C
Carlos Francisco Moreno-García
Researcher at Robert Gordon University
Publications - 54
Citations - 422
Carlos Francisco Moreno-García is an academic researcher from Robert Gordon University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 9, co-authored 43 publications receiving 248 citations. Previous affiliations of Carlos Francisco Moreno-García include Rovira i Virgili University.
Papers
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Journal ArticleDOI
New trends on digitisation of complex engineering drawings.
TL;DR: This paper presents a general framework for complex engineering drawing digitisation, a thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision, and how new trends on machine vision could be applied to this domain.
Journal ArticleDOI
Effectiveness of social marketing strategies to reduce youth obesity in European school-based interventions: a systematic review and meta-analysis
Magaly Aceves-Martins,Elisabet Llauradó,Lucia Tarro,Carlos Francisco Moreno-García,Tamy Goretty Trujillo Escobar,Rosa Solà,Montse Giralt +6 more
TL;DR: Current evidence indicates that the inclusion of at least 5 SMBC domains in school-based interventions could benefit efforts to prevent obesity in young people.
Journal ArticleDOI
CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification
TL;DR: This paper proposes a new hybrid approach aiming at reducing the dominance of the majority class instances using class decomposition and increasing the minorityclass instances using an oversampling method, resulting in a more balanced dataset and hence improving the results.
Book ChapterDOI
A graph repository for learning error-tolerant graph matching.
TL;DR: A graph repository structure such that each register is not only composed of a graph and its class, but also of a pair of graphs and a ground-truth correspondence between them, as well as their class is presented.
Journal ArticleDOI
Using artificial intelligence methods for systematic review in health sciences: A systematic review
Aymeric Blaizot,Sajesh K. Veettil,Pantakarn Saidoung,Carlos Francisco Moreno-García,Nirmalie Wiratunga,Magaly Aceves-Martins,Nai Ming Lai,Nathorn Chaiyakunapruk +7 more
TL;DR: The ambiguous benefits of the data extractions, combined with the reported advantages from 10 reviews, indicating that AI platforms have taken hold with varying success in evidence synthesis, are qualified by the reliance on the self‐reporting of the review authors.