An NLP-Driven Hybrid System for Predicting Fruit Freshness Using Text Descriptions and Adaptive Questionnaires
Disciplines
Artificial Intelligence and Robotics | Other Computer Sciences
Abstract (300 words maximum)
The core idea of this project is to build a system that predicts fruit freshness from user input. Users will first give a free-text description of the fruit’s appearance, texture, or smell, which will be classified, followed by an adaptive questionnaire that asks targeted follow-up questions based on fruit type and detected cues. The free-text classification and questionnaire responses will then be combined to generate the final freshness prediction. The workflow includes data collection and preprocessing, feature extraction for text and questionnaire responses, training and evaluating the models. Finally, using all data to deliver a clear freshness result to the user.
Use of AI Disclaimer
no
Academic department under which the project should be listed
CCSE – Computer Science
Primary Investigator (PI) Name
Md Abdullah Al Hafiz Khan
An NLP-Driven Hybrid System for Predicting Fruit Freshness Using Text Descriptions and Adaptive Questionnaires
The core idea of this project is to build a system that predicts fruit freshness from user input. Users will first give a free-text description of the fruit’s appearance, texture, or smell, which will be classified, followed by an adaptive questionnaire that asks targeted follow-up questions based on fruit type and detected cues. The free-text classification and questionnaire responses will then be combined to generate the final freshness prediction. The workflow includes data collection and preprocessing, feature extraction for text and questionnaire responses, training and evaluating the models. Finally, using all data to deliver a clear freshness result to the user.