Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Support vector machines (SVMs) have considerable potential as classifiers of remotely
sensed data. A constraint on their application in remote sensing has been their binary …

Twin support vector machines (TWSVM), a novel machine learning algorithm developing
from traditional support vector machines (SVM), is one of the typical nonparallel support …

Optimization techniques have been widely adopted to implement various data mining
algorithms. In addition to well-known Support Vector Machines (SVMs)(which are based on …

This study reports a machine vision system for the identification of the visual symptoms of
plant diseases, from coloured images. Diseased regions shown in digital pictures of cotton …

Kernel-based techniques (such as support vector machines, Bayes point machines, kernel
principal component analysis, and Gaussian processes) represent a major development in …

Network intrusion detection problem is an ongoing challenging research area because of a
huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks …

Near-infrared (NIR) spectroscopy has been successfully utilized for the rapid identification of
green, black and Oolong teas. The spectral features of each category are reasonably …

With the rapid increment of protein sequence data, it is indispensable to develop automated
and reliable predictive methods for protein function annotation. One approach for facilitating …

This study employs machine learning models assisted by symbol regression to achieve
satisfactory corrosion fatigue life prediction for T91 steel and 316L stainless steel (SS) used …