Dezembro 2024 vol. 11 num. 2 - X Simpósio Internacional de Inovação e Tecnologia

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ANALYSIS OF MACHINE LEARNING AS A VIABLE TOOL FOR SUSTAINABLE DEVELOPMENT: A BIBLIOMETRIC REVIEW

ANALYSIS OF MACHINE LEARNING AS A VIABLE TOOL FOR SUSTAINABLE DEVELOPMENT: A BIBLIOMETRIC REVIEW

CERQUEIRA, RONEY DAS MERCÊS ; Calil Junior, Marcos Antônio ; Aleluia, Jorge Lucas Gonçalves ;

Completo:

"The objective of this work was to develop a bibliometric review on algorithms based on Machine Learning as a viable tool for Sustainable Development. The proposed methodology analyzed articles published between 2014 and 2024, from the SCOPUS database. 994 articles were subjected to a General Analysis and 46 to a Specific Analysis, pointing to China and India as the countries that publish the most and Artificial Neural Networks as the most used algorithm. The conclusion reveals a rigorous and comprehensive approach, as the analyses focus on technical evaluation based on relevance, quality and pertinence."

Completo:

"The objective of this work was to develop a bibliometric review on algorithms based on Machine Learning as a viable tool for Sustainable Development. The proposed methodology analyzed articles published between 2014 and 2024, from the SCOPUS database. 994 articles were subjected to a General Analysis and 46 to a Specific Analysis, pointing to China and India as the countries that publish the most and Artificial Neural Networks as the most used algorithm. The conclusion reveals a rigorous and comprehensive approach, as the analyses focus on technical evaluation based on relevance, quality and pertinence."

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Palavras-chave: Bibliometric Review, Machine Learning, Sustainable Development,

Palavras-chave: Bibliometric Review, Machine Learning, Sustainable Development,

DOI: 10.5151/siintec2024-393281

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Como citar:

CERQUEIRA, RONEY DAS MERCÊS; Calil Junior, Marcos Antônio; Aleluia, Jorge Lucas Gonçalves; "ANALYSIS OF MACHINE LEARNING AS A VIABLE TOOL FOR SUSTAINABLE DEVELOPMENT: A BIBLIOMETRIC REVIEW", p. 929-936 . In: . São Paulo: Blucher, 2024.
ISSN 2357-7592, DOI 10.5151/siintec2024-393281

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