AIMC Topic: Monte Carlo Method

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Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.

Scientific reports
Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, ma...

Revisiting the out of Africa event with a deep-learning approach.

American journal of human genetics
Anatomically modern humans evolved around 300 thousand years ago in Africa. They started to appear in the fossil record outside of Africa as early as 100 thousand years ago, although other hominins existed throughout Eurasia much earlier. Recently, s...

Uncertainty propagation for dropout-based Bayesian neural networks.

Neural networks : the official journal of the International Neural Network Society
Uncertainty evaluation is a core technique when deep neural networks (DNNs) are used in real-world problems. In practical applications, we often encounter unexpected samples that have not seen in the training process. Not only achieving the high-pred...

Feature selection of infrared spectra analysis with convolutional neural network.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Data-driven deep learning analysis, especially for convolution neural network (CNN), has been developed and successfully applied in many domains. CNN is regarded as a black box, and the main drawback is the lack of interpretation. In this study, an i...

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 ...

Personalized brachytherapy dose reconstruction using deep learning.

Computers in biology and medicine
BACKGROUND AND PURPOSE: Accurate calculation of the absorbed dose delivered to the tumor and normal tissues improves treatment gain factor, which is the major advantage of brachytherapy over external radiation therapy. To address the simplifications ...

Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms.

American journal of epidemiology
Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data generating mechanisms. As such, numerous authors have advocated for ML methods to estimate causal effects. Unfortunately, ML algor...

Deep residual-convolutional neural networks for event positioning in a monolithic annular PET scanner.

Physics in medicine and biology
PET scanners based on monolithic pieces of scintillator can potentially produce superior performance characteristics (high spatial resolution and detection sensitivity, for example) compared to conventional PET scanners. Consequently, we initiated de...

Multi-Robot 2.5D Localization and Mapping Using a Monte Carlo Algorithm on a Multi-Level Surface.

Sensors (Basel, Switzerland)
Most indoor environments have wheelchair adaptations or ramps, providing an opportunity for mobile robots to navigate sloped areas avoiding steps. These indoor environments with integrated sloped areas are divided into different levels. The multi-lev...

DeepBeam: a machine learning framework for tuning the primary electron beam of the PRIMO Monte Carlo software.

Radiation oncology (London, England)
BACKGROUND: Any Monte Carlo simulation of dose delivery using medical accelerator-generated megavolt photon beams begins by simulating electrons of the primary electron beam interacting with a target. Because the electron beam characteristics of any ...