Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data inform...
This study aimed to develop a model for predicting the completion of clinical trials involving pregnant women using the Cox proportional hazard model and neural network model (DeepSurv) and to compare the predictive performance of both methods. We co...
In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probabil...
Neural networks : the official journal of the International Neural Network Society
Nov 5, 2021
Financial market predictions represent a complex problem. Most prediction systems work with the term time window, which is represented by exchange rate values of a real financial commodity. Such values (time window) provide the base for prediction of...
Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based on scores ranging from 0 to 1. However, Ali-M3 has not been externally validated. Our aim was to ev...
Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Thi...
Neural networks : the official journal of the International Neural Network Society
Oct 23, 2021
The Delta method is a classical procedure for quantifying epistemic uncertainty in statistical models, but its direct application to deep neural networks is prevented by the large number of parameters P. We propose a low cost approximation of the Del...
Neural networks : the official journal of the International Neural Network Society
Oct 21, 2021
We study the efficacy and efficiency of deep generative networks for approximating probability distributions. We prove that neural networks can transform a low-dimensional source distribution to a distribution that is arbitrarily close to a high-dime...
Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), es...
With the rapid development of economy and the acceleration of urbanization, the garbage produced by urban residents also increases with the increase of population. In many big cities, the phenomenon of "garbage siege" has seriously affected the devel...