The present work offers some contributions to the area of surface roughness modeling by
Artificial Neural Networks (ANNs) in machining processes. It proposes a method for an …

Deep evolutionary network structured representation (DENSER) is a novel evolutionary
approach for the automatic generation of deep neural networks (DNNs) which combines the …

Multivariate machine learning methods are increasingly used to analyze neuroimaging data,
often replacing more traditional “mass univariate” techniques that fit data one voxel at a time …

We study TV regularization, a widely used technique for eliciting structured sparsity. In
particular, we propose efficient algorithms for computing prox-operators for lp-norm TV. The …

Power load forecasting is an important task of smart grid, which is of great significance to the
sustainable development of society. In this paper, a hybrid support vector regression (HSVR) …

In order to address the challenge of accurately predicting nitrogen oxide (NOx) emission
from diesel engines in transient operation using traditional neural network models, this study …

A novel method of disease diagnosis, based on images that capture every part of a diseased
plant, such as the leaf, the fruit, the root, etc., is presented in this paper. As is well known, the …

The short-term forecasting of wind power, photovoltaic (PV) generation and loads is
important for the secure and economical dispatching of smart community with smart grid …

Imbalanced problem is concerned with the performance of classifiers on the data set with
severe class imbalance distribution. Traditional methods are misled by the majority samples …

Due to the continuous and growing spread of the novel corona virus (COVID-19) worldwide,
it is urgent, especially in the data science era, to develop accurate data driven decision …