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Keser, S. and Gezer, E. (2025) Comparative Analysis of Speaker Identification Performance Using Deep Learning, Machine Learning, and Novel Subspace Classifiers with Multiple Feature Extraction Techniques. Digital Signal Processing, 156, Article 104811.
https://doi.org/10.1016/j.dsp.2024.104811

has been cited by the following article:

• TITLE: Classifying Vibration Modes Generated by The Michelson Interferometer Using Machine Learning Methods

  AUTHORS: Xin-Han Tsai, Anthony An-Chih Yeh, Chen-Hsin Lu, Shang-Yu Chou, Shih-Wei Wang, Chi-Wei Lee, Po-Han Lee

  KEYWORDS: Michelson Interferometer, Machine Learning, Vibration Modes, Long Short-Term Memory (LSTM)

  JOURNAL NAME: Journal of Modern Physics, Vol.15 No.12, November 11, 2024

  ABSTRACT: In this paper, we explore the classification of vibration modes generated by handwriting on an optical desk using deep learning architectures. Three deep learning models—Long Short-Term Memory (LSTM) networks with attention mechanism, Video Vision Transformer (ViViT), and Long-term Recurrent Convolutional Network (LRCN)—were evaluated to determine the most effective method for analyzing time series patterns generated by a Michelson interferometer. The interferometer was used to detect vibration modes created by handwriting, capturing time-series data from the diffraction patterns. Among these models, the LSTM-Attention network achieved the highest validation accuracy, reaching up to 92%, outperforming both ViViT and LRCN. These findings highlight the potential of deep learning in material science for detecting and classifying vibration patterns. The powerful performance of the LSTM-Attention model suggests that it could be applied to similar classification tasks in related fields.