The paper investigates the powerful of hybridizing two computational intelligence methods viz., Gray Wolf Optimization (GWO) and Artificial Neural Networks (ANN) for prediction of heart disease. Gray wolf optimization is a global search method while gradient-based back propagation method is a local search one. The proposed algorithm implies the ability of ANN to find a relationship between the ...

Accurate forecasting of rainfall has been one of the most important issues in hydrological research. In this paper, a novel neural network technique, support vector regression (SVR), to monthly rainfall forecasting. The aim of this study is to examine the feasibility of SVR in monthly rainfall forecasting by comparing it with back–propagation neural networks (BPNN) and the autoregressive integr...

It is well known that metamodel or surrogate modeling techniques have been widely applied in engineering problems due to their higher efficiency. However, with the increase of the linearity and dimensions, it is difficult for the present popular metamodeling techniques to construct reliable metamodel and apply to more and more complicated high dimensional problems. Recently, neural networks (NN...

The clustering phenomenon of defects usually occurs in semiconductor manufacturing. However, previous studies did not pay much attention to the influence of clustering phenomenon for estimating fraction nonconforming of a wafer. Thus, this paper presents a systematic estimation model with considering relevant variables about clustering defects for fraction nonconforming of a wafer. The method c...

With fast evolving technology, it is necessary to design an efficient security system which can detect unauthorized access on any system. The need of the time is to implement an extremely secure, economic and perfect system for face recognition that can protect our system from unauthorized access. So, in this paper, a robust face recognition system approach is proposed for image decomposition u...

A three layer back propagation neural net is set up to determine the dependence between the semantic class of a bisyllabic Chinese word and its two characters. Simulations were performed using a three–layer back–propagation neural net (BPNN) with various combinations of inputs. The inputs are (1) semantic classes of the characters, (2) Entropy of the characters and (3) semantic strengths [1] of...

In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavi...

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, ba...

The purpose of this research is to compare the performance of two text classifiers; support vector machine (SVM) and back-propagation neural network (BPNN) within categorize messages from an online discussion. SVM has been recognized as one of the best algorithm for text categorization. BPNN is also a popular categorization method that can handle linear and non linear problems and can achieve g...

The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-on-square double layer grids with the va...