AIMC Topic: Bayes Theorem

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Extraction of low-dimensional features for single-channel common lung sound classification.

Medical & biological engineering & computing Apr 4, 2022
In this study, feature extraction methods used in the classification of single-channel lung sounds obtained by automatic identification of respiratory cycles were examined in detail in order to extract distinctive features at the lowest size. In this...
Bayes Theorem Support Vector Machine Respiratory Sounds Discriminant Analysis Humans Algorithms
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Bayesian networks elucidate complex genomic landscapes in cancer.

Communications biology Apr 4, 2022
Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they ca...
Humans Neoplasms Genomics Algorithms Artificial Intelligence Bayes Theorem
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BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks.

IEEE transactions on neural networks and learning systems Apr 4, 2022
Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood fun...
Learning Neural Networks, Computer Bayes Theorem
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Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction.

Sensors (Basel, Switzerland) Apr 1, 2022
One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) a...
Industry Discriminant Analysis Algorithms Bayes Theorem Support Vector Machine Machine Learning
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Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images.

IEEE transactions on medical imaging Apr 1, 2022
Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show that Bayes...
Bayes Theorem Breast Neural Networks, Computer Uncertainty
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Bayesian deep learning-based H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout.

Magnetic resonance in medicine Mar 28, 2022
PURPOSE: To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning-based proton MRS of the brain.
Brain Humans Bayes Theorem Deep Learning Monte Carlo Method Macromolecular Substances Uncertainty
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Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review.

Sensors (Basel, Switzerland) Mar 26, 2022
Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules...
Support Vector Machine Bayes Theorem Mobile Applications Machine Learning Algorithms Neural Networks, Computer
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A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients.

Journal of healthcare engineering Mar 26, 2022
In today's scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant ...
Humans Sepsis Machine Learning Intensive Care Units Critical Care Bayes Theorem
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Multimodal driver state modeling through unsupervised learning.

Accident; analysis and prevention Mar 24, 2022
Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral p...
Automobile Driving Unsupervised Machine Learning Heart Rate Humans Accidents, Traffic Bayes Theorem
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CircuitBot: Learning to survive with robotic circuit drawing.

PloS one Mar 24, 2022
Robots with the ability to actively acquire power from surroundings will be greatly beneficial for long-term autonomy and to survive in uncertain environments. In this work, a scenario is presented where a robot has limited energy, and the only way t...
Robotics Bayes Theorem Learning Robotic Surgical Procedures
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