A machine learning approach to detect aortic valve dysfunction through phase portrait feature extraction

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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Machine Learning Approach for Tensegrity Form Finding: Feature Extraction Problem

2020 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR) ◽

10.1109/dcnair50402.2020.9216799 ◽

2020 ◽

Author(s):

Eduard Zalyaev ◽

Sergei Savin ◽

Lyudmila Vorochaeva

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Learning Approach ◽

Form Finding ◽

Machine Learning Approach

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Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach

Journal of Biological Physics ◽

10.1007/s10867-021-09567-8 ◽

2021 ◽

Author(s):

Sankararaman Sreejyothi ◽

Ammini Renjini ◽

Vimal Raj ◽

Mohanachandran Nair Sindhu Swapna ◽

Sankaranarayana Iyer Sankararaman

Keyword(s):

Machine Learning ◽

Phase Portrait ◽

Learning Approach ◽

Machine Learning Approach

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A Tree Based Machine Learning Approach for PTB Diagnostic Dataset

Journal of Physics Conference Series ◽

10.1088/1742-6596/2115/1/012042 ◽

2021 ◽

Vol 2115 (1) ◽

pp. 012042

Author(s):

S Premanand ◽

Sathiya Narayanan

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Deep Learning ◽

Learning Algorithms ◽

Primary Objective ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Related Data ◽

Machine Learning Approach ◽

Health Related

Abstract The primary objective of this particular paper is to classify the health-related data without feature extraction in Machine Learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based Machine Learning algorithms without extracting features like in Deep Learning. This study performs better classification with Tree based Machine Learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of Machine Learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep Learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based Machine Learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively.

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Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Scientific Programming ◽

10.1155/2020/5812715 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Atif Khan ◽

Muhammad Adnan Gul ◽

M. Irfan Uddin ◽

Syed Atif Ali Shah ◽

Shafiq Ahmad ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Language Processing ◽

Big Data Analytics ◽

Extraction Technique ◽

Learning Approach ◽

Machine Learning Approach ◽

Challenging Tasks ◽

Bayes Algorithm ◽

Feature Extraction Technique

Information is exploding on the web at exponential pace, so online movie review is becoming a substantial information resource for online users. However, users post millions of movie reviews on regular basis, and it is not possible for users to summarize the reviews. Movie review classification and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is demanded to summarize the vast amount of movie reviews, and it will allow the users to speedily distinguish the positive and negative aspects of a movie. This study has proposed an approach for movie review classification and summarization. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents, and represent the review documents as a vector space model. Next, the Naïve Bayes algorithm is employed to classify the movie reviews (represented as a feature vector) into positive and negative reviews. For the task of movie review summarization, Word2vec feature extraction technique is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are used to calculate the salience score of each review sentence in clusters. Finally, the top-ranked sentences are chosen based on highest salience scores to produce the extractive summary of movie reviews. Experimental results reveal that the proposed machine learning approach is superior than other state-of-the-art approaches.

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