… a clustering routing protocol with a hole detection support. Hole detection process is based
on graph theory metrics and unsupervised soft-clustering machine learning (ML) technique. …

… The AI techniques are especially suitable for non-linear modelling and machine learning …
, an machine learning based method for breakout detection of high-speed small hole drilling …

… for coronal holes is thus difficult and the primary factor in the decision to take a machine
learning approach to coronal hole detection. To make accurate and meaningful coronal hole …

… machine learning algorithms in combination with segmentation techniques in order to distinguish
coronal holes … datasets of manually labeled coronal hole and filament channel regions …

… the spectrum hole in the case of n primary users (PUs).An unsupervised machine learning
(… Mobile Phone Activity Dataset’ [7] to detect spectrum holes. Finally, the random forest (RF) …

… The detection of black hole attack, gray hole attack, and flooding is done using the nine ML
algorithms: random forest, random tree, J48, k-nearest neighbor (KNN), gaussian naïve bayes…

… This study aims to conduct abnormality detection by applying machine learning algorithms
when drilling a carbon fiber reinforced plastic laminate. In-process signals including current, …

… We demonstrate the use of machine learning algorithms in combination with segmentation …
holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection …

… We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field
fundus images (Optos) with deep learning, which is a machine learning technology. The …

… help of machine learning algorithms the intrusion detection systems … In this study six machine
learning modelled IDSs were … gives high detection rate in the detection of black hole attack …