Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Wu, Zhizhong"'
This study introduces a pioneering Dynamic Hypergraph Networks (DHCE) model designed to predict future medical diagnoses from electronic health records with enhanced accuracy. The DHCE model innovates by identifying and differentiating acute and chro
Externí odkaz:
http://arxiv.org/abs/2408.07084
Adaptive optimizers are pivotal in guiding the weight updates of deep neural networks, yet they often face challenges such as poor generalization and oscillation issues. To counter these, we introduce sigSignGrad and tanhSignGrad, two novel optimizer
Externí odkaz:
http://arxiv.org/abs/2408.11839
This paper introduces the Multiple Greedy Quasi-Newton (MGSR1-SP) method, a novel approach to solving strongly-convex-strongly-concave (SCSC) saddle point problems. Our method enhances the approximation of the squared indefinite Hessian matrix inhere
Externí odkaz:
http://arxiv.org/abs/2408.00241
With the increasing complexity of financial markets and rapid growth in data volume, traditional risk monitoring methods no longer suffice for modern financial institutions. This paper designs and optimizes a risk monitoring system based on big data
Externí odkaz:
http://arxiv.org/abs/2407.19352
Trauma is a significant cause of mortality and disability, particularly among individuals under forty. Traditional diagnostic methods for traumatic injuries, such as X-rays, CT scans, and MRI, are often time-consuming and dependent on medical experti
Externí odkaz:
http://arxiv.org/abs/2407.16165
Super-resolution reconstruction techniques entail the utilization of software algorithms to transform one or more sets of low-resolution images captured from the same scene into high-resolution images. In recent years, considerable advancement has be
Externí odkaz:
http://arxiv.org/abs/2407.13211
With the development of deep learning technology, the detection and classification of distracted driving behaviour requires higher accuracy. Existing deep learning-based methods are computationally intensive and parameter redundant, limiting the effi
Externí odkaz:
http://arxiv.org/abs/2407.01864
Reinforcement learning continuously optimizes decision-making based on real-time feedback reward signals through continuous interaction with the environment, demonstrating strong adaptive and self-learning capabilities. In recent years, it has become
Externí odkaz:
http://arxiv.org/abs/2407.02539
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Chi, Junlin, Liu, Tonglei, Shi, Chengmin, Luo, Huayou, Wu, Zhizhong, Xiong, Binghong, Liu, Shuang, Zeng, Yujian
Publikováno v:
In Biomedicine & Pharmacotherapy October 2019 118