AIMC Topic: Uncertainty

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Role of calibration in uncertainty-based referral for deep learning.

Statistical methods in medical research Apr 3, 2023
The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in subsequent decision-making. Using data from diabetic retinopathy detection, we present an empirical e...
Humans Diabetic Retinopathy Calibration Uncertainty Neural Networks, Computer Deep Learning
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Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition.

Sensors (Basel, Switzerland) Mar 23, 2023
This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often r...
Neural Networks, Computer Humans Uncertainty Algorithms Robotics Adaptation, Psychological
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Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain.

Medical physics Mar 21, 2023
BACKGROUND: Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (...
Image Processing, Computer-Assisted Brain Humans Uncertainty Deep Learning Algorithms Pilot Projects Cone-Beam Computed Tomography
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Developing a machine learning model to detect diagnostic uncertainty in clinical documentation.

Journal of hospital medicine Mar 15, 2023
BACKGROUND AND OBJECTIVE: Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to ...
Documentation Uncertainty Natural Language Processing Humans Machine Learning Case-Control Studies Child
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Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs.

PloS one Mar 13, 2023
Machine learning (ML) algorithms to detect critical findings on head CTs may expedite patient management. Most ML algorithms for diagnostic imaging analysis utilize dichotomous classifications to determine whether a specific abnormality is present. H...
Tomography, X-Ray Computed Deep Learning Intracranial Hemorrhages Uncertainty Algorithms Retrospective Studies Humans
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Uncertainty maximization in partially observable domains: A cognitive perspective.

Neural networks : the official journal of the International Neural Network Society Mar 10, 2023
Faced with an ever-increasing complexity of their domains of application, artificial learning agents are now able to scale up in their ability to process an overwhelming amount of data. However, this comes at the cost of encoding and processing an in...
Cognition Machine Learning Markov Chains Algorithms Uncertainty
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Additive manufacturing process selection for automotive industry using Pythagorean fuzzy CRITIC EDAS.

PloS one Mar 9, 2023
For many different types of businesses, additive manufacturing has great potential for new product and process development in many different types of businesses including automotive industry. On the other hand, there are a variety of additive manufac...
Decision Making Industry Fuzzy Logic Commerce Uncertainty
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Bounded synchronization for uncertain master-slave neural networks: An adaptive impulsive control approach.

Neural networks : the official journal of the International Neural Network Society Mar 8, 2023
This paper investigates the bounded synchronization of the discrete-time master-slave neural networks (MSNNs) with uncertainty. To deal with the unknown parameter in the MSNNs, a parameter adaptive law combined with the impulsive mechanism is propose...
Time Factors Uncertainty Algorithms Neural Networks, Computer
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Uncertainty-driven dynamics for active learning of interatomic potentials.

Nature computational science Mar 6, 2023
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, th...
Uncertainty Machine Learning Glycine Molecular Dynamics Simulation
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A general framework for robust stability analysis of neural networks with discrete time delays.

Neural networks : the official journal of the International Neural Network Society Mar 1, 2023
Robust stability of different types of dynamical neural network models including time delay parameters have been extensively studied, and many different sets of sufficient conditions ensuring robust stability of these types of dynamical neural networ...
Time Factors Models, Theoretical Neural Networks, Computer Algorithms Uncertainty
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