scholarly journals Control of prosthetic hand by using mechanomyography signals based on support-vector machine classifier

2021 ◽  
Vol 23 (2) ◽  
pp. 1180
Author(s):  
Firas Saaduldeen Ahmed ◽  
Noha Abed-Al-Bary Al-jawady
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Wavelet Packet ◽  
Support Vector ◽  
Prosthetic Hand ◽  
Hand Gestures ◽  
Prosthetic Devices ◽  
Prosthetic Hands ◽  
Average Accuracy ◽  
And Control

<div>Prosthetic devices are necessary to help amputees achieve their daily activity in the natural way possible. The prosthetic hand has controlled by type of signals such as electromyography (EMG) and mechanomyography (MMG). The MMG signals have represented mechanical signals that generate during muscle contraction. These signals can be detected by accelerometers or microphones and any kind of sensors that can detect muscle vibrations. The contribution of the current paper is classifying hand gestures and control prosthetic hands depends on pattern recognition through accelerometer and microphone are to detect MMG signals. In addition to the cost of prosthetic hand less than other designs. Six subjects are involved. In this present work is the devices. In this study, two of them are amputee subjects. Each subject performs seven classes of movements. Pattern recognition (PR) is used to classify hand gestures. The wavelet packet transform (WPT) and root mean square (RMS) as features extracted from the signals and support vector machine (SVM) as a classifier. The average accuracy is 88.94% for offline tests and 84.45% for online tests. 3D printing technology is used in this study to build prosthetic hands.</div>

Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine

2017 ◽  
Vol 16 (2) ◽  
pp. 116-121 ◽  
Author(s):  
Shuihua Wang ◽  
Yang Li ◽  
Ying Shao ◽  
Carlo Cattani ◽  
Yudong Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Dendritic Spines ◽  
Wavelet Packet ◽  
Support Vector ◽  
Fuzzy Support Vector Machine

scholarly journals Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianwei Cui ◽  
Mengxiao Shan ◽  
Ruqiang Yan ◽  
Yahui Wu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Energy Difference ◽  
Test System ◽  
Support Vector ◽  
Svm Classifier ◽  
Aero Engine ◽  
Aero Engines ◽  
Optimal Set

This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

scholarly journals Data curation to improve the pattern recognition performance of B-cell epitope prediction by support vector machine

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Cen Lim ◽  
Yee Ying Lim ◽  
Yee Siew Choong
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
B Cell ◽  
Recognition Performance ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Support Vector ◽  
B Cell Epitopes ◽  
Prediction Module ◽  
B Cell Epitope

Abstract B-cell epitope will be recognized and attached to the surface of receptors in B-lymphocytes to trigger immune response, thus are the vital elements in the field of epitope-based vaccine design, antibody production and therapeutic development. However, the experimental approaches in mapping epitopes are time consuming and costly. Computational prediction could offer an unbiased preliminary selection to reduce the number of epitopes for experimental validation. The deposited B-cell epitopes in the databases are those with experimentally determined positive/negative peptides and some are ambiguous resulted from different experimental methods. Prior to the development of B-cell epitope prediction module, the available dataset need to be handled with care. In this work, we first pre-processed the B-cell epitope dataset prior to B-cell epitopes prediction based on pattern recognition using support vector machine (SVM). By using only the absolute epitopes and non-epitopes, the datasets were classified into five categories of pathogen and worked on the 6-mers peptide sequences. The pre-processing of the datasets have improved the B-cell epitope prediction performance up to 99.1 % accuracy and showed significant improvement in cross validation results. It could be useful when incorporated with physicochemical propensity ranking in the future for the development of B-cell epitope prediction module.

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