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@InProceedings{MuralikrishnaVieiSantAlme:2020:ToSoIr,
               author = "Muralikrishna, Amita and Vieira, Luis Eduardo Antunes and Santos, 
                         Rafael Duarte Coelho dos and Almeida, Adriano Pereira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Total solar irradiance forecasting with keras recurrent neural 
                         networks",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Gervasi, O. and Murgante, B. and Misra, S. and Garau, C. and 
                         Blecic, I. and Taniar, D. and Apduhan, B. O. and Rocha, A. M. A. 
                         C. and Tarantino, E. and Torre, C. M. and Karaca, Y.",
                pages = "255--269",
         organization = "International Conference on Computational Science and Its 
                         Applications,20.",
            publisher = "Springer",
                 note = "Lecture Notes in Computer Science, v.12253",
             keywords = "Remote sensing  Burned forest classification keyword  Forest 
                         fire survey and monitoring.",
             abstract = "Monitoring the large number of active fires and their consequences 
                         in such an extensive area such as the Brazilian territory is an 
                         important task. Machine Learning techniques are a promising 
                         approach to contribute to this area, but the challenge is the 
                         building of rich example datasets, whose previous examples are 
                         unavailable in many areas. Our aim in this article is to move 
                         towards the development of an approach to detect burned areas in 
                         regions for which there is no previously validated samples. We 
                         deal with that by presenting some experiments to classify burned 
                         areas through Machine Learning techniques that combine remote 
                         sensing data from nearby areas and it can distinguish between 
                         burned and non burned polygons with good results.",
  conference-location = "Cagliari, Italy",
      conference-year = "01-04 July",
                  doi = "10.1007/978-3-030-58814-4_18",
                  url = "http://dx.doi.org/10.1007/978-3-030-58814-4_18",
                 isbn = "978-303058813-7",
                 issn = "03029743",
             language = "en",
           targetfile = "amita_total.pdf",
        urlaccessdate = "28 nov. 2020"
}


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