Search Results - (( model evaluation a algorithm ) OR ( gender classification issues algorithm ))

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  1. 1

    Customer analysis with machine vision by Tiong, Wei Jie

    Published 2023
    “…The proposed solution must be able to fulfil the requirements for customer counting, customer recognition and gender classification. This study aimed to improve the human detection model by eliminating the imperfections in existing models that have a high false rate in detecting the cartoons as humans. …”
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    Final Year Project / Dissertation / Thesis
  2. 2

    Analyzing customer reviews for ARBA Travel using sentiment analysis by Abdullah, Nurulain

    Published 2025
    “…Three machine learning algorithms which are Naive Bayes, Logistic Regression, and Support Vector Machine, were implemented and evaluated using cross-validation and performance metrics such as accuracy, precision, recall, and F1- score. …”
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    Student Project
  3. 3

    Enhanced extreme learning machine for general regression and classification tasks by Mahmood, Saif F

    Published 2020
    “…To address this issue, a fast adaptive shrinkage/thresholding algorithm ELM (FASTA-ELM) which uses an extension of forward-backward splitting (FBS) to compute the smallest norm of the output weights in ELM is presented. …”
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    Thesis
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    An improvement of back propagation algorithm using halley third order optimisation method for classification problems by Abdul Hamid, Norhamreeza

    Published 2020
    “…The simulation results show that the highest improvement of H-BFGS in terms of generalisation accuracy is on the Voice Gender classification with 43.33% improvement for 60:40 data division. …”
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    Thesis
  6. 6

    Evaluation of MLP-ANN Training Algorithms for Modeling Soil Pore-Water Pressure Responses to Rainfall by Mustafa, M.R., Rezaur, R.B., Saiedi, Saied, Rahardjo, H., Isa, M.H.

    Published 2013
    “…The performance of the training algorithms was evaluated using standard performance evaluation measures—root mean square error, coefficient of efficiency, and the time and number of epochs required to reach a predefined accuracy. …”
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    Citation Index Journal
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    Neural Networks Ensemble: Evaluation of Aggregation Algorithms for Forecasting by HASSAN, SAIMA

    Published 2013
    “…The aggregation algorithms were employed on the forecasts obtained from all individual NN models as well as on a number of the best forecasts obtained from the best NN models. …”
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    Thesis
  9. 9

    Diagnosis and recommender system for diabetes patient using decision tree / Nurul Aida Mohd Zamary by Mohd Zamary, Nurul Aida

    Published 2024
    “…This project aims to develop a decision-making support model for diabetes diagnosis and treatment recommendation using the decision tree algorithm. …”
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    Thesis
  10. 10

    A Model for Evaluation of Cryptography Algorithm on UUM Portal by Norliana, Abdul Majid

    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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    Thesis
  11. 11

    Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting by Hassan, S., Khosravi, A., Jaafar, J.

    Published 2013
    “…These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). …”
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    Conference or Workshop Item
  12. 12

    Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting by Hassan, S., Khosravi, A., Jaafar, J.

    Published 2013
    “…These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). …”
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    Conference or Workshop Item
  13. 13

    Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting by Hassan, S., Khosravi, A., Jaafar, J.

    Published 2013
    “…These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). …”
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    Conference or Workshop Item
  14. 14

    Unifying the evaluation criteria of many objectives optimization using fuzzy Delphi method by Mohammed, Rawia Tahrir, Yaakob, Razali, Mohd Sharef, Nurfadhlina, Abdullah, Rusli

    Published 2021
    “…This study proposed a distinct unifying model for the MaOO evaluation criteria using the fuzzy Delphi method. …”
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    Article
  15. 15

    Evaluation of different peak models of eye blink EEG for signal peak detection using artificial neural network by Adam, A., Ibrahim, Z., Mokhtar, N., Shapiai, M.I., Mubin, M.

    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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    Article
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    Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market by Hazirah Halul

    Published 2024
    “…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. …”
    thesis::master thesis
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    Multistep forecasting for highly volatile data using new algorithm of Box-Jenkins and GARCH by Siti Roslindar, Yaziz, Roslinazairimah, Zakaria

    Published 2018
    “…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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    Conference or Workshop Item
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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

    Published 2010
    “…The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. …”
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    Article