A5103245284- Stock Market Forecasting Methods
- Energy Load and Power Forecasting
- Imbalanced Data Classification Techniques
- Face and Expression Recognition
- Financial Distress and Bankruptcy Prediction
- Data Stream Mining Techniques
- Complex Systems and Time Series Analysis
- Sparse and Compressive Sensing Techniques
- Market Dynamics and Volatility
- Financial Risk and Volatility Modeling
- Advanced Image and Video Retrieval Techniques
- Text and Document Classification Technologies
- Financial Markets and Investment Strategies
- Asphalt Pavement Performance Evaluation
- Machine Learning and ELM
- Multimodal Machine Learning Applications
- Machine Learning and Algorithms
- Grey System Theory Applications
- Infrastructure Maintenance and Monitoring
- Advanced Neural Network Applications
- Research studies in Vietnam
- Image Processing Techniques and Applications
- Monetary Policy and Economic Impact
- Blind Source Separation Techniques
- Machine Learning and Data Classification
Capital University of Economics and Business
2018-2024
Semantic Segmentation is a computer vision task for predicting the pixel labels corresponding to its belonging region or enclosing area. It an important part in many CV tasks and plays significant role machine learning. segmentation aim at understanding special object class scene. In paper, we will give survey of Segmentation. At first, make brief introduction Segmentation, introducing wide use semantic segmentation. Its range from scene understanding, humanmachine interaction, computational...
International Journal of Information Technology & Decision MakingAccepted Papers No AccessQuantile-based spillover network analysis financial institutions in chinese mainland and hong kongYinhong Yao, Zhensong Chen, Wei Xueyong LiuYinhong Chen Search for more papers by this author , Liu https://doi.org/10.1142/S0219622025410019Cited by:0 (Source: Crossref) Next AboutFiguresReferencesRelatedDetailsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend Library ShareShare...
The stock market is a very important part of the financial field, and prediction has great relationship with returns risk safety entire field. With continuous mature application machine learning deep in other fields, such as image processing text analysis, people begin to focus on use different models so predict volatility. However, view unique multi-source heterogeneous characteristics information, artificial neural network relying cannot make good it. At this time, graph that can well...
Precise prediction of stock prices leads to more profits and effective risk prevention, which is great significance both investors regulators. Recent years, various kinds information not directly-relevant with have received attention, such as texts, images or connections. These external has the potential reflecting influencing fluctuations, thus, given utilization advanced analyzing techniques, forecasting performance could be promoted substantially. For instance, graph neural network models...
Abstract Financial distress prediction (FDP) has attracted high attention from many financial institutions. Utilizing supervised learning‐based methods in FDP, however, is time consuming and labor intensive. Therefore, this paper, we exploit active‐pSVM method, which combines potential data distribution information existing expert experience to solve FDP problem. Moreover, with the increasingly popular textual information, construct several features on our protocol that are based Management...
Crack detection has drawn much attention in the last two decades, because of dramatic bloom monitoring images and urgent need corresponding crack detection. However, recent methods have not taken advantage structure information effectively, resulting low accuracy when dealing with c rack-like noises. In this paper, we propose a novel framework, which is able to identify cracks from noisy background. The main contributions paper are as follows: (1) giving new edge-based framework improve...