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Smart Basket Integration with Shopping Behavior Analysis
Abstract
In this modernized society, people have become used to shopping, where individuals go to purchase their day-to-day essential items and furthermore need to pay for that they need to remain at a charging counter for quite a long time and again it’s a rushed interaction, on the other hand, people are less likely to remember what they actually need, they often miss out the relevant products to buy, at the same time the retailers usually lack in predicting the trends to manage their inventory that leads to wastage of products. So we will propose a “SMART SHOPPING BASKET AND SHOPPING BEHAVIOR ANALYSIS” which comprises a Radio Frequency Identification (RFID) sensor, RaspberriPi microprocessor, Software Application, and a well-trained recommendation system, RFID tags are attached to every item and RFID reader reads the item data, those items will be displayed in a screen attached to the basket and with all these makes basket a smart and Intelligent Basket. It features a user-friendly application interface and recommendations of products that are frequently bought together, finally making the customer satisfied with an efficient service. By the customer data on a seasonal basis, the prediction of trends is offered, so that a retailer can utilise it to draw more customers and upscale the scales to an exponential level. This test model is planned to get rid of drawn-out shopping associations and the nature of organisational issues. The proposed structure can be easily implemented and tested on a commercial scale in future real-world scenarios. Furthermore, the proposed model is more competitive compared to others.
References
Zheng F, Liu Q. Anomalous telecom customer behavior detection and clustering analysis based on ISP’s operating data. IEEE Access. 2020 Feb 27;8:42734-48.
Lewis K, Van Horn D. Design analytics in consumer product design: A simulated study. In International Design Engineering Technical Confer- ences and Computers and Information in Engineering Conference 2013 Aug 4 (Vol. 55898, p. V03BT03A003). American Society of Mechanical Engineers.
Kajabad, E. N., Ivanov, S. V., Ramezanzade, N. (2020, June). Customer detection and tracking by deep learning and Kalman filter algorithms. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (pp. 1-6). IEEE.
Khan, M. A., Saqib, S., Alyas, T., Rehman, A. U., Saeed, Y., Zeb, A., Mohamed, E. M. (2020). Effective demand forecasting model using business intelligence empowered with machine learning. IEEE Access, 8, 116013-116023.
Erlina, T., Fikri, M. (2023). A YOLO Algorithm-based Visitor Detection System for Small Retail Stores using Single Board Computer. Journal of Applied Engineering and Technological Science (JAETS), 4(2), 908- 920.
Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., Soman, K. P. (2017, September). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 international conference on advances in computing, communications and informatics (icacci) (pp. 1643-1647). IEEE.
Bowlby, Rachel. ”Supermarket futures.” The shopping experience (2015, August). Comparative analysis of huge lines in the supermarket is a loss. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1- 8). IEEE.
Kharalkar, Shardul, Shubham Kolvekar, and Tejas Latne. ”SMART SHOPPING BASKET.” Asian Journal For Convergence In Technology (AJCT) ISSN-2350- 1146 (2019).
Fischer, M., Ferdik, M., Rack, L. O., Saxl, G., Renzler, M., Ussmueller,
T. (2019). An experimental study on the feasibility of a frequency diverse UHF RFID system. IEEE Access, 7, 132311-132323.
Wankhede, Kirti, Bharati Wukkadada, and Vidhya Nadar. ”Just walk- out technology and its challenges: A case of Amazon Go.” 2018 Inter- national Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2018.
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