Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.

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Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

Energies ◽

10.3390/en11092328 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2328 ◽

Cited By ~ 12

Author(s):

Md Shafiullah ◽

M. Abido ◽

Taher Abdel-Fattah

Keyword(s):

Machine Learning ◽

Fault Location ◽

Percentage Error ◽

Support Vector ◽

Learning Approach ◽

Efficiency Coefficient ◽

Learning Tools ◽

Performance Indices ◽

Machine Learning Approach ◽

Distribution Grids

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

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Hybrid Machine Learning Approach for Skin Disease Detection Using Optimal Support Vector Machine

Intelligent Data Communication Technologies and Internet of Things - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-3-030-34080-3_73 ◽

2019 ◽

pp. 647-658

Author(s):

K. Melbin ◽

Y. Jacob Vetha Raj

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Skin Disease ◽

Support Vector ◽

Disease Detection ◽

Learning Approach ◽

Machine Learning Approach ◽

Hybrid Machine

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Arabic English Cross-Lingual Plagiarism Detection Based on Keyphrases Extraction, Monolingual and Machine Learning Approach

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2018/v2i330075 ◽

2019 ◽

pp. 1-12

Author(s):

Mokhtar Al-Suhaiqi ◽

Muneer A. S. Hazaa ◽

Mohammed Albared

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Detection Methods ◽

Support Vector ◽

Svm Classifier ◽

Learning Approach ◽

Plagiarism Detection ◽

Machine Learning Approach ◽

Cross Lingual ◽

Cross Language

Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection. This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection. According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used.

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Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

Applied Sciences ◽

10.3390/app10165673 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5673 ◽

Cited By ~ 2

Author(s):

Daniela Cardone ◽

David Perpetuini ◽

Chiara Filippini ◽

Edoardo Spadolini ◽

Lorenza Mancini ◽

...

Keyword(s):

Machine Learning ◽

Stress State ◽

Thermal Imaging ◽

Driving Simulator ◽

Supervised Machine Learning ◽

Support Vector ◽

Learning Approach ◽

Machine Learning Approach ◽

Thermal Features ◽

Driver Stress

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.

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A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function

Structural Health Monitoring ◽

10.1177/1475921716639587 ◽

2016 ◽

Vol 15 (3) ◽

pp. 302-316 ◽

Cited By ~ 42

Author(s):

Ramin Ghiasi ◽

Peyman Torkzadeh ◽

Mohammad Noori

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Damage Detection ◽

Kernel Function ◽

Structural Damage ◽

Least Square ◽

Support Vector ◽

Learning Approach ◽

Structural Damage Detection ◽

Machine Learning Approach

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A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence

Geoscience Frontiers ◽

10.1016/j.gsf.2020.05.016 ◽

2020 ◽

Vol 11 (6) ◽

pp. 2067-2081

Author(s):

Christopher M. Yeomans ◽

Robin K. Shail ◽

Stephen Grebby ◽

Vesa Nykänen ◽

Maarit Middleton ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Learning Approach ◽

Model Confidence ◽

Machine Learning Approach

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Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach

Cancers ◽

10.3390/cancers11030431 ◽

2019 ◽

Vol 11 (3) ◽

pp. 431 ◽

Cited By ~ 11

Author(s):

Oneeb Rehman ◽

Hanqi Zhuang ◽

Ali Muhamed Ali ◽

Ali Ibrahim ◽

Zhongwei Li

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Information Gain ◽

Support Vector ◽

Learning Approach ◽

Breast Cancers ◽

Functional Studies ◽

Normal Tissues ◽

Machine Learning Approach ◽

Chi Squared

Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis.

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Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis

Life ◽

10.3390/life10090181 ◽

2020 ◽

Vol 10 (9) ◽

pp. 181

Author(s):

Christopher T. Mandrell ◽

Torrey E. Holland ◽

James F. Wheeler ◽

Sakineh M. A. Esmaeili ◽

Kshitij Amar ◽

...

Keyword(s):

Machine Learning ◽

Pancreatic Cancer ◽

Raman Spectra ◽

Pancreatic Cancer Cell Line ◽

Cancer Cell Line ◽

Human Pancreatic Cancer ◽

Support Vector ◽

Learning Approach ◽

K Nearest Neighbor ◽

Machine Learning Approach

A machine learning approach is applied to Raman spectra of cells from the MIA PaCa-2 human pancreatic cancer cell line to distinguish between tumor repopulating cells (TRCs) and parental control cells, and to aid in the identification of molecular signatures. Fifty-one Raman spectra from the two types of cells are analyzed to determine the best combination of data type, dimension size, and classification technique to differentiate the cell types. An accuracy of 0.98 is obtained from support vector machine (SVM) and k-nearest neighbor (kNN) classifiers with various dimension reduction and feature selection tools. We also identify some possible biomolecules that cause the spectral peaks that led to the best results.

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