Shuihua Wang
Biographic Data
| ID | 7971395 |
|---|---|
| NAME | Shuihua Wang |
| GIVEN NAMES | Shuihua |
| FAMILY NAME | Wang |
| SIGNATURE | WANG S |
| AFFILIATIONS | University of Leicester |
| ORCID | 0000-0003-4713-2791 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 9 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Interpretable deep learning for traffic prediction
Analyzing environment-transport relationships in metropolitan areas
From Urban Dynamics to Intelligence
Covid-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization
The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these i…
Covid-19 classification using chest X-ray images
Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray image…
Retracted
Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system. Methods: First, the n -conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. More…
Retracted
Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently. Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-wa…
Mixing Patterns in Social Trust Networks
Mixing patterns (MPs) in social trust networks (STNs) are increasingly attracting attention because they can assist analysts in designing information dissemination tactics and planning electronic word-of-mouth (eWOM) campaigns. However, the existing studies on MPs do not explain the assortative or disassortative tendencies of STNs due to their omission of the support of the sociological theory, as well as that of network theory. To address this i…
Alcoholism Identification Based on an AlexNet Transfer Learning Model
Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10 -4 , and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning. Resu…
No prominent works on this page.
Alcoholism Identification Based on an AlexNet Transfer Learning Model
Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10 -4 , and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning. Resu…
Mixing Patterns in Social Trust Networks
Mixing patterns (MPs) in social trust networks (STNs) are increasingly attracting attention because they can assist analysts in designing information dissemination tactics and planning electronic word-of-mouth (eWOM) campaigns. However, the existing studies on MPs do not explain the assortative or disassortative tendencies of STNs due to their omission of the support of the sociological theory, as well as that of network theory. To address this i…
Retracted
Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system. Methods: First, the n -conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. More…
Retracted
Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently. Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-wa…
Covid-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization
The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these i…
Covid-19 classification using chest X-ray images
Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray image…
From Urban Dynamics to Intelligence
Interpretable deep learning for traffic prediction
Analyzing environment-transport relationships in metropolitan areas
Computer Science (7 works) · Artificial Intelligence (6 works) · Anomaly Detection Techniques and Applications (5 works) · COVID-19 diagnosis using AI (4 works) · Medicine (4 works) · Deep learning (3 works) · Disease (3 works) · Human Mobility and Location-Based Analysis (3 works) · Machine learning (3 works) · Mathematics (3 works)