A survey on arabic named entity recognition: Past, recent advances, and future trends
As more and more Arabic texts emerged on the Internet, extracting important information
from these Arabic texts is especially useful. As a fundamental technology, Named entity …
from these Arabic texts is especially useful. As a fundamental technology, Named entity …
A survey on text classification: From traditional to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
A survey on text classification: From shallow to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Unified deep learning approach for efficient intrusion detection system using integrated spatial–temporal features
PR Kanna, P Santhi - Knowledge-Based Systems, 2021 - Elsevier
Intrusion detection systems (IDS) differentiate the malicious entries from the legitimate
entries in network traffic data and helps in securing the networks. Deep learning algorithms …
entries in network traffic data and helps in securing the networks. Deep learning algorithms …
Vocabulary learning via optimal transport for neural machine translation
The choice of token vocabulary affects the performance of machine translation. This paper
aims to figure out what is a good vocabulary and whether one can find the optimal …
aims to figure out what is a good vocabulary and whether one can find the optimal …
[HTML][HTML] An experimental analysis of different deep learning based models for Alzheimer's disease classification using brain magnetic resonance images
Classification of Alzheimer's disease (AD) is one of the most challenging issues for
neurologists. Manual methods are time consuming and may not be accurate all the time …
neurologists. Manual methods are time consuming and may not be accurate all the time …
Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network
EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-
invasiveness and low costs. Specifically EEG can be applied to differentiate brain states …
invasiveness and low costs. Specifically EEG can be applied to differentiate brain states …
[HTML][HTML] GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification
Exponential growth of biomedical literature and clinical data demands more robust yet
precise computational methodologies to extract useful insights from biomedical literature …
precise computational methodologies to extract useful insights from biomedical literature …
TRU-NET: a deep learning approach to high resolution prediction of rainfall
Climate models (CM) are used to evaluate the impact of climate change on the risk of floods
and heavy precipitation events. However, these numerical simulators produce outputs with …
and heavy precipitation events. However, these numerical simulators produce outputs with …
Accurate and lightweight image super-resolution with model-guided deep unfolding network
Deep neural networks (DNNs) based methods have achieved great success in single image
super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed …
super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed …