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Correction: FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records
Journal of Cloud Computing volume 13, Article number: 75 (2024)
Correction: Journal of Cloud Computing (2024) 13:57
Following publication of the original article [1], we have been notified that there is duplicate of the body text in the published article.
Now the text is:
MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)
-
1.
Dropout (p=0.2, inplace=False)
-
2.
ReLU ()
-
3.
Linear (in_features=200, out_features=2, bias=True)))
The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.
Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.
MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)
-
1.
Dropout (p=0.2, inplace=False)
-
2.
ReLU ()
-
3.
Linear (in_features=200, out_features=2, bias=True)))
It should be:
MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)
-
1.
Dropout (p=0.2, inplace=False)
-
2.
ReLU ()
-
3.
Linear (in_features=200, out_features=2, bias=True)))
The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.
Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.
The original article was updated.
Reference
Yang et al (2024) FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records. 13:57 https://doi.org/10.1186/s13677-024-00623-x
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Yang, K., Du, J., Liu, J. et al. Correction: FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records. J Cloud Comp 13, 75 (2024). https://doi.org/10.1186/s13677-024-00638-4
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DOI: https://doi.org/10.1186/s13677-024-00638-4