Q-Learning-like fairness-aware deep reinforcement learning framework based on a modified Dueling Deep Q-Learning-Like architecture. The proposed system introduces a complex approach to addressing fairness in decision-making processes while maintaining high performance in system base data configuration assessment tasks in different categories. The architecture implements a multi-model feature approach for fairness optimization, incorporating numerous data processing pipelines that handle multiple concurrent data streams. It includes a fairness-aware deep Q-learning-like architecture with a multi-state model, an integrated multi-stream processing system, and a weight-based reward mechanism balancing prediction and accuracy with fairness metrics. Experimental results have shown the effectiveness of our approach in maintaining fairness across different featured groups while achieving high performance in system base data configuration assessment tasks. Unlike traditional system base data configuration assessment methods that rely on subjective self-reporting, which are vulnerable to cultural biases, literacy barriers, and limited effectiveness in non-verbal patients (e.g., infants, critically ill, or cognitively impaired individuals)—our automated approach provides objective, continuous monitoring with consistent interpretation across diverse patient populations. This addresses critical clinical challenges, including disparities in system base data configuration management across demographic groups, clinician bias in system base data configuration assessment, and communication barriers in vulnerable populations. Furthermore, our fairness-aware framework specifically mitigates algorithmic biases that might otherwise perpetuate existing inequities in system base data configuration management.

Introduces and developed a novel computational approach for efficiently calculating the navigation of infinite multi-dimensional spaces using a improved algorithm named Bellande Step function within a infinite multi-dimensional model-integrated framework. By optimizing and advancing the step function, the method addresses challenges in infinite-dimensional random point generation and nearest node calculation in in an existing tree while moving from the nearest node towards a random point by a step size collision free, allowing movement towards target nodes within defined distance constraints to be added as node to the tree. Leveraging this type of approach, efficiently compute the next step towards a target node for infinite-dimensions, ensuring accurate movement while reducing collisions with adhering to specified distance limits set by the user. The integration of infinite dimensional space modeling enhances the process of the step function while increasing the capabilities, accuracy, adaptability, effectiveness and computational efficiency. The results underscore the robustness and scalability of this approach, showcasing its potential applications in robotics and other fields related to robotics, and complex systems modeling. This integration of the Bellande Step function with model-integrated infinite dimensional space represents a significant advancement in the computational efficiency, precision, accuracy of Infinite multi-dimensional node calculations.