This research combines two emerging technologies, the IoT and blockchain, and investigates their potential and use in the healthcare sector. In healthcare, IoT technology can be utilized for purposes such as remotely monitoring patients’ health. This paper details ongoing research towards individualized health monitoring using wearable gadgets. The goal of improving healthcare facilities and improvement of the quality of life of citizens naturally brings up Internet of Things (IoT) technologies for consideration. Health observation is exceptionally critical in terms of avoidance, especially since the early determination of illnesses can minimize trouble and treatment costs. The cornerstones of intelligent, integrated, and individualized healthcare are continuous monitoring of physical signs and evaluation of medical data. To build a more reliable and robust IoMT model, the study will monitor the application of blockchain technology in federated learning (FL). A viable way to address the heterogeneity problem in federated learning is to design the system, data, and model tiers to lessen heterogeneity and produce a high-quality, tailored model for each endpoint. Blockchain-based federated learning allows for smarter simulations, lower latency, and lower power consumption while maintaining privacy at the same time. This solution provides another immediate benefit: in addition to having a shared model upgrade, the updated model on phones will now be used automatically, giving personalized knowledge about the phone is used.

Legacy software systems remain operational in many organizations because of their long-standing business value, yet structural degradation and outdated documentation make them increasingly difficult to understand and modernize. Existing reverse-engineering and modernization approaches often address program comprehension, quality assessment, decision support, and code transformation separately, leaving a gap between understanding legacy code and prioritizing components for modernization. To address this gap, this study introduces a seven-step automated framework integrating program comprehension, Software Quality (SQ) and Business Value (BV) assessment, a BV × SQ decision mechanism, and GPT-4-assisted re-engineering with closed-loop quality validation within a unified pipeline. Architectural centrality, change-frequency signals, and structural quality metrics prioritize methods for re-engineering or maintenance, while transformed methods are iteratively re-evaluated against predefined quality criteria. Evaluated on 80 methods across four open-source legacy Java systems from the Qualitas Corpus, the framework achieved a 93.8% validation pass rate and a mean SQ improvement of 40.28 points. The closed-loop mechanism increased the pass rate from 92.5% to 93.8%, while the Maintainability Index evaluation on Apache Ant yielded a Cohen’s d of 4.964. These findings provide empirical evidence that integrating quality- and value-driven decision mechanisms with iterative LLM-assisted re-engineering can support systematic legacy software modernization.