Deep Learning Method for Classifying Items into Categories for Dutch Auctions

  • Janusz Bobulski Clemens Adam Mazgaj, Krakow and Department of Computer Science, Czestochowa University of Technology, Czestochowa
  • Sabina Szymoniak Clemens Adam Mazgaj, Krakow and Department of Computer Science, Czestochowa University of Technology, Czestochowa


Artificial Intelligence (AI) methods are widely used in our lives (phones, social media, self-driving cars, and e-commerce). In AI methods, we can find convolutional neural networks (CNN). First of all, we can use these networks to analyze images. This paper presents a method for classifying items into particular categories on an auction site. The technique prompts the seller to which category assign the item when creating a new auction. We choose a neural network with a number of image convolution layers as the best available approach to address this task. All tests were carried out in the Matlab environment using GPU and CPU. Then, the tested and verified solution was implemented in the TensorFlow environment with a CPU processor. Thanks to the cross-validation method, the effectiveness of the recognition system was fully verified in several stages. We obtained promising results. Consequently, we implemented the developed method by adding a new sales offer on the Clemens website.


Deep Learning, Internet auction, Classification,


1. B. Achour, M. Belkadi, I. Filali, M. Laghrouche, M. Lahdir, Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on convolutional neural networks (CNN), Biosystems Engineering, 198: 31–49, 2020, doi: 10.1016/j.biosystemseng.2020.07.019.
2. L. Alzubaidi et al., Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions, Journal of Big Data, 8: 53, 2021, doi: 10.1186/s40537-021-00444-8.
3. S. Aoughlis, R. Saddaoui, B. Achour, M. Laghrouche, Dairy cows’ localisation and feeding behaviour monitoring using a combination of IMU and RFID network, International Journal of Sensor Networks, 37(1): 23–35, 2021, doi: 10.1504/IJSNET.2021.117962.
4. R. Ashraf et al., Deep convolution neural network for big data medical image classification, IEEE Access, 8: 105659–105670, 2020, doi: 10.1109/ACCESS.2020.2998808.
5. M. Benouis, L.D. Medus, M. Saban, A. Ghemougui, A. Rosado-Munoz, Food tray sealing fault detection in multi-spectral images using data fusion and deep learning techniques, Journal of Imaging, 7(9): 186, 2021, doi: 10.3390/jimaging7090186.
6. J. Bobulski, M. Kubanek, Autonomous robot for plastic waste classification, [in:] International Conference on Intelligent Human Systems Integration, pp. 371–376, Springer, 2021, doi: 10.1007/978-3-030-68017-6_55.
7. J. Bobulski, M. Kubanek, Deep learning for plastic waste classification system, Applied Computational Intelligence and Soft Computing, 2021: 6626948, 2021, doi: 10.1155/2021/6626948.
8. J. Bobulski, M. Kubanek, Vehicle for plastic garbage gathering, [in:] 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), pp. 1–5, 2021.
9. J.-C. Chien, M.-T. Wu, J.-D. Lee, Inspection and classification of semiconductor wafer surface defects using CNN deep learning networks, Applied Sciences, 10(15): 5340, 2020, doi: 10.3390/app10155340.
10. Y.J. Cruz, M. Rivas, R. Quiza, G. Beruvides, R.E. Haber, Computer vision system for welding inspection of liquefied petroleum gas pressure vessels based on combined digital image processing and deep learning techniques, Sensors, 20(16): 4505, 2020, doi: 10.3390/s20164505.
11. Y.J. Cruz, M. Rivas, R. Quiza, A. Villalonga, R.E. Haber, G. Beruvides, Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process, Computers in Industry, 133: 103530, 2021, doi: 10.1016/j.compind.2021.103530.
12. F.E. Fassnacht et al., Review of studies on tree species classification from remotely sensed data, Remote Sensing of Environment, 186: 64–87, 2016, doi: 10.1016/j.rse.2016.08.013.
13. I. Filali, B. Achour, M. Belkadi, M. Lalam, Graph ranking based butterfly segmentation in ecological images, Ecological Informatics, 68: 101553, 2022, doi: 10.1016/j.ecoinf.2022.101553.
14. T. Kattenborn, J. Leitloff, F. Schiefer, S. Hinz, Review on convolutional neural networks (CNN) in vegetation remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 173: 24–49, 2021, doi: 10.1016/j.isprsjprs.2020.12.010.
15. Z. Kolar, H. Chen, X. Luo, Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images, Automation in Construction, 89: 58–70, 2018, doi: 10.1016/j.autcon.2018.01.003.
16. J.-D. Lee, J.-C. Chien, Y.-T. Hsu, C.-T. Wu, Automatic surgical instrument recognition—a case of comparison study between the faster R-CNN, mask R-CNN, and single-shot multi-box detectors, Applied Sciences, 11(17): 8097, 2021, doi: 10.3390/app11178097.
17. C.A. Mazgaj, Clemens,, accessed: 2022-06-23.
18. L.D. Medus, M. Saban, J.V. Francés-Víllora, M. Bataller-Mompeán, A. Rosado-Munoz, Hyperspectral image classification using CNN: Application to industrial food packaging, Food Control, 125: 107962, 2021, doi: 10.1016/j.foodcont.2021.107962.
19. N. Pettorelli et al., Satellite remote sensing of ecosystem functions: Opportunities, challenges and way forward, Remote Sensing in Ecology and Conservation, 4(2): 71–93, 2018, doi: 10.1002/rse2.59.
20. L.O. Rojas-Perez, J. Martinez-Carranza, Deeppilot: A CNN for autonomous drone racing, Sensors, 20(16): 4524, 2020, doi: 10.3390/s20164524.
21. L.O. Rojas-Perez, J. Martínez-Carranza, On-board processing for autonomous drone racing: An overview, Integration, 80: 46–59, 2021, doi: 10.1016/j.vlsi.2021.04.007.
22. L.O. Rojas-Perez, J. Martinez-Carranza, Towards autonomous drone racing without GPU using an OAK-D smart camera, Sensors, 21(22): 7436, 2021, doi: 10.3390/s21227436.
23. M. Saban, L.D. Medus, S. Casans, O. Aghzout, A. Rosado, Sensor node network for remote moisture measurement in timber based on bluetooth low energy and web-based monitoring system, Sensors, 21(2): 491, 2021, doi: 10.3390/s21020491.
24. S. Szymoniak, Amelia—A new security protocol for protection against false links, Computer Communications, 179: 73–81, 2021, doi: 10.1016/j.comcom.2021.07.030.
25. S. Szymoniak, A new security protocol for protection against false links, [in:] [New] Normal Technology Ethics: Proceedings of the ETHICOMP 2021, ETHICOMP 2021, pp. 307–310, 2021.
26. S. Szymoniak, Using a security protocol to protect against false links, [in:] Moving Technology Ethics at the Forefront of Society, Organisations and Governments, pp. 513–525, Universidad de La Rioja, 2021.
27. W. Tian, W. Huang, L. Yi, L. Wu, C. Wang, A CNN-based hybrid model for tropical cyclone intensity estimation in meteorological industry, IEEE Access, 8: 59158–59168, 2020, doi: 10.1109/ACCESS.2020.2982772.
28. W. Tian, W. Huangwei, X. Xu, C.Wang, Tropical cyclone maximum wind estimation from infrared satellite data with integrated convolutional neural networks, [in:] 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 575–580, IEEE, 2019.
29. W. Tian, X. Zhou, W. Huang, Y. Zhang, P. Zhang, S. Hao, Tropical cyclone intensity estimation using multidimensional convolutional neural network from multichannel satellite imagery, IEEE Geoscience and Remote Sensing Letters, 19: 1–5, 2021, doi: 10.1109/LGRS.2021.3134007.
30. P. Xu, Z. Guo, L. Liang, X. Xu, MSF-Net: Multi-scale feature learning network for classification of surface defects of multifarious sizes, Sensors, 21(15): 5125, 2021, doi: 10.3390/s21155125.
31. X. Xu, H. Zheng, Z. Guo, X. Wu, Z. Zheng, SDD-CNN: Small data-driven convolution neural networks for subtle roller defect inspection, Applied Sciences, 9(7): 1364, 2019, doi: 10.3390/app9071364.
32. Z. Zheng, H. Chen, X. Luo, A supervised event-based non-intrusive load monitoring for non-linear appliances, Sustainability, 10(4): 1001, 2018, doi: 10.3390/su10041001.
33. Z. Zheng, H. Chen, X. Luo, Spatial granularity analysis on electricity consumption prediction using LSTM recurrent neural network, Energy Procedia, 158: 2713–2718, 2019, doi: 10.1016/j.egypro.2019.02.027.
Jan 12, 2024
How to Cite
BOBULSKI, Janusz; SZYMONIAK, Sabina. Deep Learning Method for Classifying Items into Categories for Dutch Auctions. Computer Assisted Methods in Engineering and Science, [S.l.], v. 31, n. 1, p. 67–79, jan. 2024. ISSN 2956-5839. Available at: <>. Date accessed: 17 apr. 2024. doi: