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A data-aware explainable deep learning approach for next ...
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由 L Aversano 著作2023被引用 9 次 — This paper faces this issue by introducing a Data-aware Explainable Next Activity Prediction approach called DENAP based on the adoption of Long Short-Term ...
A data-aware explainable deep learning approach for next ...
ResearchGate
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Similarly, authors of [3] describe a multi-input LSTM neural network coupled with the Layer-Wise Relevance Propagation method for the next-activity prediction ...
A data-aware explainable deep learning approach for next ...
ACM Digital Library
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由 L Aversano 著作2023被引用 9 次 — The DENAP approach is validated on a set of synthetic and real logs. The obtained results show the good capability of DENAP to predict the next activity and ...
Towards an enhanced next activity prediction using attention ...
ACM Digital Library
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A data-aware explainable deep learning approach for next activity prediction. Abstract · Multi-perspective enriched instance graphs for next activity prediction ...
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Next-Activity Prediction for Non-stationary Processes with ...
ResearchGate
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2024年11月21日 — A data-aware explainable deep learning approach for next activity prediction. Article. Full-text available. Nov 2023; ENG APPL ARTIF INTEL.
Next-Activity Prediction for Non-stationary Processes with ...
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A Deep Learning Approach for Predicting Process Behaviour at Runtime · Computer Science. Business Process Management Workshops · 2016.
Explainable Deep Learning Framework for Human Activity ...
arXiv
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2024年8月21日 — The central element of our proposed framework is the incorporation of data augmentation processes into both the model training and prediction ...
PROPHET: Explainable Predictive Process Monitoring With ...
IEEE Xplore
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由 V Pasquadibisceglie 著作2024被引用 1 次 — PROPHET is designed to strike a balance between accurate predictions and interpretability, particularly focusing on the next-activity prediction task. For this ...
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Text-Aware Predictive Monitoring of Business Processes
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由 M Pegoraro 著作2021被引用 19 次 — The proposed model can take categorical, numerical and textual attributes in event data into ac- count to predict the activity and timestamp of the next event, ...
Explainable deep learning approach for advanced ...
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由 NHA Mutalib 著作2024被引用 2 次 — These neural network models are trained on datasets comprising normal activity, enabling them to compress and reconstruct input data accurately.
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