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Found 10 papers, 2 papers with code
PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations
The fourth industrial revolution creates ubiquitous sensor data in production plants.
XAI in the context of Predictive Process Monitoring: Too much to Reveal
To address this gap, we provide a framework to enable studying the effect of different PPM-related settings and ML model-related choices on characteristics and expressiveness of resulting explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
To address this gap, we investigate the effect of different PPM settings on resulting data fed into an ML model and consequently to a XAI method.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks
To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed.
Robotic Process Automation -- A Systematic Literature Review and Assessment Framework
Robotic Process Automation (RPA) is the automation of rule-based routine processes to increase efficiency and to reduce costs.
Robotics
A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions
Already today, driver assistance systems help to make daily traffic more comfortable and safer.
CONDA-PM -- A Systematic Review and Framework for Concept Drift Analysis in Process Mining
Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter.
Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior
More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density.
Detecting Production Phases Based on Sensor Values using 1D-CNNs
In the context of Industry 4. 0, the knowledge extraction from sensor information plays an important role.
Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets
By observing their environment as well as other traffic participants, humans are enabled to drive road vehicles safely.
