Rua Aburasain

Orcid: 0009-0003-5786-8011

According to our database1, Rua Aburasain authored at least 11 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
A Digital Twin-Driven Deep Reinforcement Learning Framework for End-to-End Mixed Traffic Scheduling.
IEEE Trans. Consumer Electron., February, 2026

2025
Enhanced congestion prediction of traffic flow using a hybrid attention-based deep learning model.
PeerJ Comput. Sci., 2025

Revolutionizing Traffic Flow Prediction Using a Hybrid Deep Learning Models with Kookaburra Optimization Algorithm.
J. Internet Serv. Inf. Secur., 2025

2024
A hybrid framework for glaucoma detection through federated machine learning and deep learning models.
BMC Medical Informatics Decis. Mak., December, 2024

An Efficient Long Short-Term Memory and Gated Recurrent Unit Based Smart Vessel Trajectory Prediction Using Automatic Identification System Data.
Comput. Mater. Continua, 2024

Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming.
IEEE Access, 2024

Hybrid Deep Learning with Optimized Hyperparameters Based Intrusion Detection in Internet of Things for Smart Farming.
Proceedings of the International Symposium on Networks, Computers and Communications, 2024

2021
A Coarse-to-Fine Multi-class Object Detection in Drone Images Using Convolutional Neural Networks.
Proceedings of the Digital Interaction and Machine Intelligence - Proceedings of MIDI'2021, 2021

2020
Application of convolutional neural networks in object detection, re-identification and recognition.
PhD thesis, 2020

Palm Tree Detection in Drone Images Using Deep Convolutional Neural Networks: Investigating the Effective Use of YOLO V3.
Proceedings of the Digital Interaction and Machine Intelligence - Proceedings of MIDI'2020, 2020

Drone-Based Cattle Detection Using Deep Neural Networks.
Proceedings of the Intelligent Systems and Applications, 2020


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