NeuroX: A Hybrid Self-Adaptive AI Framework for Autonomous Decision-Making in High-Volatility Environments

NeuroX is a hybrid self-adaptive AI system designed for high-volatility decision environments. It integrates deep learning, generative adversarial networks (GANs), reinforcement learning (Q-Learning, SARSA), and Bayesian forecasting into a single autonomous framework. The system is engineered to perform unsupervised, real-time decision-making with built-in behavioral regulation and risk anticipation. It also demonstrates early-stage cognitive traits such as adaptive self-halt, conditional logic interruption, and simulated awareness patterns. Originally built for dynamic environments like betting markets, NeuroX is architecture-agnostic and can adapt to any unpredictable platform. More information, demonstration videos, and system documentation are available at: h t t p s : / / m r - x n e u r o . g i t h u b . i o / n e u r o b e t - d e m o Authored by: Mr. X (Independent Researcher)