Deep learning is a growing trend in computing. It is an improvement to artificial neural network. Deep Neural Networks are used in image classification, detection and segmentation. In this paper, an overview is carried out about the usage of deep neural network in various areas of image computing including image quality assessment, document imaging, object recognition, medical imaging, content ...

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The goal of this expository paper is to bring forth the basic current elements of soft computing (fuzzy logic, neural networks, genetic algorithms and genetic programming) and the current applications in intelligent control. Fuzzy sets and fuzzy logic and their applications to control systems have been documented. Other elements of soft computing, such as neural networks and genetic algorithms,...

Wave height as well as water depth at the breaking point are two basic parameters which are necessary for studying coastal processes. In this study, the application of soft computing-based methods such as artificial neural network (ANN), fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS) and semi-empirical models for prediction of these parameters are investigated. Th...

In recent years, soft computing and artificial intelligence techniques such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been effectively used in various civil engineering applications. This study aims to examine the potential of ANN and ANFIS for modeling the compressive strength of concrete containing expanded perlite powder (EPP). For doing this, ...

In this article, a laminar magnetohydrodynamics (MHD) developing flow of an incompressible electrically conducting fluid subjected to an external magnetic field is considered. The aim of the study is to propose a correlation for computing the development length of the laminar MHD developing flow in a pipe. A numerical approach is considered to solve the problem. In the first step, the numerical...

Neural networks have recently taken the field of machine learning by storm. Their success rests upon the availability of high performance computing hardware which allows to train very wide and deep networks. Traditional neural networks have very limited biological realism. Recent work on more brain-like hardware architectures has led to first large-scale implementations of neuromorphic computin...