Prediction of the Fresh Performance of Steel Fiber Reinforced Self-Compacting Concrete Using Quadratic SVM and Weighted KNN Models

Altay, Osman; Ulaş, Mustafa; Alyamaç, Kürşat Esat

doi:10.1109/access.2020.2994562

Cited by 17 publications

(16 citation statements)

References 42 publications

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“…SVM with a quadratic polynomial kernel function [25, 26] that uses Bayesian optimization [27] for automatic parameterisation is more accurate than other classifiers for this problem. The weighted kNN [28], the Naive Bayesian classifier with Gaussian kernel [29], and a decision tree with 20 split numbers by Gini diversity index [30] were also used for this classification problem.…”

Section: Methodsmentioning

confidence: 99%

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses

Jin

et al. 2021

High Voltage

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Inspection of partial discharge before contamination flashover is of great importance for preventing exterior insulation accidents. In this study, a new method for identification of discharge severities is proposed. Specifically, a low-cost ultraviolet (UV) sensor detection system was combined with time-frequency method, texture analysis and support vector machine (SVM) classifier to classify partial discharge severities for ceramic insulators. The visible images and the root-mean-square value of leakage currents detected simultaneously are used to classify the UV signals into different discharge faults. The frequency and amplitude integration of UV pulses are minimum in corona discharge and larger in arc discharge. The images of UV signal spectrograms differ significantly at different discharge stages. The density and brightness of image textures are minimal in corona discharge and larger in arc discharge. Valid and reliable features selected by two texture feature extraction methods with SVM classifier have a reliable classification accuracy of 90.6% for ceramic insulators, and outperform a single time feature or other texture features. SVM outperforms k-Nearest Neighbour, Naive Bayes and Decision Tree. Our new method with computational effectiveness and high practicality can solve the problem of high randomness and low accuracy of UV sensor detection. It can be further applied to the deterioration diagnosis of power facilities.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Methodsmentioning

confidence: 99%

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses

Jin

et al. 2021

High Voltage

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“…In the classification phase, decision tree (DT), linear discriminant (LD) [42], naı ¨ve Bayes (NB) [43], linear SVM (LSVM) [44], quadratic SVM (QSVM) [45], Cubic SVM (CSVM) [39], Gaussian SVM (GSVM) [39], k nearest neighbor (kNN) [46,47], bagged tree (BT) [48], and multilayer perceptron (MLP) [49] classifiers were used. The plot of accuracies (%) obtained using these classifiers is shown in Fig.…”

Section: Time Complexity Analysismentioning

confidence: 99%

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Barua

Aydemir

Doğan

et al. 2022

Neural Comput & Applic

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Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model.

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“…The metrics used are listed below in the article. TP is a true positive, TN is a true negative, FN is a false negative and FP is a false positive [23].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

Abed¹,

Gitaffa²,

Issa³

2021

ETJ

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Fault detection plays a serious role in high-cost and safety-critical processes. There are two main drivers for continuous improvement in the area of early detection of process faults safety and reliability of technical plants. Detect fault in Geophone string sensors (SG-10) are very important in oil exploration to avoid loss economy. Methods are developed to enable earlier detection of process faults than the traditional limit and trend checking based on a single process variable and the development of these methods is a key matter. Classification methods will be used for pattern recognition and as such is appropriate for fault detection. In supervised training input-output pairs, both for normal and fault conditions, are presented to the network. The models were trained on the free fault and fault sensors. Then the Quadratic Support Vector Machine (QSVM) and k-Nearest Neighbor (KNN) as the classifiers are used. The test results for measuring the performance of 1232 sample classifiers from data show that the accuracy of fault-free sensor recognition is 97.4 % and 100% consecutively for these classifiers.

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Prediction of the Fresh Performance of Steel Fiber Reinforced Self-Compacting Concrete Using Quadratic SVM and Weighted KNN Models

Cited by 17 publications

References 42 publications

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

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