Developing monitoring system for paper industrial wastewater treatment system is an
important route for wastewater reuse and recycling from wastewater, which are regarded as …

The need for developing a neural network classifier in an intrusion detection system for
network security purpose is a necessary. Today, worldwide various types of sophisticated …

Abstract Convolutional Neural Networks (CNNs) are essential tools in many image
recognition tasks. In this article we propose using a Deep Convolutional Neural Network for …

Recently, learning equivariant representations has attracted considerable research
attention. Dieleman et al. introduce four operations which can be inserted into convolutional …

Intrusion detection systems play a crucial role in preventing security threats and defending
networks from attacks. Among the attacks, distributed Denial-of-Service (DDoS) attacks …

Recently, clustering algorithms based on deep AutoEncoder attract lots of attention due to
their excellent clustering performance. On the other hand, the success of PCA-Kmeans and …

Convolutional neural networks have achieved excellent performance on face recognition
(FR) by learning the high discriminative features with advanced loss functions. These …

National boundaries and country averages are commonly used as delimiters and proxies for
culture. By doing so, not enough attention is paid to cultural heterogeneity within and …

In this paper, a deep learning neural network model predictive controller (DLNNMPC) is
designed to analyse the performance of a non-linear continuous stirred tank reactor (CSTR) …

In this dissertation we report results of our research on dense distributed representations of
text data. We propose two novel neural models for learning such representations. The first …