Buranasiri, Prathan

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Buranasiri, Prathan
Alternative Name
Buranasiri, P.
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Email
prathan.bu@kmitl.ac.th

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    Item type:Publication,
    Digital holography with deep learning for algae identification and classification
    (2024-01-01)
    Ruttanasirawit, Chinnaphat
    ;
    Plaipichit, Suwan
    ;
    Thongsuwan, Setthanun
    ;
    Thonglim, Pachara
    ;
    Phunpruch, Saranya
    Recently, the characterization of marine objects, populations and biophysical interactions have become crucial within the research community. In this study, we leverage digital holographic imaging systems and deep learning networks to classify three distinct types of micro-algae: Chlamydomonas, Scenedesmus armatus, and Scenedesmus_sp-L. We employed reconstructed digital holographic images and deep learning to identify the results from both approaches. The integration of holographic imaging holds promises in replacing expensive characterization systems like AFM, x-ray diffraction, and Raman spectroscopy, offering a more costeffective solution. In our system, we utilize in-line microscopic digital holographic imaging to record and reconstruct images of the algae specimens. An essential advantage of holographic techniques is that they do not require intact samples of the specimens for effective object identification. To further enhance the process, we combined deep learning algorithms with holographic imaging, capitalizing on the advanced computers. This combination enables highly effective characterizing and classification of different types of algae. These innovative approaches pave the way for exciting advancement in marine research and monitoring.
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    Item type:Publication,
    Gender classification using convolutional neural networks based on fingerprint analysis with in-line digital holography
    (2024-01-01)
    Thonglim, Pachara
    ;
    Thongsuwan, Setthanun
    ;
    Buranasiri, Prathan
    Gender classification has found applications in various fields, including criminology, biometrics, and surveillance. Historically, different methods for gender identification have been employed, such as analyzing hand shape, gait, iris, and facial features. Fingerprints, being unique to each individual, are formed based on the control of multiple genes on chromosomes. After the 24th embryonic week, a person's fingerprint pattern remains unchanged throughout their life. Numerous studies have explored the use of fingerprints for various purposes, such as investigating mental characteristics, characteristics of hereditary diseases, and cancer screening. This paper focuses on studying fingerprints for the identification and classification of human gender through fingerprint analysis using in-line digital holography. The deep learning model constructed for this study includes two convolutional layers, pooling layers, and dense layers. It was trained on a biometric fingerprint database containing 6,000 images, achieving an impressive 99% accuracy. The model was then utilized to classify human gender based on fingerprint analysis, and its accuracy was tested using fingerprint images obtained through Inline Digital Holography (IDH) technique, achieving an 83% accuracy. The performance of the proposed system demonstrates that fingerprints contain vital features for effectively discriminating a person's gender.