A Novel Ensemble Learning Framework for Nonlinear Modeling of UV Exposure and Skin Damage

Resmi R; Sajan Jerome; Anu Babu; Anilkumar E N; Pramod P; Baiju P S

doi:10.29284/d4pqe975

Authors

Resmi R Professor LBS Institute of Technology for Women ,Thiruvananthapuram Dept.of Electronics and Communication Engineering https://orcid.org/0000-0002-0849-5509 (Corresponding author)
Sajan Jerome Associate Professor LBS Institute of Technology for Women, Thiruvananthapuram Department: Dept.of Mechanical Engineering ORCID id : https://orcid.org/0000-0003-4231-5265
Anu Babu Assistant Professor St Thomas Institute for Science and Technology, Trivandrum Dept.of Electronics and Communication Engineering ORCID id : https://orcid.org/0000-0003-4304-5391
Anilkumar E N Professor LBS Institute of Technology for Women Dept.of Mechanical Engineering ORCID id : https://orcid.org/0000-0001-5185-9489
Pramod P Professor College name: LBS College of Engineering Kasaragod Dept.of Electronics and Communication Engineering In ORCID id :https://orcid.org/0000-0002-1240-6548
Baiju P S Assistant Professor LBS College of Engineering, Kasaragod Dept.of Electronics and Communication Engineering ORCID id: https://orcid.org/0009-0008-8332-6765

DOI:

https://doi.org/10.29284/d4pqe975

Keywords:

Ultraviolet Index, Skin Damage Index, Personalized UV Risk, Environmental Sensing, Ensemble Learning, Predictive Modeling, Dermatological Risk Assessment, Mobile Health.

Abstract

The Ultraviolet Index (UVI) is widely used to communicate the intensity of solar ultraviolet radiation, yet it remains a coarse public warning rather than a truly personalized risk indicator. The same ambient UVI can lead to very different biological outcomes once exposure duration, atmospheric conditions, and individual skin sensitivity are taken into account. This revised manuscript presents a structured framework for personalized UV risk prediction that combines multisource environmental sensing with nonlinear modeling and ensemble learning. At the core of the framework is a Skin Damage Index (SDI) that grows nonlinearly with UVI and exposure time, reflecting the fact that harm does not increase in a simple one-to-one manner at higher radiation levels. A fused prediction layer then integrates Random Forest, gradient boosting, and neural networks so that noisy tabular inputs, interaction-heavy patterns, and subtle nonlinear dependencies can be handled together rather than in isolation. The paper also introduces a modular architecture for data ingestion, preprocessing, inference, and user-facing recommendation delivery, making the framework suitable for implementation in mobile health platforms, wearable systems, and preventive dermatology applications. To improve interpretability, the manuscript includes system diagrams and analytical visualizations that show how risk evolves across different UV intensities and exposure durations. Overall, the work reframes UV safety from a single-number advisory problem into a personalized, context-aware prediction problem that is better aligned with real-world skin exposure.

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