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A data-aware explainable deep learning approach for next activity prediction
16 references·0 citing·33 second hop
A data-aware explainable deep learning approach for next activity prediction (2023)A data-aware explainable deep learning ap…Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021)Deep Learning: A Comprehensiv…A Multi-View Deep Learning Approach for Predictive Business Process Monitoring (2021)A Multi-View Deep Learning Ap…Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction (2020)Predictive Business Process M…Using Convolutional Neural Networks for Predictive Process Analytics (2019)Using Convolutional Neural Ne…The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)The Performance of LSTM and B…Learning Accurate LSTM Models of Business Processes (2019)Learning Accurate LSTM Models…Activity Prediction of Business Process Instances with Inception CNN Models (2019)Activity Prediction of Busine…2018 IEEE International Conference on Big Data (Big Data) (2018)2018 IEEE International Confe…Predictive Business Process Monitoring with LSTM Neural Networks (2017)Predictive Business Process M…Predicting process behaviour using deep learning (2017)Predicting process behaviour …Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits (2017)Efficient Hyperparameter Opti…An Eye into the Future: Leveraging A-priori Knowledge in Predictive Business Process Monitoring (2017)An Eye into the Future: Lever…A Deep Learning Approach for Predicting Process Behaviour at Runtime (2017)A Deep Learning Approach for …Flexible evolutionary algorithms for mining structured process models (2014)Flexible evolutionary algorit…Data-aware remaining time prediction of business process instances (2014)Data-aware remaining time pre…Process Mining: Discovery, Conformance and Enhancement of Business Processes (2011)Process Mining: Discovery, Co…Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Practical Bayesian Optimization of Machine Learning Algorithms (2012)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Fast Bayesian hyperparameter optimization on large datasets (2017)A Survey on Practical Applications of Multi-Armed and Contextual Bandits (2019)Flexible Heuristics Miner (FHM) (2011)Replaying history on process models for conformance checking and performance analysis (2012)Decision support from financial disclosures with deep neural networks and transfer learning (2017)Process mining techniques and applications – A systematic mapping study (2019)Time prediction based on process mining (2010)Genetic algorithms for hyperparameter optimization in predictive business process monitoring (2018)Deep Learning for Predictive Business Process Monitoring: Review and Benchmark (2023)Process Mining: Data Science in Action (2016)Towards comprehensive support for organizational mining (2008)A systematic literature review on state-of-the-art deep learning methods for process prediction (2021)Predictive Process Monitoring (2022)Forecasting principles and practiceTime Series Analysis Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control (1977)Stock price prediction using deep learning and frequency decomposition (2020)Forecasting crude oil futures prices using BiLSTM-Attention-CNN model with Wavelet transform (2022)Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM (2022)Process Mining Manifesto (2012)Business Process Management: A Comprehensive Survey (2013)Digital Innovation Management: Reinventing Innovation Management Research in a Digital World (2017)Outcome-Oriented Predictive Process Monitoring (2019)Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring (2019)Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022)Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism (2023)The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks (2024)Mobile phone location data for disasters: A review from natural hazards and epidemics (2022)Explainable artificial intelligence for digital finance and consumption upgrading (2023)Speech emotion recognition approaches: A systematic review (2023)
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References

  • 2021Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions2,700 cited
  • 2019The Performance of LSTM and BiLSTM in Forecasting Time Series1,735 cited
  • 2011Process Mining: Discovery, Conformance and Enhancement of Business Processes1,725 cited
  • 2017Predictive Business Process Monitoring with LSTM Neural Networks554 cited
  • 20182018 IEEE International Conference on Big Data (Big Data)471 cited
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  • 2019Learning Accurate LSTM Models of Business Processes241 cited
  • 2017Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits127 cited
  • 2019Using Convolutional Neural Networks for Predictive Process Analytics125 cited
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  • 2020Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction114 cited
  • 2017A Deep Learning Approach for Predicting Process Behaviour at Runtime105 cited
  • 2014Data-aware remaining time prediction of business process instances100 cited
  • 2014Flexible evolutionary algorithms for mining structured process models83 cited

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2019
Activity Prediction of Business Process Instances with Inception CNN Models
80 cited
  • 2021A Multi-View Deep Learning Approach for Predictive Business Process Monitoring69 cited
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