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 …

The objective of this paper is to present a comprehensive review of the contemporary
techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs) …

Deep learning, as one of the most currently remarkable machine learning techniques, has
achieved great success in many applications such as image analysis, speech recognition …

We offer a systematic analysis of the use of deep learning networks for stock market analysis
and prediction. Its ability to extract features from a large set of raw data without relying on …

Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and
vibratory measurements in such mechanical devices are all sensitive to the existence of …

Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive
impairment (MCI), plays a critical role in possibly preventing progression of memory …

Fault diagnosis is important for the maintenance of rotating machinery. The detection of
faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this …

A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment
processes. Its control is a challenging industrial-process-control problem due to great …

Automated Modulation Classification (AMC) has been applied in various emerging areas
such as cognitive radio (CR). In our paper, we propose a deep learning-based AMC method …

Inflow forecasting applies data supports for the operations and managements of reservoirs.
A multiscale deep feature learning (MDFL) method with hybrid models is proposed in this …