Extreme learning machine (ELM) as an emerging technology has achieved exceptional performance in large-scale settings, and is well suited to binary and multi-class classification, as well as regression tasks. However, existing ELM and its variants predominantly employ single hidden layer feedforward networks, leaving the popular and potentially powerful stacked generalization principle unexploi...

Detecting ships in marine images is an essential problem in maritime surveillance systems. Although several types of deep neural networks have almost ubiquitously used for this purpose, but the performance of such networks greatly drops when they are exposed to low size and low contrast images which have been captured by passive monitoring systems. On the other hand factors such as sea waves, c...

Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme...

In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised ma...

Detecting healthcare-associated infections pose a major challenge in healthcare. Using natural language processing and machine learning applied on electronic patient records is one approach that has been shown to work. However the results indicate that there was room for improvement and therefore we have applied deep learning methods. Specifically we implemented a network of stacked sparse auto...

We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website. In this work we compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a substantial improvement by extracting features from high dimensional data durin...

Due to its importance in many applications, the incomplete data mining has received increasing attention in recent years, but there has been little study of the cost-sensitive classification on incomplete data. Therefore this paper proposes the dynamic costsensitive extreme learning machine for classification of incomplete data based on the deep imputation network (DCELMIDC). Firstly, we propos...

Deep learning has emerged as a new branch of machine learning in recent years. Some of the related algorithms have been reported to beat state-of-the-art approaches in many applications. The main aim of this paper is to verify one of the deep learning algorithms, specifically a stacked autoencoder, to detect the P300 component. This component, as a specific brain response, is widely used in the...

This paper proposes the Stacked Approximated Regression Machine (SARM), a novel, simple yet powerful deep learning (DL) baseline. We start by discussing the relationship between regularized regression models and feed-forward networks, with emphasis on the non-negative sparse coding and convolutional sparse coding models. We demonstrate how these models are naturally converted into a unified fee...