A5074188706- Video Surveillance and Tracking Methods
- Fire Detection and Safety Systems
- Green IT and Sustainability
- Advanced Sensor and Energy Harvesting Materials
- Case Reports on Hematomas
- Antiplatelet Therapy and Cardiovascular Diseases
- Bluetooth and Wireless Communication Technologies
- Heart Failure Treatment and Management
- Cardiovascular Function and Risk Factors
- Image Enhancement Techniques
- Bone and Joint Diseases
- Air Quality Monitoring and Forecasting
- Gout, Hyperuricemia, Uric Acid
University of Electronic Science and Technology of China
2023-2024
National University of Defense Technology
2022-2023
Background Short-term unplanned readmission is always neglected, especially for elderly patients with coronary heart disease (CHD). However, tools to predict are lacking. This study aimed establish the most effective predictive model 7-day in CHD using machine learning (ML) algorithms. Methods The detailed clinical data of were collected retrospectively. Five ML algorithms, including extreme gradient boosting (XGB), random forest, multilayer perceptron, categorical boosting, and logistic...
Introduction Accurate identification of the risk factors is essential for effective prevention hyperuricaemia (HUA)-related kidney damage. Previous studies have established efficacy machine learning (ML) methodologies in predicting damage due to other chronic diseases. Nevertheless, a scarcity precise and clinically applicable prediction models exists assessing HUA-related This study aims accurately predict developing using ML algorithm, which based on retrospective database. Methods...
We explore an innovative view on distribution modulation to boost Siamese trackers. Specially, we observed two cases of possible inconsistency in tracking: 1) Two branches with different sizes may be ranges after a shared backbone (including BN layers). 2) The background data affect the total feature search branch. To address these issues, proposed plug-and-play component named Progressive Perception Learning Module (P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML"...
Multiple object tracking (MOT) methods based on single are of great interest because their ability to balance efficiency and performance the strength localization capability single-target tracking. However, most only distinguish foreground background. They susceptible influence similar interfering objects during localization, while in multiple scenarios, there more is severe. Therefore, we propose a Distractor-Suppressing Graph Attention (DSGA) learn discriminative attention by reducing...