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  • International Journal Advanced Network Monitoring Controls

 

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 | 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 | 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

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 | 30-November-2018

A New Method of Improving the Traditional Traffic Identification and Accuracy

traffic traces, used machine learning techniques(SVM model[17]) to improve system performance and enable real-time traffic identification for high-speed networks. Zhao X proposed a P2P network traffic classification method based on support vector machine [19], using a statistical principle to divide the network traffic of four different types of P2P traffic applications (file sharing BitTorrent, media streaming PPLive, Internet phone Skype, instant messaging MSN), and studied network traffic

Wang Zhongsheng, Gao Jiaqiong

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

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

Research paper | 01-September-2014

KNOWLEDGE-BASED MODELING FOR PREDICTING CANE SUGAR CRYSTALLIZATION STATE

, based on support vector machine with particle swarm optimization, to improve the predictive accuracy and generalization capacity. Furthermore, the intelligent system is tested using a self-regulating intelligent comprehensive monitoring and controlling platform that represents the cane sugar process. Results demonstrate the feasibility of the system for predicting the crystallization state in a real cane sugar process.

Yanmei Meng, Xian Yu, Haiping He, Zhihong Tang, Xiaochun Wang, Jian Chen

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 942–965

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 | 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

Research paper | 10-April-2013

STUDY ON FEATURE SELECTION AND IDENTIFICATION METHOD OF TOOL WEAR STATES BASED ON SVM

extracted features, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. It is crucial to extract a smaller feature set by an effective feature selection algorithm. In this paper, an approach based on one-versus-one multi-class Support Vector Machine Recursive Feature Elimination (SVM-RFE) is proposed to solve the feature selection problem in tool wear condition monitoring. Moreover, in order to analyze a performance degradation process on the machine tool

Weilin Li, Pan Fu, Weiqing Cao

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 448–465

Research Article | 01-December-2017

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

Object based classification plays an important role in every field. Support vector machine is the popular algorithm for object based classification. Support vector machine classifies the data points using straight line. Some datasets are impossible to separate by straight line. To cope with this problem kernel function is used. The central idea of kernel function is to project points up in a higher dimensional space hoping that separability of data would improve. There are various kernels in

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

Object based classification plays an important role in every field. Support vector machine is the popular algorithm for object based classification. Support vector machine classifies the data points using straight line. Some datasets are impossible to separate by straight line. To cope with this problem kernel function is used. The central idea of kernel function is to project points up in a higher dimensional space hoping that separability of data would improve. There are various kernels in

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

Article | 11-April-2018

Research on Combination Forecasting Model of Mine Gas Emission

This paper focuses on the effective analysis of the mine gas emission monitoring data, so as to realize the accurate and reliable mine gas emission prediction. Firstly, a weighted multiple computing models based on parametric t– norm is constructed. And a new mine gas emission combination forecasting method is proposed. The BP neural network model and the support vector machine were used as the single prediction models. Finally, genetic algorithm and least square method were used to

Liang Rong, Chang Xintan, Jia Pengtao, Dong Dingwen

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 194–198

Article | 16-December-2013

Target Classification Using Pyroelectric Infrared Sensors in Unattended Wild Ground Environment

Dongfeng Xie, Huawei Liu, Baoqing Li, Qianwei Zhou, Xiaobing Yuan

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 5, 2119–2135

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-2014

 VIDEO-BASED VEHICLE DETECTION AND CLASSIFICATION IN CHALLENGING SCENARIOS   

dimension reduction. In vehicle classification part, we adopt fuzzy support vector machine, and design a novel vehicle classifier based on nested one-vs-one algorithm. Finally, experimental tests show excellent results of our methods in both vehicle detection and classification.   

Yiling Chen, GuoFeng Qin

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1077–1094

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

Article | 01-December-2016

AN INTELLIGENT FEATURE SELECTION AND CLASSIFICATION METHOD BASED ON HYBRID ABC–SVM

This paper presents a new approach to feature selection and classifcation based on support vector machine and hybrid artificial bee colony. The approach consists of two stages. At the first stage, this paper presented a hybrid artificial bee colony-based classifier model that combines artificial bee colony to improve classification accuracy with the most superior model parameter and features were selected from the original feature set. The classification accuracy and the feature subset provided

Jie Li, Qiuwen Zhang, Zhang Yongzhi, Li Chang, Xiao Jian

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1876–

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 | 05-September-2013

AN INVESTIGATION OF DECISION ANALYTIC METHODOLOGIES FOR STRESS IDENTIFICATION

data set created by the MIT Media lab is used to evaluate the relative performance of these methods. Our study show that the PCA can not only reduce the needed number of features from 22 to five, but also the number of sensors used from five to two and it only uses one type of sensor, thus increasing the application usability. The selected features can be used to quickly detect stress level with good accuracy (78.94%), if support vector machine fusion method is used.

Yong Deng, Chao-Hsien Chu, Huayou Si, Qixun Zhang, Zhonghai Wu

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1675–1699

Article | 01-June-2016

PREDICTION OF SEWAGE QUALITY BASED ON FUSION OF BPNETWORKS

Sewage treatment system is a complicated nonlinear system with multi-variables, chemical reaction, biological process and altered loads, hard to describe mathematically. Thus prediction of the effluent quality parameters of sewage treatment plant has being a challenge. In this paper we adopt fusion of two BP networks to predict sewage quality parameters with a popular process Cyclic Activated Sludge System (CASS). We take use of SVM (support vector machine) to classify the input data into two

Lijuan Wang

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 909–926

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

Article | 03-September-2013

Detection and classification of the behavior of people in an intelligent building by camera

the heating . My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use RGB color histograms and textures for LBP represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for the detection and classification of the behavior of people in this

Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1317–1342

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