Search Results - (( variable step selection algorithm ) OR ( java adaptation optimization algorithm ))

Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

“…Meanwhile, practitioners who are usually non-experts and lack of statistical knowledge will face difficulties during the modelling process. Hence, algorithm with a step by step guidance is beneficial in model building, testing and selection. …”
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Algorithmic approaches in model selection of the air passengers flows data by Ismail, Suzilah, Yusof, Norhayati, Tuan Muda, Tuan Zalizam

“…Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed to the existence of a huge variability in manual modelling process especially between expert and non-expert modellers.Many algorithms (automated model selection) have been developed to bridge the gap either through single or multiple equation modelling.This study aims to evaluate the forecasting performances of several selected algorithms on air passengers flow data based on Root Mean Square Error (RMSE) and Geometric Root Mean Square Error (GRMSE).The findings show that multiple models selection performed well in one and two step-ahead forecast but was outperformed by single model in three step-ahead forecasts.…”
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Variable order step size algorithm for solving second order ODEs by Rasedee, Ahmad Fadly Nurullah, Abdul Sathar, Mohamad Hassan, Wong, Tze Ji, Koo, Lee Feng

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Robust correlation feature selection based support vector machine approach for high dimensional datasets by Baba, Ishaq Abdullahi, Mohammed, Mohammed Bappah, Jillahi, Kamal Bakari, Umar, Aliyu, Hendi, Hasan Talib

“…The second step utilizes the estimates of weights from the first step to select the most important variables for the model. …”
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Diagonal R-point variable step variable order block method for solving second order ordinary differential equations by Zainuddin, Nooraini

“…The detailed algorithms on the selection of step sizes and orders are discussed. …”
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Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification by Abd Samad, Md Fahmi

“…Model structure selection is one of the important steps in a system identification process. …”
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Variable order variable stepsize algorithm for solving nonlinear Duffing oscillator by Rasedee, AFN, Ishak, N, Hamzah, SR, Ijam, HM, Suleiman, M, Ibrahim, ZB, Sathar, MHA, Ramli, NA, Kamaruddin, NS

Variable order variable stepsize algorithm for solving nonlinear Duffing oscillator by Rasedee, Ahmad Fadly Nurullah, Ishak, Norizarina, Hamzah, Siti Raihana, Ijam, Hazizah Mohd, Suleiman, Mohamed, Ibrahim, Zarina Bibi, Sathar, Mohammad Hasan Abdul, Ramli, Nur Ainna, Kamaruddin, Nur Shuhada

“…Numerical results have demonstrated the advantages of a variable order stepsize algorithm over conventional methods in terms of total steps and accuracy.…”
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Variable step variable order block backward differentiation formulae for solving stiff ordinary differential equations by Mohd Yatim, Siti Ainor

“…Block Backward Differentiation Formulae (BBDF) method with variable step variable order approach (VSVO) for solving stiff Ordinary Differential Equations (ODEs) is described in this thesis. …”
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Variable order variable stepsize algorithm for solving nonlinear Duffing oscillator by Nurullah Rasedee A.F., Ishak N., Hamzah S.R., Ijam H.M., Suleiman M., Ibrahim Z.B., Abdul Sathar M.H., Ramli N.A., Kamaruddin N.S.

“…Numerical results have demonstrated the advantages of a variable order stepsize algorithm over conventional methods in terms of total steps and accuracy. � Published under licence by IOP Publishing Ltd.…”

Deterministic Mutation Algorithm As A Winner Over Forward Selection Procedure by Md Fahmi, Abd Samad

“…System identification is a field of study involving the derivation of a mathematical model to explain the dynamical behaviour of a system. One of the steps in system identification is model structure selection which involves the selection of variables and terms of a model. …”
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Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study by Abu-Samah A., Abdul Razak N.N., Mohamad Suhaimi F., Jamaludin U.K., Chase G.

Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting by Afan H.A., Allawi M.F., El-Shafie A., Yaseen Z.M., Ahmed A.N., Malek M.A., Koting S.B., Salih S.Q., Mohtar W.H.M.W., Lai S.H., Sefelnasr A., Sherif M., El-Shafie 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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Block backward differentiation alpha-formulas for solving stiff ordinary differential equations by Mohd Zawawi, Iskandar Shah

“…Furthermore, the BBDF- is formulated using variable step size scheme for solving second order stiff ODEs directly. …”
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Optimized differential evolution algorithm for linear frequency modulation radar signal denoising by Al-Dabbagh, Mohanad Dawood Hasan

“…We propose an optimized crossover scheme that changes the crossover operation from being fixed-length to random-length, which has been designed to fit for the proposed variable length DE. We refer to the new DE algorithm as random variable length crossover DE (rvlx- DE) algorithm. …”
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Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms by Rachmad, Iqbal, Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Tata, Sutabri

“…A critical step in developing such intelligent systems is the feature extraction process. …”
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Optimal timber transportation planning in tropical hill forest using bees algorithm by Jamaluddin, Jamhuri

“…To limit the searching space from timbers to the first exit; landing, a geographically weighted regression (GWR) was used to select the candidate landings. The model finds the final destination from the landings attributed to the cumulative timber volume with the same steps. …”
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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

“…In addition, BRF ensures that important variables are selected at each subset selection step, thus reducing false signals and eventually improving accuracy of models. …”
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