The popular technology used in this innovative era is Computer vision for fruit recognition. Compared to other machine learning (ML) algorithms, deep neural networks (DNN) provide promising results to identify fruits in images. Currently, to identify fruits, different DNN-based classification algorithms are used. However, the issue in recognizing fruits has yet to be addressed due to similarities in size, shape and other features. This paper briefly discusses the use of deep learning (DL) for recognizing fruits and its other applications. The paper will also provide a concise explanation of convolution neural networks (CNNs) and the EfficientNet architecture to recognize fruit using the Fruit 360 dataset. The results show that the proposed model is 95% more accurate.

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Objective: To describe the current status of anxiety among students in 3 secondary schools, Khoai Chau district, Hung Yen province in 2020.Subjects and Methods: a cross-sectional descriptive study was conducted on 840 students from 3 secondary schools in Khoai Chau district, Hung Yen province. Research period was from March2020 to October 2020.Results: The rate of general anxiety accounted for 9.5%, mild anxiety accounted for 7.1% of the total number of students surveyed. The anxiety rate among male students (9.2%) was lower than that offemale students (9.8%). Among students with anxiety: mild anxiety accounted for the highest rate (75%). Class VIII students had the highest rate of anxiety (15.9%), followed by Class VII students(10.7%), Class IX students (5.9%), Class VI students (5.5%). Rate of mild anxiety in students: Class VIII students (12.1%), Class VII students (7.5%), Class VI students (4.5%), Class IX students (4.1%).Conclusion: The rate of anxiety among secondary school students was quite high, but mostly mild anxiety. The rate of anxiety among male students was lower than that of female students.