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  1. 1
    Software defect prediction framework based on hybrid metaheuristic optimization methods

    Software defect prediction framework based on hybrid metaheuristic optimization methods by Wahono, Romi Satria

    Published 2015
    “…For the purpose of this study, ten classification algorithms have been selected. The selection aims at achieving a balance between established classification algorithms used in software defect prediction. …”
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  2. 2
    Using the bees algorithm to optimise a support vector machine for wood defect classification

    Using the bees algorithm to optimise a support vector machine for wood defect classification by Pham, D.T, Muhammad, Zaidi, Mahmuddin, Massudi, Ghanbarzadeh, Afshin, Koc, Ebubekir, Otri, Sameh

    Published 2007
    “…The paper presents the results obtained to demonstrate the strengths of the Bees Algorithm as an optimization tool.…”
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  3. 3
    Enhanced Image Classification for Defect Detection on Solar Photovoltaic Modules

    Enhanced Image Classification for Defect Detection on Solar Photovoltaic Modules by Wiliani, Ninuk

    Published 2023
    “…The second algorithm uses K Nearest Neighbour using a ratio of training data and testing data of 95:05 resulting in an accuracy value of 62%. …”
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  4. 4
    Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin

    Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin by Shamsuddin, Noraishah

    Published 2011
    “…The Resilient Backpropagation (RPROP) algorithm is used to train the network. The optimized learning parameter used is 0.07 and the network has best performance when hidden neurons equal to 220. …”
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  5. 5
    Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain

    Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain by Ong, Pauline, Tieh, Tony Hieng Cai, Lai, Kee Huong, Lee, Woon Kiow, Ismon, Maznan

    Published 2019
    “…For this purpose, this study used the intensification and diversification properties of the recently proposed moth-flame optimisation (MFO) algorithm and utilised the algorithm in the proposed feature selection scheme. …”
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  6. 6
    Training feedforward neural networks for fault diagnosis of ball bearing

    Training feedforward neural networks for fault diagnosis of ball bearing by Tuan Abdul Rahman, Tuan Ahmad Zahidi, As'arry, Azizan, Abdul Jalil, Nawal Aswan, Raja Ahmad, Raja Mohd Kamil

    Published 2018
    “…The results show that the proposed approach produces comparable classification accuracy as parameters of the FNNs were optimized systematically using CFS algorithm.…”
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  7. 7
    Evaluation of the Transfer Learning Models in Wafer Defects Classification

    Evaluation of the Transfer Learning Models in Wafer Defects Classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen

    Published 2022
    “…The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train and test the algorithms. …”
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  8. 8
    Classification Analysis Of High Frequency Stress Wave For Autonomous Detection Of Defect In Steel Tubes

    Classification Analysis Of High Frequency Stress Wave For Autonomous Detection Of Defect In Steel Tubes by Abd Halim, Zakiah, Jamaludin, Nordin, Junaidi, Syarif, Syed Yahya, Syed Yusaini

    Published 2014
    “…Interpretation of propagated high frequency stress wave signals in steel tubes is noteworthy for defect identification.This paper demonstrated a successful new approach for autonomous defect detection in steel tubes using classification analysis of high frequency stress waves.Classification analysis using Principal Component Analysis (PCA) algorithm involved feature extraction to reduce the dimensionality of the complex stress waves propagation path.Two defective tubes containing a slot defect of different orientation and a reference tube are inspected using Vibration Impact Acoustic Emission (VIAE) technique.The tubes are externally excited using impact hammer.The variation of stress wave transmission path are captured by high frequency Acoustic Emission sensor.The propagated stress waves in the steel tubes are classified using PCA algorithm.Classification results are graphically illustrated using a dendrogram that demonstrated the arrangement of the natural clusters of the stress wave signals.The inspection of steel tubes showed good recognition of defect in circumferential and longitudinal orientation.This approach successfully classified stress wave signals from VIAE testing and provide fast and accurate defect identification of defective steel tubes from non-defective tubes. …”
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  9. 9
    The formulation of a transfer learning pipeline for the classification of the wafer defects

    The formulation of a transfer learning pipeline for the classification of the wafer defects by Lim, Shi Xuen

    Published 2023
    “…However, limitations such as robustness and difficulty in setting up the parameters required for image processing algorithm encourages the investigation in using Deep learning classification in detecting the wafer defects. …”
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  10. 10
    Real-time oil palm fruit bunch ripeness grading system using image processing techniques

    Real-time oil palm fruit bunch ripeness grading system using image processing techniques by Alfatni, Meftah Salem M.

    Published 2013
    “…These results are optimal based on the thorns model. A new approach was developed using expert rules-based system. …”
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  11. 11
    Neural network paradigm for classification of defects on PCB

    Neural network paradigm for classification of defects on PCB by Heriansyah, Rudi, Syed Al-Attas, Syed Abdul Rahman, Zabidi, Muhammad Mun'im Ahmad

    Published 2003
    “…A defective PCB image is used to ensure the function of the proposed technique.…”
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  12. 12
    CLASSIFICATION OF BEARING FAULTS USING EXTREME LEARNING MACHINE ALGORITHMS

    CLASSIFICATION OF BEARING FAULTS USING EXTREME LEARNING MACHINE ALGORITHMS by TEH, CHOON KEONG

    Published 2017
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  13. 13
    Cross-project software defect prediction

    Cross-project software defect prediction by Bala, Yahaya Zakariyau, Abdul Samat, Pathiah, Sharif, Khaironi Yatim, Manshor, Noridayu

    Published 2022
    “…In this work, five research questions covering the classification algorithms, dataset, independent variables, performance evaluation metrics used in CPDP studies, and as well as the performance of individual machine learning classification algorithms in predicting software defects across different software projects were addressed accordingly. …”
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  14. 14
    Evaluation of the machine learning classifier in wafer defects classification

    Evaluation of the machine learning classifier in wafer defects classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund

    Published 2021
    “…The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train, test and validate the classifier. …”
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  15. 15
    Partial discharge classification for XLPE cable joints using K nearest neighbors algorithm / Muhammad Shairazi Mohd Salleh

    Partial discharge classification for XLPE cable joints using K nearest neighbors algorithm / Muhammad Shairazi Mohd Salleh by Muhammad Shairazi , Mohd Salleh

    Published 2020
    “…The input data from the PD measurement results were used to train k-nearest neighbor (KNN) algorithm to classify each type of defect in the cable joint samples. …”
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  16. 16
    Machine learning application for concrete surface defects automatic damage classification

    Machine learning application for concrete surface defects automatic damage classification by Syahrul Fithry Senin, Khairullah Yusuf, Amer Yusuf, Rohamezan Rohim

    Published 2024
    “…Therefore, a Machine Learning classifier for concrete surface defect classification using the Discriminant Analysis Classifier was introduced to more accurately extract the types of concrete surface defects information from the digital images. …”
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  17. 17
    Design and Development of Artificial Intelligence (Al)-Based Desicion Support System For Manufacturing Applications

    Design and Development of Artificial Intelligence (Al)-Based Desicion Support System For Manufacturing Applications by Lim , Chee Peng

    Published 2016
    “…In this report, the research on welding defect detection and classification using radiograph images is presented. …”
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  18. 18
    A cascading fuzzy logic with image processing algorithm-based defect detection for automatic visual inspection of industrial cylindrical object’s surface

    A cascading fuzzy logic with image processing algorithm-based defect detection for automatic visual inspection of industrial cylindrical object’s surface by Ali, Mohammed A. H., Au, Kai Lun

    Published 2018
    “…This paper proposes a cascading fuzzy logic algorithm with image processing technique for defect detection and classification on the lateral surface of industrial cylindrical object using a camera and multiple flat mirrors. …”
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  19. 19
    Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects

    Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects by Zuwairie, Ibrahim, Tan, Shing Chiang, Watada, Junzo, Marzuki, Khalid

    Published 2014
    “…Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. …”
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  20. 20
    Classification of metal screw defect detection using FOMO on edge impulse / Muhammad Imran Daing

    Classification of metal screw defect detection using FOMO on edge impulse / Muhammad Imran Daing by Daing, Muhammad Imran

    Published 2025
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