Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications
Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …
interconnected devices, allowing the use of various smart applications. The enormous …
Deep learning in smart grid technology: A review of recent advancements and future prospects
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …
a promising landscape for high grid reliability and efficient energy management. This …
An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators
Recently, the human-like behavior on the anthropomorphic robot manipulator is increasingly
accomplished by the kinematic model establishing the relationship of an anthropomorphic …
accomplished by the kinematic model establishing the relationship of an anthropomorphic …
An appraisal of incremental learning methods
As a special case of machine learning, incremental learning can acquire useful knowledge
from incoming data continuously while it does not need to access the original data. It is …
from incoming data continuously while it does not need to access the original data. It is …
Swarm learning-based dynamic optimal management for traffic congestion in 6G-driven intelligent transportation system
Y Liu, L Huo, J Wu, AK Bashir - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
As city boundaries expand and the vehicles continues to proliferate, the transportation
system is increasingly overloaded, greatly increasing people's commuting burden and …
system is increasingly overloaded, greatly increasing people's commuting burden and …
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 …
Urban data management system: Towards Big Data analytics for Internet of Things based smart urban environment using customized Hadoop
The unbroken amplification of a versatile urban setup is challenged by huge Big Data
processing. Understanding the voluminous data generated in a smart urban environment for …
processing. Understanding the voluminous data generated in a smart urban environment for …
A distributed hierarchical deep computation model for federated learning in edge computing
H Zheng, M Gao, Z Chen, X Feng - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Deep learning has recently garnered significant interest in many applications especially for
big data analytics in the edge computing environment. Federated learning, as a novel …
big data analytics in the edge computing environment. Federated learning, as a novel …
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 …
Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing
Cloud computing is significantly contributing to the development of smart Chinese medicine.
The diagnosis and treatment of spleen and stomach diseases has been arousing great …
The diagnosis and treatment of spleen and stomach diseases has been arousing great …