A5084641325- Advanced Graph Neural Networks
- Data Mining Algorithms and Applications
- Machine Learning and Data Classification
- Parasite Biology and Host Interactions
- Imbalanced Data Classification Techniques
- Toxoplasma gondii Research Studies
- Complex Network Analysis Techniques
- Data Stream Mining Techniques
- Parasitic Infections and Diagnostics
- Helminth infection and control
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
- Text and Document Classification Technologies
- Video Analysis and Summarization
- Parasites and Host Interactions
- Domain Adaptation and Few-Shot Learning
- Herpesvirus Infections and Treatments
- Machine Learning and Algorithms
- Rough Sets and Fuzzy Logic
- Anomaly Detection Techniques and Applications
- Data Management and Algorithms
- Recommender Systems and Techniques
- Algorithms and Data Compression
- Time Series Analysis and Forecasting
- Face and Expression Recognition
Florida Atlantic University
2016-2025
Shanxi Agricultural University
2022-2024
Atlantic University College
2024
Yunnan Agricultural University
2010-2022
International Council on Mining and Metals
2022
Guangzhou University
2022
Hong Kong University of Science and Technology
2022
University of Hong Kong
2022
Guilin University of Electronic Technology
2022
Los Alamitos Medical Center
2022
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, storage, and collection capacity, are now rapidly expanding in all science engineering domains, including physical, biological biomedical sciences. This paper presents a HACE theorem that characterizes features revolution, proposes processing model, from mining perspective. data-driven model involves demand-driven aggregation information sources, analysis,...
With the widespread use of information technologies, networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation telecommunication and biological networks. Analyzing these sheds light on different aspects life structure societies, diffusion, communication patterns. In reality, however, large scale often makes network analytic tasks computationally expensive or intractable. Network representation learning has been...
The Google Android mobile phone platform is one of the most anticipated smartphone operating systems on market. open source allows developers to take full advantage operation system, but also raises significant issues related malicious applications. On hand, popularity absorbs attention for producing their applications this platform. increased numbers applications, other prepares a suitable prone some users develop different kinds malware and insert them in market or third party markets as...
Graph clustering aims to discovercommunity structures in networks, the task being fundamentally challenging mainly because topology structure and content of graphs are difficult represent for analysis. Recently, graph has moved from traditional shallow methods deep learning approaches, thanks unique feature representation capability learning. However, existing approaches can only exploit information, while ignoring information associated with nodes a graph. In this paper, we propose novel...
We propose a new online feature selection framework for applications with streaming features where the knowledge of full space is unknown in advance. define as that flow one by over time whereas number training examples remains fixed. This contrast traditional learning methods only deal sequentially added observations, little attention being paid to features. The critical challenges Online Streaming Feature Selection (OSFS) include 1) continuous growth volumes time, 2) large space, possibly...
The relevance feedback approach to image retrieval is a powerful technique and has been an active research direction for the past few years. Various ad hoc parameter estimation techniques have proposed feedback. In addition, methods that perform optimization on multi-level content model formulated. However, these only low-level features fail address images' semantic content. this paper, we propose technique, iFind, take advantage of contents images in addition features. By forming network...
This paper formulates a multi-graph learning task. In our problem setting, bag contains number of graphs and class label. A is labeled positive if at least one graph in the positive, negative otherwise. addition, genuine label each unknown, all are negative. The aim to build model from training bags predict previously unseen test with maximum accuracy. setting essentially different existing multi-instance (MIL), where instances MIL share well-defined feature values, but no features available...
In this paper, we propose a new research problem on active learning from data streams, where volumes grow continuously, and labeling all is considered expensive impractical. The objective to label small portion of stream which model derived predict future instances as accurately possible. To tackle the technical challenges raised by dynamic nature data, i.e., increasing evolving decision concepts, classifier-ensemble-based framework that selectively labels streams build classifier ensemble....