38 citations · 81 across the 27 of their papers we have counts for
28 papers
Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis
Junyu Ren, Wensheng Gan, Philip S Yu
Fault diagnosis under unseen operating conditions remains highly challenging when labeled data are scarce. Semi-supervised domain generalization fault diagnosis (SSDGFD) provides a…
High-utility Sequential Rule Mining Utilizing Segmentation Guided by Confidence
Chunkai Zhang, Jiarui Deng, Maohua Lyu +2
Within the domain of data mining, one critical objective is the discovery of sequential rules with high utility. The goal is to discover sequential rules that exhibit both high uti…
Discovering High-utility Sequential Rules with Increasing Utility Ratio
Zhenqiang Ye, Wensheng Gan, Gengsen Huang +2
Utility-driven mining is an essential task in data science, as it can provide deeper insight into the real world. High-utility sequential rule mining (HUSRM) aims at discovering se…
Enhancing Temporal Awareness in LLMs for Temporal Point Processes
Lili Chen, Wensheng Gan, Shuang Liang +1
Temporal point processes (TPPs) are crucial for analyzing events over time and are widely used in fields such as finance, healthcare, and social systems. These processes are partic…
Graph Attention-based Adaptive Transfer Learning for Link Prediction
Huashen Lu, Wensheng Gan, Guoting Chen +2
Graph neural networks (GNNs) have brought revolutionary advancements to the field of link prediction (LP), providing powerful tools for mining potential relationships in graphs. Ho…
Global-focal Adaptation with Information Separation for Noise-robust Transfer Fault Diagnosis
Junyu Ren, Wensheng Gan, Guangyu Zhang +2
Existing transfer fault diagnosis methods typically assume either clean data or sufficient domain similarity, which limits their effectiveness in industrial environments where seve…
Efficient Mining of Low-Utility Sequential Patterns
Jian Zhu, Zhidong Lin, Wensheng Gan +1
Discovering valuable insights from rich data is a crucial task for exploratory data analysis. Sequential pattern mining (SPM) has found widespread applications across various domai…
Pure Node Selection for Imbalanced Graph Node Classification
Fanlong Zeng, Wensheng Gan, Jiayang Wu +1
The problem of class imbalance refers to an uneven distribution of quantity among classes in a dataset, where some classes are significantly underrepresented compared to others. Cl…
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
Fanlong Zeng, Wensheng Gan, Philip S. Yu +1
The class imbalance problem refers to the disproportionate distribution of samples across different classes within a dataset, where the minority classes are significantly underrepr…
Advances in Large Language Models for Medicine
Zhiyu Kan, Wensheng Gan, Zhenlian Qi +1
Artificial intelligence (AI) technology has advanced rapidly in recent years, with large language models (LLMs) emerging as a significant breakthrough. LLMs are increasingly making…
Utility-based Privacy Preserving Data Mining
Qingfeng Zhou, Wensheng Gan, Zhenlian Qi +1
With the advent of big data, periodic pattern mining has demonstrated significant value in real-world applications, including smart home systems, healthcare systems, and the medica…
Targeted Mining of Time-Interval Related Patterns
Shuang Liang, Lili Chen, Wensheng Gan +2
Compared to frequent pattern mining, sequential pattern mining emphasizes the temporal aspect and finds broad applications across various fields. However, numerous studies treat te…
Graph Diffusion Network for Drug-Gene Prediction
Jiayang Wu, Wensheng Gan, Philip S. Yu
Predicting drug-gene associations is crucial for drug development and disease treatment. While graph neural networks (GNN) have shown effectiveness in this task, they face challeng…
AI-Generated Content in Landscape Architecture: A Survey
Yue Xing, Wensheng Gan, Qidi Chen +1
Landscape design is a complex process that requires designers to engage in intricate planning, analysis, and decision-making. This process involves the integration and reconstructi…
Mixture of Experts (MoE): A Big Data Perspective
Wensheng Gan, Zhenyao Ning, Zhenlian Qi +1
As the era of big data arrives, traditional artificial intelligence algorithms have difficulty processing the demands of massive and diverse data. Mixture of experts (MoE) has show…
ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training
Jiayang Wu, Wensheng Gan, Jiahao Zhang +1
In the current development of large language models (LLMs), it is important to ensure the accuracy and reliability of the underlying data sources. LLMs are critical for various app…
Graph Contrastive Learning on Multi-label Classification for Recommendations
Jiayang Wu, Wensheng Gan, Huashen Lu +1
In business analysis, providing effective recommendations is essential for enhancing company profits. The utilization of graph-based structures, such as bipartite graphs, has gaine…
Watermarking Techniques for Large Language Models: A Survey
Yuqing Liang, Jiancheng Xiao, Wensheng Gan +1
With the rapid advancement and extensive application of artificial intelligence technology, large language models (LLMs) are extensively used to enhance production, creativity, lea…
Digital Fingerprinting on Multimedia: A Survey
Wendi Chen, Wensheng Gan, Philip S. Yu
The explosive growth of multimedia content in the digital economy era has brought challenges in content recognition, copyright protection, and data management. As an emerging conte…
Targeted Mining Precise-positioning Episode Rules
Jian Zhu, Xiaoye Chen, Wensheng Gan +2
The era characterized by an exponential increase in data has led to the widespread adoption of data intelligence as a crucial task. Within the field of data mining, frequent episod…
Large Language Models for Medicine: A Survey
Yanxin Zheng, Wensheng Gan, Zefeng Chen +3
To address challenges in the digital economy's landscape of digital intelligence, large language models (LLMs) have been developed. Improvements in computational power and availabl…
Large Language Models for Education: A Survey
Hanyi Xu, Wensheng Gan, Zhenlian Qi +2
Artificial intelligence (AI) has a profound impact on traditional education. In recent years, large language models (LLMs) have been increasingly used in various applications such…
Geospatial Big Data: Survey and Challenges
Jiayang Wu, Wensheng Gan, Han-Chieh Chao +1
In recent years, geospatial big data (GBD) has obtained attention across various disciplines, categorized into big earth observation data and big human behavior data. Identifying g…
Large Language Models in Law: A Survey
Jinqi Lai, Wensheng Gan, Jiayang Wu +2
The advent of artificial intelligence (AI) has significantly impacted the traditional judicial industry. Moreover, recently, with the development of AI-generated content (AIGC), AI…