Support Vector Machines Approach for Estimating Work Function of Semiconductors: Addressing the Limitation of Metallic Plasma Model

Owolabi, Taoreed O.; Akande, Kabiru O.; Olatunji, Sunday O.

doi:10.5539/apr.v6n5p122

Cited by 17 publications

(7 citation statements)

References 18 publications

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“…Another important observation from our experiment Regularization factor, Well-C HSVR Figure 9: Sensitivity of HSVR model to regularization factor (Well-C). is that while the radial basis function (RBF) (Gaussian) kernel was the best for SVR, which has been observed and reported in many publications [8,10,26,33], we observed that the polynomial kernel (Poly) performed best for HSVR. This observation can be attributed to the reduction in the number of descriptors since HSVR only takes a single descriptor which greatly simplifies the degree of complexity for polynomial kernel and gives it an edge.…”

Section: Validation Set Proceduresupporting

confidence: 77%

A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization

Akande

Owolabi

Olatunji

et al. 2016

Applied Computational Intelligence and Soft Computing

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Hybrid computational intelligence is defined as a combination of multiple intelligent algorithms such that the resulting model has superior performance to the individual algorithms. Therefore, the importance of fusing two or more intelligent algorithms to achieve better performance cannot be overemphasized. In this work, a novel homogenous hybridization scheme is proposed for the improvement of the generalization and predictive ability of support vector machines regression (SVR). The proposed and developed hybrid SVR (HSVR) works by considering the initial SVR prediction as a feature extraction process and then employs the SVR output, which is the extracted feature, as its sole descriptor. The developed hybrid model is applied to the prediction of reservoir permeability and the predicted permeability is compared to core permeability which is regarded as standard in petroleum industry. The results show that the proposed hybrid scheme (HSVR) performed better than the existing SVR in both generalization and prediction ability. The outcome of this research will assist petroleum engineers to effectively predict permeability of carbonate reservoirs with higher degree of accuracy and will invariably lead to better reservoir. Furthermore, the encouraging performance of this hybrid will serve as impetus for further exploring homogenous hybrid system.

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Section: Validation Set Proceduresupporting

confidence: 77%

A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization

Akande

Owolabi

Olatunji

et al. 2016

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

show abstract

“…Other areas where AI techniques are adopted include the estimation of atomic radii of elements [13], diagnosing mechanical fault [14], forecasting Saudi Arabia stock prices [15], assessing the thickness of metal plates [16], automatic recognition of off-line handwritten Arabic numbers [17], material characterization [18] among others. The successes of support vector regression in estimating material properties [19,20] coupled with the need to have accurate and reliable means of estimating average surface energies of materials prompted us to delve into this research work.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of surface energies estimator (SEE) using computational intelligence technique

Owolabi

Akande

Olatunji

2015

Computational Materials Science

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“…SVR algorithm was proposed in this study because of its optimal predictive performance even with small dataset [ 41 ] and the ability to learn both linear and non-linear relationships between predictors and outcome (actual) variables [ 42 ]. Such relationships have been used in establishing a pattern whereby unknown outcomes could be predicted accurately [ 21 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression

Ibitoye

Hamzaid

Wahab

et al. 2016

Sensors

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The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R2) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.

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Support Vector Machines Approach for Estimating Work Function of Semiconductors: Addressing the Limitation of Metallic Plasma Model

Cited by 17 publications

References 18 publications

A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization

A Novel Homogenous Hybridization Scheme for Performance Improvement of Support Vector Machines Regression in Reservoir Characterization

Development and validation of surface energies estimator (SEE) using computational intelligence technique

Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression

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