AIMC Topic: Uncertainty

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Curriculum learning for improved femur fracture classification: Scheduling data with prior knowledge and uncertainty.

Medical image analysis Oct 21, 2021
An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We rely on the commonly used AO system, which describes a hierarchical knowledge tree classifying the ima...
Humans Neural Networks, Computer Deep Learning Curriculum Femur Uncertainty
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Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots.

Sensors (Basel, Switzerland) Oct 8, 2021
Human-robot interaction has received a lot of attention as collaborative robots became widely utilized in many industrial fields. Among techniques for human-robot interaction, collision identification is an indispensable element in collaborative robo...
Humans Neural Networks, Computer Robotics Uncertainty Distillation
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Explainable Uncertainty-Aware Convolutional Recurrent Neural Network for Irregular Medical Time Series.

IEEE transactions on neural networks and learning systems Oct 5, 2021
Influenced by the dynamic changes in the severity of illness, patients usually take examinations in hospitals irregularly, producing a large volume of irregular medical time-series data. Performing diagnosis prediction from the irregular medical time...
Humans Neural Networks, Computer Electronic Health Records Deep Learning Time Factors Uncertainty
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How competitors become collaborators-Bridging the gap(s) between machine learning algorithms and clinicians.

Bioethics Oct 2, 2021
For some years, we have been witnessing a steady stream of high-profile studies about machine learning (ML) algorithms achieving high diagnostic accuracy in the analysis of medical images. That said, facilitating successful collaboration between ML a...
Humans Machine Learning Algorithms Clinical Decision-Making Uncertainty
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Interpretability-Driven Sample Selection Using Self Supervised Learning for Disease Classification and Segmentation.

IEEE transactions on medical imaging Sep 30, 2021
In supervised learning for medical image analysis, sample selection methodologies are fundamental to attain optimum system performance promptly and with minimal expert interactions (e.g. label querying in an active learning setup). In this article we...
Lung Supervised Machine Learning Uncertainty
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Remaining Useful Life Estimation for Engineered Systems Operating under Uncertainty with Causal GraphNets.

Sensors (Basel, Switzerland) Sep 22, 2021
In this work, a novel approach, termed GNN-tCNN, is presented for the construction and training of Remaining Useful Life (RUL) models. The method exploits Graph Neural Networks (GNNs) and deals with the problem of efficiently learning from time serie...
Neural Networks, Computer Probability Uncertainty
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Brain emotional learning impedance control of uncertain nonlinear systems with time delay: Experiments on a hybrid elastic joint robot in telesurgery.

Computers in biology and medicine Sep 17, 2021
Telesurgical robot control is a significant example of an uncertain nonlinear system, as it involves various complexities, including unknown master/slave dynamics, environmental uncertainties, joint elasticities, and communication time delays. This p...
Brain Robotics Nonlinear Dynamics Electric Impedance Uncertainty
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A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network.

Sensors (Basel, Switzerland) Sep 17, 2021
Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monop...
Neural Networks, Computer Forecasting Electricity Uncertainty Jordan
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Structured Ensembles: An approach to reduce the memory footprint of ensemble methods.

Neural networks : the official journal of the International Neural Network Society Sep 16, 2021
In this paper, we propose a novel ensembling technique for deep neural networks, which is able to drastically reduce the required memory compared to alternative approaches. In particular, we propose to extract multiple sub-networks from a single, unt...
Neural Networks, Computer Learning Uncertainty
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Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm.

Environmental science and pollution research international Sep 15, 2021
Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting d...
Neural Networks, Computer Algorithms Water Malaysia Uncertainty
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