Proposes an AI-driven synthetic data generation framework to create realistic cybersecurity datasets for smart city research where real data is scarce or sensitive.
Exploring Optimization Dynamics: Hybrid Approaches Combining Adaptive and Traditional Techniques for Deep Learning Models,
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DBS-Adam, which scales learning rates by batch difficulty from EMA gradient norms and loss, reaches 95.22% accuracy on Bi-LSTM accident severity prediction and shows statistically significant precision gains over AMSGrad, AdamW and AdaBound.
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Bridging the Smart City Cybersecurity Data Gap Through AI-Driven Synthetic Dataset Generation
Proposes an AI-driven synthetic data generation framework to create realistic cybersecurity datasets for smart city research where real data is scarce or sensitive.