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    [0] => stdClass Object
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            [journal] => stdClass Object
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                    [id_jnl] => 74
                )

        )

    [1] => stdClass Object
        (
            [section] => stdClass Object
                (
                    [section] => 399
                )

        )

    [2] => stdClass Object
        (
            [title] => Array
                (
                    [0] => Contributions of Machine Learning in The Discovery of Dengue: A Scientometric Analysis@en
                    [1] => Contribuciones del aprendizaje automático en el descubrimiento del dengue: un análisis cienciométrico@es
                )

        )

    [3] => stdClass Object
        (
            [abstract] => Array
                (
                    [0] => Dengue is a viral disease that claims human lives year after year, generating the need to explore new computer-based solutions to achieve early and effective detection. This study aimed to identify research trends that link machine learning techniques to dengue. To this end, a scientometric and systematic analysis was performed, starting with a search for machine learning and dengue in Scopus without temporal restrictions. Three hundred seventy-seven documents were found that were published from 2010 to 2022. Subsequently, PRISMA technique was applied and the documents were filtered based on the inclusion and exclusion criteria to ensure the quality of the analysis. Using tools such as R Studio, biblioshiny library of bibliometrix and VOSviewer, the key elements of scientific production were examined such as: countries, notable authors, relevant journals and keyword co-occurrences. The results identified three focus areas: dengue diagnosis, dengue prognosis, and mosquito control. Research on using machine learning to detect dengue was found to have grown steadily and attracted more researchers as of 2016. The most commonly used machine learning techniques are: Artificial Neural Network (ANN), Decision Tree, Support Vector Machine (SVM) and a trend to use Deep learning. On the other hand, the diagnosis area uses meteorological variables such as humidity, temperature and rainfall to make forecasts of dengue outbreaks.@en
                    [1] => El dengue es una enfermedad vírica que cobra vidas humanas año tras año, lo que genera la necesidad de explorar nuevas soluciones desde la informática para lograr una detección temprana y eficaz. Este estudio tuvo como objetivo identificar las tendencias de investigación que vinculan las técnicas de aprendizaje automático (machine learning) con el dengue. Para este fin, se realizó un análisis cienciométrico y sistemático, que comenzó con una búsqueda de aprendizaje automático y dengue en Scopus sin restricciones temporales. Se hallaron 377 documentos publicados entre 2010 y 2022. Posteriormente, se aplicó la técnica PRISMA y se filtraron los documentos a partir de los criterios de inclusión y exclusión para asegurar la calidad del análisis. Mediante el empleo de herramientas como R Studio, la biblioteca biblioshiny de bibliometrix y VOSviewer se examinaron los elementos clave de la producción científica como: países, autores destacados, revistas relevantes y co-ocurrencias de palabras clave. Los resultados permitieron identificar tres áreas de enfoque: diagnóstico del dengue, pronóstico del dengue y control de mosquitos. Se encontró que la investigación en el uso del aprendizaje automático para detectar el dengue ha crecido de manera constante y ha atraído a más investigadores a partir de 2016. Las técnicas de aprendizaje automático más utilizadas son: Artificial Neural Network (ANN), Decision Tree, Support Vector Machine (SVM) y una tendencia a usar Deep learning. Por su parte, el área del diagnóstico utiliza variables meteorológicas como humedad, temperatura y lluvias para realizar los pronósticos de los brotes del dengue.@es
                )

        )

    [4] => stdClass Object
        (
            [author] => Array
                (
                    [0] => Wilson Arrubla-Hoyos
                    [1] => Andrés Solano-Barliza
                )

        )

    [5] => stdClass Object
        (
            [subject] => Array
                (
                    [0] => Machine learning@en
                    [1] => Dengue@en
                    [2] => Diagnosis@en
                    [3] => Forecast@en
                    [4] => Meteorological variables.@en
                    [5] => Machine learning@es
                    [6] => Dengue@es
                    [7] => Diagnóstico@es
                    [8] => Pronóstico@es
                    [9] => Variables meteorológicas.@es
                )

        )

    [6] => stdClass Object
        (
            [source] => stdClass Object
                (
                    [vol] => 35
                    [nr] => 
                    [year] => 2024
                    [theme] => 
                )

        )

    [7] => stdClass Object
        (
            [datePub] => Array
                (
                    [0] => 2024-07-03
                )

        )

    [8] => stdClass Object
        (
            [DOI] => Array
                (
                    [0] => stdClass Object
                        (
                            [type] => DOI
                            [value] => Array
                                (
                                    [0] => 108-19.
                                )

                        )

                )

        )

    [9] => stdClass Object
        (
            [http] => Array
                (
                    [0] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://acimed.sld.cu/index.php/acimed/article/view/2630
                                )

                        )

                    [1] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://acimed.sld.cu/index.php/acimed/article/view/2630/pdf
                                )

                        )

                    [2] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://doi.org/10.1371/journal.pone.0270933
                                )

                        )

                    [3] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://www.who.int/es/news-room/fact-sheets/detail/dengue-and-severe-dengue
                                )

                        )

                    [4] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://doi.org/10.1371/journal.pntd.0005973
                                )

                        )

                    [5] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://doi.org/10.1186/s12879-018-3066-0
                                )

                        )

                    [6] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://research-repository.uwa.edu.au/en/publications/vector-borne-viral-diseases-and-climate-change-in-the-australasia
                                )

                        )

                )

        )

    [10] => stdClass Object
        (
            [language] => Array
                (
                    [0] => es
                )

        )

    [11] => stdClass Object
        (
            [license] => Array
                (
                    [0] => Copr
                    [1] => http://creativecommons.org/licenses/by-nc-sa/4.0
                )

        )

)