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Energy-efficient networks selection based deep reinforcement learning for heterogeneous health systems
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Conference Paper)
Smart health systems improve the existing health services by integrating information and technology into health and medical practices. However, smart healthcare systems are facing major challenges including limited network ...
QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach
(
Institute of Electrical and Electronics Engineers Inc.
, 2019 , Conference Paper)
Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers ...
Hierarchical Federated Learning for Collaborative IDS in IoT Applications
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Conference Paper)
As the Internet-of-Things devices are being very widely adopted in all fields, such as smart houses, healthcare, and transportation, extremely huge amounts of data are being gathered, shared, and processed. This fact raises ...
EEG-based Analysis Study for Patients Receiving Intravenous Antibiotic Medication
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
In this paper, we conduct a biological data collection and analysis study for patients undergoing routine planned intravenous antibiotic treatment. The acquired data (i.e., Electroencephalogram (EEG), temperature and blood ...
Deep Reinforcement Learning for Network Selection over Heterogeneous Health Systems
(
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, ...
Machine Learning Based Cloud Computing Anomalies Detection
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
Recently, machine learning algorithms have been proposed to design new security systems for anomalies detection as they exhibit fast processing with real-time predictions. However, one of the major challenges in machine ...
Optimal User-Edge Assignment in Hierarchical Federated Learning Based on Statistical Properties and Network Topology Constraints
(
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 ...
A cooperative Q-learning approach for distributed resource allocation in multi-user femtocell networks
(
Institute of Electrical and Electronics Engineers Inc.
, 2016 , Conference Paper)
This paper studies distributed interference management for femtocells that share the same frequency band with macrocells. We propose a multi-agent learning technique based on distributed Q-learning, called subcarrier-based ...
A Weighted Machine Learning-Based Attacks Classification to Alleviating Class Imbalance
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
The Industrial Internet of Things (IIoT) has become very popular in recent years. However, IIoT is still an attractive and vulnerable target for attackers to exploit and experiment with different types of attacks. To ...
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an ...