Search Results - (( model application model algorithm ) OR ( defect classification _ algorithm ))*

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

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

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

“…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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Identifying Damage Types in Solar Panels Through Surface Image Analysis with Naive Bayes by Wiliani, Ninuk, T.K.A, Rahman, Ramli, Suzaimah

“…This work illustrates the potential of statistical feature extraction approaches for defect classification, while emphasizing the necessity for future improvements to boost the efficacy of feature extraction and classification techniques in practical applications.…”
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Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects by Zuwairie, Ibrahim, Tan, Shing Chiang, Watada, Junzo, Marzuki, Khalid

“…Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. …”
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Classification of metal screw defect detection using FOMO on edge impulse / Muhammad Imran Daing by Daing, Muhammad Imran

“…This project uses the FOMO (Faster Objects, More Objects) algorithm to detect surface flaws on metal screws. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee by Chong , Hue Yee

“…Hence, a wide range of studies has been focused on determining the optimal weight values of ANN models and the number of hidden neurons. In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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Identification of weld defect through the application of denoising method to the sound signal acquired during pulse mode laser welding by Yusof, M. F.M., Quazi, M. M., Aleem, S.A.A., Ishak, M., Ghazali, M. F.

“…This paper describes the improvement of weld defect identification achieved by incorporating Z-score-based thresholding into the wavelet denoising algorithm for the pre-processing of sound signals. …”
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Photogrammetric low-cost unmanned aerial vehicle for pothole detection mapping / Shahrul Nizan Abd Mukti by Abd Mukti, Shahrul Nizan

“…The lowest error of pothole polygon classification is 0.016m² from classification of Mahalanobis distance algorithm with NIR + red edge + red or Green + red edge + red combination. …”
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Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study by Mohd Yusof, Najiha 'Izzaty, Sophian, Ali, Mohd Zaki, Hasan Firdaus, Bawono, Ali Aryo, Embong, Abd Halim, Ashraf, Arselan

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Low altitude multispectral mapping for road defect detection by Shahrul Nizan Abd Mukti, Khairul Nizam Tahar

“…The comparison of the performance between the types of classifier and band combination was modeled and discussed in this study. Classifier algorithm maximum likelihood gives the lowest error of 0.108m² with a combination of NIR + red edge band. …”
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A three dimensional (3D) vision based defect inspection system for gluing application by Harun, Mohamad Haniff

“…Pixel coordinates must be converted to robot coordinates for easier understanding by the robot. Three types of defect are trained as model templates and save to the memory known as bumper, gap and bubble defect. …”
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Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review by Aliyu, R., Mokhtar, A.A., Hussin, H.

“…To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. …”
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An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images by Ibrahim, I., Ibrahim, Z., Khalil, K., Mokji, M.M., Abu Bakar, S.A.R.S., Mokhtar, N., Ahmad, W.K.W.

“…The improved PCB defect classification algorithm has been applied to real PCB images to successfully classify all of the defects. …”
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Fuzzy qualitative approach to address uncertainty in human motion analysis / Lim Chern Hong by Lim, Chern Hong

“…Improper care on the variation of human size and camera positions from the ground might results in a defect human model such as inconsistent human size, and odd human shape. …”
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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

“…The objective of the work was to find the best combination of SVM parameters and data features to maximize defect classification accuracy. The paper presents the results obtained to demonstrate the strengths of the Bees Algorithm as an optimization tool.…”
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Software defect prediction framework based on hybrid metaheuristic optimization methods by Wahono, Romi Satria

“…The classification algorithm is a popular machine learning approach for software defect prediction. …”
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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

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

“…Automated processes have been used commonly in recent years, with the judgement done by using conventional image processing algorithm. 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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An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images by Ismail, Ibrahim, Zuwairie, Ibrahim, Kamal, Khalil, Musa, Mohd Mokji, Syed Abdul Rahman, Syed Abu Bakar, Norrima, Mokhtar, Wan Khairunizam, Wan Ahmad

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