Identification of Passion Fruit Nutrients for Elderly People Using Network in Network Architecture: An Empirical Study in Thailand

Abstract

The growing elderly population has led to a rise in health issues, particularly chronic diseases. Passion fruits contain numerous nutrients that may help in the treatment of chronic diseases. However, specific recommendations for daily passion fruit nutrient intake for the elderly are currently lacking in the literature. This research aimed to identify passion fruit groups and to suggest the appropriate daily passion fruit nutrient intake for elderly people using network in network (NiN) architecture. This research demonstrates that the NiN model can be effectively applied to identify passion fruit groups for the elderly. It is more efficient than other convolutional neural network (CNN) architectures. The results show that NiN can correctly identify passion fruit groups and suggest the appropriate amount of nutrient intake for the elderly, achieving + 96.76% accuracy in the training dataset and 95.89% accuracy in the validation dataset, surpassing 84.6% accuracy achieved by EaglAI. Sensitivity analysis of the NiN model using mean absolute error (MAE) for geometric transformations revealed consistent training image results and model robustness. This research benefits elderly people with chronic diseases by providing tailored recommendations for daily passion fruit intake, based on the analysis of sugar nutrients using the NiN model.