The past few years have witnessed a growing recognition of soft computing technologies that underlie the conception, design and utilization of intelligent systems. According to Zadeh [1], soft computing consists of artificial neural networks, fuzzy inference systems, approximate reasoning and derivative free optimization techniques. In this paper, we report a performance analysis among Multivar...

In this study, six machine learning (ML) models, namely, random forest (RF), Gaussian process regression (GPR), support vector (SVR), decision tree (DT), least squares (LSSVM), and multivariate adaptive spline (MARS) were employed to reconstruct the missing daily-averaged discharge in a mega-delta from 1980 2015 using upstream-downstream multi-station data. The performance accuracy of each ML m...

The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure with artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the neural networ...

Gene expression is controlled by combinatorial regulation of transcription factors and cis-regulatory elements in eukaryotes. Experimental procedures can determine several binding sites for selected transcription factors, but they are too laborious to be applied to large-scale studies. Computational methods are thus required to detect the combinatorial regulation at the genomic level. To reveal...

We report a benchmarking of neural networks and regression techniques in a time series forecasting task. The estimation errors, computing costs and additional information obtained by Bayesian neural networks are compared with other neural network models and with Multivariate Adaptive Regression Splines (MARS). The Mackey Glass time series in chaotic regime was used to generate the two data sets...

Fracture toughness is an important phenomenon to reveal the actual strength of fractured rock materials. It is, therefore, crucial use fracture models principally for simulating performance medium. In this study, mode-I (KIC) was investigated using several soft computing techniques. For purpose, extensive literature survey carried out obtain a comprehensive database that includes simple and wid...

Crop yield forecasting is becoming more essential in the current scenario when food security must be assured, despite problems posed by an increasingly globalized community and other environmental challenges such as climate change natural disasters. Several factors influence crop prediction, which has complex non-linear relationships. Hence, to study these relationships, machine learning method...