Search Results - (( defect classification problem algorithm ) OR ( code classifications clustering algorithm ))
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An improved defect classification algorithm for six printing defects and its implementation on real printed circuit board images
Published 2012“…The improved PCB defect classification algorithm has been applied to real PCB images to successfully classify all of the defects. …”
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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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Using the bees algorithm to optimise a support vector machine for wood defect classification
Published 2007“…This paper describes a new application of the Bees Algorithm to the optimization of a Support Vector Machine (SVM) for the problem of classifying defects in plywood. …”
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An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding
Published 2020“…Classic bag of visual words algorithm is based on k-means clustering and every SIFT features belongs to one cluster and it leads to decreasing classification results. …”
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Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
Published 2014“…The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.…”
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Enhanced Image Classification for Defect Detection on Solar Photovoltaic Modules
Published 2023“…However, high similarity of characteristics among the shapes and textures has been a major challenge in defect classification process. The objective of this research was to develop and analyse feature extraction used for classification techniques for defect detection of solar photovoltaic modules surfaces. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning
Published 2016“…Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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Classification of metamorphic virus using n-grams signatures
Published 2020“…The first step is the classification model to cluster the metamorphic virus using TF-IDF technique. …”
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Detection of tube defect using the autoregressive algorithm
Published 2015“…Easy detection and evaluation of defect in the tube structure is a continuous problem and remains a significant demand in tube inspection technologies. …”
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Internal defect detection and reconstruction framework for laminated glass fibre reinforced polymer composite materials
Published 2013“…Therefore, by removmg the low frequency signals, the intemal defect detectability can be improved. Moreover, the classification of an input pattem based on the closest neighbours of the point of interest provides more accurate defect detection in comparison with the classification based on experience data as the defect pattems vary on circumstances in ultrasonic NDE problems.…”
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Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis
Published 2025“…Furthermore, classification using the Random Forest algorithm depicted that a 95.3% accuracy (k=0.768), confirming robust predictive capability in identifying course approval status and demand trends. …”
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Defect Detection And Classification Of Silicon Solar Wafer Featuring Nir Imaging And Improved Niblack Segmentation
Published 2016“…The classification combines the analysis of defect intensity features, the application of unsupervised k-mean clustering and multi-class SVM algorithms. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms
Published 2020“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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A three dimensional (3D) vision based defect inspection system for gluing application
Published 2013“…Each defect has special characteristic. Inspection system developed to identify problems occurs in gluing process. …”
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Aerial imagery paddy seedlings inspection using deep learning
Published 2022“…Deep learning algorithms in computer vision for image classification and object detection can facilitate the agriculture industry, especially in paddy cultivation, to alleviate human efforts in laborious, burdensome, and repetitive tasks. …”
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Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review
Published 2022“…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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