A survey on metric learning for feature vectors and structured data
The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
Prediction of groundwater quality using efficient machine learning technique
To ensure safe drinking water sources in the future, it is imperative to understand the quality
and pollution level of existing groundwater. The prediction of water quality with high …
and pollution level of existing groundwater. The prediction of water quality with high …
Softtriple loss: Deep metric learning without triplet sampling
Distance metric learning (DML) is to learn the embeddings where examples from the same
class are closer than examples from different classes. It can be cast as an optimization …
class are closer than examples from different classes. It can be cast as an optimization …
Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning
Recently, research on data-driven bearing fault diagnosis methods has attracted increasing
attention due to the availability of massive condition monitoring data. However, most existing …
attention due to the availability of massive condition monitoring data. However, most existing …
Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts
Timely regional air quality forecasting in a city is crucial and beneficial for supporting
environmental management decisions as well as averting serious accidents caused by air …
environmental management decisions as well as averting serious accidents caused by air …
Short-term wind speed forecasting framework based on stacked denoising auto-encoders with rough ANN
In this paper, a multi-modal short-term wind speed prediction framework has been proposed
based on Artificial Neural Networks (ANNs). Given the stochastic behavior and high …
based on Artificial Neural Networks (ANNs). Given the stochastic behavior and high …
Deep learning-based ash content prediction of coal flotation concentrate using convolutional neural network
Z Wen, C Zhou, J Pan, T Nie, C Zhou, Z Lu - Minerals Engineering, 2021 - Elsevier
Convolutional neural networks, as the current state-of-the-art in image classification, are
regarded as a promising way for flotation soft sensors based on froth images. This paper …
regarded as a promising way for flotation soft sensors based on froth images. This paper …
RSDehazeNet: Dehazing network with channel refinement for multispectral remote sensing images
Multispectral remote sensing (RS) images are often contaminated by the haze that degrades
the quality of RS data and reduces the accuracy of interpretation and classification …
the quality of RS data and reduces the accuracy of interpretation and classification …
A parasitic metric learning net for breast mass classification based on mammography
Accurate classification of different tumors in mammography plays a critical role in the early
diagnosis of breast cancer. However, owing to variations in appearance, it is a challenging …
diagnosis of breast cancer. However, owing to variations in appearance, it is a challenging …
Modulation recognition of underwater acoustic signals using deep hybrid neural networks
W Zhang, X Yang, C Leng, J Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
It is a huge challenge for the receiver to correctly identify the modulation types due to the
complex underwater channel environment and severe noise interference. Additionally, the …
complex underwater channel environment and severe noise interference. Additionally, the …