Multilayer Feedforward Neural Network (MFNN) has been administered widely for solving a wide range of supervised pattern recognition tasks. The major problem in the MFNN training phase is its long training time especially when it is trained on very huge training datasets. In this accordance, an enhanced training algorithm called Exponential Adaptive Skipping Training (EAST) Algorithm is propose...

Chronic hepatitis C (CHC) patients often stop pursuing interferon-alfa and ribavirin (IFN-alfa/RBV) treatment because of the high cost and associated adverse effects. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the IFN-alfa/RBV treatments. Single nucleotide polymorphisms (SNPs) can ...

Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation se...

An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relatio...

In this study we propose a multilayered feedforward neural network (MFNN) with Backpropagation Learning Rule Incorporating Bayesian Regularization, and apply it to the credit risk evaluation problem domain using a real world data set from a financial services company in England. We choose the MFNN because of its broad applicability to many problem domains of relevance to business: principally p...

This paper discusses the application of MultiLayer Feed Forward Neural Network (MFNN) for the differential protection of the turbogenerator based on pattern classification. The cases of all the possible internal faults in the stator of the generator with lap winding have been simulated using Modified Winding function Approach. The simulated fault currents in the phases and their parallel paths ...

Nanofluids have been applied in various fields, such as solar collectors, petroleum engineering, and chemical due to their superior properties compared traditional fluids. Among the thermophysical of nanofluids, viscosity plays a critical role thermal applications involving heat transfer fluid flow. While several conventional machine learning (ML) techniques proposed predict viscosity, these mo...

COVID-19 is a novel coronavirus that was emerged in December 2019 within Wuhan, China. As the crisis of its severe, increasing dynamic outbreak all parts globe, forecast maps and analysis confirmed cases (CS) becomes vital excellent changeling task. In this study, new forecasting model presented to analyze CS for coming days based on reported data since 22 January 2020. The proposed model, name...

Handoff management is a key issue in mobile network to provide an efficient and low-cost service. Providing seamless connectivity in high speed data networks is a challenge due to user mobility and varying user traffic patterns. The major challenges with high user mobility are (1) Frequent handoff which causes call dropping (2) Unavailability of resources in the target base station which also m...

This paper presents the application of neural networks for the non-differential protection of salient-pole synchronous generator against internal faults in any winding of the stator. The direct phase quantities and modified winding function approach has been used to simulate different types of internal and external faults using electrical parameters of generators installed by utilities. The cas...