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Author: search.filters.author.Chantamit-o-Pas, PattanapongSubject: search.filters.subject.Convolutional Neural Network

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    Deep Learning Based Automobile Identification Application
    (2023-01-01)
    Chantamit-o-Pas, Pattanapong
    ;
    Sangaroon, Pattanayu
    ;
    Srisura, Jukkapat
    Today, the high competition among domestic automobile manufacturers is intense situation than previous years. This result gives advantages in a good variety of brands, models, engine sizes and appearances. This can cause some critical issues in recognizing and recalling a car by manufacturer. In addition, an owner may modify some parts of original vehicle such as the head bumper, the rear bumper, and the head light. This modification also affects the people who are looking for pre-owned cars. Despite the fact, the details are mismatch with the vehicle registration book that issued by the Department of Land Transport. From this incident, the researchers implemented a convolutional neural network (CNN) in the identification of vehicle characteristics to reduce the ambiguity for each car’s models. The researchers conducted experiments using five algorithms. SVM, ResNet34, ResNet50 and Inception-ResNetV2. The researchers set up a library of two car models, Toyota Hilux and Honda Civic sedan and Civic Hatchback, including models from past ten years ago until the present. The images are of 224 × 224 pixels. The data are categorized into two sets, a training set has 1,449 images which is counted as 80% of total images and a testing set is having 362 images which is about 20% of total. The total images are 1,811 and 26 Classes. Our experiments compared the accuracies of SVM, ResNet34, ResNet50, and Inception-ResNetV2, which came out to be 21.4%, 55.5%, 66.6%, and 92.8% respectively. As a result, Inception-ResNetV2 outperforms among all other methods.
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    Thai Recipe Retrieval Application Using Convolutional Neural Network
    (2022-01-01)
    Phophan, Thitiwut
    ;
    Khuthanon, Rungwaraporn
    ;
    Chantamit-o-Pas, Pattanapong
    Due to the COVID-19, self-catering captured the interest of many people. This paper proposes a novel mobile application, which can share recipes and recognition material to help individuals with low prior cooking skill. It offers good, practical knowledge and can help to build cooperative teams in the cooking community among novice cooks. Choosing the ingredients for cooking can be difficult. This is especially true because of Thai vegetables look similar such as white and sweet basil particularly for new cooks not familiar with their other characteristics. This research introduces a mobile application, Kin Rai Dee App, which is based on sharing recipes and recognition material by using Roboflow with a pretrained model. To develop Thai vegetable image classification in our mobile application, the Convolutional Neural Network technique and a Thai vegetable dataset is used to evaluate the performance of our classification model. This dataset is composed of two sources including (1) Thai herb dataset from Kaggle website and (2) our own images. Therefore, there are totally 12 classes in the Thai vegetable dataset with image’s resolutions of 224 × 224 pixels. The result for image training is implemented through machine learning and Roboflow methods. The experiments process has training results accuracy at 85% and testing result at 15% in both models. The performance of our model has proven that it can achieve the result with confidence values 100% and 99.21% for specific Thai vegetables.