Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review

A Review of Epidemic Forecasting Using Artificial Neural Networks

Background and aims: Since accurate forecasts help inform decisions for preventive health-careintervention and epidemic control, this goal can only be achieved by making use of appropriatetechniques and methodologies. As much as forecast precision is important, methods and modelselection procedures are critical to forecast precision. This study aimed at providing an overview o...

Neural Networks and Artificial Intelligence

SYNOPSIS Assessment of surgical risk in patients undergoing pulmonary resection is a fundamental goal for thoracic surgeons. Usually used risk scores do not predict the individual outcome. Data Mining (DM) and Artificial Neural Networks (ANN) are artificial intelligence mathematical models that have been used for estimation of several prognostic situations. When used to assess surgical risk the...

Humanitarian health computing using artificial intelligence and social media: A narrative literature review.

INTRODUCTION According to the World Health Organization (WHO), over 130 million people are in constant need of humanitarian assistance due to natural disasters, disease outbreaks, and conflicts, among other factors. These health crises can compromise the resilience of healthcare systems, which are essential for achieving the health objectives of the sustainable development goals (SDGs) of the U...

Artificial neural networks for artificial intelligence

Artificial neural networks now have a long history as major techniques in computational intelligence with a wide range of applications for learning from data. There are many methods developed and applied so far, from multiplayer perceptrons (MLP) to the recent ones being deep neural networks and deep learning machines based on spiking neural networks. The paper addresses a main question for res...

Utilizing satellite images and artificial neural networks in estimation of vegetation fraction in arid regions