We propose a novel method for training neural networks to predict the future prices of stock indexes. Unlike previous works, we do not use target stock index data for training neural networks for index prediction. Instead, we use only the data of ind...
Convolutional Neural Networks (CNNs) have become a prominent method of AI implementation in medical classification tasks. Grading Diabetic Retinopathy (DR) has been at the forefront of the development of AI for ophthalmology. However, major obstacles...
Journal of the American College of Radiology : JACR
Apr 9, 2020
PURPOSE: The aim of this study was to enhance multispecialty CT and MRI protocol assignment quality and efficiency through development, testing, and proposed workflow design of a natural language processing (NLP)-based machine learning classifier.
BACKGROUND AND AIMS: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 ...
Neural networks : the official journal of the International Neural Network Society
Apr 9, 2020
In recent years, research on image generation has been developing very fast. The generative adversarial network (GAN) emerges as a promising framework, which uses adversarial training to improve the generative ability of its generator. However, since...
Neural networks : the official journal of the International Neural Network Society
Apr 9, 2020
In this paper, the finite-time synchronization problems of two types of driven-response memristor neural networks (MNNs) without time-delay and with time-varying delays are investigated via interval matrix method, respectively. Based on interval matr...
International journal of environmental research and public health
Apr 9, 2020
This paper focuses on quantifying the uncertainty in the specific absorption rate valuesof the brain induced by the uncertain positions of the electroencephalography electrodes placed onthe patient's scalp. To avoid running a large number of simulati...
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has re...
Computational and mathematical methods in medicine
Apr 9, 2020
In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regu...
Accurate segmentation of myocardial in cardiac MRI (magnetic resonance image) is key to effective rapid diagnosis and quantitative pathology analysis. However, a low-quality CMR (cardiac magnetic resonance) image with a large amount of noise makes it...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.