Machine learning technology in biodiesel research: A review
Biodiesel has the potential to significantly contribute to making transportation fuels more
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Fully interpretable neural network for locating resonance frequency bands for machine condition monitoring
In recent years, various neural networks have been developed to process vibration signals
for machine condition monitoring. Nevertheless, the physical interpretation of neural …
for machine condition monitoring. Nevertheless, the physical interpretation of neural …
Online dynamical learning and sequence memory with neuromorphic nanowire networks
Abstract Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems
that exploit the unique physical properties of nanostructured materials. In addition to their …
that exploit the unique physical properties of nanostructured materials. In addition to their …
[HTML][HTML] Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook
One of the main limitations to the economic sustainability of biodiesel production remains
the high feedstock cost. Modeling and optimization are crucial steps to determine if …
the high feedstock cost. Modeling and optimization are crucial steps to determine if …
An extreme learning machine-based method for computational PDEs in higher dimensions
We present two effective methods for solving high-dimensional partial differential equations
(PDE) based on randomized neural networks. Motivated by the universal approximation …
(PDE) based on randomized neural networks. Motivated by the universal approximation …
A comprehensive review of extreme learning machine on medical imaging
The feedforward neural network based on randomization has been of great interest in the
scientific community, particularly extreme learning machines, due to its simplicity, training …
scientific community, particularly extreme learning machines, due to its simplicity, training …
[HTML][HTML] A hybrid framework based on extreme learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock prediction
Accurate prediction of the stock market trend can assist efficient portfolio and risk
management. In recent years, with the rapid development of deep learning, it can make the …
management. In recent years, with the rapid development of deep learning, it can make the …
Binary imbalanced data classification based on diversity oversampling by generative models
J Zhai, J Qi, C Shen - Information Sciences, 2022 - Elsevier
In many practical applications, the data are class imbalanced. Accordingly, it is very
meaningful and valuable to investigate the classification of imbalanced data. In the …
meaningful and valuable to investigate the classification of imbalanced data. In the …
On computing the hyperparameter of extreme learning machines: Algorithm and application to computational PDEs, and comparison with classical and high-order …
S Dong, J Yang - Journal of Computational Physics, 2022 - Elsevier
We consider the use of extreme learning machines (ELM) for computational partial
differential equations (PDE). In ELM the hidden-layer coefficients in the neural network are …
differential equations (PDE). In ELM the hidden-layer coefficients in the neural network are …