Evolution and Stylistic Characteristics of Ancient Chinese Stone Carving Decoration LSTM-DL Approach with Image Visualization
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Abstract
In recent years, advancements in data analysis techniques and deep learning algorithms have revolutionized the field of art and cultural studies. Ancient Chinese stone carving decoration holds significant historical and cultural value, reflecting the artistic and stylistic evolution of different periods. This paper explored the Weighted Long Short-Term Memory Deep Learning (WLSTM – DL) evolution and stylistic characteristics of ancient Chinese stone carving decoration through the application of image visualization techniques combined with a Long Short-Term Memory (LSTM) time-series deep learning architecture. The WLSTM-DL model uses the optimized feature selection with the grasshopper optimization for the feature extraction and selection. By analyzing a comprehensive dataset of stone carving images from different periods, the WLSTM-DL model captures the temporal relationships and patterns in the evolution of stone carving decoration. The model utilizes LSTM, a specialized deep-learning architecture for time-series data, to uncover stylistic characteristics and identify significant changes over time. The findings of this study provide valuable insights into the evolution and stylistic development of ancient Chinese stone carving decoration. The application of image visualization techniques and the WLSTM-DL model showcase the potential of data analysis and deep learning in uncovering hidden narratives and understanding the intricate details of ancient artworks.
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Srikantamurthy, M. M., Rallabandi, V. P., Dudekula, D. B., Natarajan, S., & Park, J. (2023). Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning. BMC Medical Imaging, 23(1), 1-15.
Khan, M. A., & Kim, Y. (2021). Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach. Computers, Materials & Continua, 67(1).
Meliboev, A., Alikhanov, J., & Kim, W. (2022). Performance evaluation of deep learning based network intrusion detection system across multiple balanced and imbalanced datasets. Electronics, 11(4), 515.
Barzegar, R., Aalami, M. T., & Adamowski, J. (2021). Coupling a hybrid CNN-LSTM deep learning model with a boundary corrected maximal overlap discrete wavelet transform for multiscale lake water level forecasting. Journal of Hydrology, 598, 126196.
Haruna, A. A., Badi, I. A., Muhammad, L. J., Abuobieda, A., & Altamimi, A. (2023, January). CNN-LSTM Learning Approach for Classification of Foliar Disease of Apple. In 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC) (pp. 1-6). IEEE.
Haruna, A. A., Badi, I. A., Muhammad, L. J., Abuobieda, A., & Altamimi, A. (2023, January). CNN-LSTM Learning Approach for Classification of Foliar Disease of Apple. In 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC) (pp. 1-6). IEEE.
Suryawanshi, R. ., & Vanjale, S. . (2023). Brain Activity Monitoring for Stress Analysis through EEG Dataset using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 236–240. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2498
Salim, M. M., Singh, S. K., & Park, J. H. (2021). Securing Smart Cities using LSTM algorithm and lightweight containers against botnet attacks. Applied Soft Computing, 113, 107859.
Benker, M., Furtner, L., Semm, T., & Zaeh, M. F. (2021). Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo. Journal of Manufacturing Systems, 61, 799-807.
Sarumi, O. A., Aouedi, O., & Muhammad, L. J. (2022). Potential of deep learning algorithms in mitigating the spread of COVID-19. Understanding COVID-19: The Role of Computational Intelligence, 225-244.
Johny, K., Pai, M. L., & Adarsh, S. (2022). A multivariate EMD-LSTM model aided with Time Dependent Intrinsic Cross-Correlation for monthly rainfall prediction. Applied Soft Computing, 123, 108941.
Hernandez, A., Hughes, W., Silva, D., Pérez, C., & Rodríguez, C. Machine Learning for Predictive Analytics in Engineering Procurement. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/124
Abirami, S., & Chitra, P. (2022). Regional spatio-temporal forecasting of particulate matter using autoencoder based generative adversarial network. Stochastic Environmental Research and Risk Assessment, 36(5), 1255-1276.
Huang, Y., Roy, N., Dhar, E., Upadhyay, U., Kabir, M. A., Uddin, M., ... & Syed-Abdul, S. (2023). Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information. Cancers, 15(8), 2232.
Pan, J., Li, L., Yamaguchi, H., Hasegawa, K., Thufail, F. I., & Tanaka, S. (2022). 3D reconstruction of Borobudur reliefs from 2D monocular photographs based on soft-edge enhanced deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 439-450.
Saini, D. J. B. ., & Qureshi, D. I. . (2021). Feature Extraction and Classification-Based Face Recognition Using Deep Learning Architectures. Research Journal of Computer Systems and Engineering, 2(1), 52:57. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/23
Waris, M. I., Plevris, V., Mir, J., Chairman, N., & Ahmad, A. (2022). An alternative approach for measuring the mechanical properties of hybrid concrete through image processing and machine learning. Construction and Building Materials, 328, 126899.
Pan, J., Li, L., Yamaguchi, H., Hasegawa, K., Thufail, F. I., & Tanaka, S. (2021). Integrated high-definition visualization of digital archives for borobudur temple. Remote Sensing, 13(24), 5024.
Choi, Y. C., Murtala, S., Jeong, B. C., & Choi, K. S. (2021). Deep Learning-Based Engraving Segmentation of 3-D Inscriptions Extracted From the Rough Surface of Ancient Stelae. IEEE Access, 9, 153199-153212.
Ms. Mayuri Ingole. (2015). Modified Low Power Binary to Excess Code Converter. International Journal of New Practices in Management and Engineering, 4(03), 06 - 10. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/38
Im, C., Kim, Y., & Mandl, T. (2022). Deep learning for historical books: classification of printing technology for digitized images. Multimedia Tools and Applications, 81(4), 5867-5888.
Wagner, M., Müller-Stich, B. P., Kisilenko, A., Tran, D., Heger, P., Mündermann, L., ... & Bodenstedt, S. (2023). Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark. Medical Image Analysis, 86, 102770.
Wu, A., Wang, Y., Zhou, M., He, X., Zhang, H., Qu, H., & Zhang, D. (2021). MultiVision: Designing analytical dashboards with deep learning based recommendation. IEEE Transactions on Visualization and Computer Graphics, 28(1), 162-172.
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022). Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistic Surveys, 16, 1-85.
Yim, D., Malefyt, T., & Khuntia, J. (2021). Is a picture worth a thousand views? Measuring the effects of travel photos on user engagement using deep learning algorithms. Electronic Markets, 1-19.
Liu, Y., Gargesha, M., Qutaish, M., Zhou, Z., Qiao, P., Lu, Z. R., & Wilson, D. L. (2021). Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data. Scientific Reports, 11(1), 17527.
Bickel, V. T., Mandrake, L., & Doran, G. (2021). A labeled image dataset for deep learning-driven rockfall detection on the Moon and Mars. Frontiers in Remote Sensing, 2, 640034.
Bai, Z., Liu, T., Zou, D., Zhang, M., Zhou, A., & Li, Y. (2023). Image-based reinforced concrete component mechanical damage recognition and structural safety rapid assessment using deep learning with frequency information. Automation in Construction, 150, 104839.
Tang, K., Da Wang, Y., McClure, J., Chen, C., Mostaghimi, P., & Armstrong, R. T. (2022). Generalizable framework of unpaired domain transfer and deep learning for the processing of real-time synchrotron-based X-Ray microcomputed tomography images of complex structures. Physical Review Applied, 17(3), 03404