Abstract Machine learning studies automatic algorithms that improve themselves through
experience. It is widely used for analyzing and extracting value from large biomedical data …

Application of machine learning (ML) to the prediction of reaction activation barriers is a new
and exciting field for these algorithms. The works covered here are specifically those in …

WEKA is a widely used, open-source machine learning platform. Due to its intuitive interface,
it is particularly popular with novice users. However, such users often find it hard to identify …

Many different machine learning algorithms exist; taking into account each algorithm's
hyperparameters, there is a staggeringly large number of possible alternatives overall. We …

Wind power forecasting can improve the economical and technical integration of wind
energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to …

The mixing ratio of the raw materials has a significant impact on concrete compressive
strength. Although the compressive strength of concrete can be inferred from the mix ratio, it …

In this paper, we proposed a new robust twin support vector machine (called R-TWSVM) via
second order cone programming formulations for classification, which can deal with data …

Addressing the issue of SVMs parameters optimization, this study proposes an efficient
memetic algorithm based on particle swarm optimization algorithm (PSO) and pattern search …

Support vector machines (SVMs), with their roots in Statistical Learning Theory (SLT) and
optimization methods, have become powerful tools for problem solution in machine learning …

Accurate prediction of pore-pressures in the subsurface is paramount for successful
planning and drilling of oil and gas wellbores. It saves cost and time and helps to avoid …