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

Full Article - Open Access.

Idioma principal | Segundo idioma

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 ;

Full Article:

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

Full Article:

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

Download (PDF)

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

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

DOI: 10.5151/siintec2024-393281

Referências bibliográficas
  • [1] "1 MINH, D. et al. Explainable artificial intelligence: a comprehensive review. Artificial
  • [2] Intelligence Review, v. 55, n. 5, p. 3503–3568, 202
  • [3] 2 TURING, A. M. Computing Machinery and Intelligence. Mind, New Series, v. 59, n. 236, p.
  • [4] 433–460, 1950.
  • [5] 3 WEISS, S. M.; KULIKOWSKI, C. A. Computer systems that learn: classification and
  • [6] prediction methods from statistics, neural nets, machine learning, and expert systems.
  • [7] Nachdr.ed. San Mateo, Calif: Morgan Kaufman Publ, 1994.
  • [8] 4 SUFI, F. K. AI-GlobalEvents: A Software for analyzing, identifying and explaining global
  • [9] events with Artificial Intelligence. Software Impacts, v. 11, p. 100218, 2022.
  • [10] 5 WANG, L. et al. Voice‐based AI in call center customer service: A natural field experiment.
  • [11] Production and Operations Management, v. 32, n. 4, p. 1002–1018, 2023.
  • [12] 6 BURRELL, D. N. Dynamic Evaluation Approaches to Telehealth Technologies and Artificial
  • [13] Intelligence (AI) Telemedicine Applications in Healthcare and Biotechnology Organizations.
  • [14] Merits, v. 3, n. 4, p. 700–721, 2023.
  • [15] 7 RIETSCHE, R. et al. Quantum Computing. Electronic Markets, v. 32, n. 4, p. 2525–2536,
  • [16] 2022.
  • [17] 8 SACHA, G. M.; VARONA, P. Artificial intelligence in nanotechnology. Nanotechnology, v.
  • [18] 24, n. 45, p. 452002, 2013.
  • [19] 9 WALKER, A. Business Process Architecture for the Integration of Artificial Intelligence at
  • [20] Aerospace Organizations. International Journal of Aviation, Aeronautics, and Aerospace, v. 10, n. 4, 2023. Disponível em: . Acesso em:
  • [21] 13 Apr. 2024.
  • [22] 10 CERQUEIRA, R. M.; SISNANDO, A. D.; ESQUERRE, V. F. R. Metamaterial Waveguide
  • [23] Modelling by an Artificial Neural Network with Genetic Algorithm. In: FRONTIERS IN OPTICS,
  • [24] 2021, Washington, DC. Frontiers in Optics + Laser Science 2021. Washington, DC: Optica
  • [25] Publishing Group, 2021. p. JTh5A.141. Disponível em:
  • [26] . Acesso em: 18 Mar. 2024.
  • [27] 11 GOMES, J. M. D. S. et al. Deforestation of Brazilian Amazonia: Temporal Analysis and
  • [28] Mitigation Proposals. Revista de Gestão Social e Ambiental, v. 18, n. 4, p. e04693, 2024.
  • [29] 12 SUN, L. et al. Deforestation embodied in global trade: Integrating environmental extended
  • [30] input-output method and complex network analysis. Journal of Environmental Management,
  • [31] v. 325, p. 116479, 2023.
  • [32] 13 MARQUES, L. O Antropoceno como aceleração do aquecimento global. Liinc em Revista,
  • [33] v. 18, n. 1, p. e5968, 2022.
  • [34] 14 DELLAMATRICE, P. M.; MONTEIRO, R. T. R. Principais aspectos da poluição de rios
  • [35] brasileiros por pesticidas. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 18, n.
  • [36] 12, p. 1296–1301, 2014.
  • [37] 15 GUIMARÃES, R. G.; JUNIOR, J. L.; NETO, A. J. D. S. Revisão sistemática do transporte de
  • [38] microplástico do continente para o oceano. Boletim do Observatório Ambiental Alberto
  • [39] Ribeiro Lamego, v. 14, n. 1, p. 18–39, 2020.
  • [40] 16 LIMA, H. C. G. et al. O Impacto do Uso de Agrotóxico na Agricultura e os Problemas de
  • [41] Saúde Pública: uma Revisão. Brazilian Journal of Implantology and Health Sciences, v.
  • [42] 5, n. 5, p. 1491–1500, 2023.
  • [43] 17 UNITED NATIONS. The 2030 Agenda and the Sustainable Development Goals an
  • [44] opportunity for Latin America and the Caribbean. Santiago: United Nations, ECLAC, 2018.
  • [45] 18 COSTA E SILVA, M. D. V. et al. Contribuição da Inteligência Artificial no Desenvolvimento
  • [46] de Energias Renováveis: Uma Revisão Bibliométrica de Literatura. International Journal of
  • [47] Environmental Resilience Research and Science, v. 4, n. 3, p. 1–20, 2022.
  • [48] 19 CERQUEIRA, R. D. M. et al. Engenharia de Energias da UFRB: estudos e aplicação.
  • [49] RNA Aplicadas a Energia Solar. Cruz das Almas, BA: Editora UFRB, 2021. v. 1, n. 1, p. 53-
  • [50] 70. Disponível em: Access in: 07 Jul. 2024.
  • [51] 20 GUO, Y. et al. Application and Implementation of Artificial Intelligence Technology for
  • [52] Intelligent Vehicle. Journal of Physics: Conference Series, v. 2508, n. 1, p. 012049, 2023.
  • [53] 21 DE FREITAS, H. F. S. et al. Modelagem de uma Célula de Eletrólise Microbiana (Cem) por
  • [54] Meio de Redes Neurais Artificiais (RNA) Via um Algoritmo Meta-Heurístico. Revista Brasileira
  • [55] de Energias Renováveis, v. 7, n. 3, 2018. Disponível em:
  • [56] . Acesso em: 14 Apr. 2024.
  • [57] 22 BORBA, M. D. C. et al. Gestão no meio agrícola com o apoio da Inteligência Artificial: uma
  • [58] análise da digitalização da agricultura. Revista em Agronegócio e Meio Ambiente, v. 15, n.
  • [59] 3, p. 1–22, 2022.
  • [60] 23 ZHANG, Lefei; ZHANG, Liangpei. Artificial Intelligence for Remote Sensing Data Analysis:
  • [61] A review of challenges and opportunities. IEEE Geoscience and Remote Sensing
  • [62] Magazine, v. 10, n. 2, p. 270–294, 2022.
  • [63] 24 ARAÚJO, C. A. Bibliometria: evolução histórica e questões atuais. Em Questão, v. 12, n.
  • [64] 1, p. 11–32, 2006. "
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

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações