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Retraction: Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model

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Published in:PLOS ONE
Format: Online Article RSS Article
Published: 2026
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institution FRELIP
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spellingShingle Retraction: Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model
Cybersecurity, Cryptography and Privacy
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Cybersecurity, Cryptography and Privacy
Computer Science & IT
Engineering & Technology
Cybersecurity, Cryptography and Privacy
Computer Science & IT
Engineering & Technology
subject_facet Cybersecurity, Cryptography and Privacy
Computer Science & IT
Engineering & Technology
title Retraction: Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model
title_auth Retraction: Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model
title_full Retraction: Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model
title_fullStr Retraction: Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model
title_full_unstemmed Retraction: Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model
title_short Retraction: Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model
title_sort retraction: advancing enterprise risk management with deep learning: a predictive approach using the xgboost-cnn-bilstm model
topic Cybersecurity, Cryptography and Privacy
Computer Science & IT
Engineering & Technology
url https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0343285