In the last years, computer vision systems for the detection of skin cancer have been proposed, especially using machine learning techniques for the classification of the disease and features based on the ABCD dermatology criterion, which gives information on the status of the skin lesion based on static properties such as geometry, color, and texture, making it an appropriate criterion for medical diagnosis systems that work through images. This paper proposes a novel skin cancer classification system that works on images taken from a standard camera and studies the impact on the results of the smoothed bootstrapping, which was used to augment the original dataset. Eight classifiers with different topologies (KNN, ANN, and SVM) were compared, with and without data augmentation, showing that the classifier with the highest performance as well as the most balanced one was the ANN with data augmentation, achieving an AUC of 87.1%, which saw an improvement from an AUC of 84.3% of the ANN trained with the original dataset.This is an open access article under the CC BY-SA license.
The age-related mortality and morbidity risk of COVID-19 has been considered speculative without enough scientific evidence. This study aimed to collect more evidence on the association between patient age and risk of severe disease state and/or mortality from SARS-CoV-2 infection. Genomic dataset along with metadata (3608 samples) retrieved from GISAID from different geographical regions were grouped into 10 age groups (0-10, 11-20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, 81-90, 91-100 years) as well as high-risk or low-risk according to patient clinical status. Genomic sequences were aligned and analyzed using MAFFT and FASTTREE to build a phylogenetic tree in order to identify age-risk associations based on phylogenetic clustering. Case fatality rates (CFR), as well as the Odds ratio (OR) for high-risk outcomes, were calculated for different age groups. Results revealed that individuals aged between 25-50 years have the best immune response to the infection. On the other hand, disease fatality was higher in patients aging above 50 years. We created an application to calculate the OR of being at high risk given a certain age threshold from GISAID datasets. OR values increased between ages 1-10 years (1.271) and 11-20 years (1.313) but reduced at age range 21-30 years (1.290) and increased again for 61-70 years (2.465). CFR calculated for each of the age groups had peak values at 90-100 years (26.8%) and the lowest at 0-10 years (0%). The CFR for ages above 50 years was about twice greater (11.6%-26.8%) than that for ages below (0-6.6%). The phylogenetic analysis revealed that the majority of samples obtained from India showed low-risk among different age groups and were defined as clade GH. Another cluster from Singapore visualization showed unfavorable patient outcome across several age groups and were classified under clade O. To conclude, this study analyses showed a variety of age-risk associations. As scientists from different countries upload more genomes to globally shared databases, more evidence will reinforce mortality risk associations in COVID-19 patients.
: To ensure high-quality educational processes, more educational research should be developed about student experiences during the COVID-19 pandemic. Furthermore, the student's perception about distance learning education (under the current situation) could be used as input for instructors and educational institutions to guide their distance learning process. The primary aim of this research is to explore engineering undergraduate student's perceptions about the online assessment methods during the COVID-19 age. This paper presents an exploratory-descriptive study based on content and quantitative analysis tools. The main findings of this research concern the factors perceived as the main differences between the face-to-face and online assessment by a group of engineering students during the current pandemic. These factors are: teaching presence, self-efficacy, autonomy, teamwork, and coherence between assessment and class. Furthermore, in the analyzed state-of-the-art, the last three factors have not been reported. These results will be used to guide the improvement of future online assessment methods in our engineering school. Keywords: Assessment's methods; COVID-19; Distance Learning; On-line Learning.
Discrete complex exponentials are almost periodic signals, not always periodic; when periodic, the frequency determines the period, but not viceversa, the period being a chaotic function of the frequency, expressible in terms of Thomae's function. The absolute value of the frequency is an increasing function of the subadditive functional of average variation. For discrete signals that are either sums or series of periodic complex exponentials, the decomposition into their periodic, additive components allows for their filtering according to period. Likewise, their period-frequency spectrum makes predictable the effects on period of convolution filtering. Ramanujan-Fourier series are a particular case of the signal class of series of periodic complex exponentials, a broad class of signals on which a transform, discrete both in time and in frequency, called the DFDT Transform, is defined.
La utilización de las herramientas para desarrollo de aplicaciones robóticas y la integración con plataformas de inteligencia artificial se ha incrementado en entornos académicos y de investigación en los últimos años, tendencia creciente que siguen de cerca los entonos industriales que reclaman cada vez más la automatización e interconexión de sus procesos en lo que se ha denominado Industria 4.0. El Gobierno Nacional de Colombia, estableció el Pacto por la Transformación Digital del país en su Plan Nacional de Desarrollo 2018-2022, en el cual se incluye a la robótica y a la inteligencia artificial como parte de las tecnologías disruptivas capaces de potenciar de productividad tanto en el sector privado como en el público(Gobierno-Nacional, 2018). En este contexto, el Sistema Operativo para Robots (ROS - Robot Operating System) adquiere una importancia estratégica, siendo uno de los entornos de desarrollo de aplicaciones en robótica más utilizados de la actualidad. Dada la importancia de tales herramientas, y la urgente necesidad de profesionales expertos en estas áreas se creó, por iniciativa de un grupo de estudiantes, el semillero de investigación derivado de la rama estudiantil RAS – IEEE (Robotic & Automation Society), en donde estudiantes de diferentes carreras y semestres aprenden las herramientas para desarrollo de aplicaciones sobre plataformas robóticas comerciales utilizadas a nivel industria y aplicaciones de inteligencia artificial orientas a robótica. El Semillero de investigación RAS - IEEE vincula actualmente estudiantes de Ingeniería Electrónica, Ingeniería Mecatrónica, Ingeniería Industrial, Ingeniería de Sistemas y Ciencia de Datos. Para lograr los objetivos del semillero tanto en formación como en generación de conocimiento, se establece un trabajo basado en proyectos semestrales que construyan cuerpo de conocimiento en las áreas de robótica e inteligencia artificial y en segunda instancia, un modelo de formación mediante un programa de mentorías para ayudar la transición de los nuevos miembros al aprendizaje y uso de las herramientas. El presente artículo discute los retos de la creación del semillero de investigación e impactos positivos observados a lo largo de cuatro semestres de trabajo, así como proyecciones futuras y recomendaciones.
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