How can physics-based AI models be used to detect anomalies in real-world applications?
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Physics-based AI models can be used to detect anomalies in real-world applications by analyzing data and identifying abnormal patterns or behaviors. These models utilize AI algorithms to analyze captured packets or extracted features and determine if there are any network attacks or abnormal traffic . By detecting anomalies, these models can adjust or isolate harmful network data transmission behaviors, ensuring the information security of devices . In the case of earthquake studies, physics-based AI models can be used to examine the correlation between Radon gas and Ionospheric total electron content (TEC) changes, providing insights into the Lithosphere-Atmosphere-Ionosphere coupling phenomenon . Additionally, in the context of traffic accidents, AI techniques can be applied to merge extracted features from dashcam video datasets and model their temporal occurrence, effectively identifying traffic anomalies and classifying them into different accident types .
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19 Oct 2022 | The provided paper does not mention the use of physics-based AI models to detect anomalies in real-world applications. |
19 Oct 2022 | The provided paper does not discuss the use of physics-based AI models to detect anomalies in real-world applications. |
2 Citations | The provided paper does not specifically discuss the use of physics-based AI models for anomaly detection in real-world applications. |
20 Feb 2023 1 Citations | The provided paper does not mention the use of physics-based AI models to detect anomalies in real-world applications. |
20 Feb 2023 | The provided paper does not mention the use of physics-based AI models to detect anomalies in real-world applications. |
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