A Novel Over-Exposed Region Detection Algorithm Using Non-Linear Support Vector Machine

2017 ◽  
Author(s):  
An Fan ◽  
Ying Liu ◽  
Keng Pang Lim ◽  
Haitao Dong ◽  
Zhaojie Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Detection Algorithm ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Region Detection ◽  
Exposed Region ◽  
Non Linear

EEG Classification of Imaginary Left and Right Leg Movements based on Multivariate Empirical Mode Decomposition and Non-Linear Support Vector Machine

2021 ◽  
Vol 39 (11) ◽  
Author(s):  
Sahar Zolfaghari ◽  
Mohammad Hamiruce Marhaban ◽  
Siti Anom Ahmad ◽  
Asnor Juraiza Ishak ◽  
Pegah Khosropanah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Classification Model ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Non Linear ◽  
Left And Right

Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.

Engine Fault Diagnostic System Based on Non-Linear Support Vector Machine

2012 ◽  
Vol 229-231 ◽  
pp. 534-537
Author(s):  
Gao Huan Xu ◽  
Jun Xiang Ye
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Diagnostic System ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Diagnosis System ◽  
The Core ◽  
Wavelet Energy ◽  
Non Linear ◽  
Fault Diagnosis System

The car engine failures in the course of time and place have many possibilities. The engine fault diagnosis system developed in .NET platform. The core of the system make use of noise wavelet energy features and non-linear support vector machine classification. After the experiment, the system has fairly good results.

scholarly journals Umpire Gesture Detection and Recognition using HOG and Non-Linear Support Vector Machine (NL-SVM) Classification of Deep Features in Cricket Videos

2021 ◽  
Vol 2070 (1) ◽  
pp. 012148
Author(s):  
Suvarna Nandyal ◽  
Suvarna Laxmikant Kattimani
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Gesture Recognition ◽  
Medical Rehabilitation ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Svm Classification ◽  
Gesture Analysis ◽  
Non Linear

Abstract Gesture Recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation, monitoring patients or elder people, surveillance systems, sports gesture analysis, human behaviour analysis etc., to virtual reality. In recent years, there has been increased interest in video summarization and automatic sports highlights generation in the game of Cricket. In Cricket, the Umpire has the authority to make important decisions about events on the field. The Umpire signals important events using unique hand on signals and gestures. The primary intention of our work is to design and develop a new robust method for Umpire Action and Non-Action Gesture Identification and Recognition based on the Umpire Segmentation and the proposed Histogram Oriented Gradient (HOG) feature Extraction oriented Non-Linear Support Vector Machine (NL-SVM) classification of Deep Features. Primarily the 80% of Umpire action and non-action images in a cricket match, about 1, 93, 000 frames, the Histogram of Oriented Gradient Deep Features are calculated and trained the system having six gestures of Umpire pose. The proposed HOG Feature Extraction oriented Non-Linear Support Vector Machine classification method achieves the maximal accuracy of 97.95%, the maximal sensitivity of 98.87%, the maximal specificity of 98.89% and maximal Precision of 97.02% which indicates its superiority.

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