Abstract
Sequence recognition is a critical task across numerous disciplines. While traditional methods utilizing Finite State Machines (FSMs) offer a structured data representation and high interpretability, their flexibility is limited. Contemporary Machine Learning (ML) algorithms exhibit high accuracy but demand substantial computational resources. Combining these paradigms can enhance the effectiveness of complex sequence recognition. This study explores the integration of FSMs with ML techniques to address sequence analysis problems. Three distinct applications are examined: text classification (spam detection), recognition of genetic sequences related to Alzheimer's disease, and image-based gesture identification. For each, hybrid models were developed and tested, combining Deterministic Finite Automata (DFA), Non-deterministic Finite Automata (NFA), and ML algorithms such as Random Forest, Gradient Boosting, and Multilayer Perceptrons (MLP). Experimental results indicate that these hybrid models achieve performance comparable to traditional ML methods, and in some instances, yield more accurate predictions. In spam classification, neural network models demonstrated the best results, with FSM-neural network combinations providing similar effectiveness. For genetic sequence analysis, gradient boosting-based models exhibited the highest accuracy, with the inclusion of FSMs maintaining performance while enhancing interpretability. In gesture recognition, neural network approaches proved most effective, but integrating FSMs with ensemble methods achieved a high level of predictive capability, surpassing conventional ML models. In conclusion, the integration of FSMs and ML presents a promising avenue in sequence analysis. Future research could focus on optimizing model architectures and applying them to other domains requiring high-precision recognition of intricate structures.