Based on SVM Classification of Connection Pools Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 2435-2438
Author(s):  
Juan Zhao ◽  
Hui Yun Xiong
Keyword(s):  
Classification Algorithm ◽  
Experimental Results ◽  
Svm Classifier ◽  
Database Access ◽  
Service Platform ◽  
Buffer Pool ◽  
Svm Classification ◽  
Connection Pool

The connection pool technology has become a deal with large amount of data requested a solution that is widely used now. This paper used the SVM classification algorithm for classified all database requests quickly, so the corresponding database request could be assigned to different connection pool distribution. We applied the connection pool to measurement service platform and tested on the accuracy of the SVM classifier and buffer pool hit ratios of the connection pool module. The experimental results show that the connection pools can improve the efficiency of database access obviously.

scholarly journals A Hybrid Fuzzy-SVM classifier for automated lung diseases diagnosis

2016 ◽  
Vol 22 (4) ◽  
pp. 97-103 ◽  
Author(s):  
Donia Ben Hassen ◽  
Sihem Ben Zakour ◽  
Hassen Taleb
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Lung Diseases ◽  
Roc Curves ◽  
Chest Radiographs ◽  
Experimental Results ◽  
Svm Classifier ◽  
Forward Selection ◽  
Svm Classification ◽  
Components Analysis

Abstract A novel scheme for lesions classification in chest radiographs is presented in this paper. Features are extracted from detected lesions from lung regions which are segmented automatically. Then, we needed to eliminate redundant variables from the subset extracted because they affect the performance of the classification. We used Stepwise Forward Selection and Principal Components Analysis. Then, we obtained two subsets of features. We finally experimented the Stepwise/FCM/SVM classification and the PCA/FCM/SVM one. The ROC curves show that the hybrid PCA/FCM/SVM has relatively better accuracy and remarkable higher efficiency. Experimental results suggest that this approach may be helpful to radiologists for reading chest images.

scholarly journals Effect of Wireless Channels on Detection and Classification of Asthma Attacks in Wireless Remote Health Monitoring Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Orobah Al-Momani ◽  
Khaled M. Gharaibeh
Keyword(s):  
Wireless Channels ◽  
Wireless Channel ◽  
Point Of View ◽  
Support Vector ◽  
Svm Classifier ◽  
Svm Classification ◽  
Clinical Point ◽  
Health Monitoring Systems ◽  
Remote Health

This paper aims to study the performance of support vector machine (SVM) classification in detecting asthma attacks in a wireless remote monitoring scenario. The effect of wireless channels on decision making of the SVM classifier is studied in order to determine the channel conditions under which transmission is not recommended from a clinical point of view. The simulation results show that the performance of the SVM classification algorithm in detecting asthma attacks is highly influenced by the mobility of the user where Doppler effects are manifested. The results also show that SVM classifiers outperform other methods used for classification of cough signals such as the hidden markov model (HMM) based classifier specially when wireless channel impairments are considered.

scholarly journals Arteriovenous Fistula Flow Dysfunction Surveillance: Early Detection Using Pulse Radar Sensor and Machine Learning Classification

Biosensors ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 297
Author(s):  
Cheng-Hsu Chen ◽  
Teh-Ho Tao ◽  
Yi-Hua Chou ◽  
Ya-Wen Chuang ◽  
Tai-Been Chen
Keyword(s):  
Arteriovenous Fistula ◽  
Classification Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
High Morbidity ◽  
Radar Sensor ◽  
Svm Classification ◽  
Ultrasound Dilution ◽  
Machine Learning Classification ◽  
Pulse Radar

Vascular Access (VA) is often referred to as the “Achilles heel” for a Hemodialysis (HD)-dependent patient. Both the patent and sufficient VA provide adequacy for performing dialysis and reducing dialysis-related complications, while on the contrary, insufficient VA is the main reason for recurrent hospitalizations, high morbidity, and high mortality in HD patients. A non-invasive Vascular Wall Motion (VWM) monitoring system, made up of a pulse radar sensor and Support Vector Machine (SVM) classification algorithm, has been developed to detect access flow dysfunction in Arteriovenous Fistula (AVF). The harmonic ratios derived from the Fast Fourier Transform (FFT) spectrum-based signal processing technique were employed as the input features for the SVM classifier. The result of a pilot clinical trial showed that a more accurate prediction of AVF flow dysfunction could be achieved by the VWM monitor as compared with the Ultrasound Dilution (UD) flow monitor. Receiver Operating Characteristic (ROC) curve analysis showed that the SVM classification algorithm achieved a detection specificity of 100% at detection thresholds in the range from 500 to 750 mL/min and a maximum sensitivity of 95.2% at a detection threshold of 750 mL/min.

scholarly journals Classification of Chronic Obstructive Pulmonary Disease (COPD) using Gabor Filter With SVM Classifier

2019 ◽  
Vol 9 (1) ◽  
pp. 787-790
Keyword(s):  
Medical Diagnosis ◽  
Extraction Method ◽  
Gabor Filter ◽  
Svm Classifier ◽  
Chronic Obstructive ◽  
Structural Representation ◽  
Obstructive Pulmonary Disease ◽  
Svm Classification ◽  
Feature Extraction Method

In the field of medical diagnosis, early detection of COPD symptoms is difficult. The use of predictive tests leads to the treatment of COPD and also helps to identify signs of COPD early. Therefore, we present in this paper a feature extraction method for the structural representation of COPD images using the Gabor Filter. In addition, we train and evaluate COPD extracted function or category with SVM classification. The results show that the proposed method can be more accurate, more flexible, and more reliable. This approach is well adapted for early diagnosis of COPD.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

scholarly journals Classification of SSVEP-based BCIs using Genetic Algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamideh Soltani ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Keivan Maghooli
Keyword(s):  
Genetic Algorithm ◽  
Dimension Reduction ◽  
Classification Accuracy ◽  
Bayesian Method ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Effective Dimension ◽  
Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

scholarly journals Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zhu-Hong You ◽  
Yu-Hong Fang ◽  
Yu-Jun Zhao ◽  
...  
Keyword(s):  
Protein Sequences ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Computational Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Local Phase ◽  
Local Phase Quantization ◽  
Phase Quantization ◽  
Better Than

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments onYeastandHumandatasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on theYeastdataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

MAGNETOREOLOGICAL JOURNAL BEARING: EXPERIMENTAL RESULTS

2020 ◽  
Vol 3 ◽  
pp. 83-90
Author(s):  
A.S. FETISOV ◽  
V.O. TYURIN
Keyword(s):  
Experimental Study ◽  
Journal Bearing ◽  
Experimental Results ◽  
Measuring System ◽  
Information Measuring ◽  
Experimental Stand ◽  
Information Measuring System

The article presents the classification of magnetorheological devices. The classification of bearings of rotor machines is given. An experimental stand is described that includes a magnetorheological journal bearing. The information–measuring system of the experimental stand is presented. The results of experimental study is presented.

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