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Accurate estimation technique that accommodates few data points is useful and desired in tackling the difficulties in experimental determination of surface energies of materials. We hereby propose a computational intelligence technique on the platform of support vector regression (SVR) using test-set-cross-validation method to develop surface energies(More)
Transition metal carbides (TMC) are characterized with high melting points which make experimental determination of their average surface energies a difficult task. A database of 3d, 4d and 5d TMC is hereby established using machine learning technique on the platform of support vector regression (SVR). SVR was built, trained and validated using some(More)
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(More)
Concrete compressive strength prediction is very important in structure and building design, particularly in specifying the quality and measuring performance of concrete as well as determination of its mix proportion. The conventional method of determining the strength of concrete is complicated and time consuming hence artificial neural network (ANN) is(More)
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