Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

Abstract The Restricted Boltzmann Machine (RBM) has aroused wide interest in machine
learning fields during the past decade. This review aims to report the recent developments in …

Although the existing traditional image classification methods have been widely applied in
practical problems, there are some problems in the application process, such as …

Using macroeconomic and financial conditions to forecast credit default swap (CDS)
spreads is a challenging task. In this paper, we propose the Merton-LSTM model, a modified …

This paper presents the framework of the smoothing group L 1/2 regularized discriminative
broad learning system for pattern classification and regression. The core idea is to improve …

An enhanced intelligent diagnosis method is proposed based on a joint pairwise graph
embedded sparse deep belief network with partial least square fine-tuning (J-PDBN). In this …

The growth and success of deep learning approaches can be attributed to two major factors:
availability of hardware resources and availability of large number of training samples. For …

Unsupervised feature extraction is gaining a lot of research attention following its success to
represent any kind of noisy data. Owing to the presence of a lot of training parameters, these …

As has been demonstrated, the long short-term memory (LSTM) algorithm has the special
ability to process sequenced data; however, LSTM suffers from high dimensionality, and its …

Recently, deep learning is receiving renewed attention in the field of artificial intelligence.
Deep belief network (DBN) has a deep network architecture that can represent multiple …