tella, A.

“…The random forest outperformed other algorithms with a very high R2 of 0.970, low RMSE of 2.737 and MAE of 1.824, followed by linear regression, support vector regression and decision tree regression, respectively. …”
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The use of Cox regression and genetic algorithm (CoRGA) for identifying risk factors for mortality in older people by Ahmad, Rabiah, Bath, Peter A

“…This article describes ongoing research to overcome these limitations through the CoRGA program, which combines Cox regression with a genetic algorithm for the variable selection process. …”
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Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms by Ziyad Sami B.H., Ziyad Sami B.F., Kumar P., Ahmed A.N., Amieghemen G.E., Sherif M.M., El-Shafie A.

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Weather prediction in Kota Kinabalu using linear regressions with multiple variables by Teong, Khan Vun, Chung, Gwo Chin, Jedol Dayou

“…Numerical weather prediction is the process of using existing numerical data on weather conditions to forecast the weather using machine learning algorithms. This study employs machine learning algorithms, a linear regression model using statistics, and two optimization approaches, the normal equation approach, and gradient descent approach to predict the weather based on a few variables. …”
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Bayesian logistic regression model on risk factors of type 2 diabetes mellitus by Chiaka, Emenyonu Sandra

“…Logistic regression model has long been known and it is commonly used in analysing a binary outcome or dependent variable and connects the binary dependent variable to several independent variables. …”
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Comparing three methods of handling multicollinearity using simulation approach by Adnan, Norliza

“…In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. …”
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A hybrid genetic algorithm and linear regression for prediction of NOx emission in power generation plant by Bunyamin M.A., Yap K.S., Aziz N.L.A.A., Tiong S.K., Wong S.Y., Kamal M.F.

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Demand analysis of flood insurance by using logistic regression model and genetic algorithm by Sidi, P., Mamat, M.B., Sukono, ., Supian, S., Putra, A.S.

“…The analysis was done by using logistic regression model, and to estimate model parameters, it is done with genetic algorithm. …”
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Comparison of Conjugate Gradient methods for developing the multiple linear regression model for rubber yield in Malaysia / Anis Shahida Rohimi Ozeman by Rohimi Ozeman, Anis Shahida

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Multiple Linear Regression Models For Estimating True Subsurface Resistivity From Apparent Resistivity Measurements by Bala, Muhammad Sabiu

“…The parameters investigated are apparent resistivity ( a  ), horizontal location (x) and depth (z) as independent variable; while true resistivity ( t  ) is dependent variable.…”
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Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models by Khairudin K., Ul-Saufie A.Z., Senin S.F., Zainudin Z., Rashid A.M., Abu Bakar N.F., Anas Abd Wahid M.Z., Azha S.F., Abd-Wahab F., Wang L., Sahar F.N., Osman M.S.

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A Comparative Study On Some Methods For Handling Multicollinearity Problems by Adnan, Norliza, Ahmad, Maizah Hura, Adnan, Robiah

“…In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. …”
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A comparative study on some methods for handling multicollinearity problems by Adnan, Norliza, Ahmad, Maizah Hura, Adnan, Robiah

“…In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. …”
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Linear regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography / Yong Yan Yin by Yong, Yan Ling

“…In addition, an inter-observer variability test was performed and has shown that the proposed algorithm has comparable variability against manual luminal area estimations by expert human observers. …”
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Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach by Bahadar A., Kanthasamy R., Sait H.H., Zwawi M., Algarni M., Ayodele B.V., Cheng C.K., Wei L.J.