Online Home Appliances Services using Machine Learning Techniques

DOI

10.2991/978-94-6463-738-0_93How to use a DOI?

Keywords

supervised learning; time-series analysis; machine learning; predictive maintenance; home appliance

Abstract

Predictive maintenance, failure detection, and improved customer assistance are some of the ways that machine learning (ML) integration changes home appliance services. Appliance performance data is analysed using both supervised and unsupervised machine learning approaches, including clustering and decision trees. The study demonstrates how ML may increase customer happiness, decrease downtime, and enhance service quality. IoT integration and real-time adaptive systems for better services are examples of future developments.

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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TY  - CONF
AU  - Rakesh Kumar Khare
AU  - Priya Chakradhari
AU  - Akshada Puranik
AU  - Anutosh Pandey
PY  - 2025
DA  - 2025/06/22
TI  - Online Home Appliances Services using Machine Learning Techniques
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 1219
EP  - 1230
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_93
DO  - 10.2991/978-94-6463-738-0_93
ID  - Khare2025
ER  -