This paper focuses on the enhancement of the generalization ability and training stability of
deep neural networks (DNNs). New activation functions that we call bounded rectified linear …

Land-use information provides a direct representation of the effect of human activities on the
environment, and an accurate and efficient land-use classification of remote sensing images …

This study investigates the effectiveness of multiple maxout activation function variants on 18
datasets using Convolutional Neural Networks. A network with maxout activation has a …

The brain can be viewed as a complex modular structure with features of information
processing through knowledge storage and retrieval. Modularity ensures that the knowledge …

This brief paper presents two implementations of feed-forward artificial neural networks in
FPGAs. The implementations differ in the FPGA resources requirement and calculations …

The implementation of non-linear Activation Functions (AFs) within the Artificial Neural
Network (ANN) on the Field Programmable Gate Array (FPGA) is substantial due to the …

This paper presents implementation of a hand gestures tracking system with a fully
connected multilayer perceptron and a supervised sequential learning algorithm on low cost …

Reconfigurable architectures targeting neural networks are an attractive option. They allow
multiple neural networks of different types to be hosted on the same hardware, in parallel or …

Globally, health care losses due to fraud rise every year, and for this reason fraud detection
is an active research area that, in the US alone, can potentially save billions of dollars. We …

This paper proposes a novel optimization method that utilizes a multi-population parallel
genetic algorithm (GA) to simultaneously optimize the structure and system latency of …