Functional Requirements and Object-Oriented System Modeling for Designing AI-Driven Intelligent Catering Systems

Summary

This study introduces an AI-based restaurant catering system that allows for contactless communication, customized meal suggestions, and satisfaction prediction. By utilizing NLP with LDA, Conv-RNN, and Conv-LSTM, it surpasses rule-based techniques with more accuracy, precision, recall, and reduced mistake rates, demonstrating AI's revolutionary potential in the food service industry.

Abstract

The food industry has undergone a significant transformation in recent decades due to globalization, technological advancements, and evolving customer expectations. Artificial Intelligence (AI) and the Internet of Things (IoT) are now playing a critical role in enhancing food production, marketing, and service delivery. This study proposes an AI-driven intelligent system to improve restaurant catering services through contactless service using Natural Language Processing(NLP) and Linear Discriminant Analysis(LDA), personalized food recommendations through a Convolutional Recurrent Neural Network(Conv-RNN) model, and customer satisfaction prediction using an optimized Convolutional Long Short Term Memory(Conv-LSTM) model. Real-world experiments demonstrate that the proposed system outperforms traditional rule-based methods, achieving 91.5% accuracy, 91% precision, 91.1% recall, and an F1 score of 89.7% with Word2Vec-LDA; 98.5% accuracy with a loss of 0.02 in the Conv-RNN model; and an RMSE of 0.1011 with an R² of 0.9812 in the Conv-LSTM system. These results highlight the transformative potential of AI in automating and enhancing customer service in the restaurant industry.

Introduction

Adoption of AI has been a crucial part of digital technology growth for the last decade. It has given several industries, including the hospitality sector, both possibilities and challenges since its start¹, and numerous AI-powered inventions have been developed that have the potential to improve people's quality of life and thereby enhance the economy. In the very competitive restaurant industry, maintaining top-notch food and customer service is essential to success. As technology advances and dining experiences shift, AI is becoming a game-changing tool to increase operational effectiveness and customer satisfaction. AI-powered monitorin....

Protocol

This study was conducted in accordance with the guidelines of the Research Ethics Committee of The National University of Malaysia (UKM) and approved under approval number UKM FST/2025-AI/023. Written informed consent was obtained from all participants prior to the collection of chatbot queries. All data were anonymized to ensure participant confidentiality and privacy

Study overview

The overview of the proposed intelligent catering system assisted with AI technologies is shown in Figure 1. As illustrated, the customer input is preprocessed with the NLP techniques such as word embeddin....

Results

This study thoroughly tested and validated several models to guarantee the authenticity and dependability of the developed ICS. The most efficient setup for ICS was determined by performing a comparative study of several word embedding and classifier combinations. Each experiment was conducted 10x and the results were presented as average values with standard errors enclosed in parentheses. This method brought attention to the model's unpredictability and consistency in performance. The s.......

Discussion

The overall performance of the suggested ICS model using AI technologies is compared with the k-means with SVR²⁴, quick service restaurant with LSTM (QSR-LSTM)²⁵, and NLP-ANN³⁸. Comparatively, the proposed model secured a reduced computation time compared to the considered approaches, as shown in Figure 12. As the number of iterations increases, the computation time for all the models increases gradually. The suggest.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Programming Language	Python (used for model development, NLP, and deep learning)	https://www.python.org/	Python 3.8+
Database	MySQL or SQLite (for storing user interaction logs)	https://www.mysql.com/; https://www.sqlite.org/	MySQL 8.0 or SQLite3
Dataset	User queries collected from local restaurant ordering chatbot		Manually annotated
Deep Learning Framework	TensorFlow / Keras	https://www.tensorflow.org/; Keras 2.11 → https://keras.io/	TensorFlow 2.11 or Keras 2.11
Development Environment	Jupyter Notebook / Google Colab	https://jupyter.org/; https://colab.research.google.com/	JupyterLab 3+ / Colab (free)
Evaluation Metrics	scikit-learn metrics: precision, recall, cross-entropy, R²	https://scikit-learn.org/	scikit-learn 1.0+
Natural Language Toolkit	spaCy / NLTK (for intent detection preprocessing)	https://spacy.io/; https://www.nltk.org/	spaCy 3.0 / NLTK 3.6
Recurrent Neural Network Models	RNN, LSTM, Conv-LSTM	https://keras.io/	Implemented in Keras
System Hardware	Intel Core i7, 16GB RAM, NVIDIA GTX 1660 Ti GPU		Local system
Topic Modeling Tool	Gensim (used for Latent Dirichlet Allocation)	https://radimrehurek.com/gensim/	Gensim 4.1.2
Visualization Tools	Matplotlib, Seaborn (for plotting performance graphs)	https://seaborn.pydata.org/; https://matplotlib.org/	Matplotlib 3.5+, Seaborn 0.11
Word Embedding	Word2Vec / GloVe pre-trained embeddings	https://nlp.stanford.edu/projects/glove/	GloVe (100D), Stanford NLP