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Found 6 papers, 3 papers with code
A Novel Approach to Discover Switch Behaviours in Process Mining
Process mining is a relatively new subject which builds a bridge between process modelling and data mining.
Detecting and Understanding Branching Frequency Changes in Process Models
In this paper, we propose a method which takes both event logs and process models as input and generates a choice sequence for each exclusive choice in the process model.
Discovering Redundant Activities in Event Logs for the Simplification of Process Models
The key to process mining is to discovery understandable process models.
AMI-Net+: A Novel Multi-Instance Neural Network for Medical Diagnosis from Incomplete and Imbalanced Data
In medical real-world study (RWS), how to fully utilize the fragmentary and scarce information in model training to generate the solid diagnosis results is a challenging task.
Attention-based Multi-instance Neural Network for Medical Diagnosis from Incomplete and Low Quality Data
However, in many real-world cases, data is often of low-quality due to a variety of reasons, such as data consistency, integrity, completeness, accuracy, etc.
CNN based Multi-Instance Multi-Task Learning for Syndrome Differentiation of Diabetic Patients
Inspired from it, we employ multi-instance multi-task learning combined with the convolutional neural network (MIMT-CNN) for syndrome differentiation, which takes region proposals as input and output image labels directly.
