Investigating the effect of correlation-based feature selection on the performance of support vector machines in reservoir characterization

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

“…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.…”

Estimation of surface tension of methyl esters biodiesels using computational intelligence technique

“…Although, large number of hidden neurons slows down the training period of a network but our developed MESTE achieves good stability within short period of time due to its adoption of small number of data-points in attaining its excellent generalization ability which further makes the time effect of large hidden neuron inconsequential [16]. Furthermore, the strategy (SBLLM) utilized by the developed MESTE also saves the computational time [19].…”

“…This present paper introduces SBLLM in making excellent and accurate estimation of surface tension of several classes of methyl esters biodiesel at different temperatures using their corresponding molecular weight. SBLLM is among machine learning tools [10][11][12][13][14][15][16][17][18] that acquires pattern between the descriptor and target. Despite the fact that the model (SBLLM) is less complex, it saves computational time, achieves good stability within short period of time and eliminates local convergence.…”

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

“…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.…”