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Edge computing for smart health: Context-aware approaches, opportunities, and challenges
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Institute of Electrical and Electronics Engineers Inc.
, 2019 , Article)
Improving the efficiency of healthcare systems is a top national interest worldwide. However, the need to deliver scalable healthcare services to patients while reducing costs is a challenging issue. Among the most promising ...
MEdge-Chain: Leveraging Edge Computing and Blockchain for Efficient Medical Data Exchange
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Medical data exchange between diverse e-health entities can lead to a better healthcare quality, improving the response time in emergency conditions, and a more accurate control of critical medical events (e.g., national ...
SsHealth: Toward secure, blockchain-enabled healthcare systems
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Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
The future of healthcare systems is being shaped by incorporating emerged technological innovations to drive new models for patient care. By acquiring, integrating, analyzing, and exchanging medical data at different system ...
Deep Reinforcement Learning for Network Selection over Heterogeneous Health Systems
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IEEE Computer Society
, 2022 , Article)
Smart health systems improve our quality oflife by integrating diverse information and technologies into health and medical practices. Such technologies can significantly improve the existing health services. However, ...
Optimal User-Edge Assignment in Hierarchical Federated Learning Based on Statistical Properties and Network Topology Constraints
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IEEE Computer Society
, 2022 , Article)
Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local ...
Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data
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Elsevier B.V.
, 2022 , Article)
Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring ...
B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
In order to maintain a satisfactory performance in the midst of rapid growth of mobile traffic, the mobile network infrastructure needs to be scaled. Thus there has been significant interest in scalability of mobile core ...
Active Learning with Noisy Labelers for Improving Classification Accuracy of Connected Vehicles
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. Reacting to such situations requires accurate classification ...