Quantitative Verification-Aided Machine Learning: A Tandem Approach for Architecting Self-Adaptive IoT Systems

Author:

Camara Javier,Muccini Henry,Vaidhyanathan Karthik

Publisher

IEEE

Cited by 33 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Reimagining Self-Adaptation in the Age of Large Language Models;2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C);2024-06-04

2. A Message Broker Architecture for Adaptive Data Exchange in the IoT;2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C);2024-06-04

3. SWITCH: An Exemplar for Evaluating Self-Adaptive ML-Enabled Systems;Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems;2024-04-15

4. Towards Proactive Decentralized Adaptation of Unmanned Aerial Vehicles for Wildfire Tracking;Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems;2024-04-15

5. Self-adaptive Internet of Things Systems: A Systematic Literature Review;Communications in Computer and Information Science;2024

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