Modeling the choice of an online course for information hygiene skills using the Saaty method

• Autorzy: Rudenko Yuliia , Ahadzhanov-Honsales Karen , Ahadzhanova Svitlana , Batalova Alla , Bieliaieva Olena , Yurchenko Artem , Semenikhina Olena

Identyfikatory

DOI

10.35784/iapgos.5691

• Języki publikacji: EN

Abstrakty

PL

Modelowanie wyboru kursu online dla umiejętności higieny informacji metodą Saaty

Słowa kluczowe

EN

Saaty method; hierarchy analysis method; information hygiene skills; online course; information hygiene skills formation; education

PL

metoda Saaty; metoda analizy hierarchicznej; umiejętności higieny informacji; kurs online; kształtowanie umiejętności higieny informacji; edukacja

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2024
• Tom: T. 14, nr 2
• Strony: 127--132
• Opis fizyczny: Bibliogr. 24 poz., tab.

Twórcy

autor

Rudenko Yuliia

• Sumy National Agrarian University, Department of Cybernetics and Informatics, Sumy, Ukraine

• https://orcid.org/0000-0003-3162-1216

autor

Ahadzhanov-Honsales Karen

• Sumy National Agrarian University, Department of Cybernetics and Informatics, Sumy, Ukraine

• https://orcid.org/0000-0002-1409-4648

autor

Ahadzhanova Svitlana

• Sumy National Agrarian University, Department of Cybernetics and Informatics, Sumy, Ukraine

• https://orcid.org/0000-0002-0486-3511

autor

Batalova Alla

• Sumy National Agrarian University, Department of Cybernetics and Informatics, Sumy, Ukraine

• https://orcid.org/0000-0003-2059-9594

autor

Bieliaieva Olena

• Sumy State Pedagogical University named after A.S. Makarenko, Department Law and Methods of Teaching Jurisprudence, Sumy, Ukraine

• https://orcid.org/0000-0002-7246-4694

autor

Yurchenko Artem

• Sumy State Pedagogical University named after A.S. Makarenko, Department of Computer Science, Sumy, Ukraine

• https://orcid.org/0000-0002-6770-186X

autor

Semenikhina Olena

• Sumy State Pedagogical University named after A.S. Makarenko, Department of Computer Science, Sumy, Ukraine

• https://orcid.org/0000-0002-3896-8151

Bibliografia

• [1] Ahuja A. S. et al.: Artificial intelligence in ophthalmology: A multidisciplinaryapproach. Integrative Medicine Research 11(4), 2022, 100888.
• [2] Al-Shamdeen M. J., Younis A. N., Younis H. A.: Metaheuristic algorithmfor capital letters images recognition. Computer Science 16(2), 2020, 577–588.
• [3] Bhujel S., Shakya S.: Rice Leaf Diseases Classification Using DiscriminativeFine Tuning and CLR on EfficientNet. Journal of Soft Computing Paradigm4(3), 2022, 172–187.
• [4] Chabi Adjobo E. et al.: Automatic Localization of Five Relevant DermoscopicStructures Based on YOLOv8 for Diagnosis Improvement. Journal of Imaging9(7), 2023, 148.
• [5] Deng J. et al.: Retinaface: Single-stage dense face localisation in the wild. arXivpreprint arXiv: 1905.00641, 2019.
• [6] Diwan T., Anirudh G., Tembhurne J. V.: Object detection using YOLO:Challenges, architectural successors, datasets and applications. multimediaTools and Applications 82(6), 2023, 9243–9275.
• [7] Elharrouss O. et al.: Backbones-review: Feature extraction networks for deeplearning and deep reinforcement learning approaches. arXiv preprint arXiv:2206.08016, 2022.
• [8] Gunawan T.S. et al.: Development of video-based emotion recognition usingdeep learning with Google Colab. TELKOMNIKA (TelecommunicationComputing Electronics and Control) 18(5), 2020, 2463–2471.
• [9] Ju R. Y., Cai W.: Fracture Detection in Pediatric Wrist Trauma X-ray ImagesUsing YOLOv8 Algorithm. arXiv preprint arXiv: 2304.05071, 2023.
• [10] Kelleher J. D.: Deep learning. MIT Press, 2019.
• [11] Kumar A., Kalia A., Kalia A.: ETL-YOLO v4: A face mask detection algorithmin era of COVID-19 pandemic. Optik, 259, 2022, 169051.
• [12] Loey M. et al.: A hybrid deep transfer learning model with machine learningmethods for face mask detection in the era of the COVID-19 pandemic.Measurement 167, 2021, 108288.
• [13] Lou H. et al.: DC-YOLOv8: Small-Size Object Detection Algorithm Basedon Camera Sensor. Electronics 12(10), 2023, 2323.
• [14] Mbunge E. et al.: Application of deep learning and machine learning modelsto detect COVID-19 face masks-A review. Sustainable Operationsand Computers 2, 2021, 235–245.
• [15] Mohammed Ali F. A., Al-Tamimi M. S.: Face mask detection methodsand techniques: A review. International Journal of Nonlinear Analysisand Applications 13(1), 2022, 3811–3823.
• [16] Nowrin A. et al.: Comprehensive review on facemask detection techniquesin the context of covid-19. IEEE access 9, 2021, 106839–106864.
• [17] Padilla R., Netto S. L., Da Silva E. A.: A survey on performance metricsfor object-detection algorithms. in 2020 international conference on systems,signals and image processing (IWSSIP), IEEE, 2020.
• [18] Phan Q. B., Nguyen T.: A Novel Approach for PV Cell Fault Detection usingYOLOv8 and Particle Swarm Optimization, 2023.
• [19] Rajeshwari P. et al.: Object detection: an overview. Int. J. Trend Sci. Res. Dev.(IJTSRD) 3(1), 2019, 1663–1665.
• [20] Reis D. et al.: Real-Time Flying Object Detection with YOLOv8. arXiv preprintarXiv: 2305.09972, 2023.
• [21] Solawetz J.: What is YOLOv8? The Ultimate Guide, 2023,[https://blog.roboflow.com/whats-new-in-yolov8/] (available: 1.01.2024).
• [22] Talaat F. M., ZainEldin H.: An improved fire detection approach basedon YOLO-v8 for smart cities. Neural Computing and Applications, 2023, 1–16.
• [23] Terven J., Cordova-Esparza D.: A comprehensive review of YOLO: FromYOLOv1 and beyond. arXiv 2023. arXiv preprint arXiv: 2304.00501.
• [24] Tian Y. et al.: Role of masks in mitigating viral spread on networks. PhysicalReview E 108(1), 2023, 014306
• [25] Vibhuti et al.: Face mask detection in COVID-19: a strategic review.Multimedia Tools and Applications 81(28), 2022, 40013–40042.
• [26] Vrigkas M. et al.: Facemask: A new image dataset for the automatedidentification of people wearing masks in the wild. Sensors 22(3), 2022, 896.
• [27] Wani M. A. et al.: Advances in deep learning. Springer, 2020.
• [28] Wu W. et al.: Application of local fully Convolutional Neural Networkcombined with YOLO v5 algorithm in small target detection of remote sensingimage. PloS one 16(10), 2021, e0259283.
• [29] Yunus E.: YOLO V7 and Computer Vision-Based Mask-Wearing WarningSystem for Congested Public Areas. Journal of the Institute of Scienceand Technology 13(1), 2023, 22–32.