Enhancing house inspections: UAVs integrated with LLMs for efficient AI-powered surveillance-Reference-Cited by-同舟云学术

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Enhancing house inspections: UAVs integrated with LLMs for efficient AI-powered surveillance

Published:2024-06-30 Issue: Volume:abs/2106.00666 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
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Author:

Nguyen Vu Trung¹,Zhou Jingwen²,Dong Chengzu³,Cui Guangming⁴,An Qi⁵,Vinnakota Sunny⁶

Affiliation:

1. Victoria University,School of Information Technology,Melbourne,Australia

2. Deakin University,Data61, CSIRO, School of Information Technology, Faculty of Sci Eng & Built Env,Australia

3. Deakin University,School of Information Technology, Faculty of Sci Eng & Built Env,Australia

4. University of Information Science and Technology,School of Software,Nanjing,China

5. Deakin University,School of Information Technology, Faculty of Sci Eng & Built Env,Geelong,Australia

6. Academies Australasia Polytechnic,Melbourne,Australia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650902.pdf?arnumber=10650902

Reference36 articles.

1. A Federated Multi-Modal Learning Framework Powered by Distributed Ledgers for Cyber-safe and Efficient UAV Delivery Systems

2. FedEdge: Accelerating Edge-Assisted Federated Learning

3. VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems

4. Defending Poisoning Attacks in Federated Learning via Adversarial Training Method

5. Network Anomaly Detection Using Federated Learning and Transfer Learning

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