AIMC Topic: Probability

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Extremely randomized neural networks for constructing prediction intervals.

Neural networks : the official journal of the International Neural Network Society
The aim of this paper is to propose a novel prediction model based on an ensemble of deep neural networks adapting the extremely randomized trees method originally developed for random forests. The extra-randomness introduced in the ensemble reduces ...

A stochastic modeling approach for analyzing water resources systems.

Journal of contaminant hydrology
Many uncertain factors exist in the water resource systems, leading to dynamic characteristics of the water distribution process. Especially for the watershed including irrigation area with multiple water sources and water users, it is complicated th...

Nonfragile H State Estimation for Recurrent Neural Networks With Time-Varying Delays: On Proportional-Integral Observer Design.

IEEE transactions on neural networks and learning systems
In this article, a novel proportional-integral observer (PIO) design approach is proposed for the nonfragile H state estimation problem for a class of discrete-time recurrent neural networks with time-varying delays. The developed PIO is equipped wit...

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network.

PloS one
Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important infl...

In silico saturation mutagenesis of cancer genes.

Nature
Despite the existence of good catalogues of cancer genes, identifying the specific mutations of those genes that drive tumorigenesis across tumour types is still a largely unsolved problem. As a result, most mutations identified in cancer genes acros...

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.

Scientific reports
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID compu...

Research on Network Security Situation Awareness Based on the LSTM-DT Model.

Sensors (Basel, Switzerland)
To better understand the behavior of attackers and describe the network state, we construct an LSTM-DT model for network security situation awareness, which provides risk assessment indicators and quantitative methods. This paper introduces the conce...

Lesion probability mapping in MS patients using a regression network on MR fingerprinting.

BMC medical imaging
BACKGROUND: To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to [Formula: see text], [Formula: see text], NAWM, and GM- proba...

A sensitivity analysis of probability maps in deep-learning-based anatomical segmentation.

Journal of applied clinical medical physics
PURPOSE: Deep-learning-based segmentation models implicitly learn to predict the presence of a structure based on its overall prominence in the training dataset. This phenomenon is observed and accounted for in deep-learning applications such as natu...

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification.

Sensors (Basel, Switzerland)
Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavi...