Weighted-guided-filter-aided texture classification using recursive feature elimination-based fusion of feature sets

Choudhury, Dwaipayan; Bhattacharya, Arghya

doi:10.1109/cgvis.2015.7449906

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2022

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“…Machine learning techniques have made it possible to optimize the time and accuracy of operations in industrial, business and academic environments, among others. Bibliometric analysis evidences the growing interest in auto-mathematical learning in the textile industry [2] [3] [4][5][6][7][8][9][10][11][12], algorithms that study and detect fibers in images [13], among others. This research is based on the bibliometric analysis methodology proposed by [1] for the analysis of bibliographic interest in the proposed topic of study.…”

Section: Introductionmentioning

confidence: 99%

Textile and machine learning: A Bibliometric analysis

Umaquinga

Toro

COLLAGUAZO

2022

IDEAS

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Abstract. In the present research, a bibliometric analysis of scientific publications in the area of textile sciences from the area of machine learning is carried out. The search equation identifies as keywords (i) textile and (ii) machine learning from the scopus database, obtaining as initial result 308 publications, being 281 the final publications to be studied from 1991 to 2022. The bibliometric analysis was carried out using bibliometrix and VOS Viewer, showing the growing interest of the scientific community and authors in this area of research.

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