Improving Sentence Filtering with Levy-Based PSO and Bi-LSTM

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The proposed framework, Repulsive Levy Random Walk Established PSO within Bi-LSTM for Filthy Sentence Selection, opens several promising research directions. By combining the stochastic exploration ability of Levy random walks with the optimization efficiency of Particle Swarm Optimization (PSO), the approach addresses challenges of local minima in Bi-LSTM training, particularly when detecting complex and ambiguous filthy expressions. From a theoretical perspective, the integration of repulsive Levy walks introduces a balance between diversification and intensification, ensuring broader search coverage while still exploiting promising solution regions. This could significantly improve sentence-level classification accuracy by reducing overfitting to specific linguistic patterns. From a computational perspective, the method demonstrates potential for optimizing deep recurrent architectures without extensive manual hyperparameter tuning. Future extensions could investigate adaptive learning strategies, where the repulsion factor is dynamically adjusted based on sentence complexity, thereby further enhancing robustness across diverse datasets. From an application perspective, the framework can be extended beyond filthy sentence detection to other sensitive domains such as hate speech detection, misinformation filtering, and toxic comment moderation. Moreover, deploying the model in real-time environments, such as social media monitoring or educational platforms, could provide immediate filtering capabilities, contributing to safer digital communication. Overall, this research bridges optimization heuristics and deep learning architectures, paving the way for more interpretable, adaptive, and efficient models for text purification tasks. Future work may also consider incorporating multimodal features (e.g., speech or visual cues) and explainable AI techniques to enhance both performance and transparency.

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