Advancing Dynamic Learning Pattern Recognition with Enhanced Prefix Span Algorithm Model and LSTM
Ramesh M
Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, India.
Department of Computer Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India.
In Dynamic Learning Pattern Recognition, the progress needs new ways of reliably detecting and describing dynamic patterns in big data. Additionally, based on the above research, this thesis proposes a novel cycle of the development of the dynamic learning pattern recognition through the combination of Enhanced Prefix Span Algorithm Model, as well as Long Short-Term Memory (LSTM) networks. An important part of our work in solving the issue of identifying dynamic patterns in textual data in online caselaw finding applications of sequential pattern mining methods and deep learning methods. On that basis, this study will then accelerate the traditional PrefixSpan algorithm to extract the sequential patterns of the data set taking into consideration the dynamic nature of the pattern. Then add LSTM networks to model and learn such sequential patterns and thus identify long term dependencies and time information. The suggested methodology has been researched and the performance of the designed methodology compared to other classifiers such as MultinomialNB, Logistical Regression. The findings demonstrate the high performance of the proposed model whose accuracy, recall, and F1-score are 98.45, 98.62, and 97.3, respectively. This study is to combine sequential pattern mining with the deep learning, which serves to the advancement of dynamic learning pattern recognition methods and to give certain promising suggestions in the cases such as natural language processing and predictive modeling. It is an effort by python implementation to promote the efficiency and precision of recognition of learning patterns in different modalities. Here, we apply it in assembling a sound framework of the effective recognition and adaptation to changing patterns within textual data to assist in enhancing and more efficient learning on a variety of modalities in different situations.
M. Yang, B. Yang, M. Liao, Y. Zhu, and X. Bai, “Sequential visual and semantic consistency for semi-supervised text recognition,” Pattern Recognition Letters, vol. 178, pp. 174–180, Feb. 2024, doi: 10.1016/j.patrec.2024.01.008.
G. Peng, “Segmentation-free Recognition Algorithm Based on Deep Learning for Handwritten Text Image,” Journal of Artificial Intelligence and Technology, Mar. 2024, doi: 10.37965/jait.2024.0473.
X. Zhang et al., ‘Realcompo: Dynamic equilibrium between realism and compositionality improves text-to-image diffusion models’, arXiv preprint arXiv:2402.12908, 2024.
K. Sai and R. Thokala, “Pattern Recognition in Medical Decision Support and Estimating Redundancy in Clinical Text,” Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India, 2024, doi: 10.4108/eai.23-11-2023.2343233.
S. Venkatachalam and Rajiv Kannan, “Optimizing dynamic keystroke pattern recognition with hybrid deep learning technique and multiple soft biometric factors,” International Journal of Computers Communications Control, vol. 19, no. 2, Mar. 2024, doi: 10.15837/ijccc.2024. 2.6097.
D. Zhong et al., “NDOrder: Exploring a novel decoding order for scene text recognition,” Expert Systems with Applications, vol. 249, p. 123771, Sep. 2024, doi: 10.1016/j.eswa.2024.123771.
C. Thaokar, J. K. Rout, M. Rout, and N. K. Ray, “N-Gram Based Sarcasm Detection for News and Social Media Text Using Hybrid Deep Learning Models,” SN Computer Science, vol. 5, no. 1, Jan. 2024, doi: 10.1007/s42979-023-02506-5.
X. Wu, K. Zhao, Q. Huang, Q. Wang, Z. Yang, and G. Hao, “MISL: Multi-grained image-text semantic learning for text-guided image inpainting,” Pattern Recognition, vol. 145, p. 109961, Jan. 2024, doi: 10.1016/j.patcog.2023.109961.
S. Huang, W. Fu, Z. Zhang, and S. Liu, “Global-local fusion based on adversarial sample generation for image-text matching,” Information Fusion, vol. 103, p. 102084, Mar. 2024, doi: 10.1016/j.inffus.2023.102084.
F. Yang, S. Yang, M. A. Butt, J. van de Weijer, and others, ‘Dynamic prompt learning: Addressing cross-attention leakage for text-based image editing’, Advances in Neural Information Processing Systems, vol. 36, 2024.
J. Huo, J. Yu, M. Wang, Z. Yi, J. Leng, and Y. Liao, “Coexistence of Cyclic Sequential Pattern Recognition and Associative Memory in Neural Networks by Attractor Mechanisms,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 4959–4970, Mar. 2025, doi: 10.1109/tnnls.2024.3368092.
M. Yang, B. Yang, M. Liao, Y. Zhu, and X. Bai, “Class-Aware Mask-guided feature refinement for scene text recognition,” Pattern Recognition, vol. 149, p. 110244, May 2024, doi: 10.1016/j.patcog.2023.110244.
S. Momeni and B. BabaAli, “A transformer-based approach for Arabic offline handwritten text recognition,” Signal, Image and Video Processing, vol. 18, no. 4, pp. 3053–3062, Jan. 2024, doi: 10.1007/s11760-023-02970-9.
J. Zheng, L. Zhang, Y. Wu, and C. Zhao, “BPDO: Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection,” ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5345–5349, Apr. 2024, doi: 10.1109/icassp48485.2024.10447371.
Z. Wei, Y. Gao, X. Zhang, X. Li, and Z. Han, “Adaptive marine traffic behaviour pattern recognition based on multidimensional dynamic time warping and DBSCAN algorithm,” Expert Systems with Applications, vol. 238, p. 122229, Mar. 2024, doi: 10.1016/j.eswa.2023.122229.
X. Zhang, B. Zhu, X. Yao, Q. Sun, R. Li, and B. Yu, “Context-Based Contrastive Learning for Scene Text Recognition,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 3, pp. 3353–3361, Jun. 2022, doi: 10.1609/aaai.v36i3.20245.
M. Yang et al., “Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition,” Proceedings of the 30th ACM International Conference on Multimedia, pp. 4214–4223, Oct. 2022, doi: 10.1145/3503161.3547784.
D. Coquenet, C. Chatelain, and T. Paquet, “End-to-End Handwritten Paragraph Text Recognition Using a Vertical Attention Network,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 508–524, Jan. 2023, doi: 10.1109/tpami.2022.3144899.
K. Li, Y. Zhang, K. Li, Y. Li, and Y. Fu, “Image-Text Embedding Learning via Visual and Textual Semantic Reasoning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 641–656, Jan. 2023, doi: 10.1109/tpami.2022.3148470.
E. Vidal, A. H. Toselli, A. Ríos-Vila, and J. Calvo-Zaragoza, “End-to-End page-Level assessment of handwritten text recognition,” Pattern Recognition, vol. 142, p. 109695, Oct. 2023, doi: 10.1016/j.patcog.2023.109695.
L. M. Francis and N. Sreenath, “Robust scene text recognition: Using manifold regularized Twin-Support Vector Machine,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 3, pp. 589–604, Mar. 2022, doi: 10.1016/j.jksuci.2019.01.013.
‘Learning Style (VAK)’. Accessed: Mar. 26, 2024. [Online]. Available: https://www.kaggle.com/datasets/zeyadkhalid/learning-style-vak
S.-S. Park and K. Chung, “MMCNet: deep learning–based multimodal classification model using dynamic knowledge,” Personal and Ubiquitous Computing, vol. 26, no. 2, pp. 355–364, Aug. 2019, doi: 10.1007/s00779-019-01261-w.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Ramesh M and Jayashree R;
Methodology: Ramesh M;
Software: Jayashree R;
Data Curation: Ramesh M;
Writing- Original Draft Preparation: Ramesh M and Jayashree R;
Visualization: Ramesh M;
Investigation: Jayashree R;
Supervision: Ramesh M;
Validation: Jayashree R;
Writing-Reviewing and Editing: Ramesh M and Jayashree R;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
The author wishes to acknowledge and express thanks to the institutional support provided by the Microwave Lab, ECE Division, Karunya Institute of Technology and Sciences for the Antenna Fabrication and Testing.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Jayashree R
Department of Computer Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Ramesh M and Jayashree R, “Advancing Dynamic Learning Pattern Recognition with Enhanced Prefix Span Algorithm Model and LSTM”, Journal of Machine and Computing, vol.6, no.1, pp. 014-026, 2026, doi: 10.53759/7669/jmc202606002.
