Vasyl Kovalchuk

Work place: Oleksandr Dovzhenko Hlukhiv National Pedagogical University, Hlukhiv, 41400, Ukraine



Research Interests: Web Technologies, Educational Technology, Higher Education


Vasyl Kovalchuk, Studied at Yuri Fedkovych Chernivtsi State University and got specialist degree in Professional education (1993-1998). In 2014 defended doctoral dissertation “Theoretical and methodological principles of developing pedagogical skills of vocational schools professional training masters in postgraduate education” and became Doctor of Pedagogical Sciences. Professor, head of the department of professional education and technologies of agricultural production, Oleksandr Dovzhenko Hlukhiv National Pedagogical University, Hlukhiv, Ukraine.
Research Interests: include professional and higher education development, introducing innovative teaching technologies in the educational process, teachers’ pedagogical skills development, introducing digital technologies in the educational process, emotional intelligence development, leadership.

Author Articles
Face Mask Recognition by the Viola-Jones Method Using Fuzzy Logic

By Serhiy Balovsyak Oleksandr Derevyanchuk Vasyl Kovalchuk Hanna Kravchenko Maryna Kozhokar

DOI:, Pub. Date: 8 Jun. 2024

In the work, the software implementation of the face mask recognition system using the Viola-Jones method and fuzzy logic is performed. The initial images are read from digital video cameras or from graphic files. 
Detection of face, eye and mouth positions in images is performed using appropriate Haar cascades. The confidence of detecting a face and its features is determined based on the set parameters of Haar cascades.
Face recognition in the image is performed based on the results of face and eye detection by means of fuzzy logic using the Mamdani knowledge base. Fuzzy sets are described by triangular membership functions. Face mask recognition is performed based on the results of face recognition and mouth detection by means of fuzzy logic using the Mamdani knowledge base. Comprehensive consideration of the results of different Haar cascades in the detection of face, eyes and mouth allowed to increase the accuracy of recognition face and face mask.
The software implementation of the system was made in Python using the OpenCV, Scikit-Fuzzy libraries and Google Colab cloud platform. The developed recognition system will allow monitoring the presence of people without masks in vehicles, in the premises of educational institutions, shopping centers, etc. In educational institutions, a face mask recognition system can be useful for determining the number of people in the premises and for analyzing their behavior.

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STEM Project for Vehicle Image Segmentation Using Fuzzy Logic

By Serhiy Balovsyak Oleksandr Derevyanchuk Vasyl Kovalchuk Hanna Kravchenko Yuriy Ushenko Zhengbing Hu

DOI:, Pub. Date: 8 Apr. 2024

A STEM project was implemented, which is intended for students of technical specialties to study the principles of building and using a computer system for segmentation of images of railway transport using fuzzy logic. The project consists of 4 stages, namely stage #1 "Reading images from video cameras using a personal computer or Raspberry Pi microcomputer", stage #2 "Digital image pre-processing (noise removal, contrast enhancement, contour selection)", stage #3 "Segmentation of images", stage #4 "Detection and analysis of objects on segmented images by means of fuzzy logic". Hardware and software tools have been developed for the implementation of the STEM project. A personal computer and a Raspberry Pi 3B+ microcomputer with attached video cameras were used as hardware. Software tools are implemented in the Python language using the Google Colab cloud platform. At each stage of the project, students deepen their knowledge and gain practical skills: they perform hardware and software settings, change program code, and process experimental images of vehicles. It is shown that the processing of experimental images ensures the correct selection of meaningful parts in images of vehicles, for example, windows and number plates in images of locomotives. Assessment of students' educational achievements was carried out by testing them before the start of the STEM project, as well as after the completion of the project. The topics of the test tasks corresponded to the topics of the stages of the STEM project. Improvements in educational achievements were obtained for all stages of the project.

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