Abstract Natural fractures play an essential role in the characterization and modeling of hydrocarbon reservoirs. Modeling fractured reservoirs requires understanding fracture characteristics. Fractured zones can be detected by using seismic data, petrophysical logs, well tests, drilling mud loss history core description. In this study, feed-forward neural networks (FFNN), cascade feed forward ...

Malignant lymphoma is one of the types malignant tumors that can lead to death. The diagnostic method for identifying a histopathological analysis tissue images. Because similar morphological characteristics types, it difficult doctors and specialists manually distinguish lymphomas. Therefore, deep automated learning techniques aim solve this problem help clinicians reconsider their decisions. ...

This paper examines the e ectiveness of using a quasi-Newton based training of a feedforward neural network for forecasting. We have developed a novel quasi-Newton based training algorithm using a generalized logistic function. We have shown that a well designed feed forward structure can lead to a good forecast without the use of the more complicated feedback/feedforward structure of the recur...

• For the first time, automatic detection of heart failure (HF) from speech is studied. Both vocal tract and glottal source parameters are used in feature extraction. Four machine learning algorithms as classifiers. Applying Feature selection on + features improved classification accuracy. Among classifiers, neural network gave best performance. Heart a major global health concern increasing pr...

suspended sediment estimation is an important factor from different aspects including, farming, soil conservation, dams, aquatic life, as well as various aspects of the research. there are different methods for suspended sediment estimation. this study aims to estimate suspended sediment using feed forward neural network with error back propagation with levenberg-marquardt back propagation algo...

Wind Power forecasting is an important subject of concern for reliable operations of grid and it has been studied from different points of views of both accuracy and reliability. So with an aim of improvement in prediction accuracy this paper presents a hybrid wind power prediction machine for Ontario Electricity Market (OEM) on single step ahead basis in which Wavelet Transform (WT) is used fo...

Some results from a method for generating recurrent neural networks (RNN) for prediction of financial and macroeconomic time series are presented. In the presented method, a feedforward neural network (FFNN) is first obtained using backpropagation. While backpropagation is usually able to find a fairly good predictor, all FFNN are limited by their lack of short-term dynamic memory. RNNs, by con...