In this paper, sixty-eight research articles published between 2000 and 2017 as well as
textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN) …

Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of
modern industrial systems. As an emerging field in industrial applications and an effective …

Abstract Machine Learning methods and, in particular, Artificial Neural Networks (ANNs)
have demonstrated promising capabilities in material constitutive modeling. One of the main …

To mitigate the impact of climate change and global warming, the use of renewable energies
is increasing day by day significantly. A considerable amount of electricity is generated from …

The uncertainty associated with modeling and performance prediction of solar photovoltaic
systems could be easily and efficiently solved by artificial intelligence techniques. During the …

Image restoration is a long-standing problem in low-level computer vision with many
interesting applications. We describe a flexible learning framework based on the concept of …

Fault detection prior to their occurrence or complete shut-down in induction motor is
essential for the industries. The fault detection based on condition monitoring techniques …

We propose a novel deep network architecture for grayscale and color image denoising that
is based on a non-local image model. Our motivation for the overall design of the proposed …

We design a novel network architecture for learning discriminative image models that are
employed to efficiently tackle the problem of grayscale and color image denoising. Based on …

Electronic structure calculations, such as those employing Kohn–Sham density functional
theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of …