Principle of neural network and its main types
AN Sharkawy - Journal of Advances in Applied & …, 2020 - avantipublisher.com
In this paper, an overview of the artificial neural networks is presented. Their main and
popular types such as the multilayer feedforward neural network (MLFFNN), the recurrent …
popular types such as the multilayer feedforward neural network (MLFFNN), the recurrent …
A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids
Nanofluids are broadly applied in energy systems such as solar collector, heat exchanger
and heat pipes. Dynamic viscosity of the nanofluids is among the most important features …
and heat pipes. Dynamic viscosity of the nanofluids is among the most important features …
Breast cancer diagnosis using GA feature selection and Rotation Forest
Breast cancer is one of the primary causes of death among the women worldwide, and the
accurate diagnosis is one of the most significant steps in breast cancer treatment. Data …
accurate diagnosis is one of the most significant steps in breast cancer treatment. Data …
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders
A Subasi - Computers in biology and medicine, 2013 - Elsevier
Support vector machine (SVM) is an extensively used machine learning method with many
biomedical signal classification applications. In this study, a novel PSO-SVM model has …
biomedical signal classification applications. In this study, a novel PSO-SVM model has …
The prediction of fire performance of concrete-filled steel tubes (CFST) using artificial neural network
Search for enhancing the efficiency has led to composite structures such as concrete-filled
steel tubes (CFST) with increasing applications across the world. The fire performance of …
steel tubes (CFST) with increasing applications across the world. The fire performance of …
Using radial basis function networks for function approximation and classification
Y Wu, H Wang, B Zhang, KL Du - … Scholarly Research Notices, 2012 - Wiley Online Library
The radial basis function (RBF) network has its foundation in the conventional approximation
theory. It has the capability of universal approximation. The RBF network is a popular …
theory. It has the capability of universal approximation. The RBF network is a popular …
Clustering: A neural network approach
KL Du - Neural networks, 2010 - Elsevier
Clustering is a fundamental data analysis method. It is widely used for pattern recognition,
feature extraction, vector quantization (VQ), image segmentation, function approximation …
feature extraction, vector quantization (VQ), image segmentation, function approximation …
Design of power-efficient approximate multipliers for approximate artificial neural networks
Artificial neural networks (NN) have shown a significant promise in difficult tasks like image
classification or speech recognition. Even well-optimized hardware implementations of …
classification or speech recognition. Even well-optimized hardware implementations of …
Combined machine-learning and optimization models for predicting carbon dioxide trap** indexes in deep geological formations
Emissions of carbon dioxide (CO 2) are a major source of atmospheric pollution contributing
to global warming. Carbon geological sequestration (CGS) in saline aquifers offers a …
to global warming. Carbon geological sequestration (CGS) in saline aquifers offers a …
Machine learning-based approaches for modeling thermophysical properties of hybrid nanofluids: A comprehensive review
Thermophysical properties of hybrid nanofluids remarkably affect their behavior in
engineering systems. Among these properties, dynamic viscosity and thermal conductivity …
engineering systems. Among these properties, dynamic viscosity and thermal conductivity …