Application of artificial intelligence techniques in studies on eating habits
Bibliometric analysis
DOI:
https://doi.org/10.51252/rcsi.v3i1.489Keywords:
neural networks, recommender systems, deep learning, nutritionAbstract
The study presents a bibliometric analysis of the application of artificial intelligence techniques in research related to eating habits. A total of 233 documents were analyzed from the Scopus database between 1990 and 2020, identifying the main trends in scientific production, publication sources, institutional affiliations, and collaboration networks. The results show an exponential growth in the number of publications since 2015, attributable to advancements in AI and the increasing interest in public health. The journal "Lecture Notes in Computer Science" is the source with the most publications in this field, followed by the "ACM International Conference Proceeding Series." The institutions with the highest production are the "Weizmann Institute of Science" and the "University of Bari." Furthermore, the keyword analysis highlights the relevance of techniques such as "machine learning," "deep learning," and "neural networks." Collaboration maps reveal that the United States and China are leaders in production and co-authorship. The study concludes that AI has had a growing impact on research into eating habits, highlighting its importance as an emerging tool for improving the understanding of dietary behaviors and promoting personalized and effective public health interventions.
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