Results 211 to 220 of about 99,068 (260)

Bayesian Networks and Causal Discovery. [PDF]

Entropy (Basel)
Gao X, Wang Z.
europepmc   +1 more source

SE-SNN: Squeeze-and-Excitation-Enhanced Spiking Neural Networks with Learnable Neuron Dynamics for Event-Based Vision. [PDF]

Biomimetics (Basel)
Liu C, Chen Y.
europepmc   +1 more source

Early detection of chronic kidney disease using deep learning: a Mini review. [PDF]

Front Digit Health
Hossen MJ, Bannah H, Sadib RJ.
europepmc   +1 more source

Use of Artificial Intelligence in the Interpretation of Electroretinography (ERG) Studies. [PDF]

Int J Mol Sci
Hegde M, Thomis A, Shirke S.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Artificial Neural Networks and Deep Learning in the Visual Arts: a review

Neural Computing and Applications, 2021
In this article, we perform an exhaustive analysis of the use of Artificial Neural Networks and Deep Learning in the Visual Arts. We begin by introducing changes in Artificial Intelligence over the years and examine in depth the latest work carried out in prediction, classification, evaluation, generation, and identification through Artificial Neural ...
Adrian Carballal, Iria Santos, Alvaro Torrente-Patiño   +2 more
exaly   +2 more sources

Deep Learning and Artificial Neural Networks

2020
Artificial neural networks is an important tool used in artificial intelligence and machine learning or deep learning. An example of their applications is to image recognition.
openaire   +2 more sources

Convolutional deep-learning artificial neural networks

Journal of Optical Technology, 2015
This paper discusses the history of the appearance and development of the concept of convolutional artificial neural networks, which, because they use a learning technology based on back-propagation of the error signal, have become one of the most efficient tools of automatic image classification.
openaire   +1 more source

Artificial Neural Networks and Deep Learning in Solid Organ Transplantation

Transplantation
Artificial neural networks are now used across many fields, from medicine to chatbots. However, they are often complicated and give “black-box” predictions. To better understand these models, we outline some of the terminology, provide example applications in solid organ transplantation, and offer recommendations for those interested in starting to use
Byron H, Smith, Aleksandar, Denic
openaire   +2 more sources