Next-Generation Predictive Modeling with Machine Learning: Advancing Cross-Industry Intelligence through Federated, Adaptive and Interpretable Systems

S. Prabagar, Deepika Pradeep Patil, S. Rajeswari, M. Jeevaa, R. Vishalakshi, Akilan S.

2025

Abstract

The rise of machine learning has rapidly changed predictive modeling in any industrial sector, where systems have transformed from being data-driven to adaptive, safe and interpretable. The present work investigates, how the benefits of emerging machine learning frameworks such as federated learning, ensemble strategies, and transfer learning – can be combined to address limitations that exist with regards to scalability, bias, and real-time capabilities. By reviewing healthcare diagnosis, financial fraud detection, environmental prediction and industry 4.0 applications, the study shows how our new class of ML algorithms can offer both explainability and actionable results, and at the same time, offer data privacy and resistance to adversarial attacks. The proposed structure highlights its adjustable nature on volatile datasets, transparency in decision-making, and applicability to various industries. This paper anchors machine learning as not only predictive, but as a strategic enabler of intelligent automation at scale in all industries.

Paper Citation

in Harvard Style

Prabagar S., Patil D., Rajeswari S., Jeevaa M., Vishalakshi R. and S. A. (2025). Next-Generation Predictive Modeling with Machine Learning: Advancing Cross-Industry Intelligence through Federated, Adaptive and Interpretable Systems. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 827-832. DOI: 10.5220/0013944400004919

in Bibtex Style

@conference{icrdicct`2525,
author={S. Prabagar and Deepika Patil and S. Rajeswari and M. Jeevaa and R. Vishalakshi and Akilan S.},
title={Next-Generation Predictive Modeling with Machine Learning: Advancing Cross-Industry Intelligence through Federated, Adaptive and Interpretable Systems},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={827-832},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013944400004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Next-Generation Predictive Modeling with Machine Learning: Advancing Cross-Industry Intelligence through Federated, Adaptive and Interpretable Systems
SN - 978-989-758-777-1
AU - Prabagar S.
AU - Patil D.
AU - Rajeswari S.
AU - Jeevaa M.
AU - Vishalakshi R.
AU - S. A.
PY - 2025
SP - 827
EP - 832
DO - 10.5220/0013944400004919
PB - SciTePress