Deep convolutional neural networks (deep CNN) show a large power for robust recognition
of visual patterns. The neocognitron, which was first proposed by Fukushima (1979), is a …

Interference signals degrade and disrupt Global Navigation Satellite System (GNSS)
receivers, impacting their localization accuracy. Therefore, they need to be detected …

Deep convolutional neural networks (deep CNNs) show a large power for robust recognition
of visual patterns. The neocognitron, which was first proposed by Fukushima (1979), is …

The neocognitron is a deep (multi-layered) convolutional neural network that can be trained
to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local …

In many deep neural networks for pattern recognition, the input pattern is classified in the
deepest layer based on features extracted through intermediate layers. IntVec (interpolating …

In 1979 Fukushima developed a hierarchical, multilayered neural network called
Neocognitron and used it for the automatic recognition of handwritten Japanese symbols …

Structures of neural networks are usually designed by experts to fit target problems. This
study proposes a method to automate small network design for a regression problem based …

In the past decade, with the increase of data analytics and technology and the growth of e-
commerce, many new applications have been developed. One example of these …

Abstract Training a Convolutional Neural Network (CNN) based classifier is dependent on a
large number of factors. These factors involve tasks such as aggregation of apt dataset …

The visual Pathway system of our brain is very complicated to understand. The Primary
visual cortex is used for the vision in our brain. These processes of vision starting from the …