scholarly journals Machine Learning-Based Fault Diagnosis of Self-Aligning Bearings for Rotating Machinery Using Infrared Thermography

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ankush Mehta ◽  
Deepam Goyal ◽  
Anurag Choudhary ◽  
B. S. Pabla ◽  
Safya Belghith
Keyword(s):  
Fault Diagnosis ◽  
Infrared Thermography ◽  
Condition Monitoring ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mechanical Equipment ◽  
K Nearest Neighbor ◽  
Linear Discriminant

Bearings are considered as indispensable and critical components of mechanical equipment, which support the basic forces and dynamic loads. Across different condition monitoring (CM) techniques, infrared thermography (IRT) has gained the limelight due to its noncontact nature, high accuracy, and reliability. This article presents the use of IRT for the bearing fault diagnosis. A two-dimensional discrete wavelet transform (2D-DWT) has been applied for the decomposition of the thermal image. Principal component analysis (PCA) has been used for the reduction of dimensionality of extracted features, and thereafter the most relevant features are accomplished. Furthermore, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN) as the classifiers were considered for classification of faults and performance assessment. The results reveal that the SVM outperformed LDA as well as KNN. Noncontact condition monitoring shows a great potential to be implemented in determining the health of machine. The utilization of noncontact thermal imaging-based instruments has enormous potential in anticipating the maintenance and increased machine availability.

scholarly journals Detecting Types of Sleep Apneas Through Nonlinear Features of Electrocardiogram and Electroencephalogram Signals

2021 ◽  
Author(s):  
Shaghayegh Saghafi ◽  
Fereidoun Nowshiravan Rahatabad ◽  
Keivan Maghooli
Keyword(s):  
Sleep Apnea ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Majority Voting ◽  
Support Vector ◽  
Svm Classifier ◽  
Common Disease ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nonlinear Features

Purpose: Sleep apnea is a common disease among women, and mainly men. The most dangerous complication of this disorder is heart stroke. Other complications include insufficient sleep and resulting daytime tiredness and illness that affect the individual's activities during the day, disrupt their life. Therefore, identifying this disease is important. Materials and Methods: We used Electroencephalogram (EEG) and Electrocardiogram (ECG) channels from the data of 25 patients with sleep apnea, for each type of sleep apnea, 8 nonlinear-like features, including fractal dimension, correlation dimension, certainty, recurrence rate, mean diagonal lines, the entropy of recursive quantification analysis, sample Entropy, and Shannon entropy were extracted. Then, feature matrices were sorted using principal component analysis in the order of linear combination of features, and the 20 selected features were chosen, normalized using common methods, and fed to different classifiers. Two 5-class and 2-class classification methods were assessed. In the 5-classification, three classifiers were used; the support vector machine, k-nearest neighbor, and multilayer perceptron. Results: The results showed that the highest mean validity, accuracy, sensitivity, and specificity for the SVM classifier was 88.45%, 88.35%, 88.33%, and 88.32%, respectively. In the 2-class approach, in addition to the mentioned classifiers, linear discriminant analysis, Bayes, and majority voting were used, and each class was considered against all classes. The highest average validity, average accuracy, average sensitivity, average specificity using the majority rule voting was 94.35%, 94.30%, 94.32%, and 94.15% respectively. Conclusion: When the results of classifiers are combined with the majority voting method, the validity of identifying the classes increases. The average validity for this method was obtained at 94.42%, which was higher than several other studies. It is recommended that databases with a larger sample size be used. This would lead to increased reliability of the proposed analysis method. Moreover, using novel deep-learning-based methods could help obtain better results.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

scholarly journals Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2936 ◽  
Author(s):  
Xianghao Zhan ◽  
Xiaoqing Guan ◽  
Rumeng Wu ◽  
Zhan Wang ◽  
You Wang ◽  
...  
Keyword(s):  
Herbal Medicine ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Herbal Medicines ◽  
Principal Component ◽  
Support Vector ◽  
Linear Discriminant ◽  
Convenient Means ◽  
Electronic Nose System ◽  
Insight Into

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

2020 ◽  
Author(s):  
Nazrul Anuar Nayan ◽  
Hafifah Ab Hamid ◽  
Mohd Zubir Suboh ◽  
Noraidatulakma Abdullah ◽  
Rosmina Jaafar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Nearest Neighbor ◽  
Total Cholesterol Level ◽  
Population Based ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cvd Risk ◽  
Linear Discriminant ◽  
Artificial Neural Network Ann

Abstract Background: Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. Results: This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. Conclusions: In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

scholarly journals A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2814 ◽  
Author(s):  
Xiaoguang Liu ◽  
Huanliang Li ◽  
Cunguang Lou ◽  
Tie Liang ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Fall Detection ◽  
Classification Performance ◽  
Daily Activities ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Parallel Channels

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

scholarly journals A Study on the Psychological Analysis System Using Machine Learning

2018 ◽  
Vol 7 (3.33) ◽  
pp. 128
Author(s):  
Ki Young Lee ◽  
Kyu Ho Kim ◽  
Jeong Jin Kang ◽  
Sung Jai Choi ◽  
Yong Soon Im ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Smart Phone ◽  
Principal Component ◽  
Digital Data ◽  
Expression Recognition ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Psychological Analysis ◽  
Analysis System

Real-time facial expression recognition and analysis technology is recently drawing attention in areas of computer vision, computer graphics, and HCI. Recognition of user’s emotion on the basis of video and voice is drawing particular interest. The technology may help managers of households or hospitals. In the present study, video and voice were converted into digital data through MATLAB by using PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis), KNN(K Nearest Neighbor) algorithms to analyze emotions through machine learning. The manager of the psychological analysis counseling system may understand a user’s emotion in an smart phone environment. This system of the present study may help the manager to have a smooth conversation or develop a smooth relationship with a user on the basis of the provided psychological analysis results. 

scholarly journals Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

2020 ◽  
Vol 16 (07) ◽  
pp. 34
Author(s):  
Nayan Nazrul Anuar ◽  
Ab Hamid Hafifah ◽  
Suboh Mohd Zubir ◽  
Abdullah Noraidatulakma ◽  
Jaafar Rosmina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Nearest Neighbor ◽  
Total Cholesterol Level ◽  
Population Based ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cvd Risk ◽  
Linear Discriminant ◽  
Artificial Neural Network Ann

<p>Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.</p>

scholarly journals Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Mahmood I. Alhusseini ◽  
Firas Abuzaid ◽  
Albert J. Rogers ◽  
Junaid A.B. Zaman ◽  
Tina Baykaner ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Unique Identifier ◽  
Multiple Systems ◽  
Linear Discriminant ◽  
Link Type ◽  
Decision Logic ◽  
The Hilbert Transform

Background: Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation in AF, which could be applied clinically if CNN classifications could also be explained. Methods: We performed panoramic recording of bi-atrial electrical signals in AF. We used the Hilbert-transform to produce 175 000 image grids in 35 patients, labeled for rotational activation by experts who showed consistency but with variability (kappa [κ]=0.79). In each patient, ablation terminated AF. A CNN was developed and trained on 100 000 AF image grids, validated on 25 000 grids, then tested on a separate 50 000 grids. Results: In the separate test cohort (50 000 grids), CNN reproducibly classified AF image grids into those with/without rotational sites with 95.0% accuracy (CI, 94.8%–95.2%). This accuracy exceeded that of support vector machines, traditional linear discriminant, and k-nearest neighbor statistical analyses. To probe the CNN, we applied gradient-weighted class activation mapping which revealed that the decision logic closely mimicked rules used by experts (C statistic 0.96). Conclusions: CNNs improved the classification of intracardiac AF maps compared with other analyses and agreed with expert evaluation. Novel explainability analyses revealed that the CNN operated using a decision logic similar to rules used by experts, even though these rules were not provided in training. We thus describe a scaleable platform for robust comparisons of complex AF data from multiple systems, which may provide immediate clinical utility to guide ablation. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02997254. Graphic Abstract: A graphic abstract is available for this article.

scholarly journals Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non-invasive BCI

2019 ◽  
Vol 8 (9) ◽  
pp. 1689-1694
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Non Invasive ◽  
Band Limited ◽  
Ca Sensitivity

Electroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.

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