scholarly journals Personal Identification Based on Vectorcardiogram Derived from Limb Leads Electrocardiogram

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Jongshill Lee ◽  
Youngjoon Chee ◽  
Inyoung Kim
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Personal Identification ◽  
Experimental Results ◽  
New Method ◽  
Support Vector ◽  
Inverse Transform ◽  
Transform Matrix ◽  
Electrocardiogram Ecg

We propose a new method for personal identification using the derived vectorcardiogram (dVCG), which is derived from the limb leads electrocardiogram (ECG). The dVCG was calculated from the standard limb leads ECG using the precalculated inverse transform matrix. Twenty-one features were extracted from the dVCG, and some or all of these 21 features were used in support vector machine (SVM) learning and in tests. The classification accuracy was 99.53%, which is similar to the previous dVCG analysis using the standard 12-lead ECG. Our experimental results show that it is possible to identify a person by features extracted from a dVCG derived from limb leads only. Hence, only three electrodes have to be attached to the person to be identified, which can reduce the effort required to connect electrodes and calculate the dVCG.

An New Fuzzy Support Vector Machine for Binary Classification

2012 ◽  
Vol 433-440 ◽  
pp. 2856-2861 ◽  
Author(s):  
Rui Zhang ◽  
Tong Bo Liu ◽  
Ming Wen Zheng
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Experimental Results ◽  
New Method ◽  
Support Vector ◽  
Fuzzy Support Vector Machine

In this paper, we proposed a new fuzzy support vector machine(called L2–FSVM here), which error part of object is L2–norm.Meanwhile we introduce a new method of generating fuzzy memberships so as to reduce to effects of outliers. The experimental results demonstrate that the L2-FSVM method provides improved ability to reduce to effects of outliers in comparison with traditional SVMs and FSVMs, and claim that L2–FSVM is the best way to solve the binary classification in the three methods stated above.

Application of ACO-SVM in Chinese Text Feature Selection

2012 ◽  
Vol 155-156 ◽  
pp. 770-775
Author(s):  
Jin Xiu Cui ◽  
Xiao Xia Huang
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Chinese Text ◽  
Classification Accuracy ◽  
Considerable Increase ◽  
Experimental Results ◽  
Support Vector ◽  
Text Feature

The algorithm proposed in this paper applies ACO in combination with support vector machine (SVM) in Chinese text feature selection. It obtains classifier models for each category at last. The experimental results show that the proposed method is feasible and lead to a considerable increase of classification accuracy.

Infinite AdaBoost and its Application on Fault Diagnosis for Analog Circuits

2011 ◽  
Vol 201-203 ◽  
pp. 2070-2074
Author(s):  
He Xun Wang ◽  
Chong Liu ◽  
Yu Qing Sun
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Ensemble Learning ◽  
Analog Circuits ◽  
Classification Accuracy ◽  
Experimental Results ◽  
Support Vector ◽  
Adaboost Algorithm ◽  
Learning Framework

AdaBoost algorithm can achieve better performance by averaging over the predictions of some weak hypotheses. To improve the power of classification ability of AdaBoost, an infinite ensemble learning framework based on the Support Vector Machine was formulated. The framework can output an infinite AdaBoost through embedding infinite hypotheses into a new kernel of Support Vector Machine. The stump kernel embodies infinite decision stumps. At last, the algorithm was used in fault diagnosis for analog circuits. Experimental results show that infinite AdaBoost with Support Vector Machine is superior than finite AdaBoost with the same base hypothesis set. The purpose of enhancing classification accuracy of AdaBoost algorithm is achieved.

A NEW TECHNIQUE FOR SELECTING FEATURES FROM PROTEIN SEQUENCES

2006 ◽  
Vol 20 (02) ◽  
pp. 271-283 ◽  
Author(s):  
XING-MING ZHAO ◽  
JI-XIANG DU ◽  
HONG-QIANG WANG ◽  
YUNPING ZHU ◽  
YIXUE LI
Keyword(s):  
Support Vector Machine ◽  
Relative Entropy ◽  
Protein Sequences ◽  
Entropy Method ◽  
Experimental Results ◽  
New Method ◽  
Support Vector ◽  
Svm Classifier ◽  
A New Technique ◽  
Relative Entropy Method

A new method for selecting features from protein sequences is proposed in this paper. First, the protein sequences are converted into fixed-dimensional feature vectors. Then, a subset of features is selected using relative entropy method and used as the inputs for Support Vector Machine (SVM). Finally, the trained SVM classifier is utilized to classify protein sequences into certain known protein families. Experimental results over proteins obtained from PIR database and GPCRs have shown that our proposed approach is really effective and efficient in selecting features from protein sequences.

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

2020 ◽  
Vol 13 (2) ◽  
pp. 153-164
Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Time Window ◽  
Optimal Solution ◽  
Similarity Score ◽  
Learning Objects ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Computationally Efficient ◽  
User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

Detection and Recognition of RF Devices Using Support Vector Machine

2013 ◽  
Vol 5 (4) ◽  
pp. 13-20
Author(s):  
Shikhar P. Acharya ◽  
Ivan G. Guardiola
Keyword(s):  
Support Vector Machine ◽  
Radio Frequency ◽  
Experimental Results ◽  
Support Vector ◽  
Noise Band ◽  
Detection And Identification ◽  
Electromagnetic Emissions ◽  
Rf Devices ◽  
Unintended Electromagnetic Emissions ◽  
Detection And Recognition

Radio Frequency (RF) devices produce some amount of Unintended Electromagnetic Emissions (UEEs). UEEs are generally unique to a device and can be used as a signature for the purpose of detection and identification. The problem with UEEs is that they are very low in power and are often buried deep inside the noise band. The research herein provides the application of Support Vector Machine (SVM) for detection and identification of RF devices using their UEEs. Experimental Results shows that SVM can detect RF devices within the noise band, and can also identify RF devices using their UEEs.

Sign in / Sign up

Export Citation Format

Share Document