Machine Learning and Deep Learning are computational tools that fall within the domain of artificial intelligence. In recent years, numerous research works have advanced the application of machine and deep learning in various fields, including optics and photonics. In this article, we employ machine learning algorithms to investigate the feasibility of predicting a stochastic phenomena: random laser emissions. Our results indicate that machine and deep learning have the capacity to accurately reproduce fluctuations characteristic of random lasers. By employing simple supervised learning algorithms, we demonstrate that the random laser intensity fluctuations can be predicted using spontaneous emission and pump intensity as input parameters in the models. Applications based on the demonstrated results are discussed.
This study's objective was to analyze the effect of land cover change, between 1965 and 2018, using statistical metrics and geoprocessing tools. And consequently, to provide information of area (ha) and spatial fragmentation of the Atlantic Forest in the municipality of Rio Largo/AL, Brazil. The samples were collected and transferred by CECA, CADEH, and INCRA, between November 2019 and April 2020. The basic materials used in this work were multi-temporal aerial images in digital format, derived from the 1965 aerophotogrametric survey on the scale 1:25000, belonging to the collection of the Engineering and Agrarian Sciences Campus - UFAL, and images of Landsat satellites (5 and 8) processed and made available by the Mapbiomas Project. The statistic landscape metrics were calculated using Landscape ecology Statistics (LECOs), a QGIS plugin. The analysis of forest fragmentation areas over the 53 years showed a reduction between 32.17% (1965) and 12.04% (2018) concerning the total extension of the municipality. In 1965, the average area obtained from 49 fragments was 201.13 ha. The values show a higher distance of forest fragments between 1965 and 1989, and disappearance by 2018.The Pearson correlation coefficient for 1965 and 2018 presented the value of r = -0.525, indicating a moderate and negative correlation between the mean values of areas (ha) of forest fragments and the number of forest fragments. The worst-case scenario for the maintenance of native forests occurred in 1989, where the reduction of continuous forest areas had 10.87 ha for forest area average, being spaced in 327 fragments. In the period 1986 and 1996, there was a decrease in fragmentation, reaching 200 fragments. In 1996 and 1997, there was an imbalance in forest maintenance, again increasing the number of fragments to 250 areas, and being explained by the loosening of surveillance in previous years, followed by deforestation.
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