Gaith Rjoub

Orcid: 0000-0002-7282-0687

According to our database1, Gaith Rjoub authored at least 23 papers between 2017 and 2024.

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Bibliography

2024
Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection.
Inf. Syst. Frontiers, August, 2024

Trust-driven reinforcement selection strategy for federated learning on IoT devices.
Computing, April, 2024

A comprehensive survey on applications of transformers for deep learning tasks.
Expert Syst. Appl., 2024

Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology.
CoRR, 2024

2023
A Survey on Explainable Artificial Intelligence for Cybersecurity.
IEEE Trans. Netw. Serv. Manag., December, 2023

A Survey on Explainable Artificial Intelligence for Network Cybersecurity.
CoRR, 2023

Explainable Trust-aware Selection of Autonomous Vehicles Using LIME for One-Shot Federated Learning.
Proceedings of the International Wireless Communications and Mobile Computing, 2023

2022
Cloud Computing as a Platform for Monetizing Data Services: A Two-Sided Game Business Model.
IEEE Trans. Netw. Serv. Manag., 2022

Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems.
Inf. Sci., 2022

Formal verification of group and propagated trust in multi-agent systems.
Auton. Agents Multi Agent Syst., 2022

Active Federated YOLOR Model for Enhancing Autonomous Vehicles Safety.
Proceedings of the Mobile Web and Intelligent Information Systems, 2022

Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving.
Proceedings of the 2022 International Wireless Communications and Mobile Computing, 2022

One-Shot Federated Learning-based Model-Free Reinforcement Learning.
Proceedings of the International Conference on Deep Learning, 2022

2021
Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems.
Concurr. Comput. Pract. Exp., 2021

Improving Autonomous Vehicles Safety in Snow Weather Using Federated YOLO CNN Learning.
Proceedings of the Mobile Web and Intelligent Information Systems, 2021

Cloud as platform for monetizing complementary data for AI-driven services: A two-sided cooperative game.
Proceedings of the IEEE International Conference on Services Computing, 2021

2020
An endorsement-based trust bootstrapping approach for newcomer cloud services.
Inf. Sci., 2020

BigTrustScheduling: Trust-aware big data task scheduling approach in cloud computing environments.
Future Gener. Comput. Syst., 2020

Formalizing Group and Propagated Trust in Multi-Agent Systems.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

A Trust and Energy-Aware Double Deep Reinforcement Learning Scheduling Strategy for Federated Learning on IoT Devices.
Proceedings of the Service-Oriented Computing - 18th International Conference, 2020

A Game-Based Secure Trading of Big Data and IoT Services: Blockchain as a Two-Sided Market.
Proceedings of the Service-Oriented Computing - 18th International Conference, 2020

2019
Deep Smart Scheduling: A Deep Learning Approach for Automated Big Data Scheduling Over the Cloud.
Proceedings of the 7th International Conference on Future Internet of Things and Cloud, 2019

2017
Cloud Task Scheduling Based on Swarm Intelligence and Machine Learning.
Proceedings of the 5th IEEE International Conference on Future Internet of Things and Cloud, 2017


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