D = Data engineering, M = Machine learning, B = Biometrics & computer vision
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Data Engineering
- Data Engineering
- [D] A probabilistic approach towards handling data quality problems and imperfect data integration tasks
- [D] Data Physicalization – What Happens in the Brain?
- [D][M] BIO marker-based COVID Severity prediction and data quality exploration
- [D] Process Mining for Logistics Applications
- [D] Process Mining Software Logs
- [D] Subpopulation process mining based on graph matching
- [D] Subpopulation Process Mining in Health
- [D][M] Synthetic data production and privacy evaluation for Hospital Information System(HIS)
- [D] Tracing data utility for noise adding plugins in process mining
Biometrics
- Biometrics
- Finger vein recognition
- Iris on RPI
- Self Driving Challenge
- [B] Hazardous Materials Signs Detection on Freight Trains
- [B] Quantative Weather Simulations on Optical Character Reading Problems
- [B] Finishing and improving of finger vein system
- [B] Morphing detection based on analysis of local spectrum
- [B] Morphing detection based on facial marks
- [B] Morphing robust face recognition
- [B] Team formation. A bibliometric review of the last 30 years
Machine Learning
- Machine Learning
- [M] Adversarial noise in convolutional deep networks
- [M] Advancing Healthcare Process Simulation: Automating Discrete Event Models from Process Data
- [M] Deep learning models for point cloud classification/semantic segmentation.
- [M][B] Fourier-basis noise data augmentation
- [M] A framework for ethical AI
- [M] Machine Learning for Model-based Diagnosis of Cyberphysical Systems
- [M] Making Bayesian Networks Available for Clinical Decision Support
- [M][B] Computer vision for image and video analysis