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Development of the computer vision system based on machine learning for educational purposes

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dc.contributor.author Семеріков, Сергій Олексійович
dc.contributor.author Vakaliuk, Tetiana A.
dc.contributor.author Мінтій, Ірина Сергіївна
dc.contributor.author Гаманюк, Віта Анатоліївна
dc.contributor.author Соловйов, Володимир Миколайович
dc.contributor.author Бондаренко, Ольга Володимирівна
dc.contributor.author Нечипуренко, Павло Павлович
dc.contributor.author Шокалюк, Світлана Вікторівна
dc.contributor.author Моісеєнко, Наталя Володимирівна
dc.contributor.author Ruban, Vitalii R.
dc.contributor.author Вакалюк, Тетяна Анатоліївна
dc.contributor.author Рубан, Віталій Романович
dc.date.accessioned 2023-01-02T16:54:32Z
dc.date.available 2023-01-02T16:54:32Z
dc.date.issued 2021-12-09
dc.identifier.citation Semerikov S. O. Development of the computer vision system based on machine learning for educational purposes / Serhiy O. Semerikov, Tetiana A. Vakaliuk, Iryna S. Mintii, Vita A. Hamaniuk, Vladimir N. Soloviev, Olga V. Bondarenko, Pavlo P. Nechypurenko, Svitlana V. Shokaliuk, Natalia V. Moiseienko, Vitalii R. Ruban // Educational Dimension. – 2021. – Vol. 57. – P. 8–60. – DOI : 10.31812/educdim.4717. uk
dc.identifier.issn 2708-4612
dc.identifier.uri https://doi.org/10.31812/educdim.4717
dc.identifier.uri http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/6996
dc.description 2021. Google Ngram Viewer. https://books.google.com/ngrams/graph?content=computer+vision%2C+machine+vision&year_start=1800&year_end= 2019&corpus=26&smoothing=3&direct_url=t1%3B%2Ccomputer%20vision% 3B%2Cc0%3B.t1%3B%2Cmachine%20vision%3B%2Cc0#t1%3B%2Ccomputer%20vision%3B%2Cc0%3B.t1%3B%2Cmachine%20vision%3B%2Cc0 Adaptive Vision. Libraries comparison. https://docs.adaptive-vision.com/avl/technical_issues/LibrariesComparison.html Lakshya Agarwal, Manan Mukim, Harish Sharma, Amit Bhandari, and Atul Mishra. 2021. Face Recognition Based Smart and Robust Attendance Monitoring using Deep CNN. In 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom). 699–704 (2021). doi: 10.1109/INDIACom51348.2021.00124 Dana, H.: Ballard and Christopher M. Brown. Computer Vision. Prentice Hall, Englewood Cliffs. https://archive.org/details/computervision0000ball (1982). 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Deep Learning applications for COVID-19. Journal of Big Data 8 (1), (11 Jan 2021), 18 (2021). doi: 10.1186/s40537-020-00392-9 DOI: https://doi.org/10.1186/s40537-020-00392-9 Sivakumar, S. A., John, J. T., Selvi, G. T., Madhu, B., Shankar, S. U, Arjun, K. P.: 2021. IoT based Intelligent Attendance Monitoring with Face Recognition Scheme. In 2021 5 th International Conference on Computing Methodologies and Communication (ICCMC). 349–353 (2021). doi: 10.1109/ ICCMC51019.2021.9418264 DOI: https://doi.org/10.1109/ICCMC51019.2021.9418264 Ivan Edward Sutherland. Sketchpad, a man-machine graphical communication system. Ph. D. Dissertation. Massachusetts Institute of Technology. http://images.designworldonline.com.s3.amazonaws.com/CADhistory/Sketchpad_A_Man-Machine_Graphical_Communication_System_Jan63.pdf (1963). Accessed 13 Nov 2021 DOI: https://doi.org/10.1145/1461551.1461591 Tkachuk, V., Yechkalo, Yu., Semerikov, S., Kislova, M, Hladyr, Y.: Using Mobile ICT for Online Learning During COVID-19 Lockdown. In Information and Communication Technologies in Education, Research, and Industrial Applications, Andreas Bollin, Vadim Ermolayev, Heinrich C. Mayr, Mykola Nikitchenko, Aleksander Spivakovsky, Mykola Tkachuk, Vitaliy Yakovyna, and Grygoriy Zholtkevych (eds.). Springer International Publishing, Cham, 46–67 (2021). doi: 10.1007/978-3-030-77592-6_3 DOI: https://doi.org/10.1007/978-3-030-77592-6_3 viso.ai. Abandoned Luggage. https://viso.ai/application/abandonedluggage-detection (2021). Accessed 13 Nov 2021 viso.ai. Face Recognition. https://viso.ai/application/face-recognition (2021). Accessed 13 Nov 2021 viso.ai. Facial Emotion Analysis. https://viso.ai/application/emotionanalysis (2021). Accessed 13 Nov 2021 viso.ai. Intrusion Detection. https://viso.ai/application/intrusion-detection (2021). 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dc.description.abstract The article provides an overview of the origins and current state of machine vision systems, examples of machine vision problems. The article describes the use of computer vision systems in education in both conventional and pandemic conditions. The COVID-19 pandemic has triggered changes in education that have modified existing educational applications of computer vision systems and spawned new ones, including social distancing, facial mask recognition, detection of infiltration into universities and schools, prevention of vandalism and detection of suspicious objects, attendance monitoring, recognition of emotions on faces in and without masks. Computer vision systems can also be used in education to introduce immersive educational resources. On the basis of the analysis of autonomous libraries for the identification of dynamic objects, it is concluded that in the creation of computer vision systems for educational purposes it is advisable to use computer vision libraries based on in-depth learning (in particular, the implementation of convolutional neural networks). A prototype computer vision system developed on the basis of Microsoft Cognitive Toolkit and deployed in the Microsoft Azure cloud is described. The system allows you to perform with a high degree of reliability the main functions: identification of emotions and the presence of a mask on the face, as well as allows you to determine sex, age, hair color, smile intensity, the presence of makeup, glasses, etc. uk
dc.description.sponsorship Ministry of Education and Science of Ukraine Grant numbers 0121U113711 uk
dc.language.iso uk uk
dc.publisher Academy of Cognitive and Natural Sciences uk
dc.subject computer vision uk
dc.subject COVID-19 uk
dc.subject mask detection uk
dc.subject education uk
dc.title Development of the computer vision system based on machine learning for educational purposes uk
dc.type Article uk


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