Designing Machine Learning Software for Fragrance-Based Air Quality Optimization

Abstract

This paper presents the design of a machine learning-based software system for fragrance-based indoor air quality optimization, integrating advanced sensors and adaptive algorithms to enhance environmental conditions and user comfort. The proposed framework is a conceptual model that utilizes real-time data acquisition, predictive modeling, and intelligent fragrance diffusion for dynamically regulates air quality and humidity. By leveraging machine learning techniques, the system analyzes air pollutants, personalizes fragrance profiles, and optimizes indoor environments for energy-efficient air purification. Unlike conventional air quality monitoring systems, this approach actively enhances indoor air quality rather than merely detecting pollutants. The system's adaptive fragrance diffusion mechanism ensures cost-effectiveness and broad applicability across various indoor settings. However, as this study focuses on system design without practical implementation, further validation through prototype development and empirical evaluation is essential to assess its feasibility and effectiveness. Future research should explore real-world testing, additional AI-driven optimization techniques, and integration with IoT-based smart environments to refine the proposed system's capabilities.