scholarly journals Support Vector Machine Performance with Optimal Parameters Identification in Recognising Asphyxiated Infant Cry

2018 ◽  
Vol 7 (3.15) ◽  
pp. 114
Author(s):  
R Sahak ◽  
W Mansor ◽  
Khuan Y. Lee ◽  
A Zabidi
Keyword(s):  
Support Vector Machine ◽  
Optimal Parameter ◽  
Early Stage ◽  
Computation Time ◽  
Principal Component ◽  
Parameters Identification ◽  
Least Square ◽  
Support Vector ◽  
Optimal Parameters ◽  
Infant Cry

Detection of asphyxia in infant at an early stage is crucial to reduce the rate of infant morbidity. The information regarding asphyxia can be extracted from infant cry signals using support vector machine (SVM) combined with effective feature selection methods such as principal component analysis (PCA) or orthogonal least square (OLS). The performance of SVM in recognizing infant cry with asphyxia after undergone comprehensive identification of optimal parameters at the feature extraction and classification stages has not been     reported. This paper describes the two stages of optimal parameter identification; at Mel-frequency Cepstral coefficient (MFCC) analysis stage, SVM with and without employing the PCA and OLS stages, and the performance of the SVM in recognizing infant cry with asphyxia resulted from all levels of optimal parameters identification. The SVM was first optimized after performing MFCC analysis to find the optimum parameters. Two types of kernels were used, the polynomial and RBF kernels. The subsequent SVM optimizations were conducted after PCA and OLS were employed. In the PCA, the significant features were selected using eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE). The SVM performance was evaluated based on classification accuracy and computation time. The experimental results have shown that the optimized SVM was able to recognize the asphyxiated infant cry with an accuracy of 94.84% and computation time of 1.98s using PCA with EOC and RBF kernel.  

scholarly journals Performance of Principal Component Analysis and Orthogonal Least Square on Optimized Feature Set in Classifying Asphyxiated Infant Cry Using Support Vector Machine

2018 ◽  
Vol 9 (1) ◽  
pp. 139
Author(s):  
R. Sahak ◽  
W. Mansor ◽  
Khuan Y. Lee ◽  
A. Zabidi
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Least Square ◽  
Support Vector ◽  
Infant Cry ◽  
Orthogonal Least Square

<p>An investigation into optimized support vector machine (SVM) integrated with principal component analysis (PCA) and orthogonal least square (OLS) in classifying asphyxiated infant cry was performed in this study. Three approaches were used in the classification; SVM, PCA-SVM, and OLS-SVM. Various numbers of features extracted from Mel-frequency Cepstral coefficient (MFCC) were tested to obtain the optimal parameters of SVM kernels. Once the optimal feature set is obtained, PCA and OLS selected the most significant features and the optimized SVM then classified the selected cry patterns. In PCA-SVM, eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE) were used to select the most significant features. SVM with radial basis function (RBF) kernel was chosen in the classification stage. The classification accuracy and computation time were computed to evaluate the performance of each method. The best method for classifying asphyxiated infant cry is PCA-SVM with EOC since it produces the highest classification accuracy which is 94.84%. Using PCA-SVM, the classification process was performed in 1.98s only. The results also show that employing feature selection techniques could enhance the classifier performance.</p>

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

2021 ◽  
Author(s):  
Tim Brandes ◽  
Stefano Scarso ◽  
Christian Koch ◽  
Stephan Staudacher
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Principal Component ◽  
General Term ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Model ◽  
Precision Error ◽  
Operational Conditions ◽  
Wide Range

Abstract A numerical experiment of intentionally reduced complexity is used to demonstrate a method to classify flight missions in terms of the operational severity experienced by the engines. In this proof of concept, the general term of severity is limited to the erosion of the core flow compressor blade and vane leading edges. A Monte Carlo simulation of varying operational conditions generates a required database of 10000 flight missions. Each flight is sampled at a rate of 1 Hz. Eleven measurable or synthesizable physical parameters are deemed to be relevant for the problem. They are reduced to seven universal non-dimensional groups which are averaged for each flight. The application of principal component analysis allows a further reduction to three principal components. They are used to run a support-vector machine model in order to classify the flights. A linear kernel function is chosen for the support-vector machine due to its low computation time compared to other functions. The robustness of the classification approach against measurement precision error is evaluated. In addition, a minimum number of flights required for training and a sensible number of severity classes are documented. Furthermore, the importance to train the algorithms on a sufficiently wide range of operations is presented.

Prediction of Undervoltage Load Shedding Using Quantum-Inspired Evolutionary Programming-Support Vector Machine

2015 ◽  
Vol 785 ◽  
pp. 43-47
Author(s):  
Zuhaila Mat Yasin ◽  
Zuhaina Zakaria ◽  
Titik Khawa Abdul Rahman
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Evolutionary Programming ◽  
Test System ◽  
Least Square ◽  
Support Vector ◽  
Rbf Kernel ◽  
Validation Procedure ◽  
Programming Support ◽  
A New Technique

This paper presents a new technique to predict the optimal amount of load to be shed at various loading conditions using Quantum-Inspired Evolutionary Programming–Support Vector Machine (QIEP-SVM). QIEP is utilised to optimise the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimisation is to minimise the mean square error (MSE). The performance of QIEP-SVM technique was compared with those obtained from LS-SVM technique with prediction accuracy through a 10-fold cross-validation procedure. All simulations in this study were carried out using IEEE 69-bus distribution test system. QIEP-SVM model had shown better prediction performance as compared to LS-SVM. The results also indicate that the proposed approach outperforms the most recently reported technique in terms of accuracy and fast computation time.

scholarly journals A Novel Shearer Cutting State Recognition Method Based on Improved Variational Mode Decomposition and LSSVM with Acoustic Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhongbin Wang ◽  
Bin Liang ◽  
Lei Si ◽  
Kuangwei Tong ◽  
Chao Tan
Keyword(s):  
Search Algorithm ◽  
Optimal Parameter ◽  
Gravitational Search Algorithm ◽  
Principal Component ◽  
Least Square ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Recognition Method ◽  
Mode Decomposition

The recognition of shearer cutting state is the key technology to realize the intelligent control of the shearer, which has become a highly difficult subject concerned by the world. This paper takes the sound signal as analytic objects and proposes a novel recognition method based on the combination of variational mode decomposition (VMD), principal component analysis method (PCA), and least square support vector machine (LSSVM). VMD can decompose a signal into various modes by using calculus of variation and effectively avoid the false component and mode mixing problems. On this basis, an improved gravitational search algorithm (IGSA) is designed by using the position update mechanism of Levy flight strategy to find the optimal parameter combination of VMD. Then, the feature extraction is achieved by calculating the envelope entropy and kurtosis of the decomposed intrinsic mode functions (IMFs). To avoid dimensional disasters and reinforce the classification performance, PCA is introduced to choose useful features, and the LSSVM-based classifier is reasonably constructed. Finally, the experimental results indicate that the proposed method is more feasible and superior in the recognition of shearer cutting states.

A hybrid combination of LS-SVM and KPCA with bat algorithm for intrusion detection

2019 ◽  
Vol 12 ◽  
Author(s):  
Thiruppathy Kesavan. V ◽  
Loheswaran K
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Principal Component ◽  
Bat Algorithm ◽  
Least Square ◽  
Kernel Principal Component Analysis ◽  
Support Vector

: Intrusion Detection System is one of the prominent ways to identify the attacks by effectively monitoring the network. Designing an intrusion detection system that utilizes the resources efficiently by improving the precision is a challenging factor. This paper proposes a Least Square Support Vector Machine (LS-SVM) based on bat algorithm (BA) for efficient intrusion detection. The proposed technique is divided into two phases. In the first phase, the Kernel principal component analysis (KPCA) is utilized as a pre-processing of LS-SVM to decrease the dimension of feature vectors and abbreviates the preparing time with a specific end goal to decrease the noise caused by feature contrasts and enhance the implementation of LS-SVM. In the second phase, the LS-SVM with bat algorithm is applied for the classification of detection. BA utilizes programmed zooming to adjust investigation and abuse among the hunting procedure. Finally, as per the ideal feature subset, the feature weights and the parameters of LS-SVM are optimized at the same time. The proposed algorithm is named as Kernel principal component analysis based least square support vector machine with bat algorithm (KPCA-BA-LS-SVM). To show the adequacy of proposed method, the tests are completed on KDD 99 dataset which is viewed as an accepted benchmark for assessing the execution of intrusions detection. Furthermore, our proposed hybridization method gets a sensible execution regarding precision and efficiency.

A soft computing approach for diabetes disease classification

2016 ◽  
Vol 24 (4) ◽  
pp. 379-393 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Othman Bin Ibrahim ◽  
Abbas Mardani ◽  
Ali Ahani ◽  
Ahmad Jusoh
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Intelligent System ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Removal ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Support Vector

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

Sign in / Sign up

Export Citation Format

Share Document