Data Integration

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.

Background: Digital twin (DT) technologies have become significant enablers of urban management, utilising real-time information, data analytics, and IoT connectivity to manage challenging urban issues. Nonetheless, existing studies reveal the capacity of the DTs, while their generalization, flexibility, and cross-disciplinary application for various urban environments are not thoroughly studied yet. Objective: This article aims to evaluate the effectiveness of DT technologies in improving traffic management, energy efficiency, infrastructure maintenance, and public safety across six case study cities: There are Singapore, Helsinki, Barcelona, Dubai, New York, and Tokyo. The study examines how DTs can be extended and implemented to target urban issues and how their use operational performance might be optimized. Methods: The study used quantitative data processing, on-line data analysis with factorization and machine learning, and assessment of the case studies. Quantitative measures which included traffic flow, energy loss, down time, and response to emergency situations were investigated pre and post DT application. The improvements mentioned were statistically confirmed, and the metrics of scalability and adaptability were evaluated in the course of the cities. Results: DT technologies increased traffic flow by up to 42.9%, reduced energy losses by 35%, minimum down time was 42%, emergency response was 44.9%. This was the case because the network had high IoT coverage and because DTs were applied to the context when it specifically needed them. Conclusion: The study proves that DTs can be implemented in different environments due to their flexibility to accommodate different urban conditions. AI and cross domain integration can add to the effectiveness of DT in general and both are inarguably now crucial for the management of contemporary urban environment.