Search Results - (( defect classification methods algorithm ) OR ( code classification rules algorithm ))
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Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
Published 2014“…This paper presents a classification of android malware using candidate detectors generated from an unsupervised association rule of Apriori algorithm improved with particle swarm optimization to train three different supervised classifiers. …”
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Proceeding Paper -
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Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization
Published 2015“…However, supervised learning technique has limitations for malware classification task. This paper presents a classification approach on android malware using candidate detectors generated from an unsupervised association rule of Apriori Algorithm. …”
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Article -
3
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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Thesis -
4
Evaluation of the Transfer Learning Models in Wafer Defects Classification
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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Conference or Workshop Item -
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Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
Published 2018“…This research proposed an algorithm named Genetic Algorithm Fuzzy Logic (GAFL) with Pittsburg approach for rules learning and induction in genetic fuzzy system knowledge discovery. …”
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Article -
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Genetic algorithm fuzzy logic for medical knowledge-based pattern classification
Published 2023“…This research proposed an algorithm named Genetic Algorithm Fuzzy Logic (GAFL) with Pittsburg approach for rules learning and induction in genetic fuzzy system knowledge discovery. …”
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Automated mold defects classification in paintings: a comparison of machine learning and rule-based techniques.
Published 2025“…Results indicate that both methods improve the accuracy and precision of mold defect detection compared to no classifier. …”
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Cross-project software defect prediction
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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Partial discharge classification for XLPE cable joints using K nearest neighbors algorithm / 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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10
Chain coding and pre processing stages of handwritten character image file
Published 2010“…Fuzzy Logic is used in the classification phase while HMM is used in the process of extracting features for the preparation of linguistic variables of the fuzzy rules. …”
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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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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool
Published 2018“…From the data analysis using WEKA software, the production rules classifier (PART) is found to be the most accurate classification algorithm in classifying the emotion which yields the highest precision percentage of 99.6% compared to J48 (99.5%) and Naïve Bayes (96.2%). …”
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Thesis -
13
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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14
A cascading fuzzy logic with image processing algorithm-based defect detection for automatic visual inspection of industrial cylindrical object’s surface
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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15
Classification of metal screw defect detection using FOMO on edge impulse / Muhammad Imran Daing
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Student Project -
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POWER QUALITY CLASSIFICATION WITH DE-NOISING SCHEME USING WAVELET TRANSFORM AND RULE- BASED METHOD
Published 2012“…Unique features from the I", 4t h ,7th and 8thl evel details are obtained as criteria for developing a Rules-Based Algorithm for classifying disturbances that have occurred. …”
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Internal defect detection and reconstruction framework for laminated glass fibre reinforced polymer composite materials
Published 2013“…(iii) The image ofthe defect region is reconstructed by using the attenuation of the reflected ultrasound signal (iv) Entropy-based fuzzy k-nearest neighbour classification method is used to extract the feature of the defects. …”
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Identifying Damage Types in Solar Panels Through Surface Image Analysis with Naive Bayes
Published 2024“…This study investigates the utilization of statistical feature extraction methods alongside Bernoulli Naive Bayes (BNB) and Gaussian Naive Bayes (GNB) algorithms to categorize different defect types, such as cracks, scratches, spots, and non-defective surfaces, through digital image analysis. …”
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Journal -
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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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Thesis
