Paper Citation Record · LEDGER

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring

As of 8 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2506.03696.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.03696 v2

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:41:58.583367Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T00:09:15.111441Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 6f4a5740-33f6-4c7a-a0ec-d991be90f936 · outbound

This paper cites Tuning machine learning to address process mining requirements,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Tuning machine learning to address process mining requirements,

Reference 1

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Observation df8876c7-fc36-4234-9460-4684c775995f · outbound

This paper cites A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs,

Reference 2

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Observation 2f6e6016-2391-4b96-9f0b-543484ab6427 · outbound

This paper cites Clustering-based predictive process monitoring,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Clustering-based predictive process monitoring,

Reference 3

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Observation 88a1a1c5-acda-46b1-a11b-aa207a2494ed · outbound

This paper cites Complex symbolic sequence encodings for predictive monitoring of business processes,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Complex symbolic sequence encodings for predictive monitoring of business processes,

Reference 4

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Observation a39d2b78-5353-446b-8e6a-da20331ddeb7 · outbound

This paper cites Predictive analytics for semi-structured case oriented business processes,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Predictive analytics for semi-structured case oriented business processes,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:44.272127Z

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Observation af0ac9ec-3f23-45cd-a2ef-cf2be95ac78e · outbound

This paper cites Enhancing predictive process monitoring with time-related feature engineering,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Enhancing predictive process monitoring with time-related feature engineering,

Reference 6

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Observation 6fd5e742-4e37-462e-9156-cf8d75a50409 · outbound

This paper cites Trace encoding in process mining: A survey and benchmarking,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Trace encoding in process mining: A survey and benchmarking,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:44.255932Z

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Observation 294ee12b-ac47-4321-b8e7-d1547352f81e · outbound

This paper cites Predicting process behaviour using deep learning,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Predicting process behaviour using deep learning,

Reference 8

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Observation 04ac9328-5883-4302-a307-e1890968ad08 · outbound

This paper cites Predictive business process monitoring with lstm neural networks,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Predictive business process monitoring with lstm neural networks,

Reference 9

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Observation dbb14e16-5f3e-4ea9-a83e-6bd61501d617 · outbound

This paper cites Predictive monitoring of business processes,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Predictive monitoring of business processes,

Reference 10

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Observation 970836b8-0a88-4f0f-a0e3-4914e4c2010e · outbound

This paper cites Evaluating and predicting overall process risk using event logs,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Evaluating and predicting overall process risk using event logs,

Reference 11

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Observation b0e44b3b-3fc5-45fc-92fb-6c658e7e6ba0 · outbound

This paper cites Outcome- oriented predictive process monitoring: Review and benchmark,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Outcome- oriented predictive process monitoring: Review and benchmark,

Reference 12

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Observation 6d08350f-09c2-46e1-b866-54cd7b3e7515 · outbound

This paper cites Pre- dicting critical behaviors in business process executions: when evidence counts,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Pre- dicting critical behaviors in business process executions: when evidence counts,

Reference 13

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Observation 534bdedd-7caa-41b2-a581-6413f3322c2d · outbound

This paper cites Predictive process monitoring,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Predictive process monitoring,

Reference 14

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Observation b4e7f42e-5603-4933-b25d-97f6115682e7 · outbound

This paper cites Orange: outcome-oriented predictive process monitoring based on image encoding and cnns,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Orange: outcome-oriented predictive process monitoring based on image encoding and cnns,

Reference 15

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Observation 909a86f5-15b1-4fef-b828-60f10a161671 · outbound

This paper cites Specification-driven multi-perspective predictive business process monitoring,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Specification-driven multi-perspective predictive business process monitoring,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:44.185637Z

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Observation b7e35411-1010-4c7b-b270-481822eda6e0 · outbound

This paper cites Improving business process quality through exception understanding, prediction, and pre- vention,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Improving business process quality through exception understanding, prediction, and pre- vention,

Reference 17

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Observation eb52721a-41c4-40a7-9717-72f6d547c099 · outbound

This paper cites Business process intelligence,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Business process intelligence,

Reference 18

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Observation 55480e0d-5607-4c95-90d6-126f6ff230a7 · outbound

This paper cites Predictive business operations management,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Predictive business operations management,

Reference 19

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Observation 827d8f62-43ae-4b8b-8a88-ce11b9dddc10 · outbound

This paper cites Intra and inter-case features in predictive process mon- itoring: A tale of two dimensions,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Intra and inter-case features in predictive process mon- itoring: A tale of two dimensions,

Reference 20

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Observation fa907e37-e5cb-4a40-aef9-ca3275e52362 · outbound

This paper cites Periodic performance prediction for real-time business process monitoring,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Periodic performance prediction for real-time business process monitoring,

Reference 21

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Observation 961f01f4-8537-47a3-80f0-dd5d7e180446 · outbound

This paper cites Deep learning for predictive business process monitoring: Review and benchmark,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Deep learning for predictive business process monitoring: Review and benchmark,

Reference 22

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Observation bace2ff1-ae70-4f36-8b3c-16e85a21102b · outbound

This paper cites Deep learn- ing process prediction with discrete and continuous data features,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Deep learn- ing process prediction with discrete and continuous data features,

Reference 23

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Observation 09a5c829-5d14-4588-8002-e44dc9cca117 · outbound

This paper cites Lstm networks for data-aware remaining time prediction of business process instances,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Lstm networks for data-aware remaining time prediction of business process instances,

Reference 24

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Observation 6827760b-7544-4d4d-a4e5-0ed1e7361040 · outbound

This paper cites Learning accurate lstm models of business processes,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Learning accurate lstm models of business processes,

Reference 25

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This paper cites Ham-net: Predictive business process monitoring with a hierarchical attention mechanism,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Ham-net: Predictive business process monitoring with a hierarchical attention mechanism,

Reference 26

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This paper cites Time matters: Time-aware lstms for predictive business process monitoring,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Time matters: Time-aware lstms for predictive business process monitoring,

Reference 27

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Observation 2c119f6f-c184-4f97-a98f-539dfbe5d33a · outbound

This paper cites Mm-pred: A deep predictive model for multi-attribute event sequence,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Mm-pred: A deep predictive model for multi-attribute event sequence,

Reference 28

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This paper cites Harane and S.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Harane and S

Reference 29

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Observation d0250ce6-679c-4933-a42f-30abc8d7a13a · outbound

This paper cites Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring,

Reference 30

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Observation 330a4649-90be-438f-bd78-b8d3dcb96310 · outbound

This paper cites Text-aware predictive monitoring of business processes,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Text-aware predictive monitoring of business processes,

Reference 31

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This paper cites A systematic literature review on state-of-the-art deep learning methods for process prediction,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring A systematic literature review on state-of-the-art deep learning methods for process prediction,

Reference 32

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Observation e0686d92-4aa2-40c3-898f-08f5a5ffb1df · outbound

This paper cites Predictive business process monitoring with structured and unstructured data,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Predictive business process monitoring with structured and unstructured data,

Reference 33

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Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Unresolved cited work

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This paper cites Comparing and combining pre- dictive business process monitoring techniques,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Comparing and combining pre- dictive business process monitoring techniques,

Reference 35

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Observation 982d6bd9-7fa0-4107-8175-e64acf68f41b · outbound

This paper cites Genetic algorithms for hyperparam- eter optimization in predictive business process monitoring,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Genetic algorithms for hyperparam- eter optimization in predictive business process monitoring,

Reference 36

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Observation 5d03251e-f9e2-4462-adba-0d679295df53 · outbound

This paper cites Outcome-oriented predictive pro- cess monitoring with attention-based bidirectional lstm neural networks,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Outcome-oriented predictive pro- cess monitoring with attention-based bidirectional lstm neural networks,

Reference 37

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This paper cites Learning effective neural nets for outcome prediction from partially labelled log data,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Learning effective neural nets for outcome prediction from partially labelled log data,

Reference 38

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This paper cites Classifying process instances using recurrent neural networks,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Classifying process instances using recurrent neural networks,

Reference 39

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This paper cites Pustejovsky and A.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Pustejovsky and A

Reference 40

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This paper cites Change patterns and change support features–enhancing flexibility in process-aware infor- mation systems,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Change patterns and change support features–enhancing flexibility in process-aware infor- mation systems,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.907634Z

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Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Graves, Supervised sequence labelling

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.899628Z

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Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring An exploration of dropout with lstms

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.891802Z

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Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring A theoretically grounded application of dropout in recurrent neural networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.883951Z

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Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Neural networks for machine learning, lecture 6a overview of mini-batch gradient descent,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.875910Z

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This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 46

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Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Deep sparse rectifier neural networks,

Reference 47

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Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Methods for interpreting and understanding deep neural networks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.857839Z

Source-reported events for the cited work

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Observation 4b1b1bd2-05a5-4ee6-9988-0d45bc43672c · outbound

This paper cites Deep learning of representations: Looking forward,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Deep learning of representations: Looking forward,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.849958Z

Source-reported events for the cited work

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Observation 0a02e026-c990-4176-b476-f59c0869c88b · outbound

This paper cites On the expressive power of deep neural networks,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring On the expressive power of deep neural networks,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.842369Z

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Observation 982c6103-02ce-4667-9562-eae1a7dfb681 · outbound

This paper cites On the importance of initialization and momentum in deep learning,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring On the importance of initialization and momentum in deep learning,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T11:02:43.683583Z

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Observation c7cb47da-1486-4bd6-a863-f855c4ddd969 · outbound

This paper cites Llr: Learning learning rates by lstm for training neural networks,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Llr: Learning learning rates by lstm for training neural networks,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.829213Z

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Observation 74b5eac8-8d43-4c9c-8e02-e7292b920a18 · outbound

This paper cites A comparison of lstm and gru networks for learning symbolic sequences,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring A comparison of lstm and gru networks for learning symbolic sequences,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.821518Z

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Observation 7fa3e0f0-f759-4d73-823c-839c444a55de · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T11:02:43.690953Z

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Observation 4f78cca5-cdba-42bb-a334-01ffb4b41efa · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Adam: A Method for Stochastic Optimization

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T11:02:43.693263Z

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Observation d673ebf5-ba8e-4310-ae23-775d73a3d6f7 · outbound

This paper cites Two-layer intelligent learn- ing control using output recurrent fuzzy neural lstm-bls with rmsprop,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Two-layer intelligent learn- ing control using output recurrent fuzzy neural lstm-bls with rmsprop,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.808108Z

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Observation 4757a23c-61b5-496a-9b8b-3373b6f178c2 · outbound

This paper cites Glove: Global vectors for word representation,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Glove: Global vectors for word representation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.799438Z

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Observation 6fa656c9-f0a9-45e6-b2cc-a7701183000c · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Efficient Estimation of Word Representations in Vector Space

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T11:02:43.700535Z

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Observation 19789246-01f7-4ee7-9dc4-71765ef49ab2 · outbound

This paper cites Lstm hyper-parameter selection for malware detection: Interaction effects and hierarchical selection approach,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Lstm hyper-parameter selection for malware detection: Interaction effects and hierarchical selection approach,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.790443Z

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Observation 0fcba66a-0bc9-4cda-81a0-a33b51bfee12 · outbound

This paper cites Bpi challenge 2012,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Bpi challenge 2012,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.782004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5b926c8b-9ca4-4879-98e5-7518931b49a1 · outbound

This paper cites Outcome-oriented prescriptive process monitoring based on temporal logic patterns,.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Outcome-oriented prescriptive process monitoring based on temporal logic patterns,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.773485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f5238c7d-a91c-4a0d-acc7-7cd85c52558a · outbound

This paper cites Process outcome prediction: Cnn vs. lstm (with attention),.

Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring Process outcome prediction: Cnn vs. lstm (with attention),

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:02:43.765415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 286a74ab-b942-4289-9118-b732855e9029 · inbound

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data cites this paper.

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T11:41:58.583367Z

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Observation 1bd4619c-5717-4424-9c91-af45a379783a · inbound

Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring cites this paper.

Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:09:15.113440Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.