Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review
MN Kanyama, FB Shava, AM Gamundani… - … of the Earth, Parts A/B/C, 2024 - Elsevier
The digitization of the water sector has led to the emergence of Smart Water Metering
Networks (SWMNs), which enable automated and continuous water consumption …
Networks (SWMNs), which enable automated and continuous water consumption …
Interactive reinforced feature selection with traverse strategy
In this paper, we propose a single-agent Monte Carlo-based reinforced feature selection
method, as well as two efficiency improvement strategies, ie, early stopping strategy and …
method, as well as two efficiency improvement strategies, ie, early stopping strategy and …
Mad-sgcn: Multivariate anomaly detection with self-learning graph convolutional networks
Today's Cyber Physical Systems (CPSs) are large and complex data-intensive systems.
Constant monitoring and analysis of the data generated by a multitude of interconnected …
Constant monitoring and analysis of the data generated by a multitude of interconnected …
Masked graph neural networks for unsupervised anomaly detection in multivariate time series
Anomaly detection has been widely used in grid operation and maintenance, machine fault
detection, and so on. In these applications, the multivariate time-series data from multiple …
detection, and so on. In these applications, the multivariate time-series data from multiple …
Efficient reinforced feature selection via early stopping traverse strategy
In this paper, we propose a single-agent Monte Carlo based reinforced feature selection
(MCRFS) method, as well as two efficiency improvement strategies, ie, early stopping (ES) …
(MCRFS) method, as well as two efficiency improvement strategies, ie, early stopping (ES) …
Deep human-guided conditional variational generative modeling for automated urban planning
Urban planning designs land-use configurations and can benefit building livable,
sustainable, safe communities. Inspired by image generation, deep urban planning aims to …
sustainable, safe communities. Inspired by image generation, deep urban planning aims to …
Automated urban planning for reimagining city configuration via adversarial learning: quantification, generation, and evaluation
Urban planning refers to the efforts of designing land-use configurations given a region.
However, to obtain effective urban plans, urban experts have to spend much time and effort …
However, to obtain effective urban plans, urban experts have to spend much time and effort …
Automated urban planning aware spatial hierarchies and human instructions
Traditional urban planning demands urban experts to spend much time producing an
optimal urban plan under many architectural constraints. The remarkable imaginative ability …
optimal urban plan under many architectural constraints. The remarkable imaginative ability …
Towards Semantically-Rich Spatial Network Representation Learning via Automated Feature Topic Pairing
Automated characterization of spatial data is a kind of critical geographical intelligence. As
an emerging technique for characterization, spatial Representation Learning (SRL) uses …
an emerging technique for characterization, spatial Representation Learning (SRL) uses …
Automated feature-topic pairing: Aligning semantic and embedding spaces in spatial representation learning
Automated characterization of spatial data is a kind of critical geographical intelligence. As
an emerging technique for characterization, Spatial Representation Learning (SRL) uses …
an emerging technique for characterization, Spatial Representation Learning (SRL) uses …