Electroencephalography (EEG) signals can be used to monitor the brain activities of all human beings. As a result, it can be used to detect abnormalities in the brain functioning. In this study, using Artificial Neural Network (ANN) EEG signals of children with Autism Spectrum Disorder (ASD) and non-ASD children were classified. Two neural network models namely Pattern Recognition Neural Networ...

In a recent short paper and a report (see [Neto et al. 98] and [Neto and Costa 99]) it was shown that programming languages can be translated efficiently on recurrent (analog, rational weighted) neural nets, using bounded resources. This fact was achieved by creating a neural programming language called NETDEF, such that each program corresponds to a modular neural net that computes it. This fr...

Despite the many attempts and approaches for anomaly de- tection explored over years, automatic detection of rare events in data communication networks remains a com- plex problem. In this paper we introduce Net-GAN, novel approach to network time-series, us- ing recurrent neural (RNNs) generative ad- versarial (GAN). Different from state art, which traditionally focuses on univariate measureme...

Machine Learning (ML) such as Artificial Neural Network (ANN), Deep learning, Recurrent Networks (RNN), Alex Net, and ResNet can be considered a broad research direction in the identification classification of critical diseases. CNN its particular variant, usually named U-Net Segmentation, has made revolutionary advancement medical diseases, specifically retinal However, because feature extract...

~ (Received 8 July 1969, and in revised form 10 November 1969) lI, Some possible neural mechanisms of pattern discrimination are discussed, leading to neural networks which can discriminate any number of essentially arbitrarily complicated space-time patterns and activate cells which can then learn and perform any number of essentially arbitrarily complicated space-time patterns in response to ...

To improve speaker verification performance, we extend the wellknown Probabilistic Neural Networks (PNN) to Locally Recurrent Probabilistic Neural Networks (LRPNN). In contrast to PNNs that possess no feedbacks, LRPNNs incorporate internal connections to the past outputs of all recurrent neurons, which render them sensitive to the context in which events occur. Thus, LRPNNs are capable of ident...

Recurrent pregnancy loss is usually defined as the loss of two or more consecutive pregnancies before 20 weeks of gestation, which occurs in approximately 5% of reproductive-aged women. It has been suggested that women with thrombophilia have an increased risk of pregnancy loss and other adverse pregnancy outcomes. Thrombophilia is an important predisposition to blood clot formation and is cons...