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Investigation variable star classification through light curve analysis using machine learning approach
(2025-01-01)
Tongleak, Chutipon
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Thongsuwan, Setthanun
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Srithongtae, Kewalee
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Kitrattana, Borirak
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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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Digital holography with deep learning for algae identification and classification
(2024-01-01)
Ruttanasirawit, Chinnaphat
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Plaipichit, Suwan
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Thongsuwan, Setthanun
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Thonglim, Pachara
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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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Gender classification using convolutional neural networks based on fingerprint analysis with in-line digital holography
(2024-01-01)
Thonglim, Pachara
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Thongsuwan, Setthanun
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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.
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The Moina Gender Investigation using In-Line Digital Holographic Microscopy
(2023-01-01)
Voochaiyaphum, Bunyarit
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Thongsuwan, Setthanun
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Ruttanasirawit, Chinnapat
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Plaipichit, Suwan
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Thonglim, Pachara
The investigation of Moina was performed using in-line DHM. The reconstructed images of Moinas have been applied for investigation their surface area. Then the surface area has been used for gender investigation.
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A Deep Single-Pass Learning for Recognition of Handwritten Digits
(2022-03-01)
Thongsuwan, Setthanun
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Jaiyen, Saichon
We describe a deep learning model-Deep Single-Pass Learning (DSPL)-that can learn a data set, with a single pass for recognition, and predict with high accuracy, when evaluated for visual recognition of handwritten digits. DSPL consists of several stacked convolutional layers to learn features automatically and Extreme gradient boosting (XGBoost) was set as the last layer for predicting class labels. The learning time complexity is O(Lc<sup>2</sup> mnpq), or less than the learning time of deep learning-Convolutional Neural Networks (CNNs). The network does not need iteration to re-adjust weights during feature learning. Tests showed that our model provided better accuracy than other models i.e. CNNs, XGBoost, LR, ETC, GBC, RFC, GNB, and DTC, including MLP and SVC families: in the worst case, DSPL provided 99.95% accuracy.
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ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost
(2021-02-01)
Thongsuwan, Setthanun
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Jaiyen, Saichon
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Padcharoen, Anantachai
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Agarwal, Praveen
We describe a new deep learning model - Convolutional eXtreme Gradient Boosting (ConvXGB) for classification problems based on convolutional neural nets and Chen et al.’s XGBoost. As well as image data, ConvXGB also supports the general classification problems, with a data preprocessing module. ConvXGB consists of several stacked convolutional layers to learn the features of the input and is able to learn features automatically, followed by XGBoost in the last layer for predicting the class labels. The ConvXGB model is simplified by reducing the number of parameters under appropriate conditions, since it is not necessary re-adjust the weight values in a back propagation cycle. Experiments on several data sets from UCL Repository, including images and general data sets, showed that our model handled the classification problems, for all the tested data sets, slightly better than CNN and XGBoost alone and was sometimes significantly better.

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