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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
    A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier

    A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier by Noormadinah Allias, Megat NorulAzmi Megat Mohamed Noor, Mohd. Nazri Ismail, Kim de Silva, (UniKL MIIT)

    Published 2014
    “…A performance of anti-spam filter not only depends on the number of features and types of classifier that are used, but it also depends on the other parameter settings. Deriving from previous experiments, we extended our work by investigating the effect of population sizes from our proposed method of feature selection on different learning classifier algorithms using Random Forest, Voting, Decision Tree, Support Vector Machine and Stacking. …”
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  4. 4
    An extended adaptive mechanism of evolutionary based channel assignment via reinforcement

    An extended adaptive mechanism of evolutionary based channel assignment via reinforcement by Teo, Kenneth Tze Kin, Yew, Hoe Tung, Lye, Scott Carr Ken, Lim, Kit Guan, Ang, Soo Siang, Khairul Anuar Mohamad, Ali Chekima, Liau, Chung Fan, Aroland Jilui Kiring

    Published 2012
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    Research Report
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  5. 5
    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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  6. 6
    Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid

    Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid by Wahid, Muhammad Hazim

    Published 2022
    “…One of the major challenges when applying machine learning is to identify the best algorithm from a variety of algorithms to solve a problem. …”
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  7. 7
    Information Theoretic-based Feature Selection for Machine Learning

    Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

    Published 2018
    “…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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  8. 8
    Machine learning methods for herschel-bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation

    Machine learning methods for herschel-bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation by Kumar, A., Ridha, S., Ganet, T., Vasant, P., Ilyas, S.U.

    Published 2020
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  9. 9
    Investigation of Meta-heuristics Algorithms in ANN Streamflow Forecasting

    Investigation of Meta-heuristics Algorithms in ANN Streamflow Forecasting by Wei Y., Hashim H., Chong K.L., Huang Y.F., Ahmed A.N., El-Shafie A.

    Published 2024
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  10. 10
    A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee

    A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee by Wong , Wen Yee

    Published 2023
    “…Any resampling algorithm is not a necessity in the case of this proposed algorithm. …”
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  11. 11
    Kernel and multi-class classifiers for multi-floor wlan localisation

    Kernel and multi-class classifiers for multi-floor wlan localisation by Abd Rahman, Mohd Amiruddin

    Published 2016
    “…Unlike the classical kNN algorithm which is a regression type algorithm, the proposed localisation algorithms utilise machine learning classification for both linear and kernel types. …”
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  12. 12
    Hybrid learning control schemes with input shaping of a flexible manipulator system.

    Hybrid learning control schemes with input shaping of a flexible manipulator system. by Md. Zain, M. Z., Tokhi, M. O., Mohamed, Z.

    Published 2006
    “…A collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control with acceleration feedback and genetic algorithms (GAs) for optimization of the learning parameters and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. …”
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  13. 13
    Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning

    Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning by Ismail, Amelia Ritahani, Azhary, Muhammad Zulhazmi Rafiqi, Hitam, Nor Azizah

    Published 2025
    “…Optimizers play an essential role in adjusting the model’s parameters to minimize errors, assisting the learning process during the model development. …”
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  14. 14
    Vision based automatic steering control using a PID controller

    Vision based automatic steering control using a PID controller by Abdullah, A.S., Hai, L.K., Osman, N.A.A., Zainon, M.Z.

    Published 2006
    “…Initially, a collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control with genetic algorithm (GA) to optimize the learning parameters and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. …”
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  15. 15
    Performance of hybrid learning control with input shaping for input tracking and vibration suppression of a flexible manipulator

    Performance of hybrid learning control with input shaping for input tracking and vibration suppression of a flexible manipulator by Md. Zain, M. Z., Tokhi, M. O., Mohamed, Z.

    Published 2006
    “…Initially, A Collocated Proportional-Derivative (PD) Controller Utilizing Hub-Angle And Hub-Velocity Feedback Is Developed For Control Of Rigid-Body Motion Of The System. This Is Then Extended To Incorporate Iterative Learning Control With Genetic Algorithm (GA) To Optimize The Learning Parameters And A Feedforward Controller Based On Input Shaping Techniques For Control Of Vibration (Flexible Motion) Of The System. …”
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  16. 16
    The effect of human learning and forgetting on fuzzy EOQ model with backorders / Nima Kazemi

    The effect of human learning and forgetting on fuzzy EOQ model with backorders / Nima Kazemi by Nima , Kazemi

    Published 2017
    “…When estimating the key cost parameters of an inventory model the experience and learning capabilities of the planners affect efficiency of the inventory system. …”
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  17. 17
    A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

    A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting by Hassan, S., Khosravi, A., Jaafar, J., Khanesar, M.A.

    Published 2016
    “…The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. …”
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  18. 18
    New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification

    New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification by Balakrishnan, Malarvili, Ting, Chee Ming, Shaikh Salleh, Sheikh Hussain

    Published 2009
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  19. 19
    Adapting robot kinematics for human-arm motion recognition

    Adapting robot kinematics for human-arm motion recognition by Chan, C.S., Liu, H., Brown, D.

    Published 2007
    “…Finally, classification of the human-arm motion is achieved by comparing the QNTs to the parameters learnt with particle filter based motion tracking algorithm. …”
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  20. 20
    Adaptive complex neuro-fuzzy inference system for non linear modeling and time series prediction

    Adaptive complex neuro-fuzzy inference system for non linear modeling and time series prediction by Shoorangiz, Mohammadreza

    Published 2013
    “…In this regard, genetic algorithm generates different initial conditions of premise parameters to and the best one. …”
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