This paper proposes a methodology named OP-ELM, based on a recent development –the Extreme Learning Machine– decreasing drastically the training speed of networks. Variable selection is beforehand performed on the original dataset for proper results by OP-ELM: the network is first created using Extreme Learning Process, selection of the most relevant nodes is performed using Least Angle Regress...

Twin support vector machine (TWSVM) was initially designed for binary classification. However, real-world problems often require the discrimination more than two categories. To tackle multi-class classification problem, in this paper, a multiple least squares twin support vector machine is proposed. Our Multi-LSTSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each pr...

This paper proposed a gas emission prediction method based on feature selection and improved machine learning, as traditional models are neither accurate nor universally applicable. Through analysis, this identified 12 factors that affected emissions. A total of 30 groups typical data for outflow were standardized, after which full subset regression was used to categorize influencing into diffe...

Retaining walls are geostructures providing permanent lateral support to vertical slopes of soil, and it is essential analyze the failure probability such a structure. To keep importance geotechnics on par with advancement in technology, implementation artificial intelligence techniques done for reliability analysis Designing structure based leads an economical design. Machine learning models u...