Jearanaitanakij, Kietikul

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Jearanaitanakij, Kietikul
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kietikul.je@kmitl.ac.th

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    Item type:Publication,
    Improving knn algorithm based on weighted attributes by pearson correlation coefficient and pso fine Tuning
    (2020-10-21)
    Sinhashthita, Wanarase
    ;
    Jearanaitanakij, Kietikul
    Assigning proper weights to attributes in some datasets according to their importances can significantly improve the classification accuracy. Weighted attributes can support the classification methods effectively if their weights truly represent by their importances. In this research, we improve the K-Nearest Neighbors (KNN) algorithm by using Pearson correlation coefficient along with Particle Swarm Optimization (PSO) to find the optimal set of weights for attributes in the dataset. The experimental results show that the proposed method can significantly improve the classification accuracy when compared to the traditional KNN algorithm.
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    DEEP LEARNING DEVELOPMENTAL MODEL FOR EMBRYO GRADING IN THAI POPULATION
    (2024-01-01)
    Wiriyasirivaj, Budsaba
    ;
    Limkiatsataporn, Sawit
    ;
    Pukinghin, Apisit
    ;
    Kuekulkomain, Phatrapron
    ;
    Promrungrueng, Pornprom
    In light of the growing challenges associated with infertility, an increasing number of researchers are resorting to assisted reproductive technologies such as In Vitro Fertilization (IVF). Embryo grading is a crucial step in the IVF process that requires embryologists’ expertise. However, their limited availability has led to the exploration of technological alternatives. This study aims to use deep learning for human embryo grading, with models specifically designed for the dataset in Thailand at Vajira Hospital. The process of IVF at Vajira Hospital presents its own set of challenges since its embryo classification extends beyond the Istanbul consensus. Furthermore, classes that occur infrequently are removed and classes with similarities are merged. We apply transfer learning to pretrained deep learning models like VGG19, VGG16, ResNet152, Resnet101, Xception, InceptionV3, and EfficientNet, to create a system capable of accurately classifying embryo quality scores. Experimental results from the embryo dataset collected from Vajira Hospital in Thailand demonstrate the proposed classifier’s accuracy, precision, recall, f1-score, and AUC superiority. This research contributes to the field of IVF in Thailand by potentially reducing human errors and addressing the demand for skilled embryologists.