Artificial neural network (ANN) introduces different types of neural network structures and
has been applied successfully in diverse domains of real-world problems. Among various …

Abstract An Artificial Neural Network (ANN) is an abstract representation of the biological
nervous system which has the ability to solve many complex problems. The interesting …

Traditionally, optimizing the structure of a feed-forward neural-network is time-consuming
and it needs to balance the trade-off between the network size and network performance. In …

Adversarial examples pose significant challenges for Natural Language Processing (NLP)
model robustness, often causing notable performance degradation. While various detection …

Data mining is an emerging technology for applications such as text based mining, web
based mining and it performs a major role in various domains for numerical data analysis …

Over the past several decades, neural networks have evolved into powerful computation
systems, which are able to learn complex nonlinear input-output relationship from data …

The burning through point (BTP) is a crucial parameter in sintering process, which directly
determines the quality of the final sinter. Since the BTP is difficult to directly detect online, it is …

In this letter we explore the sparse sensing and learning mechanism of the human visual
system, to propose a dual-sparse single-hidden-layer feedforward neural network (SLFNN) …

Microphone arrays are today employed to specify the sound source locations in numerous
real time applications such as speech processing in large rooms or acoustic echo …

This work aims to propose an approach for pruning a bagging ensemble regression (BER)
model based on sparse representation, which we call sparse representation pruning (SRP) …