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  • In Jour Smart Sensing And Intelligent Systems
  • International Journal Advanced Network Monitoring Controls

 

Article | 27-December-2017

FACE DETECTION IN PROFILE VIEWS USING FAST DISCRETE CURVELET TRANSFORM (FDCT) AND SUPPORT VECTOR MACHINE (SVM)

YCgCr (luminance - green chrominance - red chrominance) color models is used to extract skin blocks. The segmentation scheme utilizes only the S and CgCr components, and is therefore luminance independent. Features extracted from three frequency bands from curvelet decomposition are used to detect face in each block. A support vector machine (SVM) classifier is trained for the classification task. In the performance test, the results showed that the proposed algorithm can detect profile faces in

Bashir Muhammad, Syed Abd Rahman Abu-Bakar

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 1, 108–123

Article | 30-November-2018

A New Method of Improving the Traditional Traffic Identification and Accuracy

proposed method is also applicable to encrypted network traffic. II. PROPOSED METHOD A. Support Vector Machine (SVM) model SVM is a machine learning method that is based on one of the statistical algorithms with good generalization ability. It is mainly used to solve small samples. The feature vectors of the data stream in the network are more or less, and too many features will affect the efficiency and accuracy of the SVM algorithm. Therefore, to reduce redundant features, feature combinations

Wang Zhongsheng, Gao Jiaqiong

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 3, 53–60

Article | 01-March-2015

ROBUST VISUAL TRACKING BASED ON SUPPORT VECTOR MACHINE AND WEIGHTED SAMPLING METHOD

Visual tracking algorithm based on binary classification has become the research hot issue. The tracking algorithm firstly constructs a binary classifier between object and background, then to determine the object’s location by the probability of the classifier. However, such binary classification may not fully handle the outliers, which may cause drifting. To improve the robustness of these tracking methods, a novel object tracking algorithm is proposed based on support vector machine (SVM

Gao Xiaoxing, Liu Feng

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 255–271

Article | 05-September-2013

PREDICTION OF PCCP FAILURE BASED ON HYDROPHNE DETECTING

Prestressed Concrete Cylinder Pipe (PCCP) is a widely used water pipe all over the world. A major cause of PCCP failure is the internal wire break, which will emit acoustic signal. In this paper, a hydrophone-based PCCP real-time monitoring and failure-prediction system was proposed. By applying wavelet energy normalization analysis to signal feature extraction and Support Vector Machine (SVM) to signal recognition, a high prediction accuracy of 98.33% was achieved. The result showed that the

Yuan Zhang, Yibo Li

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1582–1598

Research Article | 01-September-2017

MACHINE VISION BASED MISSING FASTENER DETECTION IN RAIL TRACK IMAGES USING SVM CLASSIFIER

Missing fastener detection is a critical task due to its similar characteristics with surrounding environments. In this paper, a machine vision based fully automatic detection and classification of missing fastener detection system is proposed using Support Vector Machine (SVM) classifier. This proposed system consists of preprocessing, transformation, feature extraction and classifications. Image resizing is performed as preprocessing step and Gabor transform is used as transformation

R. Manikandan, M. Balasubramanian, S. Palanivel

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 574–589

Research Article | 27-December-2017

INDEX FINGER MOTION RECOGNITION USING SELF-ADVISE SUPPORT VECTOR MACHINE

and well-known classifier, Support Vector Machine (SVM). An improvement of SVM, Self-advise SVM (SA-SVM), is tested to evaluate and compare its performance with the original one. The experimental result shows that SA-SVM improves the classification performance by on average 0.63 %.

Khairul Anam, Adel Al Jumaily, Yashar Maali

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 2, 644–657

Article | 14-October-2020

A Comparative Study of Face Recognition Classification Algorithms

. The methods used involve linear logistic regression, linear discriminant analysis (LDA), K-Nearest Neighbor (KNN), support vector machine (SVM), Naïve Bayes (NB) and other methods. The definition and advantages and disadvantages of the act are briefly explained. Finally, the five methods are compared and analyzed according to the evaluation indicators such as the accuracy rate, recall rate, F1-score, and AUC area commonly used in machine learning. II. RELATED WORK A. Principal component

Changyuan Wang, Guang Li, Pengxiang Xue, Qiyou Wu

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 23–29

Article | 01-September-2012

AN APPROPRIATE PROCEDURE FOR DETECTION OF JOURNAL-BEARING FAULT USING POWER SPECTRAL DENSITY, K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE

Journal-bearings play a significant role in industrial applications and the necessity of condition monitoring with nondestructive tests is increasing. This paper deals a proper fault detection technique based on power spectral density (PSD) of vibration signals in combination with K-Nearest Neighbor and Support Vector Machine (SVM). The frequency domain vibration signals of an internal combustion engine with three journal-bearing conditions were gained, corresponding to, (i) normal, (ii

A. Moosavian, H. Ahmadi, A. Tabatabaeefar, B. Sakhaei

International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 3, 685–700

Article | 01-March-2016

VIBRATION BASED HEALTH MONITORING OF HONEYCOMB CORE SANDWICH PANELS USING SUPPORT VECTOR MACHINE

monitoring of these structures using support vector machine (SVM). The proposed technique is first used on simulated mode shape data of the structure and then the technique is validated using experimental mode shape data. The experimental set up has been developed in laboratory and Laser Doppler Vibrometer (LDV) is used to extract the experimental mode shapes. The results have been obtained using both support vector classification and regression analysis and it is found that that the former is better at

Saurabh Gupta, Satish B Satpal, Sauvik Banerjee, Anirban Guha

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 1, 215–232

Research Article | 05-September-2018

Multi-algorithmic Palmprint Authentication System Based on Score Level Fusion

transform. The match scores obtained from matching modules were normalized using z-score and fused with different score level fusion schemes namely sum-rule,weighted sum-rule, and Support Vector Machine (SVM) fusion, respectively. The experimental results show that SVM score level fusion lead to an increased performance for the proposed multi-algorithmic palmprint authentication system with genuine acceptance rate of 98% for 0.1% false acceptance rate, and equal error rate of 1.5% compared to weighted

C. Murukesh, G. Arul Elango

International Journal on Smart Sensing and Intelligent Systems, Volume 11 , ISSUE 1, 1–11

Article | 07-May-2018

Multiple Vehicle License Plate Location in Complex Background

the color, dimension, texture and match the similarity to choose, search and merger area in the image, suspicious area of the license plate is obtained. Using visual word package to express rectangular profile after coarse positioning. Using support vector machine (SVM) to classify and identify rectangular area of license plate. Accurate positioning license plate location is positioned accurately. The method of accuracy is 96.4% for 135 pieces of test sample positioning, strong anti-jamming.

Yaxin Zhao, Li Zhao, Ya Li

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 1, 62–68

Research Article | 01-December-2017

Dwipa Ontology III: Implementation of Ontology Method Enrichment on Tourism Domain

This article summarizes some research results related to ontology enrichment specific to tourism domains from 2014 to 2017. Currently, some ontology enrichment approaches can use learning machinery such as support vector machine (SVM), Conditional Random Field (CRF) and kNN. Several studies have also been successful in evaluating ontology enrichment results with several parameters such as precision, recall and F-Measure. In addition, our research can enrich Dwipa Ontology II which has been

Guson Prasamuarso Kuntarto, Irwan Prasetya Gunawan, Fahmi L. Moechtar, Yudhiansyah Ahmadin, Berkah I. Santoso

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 4, 903–919

Research Article | 01-December-2017

PERFORMANCE EVALUATION OF SVM KERNELS ON MULTISPECTRAL LISS III DATA FOR OBJECT CLASSIFICATION

the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of SVM .By changing the value of ‘C’ and γ varying results are observed. Among these RBF kernel with a value of C = 1000 and gamma=0.75 got an excellent accuracy of 99.1509%.The SVM-RBF

S.V.S. Prasad, T. Satya Savithri, Iyyanki V. Murali Krishna

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 4, 829–844

Research Article | 01-December-2017

PERFORMANCE EVALUATION OF SVM KERNELS ON MULTISPECTRAL LISS III DATA FOR OBJECT CLASSIFICATION

the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of SVM .By changing the value of ‘C’ and γ varying results are observed. Among these RBF kernel with a value of C = 1000 and gamma=0.75 got an excellent accuracy of 99.1509%.The SVM-RBF kernel gave an edge

S.V.S. Prasad, T. Satya Savithri, Iyyanki V. Murali Krishna

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 4, 863–878

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