Design and Implementation of a Smart Mess Management System Utilizing ArtificialIntelligence, Blockchain, and Biometrics using Machine Learning
Keywords:
Smart Mess Management, Artificial Intelligence, Blockchain, Biometrics, Face Recognition, Body Mass Index (BMI), Food Waste Reduction, Leave Management, Nutritional Guidance, Zero-Trust Architecture, Automated Attendance, Generative AI.Abstract
Messes, which provide communal dining facilities for students and employees in many colleges, universities and business settings, are an important part of the overall infrastructure of these types of facilities. Unfortunately, the manner in which traditional messes rely heavily on manual administrative functions often creates inefficient operationsleading to the frequently experienced problems of inaccurate record-keeping for transactions as well as attendance records, a lack of transparency into financial statements and a high level of food waste. This paper describes how the Smart Mess Management System was designed and implemented. This paper explains how the Smart Mess Management System provides a new and enhanced method of operating a physical mess by the use of modern technology that includes automated and optimised (through advanced technologies) and the replacement of traditional manual processes. The use of facial recognition technology ensures that automatic attendance can be provided with no proxies. This paper also describes how blockchain technology was used to provide a single source of truth for all financial transactions (e.g., peer-to peer payments, etc.) and for proper receivables accounting of fees owed by each student or employee that used the mess. The use of artificial intelligence allows the system to provide students and employees with customised nutrition plans based on their BMI (body mass index) and an AI-based recipe engine that maximises the usability of the current inventory of food items at the campus mess and eliminates the need for additional procurement of those inventory items. Finally, the Smart Mess Management System includes pre emptive leave management that is based on predictive models and an AI-based filtration system for feedback provided by users to reduce food waste and to preserve the integrity of the system-generated data. As a result, implementing this complete architecture establishes a modern, secure and user-friendly environment that greatly improves user satisfaction and operational efficiency.
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