Search Results - (( parameter estimation using algorithm ) OR ( (variable OR variables) selection based algorithm ))

Model selection approaches of water quality index data by Kamarudin, Nur Azulia, Ismail, Suzilah

“…Automatic model selection by using algorithm can avoid huge variability in model specification process compared to manual selection.With the employment of algorithm, the right model selected is then also used for forecasting purposes. …”
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Determination of tree stem volume : A case study of Cinnamomum by Noraini Abdullah

“…A modeling approach is developed which focuses on the phases in the model-building procedures, effects of interactions variables on the model, minimizing the effects of multicollinearity on the variables and recommending remedial techniques to overcome them, identification of the significant variables by removing insignificant variables, selecting the best model using the eight selection criteria (8SCs), and finally using the residual analysis to validate the chosen best model. …”
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Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application by Hossain Lipu M.S., Hannan M.A., Hussain A.

Penalized LAD-SCAD estimator based on robust wrapped correlation screening method for high dimensional models by Baba, Ishaq Abdullahi, Midi, Habshah, Leong, Wah June, Ibragimov, Gafurjan I.

“…The SIS method uses the rank correlation screening (RCS) algorithm in the pre-screening step and the traditional Pathwise coordinate descent algorithm for computing the sequence of the regularization parameters in the post screening step for onward model selection. …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake. by Malek, S., Syed Ahmad, S. M., Singh, S. K., Milow, P., Salleh, A.

“…HEA model used parameters selected using genetic algorithm (GA). …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake. by Syed Ahmad Abdul Rahman, Sharifah Mumtazah, Malek, Sorayya, Kashmir Singh, Sarinder Kaur, Milow, Pozi, Salleh, Aishah

“…HEA model used parameters selected using genetic algorithm (GA). …”
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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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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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Properties of selected garma models and their estimation procedures by Ramiah Pillai, Thulasyammal

“…The focus of this study is to investigate the properties specically the variance and autocovariance of the GARMA (p; q; ±1; ±2) models. We also study the estimation of the parameters of these models. Evaluation of the performance of two estimators based on the Hannan-Rissanen Algorithm Estimator (HRA) and the Whittle's Estimator (WE) through a series of simulation studies have been conducted in this thesis. …”
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Air quality measurement using remote sensing and digital images processing techniques / Lim, H. S. … [et al.] by Lim, H. S., Jafri, Mat, Abdullah, M. Z., Ahmad, A.N.

“…The coefficients of the calibrated algorithm were determined and used in estimating the air pollution level. …”
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Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network by Mohd Hamdan, Eleena Yasmeen

“…As a result, 60% training with five hidden nodes demonstrated the best performance with R- value of 0.827 and MSE value of 52.283. The ANN-based models could serve as reliable and useful tools in estimating the WQI of the river.…”
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Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms by Abed M., Imteaz M.A., Ahmed A.N., Huang Y.F.

Neural network based adaptive pid controller for shell-and-tube heat exchanger by Othman, Mohamad Hakimi

“…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
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Neural network based adaptive pid controller for shell-and-tube heat exchanger: article by Othman, Mohamad Hakimi

“…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
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Price prediction model of green building based on machine learning algorithms / Nur Syafiqah Jamil by Jamil, Nur Syafiqah

“…The selection features involved were based on Experiment 1 which included 17 IVs (all features) without excluding the most significant variable for this research.…”
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Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis by Adnani, Seyedeh Atena

“…Subsequent quantitative studies on main effects of parameters governing the reactions based on conversion of fatty acid were conducted by titration analysis. …”
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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

“…To evaluate the performance of the Weibull parameters’ estimator methods, two sets of data are considered, one based on simulated data with different random variable size and the other based on actual data collected from a wind farm in Iran. …”
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Perturbation stochastic model updating of a bolted structure / Mohamad Azam Shah Aziz Shah by Aziz Shah, Mohamad Azam Shah

“…The SMU method based on the improved model was used to predict the variability of the dynamic behaviour of the structure. …”
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Conceptual Design And Dynamical Analysis Of Aerostat System by Mahmood, Khurrum

“…The baseline configuration for the desired mission has been obtained using a design algorithm. The statistical values of the selected design variables that include hull fineness ratio, fin area and fin position of the existing aerostat are used to obtain the baseline configuration. …”
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Forecasting of meteorological drought using ensemble and machine learning models by Pande C.B., Sidek L.M., Varade A.M., Elkhrachy I., Radwan N., Tolche A.D., Elbeltagi A.

“…The developed models have been compared with each other, with the best combination of input variables selected using subset regression models and sensitivity studies. …”