Search Results - (( variable affecting _ algorithm ) OR ( java interactive learning algorithm ))

Teaching and learning via chatbots with immersive and machine learning capabilities by Nantha Kumar Subramaniam

“…These chatbots support learning of Java via problem-solving steps through “learning by doing”. …”
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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

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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

“…By selecting the appropriate restrictions, the VOS algorithm provides a cost efficient computational code without affecting its accuracy. …”
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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

Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. …”
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Mixed variable ant colony optimization technique for feature subset selection and model selection by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This paper presents the integration of Mixed Variable Ant Colony Optimization and Support Vector Machine (SVM) to enhance the performance of SVM through simultaneously tuning its parameters and selecting a small number of features.The process of selecting a suitable feature subset and optimizing SVM parameters must occur simultaneously,because these processes affect each ot her which in turn will affect the SVM performance.Thus producing unacceptable classification accuracy.Five datasets from UCI were used to evaluate the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with the small size of features subset.…”
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Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data by Baba, Ishaq Abdullahi

“…However, it cannot be applied when the sample size is less than the number of predictor variables. In addressing this problem, some robust procedures for high dimensional dataset via the RFCH algorithm are developed. …”
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Comparing seabed roughness result from QPS fledermaus software, benthic trrain modeler [BTM] and developed model derived FRM slope variability algorithm for hard coral reef detecti... by Mohd Sayud, Nur Asikin

“…In this study, several models has been created which are from QPS Fledermaus model, BTM model and Slope Variability model. Slope variability model is an algorithm that is being used for detecting terrain roughness. …”
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Research On Photovoltaic Mppt Control Method Based On Improved Bas Algorithm And Variable Step-Size Inc Method by Li, Wenli

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Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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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.

“…By selecting the appropriate restrictions, the VOS algorithm provides a cost efficient computational code without affecting its accuracy. …”

Assessing the simulation performances of multiple model selection algorithm by Yusof, Norhayati, Ismail, Suzilah, Tuan Muda, Tuan Zalizam

“…The capability of the algorithm in finding the true specification of multiple models is measured by the percentage of simulation outcomes.Overall results show that the algorithm has performed well for a model with two equations.The findings also indicated that the number of variables in the true models affect the algorithm performances. …”
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A bayesian network approach to identify factors affecting learning of Additional Mathematics by Ong, Hong Choon, Kumarenthiran A/L Chandrasekaran

“…Constraint-based algorithms and score-based algorithms are used to generate the networks into several categories to compare and identify the strong relationships among the factors that affect the students’ learning of the subject. …”
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Comparison of developed FLC and P&O MPPT algorithms for improving PV system performance at variable irradiance conditions by Alhamdawee, Ehsan Mohsin, Mailah, Nashiren Farzilah, Mohd Radzi, Mohd Amran, Shafie, Suhaidi, Hajighorbani , Shahrooz, Turki, Ahmed Qasim

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Test of Self Por [decoded] trait no. 2 / Syafiq Abdul Samat by Abdul Samat, Syafiq

“…Inspired by some of the generative art made via programming, I was able to create my own algorithmic concept, using these “noises” as factors or variables to affect the creation of the proposed visual. …”
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Integrated ACOR/IACOMV-R-SVM Algorithm by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This process will result in loss of information which affects the classification accuracy. This paper presents two algorithms that can simultaneously tune SVM parameters and select the feature subset. …”
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Minimization of Test Cases and Fault Detection Effectiveness Improvement through Modified Reduction with Selective Redundancy Algorithm by Nikfal, Shima

“…Then the algorithm gathers all the test cases based on the definition occurrence and def-use pair if they cover same definition occurrence of one variable but they don’t cover def-use pair of the same variable. …”
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A Hybrid Adaptive Leadership GWO Optimization with Category Gradient Boosting on Decision Trees Algorithm for Credit Risk Control Classification by Suihai, Chen, Chih How, Bong, Po Chan, Chiu

“…Secondly, an improved CatBoost algorithm (EBGWO-CatBoost) was proposed, which was a combination of improved GWO algorithm (EBGWO) and CatBoost algorithm, and the optimized GWO algorithm was used to offset the defects of CatBoost algorithm in parameter tuning. …”
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Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables by ALAhmad A.K., Verayiah R., Ramasamy A., Marsadek M., Shareef H.

“…The IEEE 118-bus system is adopted as the large-scale testing system to assess the performance of the proposed methodology and the convergence capability of the hybrid algorithm in rejecting the disturbances in the system caused by the existence of 132 different input correlated stochastic variables. …”

To study the multi-objective optimization of EDM using genetic algorithm by Fairuz, Idris

“…Development of EDM process has resulted in significant improvements in operating techniques, productivity and accuracy, which the result of this machining development has helped variability in EDM process. The main purpose of this study is to optimize the parameters used in EDM machining such as non-electrical parameter, electrical parameters, the characteristics of the machining, work piece and the variable parameters that will affect the actual machining performances such as material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). …”
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