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  1. 1
    Evaluation of cracks and disintegrations using close-range digital photogrammetry and image processing technique

    Evaluation of cracks and disintegrations using close-range digital photogrammetry and image processing technique by Ling, Tung Chai

    Published 2005
    “…Furthermore, the automated method can improve the objectivity, accuracy, and consistency of distress survey data. …”
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  2. 2
    Automated pavement imaging program (APIP) for pavement cracks classification and quantification – a photogrammetric approach

    Automated pavement imaging program (APIP) for pavement cracks classification and quantification – a photogrammetric approach by Mustaffar, Mushairry, Puan, O. C., Ling, Tung Chai

    Published 2008
    “…Advancement in digital photogrammetric technology creates an opportunity to overcome some problems associated with the manual methods. …”
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  3. 3
    Validation of deep convolutional neural network for age estimation in children using mandibular premolars on digital panoramic dental imaging / Norhasmira Mohammad

    Validation of deep convolutional neural network for age estimation in children using mandibular premolars on digital panoramic dental imaging / Norhasmira Mohammad by Mohammad, Norhasmira

    Published 2022
    “…The semi-automated dental staging system developed in this study is based on the Malay children’s population and uses a brain-inspired learning algorithm termed "deep learning". …”
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  4. 4
    Semiparametric inference procedure for the accelarated failure time model with interval-censored data

    Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

    Published 2019
    “…A computationally simple two-step iterative algorithm, called estimationapproximation algorithm, is introduced for estimating the parameters of the model on the basis of the rank estimators. …”
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  5. 5
    Multiple equations model selection algorithm with iterative estimation method

    Multiple equations model selection algorithm with iterative estimation method by Kamarudin, Nur Azulia, Ismail, Suzilah

    Published 2016
    “…In particular, an algorithm on model selection for seemingly unrelated regression equations model using iterative feasible generalized least squares estimation method is proposed. …”
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  6. 6
    An automated strabismus classification using machine learning algorithm for binocular vision management system

    An automated strabismus classification using machine learning algorithm for binocular vision management system by Rohismadi, Muhammad Amirul Isyraf, Mat Raffei, Anis Farihan, Zulkifli, Nor Saradatul Akmar, Ithnin, Mohd. Hafidz, Othman, Shah Farez

    Published 2023
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  7. 7
    An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems

    An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems by Muhammad Akmal, Remli, Mohd Saberi, Mohamad, Safaai, Deris, Sinnott, Richard O., Suhaimi, Napis

    Published 2019
    “…Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building.…”
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  8. 8
    An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems

    An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems by Remli, Muhammad Akmal, Mohamad, Mohd Saberi, Deris, Safaai, Sinnott, Richard, Napis, Suhaimi

    Published 2019
    “…Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building.…”
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  9. 9
    An automated strabismus classification using machine learning algorithm for binocular vision management system

    An automated strabismus classification using machine learning algorithm for binocular vision management system by Muhammad Amirul Isyraf, Rohismadi, Anis Farihan, Mat Raffei, Nor Saradatul Akmar, Zulkifli, Mohd. Hafidz, Ithnin, Shah Farez, Othman

    Published 2023
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  10. 10
    Elimination of raindrops effects in infrared sensitive camera

    Elimination of raindrops effects in infrared sensitive camera by Abdullah, Ahmad Sharmi

    Published 2007
    “…The effects of rain brought some drawback to the automated surveillance systems especially during rainy night time which degrade the performance of the tracking system. …”
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  11. 11
    Organizational Culture Automated Audit System (OCAAS)

    Organizational Culture Automated Audit System (OCAAS) by Al - Jubair, Md. Abdullah

    Published 2017
    “…The study suggests the development of data mining based on knowledge management technology that can be utilized in automated OCA to overcome the many drawbacks. A new framework was proposed for automated OCA and development of the OCA system named as Organizational Culture Automated Audit System (OCAAS). …”
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  12. 12
    Automated Student Timetable Scheduling System based on Genetic Algorithm

    Automated Student Timetable Scheduling System based on Genetic Algorithm by Amirul Azuani, Romlee, Noor Ain, Rosly, Meng, Chuan Haw

    Published 2019
    “…In order to solve these problems, automated timetable scheduling system based on genetic algorithm is proposed which is estimated to reduce the chances of class clashing and prevent continuous lecture time. …”
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  13. 13
    An Automated Strabismus Classification Using Case-Based Reasoning Algorithm For Binocular Vision Management System

    An Automated Strabismus Classification Using Case-Based Reasoning Algorithm For Binocular Vision Management System by Muhammad Amirul Isyraf, Rohismadi

    Published 2023
    “…Thus, with the Agile methodology, the Binocular Vision Management system is proposed which comprised of two components, a web-based component for patient, treatment, and appointment management, and a machine learning component for automating the strabismus classification by using case-based reasoning algorithm. …”
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    Undergraduates Project Papers
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  14. 14
    Model selection approaches of water quality index data

    Model selection approaches of water quality index data by Kamarudin, Nur Azulia, Ismail, Suzilah

    Published 2016
    “…In order to select the best model, it is vital to ensure that proper estimation method is chosen in the modelling process.Different estimators have been proposed for the estimation of parameters of a model, including the least square and iterative estimators.This study aims to evaluate the forecasting performances of two algorithms on water quality index (WQI) of a river in Malaysia based on root mean square error (RMSE) and geometric root mean square error (GRMSE).Feasible generalised least squares (FGLS) and iterative maximum likelihood (ML) estimation methods are used in the algorithms, respectively.The results showed that SUREMLE-Autometrics has surpassed SURE-Autometrics; another simultaneous selection procedure of multipleequation models.Two individual selections, namely Autometrics-SUREMLE and Autometrics-SURE, though showed consistency only for GRMSE.All in all, ML estimation is a more appropriate method to be employed in this seemingly unrelated regression equations (SURE) model selection.…”
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  15. 15
    Multi-Robot Path-Planning algorithm for automated parcel sorting centre with ring road layout

    Multi-Robot Path-Planning algorithm for automated parcel sorting centre with ring road layout by Jong, Qian Biao

    Published 2023
    “…Automated warehouses use automated guided vehicles (AGV) or autonomous mobile robots (AMR) to help sort parcels. …”
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  16. 16
    Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm

    Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm by Suparman, S., Rusiman, Mohd Saifullah

    Published 2018
    “…The performance of the algorithm is tested by using simulated data. The test results show that the algorithm can estimate the order and coefficients of the autoregressive model very well. …”
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  17. 17
    PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah

    PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah by Abdullah, Siti Muniroh

    Published 2017
    “…The parameter estimation step is concerned with the estimation of model parameters once the structure is known. …”
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  18. 18
    An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli

    An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli by Mohammed Adam, Kunna, Tuty Asmawaty, Abdul Kadir, Muhammad Akmal, Remli, Noorlin, Mohd Ali, Kohbalan, Moorthy, Noryanti, Muhammad

    Published 2020
    “…In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. …”
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  19. 19
    The development of parameter estimation method for Chinese hamster ovary model using black widow optimization algorithm

    The development of parameter estimation method for Chinese hamster ovary model using black widow optimization algorithm by Nurul Aimi Munirah, ., Muhammad Akmal, Remli, Noorlin, Mohd Ali, Hui, Wen Nies, Mohd Saberi, Mohamad, Khairul Nizar Syazwan, Wan Salihin Wong

    Published 2020
    “…It will help the researcher get the fitted graph model, correct data, and estimate the value based on the data’s behaviour. …”
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  20. 20
    The compact genetic algorithm for likelihood estimator of first order moving average model

    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.

    Published 2012
    “…In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). …”
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