Search Results - (( regression ((models algorithm) OR (bees algorithm)) ) OR ( regression modified algorithm ))

A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm by Ba-Quttayyan, Bakr Salim Abdullah

“…Regression test case prioritisation (TCP) is used to revalidate modified software, ensuring its quality before release on the digital market. …”
Get full text
Get full text
Get full text
Get full text

Statistical modeling via bootstrapping and weighted techniques based on variances by Wan Ahmad, Wan Muhamad Amir, Aleng, Nor Azlida, Ali, Z, Mohd Ibrahim, Mohamad Shafiq

“…This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. …”
Get full text
Get full text
Get full text

Comparison between fuzzy bootstrap weighted multiple linear regression and multiple linear regression: a case study for oral cancer modelling by Mohd Ibrahim, Mohamad Shafiq, Wan Ahmad, Wan Muhamad Amir, Hasan, Ruhaya, Harun, Masitah Hayati

“…(MLR) is the most common type of linear regression analysis. Current technology advancement and increasing of development of the new or modified methodology building leads to the development of an alternative method for multiple linear regression model calculation. …”
Get full text
Get full text

Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms by Hossain S.K.S., Ali S.S., Rushd S., Ayodele B.V., Cheng C.K.

Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives by Jegatheesan N., Ibrahim M.R., Ahmed A.N., Koting S., El-Shafie A., Katman H.Y.B.

Subjects: “…Regression analysis…”

The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction by Adnan R.M., Kisi O., Mostafa R.R., Ahmed A.N., El-Shafie A.

“…Balancing; Forecasting; Stream flow; Support vector machines; Exploitation and explorations; Machine learning models; Optimisations; Optimization algorithms; Prediction modelling; Simulated annealing integrated with mayfly optimization; Streamflow prediction; Support vector regression models; Support vector regressions; Support vectors machine; Simulated annealing; algorithm; mayfly; optimization; prediction; streamflow; support vector machine; Jhelum River…”

Stochastic And Modified Sequent Peak Algorithm For Reservoir Planning Analysis Considering Performance Indices by Oskoui, Issa Saket

“…Subsequently, Auto-regressive lag one, AR(1), coupled with Valencia-Schaake (V-S) disaggregation model are applied to generate synthetic streamflow data. …”
Get full text
Get full text

Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm by Basri Badyalina, Nurkhairany Amyra Mokhtar, Nur Amalina Mat Jan, Muhammad Fadhil Marsani, Mohamad Faizal Ramli, Muhammad Majid, Fatin Farazh Ya'acob

“…It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. …”
Get full text
Get full text
Get full text

Modification of the CREAMS Nutrient submodel by Saleh, Abdul Razak

“…The CREAMS nutrient submodel was modified to improve the prediction, of the nitrogen loss from a flat agricultural field with a fluctuating water table.The CREAMS nutrient submodal was modified by incorporating a water function in the CREAMS denitrification algorithm.The capability of the CREAMS nutrient submodel and modified CREAMS nutrient submodel in predicting nitrogen loss was evaluated by using linear regression analysis, t-test on the slope and intercept of the regression equation, standard deviation of differences, absolute average differences, and percent error.Observed data from an experimental plot near Baton Rouge, Louisiana, USA were used in this study.The modified model underestimated the total nitrogen losses by 2% compared to 35% overestimation by the CREAMS model.Overall performance of the modified model in predicting nitrogen losses was satisfactory.…”
Get full text
Get full text

Modified zero inflated poisson regression analysis and its application to public health data by Wan Ahmad, Wan Muhamad Amir, Zafakali, Nur Syabiha, Aleng, Nor Azlida, Mohd Ibrahim, Mohamad Shafiq, Hasan, Ruhaya, Mokhtar, Kasypi

“…This paper focuses on the programming of zero inflated Poisson regression (ZIPR) with combination of fuzzy regression method through SAS algorithm. …”
Get full text
Get full text
Get full text

Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Jamaluddin, Hishamuddin, Abd. Samad, M. F., Ahmad, Robiah, Yaacob, M. S.

“…One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. …”
Get full text
Get full text

Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Jamaluddin, H., Samad, M. F. A., Ahmad, R., Yaacob, M. S.

“…One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. …”
Get full text
Get full text

Standard equations for predicting the discharge coefficient of a modified high-performance side weir by Zaji, Amir Hossein, Bonakdari, Hossein, Shamshirband, Shahaboddin

“…Four different forms of the equations and two non-dimensional input combinations were used to develop the most appropriate model. The results obtained by our simple standard equations optimized by the PSO algorithm were compared with those of complex nonlinear regression equations, and our equations were more accurate in modeling the discharge coefficient. …”
Get full text
Get full text

Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data by Joshua, Ibidoja Olayemi

“…For the modified model, LASSO with M Bi square estimator showed that better significant results were obtained with 1.31% outliers. …”
Get full text
Get full text

Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Abd Samad, Md Fahmi

“…One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. …”
Get full text
Get full text
Get full text
Get full text

Structural Equation Modeling Algorithm and Its Application in Business Analytics by Sorooshian, Shahryar

“…Structural Equation Modeling (SEM) is a statistical-based multivariate modeling methods, Application of SEM is similar but more powerful than regression analysis; and number of scientists using SEM in their research is rupidly inereasing. …”
Get full text
Get full text
Get full text

Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior by Ahmed, Al Omari Mohammed

“…We then incorporate covariates into the Weibull model. Under this regression model with regards to Bayesian, the usual method was not possible. …”
Get full text
Get full text

A hybrid ART-GRNN online learning neural network with a ?-insensitive loss function by Yap K.S., Lim C.P., Abidin I.Z.

Subjects:

An Analytical Algorithm for Delphi Method for Consensus Building and Organizational Productivity by Abd Hamid, Zahidy, Noor Azlinna, Azizan, Sorooshian, Shahryar

“…Structural Equation Modeling (SEM) is a statistical-based multivariate modeling methods, Application of SEM is similar but more powerful than regression analysis; and number of scientists using SEM in their research is rupidly inereasing. …”
Get full text
Get full text
Get full text

Robust techniques for linear regression with multicollinearity and outliers by Mohammed, Mohammed Abdulhussein

“…The ordinary least squares (OLS) method is the most commonly used method in multiple linear regression model due to its optimal properties and ease of computation. …”
Get full text
Get full text