A two-step machine learning approach for predictive maintenance and anomaly detection in environmental sensor systems
Environmental sensor systems are essential for monitoring infrastructure and environmental quality but are prone to unreliability caused by sensor faults and environmental anomalies. Using Environmental Sensor Telemetry Data, this study introduces a novel methodology that combines unsupervised and s...
Saved in:
Main Authors: | Saiprasad Potharaju, Ravi Kumar Tirandasu, Swapnali N. Tambe, Devyani Bhamare Jadhav, Dudla Anil Kumar, Shanmuk Srinivas Amiripalli |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-06-01
|
Series: | MethodsX |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000299 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
iMESc – an interactive machine learning app for environmental sciences
by: Danilo Cândido Vieira, et al.
Published: (2025-01-01) -
A Systematic Analysis of Various Word Sense Disambiguation Approaches
by: Chandra Ganesh, et al.
Published: (2024-12-01) -
Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview
by: Peyman Vafadoost Sabzevar, et al.
Published: (2024-12-01) -
Use of supervised and unsupervised approaches to make zonal application maps for variable-rate application of crop growth regulators in commercial cotton fields
by: Maria C. da S. Andrea, et al.
Published: (2025-01-01) -
Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction
by: Raik Orbay, et al.
Published: (2025-01-01)