Integrating bio-electrochemical sensors and machine learning to predict the efficacy of biological nutrient removal processes at water resource recovery facilities
SA Emaminejad, J Sparks… - Environmental Science & …, 2023 - ACS Publications
Monitoring biological nutrient removal (BNR) processes at water resource recovery facilities
(WRRFs) with data-driven models is currently limited by the data limitations associated with …
(WRRFs) with data-driven models is currently limited by the data limitations associated with …
Federated feature selection for horizontal federated learning in iot networks
Under horizontal federated learning (HFL) in the Internet of Things (IoT) scenarios, different
user data sets have significant similarities on the feature spaces, the final goal is to build a …
user data sets have significant similarities on the feature spaces, the final goal is to build a …
[HTML][HTML] A novel hybrid ensemble learning for anomaly detection in industrial sensor networks and SCADA systems for smart city infrastructures
Abstract Critical Infrastructures (CIs) use Supervisory Control and Data Acquisition (SCADA)
systems for monitoring and remote control. Sensor networks are being integrated into all …
systems for monitoring and remote control. Sensor networks are being integrated into all …
A proposed ensemble feature selection method for estimating forest aboveground biomass from multiple satellite data
Y Zhang, J Liu, W Li, S Liang - Remote Sensing, 2023 - mdpi.com
Feature selection (FS) can increase the accuracy of forest aboveground biomass (AGB)
prediction from multiple satellite data and identify important predictors, but the role of FS in …
prediction from multiple satellite data and identify important predictors, but the role of FS in …
Clustering and Interpretation of time-series trajectories of chronic pain using evidential c-means
The most well-known unsupervised classification algorithms allow for the identification of
hard or probabilistic partitions. However, when working with complex datasets such as those …
hard or probabilistic partitions. However, when working with complex datasets such as those …
GEO spacecraft maneuver detection based on causal inference
X Long, Y Le**, C Weiwei, L **ghong - Advances in Space Research, 2023 - Elsevier
Geosynchronous (GEO) spacecraft maneuver detection is deemed crucial for space domain
awareness (SDA) as it helps to maintain catalogs of high value resident space objects …
awareness (SDA) as it helps to maintain catalogs of high value resident space objects …
Multivariate time series anomaly detection with generative adversarial networks based on active distortion transformer
Detecting anomalies for multivariate time series is of great importance in modern industrial
applications. However, due to the complex temporal dynamics in modern systems, finding a …
applications. However, due to the complex temporal dynamics in modern systems, finding a …
Normal-only Anomaly detection in environmental sensors in CPS: A comprehensive review
Anomaly detection is crucial for maintaining the reliability and security of environmental
sensors in Cyber-Physical Systems (CPS). This paper presents a comprehensive review that …
sensors in Cyber-Physical Systems (CPS). This paper presents a comprehensive review that …
Anomaly detection in IoT environment using machine learning
H Bilakanti, S Pasam, V Palakollu… - Security and …, 2024 - Wiley Online Library
This research paper delves into the security concerns within Internet of Things (IoT)
networks, emphasizing the need to safeguard the extensive data generated by …
networks, emphasizing the need to safeguard the extensive data generated by …
Target detection in sea clutter via contrastive learning
This article considers the target detection problem using limited labeled samples in the
nonhomogeneous sea clutter environment and proposes an effective method of radar visual …
nonhomogeneous sea clutter environment and proposes an effective method of radar visual …