No 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 research undertakes an empirical evaluation of recent transfer learning models for deep learning feature extraction for a food recognition model. The VIREO-Food172 Dataset and a newly established Sabah Food Dataset are used to evaluate the food recognition model. Afterwards, the model is implemented into a web application system as an attempt to automate food recognition. In this model, a fully connected layer with 11 and 10 Softmax neurons is used as the classifier for food categories in both datasets. Six pre-trained Convolutional Neural Network (CNN) models are evaluated as the feature extractors to extract essential features from food images. From the evaluation, the research found that the EfficientNet feature extractor-based and CNN classifier achieved the highest classification accuracy of 94.01% on the Sabah Food Dataset and 86.57% on VIREO-Food172 Dataset. EFFNet as a feature representation outperformed Xception in terms of overall performance. However, Xception can be considered despite some accuracy performance drawback if computational speed and memory space usage are more important than performance.

No abstract

This paper presents the evaluation of positive emotion in children's mobile learning applications. The mobile learning application is a teaching aid that can help students to self-study and increase the students’ interest in learning especially children. This paper will discuss how mobile learning application affects the children interest in school. The evaluation method implemented to evaluate the rate of positive emotion elicited by the children using mobile learning applications was a mixed method of qualitative and quantitative methods. Since emotion can be either negative or positive, the identification of a proper method or perspective was required to prove that positive emotion was really elicited. Next, the data was collected through the children’s assessment score, Electroencephalograms (EEG) device, Emotion identification using micro-expression (facial expression), Kort Scale and interview to confirm the positive emotion felt by the students. The result shows that all five perspectives or methods have shown that positive emotion is produced. It is found that the Mobile learning application can really trigger the children’s positive emotions.