Improving Demand Planning and Forecasting for Resilience in Fast-Changing Global Retail Markets

Abstract

In conclusion, this thesis establishes that Transformer and hybrid architectures can serve as a powerful foundation for non-invasive respiratory diagnostic tools. By combining advanced models with optimal feature representation and carefully curated data, it is possible to develop reliable and scalable screening systems. The research provides an evidence-based framework for future development, emphasizing robust validation and ethical considerations for clinical deployment.