Search Results - (( model evaluation model algorithm ) OR ( based education case algorithm ))
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Modeling total quality management in higher education with case-based reasoning
Published 2023Subjects: “…Case-based reasoning (CBR)…”
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Optimum Feeder Routing and Distribution Substation Placement and Sizing using PSO and MST
Published 2014“…The proposed algorithm has been evaluated on the distribution network case with 500 consumers which are consisting of residential and commercial loads. …”
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Cognitive knowledge-based model for adaptive feedback: A case in physics / Andrew Thomas Bimba
Published 2019“…This form of representation is achieved through the knowledge elicitation, knowledge bonding, and adaptive feedback algorithms. The model is evaluated based on the feedback it suggests to students while solving Physics problems. …”
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Predictive modelling of student academic performance using machine learning approaches : a case study in universiti islam pahang sultan ahmad shah
Published 2024“…Particularly, the decision tree is identified as the most accurate predictive model, having a 0.60 accuracy value. The model also has the highest value for recall and F1-score compared to other models. …”
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Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad
Published 2023“…The system’s functionality based on the use case and usability tested by the Human Resources expert has been tested to evaluate its system requirements. …”
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Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad, Khyrina Airin Fariza Abu Samah and Nuwairah Aimi Ahmad...
Published 2023“…The system’s functionality based on the use case and usability tested by the Human Resources expert has been tested to evaluate its system requirements. …”
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Technical efficiency performance of Malaysian public research universities: Fuzzy data envelopment analysis / Saber Abdelall Mohamed Ahmed
Published 2023“…By utilizing Fuzzy DEA (FDEA) model with algorithm, this study observes the PRUMs efficiency status during the study period and the expected average efficiency for the next 2021/2022 academic year. …”
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A new fuzzy criterion-referenced assessment with a fuzzy rule selection technique and a monotonicity-preserving similarity reasoning scheme
Published 2013“…Besides, this paper also contributes to a new application of SR in education assessment. The proposed approach is evaluated with a case study relating to a lab assessment in Universiti Malaysia Sarawak (UNIMAS).…”
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Evaluation of MLP-ANN Training Algorithms for Modeling Soil Pore-Water Pressure Responses to Rainfall
Published 2013“…The performance of four artificial neural network (ANN) training algorithms was evaluated to identify the training algorithm appropriate for modeling the dynamics of soil pore-water pressure responses to rainfall patterns using multilayer perceptron (MLP) ANN. …”
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Diagnosis and recommender system for diabetes patient using decision tree / Nurul Aida Mohd Zamary
Published 2024“…To evaluate the model, the model accuracy, precision, recall, F1- score, and confusion matrix were used. …”
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Neural Networks Ensemble: Evaluation of Aggregation Algorithms for Forecasting
Published 2013“…The outputs from the individual NN models were combined by four different aggregation algorithms in NNs ensemble. …”
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A Model for Evaluation of Cryptography Algorithm on UUM Portal
Published 2004“…The purpose of this project are to construct and provide guidelines to develop a simulation model to evaluate cryptography algorithm in terms of encryption speed and descryption speed on UUM portal. …”
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Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market
Published 2024“…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. …”
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Evaluation of different peak models of eye blink EEG for signal peak detection using artificial neural network
Published 2016“…This study evaluates the performance of eye blink EEG signal peak detection algorithm for four different peak models which are Dumpala's, Acir's, Liu's, and Dingle's peak models. …”
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Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network
Published 2016“…This study evaluates the performance of eye blink EEG signal peak detection algorithm for four different peak models which are Dumpala’s, Acir’s, Liu’s, and Dingle’s peak models. …”
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Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH
Published 2018“…The study of the multistep ahead forecast is significant for practical application purposes using the proposed statistical model. This study is proposing a new algorithm of Box-Jenkins and GARCH (or BJ-G) in evaluating the multistep forecasting performance of the BJ-G model for 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
Published 2010“…The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. …”
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Algorithmic approaches in model selection of the air passengers flows data
Published 2015“…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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