UniRetail is an innovative mobile application designed to revolutionize retail operations by integrating advanced machine learning ML capabilities and modern technologies. The platform enables retailers to centralize and streamline critical functions, including inventory management, dynamic pricing, and targeted promotions, while delivering a seamless, personalized shopping experience for customers. Machine learning algorithms are at the core of UniRetail, empowering store owners with predictive analytics for demand forecasting, personalized product recommendations using collaborative filtering, and customer segmentation through clustering techniques. Natural language processing NLP enhances product search and filtering, while sentiment analysis refines customer feedback to improve service quality. Additionally, fraud detection models ensure secure and efficient multi-payment checkouts. UniRetail’s robust, cloud-based architecture, built on microservices, supports scalability and integrates seamlessly with existing retail systems. The use of technologies like TensorFlow, PyTorch, and Apache Spark ensures efficient data processing and real-time ML model deployment through Kubernetes and AWS SageMaker. By uniting operational efficiency, data driven insights, and customer engagement, UniRetail aims to be a transformative tool for modern retail enterprises, helping them thrive in the digital landscape.
100
75
43
100
75
43
100
75
43
Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author:
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Or share your Opinion
The increasing prevalence of Internet of Things (IoT) devices in various domains underscores the necessity for robust security...
The rapid advancement in processing power has empowered deep learning algorithms to produce remarkably convincing human-synthesized videos, commonly...
Because of the inherent volatility and complexity of the financial markets, accurately predicting stock prices is a challenging...
Automated detection and diagnosis of pulmonary diseases such as pneumonia, tuberculosis, lung cancer, and COVID-19 play a crucial...
The goal of this project is to develop a sophisticated and intelligent system that tracks soil moisture levels...
The primary objective of the present paper is to assess the growth of economic sectors in the Indian...
The Secured data safe guard transaction with multi-tenant environments run on private-protected authenticate platforms runs by secured handed...
The current paper concentrates on the data of food safety sensors on four key environmental parameters, including the...
Comments(0)