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The concept of an intelligent system of an outfit completion

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article considers the main criteria for the selection and formation of the wardrobe, which is one of the areas of application of methods and means for image classification. Typical software solutions for the task are analyzed, and the Analytic Hierarchy Process was used to analyze such applications. To improve the wardrobe selection process, the concept of an intelligent information system based on the use of convolutional neural networks was proposed.
Twórcy
  • Lviv Polytechnic National University, Lviv, Ukraine
  • Lviv Polytechnic National University, Lviv, Ukraine
Bibliografia
  • 1. Matiushyna V. 2000. В.И. Children's and teenage fashion, Garment industry, № 2, pp. 37–39.
  • 2. Steel G. 2021. Going global – going digital. Diaspora networks and female online entrepreneurship in Khartoum, Sudan, Geoforum, Volume 120, pp. 22-29.
  • 3. Lorenzo-Romero C., Andrés-Martínez M.-E., Cordente-Rodríguez M., Gómez-Borja M.Á. 2021. Active Participation of E-Consumer: A Qualitative Analysis From Fashion Retailer Perspective, SAGE Open, 11 (1).
  • 4. Andrunyk V., Pasichnyk V., Antonyuk N., Shestakevych T. 2020. A complex system for teaching students with autism: The concept of analysis. formation of IT teaching complex, Advances in Intelligent Systems and Computing, 1080 AISC, pp. 721-733.
  • 5. Andrunyk V., Prystai Y., Shestakevych T. 2020. Analytic Hierarchy Process for Personalization of Education IT (for students with autism), in 2020 IEEE 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2020, 2, art. no. 9322050, pp. 301-305.
  • 6. Shestakevych T., Pasichnyk V., Nazaruk M., Medykovskiy M., Antonyuk N. 2019. Web-Products, Actual for Inclusive School Graduates: Evaluating the Accessibility, Advances in Intelligent Systems and Computing, 871, pp. 350-363.
  • 7. Kong G., Jiang L., Yin X., Wang T., Xu D.-L., Yang J.-B., Hu Y. 2018. Combining principal component analysis and the evidential reasoning approach for healthcare quality assessment, Annals of Operations Research, 271 (2), pp. 679-699.
  • 8. Gómez-Navarro T., García-Melón M., Guijarro F., Preuss M. 2018. Methodology to assess the market value of companies according to their financial and social responsibility aspects: An AHP approach, Journal of the Operational Research Society, 69 (10), pp. 1599-1608.
  • 9. D'Inverno G., Carosi L., Romano G., Guerrini A. 2018. Water pollution in wastewater treatment plants: An efficiency analysis with undesirable output, European Journal of Operational Research, 269 (1), pp. 24-34.
  • 10. Uchkin D., Korotyeyeva T., Shestakevych T. 2020. Bitmap Image Recognition with Neural Networks, ECONTECHMOD, Vol. 09, No. 1, pp. 30-35.
  • 11. Veres O., Kis Ya., Kugivchak V., Rishniak I. 2018. Development of a Reverse-search System of Similar or Identical Images, ECONTECHMOD, Vol. 07, No. 2, pp. 23-30.
  • 12. Shumeiko A., Smorodskyi V. 2017. Discrete trigonometric transform and its usage in digital image processing, ECONTECHMOD, Vol. 06, No. 4, pp. 21- 26.
  • 13. Mieszkalski L., Wojdalski J. 2017. A mathematical method for modeling the shape of apples Part 1. Description of the method, ECONTECHMOD, Vol. 06, No. 3, pp. 97–104.
  • 14. Alldieck T., Magnor M., Xu W., Theobalt C., Pons-Moll G. 2018. Video Based Reconstruction of 3D People Models, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 8387-8397, doi: 10.1109/CVPR.2018.00875.
  • 15. Lao D., Sundaramoorthi G. 2019. Minimum Delay Object Detection From Video, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp. 5096-5105, doi: 10.1109/ICCV.2019.00520.
  • 16. Seoud M.S.A., Taj-Eddin I.A.T.F. 2019. An android augmented reality application for retail fashion shopping, International Journal of Interactive Mobile Technologies, 13 (1), pp. 4-19.
  • 17. Prato G., Sallemi F., Cremonesi P., Scriminaci M., Gudmundsson S., Palumbo S. 2020. Outfit completion and clothes recommendation, Conference on Human Factors in Computing Systems - Proceedings, art. no. 3383076.
  • 18. Guo D., Ktena S.I., Myana P.K., Huszar F., Shi W., Tejani A., Kneier M., Das S. 2020. Deep Bayesian Bandits: Exploring in Online Personalized Recommendations, RecSys 2020 - 14th ACM Conference on Recommender Systems, pp. 456-461.
  • 19. Krizhevsky A., Sutskever I., Hinton G. E. 2012. ImageNet classification with deep convolutional neural networks, Advances in neural information processing systems. DOI: 10.1145/3065386
  • 20. Suatap C., Patanukhom K. 2019. Game Genre Classification from Icon and Screenshot Images Using Convolutional Neural Networks, ACM International Conference Proceeding Series, pp. 51-58.
  • 21. Kang J., Zheng J., Bai H., Xue X., Zhou Y., Guo J., Gan D. 2019. A video analysis method on wanfang dataset via deep neural network, ACM International Conference Proceeding Series, pp. 126-131.
  • 22. Zhang D., Cao D., Chen H.2019. Deep Learning Decoding of Mental State in Non-invasive Brain Computer Interface, ACM International Conference Proceeding Series, art. no. a6,.
  • 23. Konovalov D. A., Saleh A., Efremova D. B., Domingos J. A., Jerry D. R. 2019. Automatic Weight Estimation of Harvested Fish from Images, 2019 Digital Image Computing: Techniques and Applications (DICTA), Perth, WA, Australia, pp. 1-7, doi: 10.1109/DICTA47822.2019.8945971.
  • 24. Hosseini S., Guo X. 2019. Deep convolutional neural network for automated detection of mind wandering using EEG signals (2019) ACM-BCB 2019, Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 314-319.
  • 25. Alnaim N., Abbod M. Albar A. 2019. Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke, 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, pp. 1-6, doi: 10.1109/ISMSIT.2019.8932739
  • 26. Ristea N. -C., Duţu L. C., Radoi A. 2019. Emotion Recognition System from Speech and Visual Information based on Convolutional Neural Networks, 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), Timisoara, Romania, pp. 1-6, doi: 10.1109/SPED.2019.8906538
  • 27. Pham L., McLoughlin I., Phan H., Palaniappan R. 2019. A Robust Framework for Acoustic Scene Classification. Proc. Interspeech 2019, pp. 3634-3638, DOI: 10.21437/Interspeech.2019-1841
  • 28. Slavova O., Lebid S.2020. Denoising and Analysis Methods of Computer Tomography Results of Lung Diagnostics for Use in Neural Network Technology, ECONTECHMOD, Vol. 09, No. 1, pp. 19-24
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b45bba9a-81b4-48d2-aae9-384a1be22b21
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