Risk assessment and prevention in airport security assurance by integrating LSTM algorithm
2025
Improving Airport Security with LSTM Algorithm
Sample size: 2190
publication
10 minutes
Evidence: high
Author Information
Author(s): Hu Yao, Qiao Liguang, Gu Feng
Primary Institution: Civil Aviation Flight University of China
Hypothesis
Can integrating the LSTM algorithm enhance risk assessment and prevention in airport security?
Conclusion
The LSTM algorithm significantly improves the accuracy and efficiency of airport security risk assessments.
Supporting Evidence
- The LSTM model showed a standard error of less than 0.18.
- Decision coefficients were all greater than 0.9.
- The predicted data was highly consistent with actual data.
- The model can enhance real-time monitoring and decision-making.
- Risk assessment can be automated from data collection to prediction.
Takeaway
This study shows that using a special computer program can help keep airports safer by predicting problems before they happen.
Methodology
The study used LSTM to analyze historical flight safety data and predict potential risks.
Limitations
The dataset has a small time span and scale, which may affect the model's generalizability.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website