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  1. 1
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…Finally, the algorithm found, which would solve the image segmentation problem.…”
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  2. 2
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
    “…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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  3. 3
    Development of seven segment display recognition using TensorFlow on Raspberry Pi

    Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

    Published 2022
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    Proceeding Paper
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  4. 4
    Shunt active power filter using hybrid fuzzy-proportional and crisp-integral control algorithms for total harmonic distortion improvement

    Shunt active power filter using hybrid fuzzy-proportional and crisp-integral control algorithms for total harmonic distortion improvement by Abdul Rahman, Nor Farahaida

    Published 2016
    “…Utilization of soft-computing algorithms in the operation of Shunt Active Power Filters (SAPFs) becomes a latest trend. …”
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  5. 5
    Discrimination of pathological voices using systole activated neural network

    Discrimination of pathological voices using systole activated neural network by Murugesa Pandiyan, Paulraj, Prof. Madya Dr, Sazali, Yaacob, Prof. Dr., Hariharan, Muthusamy, Dr.

    Published 2012
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    Working Paper
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  6. 6
    Effort Estimation Model for Function Point Measurement

    Effort Estimation Model for Function Point Measurement by Koh, Tieng Wei

    Published 2007
    “…Generally, most of the algorithmic models were generated based on historical projects, and therefore, the same methodology has been applied in this research work. …”
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  7. 7
    Nonlinear adaptive algorithm for active noise control with loudspeaker nonlinearity

    Nonlinear adaptive algorithm for active noise control with loudspeaker nonlinearity by Dehkordi, Sepehr Ghasemi

    Published 2014
    “…Adaptive algorithms are prevalently applied in the design of nonlinear active noise control (ANC) system. …”
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  8. 8
    Nonlinear THF-FXLMS algorithm for active noise control with loudspeaker nonlinearity

    Nonlinear THF-FXLMS algorithm for active noise control with loudspeaker nonlinearity by Ghasemi, Sepehr, Raja Ahmad, Raja Mohd Kamil, Marhaban, Mohammad Hamiruce

    Published 2016
    “…Adaptive algorithms are prevalently applied in the design of nonlinear active noise control (ANC) systems. …”
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  9. 9
    Identification of vocal and voice disorders

    Identification of vocal and voice disorders by Murugesa Pandiyan, Paulraj, Prof. Madya Dr., Sazali, Yaacob, Prof. Dr., Mohd Rizon, Mohammed Juhari, Prof. Dr., Sivanandam, S. N., Muthusamy, Hariharan, Dr.

    Published 2011
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  10. 10
    Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control

    Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control by Srazhidinov, Radik

    Published 2016
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  11. 11
    Performance comparison of LFXLMS, MOVFXLMS and THF-NLFXLMS algorithms for Hammerstein NANC

    Performance comparison of LFXLMS, MOVFXLMS and THF-NLFXLMS algorithms for Hammerstein NANC by Srazhidinov, Radik, Raja Ahmad, Raja Mohd Kamil

    Published 2016
    “…In previous works, Tangential Hyperbolic Function NLFXLMS (THF-NLFXLMS) algorithm has been developed whereby the degree of nonlinearity is estimated using tangential hyperbolic function (THF). …”
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    Conference or Workshop Item
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  12. 12
    Real time nonlinear filtered-x lms algorithm for active noise control

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

    Published 2012
    “…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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  13. 13
    Jogging activity recognition using k-NN algorithm

    Jogging activity recognition using k-NN algorithm by Afifah Ismail

    Published 2022
    “…The objective of this project are 1) to investigate human activity recognition (HAR) for jogging activity and k-Nearest Neighbors (k-NN) algorithm for jogging classifier, 2) to apply HAR AND k-NN for jogging recognition and classification and, 3) to test the functionality of the k-NN algorithm of jogging recognition and classification. …”
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    Academic Exercise
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  14. 14
    Developing A Control Protocol Algorithm For Stimulating The Street Light System

    Developing A Control Protocol Algorithm For Stimulating The Street Light System by Wong, Kwo Yiang

    Published 2017
    “…Based on the previous research and previous works, the automatic street lighting system is only considered by using Light Dependent Resistor (LDR) to activate the system. …”
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    Monograph
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  15. 15
    Adaptive linear neural network approach for three-phase four-wire active power filtering under non-ideal grid and unbalanced load scenarios

    Adaptive linear neural network approach for three-phase four-wire active power filtering under non-ideal grid and unbalanced load scenarios by Hoon, Yap, Mohd Radzi, Mohd Amran, Al-Ogaili, Ali Saadon

    Published 2019
    “…Hence, in this work, a control algorithm (named as enhanced-ADALINE) which combines the strength of highly selective filter (HSF), ADALINE concept and averaging function is proposed, to manage harmonics mitigation by shunt active power filter (SAPF) under non-ideal grid and unbalanced load scenarios. …”
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  16. 16
    A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm

    A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm by Kahourzade, S., Mahmoudi, A., Mokhlis, Hazlie

    Published 2015
    “…This study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. …”
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  17. 17
    Adaptive linear neural network approach for three-phase four-wire active power filtering under non-ideal grid and unbalanced load scenarios

    Adaptive linear neural network approach for three-phase four-wire active power filtering under non-ideal grid and unbalanced load scenarios by Hoon Y., Radzi M.A.M., Al-Ogaili A.S.

    Published 2023
    “…Hence, in this work, a control algorithm (named as enhanced-ADALINE) which combines the strength of highly selective filter (HSF), ADALINE concept and averaging function is proposed, to manage harmonics mitigation by shunt active power filter (SAPF) under non-ideal grid and unbalanced load scenarios. …”
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  18. 18
    DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMZATION FRAMEWORK FOR INDUSTRIAL PROCESSES

    DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMZATION FRAMEWORK FOR INDUSTRIAL PROCESSES by GANESAN, TIMOTHY

    Published 2014
    “…This work investigates the performance and solution characteristics of metaheuristic algorithms when applied to such problems. …”
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  19. 19
    Active contour for Intelligent Road Tracking System

    Active contour for Intelligent Road Tracking System by Muhammad Asif, Muhammad Azwan, Nasirudin, Mohd Rizal, Arshad

    Published 2009
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    Working Paper
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  20. 20
    A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds

    A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds by Salim, Naomie, Shah, J. Z.

    Published 2007
    “…In this work a fuzzy hierarchical algorithm is developed which provides a mechanism not only to benefit from the fuzzy clustering process but also to get advantage of the multiple membership function of the fuzzy clustering. …”
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