BETS
Author:
Affiliation:
1. Netrix S.A., Lublin, Poland
2. University of Economics and Innovation, Lublin, Poland
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3560905.3568057
Reference6 articles.
1. Supervised Learning Classifiers for Electrical Impedance-based Bladder State Detection
2. USING NEURAL NETWORKS AND DEEP LEARNING ALGORITHMS IN ELECTRICAL IMPEDANCE TOMOGRAPHY
3. Overactive Bladder Syndrome: Evaluation and Management
4. Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review
5. Hybrid machine learning in electrical impedance tomography
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3. Poster Abstract: A Wearable for Non-Invasive Monitoring and Diagnosing Functional Disorders of the Lower Urinary Tract;Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems;2023-11-12
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