Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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A Context-Aware Proactive Algorithm for Health Recommendations using Machine Learning

Authors
Pranali G. Chavhan1, *, Ritesh V. Patil2
1Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune-48, Affiliation to Savitribai Phule Pune University (SPPU), Pune, India
2Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune-48, Affiliation to Savitribai Phule Pune University (SPPU), Pune, India
*Corresponding author. Email: pranali.chavhan@vit.edu
Corresponding Author
Pranali G. Chavhan
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_37How to use a DOI?
Keywords
Machine Learning; User Behavior Forecasting; Model Comparison and Data Analysis
Abstract

This paper presents an enhanced machine learning framework for delivering high-precision, context-aware recommendations and adaptive user modeling. This system brings together different kinds of data sources, such as IoT devices, smart home systems, cell phones, and wearable tech, to get multi-dimensional contextual information like people's interaction, activity patterns, and the movement of people in and out of the location. Data privacy, acquisition integrity, and output security are guaranteed by a secure and scalable system authority layer. Contextual data goes through a hybrid edge-cloud architecture which allows for low-latency responses at the edge and large-scale computation in the cloud. Optimized machine learning algorithms use both historical and real-time data to forecast user activities and deliver personal recommendations. The experiments performed show better accuracy, adaptability, and efficiency; thus, the framework can be applied in a variety of fields like personalized services, smart environments, and proactive decision-making.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_37How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Pranali G. Chavhan
AU  - Ritesh V. Patil
PY  - 2026
DA  - 2026/01/06
TI  - A Context-Aware Proactive Algorithm for Health Recommendations using Machine Learning
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 530
EP  - 543
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_37
DO  - 10.2991/978-94-6463-948-3_37
ID  - Chavhan2026
ER  -