Robust and explainable semi-supervised deep learning model for anomaly detection in aviation

HIGHLIGHTS

• who: Milad Memarzadeh et al. from the Moffett Field, CA, USA have published the paper: Robust and Explainable Semi-Supervised Deep Learning Model for Anomaly Detection in Aviation, in the Journal: Aerospace 2022, 9, 437. of /2022/
• what: The authors develop a Robust and Explainable Semi-supervised deep learning model for Anomaly Detection (RESAD) in aviation data. The authors develop a case study of multi-class anomaly detection in the approach to landing of commercial aircraft in to benchmark RESAD's performance to baseline methods. The authors develop an optimization scheme where the model is . . .
  

   

  

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