Research of Software Solutions to Determine the Optimal Solutionfor the Specified Parameters
( Pp. 78-86)
More about authors
Pugacheva Darya B.
Department of Computer Design, Institute of Advanced Technologies and Industrial Programming
MIREA – Russian Technological University
Moscow, Russian Federation Yudina Maya V. Cand. Sci. (Eng.); associate professor, Department of Computer Design, Institute of Advanced Technologies and Industrial Programming; MIREA – Russian Technological University; Moscow, Russian Federation.
MIREA – Russian Technological University
Moscow, Russian Federation Yudina Maya V. Cand. Sci. (Eng.); associate professor, Department of Computer Design, Institute of Advanced Technologies and Industrial Programming; MIREA – Russian Technological University; Moscow, Russian Federation.
Abstract:
This paper examines the study of software solutions for optimizing decision-making, in particular, choosing the most appropriate clothing size. The purpose of the work is to conduct research and compare three machine learning methods regarding the issue of predicting clothing size. Software solutions were developed based on an open data set containing user measurements, information about products, sizes and types of ordered goods, reviews and comments on orders. During the work, three machine learning algorithms were implemented: the k-nearest neighbor method, the use of a multilayer fully connected neural network, and the use of a neural network with funny data inputs. Possible solutions and architectures of neural networks are presented and tested regarding the issue of optimizing decision-making regarding size according to the criteria of the user himself. It is proposed to use a neural network with mixed data inputs in the JavaScript programming language using TensorFlow.JS, where mixed inputs mean data on the user’s personal measurements and comments left on the compliance of the declared size. The subsequent implementation of the proposed solution is possible as an independent web application or to integrate the module into web sites with the appropriate subject.
How to Cite:
Pugacheva D.B., Yudina M.V. Research of Software Solutions to Determine the Optimal Solution for the Specified Parameters. Computational Nanotechnology. 2024. Vol. 11. No. 5. Pp. 78–86. (In Rus.). DOI: 10.33693/2313-223X-2024-11-5-78-86. EDN: BUKIIM
Reference list:
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Goodfellow Ya., Benjio I., Courville A. Deep learning. A.A. Slinkin (transl. from English). 2nd ed., cor. Moscow: DMK Press, 2018. 652 p.
Morales M. Grokking deep reinforcement learning. St. Petersburg: Piter, 2023. 464 p. (Series “Programmer’s Library”).
Rishabh M., Mengting Wan, McAuley J. Decomposing fit semantics for product size recommendation in metric spaces. In: Proceedings of the 12th ACM Conference on Recommender Systems. 2018. Pp. 422–426.
Rishabh M., Grover J. Sculpting data for ML: The first act of machine learning. 2021. ISBN: 9798585463570.
Sklearn API Reference. scikit-learn. URL: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors (data of accesses: 18.03.2024).
Sahoo S., Kumar P., Lakshmi B.R. Mixed data through multiple input for price prediction with multilayer perception and mini VGG. International Journal of Recent Technology and Engineering (IJRTE). No. 8 (2). Pp. 6317–6320.
Ramé A., Sun R., Cord M. MixMo: Mixing multiple inputs for multiple outputs via deep subnetworks. 2020. URL: https://www.researchgate.net/publication/349963564_MixMo_Mixing_Multiple_Inputs_for_Multiple_Outputs_via_Deep_Subnetworks
Linjun Zhang, Zhun Deng, Kawaguchi K. et al. How does mixup help with robustness and generalization? ICLR. 2021.
Borugadda P. et al. Transfer learning VGG16 model for classification of tomato plant leaf diseases: A novel approach for multi-level dimensional reduction. Pertanika Journal of Science and Technology. 2023. N. pag.
Analysis of the clothing market in Russia – demo version of the BusinesStat report. BusinesStat. URL: https://businesstat.ru/images/demo/clothes_russia_demo_businesstat.pdf (data of accesses: 25.03.2024).
The Internet trade market in Russia. Association of Internet Trade Companies – AKIT. URL: https://akit.ru / (data of accesses: 21.03.2024).
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Keywords:
Python, TensorFlow, JavaScript, TensorFlow.JS, neural network, Python, TensorFlow, data processing, JavaScript, TensorFlow.JS, size prediction.
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