Deep learning in mobile and wireless networking: A survey
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …
services pose unprecedented demands on mobile and wireless networking infrastructure …
[HTML][HTML] Deep learning for network traffic monitoring and analysis (NTMA): A survey
Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
ABL-TC: A lightweight design for network traffic classification empowered by deep learning
W Wei, H Gu, W Deng, Z Xiao, X Ren - Neurocomputing, 2022 - Elsevier
Network traffic classification is an increasingly significant prerequisite for network
management. An accurate traffic classifier can contribute to traffic engineering, traffic …
management. An accurate traffic classifier can contribute to traffic engineering, traffic …
Autonomous unknown-application filtering and labeling for dl-based traffic classifier update
Network traffic classification has been widely studied to fundamentally advance network
measurement and management. Machine Learning is one of the effective approaches for …
measurement and management. Machine Learning is one of the effective approaches for …
Scope of machine learning applications for addressing the challenges in next‐generation wireless networks
RK Samanta, B Sadhukhan… - CAAI Transactions …, 2022 - Wiley Online Library
The convenience of availing quality services at affordable costs anytime and anywhere
makes mobile technology very popular among users. Due to this popularity, there has been …
makes mobile technology very popular among users. Due to this popularity, there has been …
Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)
J Abdollahi, B Nouri-Moghaddam… - arXiv preprint arXiv …, 2021 - arxiv.org
learning algorithms. In this paper, we review the classification algorithms used in the health
care system (chronic diseases) and present the neural network-based Ensemble learning …
care system (chronic diseases) and present the neural network-based Ensemble learning …
A network intrusion detection method based on deep multi-scale convolutional neural network
X Wang, S Yin, H Li, J Wang, L Teng - International Journal of Wireless …, 2020 - Springer
Network intrusion detection (NID) is an important method for network system administrators
to detect various security holes. The performance of traditional NID methods can be affected …
to detect various security holes. The performance of traditional NID methods can be affected …
Estimation of railway track longitudinal irregularity using vehicle response with information compression and Bayesian deep learning
In railway transportation, track geometry irregularity is one of the main factors in controlling
train safety. At present, railway practitioners typically use the track geometry car (TGC) …
train safety. At present, railway practitioners typically use the track geometry car (TGC) …
DMCNN: a deep multiscale convolutional neural network model for medical image segmentation
L Teng, H Li, S Karim - Journal of Healthcare Engineering, 2019 - Wiley Online Library
Medical image segmentation is one of the hot issues in the related area of image
processing. Precise segmentation for medical images is a vital guarantee for follow‐up …
processing. Precise segmentation for medical images is a vital guarantee for follow‐up …
Recycling waste classification using emperor penguin optimizer with deep learning model for bioenergy production
AI Khan, ASA Alghamdi, YB Abushark, F Alsolami… - Chemosphere, 2022 - Elsevier
The growth and implementation of biofuels and bioenergy conversion technologies play an
important part in the production of sustainable and renewable energy resources in the …
important part in the production of sustainable and renewable energy resources in the …