| _version_ | 1866901751462887424 |
|---|---|
| author | Anderson, Olivia Marie Daniels, Emmanuel Chukwuemeka |
| author_facet | Anderson, Olivia Marie Daniels, Emmanuel Chukwuemeka |
| contents | <p><span>The rapid advancement of artificial intelligence (AI) has opened new avenues for innovation in retail, particularly in the domain of grocery management. This paper presents a Smart Grocery Ordering System that utilizes AI-driven predictive analytics and personalized health recommendations to optimize the shopping experience. The proposed system analyzes individual consumption behavior using machine learning techniques to forecast future grocery needs, automate routine ordering, and provide nutritional guidance tailored to specific medical conditions such as diabetes and hypertension. The architecture integrates modules for purchase pattern recognition, automatic order scheduling, and real-time health alerts based on user profiles. Experimental evaluation indicates a notable improvement in shopping efficiency, a reduction in food wastage, and increased adherence to dietary recommendations. The results highlight the potential of AI-powered solutions to transform traditional grocery shopping into a more intelligent, personalized, and health-conscious process, paving the way for future innovations in smart retail automation</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20307690 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | PERSONALIZED GROCERY SHOPPING THROUGH AI-DRIVEN SMART ORDERING TECHNOLOGIES Anderson, Olivia Marie Daniels, Emmanuel Chukwuemeka Artificial intelligence, grocery automation, food waste reduction, health-based recommendations, machine learning, predictive analytics, personalized retail.Top of Form <p><span>The rapid advancement of artificial intelligence (AI) has opened new avenues for innovation in retail, particularly in the domain of grocery management. This paper presents a Smart Grocery Ordering System that utilizes AI-driven predictive analytics and personalized health recommendations to optimize the shopping experience. The proposed system analyzes individual consumption behavior using machine learning techniques to forecast future grocery needs, automate routine ordering, and provide nutritional guidance tailored to specific medical conditions such as diabetes and hypertension. The architecture integrates modules for purchase pattern recognition, automatic order scheduling, and real-time health alerts based on user profiles. Experimental evaluation indicates a notable improvement in shopping efficiency, a reduction in food wastage, and increased adherence to dietary recommendations. The results highlight the potential of AI-powered solutions to transform traditional grocery shopping into a more intelligent, personalized, and health-conscious process, paving the way for future innovations in smart retail automation</span></p> |
| title | PERSONALIZED GROCERY SHOPPING THROUGH AI-DRIVEN SMART ORDERING TECHNOLOGIES |
| topic | Artificial intelligence, grocery automation, food waste reduction, health-based recommendations, machine learning, predictive analytics, personalized retail.Top of Form |
| url | https://doi.org/10.5281/zenodo.20307690 |