Artículo

Exploring the landscape of machine learning-aided research in biofuels and biodiesel: A bibliometric analysis

Resumen

This bibliometric analysis explores machine learning applications in biofuels and biodiesel research using Elsevier’s Scopus database from 2013 to 2023. The research employs co-authorship, co-occurrence, citation, and co-citation analyses with fractional counting. Results indicate a significant rise in publications. Prominent funding agencies along this field include the National Natural Science Foundation of China, Brazil’s Conselho Nacional de Desenvolvimento Científico e Tecnológico and the U.S. Department of Energy. Co-authorship analysis reveals contributions from 268 authors across 951 organizations in 71 countries, with strong collaboration in Asia. Citation analysis shows that 95% of articles have received at least one citation, with China and the United States leading in citation counts. This study highlights the interdisciplinary and collaborative nature of machine learning research in biofuels and biodiesel, driven by substantial contributions from key funding bodies and researchers worldwide.
Jrade, Ahmad (12804778900); Jalaei, Farnaz (57212026747); Zhang, Jieying Jane (59228484300); Jalilzadeh Eirdmousa, Saeed (59228444800); Jalaei, Farzad (54894358100)
Potential Integration of Bridge Information Modeling and Life Cycle Assessment/Life Cycle Costing Tools for Infrastructure Projects within Construction 4.0: A Review
2023
10.3390/su152015049
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199226134&doi=10.3390%2fsu152015049&partnerID=40&md5=b503d960a132c3df787b4cb99ad749dc
Department of Civil Engineering, University of Ottawa, Ottawa, K1N 6N5, ON, Canada; National Research Council Canada, Government of Canada, Ottawa, K1A 0R6, ON, Canada
All Open Access; Gold Open Access
Scopus
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