Search Results - (( model evaluation a algorithm ) OR ( defect classification model algorithm ))
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Evaluation of the Transfer Learning Models in Wafer Defects Classification
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Conference or Workshop Item -
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Cross-project software defect prediction
Published 2022“…The feasibility of building a software defect prediction (SDP) model in the absence of previous records has been increased by the introduction of the Cross-Project Defect Prediction (CPDP) method. …”
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Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…The classification algorithm is a popular machine learning approach for software defect prediction. …”
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4
The formulation of a transfer learning pipeline for the classification of the wafer defects
Published 2023“…Thus far, there are still limited studies that investigate the classification of wafer defects using TL combined with a classical Machine learning (ML) pipeline. …”
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Automated mold defects classification in paintings: a comparison of machine learning and rule-based techniques.
Published 2025“…Subsequently, these regions are classified as mold defects using either morphological filtering or machine learning models such as Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). …”
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Photogrammetric low-cost unmanned aerial vehicle for pothole detection mapping / Shahrul Nizan Abd Mukti
Published 2022“…The most significant classifier algorithms to distinguish a pothole defect is Maximum likelihood with 29 over 40 band combination win rate. …”
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Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study
Published 2024“…In this work, YOLOv5, YOLOv6 and YOLOv7 models have been implemented and trained using a custom dataset of road cracks and potholes and their performances have been evaluated and compared. …”
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Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review
Published 2022“…Findings from the literature review shows that the neural network (NN) is the most prevalent algorithm employed in studies, followed by the Bayesian network (BN), support vector machine (SVM), and hybrid models. …”
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Aerial imagery paddy seedlings inspection using deep learning
Published 2022“…In this work we aimed to explore several deep convolutional neural networks (DCNN) models to determine which one performs the best for defective paddy seedling detection using aerial imagery. …”
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Machine learning application for concrete surface defects automatic damage classification
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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Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
Published 2014“…In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. …”
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Classification of metal screw defect detection using FOMO on edge impulse / Muhammad Imran Daing
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Student Project -
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Identifying Damage Types in Solar Panels Through Surface Image Analysis with Naive Bayes
Published 2024“…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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Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin
Published 2011“…This thesis presents a research work on a diagnosis system for heart sound based on nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of several heart diseases. …”
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Real-time oil palm fruit bunch ripeness grading system using image processing techniques
Published 2013“…Third, GLCM and ANN applied on the ROI1 achieved 87% accuracy and a 3.7 second processing time for the Nigrescens thorns model, whereas BGLAM applied on the ROI3 based on SVM achieved 91% accuracy and a 1.20 second processing time for the Oleifera thorns model as well as 88% accuracy and a 0.83 second processing time for the Virescens colour model. …”
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Identification of weld defect through the application of denoising method to the sound signal acquired during pulse mode laser welding
Published 2023“…The root-mean-square and spectral entropy of both unfiltered and filtered signals were extracted, and classification models were developed using these data sets. …”
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Heart sound diagnosis using nonlinear arx model / Noraishah Shamsuddin
Published 2012“…This thesis presents a research work on a diagnosis system for heart sound based on nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of several heart diseases. …”
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Book Section -
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Low altitude multispectral mapping for road defect detection
Published 2021“…Pothole’s defect is major damages indicated the road condition visually, and the structural defects due to some potential causes. …”
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