Transition Initiation Sites (TIS) prediction is a challenging problem in computational biology. In the literature TIS is predicted using various machine learning techniques such as Neural Network (NN), Support Vector Machine, etc. We have applied Principal Component Analysis (PCA) to remove highly correlated features which improves the performance in terms of time and accuracy. In this paper we...

Accurate prediction of water level (WL) is essential for the optimal management different resource projects. The development a reliable model WL remains challenging task in resources management. In this study, novel hybrid models, namely, Generalized Structure-Group Method Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict d...

nowadays, due to increasing the complexity of ic engines, calibration task becomes more severe and the need to use surrogate models for investigating of the engine behavior arises. accordingly, many black box modeling approaches have been used in this context among which network based models are of the most powerful approaches thanks to their flexible structures. in this paper four network base...

A novel approach is presented in this article for obtaining inverse mapping of thermodynamically Pareto-optimized ideal turbojet engines using group method of data handling (GMDH)-type neural networks and evolutionary algorithms (EAs). EAs are used in two different aspects. Firstly, multi-objective EAs (non–dominated sorting genetic algorithm-II) with a new diversity preserving mechanism are us...

Accurate forecasting of electricity load is one of the most important issues in the electricity industry. It is essential part of an efficient power system planning and operation. Due to the limited generic structure of conventional group method of data handling (GMDH) network (quadratic twovariable polynomial), it tends to produce an exceedingly complex network when it comes to highly nonlinea...

Abstract In this study, modeling of discharge was performed in compound open channels with non-prismatic floodplains (CCNPF) using soft computation models including multivariate adaptive regression splines (MARS) and group method data handling (GMDH), then their results were compared the multilayer perceptron neural networks (MLPNN). addition to total discharge, separation between floodplain ma...