scholarly journals Predicting Multi-Attribute Host Resource Utilization Using Support Vector Regression Technique

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 66048-66067
Author(s):  
Labeb Abdullah ◽  
Huixi Li ◽  
Shamsan Al-Jamali ◽  
Abdulrahman Al-Badwi ◽  
Chang Ruan
Keyword(s):  
Support Vector Regression ◽  
Resource Utilization ◽  
Regression Technique ◽  
Support Vector ◽  
Host Resource

Prediction of the solar radiation on the Earth using support vector regression technique

2015 ◽  
Vol 68 ◽  
pp. 179-185 ◽  
Author(s):  
Jamshid Piri ◽  
Shahaboddin Shamshirband ◽  
Dalibor Petković ◽  
Chong Wen Tong ◽  
Muhammad Habib ur Rehman
Keyword(s):  
Solar Radiation ◽  
Support Vector Regression ◽  
Regression Technique ◽  
Support Vector ◽  
The Earth

Forecasting Demand With Support Vector Regression Technique Incorporating Feature Selection in the Presence of Calendar Effect

2018 ◽  
pp. 302-316 ◽  
Author(s):  
Malek Sarhani ◽  
Abdellatif El Afia
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Supply Chains ◽  
Support Vector Regression ◽  
Case Studies ◽  
Time Series Forecasting ◽  
Regression Technique ◽  
Support Vector ◽  
Reliable Prediction ◽  
Forecasting Methods

Reliable prediction of future demand is needed to better manage and optimize supply chains. However, a difficulty of forecasting demand arises due to the fact that heterogeneous factors may affect it. Analyzing such data by using classical time series forecasting methods will fail to capture such dependency of factors. This chapter addresses these problems by examining the use of feature selection in forecasting using support vector regression while eliminating the calendar effect using X13-ARIMA-SEATS. The approach is investigated in three different case studies.

scholarly journals Improved Direction-of-Arrival Estimation of an Acoustic Source Using Support Vector Regression and Signal Correlation

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2692
Author(s):  
Faisal Alam ◽  
Mohammed Usman ◽  
Hend I. Alkhammash ◽  
Mohd Wajid
Keyword(s):  
Support Vector Regression ◽  
Cross Correlation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Regression Technique ◽  
Support Vector ◽  
Correlation Technique ◽  
Multilevel Regression ◽  
Acoustic Source ◽  
Regression Techniques

The direction-of-arrival (DoA) estimation of an acoustic source can be estimated with a uniform linear array using classical techniques such as generalized cross-correlation, beamforming, subspace techniques, etc. However, these methods require a search in the angular space and also have a higher angular error at the end-fire. In this paper, we propose the use of regression techniques to improve the results of DoA estimation at all angles including the end-fire. The proposed methodology employs curve-fitting on the received multi-channel microphone signals, which when applied in tandem with support vector regression (SVR) provides a better estimation of DoA as compared to the conventional techniques and other polynomial regression techniques. A multilevel regression technique is also proposed, which further improves the estimation accuracy at the end-fire. This multilevel regression technique employs the use of linear regression over the results obtained from SVR. The techniques employed here yielded an overall 63% improvement over the classical generalized cross-correlation technique.

Estimating Soil Moisture With the Support Vector Regression Technique

2011 ◽  
Vol 8 (6) ◽  
pp. 1080-1084 ◽  
Author(s):  
Luca Pasolli ◽  
Claudia Notarnicola ◽  
Lorenzo Bruzzone
Keyword(s):  
Soil Moisture ◽  
Support Vector Regression ◽  
Regression Technique ◽  
Support Vector

scholarly journals Prediction of Diesel Engine Performance using Support Vector Regression Technique

2020 ◽  
Vol 9 (10) ◽  
pp. 260-264
Keyword(s):  
Diesel Engine ◽  
Support Vector Regression ◽  
Performance Metrics ◽  
Engine Performance ◽  
Threshold Value ◽  
Regression Technique ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Actual Performance ◽  
And Performance

Extensive research has been carried out on the prediction of diesel engine performance. Machine learning techniques such as support vector regression technique makes the performance predictions simpler. Support vector regression is a regression algorithm used to minimize the error with a threshold value and tries to fit the best line with a threshold value. In this paper, a detailed study of diesel engine performance using support vector regression and performance metrics such as brake thermal efficiency and accuracy are explored. Findings specify that support vector regression is an efficient technique for diesel engine performance that validates and compares the actual performance with high accuracy. For engine performance, the support vector machine supports to reduce the time and cost of testing.

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