Area of research
Artificial Intelligence · Computer Networks and Communications
Research interest
Research interests include Computer science, Anomaly detection, Artificial intelligence, Pattern recognition (psychology), Outlier, and Autoencoder.
Generative adversarial synthetic neighbors-based unsupervised anomaly detection
Generative adversarial message passing-based anomaly detection
Unsupervised Dual-discriminative Graph Neural Network for Anomaly Detection
Generative adversarial local density-based unsupervised anomaly detection
A novel attention-based long short-term memory latency prediction model for stream processing applications
Bearing fault detection by using graph autoencoder and ensemble learning
Self-supervised enhanced denoising diffusion for anomaly detection
DGTAD: decomposition GAN-based transformer for anomaly detection in multivariate time series data
Graph Sampling and Aggregation Network-Based Unsupervised Anomaly Detection
Cardinality estimation for property graph queries with gated learning approach on the graph database
Bearing fault detection based on area equalization
Generative adversarial nets for unsupervised outlier detection
Video Anomaly Detection Based on Attention Mechanism
Fluctuation-based outlier detection
Query cost estimation in graph databases via emphasizing query dependencies by using a neural reasoning network
Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database
Outlier Detection Based on Autoencoder Ensembles with Denoising layer and Attention Mechanism
Graph autoencoder-based unsupervised outlier detection
Cluster-Based Improved Isolation Forest
Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk
Task’s Choice: Pruning-Based Feature Sharing (PBFS) for Multi-Task Learning
Graph Convolutional Networks and Attention-Based Outlier Detection
Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm
Fair Outlier Detection Based on Adversarial Representation Learning
Faiad: Feature Adaptive-Based Image Anomaly Detection
Faiad: Feature Adaptive-Based Image Anomaly Detection
FAIAD: Feature Adaptive-based Image Anomaly Detection
Fish Classification Using DNA Barcode Sequences through Deep Learning Method
A deep learning model for fish classification base on DNA barcode