Advancing Dynamic Learning Pattern Recognition with Enhanced Prefix Span Algorithm Model and LSTM

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.