Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.
Como objetivo deste trabalho propõe-se um estudo prospectivo sobre o impacto que as mídias sociais terão no futuro da educação superior no Brasil. Este estudo utilizou o método Delphi, onde buscou-se um consenso nas opiniões em relação ao uso das mídias sociais na educação. Apesar de ter havido um consenso entre os respondentes em relação às tendências de mídias sociais apresentadas, ainda percebe-se que alguns poucos painelistas se mantêm neutros às suas opiniões em relação ao uso de mídias sociais e tecnologias aplicadas à educação até o ano de 2040.
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