Video Experimental Relacionado

Updated: Jun 23, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

IA Explicable Asistida por Algoritmos Híbridos de Mapas Autoorganizados y Aprendizaje Profundo para la Detección de

Osman Taylan1, Menekse Bulut2,3, Nur Cebi4

  • 1Department of Management Engineering, Faculty of Management, Istanbul Technical University, Istanbul, Macka, Türkiye.

Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment
|January 16, 2026
PubMed
Resumen

Los investigadores desarrollaron un modelo de IA utilizando espectroscopía Raman para detectar la adulteración de guisantes en pistachos. Este enfoque híbrido de Mapas Autoorganizados (SOM) y Red Neuronal Convolucional (CNN) logró una precisión del 97% en la identificación de muestras fraudulentas de pistacho.

Palabras clave:
Adulteración de pistacho con guisantesredes neuronales convolucionalesprocesamiento de imágenesmapas autoorganizados

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