O estudo da atuação feminina nos cursos daárea de Tecnologia da Informação e Comunicação (TIC) é relevante para o mercado e a academia no contexto da promoção da igualdade de gênero. Contudo, ainda são poucas as pesquisas que retratam o perfil das alunas daárea de TIC. Tais pesquisas são importantes para suporte à implementação de estratégias de permanência e êxito das alunas. Neste trabalho, é delineado o perfil das alunas do curso de Bacharelado em Ciência da Computação do IFCE campus Aracati. Dentre os resultados obtidos, destaca-se a discrepância entre o perfil das alunas matriculadas e desistentes. Os resultados também auxiliaram no desenvolvimento de ações de incentivo à permanência e êxito das alunas.
Fraud, misidentification, and adulteration of food, whether unintentional or purposeful, are a worldwide and growing concern. Aquaculture and fisheries are recognized as one of the sectors most vulnerable to food fraud. Besides, a series of risks related to health and distrust between consumer and popular market makes this sector develop an effective solution for fraud control. Species identification is an essential aspect to expose commercial fraud. Convolutional neural networks (CNNs) are one of the most powerful tools for image recognition and classification tasks. Thus, the objective of this study is to propose a model of recognition of fish species based on CNNs. After the implementation and comparison of the results of the CNNs, it was found that the Xception architecture achieved better performance with 86% accuracy. It was also possible to build a web application mockup. The proposal is easily applied in other aquaculture areas such as the species recognition of lobsters, shrimp, among other seafood.
Fraud, misidentification, and adulteration of food, whether unintentional or purposeful, are a worldwide and growing concern. Aquaculture and fisheries are recognized as one of the sectors most vulnerable to food fraud. Besides, a series of risks related to health and distrust between consumer and popular market that this sector develop an effective solution for fraud control. Species identification is an essential aspect to expose commercial fraud. Convolutional neural networks (CNNs) are one of the most powerful tools for image recognition and classification tasks. Thus, the objective of this study is to propose a model of recognition of fish species based on CNNs. The results obtained show an algorithm with an accuracy of 86%, providing an effective solution to prevent fish fraud.
Recognition of fish species is of great importance to marine biology and aquaculture. Ubiquitous devices represent an efficient solution for the preservation of species through the monitoring of fish in the risk of extinction. This approach is essential for endangered population assessment as well as for ecosystem preservation. Several methods have been assessed in these devices to solve the complex task of identifying at-risk of overfishing. As an alternative, the convolutional neural networks (CNNs) represent an accurate method for pattern recognition. Hence, this paper proposes the performance evaluation of a fish recognition model based on CNNs in ubiquitous devices, focusing on the preservation of these species principally during the closed period.
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