Gustavo A. Alonso-Silverio

TL;DR: The proposed trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts has the potential to increase the self-confidence of trainees and to be applied to programs with limited resources.

TL;DR: Together with motion analysis and three laparoscopic tasks of the Fundamental Laparoscopic Surgery Program, these classifiers provide a means for objectively classifying surgical competence of the surgeons for existing laparoscope box trainers.

TL;DR: An Internet of Things (IoT)-based framework for non-invasive blood glucose monitoring is described based on Raspberry Pi Zero energised with a power bank, using a visible laser beam and a Raspberry Pi Camera, all implemented in a glove.

TL;DR: In this paper, the authors presented the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework for assessing the landslide susceptibility using remote sensing data, spatial databases, or geological catalogues.

TL;DR: In this article, the authors presented the performance of five machine learning methods (k-nearest neighbor (KNN), stochastic gradient descendent (SGD), support vector machine radial basis function (SVM RBF Kernel), SVM linear kernel, and AdaBoost) in landslide detection in a zone of the state of Guerrero in southern Mexico, using continuous change maps and primary landslide factors, such as slope angle, terrain orientation (aspect), and lithology, as inputs.