Adaptive learning

In a situation where the country's banking system is vulnerable, the behavior of depositors based on their assessment of the banks' risk level can lead to an increase in the probability of systemic crises and instability of the banking network. Recently, network theory and agent-based simulation are used to investigate complex banking systems. Agent-based modeling (ABM) is a new computational method that studies economic phenomena by representing the behavior of individuals and agents. Using this approach, the present study evaluates the phenomenon of bank runs and the effect of deposit insurance on the country's banking network. The agents in this ABM include banks, central bank, firms and depositors. Banks and depositors are intelligent agents that operate on an adaptive learning model. This research was conducted with the aim of investigating the effect of depositors' behavior on the banking network and the safety policy of deposit insurance based on the balance sheets of 25 banks that are members of the Iranian interbank market during the years 2006 to 2019. We find that when depositors act strategically, bank operations occur and banks choose adaptive strategies with lower capital adequacy ratios (CARs). Also, our findings are that the safety of the banking network through deposit insurance has not been significantly successful in reducing the risk of contagion in the system.

This study aims to present a novel generative AI-driven system for hyper-personalized health monitoring. Dynamic data processing, predictive modeling, and flexible learning improve real-time health evaluations. By combining weighted feature aggregation, iterative least squares estimation, and selective feature extraction, the suggested strategy makes predictions that are more accurate while using less computer power. Abnormality detection methods like adaptive thresholding and Kalman filtering provide accurate health monitoring. Attention, gradient-based optimization, and sequence learning improve health trend forecasts as the model improves. Generative AI-driven wearables outperform conventional and AI-based alternatives in many key performance tests. These evaluations include prediction accuracy (94%), real-time monitoring efficiency (93%), adaptability (92%), data integration quality (95%), and system reaction time (90 ms). These devices are safer (96%), have longer battery life (32 hours), and are simpler, more comfortable, and scalable. The results suggest that creative AI can transform personal healthcare into something more adaptable, safe, and affordable. Generative AI-powered smart gadgets are the most sophisticated means to monitor health in real time and deliver individualized, data-driven medical treatment. Future research will concentrate on improving prediction models and developing AI-driven modification approaches to make them more effective in additional healthcare scenarios.