scholarly journals Support Vector Machine Based Mobility Prediction Scheme in Heterogeneous Wireless Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jiamei Chen ◽  
Lin Ma ◽  
Yubin Xu
Keyword(s):  
Support Vector Machine ◽  
Wireless Networks ◽  
Prediction Accuracy ◽  
Heterogeneous Wireless Networks ◽  
Support Vector ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Prediction Scheme ◽  
The Cost ◽  
Different Characteristics

To improve the intelligence of the mobile-aware applications in the heterogeneous wireless networks (HetNets), it is essential to establish an advanced mechanism to anticipate the change of the user location in every subnet for HetNets. This paper proposes a multiclass support vector machine based mobility prediction (Multi-SVMMP) scheme to estimate the future location of mobile users according to the movement history information of each user in HetNets. In the location prediction process, the regular and random user movement patterns are treated differently, which can reflect the user movements more realistically than the existing movement models in HetNets. And different forms of multiclass support vector machines are embedded in the two mobility patterns according to the different characteristics of the two mobility patterns. Moreover, the introduction of target region (TR) cuts down the energy consumption efficiently without impacting the prediction accuracy. As reported in the simulations, our Multi-SVMMP can overcome the difficulties found in the traditional methods and obtain a higher prediction accuracy and user adaptability while reducing the cost of prediction resources.

scholarly journals Characterization of Primary Users in Cognitive Radio Wireless Networks using Support Vector Machine

2017 ◽  
Vol 10 (32) ◽  
pp. 1-12
Author(s):  
Danilo Lopez Sarmiento ◽  
Edwin Rivas Trujillo ◽  
Oscar Eduardo Gualdron ◽  
◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wireless Networks ◽  
Cognitive Radio ◽  
Support Vector ◽  
Primary Users

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

2013 ◽  
Vol 295-298 ◽  
pp. 644-647 ◽  
Author(s):  
Yu Kai Yao ◽  
Hong Mei Cui ◽  
Ming Wei Len ◽  
Xiao Yun Chen
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Mining Algorithm ◽  
Grid Search ◽  
Mining Algorithm ◽  
Stratified Sample

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

2021 ◽  
pp. 0309524X2110568
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Power Grid ◽  
Wavelet Denoising ◽  
Slime Mold ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction

The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

A New Fuzzy Support Vector Machine for Credit Scoring

2011 ◽  
Vol 109 ◽  
pp. 636-640
Author(s):  
Bo Tang ◽  
Min Xia
Keyword(s):  
Support Vector Machine ◽  
Economic Development ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Good Prediction ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine ◽  
Good Prediction Accuracy

With China's rapid economic development, credit scoring has become very important. This paper presents a new fuzzy support vector machine algorithm used to solve the problems of credit scoring. The empirical results show that the proposed fuzzy membership model is valid ,the algorithm has good prediction accuracy and anti-noise ability.

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

scholarly journals Automatic Task Classification via Support Vector Machine and Crowdsourcing

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hyungsik Shin ◽  
Jeongyeup Paek
Keyword(s):  
Support Vector Machine ◽  
Mobile Devices ◽  
Prediction Accuracy ◽  
Training Data ◽  
Amazon Mechanical Turk ◽  
Support Vector ◽  
Data Set ◽  
English Sentence ◽  
Task Classification ◽  
Personal Assistant

Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

scholarly journals Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yan Liu ◽  
Wenxiang Gu ◽  
Wenyi Zhang ◽  
Jianan Wang
Keyword(s):  
Support Vector Machine ◽  
Amino Acid ◽  
Prediction Accuracy ◽  
Support Vector Machine Classifier ◽  
Biological Function ◽  
Reducing Sugar ◽  
Computational Method ◽  
Protein Glycation ◽  
Feature Analysis ◽  
Support Vector

Glycation is a nonenzymatic process in which proteins react with reducing sugar molecules. The identification of glycation sites in protein may provide guidelines to understand the biological function of protein glycation. In this study, we developed a computational method to predict protein glycation sites by using the support vector machine classifier. The experimental results showed that the prediction accuracy was 85.51% and an overall MCC was 0.70. Feature analysis indicated that the composition ofk-spaced amino acid pairs feature contributed the most for glycation sites prediction.

scholarly journals ENHANCED NETWORK SELECTION AND HANDOVER SCHEMA FOR HETEROGENEOUS WIRELESS NETWORKS

2019 ◽  
Vol 01 (03) ◽  
pp. 160-171 ◽  
Author(s):  
Duraipandian M
Keyword(s):  
Wireless Networks ◽  
Heterogeneous Wireless Networks ◽  
Vertical Handover ◽  
User Preference ◽  
Network Selection ◽  
Order Preference ◽  
Simple Additive Weighting ◽  
Topsis Technique ◽  
The Cost ◽  
Grey Relational

The seamless communication between the devices of the heterogeneous wireless networks still remains as a challenge due to the delay incurred, the cost difference and the bandwidth variations.so it becomes necessary for a perfect management of the hand over and the selection of the network to provide a continuous conveyance in the broadcasting or the sharing of the information’s. So the paper initiates an enhanced network selection and a vertical hand over schema that is context aware and based on the user preference utilizing the grey relational analysis integrated with the particle swarm optimization. The performance analysis of the proposed method of vertical handover evinces the perfect network selection that enables a continuous connection for the heterogeneous networks and shows an enhanced throughput and latency in handover compared to the other methods like TOPSIS (technique for order preference by similarity to ideal solution) and SAW(simple additive weighting).

scholarly journals Application of support vector machine for the prediction of strip crown in hot strip rolling

2020 ◽  
Author(s):  
Ya-feng Ji ◽  
Le-Bao Song ◽  
Hao Yuan ◽  
Wen Peng ◽  
Hua-Ying Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Prediction Performance ◽  
Support Vector ◽  
Hot Strip Rolling ◽  
Strip Rolling ◽  
Hot Strip ◽  
Strip Crown

Abstract In order to enhance the prediction accuracy of the strip crown and improve the quality of final product in the hot strip rolling, an optimized model based upon support vector machine (SVM) is proposed firstly. Meanwhile, for purposes of enriching data information and ensuring data quality, the actual data from a hot-rolled plant are collected to establish prediction model, as well as the prediction performance of models was evaluated by using multiple indicators. Besides, the traditional SVM model and the combined prediction models with the particle swarm optimization (PSO) and the cuckoo search (CS) optimization algorithm are also proposed. Furthermore, the prediction performance comparisons of the three different methods are discussed and validated. The results show that the CS-SVM has the highest prediction accuracy compared to the other two methods, and the root mean squared error (RMSE) of the proposed CS-SVM is 2.05µm, and 98.11% of prediction data have an absolute error below 4.5μm. In addition, the results also demonstrated that the CS-SVM not only with faster convergence speed and higher prediction accuracy but can be well applied to the actual hot strip rolling production.

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