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

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

doi:10.1016/j.commatsci.2015.01.020

Cited by 43 publications

(15 citation statements)

References 35 publications

(41 reference statements)

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“…It acquires pattern or relation that exists between the target ( ) and descriptor (room temperature resistivity) and adopts the acquired pattern for future estimation of the unknown target with the aid of the descriptors. Its excellent predictive ability is made which is used in tackling several problems in medical field [28], material science [26,[29][30][31][32], and oil and gas industries [33,34], to name but a few. The excellent predictive and generalization ability of SVR in solving numerous problems coupled with the need to have accurate, direct, and effective way of estimating the effects of disorder on of MgB 2 superconductor serves as motivation for carrying out this research work.…”

Section: Applied Computational Intelligence and Soft Computingmentioning

confidence: 99%

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Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB₂Superconductors Using Room Temperature Resistivity

Owolabi

Akande

Olatunji

2016

Applied Computational Intelligence and Soft Computing

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Doping and fabrication conditions bring about disorder in MgB2superconductor and further influence its room temperature resistivity as well as its superconducting transition temperature (TC). Existence of a model that directly estimatesTCof any doped MgB2superconductor from the room temperature resistivity would have immense significance since room temperature resistivity is easily measured using conventional resistivity measuring instrument and the experimental measurement ofTCwastes valuable resources and is confined to low temperature regime. This work develops a model, superconducting transition temperature estimator (STTE), that directly estimatesTCof disordered MgB2superconductors using room temperature resistivity as input to the model. STTE was developed through training and testing support vector regression (SVR) with ten experimental values of room temperature resistivity and their correspondingTCusing the best performance parameters obtained through test-set cross validation optimization technique. The developed STTE was used to estimateTCof different disordered MgB2superconductors and the obtained results show excellent agreement with the reported experimental data. STTE can therefore be incorporated into resistivity measuring instruments for quick and direct estimation ofTCof disordered MgB2superconductors with high degree of accuracy.

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Section: Applied Computational Intelligence and Soft Computingmentioning

confidence: 99%

“…In order to obtain the solution of the optimization problem, the saddle points of the Lagrangian function are obtained by equating the partial derivative of Lagrangian with respect to , , , and to zero which gives rise to (5) as described in [29]:…”

Section: Description Of the Proposed Modelmentioning

confidence: 99%

Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB₂Superconductors Using Room Temperature Resistivity

Owolabi

Akande

Olatunji

2016

Applied Computational Intelligence and Soft Computing

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“…The popular kernel functions and those considered in this work are Polynomial and Radial basis function kernel [21] .The performance of SVR is optimized by user-defined parameters such as regularization factor C, type of kernel function and -insensitive loss function which are carefully selected by the user in order to attain optimal settings. SVR employs the use of epsilon errorinsensitive zone [26] while ANN uses weight decay [27]. While ANN has been developed from extensive application and experimentation known as heuristic approach, SVR has a sound mathematical and theoretical foundation before proceeding to implementation and experiments [23].…”

Section: Svm Svm Was First Proposed By Cortes and Vapnikmentioning

confidence: 99%

Comparative Analysis of Feature Selection-Based Machine Learning Techniques in Reservoir Characterization

et al. 2015

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Comparative study and analysis of the generalization performance and predictive capability of machine learning (ML) techniques in reservoir characterization and modeling is presented in this work by utilizing two distinct Oil well data sets. The performances of the ML techniques (artificial neural network (ANN) and support vector regression (SVR)) have been boosted by proposing a correlation-based feature selection approach which employs fewer datasets resulting in less computing time and processing power. Predictive accuracy of both ML techniques in permeability prediction has been improved as a result of the feature-selection approach. Furthermore, ANN shows superior performance in case of large dataset while SVR shows better performance in case of small dataset. The results of this work provide excellent insight and guidance to practitioners in improving ML performance and determining the most appropriate technique in cases of small and large datasets.

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“…It is built on sound mathematical foundation and does not converge to local minima. It has enjoyed a wide range of applications in material sciences [20][21][22][23][24][25], medicine [26,27] and other areas of study [28,29]. Its hybridization proposed in this present work involves combination of two SVR in which one of it is trained and tested using molecular weight and number of carbon to carbon double bound as the descriptors, while the other SVR is developed using the estimated melting points of the first one.…”

Section: Introductionmentioning

confidence: 98%

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

Owolabi

Zakariya

Olatunji

et al. 2016

Neural Comput & Applic

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This work develops a hybridized support vector regression (HSVR)-based model for accurate estimation of melting points of fatty acids using their molecular weights and the number of carbon-carbon double bond as descriptors. The development of HSVR-based model is characterized with two stages. The first stage involves training and testing SVR using test-set-cross validation technique with molecular weights and the number of carbon-carbon double bond as descriptors, while the second stage utilizes the estimated melting points obtained from the first stage as descriptor for further training and testing of SVR. The proposed hybrid system therefore demonstrates a better predictive and generalization ability than ordinary SVR. Furthermore, the melting points of sixty-two fatty acids estimated using the proposed HSVR-based model show persistence closeness with the experimental values than the results of other existing predictive models for fatty acids melting points estimation such as Guijie et al. model and Guendouzi model. The developed HSVRbased model is also characterized with higher value of coefficient of correlation and lower value of mean absolute error than that of the existing predictive models. Superiority of the developed HSVR-based model over the existing predictive models in terms of the ease of obtaining its descriptors and the accuracy of its estimates is advantageous to unravel estimation challenges associated with determination of fatty acids melting points.

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

Cited by 43 publications

References 35 publications

Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB₂Superconductors Using Room Temperature Resistivity

Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB₂Superconductors Using Room Temperature Resistivity

Comparative Analysis of Feature Selection-Based Machine Learning Techniques in Reservoir Characterization

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

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

Cited by 43 publications

References 35 publications

Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB2Superconductors Using Room Temperature Resistivity

Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB2Superconductors Using Room Temperature Resistivity

Comparative Analysis of Feature Selection-Based Machine Learning Techniques in Reservoir Characterization

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

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Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB₂Superconductors Using Room Temperature Resistivity

Computational Intelligence Approach for Estimating Superconducting Transition Temperature of Disordered MgB₂Superconductors Using Room Temperature Resistivity