125 citations · 148 across the 7 of their papers we have counts for
7 papers
Transaction Fraud Detection via an Adaptive Graph Neural Network
Yue Tian, Guanjun Liu, Jiacun Wang +1
Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security of individuals and banks. However, mo…
Transfer Learning for Fault Diagnosis of Transmission Lines
Fatemeh Mohammadi Shakiba, Milad Shojaee, S. Mohsen Azizi +1
Recent artificial intelligence-based methods have shown great promise in the use of neural networks for real-time sensing and detection of transmission line faults and estimation o…
Frequent Itemset-driven Search for Finding Minimum Node Separators in Complex Networks
Yangming Zhou, Xiaze Zhang, Na Geng +2
Finding an optimal set of critical nodes in a complex network has been a long-standing problem in the fields of both artificial intelligence and operations research. Potential appl…
IoT-based Route Recommendation for an Intelligent Waste Management System
Mohammadhossein Ghahramani, Mengchu Zhou, Anna Molter +1
The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physica…
A Novel Load Balancing Scheme for Mobile Edge Computing
Zhenhua Duan, Cong Tian, Nan Zhang +5
To overcome long propagation delays for data exchange between the remote cloud data center and end devices in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) is merging t…
Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness
Xuwei Tan, Yangming Zhou, MengChu Zhou +1
Detecting critical nodes in sparse graphs is important in a variety of application domains, such as network vulnerability assessment, epidemic control, and drug design. The critica…
Learning Smooth Representation for Unsupervised Domain Adaptation
Guanyu Cai, Lianghua He, Mengchu Zhou +2
Typical adversarial-training-based unsupervised domain adaptation methods are vulnerable when the source and target datasets are highly-complex or exhibit a large discrepancy betwe…