scholarly journals Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mengqiu Cao ◽  
Shiteng Suo ◽  
Xiao Zhang ◽  
Xiaoqing Wang ◽  
Jianrong Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Idh1 Mutation ◽  
Quantitative Mri ◽  
Learning Approach ◽  
Lower Grade ◽  
Gray Level ◽  
Qualitative And Quantitative ◽  
Machine Learning Approach

Purpose. Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs. Materials and Methods. A total of 102 LGG patients were allocated to training ( n = 67 ) and validation ( n = 35 ) cohorts and were subject to Visually Accessible Rembrandt Images (VASARI) feature extraction (23 features) from conventional multimodal MRI and radiomics feature extraction (56 features) from apparent diffusion coefficient maps. Feature selection was conducted using the maximum Relevance Minimum Redundancy method and 0.632+ bootstrap method. A machine learning model to predict IDH1 mutation was then established using a random forest classifier. The predictive performance was evaluated using receiver operating characteristic (ROC) curves. Results. After feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879. Conclusion. The proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.

scholarly journals Implementation of Rule based Classifiers for Wind Turbine Blade Fault Diagnosis using Vibration Signals

2019 ◽  
Vol 8 (2S11) ◽  
pp. 320-331
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Learning Approach ◽  
Feature Classification ◽  
Rule Based ◽  
Machine Learning Approach

The fast developing wind industry has revealed a requirement for more multifaceted fault diagnosis system in the segments of a wind turbine. “Present wind turbine researches concentrate on enhancing their dependability quality and decreasing the cost of energy production, especially when wind turbines are worked in off-shore places. Wind turbine blades are ought to be an important component among the other basic segments in the wind turbine framework since they transform dynamic energy of wind into useable power and due to environmental conditions, it get damage often and cause lack in productivity. The main objective of this study is to carry out a fault identification model for wind turbine blade using a machine learning approach through vibration data to classify the blade condition. Here five faults namely, blade bend, hub-blade loose connection, blade cracks, blade erosion and pitch angle twist have been considered. Machine learning approach has three steps namely feature extraction, feature selection and feature classification. Feature extraction was carried out by statistical analysis followed by feature selection using J48 decision tree algorithm. Feature classification was done using twelve rule based classifiers using WEKA. The results were compared with respect to the classification accuracy and the computational time of the classifier.”

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

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

scholarly journals MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

2018 ◽  
Vol 17 ◽  
pp. 306-311 ◽  
Author(s):  
Yiming Li ◽  
Zenghui Qian ◽  
Kaibin Xu ◽  
Kai Wang ◽  
Xing Fan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Lower Grade ◽  
Mri Features ◽  
Machine Learning Approach ◽  
P53 Status

scholarly journals A machine learning approach for feature selection traffic classification using security analysis

2018 ◽  
Vol 74 (10) ◽  
pp. 4867-4892 ◽  
Author(s):  
Muhammad Shafiq ◽  
Xiangzhan Yu ◽  
Ali Kashif Bashir ◽  
Hassan Nazeer Chaudhry ◽  
Dawei Wang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Security Analysis ◽  
Learning Approach ◽  
Traffic Classification ◽  
Machine Learning Approach

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261040
Author(s):  
Zazilah May ◽  
M. K. Alam ◽  
Nazrul Anuar Nayan ◽  
Noor A’in A. Rahman ◽  
Muhammad Shazwan Mahmud
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Feature Extraction ◽  
Wavelet Packet ◽  
Support Vector ◽  
Learning Approach ◽  
Severity Assessment ◽  
Corrosion Detection ◽  
Support Vector Classifier ◽  
Machine Learning Approach

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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