Accurate air quality forecasting is critical for systematic pollution control as well as public
health and wellness. Most of the traditional forecasting techniques have shown inconsistent …

Deep learning of artificial neural networks has become the de facto standard approach to
solving data analysis problems in virtually all fields of science and engineering. Also in …

To overcome the shortcomings in current study of oil well production prediction, we propose
a combined model (CNN-LSTM-SA) with the convolutional neural network (CNN), the long …

The objective of this study is to develop a convolutional neural network (CNN) for computed
tomography (CT) image super-resolution. The network learns an end-to-end map** …

Purpose We sought to distinguish lung adenocarcinoma (ADC) from squamous cell
carcinoma using a machine-learning algorithm with PET-based radiomic features. Methods …

Molecular imaging has emerged at the end of the last century as an interdisciplinary method
involving in vivo imaging and molecular biology aiming at identifying living biological …

Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively
accurate positron emission tomography (PET) images. PET AC based on computed …

Dementia, a chronic and progressive cognitive declination of brain function caused by
disease or impairment, is becoming more prevalent due to the aging population. A major …

We propose a new deep learning–based approach to provide more accurate whole-body
PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method …

Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …