PREDICTION SYSTEM OF ERYTHEMAS FOR PHOTOTYPES I AND II, USING DEEP-LEARNING
Background: The sun is a natural source of electromagnetic radiation, upon which are found the ultraviolet (UV) rays, where only the types A and B are able to irradiate over the surface of the Earth in different proportions. Although the sun helps human skin in the formation of vitamin D, the minera...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
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Universidad de Antioquia
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| Acceso en línea: | https://doaj.org/article/75fb4653977647fcb78604e4c4e49f3a |
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| Sumario: | Background: The sun is a natural source of electromagnetic radiation, upon which are found the ultraviolet (UV) rays, where only the types A and B are able to irradiate over the surface of the Earth in different proportions. Although the sun helps human skin in the formation of vitamin D, the mineralization of bones, and absorption of calcium and phosphorus in the organism, it can cause damage on the skin by prolonged exposure to UV radiation, generating adverse effects on human health like erythema formation, photo-toxicity, photo-allergy, idiopathic lesions, and photo-dermatitis, among others. This paper, shows the results of developing a prediction system of the exposure time of a person to UV rays coming from the sun, which can cause erythema on human skin, using the standards in UV index and the dose limits of radiation allowed for phototypes I and II, aiming to foresee the generation of these kind of lesions. This was made by the implementation of artificial intelligence algorithms like Deep Belief Networks and Backpropagation, based in the Deep Learning technique. These algorithms use as training parameters for the neural network, the meteorological data such as the sky clearness index, the radiation on the horizontal surface and average air temperature, supplied by the National Aeronautics and Space Administration (NASA). With the data, a neural network aiming to foresee the UV index for the following year of the data input was trained, in addition some mathematical regressions were applied allowing in this way, to obtain an approach to the behavior of the UV index along the day. Likewise, this information was used to estimate the maximum time of sun exposure, for the period of time contained between 6:00 a.m. and 6:00 p.m. This paper, also presents some conclusions based in the results found, which try to establish some important considerations in order to implement the neural networks. |
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