|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Color image enhancement based on HVS and PCNN显示文摘To enhance color images more effectively, a novel strategy is presented in this paper. We firstly translate the image to be enhanced from RGB space into HIS space, secondly keep its H component unchanged, and thirdly stretch its S component exponentially, and at last process its I component in the following manner: couple both the gray value and the spatial information into an inner activity item of corresponding neuron, integrate the human visual system into a dynamic component of corresponding neuron, and compare the inner activity item with dynamic component to obtain the enhanced image. Experiments demonstrate the effectiveness and validity of our strategy. | ZHANG YuDong, WU LeNan , WANG ShuiHua & WEI Geng School of Information Science & Engineering, Southeast University, Nanjing 210096, China | 2010 | Science China(Information Sciences)2010，53，10： | 12 |
| 2 | A Survey on Artificial Intelligence in Posture Recognition显示文摘Over the years,the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded.The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years,such as scale-invariant feature transform,histogram of oriented gradients,support vectormachine(SVM),Gaussian mixturemodel,dynamic time warping,hiddenMarkovmodel(HMM),lightweight network,convolutional neural network(CNN).We also investigate improved methods of CNN,such as stacked hourglass networks,multi-stage pose estimation networks,convolutional posemachines,and high-resolution nets.The general process and datasets of posture recognition are analyzed and summarized,and several improved CNNmethods and threemain recognition techniques are compared.In addition,the applications of advanced neural networks in posture recognition,such as transfer learning,ensemble learning,graph neural networks,and explainable deep neural networks,are introduced.It was found that CNN has achieved great success in posture recognition and is favored by researchers.Still,a more in-depth research is needed in feature extraction,information fusion,and other aspects.Among classification methods,HMM and SVM are the most widely used,and lightweight network gradually attracts the attention of researchers.In addition,due to the lack of 3Dbenchmark data sets,data generation is a critical research direction. | Xiaoyan Jiang Zuojin Hu Shuihua Wang Yudong Zhang | 2023 | Computer Modeling in Engineering & Sciences2023，，10： | 2 |
| 3 | Association between adverse clinical outcome in human disease caused by novel influenza A H7N9 virus and sustained viral shedding and emergence of antiviral resistance显示文摘 | Yunwen Hu Shuihua Lu Zhigang Song Wei Wang Pei Hao Jianhua Li Xiaonan Zhang Hui-Ling Yen Bisheng Shi Tao Li Wencai Guan Lei Xu Yi Liu Sen Wang Xiaoling Zhang Di Tian Zhaoqin Zhu Jing He Kai Huang Huijie Chen Lulu Zheng Xuan Li Jie Ping Bin Kang Xiuhong Xi | 2013 | The Lancet2013，，9885： | 1 |
| 4 | Li-hopfield neural network for Chinese character recognition显示文摘 | Zhang Yudong Wu Lenan Wang Shuihua | 2010 | Journal of Nanjing University of Information Science&Technology:Natural Science Edition2010，2，1： | 1 |
| 5 | Simulation and experimental study on the impact of breadth of back blade on axial force balance显示文摘 | Wang Hui Mu Jiegang Zheng Shuihua | 2012 | Applied Mechanics and Materials2012，130，： | 1 |
| 6 | Carbon nanofibers supported Pt–Ru electrocatalysts for direct methanol fuel cells显示文摘 | Junsong Guo Gongquan Sun Qi Wang Guoxiong Wang Zhenhua Zhou Shuihua Tang Luhua Jiang Bing Zhou Qin Xin | 2005 | Carbon2005，，： | 1 |
| 7 | Association between adverse clinical outcome in human disease caused by novel influenza A H7N9 virus and sustained viral shedding and emergence of antiviral resistance显示文摘 | Yunwen Hu Shuihua Lu Zhigang Song Wei Wang Pei Hao Jianhua Li Xiaonan Zhang Hui-Ling Yen Bisheng Shi Tao Li Wencai Guan Lei Xu Yi Liu Sen Wang Xiaoling Zhang Di Tian Zhaoqin Zhu Jing He Kai Huang Huijie Chen Lulu Zheng Xuan Li Jie Ping Bin Kang Xiuhong Xi | 2013 | The Lancet2013，，9885： | 1 |
| 8 | Grad-CAM:Understanding AI Models显示文摘Artificial intelligence(AI)[1,2]allows computers to think and behave like humans,so it is now becoming more and more influential in almost every field[3].Hence,users in businesses,industries,hospitals[4],etc.,need to understand how these AI models work[5]and the potential impact of using them. | Shuihua Wang Yudong Zhang | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 9 | Shanghai Petrochemical Company Limited egins a New Round of Leap显示文摘 On June 18, 2000 the construction of the fourth phase project worth 6.5 billion RMB was officially kicked off at Shanghai Petrochemical Company Limited (SPC).Mr. Chen Jinghua, the vice-chairman of Chinese People's Political Consultative Conference, Mr. Jiang Yiren,member of the Standing Committee of Shanghai Communist Party and the vice-mayor of Shanghai, and Mr. Chen Tonghai, the deputy president of SINOPEC and vice-chairman of Board of China Petroleum and Chemical Corporation, attended the opening ceremony of the construction project, symbolizing a new round of leap forward at the turn of century by Shanghai Petrochemical Company Limited after having created previously brilliant achievements.…… | Wang Shuihua | 2000 | China Petroleum Processing & Petrochemical Technology2000，2，4： | 0 |
| 10 | SNSVM:SqueezeNet-Guided SVM for Breast Cancer Diagnosis显示文摘Breast cancer is a major public health concern that affects women worldwide.It is a leading cause of cancer-related deaths among women,and early detection is crucial for successful treatment.Unfortunately,breast cancer can often go undetected until it has reached advanced stages,making it more difficult to treat.Therefore,there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage.The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images.The extracted features are then utilized to train a support vector machine(SVM)for mammography image classification.The SqueezeNet-guided SVMmodel,known as SNSVM,achieved promising results,with an accuracy of 94.10% and a sensitivity of 94.30%.A 10-fold cross-validation was performed to ensure the robustness of the results,and the mean and standard deviation of various performance indicators were calculated across multiple runs.This model also outperforms state-of-the-art models in all performance indicators,indicating its superior performance.This demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography images.The superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer diagnosis.This may have significant implications for reducing breast cancer mortality rates. | Jiaji Wang Muhammad Attique Khan Shuihua Wang Yudong Zhang | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 11 | Bird community patterns in response to the island features of urban woodlots in eastern China显示文摘Many studies have demonstrated the changes in the spatial patterns of plant and animal communities with respect to habitat fragmentation.Insular communities tend to exhibit some special patterns in connection with the characteristics of island habitats.In this paper,the relationships between richness,assemblage,and abundance of bird communities with respect to island features were analyzed in 20 urban woodlots in Hangzhou,China.Field investigations of bird communities,using the line transect method,were conducted from January to December,1997.Each woodlot was surveyed 16 times during the year.Results indicated that bird richness was higher,per unit area,in the smaller woodlots than the larger ones,and overall bird density decreased with the increase in the size of woodlot.However,the evenness of species abundance increased with the area,and small woodlots were usually dominated by higher density species and large woodlots by medium density species.Most species occurring in the small woodlots also occurred in larger woodlots.Also,bird communities among urban woodlots showed a nestedness pattern in assemblage.These patterns implied that the main impacts of woodland habitat fragmentation are:(1)species are constricted and thus species number will increase at a given sample size;(2)as surface area decreases,the proportion of forest edge species as to interior species will increase;(3)community abundance will therefore increase per unit area but most individuals will be from a few dominant species;and(4)overall species diversity will decrease at a habitat level as well as at a region level.These patterns of community in response to the island features were therefore summarized as“island effects in community”.The underlying processes of such observations were also examined in this paper.Woodlot area,edge ratio,isolation,and habitat nestedness were considered as the important factors forming the island effects in community.High heterogeneity between habitats usually contributed most to the maintenance of regional biodiversity,especially in urban woodlots. | CHEN Shuihua DING Ping ZHENG Guangmei WANG Yujun | 2006 | Frontiers in Biology2006，1，4： | 0 |
| 12 | Variation in reproductive life-history traits of Chinese Bulbuls(Pycnonotus sinensis)along the urbanization gradient in Hangzhou,China显示文摘Urbanization brings new selection pressures to wildlife living in cities,and changes in the life-history traits of urban species can reflect their responses to such pressures.To date,most of the studies investigating the impacts of urbanization on avian life-history traits are conducted in Europe and North America,while such studies are often lacking in quickly developing countries in Asia(e.g.,China).In this study,we examined the variations in reproductive life-history traits of Chinese Bulbuls(Pycnonotus sinensis)along the urbanization gradient in Hangzhou,China.We detected 234 natural nests of Chinese Bulbuls and continuously monitored them in two continuous breeding seasons from 2012 to 2013.We collected data on seven life-history traits(laying date,incubation period,nestling period,clutch size,egg volume,hatching success rate,and fledging success rate).We used infrared cameras to record the number of feedings per hour as the measure of food resources for the nestlings.We measured nest predation pressure by monitoring 148 natural breeding nests during breeding seasons and 54 artificial nests immediately after breeding seasons.We then calculated the urbanization synthetic index(USI)as a measure of the level of urbanization and examined its relationship with the seven life-history traits.We found that Chinese Bulbuls laid eggs significantly earlier with increasing USI.However,the other six life-history traits did not vary significantly with the USI.Moreover,the feeding frequency of chicks increased significantly with the USI,but the nest predation pressure of Chinese Bulbuls decreased significantly with the USI.Increased food resources and reduced nest predation pressure in cities may lead to earlier laying date of Chinese Bulbuls.Further study should test whether the earlier laying date of Chinese Bulbuls is the result of phenotypic plasticity or genetic change. | Xingmin Chen Qin Zhang Sisi Lan Qin Huang Shuihua Chen Yanping Wang | 2023 | Avian Research2023，14，2： | 0 |
| 13 | Introduction to the Special Issue on Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph显示文摘The chest radiograph has been one of the most frequently performed radiological investigation tools.In clinical medicine,the chest radiograph can provide technical basis and scientific instruction to recognize a series of thoracic diseases(such as Atelectasis,Nodule,and Pneumonia,etc.).Importantly,it is of paramount importance for clinical screening,diagnosis,treatment planning,and efficacy evaluation.However,it remains challenging for automated chest radiograph diagnosis and interpretation at the level of an experienced radiologist.In recent years,many studies on biomedical image processing have advanced rapidly with the development of artificial intelligence especially deep learning techniques and algorithms.How to build an efficient and accurate deep learning model for automatic chest radiograph processing is an important scientific problem that needs to be solved. | Shuihua Wang Zheng Zhang Yuankai Huo | 2022 | Computer Modeling in Engineering & Sciences2022，，9： | 0 |
| 14 | Effect of Data Augmentation of Renal Lesion Image by Nine-layer Convolutional Neural Network in Kidney CT显示文摘Artificial Intelligence(AI)becomes one hotspot in the field of the medical images analysis and provides rather promising solution.Although some research has been explored in smart diagnosis for the common diseases of urinary system,some problems remain unsolved completely A nine-layer Convolutional Neural Network(CNN)is proposed in this paper to classify the renal Computed Tomography(CT)images.Four group of comparative experiments prove the structure of this CNN is optimal and can achieve good performance with average accuracy about 92.07±1.67%.Although our renal CT data is not very large,we do augment the training data by affine,translating,rotating and scaling geometric transformation and gamma,noise transformation in color space.Experimental results validate the Data Augmentation(DA)on training data can improve the performance of our proposed CNN compared to without DA with the average accuracy about 0.85%.This proposed algorithm gives a promising solution to help clinical doctors automatically recognize the abnormal images faster than manual judgment and more accurately than previous methods. | Liying Wang Zhiqiang Xu Shuihua Wang | 2020 | Computer Modeling in Engineering & Sciences2020，，9： | 0 |
| 15 | Weakly supervised machine learning显示文摘Supervised learning aims to build a function or model that seeks as many mappings as possible between the training data and outputs,where each training data will predict as a label to match its corresponding ground‐truth value.Although supervised learning has achieved great success in many tasks,sufficient data supervision for labels is not accessible in many domains because accurate data labelling is costly and laborious,particularly in medical image analysis.The cost of the dataset with ground‐truth labels is much higher than in other domains.Therefore,it is noteworthy to focus on weakly supervised learning for medical image analysis,as it is more applicable for practical applications.In this re-view,the authors give an overview of the latest process of weakly supervised learning in medical image analysis,including incomplete,inexact,and inaccurate supervision,and introduce the related works on different applications for medical image analysis.Related concepts are illustrated to help readers get an overview ranging from supervised to un-supervised learning within the scope of machine learning.Furthermore,the challenges and future works of weakly supervised learning in medical image analysis are discussed. | Zeyu Ren Shuihua Wang Yudong Zhang | 2023 | CAAI Transactions on Intelligence Technology2023，8，3： | 0 |
| 16 | Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization显示文摘Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes. | Zhuqing Jiao Yixin Ji Tingxuan Jiao Shuihua Wang | 2020 | Computer Modeling in Engineering & Sciences2020，，5： | 0 |
| 17 | Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification显示文摘Hearing loss(HL)is a kind of common illness,which can significantly reduce the quality of life.For example,HL often results in mishearing,misunderstanding,and communication problems.Therefore,it is necessary to provide early diagnosis and timely treatment for HL.This study investigated the advantages and disadvantages of three classical machine learning methods:multilayer perceptron(MLP),support vector machine(SVM),and least-square support vector machine(LS-SVM)approach andmade a further optimization of the LS-SVM model via wavelet entropy.The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function and least-square optimizationmethod.Besides,a wavelet selection method was proposed,and we found db4 can achieve the best results.The experiments showed that the LS-SVM method can identify the hearing loss disease with an overall accuracy of three classes as 84.89±1.77,which is superior to SVM andMLP.The results show that the least-square support vector machine is effective in hearing loss identification. | Chaosheng Tang Deepak Ranjan Nayak Shuihua Wang | 2020 | Computer Modeling in Engineering & Sciences2020，，10： | 0 |
| 18 | ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules显示文摘Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques accurately.Methods:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules.First,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask learning.Second,we propose the DualLoss function,tailored to the thyroid nodule localization and classification tasks.It balances the learning of the localization and classification tasks to help improve the model’s generalization ability.Third,we introduce strategies for augmenting the data.Finally,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid nodules.Results:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and TransUnet.Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules.It achieved improved accuracy of 3.9%and 1.5%,respectively.Conclusion:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks.Future research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis. | Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang | 2024 | Computer Modeling in Engineering & Sciences2024，139，4： | 0 |
| 19 | SNELM:SqueezeNet-Guided ELM for COVID-19 Recognition显示文摘(Aim)The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022.Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients.(Method)Two datasets are chosen for this study.The multiple-way data augmentation,including speckle noise,random translation,scaling,salt-and-pepper noise,vertical shear,Gamma correction,rotation,Gaussian noise,and horizontal shear,is harnessed to increase the size of the training set.Then,the SqueezeNet(SN)with complex bypass is used to generate SN features.Finally,the extreme learning machine(ELM)is used to serve as the classifier due to its simplicity of usage,quick learning speed,and great generalization performances.The number of hidden neurons in ELM is set to 2000.Ten runs of 10-fold cross-validation are implemented to generate impartial results.(Result)For the 296-image dataset,our SNELM model attains a sensitivity of 96.35±1.50%,a specificity of 96.08±1.05%,a precision of 96.10±1.00%,and an accuracy of 96.22±0.94%.For the 640-image dataset,the SNELM attains a sensitivity of 96.00±1.25%,a specificity of 96.28±1.16%,a precision of 96.28±1.13%,and an accuracy of 96.14±0.96%.(Conclusion)The proposed SNELM model is successful in diagnosing COVID-19.The performances of our model are higher than seven state-of-the-art COVID-19 recognition models. | Yudong Zhang Muhammad Attique Khan Ziquan Zhu Shuihua Wang | 2023 | Computer Systems Science & Engineering2023，46，7： | 0 |
| 20 | Deep Learning for COVID-19 Diagnosis via Chest Images显示文摘COVID-19 is a vastly infectious disease caused by the new coronavirus,officially recognized as severe acute respiratory syndrome coronavirus 2[1].This virus has multiplied fast worldwide,causing a global pandemic[2].It has caused 6.87 million death tolls until 20/March/2023. | Shuihua Wang Yudong Zhang | 2023 | Computers, Materials & Continua2023，，7： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员