Human-database interaction is inevitable in intelligent system applications, and accurately converting user-entered natural language into database query language is a critical step. To improve the accuracy, generalization, and robustness of text-to-SQL, we propose Nabil (a model for natural language conversion query language based on brain-inspired computing technology and a large language model). This model first leverages the spatiotemporal encoding capabilities of spiking neural networks to capture semantic features of natural language, then fuses these features with those generated by a large language model. Finally, a champion model is designed to select the optimal query from multiple candidate SQLs. Experiments were conducted on three database engines, DuckDB, MySQL, and PostgreSQL, and the model’s effectiveness was verified on benchmark datasets such as BIRD. The results show that Nabil outperforms existing baseline methods in both execution accuracy and effective efficiency scores. Furthermore, our proposed normalization and syntax tree abstraction algorithms further enhance the champion model’s discriminative capabilities, providing new insights for text-to-SQL research.

Skin cancer risk prediction plays a critical role in early diagnosis and personalized healthcare. However, existing approaches often struggle to achieve both rich feature representation and computational efficiency. To address these issues, this study proposes Bicom, a brain-inspired framework for skin cancer risk prediction that integrates efficient attention mechanisms, multi-scale feature fusion, and confidence-aware refinement. Specifically, we develop F-ResNeSt, a multi-scale feature-extraction architecture that enhances the ResNeSt backbone by incorporating a Feature Pyramid Network (FPN) and Linformer-based linear-complexity attention. In addition, we propose L-CoAtNet, an optimized classification network that replaces conventional relative attention with Linformer-based attention to achieve scalable global contextual modeling. Furthermore, a brain-inspired Spiking Neural Network (SNN) module is introduced as a confidence-aware refinement mechanism to enhance prediction reliability for ambiguous samples. Comprehensive experiments on public and subject datasets demonstrate that the proposed framework achieves consistently competitive performance across multiple evaluation metrics, indicating its effectiveness, robustness, and scalability for assisted skin cancer risk prediction.