Search Results - (( model evaluation ((a algorithm) OR (_ algorithm)) ) OR ( model application based algorithm ))

Real time nonlinear filtered-x lms algorithm for active noise control by Sahib, Mouayad Abdulredha

“…The NLFXLMS algorithm is a stochastic gradient algorithm that incorporates the derivative of a nonlinear plant model which is represented by the scaled error function (SEF) in the controller design. …”
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Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH by Siti Roslindar, Yaziz, Roslinazairimah, Zakaria

“…Based on the empirical results, the proposed algorithm of multistep ahead forecast to the algorithm of BJ-G provides a promising procedure to assess the performance of the BJ-G model in forecasting a highly volatile time series data. …”
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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition by Yahya, Anwar Ali, Mahmod, Ramlan, Ramli, Abd Rahman

“…The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

“…This thesis describes original research in the field of software quality model by presenting a Feature Ranking Algorithm (FRA) for Pragmatic Quality Factor (PQF) model. …”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

“…Finally, a GA based BPNN called (GA-BPNN) is designed and evaluated. …”
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Laptop price prediction using decision tree algorithm / Nurnazifah Abd Mokti by Abd Mokti, Nurnazifah

“…This research project focuses on developing a laptop price prediction model using the decision tree algorithm based on laptop specifications. …”
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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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Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost by Chen, Suihai, Bong, Chih How, Chiu, Po Chan

“…To a certain extent, it avoids the inefficiency of the Gray Wolf algorithm, balances the ability of local search and global development, and improves the accuracy of the model. …”
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An adaptive ant colony optimization algorithm for rule-based classification by Al-Behadili, Hayder Naser Khraibet

“…Differing from other complex and difficult classification models, rules-based classification algorithms produce models which are understandable for users. …”
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Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications by Sabry, Ahmad H., Wan Hasan, Wan Zuha, Ab Kadir, M. Zainal A., Mohd Radzi, Mohd Amran, Shafie, Suhaidi

“…The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. …”
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Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market by Hazirah Halul

“…Previous studies usually concentrate on the forecasting stock index or selecting a few stocks with restricted features. Therefore, this study focused to contribute on evaluating different algorithm models such as traditional ML and deep learning models with big stock data of multiple parameters from selected companies in Bursa Malaysia. …”

Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters by Yousri D., Thanikanti S.B., Allam D., Ramachandaramurthy V.K., Eteiba M.B.

Workflow optimization in distributed computing environment for stream-based data processing model / Saima Gulzar Ahmad by Saima Gulzar, Ahmad

“…The workflows derived from real world applications include Montage and Cybershake. Synthesized workflows were generated with different sizes, shapes and densities to evaluate the proposed algorithms. …”
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The compact genetic algorithm for likelihood estimator of first order moving average model by Al-Dabbagh, R.D., Baba, M.S., Mekhilef, Saad, Kinsheel, A.

“…One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. …”
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Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm by Nukman, Y., Hassan, M.A., Harizam, M.Z.

“…To overcome such problem, a hybrid genetic algorithm-based Taguchi ANN (GA-Taguchi ANN) has been developed. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

“…Then, this study aims to optimize the hyperparameters of the developed DNN model using the Arithmetic Optimization Algorithm (AOA) and, lastly, to evaluate the performance of the newly proposed deep learning model with Simulated Kalman Filter (SKF) algorithm in solving image encryption application. …”
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An artificial immune system model as talent performance predictor / Siti ‘Aisyah Sa’dan, Hamidah Jantan and Mohd Hanapi Abdul Latif by Sa’dan, Siti ‘Aisyah, Jantan, Hamidah, Abdul Latif, Mohd Hanapi

“…The objective of this study is to propose a prediction model based on bio-inspired algorithm for talent knowledge discovery through some experiments. …”
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Performance Analysis of ARMA based Magnetic Resonance Imaging (MRI) Reconstruction Algorithm by Salami, Momoh Jimoh Emiyoka, Najeeb, Athaur Rahman

“…Future work include extending this modelling method to two dimensional domain, evaluating the performance of the proposed CVNN-CARMA and using a trained artificial neural network to automatically obtain the model order of a complex valued data. …”
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Dingle's Model-based EEG Peak Detection using a Rule-based Classifier by Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Zuwairie, Ibrahim, Mohd Ibrahim, Shapiai

“…In this study, the performances of four different peak models of time domain approach which are Dumpala's, Acir's, Liu's, and Dingle's peak models are evaluated for electroencephalogram (EEG) signal peak detection algorithm. …”
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