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Author: search.filters.author.Boongasame, LaorSubject: search.filters.subject.classification

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    Classification of Thai Rice Varieties Using Image Processing and Deep Learning Techniques
    (2025-01-01)
    Kongmanee, Panpatsorn
    ;
    Puengpradith, Sorapojana
    ;
    Boongasame, Laor
    The methods for identifying Thai rice varieties are complex, time-consuming, and require high expertise to achieve accurate results. This research explores different deep learning techniques to efficiently classify the strains of Thai rice that optimize accuracy and speed. The focus rice varieties are Khao Hom-Mali Thai and Thai Hom Pathum Thani 1 fragrant rice; both have similar shapes and characteristics but differ in price, market value, and recognition. The proposed model is based on an instance segmentation model of YOLOv8, which is compared against popular instance segmentation models such as YOLACT, SOLOv2, and Mask R-CNN. Additionally, hyperparameter tuning is performed to ascertain the most optimal values. The evaluation of the model performance reports in the form of mean average precision (mAP), inference time, and model stability. Experimental results indicate that YOLOv8n-seg, with the fewest parameters, achieves the highest accuracy comparable to other YOLOv8-based models with more parameters. The proposed model demonstrates superior accuracy and processing speed performance compared to other state-of-the-art models.
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    Enhanced Feature Selection via Hierarchical Concept Modeling
    (2024-12-01)
    Saelee, Jarunee
    ;
    Wetchapram, Patsita
    ;
    Wanichsombat, Apirat
    ;
    Intarasit, Arthit
    ;
    Muangprathub, Jirapond
    The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. With big data, it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. In this study, we present a new feature selection approach based on hierarchical concept models using formal concept analysis (FCA) and a decision tree (DT) for selecting a subset of attributes. The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). The hierarchical concept model is built from a dataset, and it is selected by top-down considering features (attributes) node for each level of structure. Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on a paired-samples t-test. To compare the identified models in order to evaluate feature selection effects, the indicators used were information gain (IG) and chi-squared (CS), while both forward selection (FS) and backward elimination (BS) were tested with the datasets to assess whether the presented model was effective in reducing the number of features used. The results show clearly that the proposed models when using DT or using FCA, needed fewer features than the other methods for similar classification performance.