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Machine learning techniques to detect a DDoS attack in SDN: A systematic review
The recent advancements in security approaches have significantly increased the ability to
identify and mitigate any type of threat or attack in any network infrastructure, such as a …
identify and mitigate any type of threat or attack in any network infrastructure, such as a …
[HTML][HTML] Current advances in imaging spectroscopy and its state-of-the-art applications
Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single
system and has gained widespread acceptance as a non-destructive scientific instrument for …
system and has gained widespread acceptance as a non-destructive scientific instrument for …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Feature selection for classification with Spearman's rank correlation coefficient-based self-information in divergence-based fuzzy rough sets
Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …
A fast and compact 3-D CNN for hyperspectral image classification
Hyperspectral images (HSIs) are used in a large number of real-world applications. HSI
classification (HSIC) is a challenging task due to high interclass similarity, high intraclass …
classification (HSIC) is a challenging task due to high interclass similarity, high intraclass …
GTFN: GCN and transformer fusion network with spatial-spectral features for hyperspectral image classification
Transformer has been widely used in classification tasks for hyperspectral images (HSIs) in
recent years. Because it can mine spectral sequence information to establish long-range …
recent years. Because it can mine spectral sequence information to establish long-range …
Multiscale dual-branch residual spectral–spatial network with attention for hyperspectral image classification
The development of remote sensing images in recent years has made it possible to identify
materials in inaccessible environments and study natural materials on a large scale. But …
materials in inaccessible environments and study natural materials on a large scale. But …
[HTML][HTML] Weighted kappa measures for ordinal multi-class classification performance
Assessing the classification performance of ordinal classifiers is a challenging problem
under imbalanced data compositions. Considering the critical impact of the metrics on the …
under imbalanced data compositions. Considering the critical impact of the metrics on the …
Sapenet: Self-attention based prototype enhancement network for few-shot learning
Few-shot learning considers the problem of learning unseen categories given only a few
labeled samples. As one of the most popular few-shot learning approaches, Prototypical …
labeled samples. As one of the most popular few-shot learning approaches, Prototypical …
Hyperspectral image classification using graph convolutional network: A comprehensive review
With the development of hyperspectral sensors, more and more hyperspectral images can
be acquired, and the pixel-oriented classification of hyperspectral images has attracted the …
be acquired, and the pixel-oriented classification of hyperspectral images has attracted the …