Buranasiri, Prathan

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Buranasiri, Prathan
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Buranasiri, P.
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prathan.bu@kmitl.ac.th

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    Investigation variable star classification through light curve analysis using machine learning approach
    (2025-01-01)
    Tongleak, Chutipon
    ;
    Thongsuwan, Setthanun
    ;
    Srithongtae, Kewalee
    ;
    Kitrattana, Borirak
    ;
    Tanirat, Purin
    With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.
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    Off-Axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification
    (2022-12-01)
    Lam, Hoson
    ;
    Zhu, Yanmin
    ;
    Buranasiri, Prathan
    This paper proposes a method with an off-axis interferometer and an ensemble deep learning (I-EDL) hologram-classifier to interpret noisy digital holograms captured from the tissues of flawed biological specimens. The holograms are captured by an interferometer, which serves as a digital holographic scanner to scan the tissue with 3D information. The method achieves a high success rate of 99.60% in identifying the specimens through the tissue holograms. It is found that the ensemble deep learning hologram-classifier can effectively adapt to optical aberration coming from dust on mirrors and optical lens aberrations such as the Airy-plaque-like rings out-turn from the lenses in the interferometer. The deep learning network effectively adapts to these irregularities during the training stage and performs well in the later recognition stage without prior optical background compensations. The method does not require an intact sample with a full outline shape of the specimens or the organs to understand the objects’ identities. It demonstrates a new paradigm in object identification by ensemble deep learning through a direct wavefront recognition technique.