information Article Indigenous Food Recognition Model Based on Various Convolutional Neural Network Architectures for Gastronomic Tourism Business Analytics Mohd Norhisham Razali 1, Ervin Gubin Moung 2,* , Farashazillah Yahya 2 , Chong Joon Hou 2, Rozita Hanapi 1, Raihani Mohamed 3 and Ibrahim Abakr Targio Hashem 4 1 Faculty of Business Management, Universiti Teknologi Mara Cawangan Sarawak, Kota Samarahan 94350, Sarawak, Malaysia;
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[email protected] Abstract: In gastronomic tourism, food is viewed as the central tourist attraction. Specifically, indigenous food is known to represent the expression of local culture and identity. To promote gastronomic tourism, it is critical to have a model for the food business analytics system. This Citation: Razali, M.N.; Moung, E.G.; research undertakes an empirical evaluation of recent transfer learning models for deep learning Yahya, F.; Hou, C.J.; Hanapi, R.; feature extraction for a food recognition model. The VIREO-Food172 Dataset and a newly established Mohamed, R.; Hashem, I.A.T. Sabah Food Dataset are used to evaluate the food recognition model. Afterwards, the model is Indigenous Food Recognition Model implemented into a web application system as an attempt to automate food recognition.