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学者姓名：李建强
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摘要 ：
大气污染领域本体的半自动构建及语义推理
关键词 ：
语义推理 语义推理 注意力机制 注意力机制 大气污染 大气污染 自然语言处理 自然语言处理 实体关系抽取 实体关系抽取 本体 本体
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| GB/T 7714 | 刘博 , 张佳慧 , 李建强 et al. 大气污染领域本体的半自动构建及语义推理 [J]. | 刘博 , 2021 , 47 (3) : 246-259 . |
| MLA | 刘博 et al. "大气污染领域本体的半自动构建及语义推理" . | 刘博 47 . 3 (2021) : 246-259 . |
| APA | 刘博 , 张佳慧 , 李建强 , 李永 , 郎建垒 , 北京工业大学学报 . 大气污染领域本体的半自动构建及语义推理 . | 刘博 , 2021 , 47 (3) , 246-259 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明提供一种图像分割方法及系统，该方法包括：将待分割图像依次经过图像分割模型中的各下采样模块，获取最后一个下采样模块输出的特征图；将最后一个下采样模块输出的特征图依次经过图像分割模型中的各上采样模块，获取最后一个上采样模块输出的特征图；对最后一个上采样模块输出的特征图进行分割，获取最后一个上采样模块输出的特征图的分割结果；其中，任一上采样层的下一层的输入由该上采样层输出的特征图和将该上采样层所属的上采样模块对应的下采样模块输出的特征图输入金字塔池化层后输出的特征图融合获取。本发明实现融合后的特征图包含丰富的浅层特征和深层特征，可以减少特征信息的损失，有效提高图像分割的准确性。
引用：
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| GB/T 7714 | 李建强 , 刘青 . 图像分割方法及系统 : CN202110048037.1[P]. | 2021-01-14 . |
| MLA | 李建强 et al. "图像分割方法及系统" : CN202110048037.1. | 2021-01-14 . |
| APA | 李建强 , 刘青 . 图像分割方法及系统 : CN202110048037.1. | 2021-01-14 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder's location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder's information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.
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| GB/T 7714 | Qureshi, Sirajuddin , Li, Jianqiang , Akhtar, Faheem et al. Analysis of Challenges in Modern Network Forensic Framework [J]. | SECURITY AND COMMUNICATION NETWORKS , 2021 , 2021 . |
| MLA | Qureshi, Sirajuddin et al. "Analysis of Challenges in Modern Network Forensic Framework" . | SECURITY AND COMMUNICATION NETWORKS 2021 (2021) . |
| APA | Qureshi, Sirajuddin , Li, Jianqiang , Akhtar, Faheem , Tunio, Saima , Khand, Zahid Hussain , Wajahat, Ahsan . Analysis of Challenges in Modern Network Forensic Framework . | SECURITY AND COMMUNICATION NETWORKS , 2021 , 2021 . |
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摘要 ：
The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis (OMG) is time-consuming and laborious, and it lacks quantitative standards. An aided diagnostic system for OMG is proposed to solve this problem. The values calculated by the system include three clinical indicators: eyelid distance, sclera distance, and palpebra superior fatigability test time. For the first two indicators, the semantic segmentation method was used to extract the pathological features of the patient's eye image and a semantic segmentation model was constructed. The patient eye image was divided into three regions: iris, sclera, and background. The indicators were calculated based on the position of the pixels in the segmentation mask. For the last indicator, a calculation method based on the Eyelid Aspect Ratio (EAR) is proposed; this method can better reflect the change of eyelid distance overtime. The system was evaluated based on the collected patient data. The results show that the segmentation model achieves a mean Intersection-Over-Union (mIoU) value of 86.05%. The paired-sample T-test was used to compare the results obtained by the system and doctors, and the p values were all greater than 0.05. Thus, the system can reduce the cost of clinical diagnosis and has high application value.
关键词 ：
Convolution Convolution Facial features Facial features Faces Faces Image segmentation Image segmentation ocular myasthenia gravis ocular myasthenia gravis semantic segmentation semantic segmentation computer-aided system computer-aided system Standards Standards Eyelids Eyelids eyelid aspect ratio eyelid aspect ratio Feature extraction Feature extraction
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| GB/T 7714 | Liu, Guanjie , Wei, Yan , Xie, Yunshen et al. A Computer-Aided System for Ocular Myasthenia Gravis Diagnosis [J]. | TSINGHUA SCIENCE AND TECHNOLOGY , 2021 , 26 (5) : 749-758 . |
| MLA | Liu, Guanjie et al. "A Computer-Aided System for Ocular Myasthenia Gravis Diagnosis" . | TSINGHUA SCIENCE AND TECHNOLOGY 26 . 5 (2021) : 749-758 . |
| APA | Liu, Guanjie , Wei, Yan , Xie, Yunshen , Li, Jianqiang , Qiao, Liyan , Yang, Ji-Jiang . A Computer-Aided System for Ocular Myasthenia Gravis Diagnosis . | TSINGHUA SCIENCE AND TECHNOLOGY , 2021 , 26 (5) , 749-758 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
目的:利用人脸图像,构建基于深度学习的特纳综合征(Turner syndrome,TS)分类模型,旨在提高TS诊断准确率,降低诊断开销.方法:首先,将通道域注意力机制和空间域注意力机制以及残差结构相结合,提出一种具有混合域注意力模块的残差网络,然后使用深度迁移学习技术完成模型的初始化,最后使用TS人脸数据集对网络模型进行微调.结果:该模型对TS的分类准确率为0.9171.结论:所提出的TS分类模型优于现有TS识别方法,能更为有效地辅助TS的临床诊断.
关键词 ：
残差网络 残差网络 通道域注意力机制 通道域注意力机制 空间域注意力机制 空间域注意力机制 特纳综合征 特纳综合征
引用：
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| GB/T 7714 | 刘璐 , 李建强 , 陈适 . 基于混合域注意力机制和残差网络的特纳综合征分类研究 [J]. | 中国数字医学 , 2021 , 16 (2) : 16-20 . |
| MLA | 刘璐 et al. "基于混合域注意力机制和残差网络的特纳综合征分类研究" . | 中国数字医学 16 . 2 (2021) : 16-20 . |
| APA | 刘璐 , 李建强 , 陈适 . 基于混合域注意力机制和残差网络的特纳综合征分类研究 . | 中国数字医学 , 2021 , 16 (2) , 16-20 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
With rapid industrial development, air pollution problems, especially in urban and metropolitan centers, have become a serious societal problem and require our immediate attention and comprehensive solutions to protect human and animal health and the environment. Because bad air quality brings prominent effects on our daily life, how to forecast future air quality accurately and tenuously has emerged as a priority for guaranteeing the quality of human life in many urban areas worldwide. Existing models usually neglect the influence of wind and do not consider both distance and similarity to select the most related stations, which can provide significant information in prediction. Therefore, we propose a Geographic Self-Organizing Map (GeoSOM) spatiotemporal gated recurrent unit (GRU) model, which clusters all the monitor stations into several clusters by geographical coordinates and time-series features. For each cluster, we build a GRU model and weighted different models with the Gaussian vector weights to predict the target sequence. The experimental results on real air quality data in Beijing validate the superiority of the proposed method over a number of state-of-the-art ones in metrics, such as R-2, mean relative error (MRE), and mean absolute error (MAE). The MAE, MRE, and R-2 are 16.1, 0.79, and 035 at the Gucheng station and 19.53, 0.82, and 036 at the Dongsi station.
关键词 ：
Air quality Air quality environment pollution environment pollution prediction prediction recurrent neural network (RNN) recurrent neural network (RNN) spatiotemporal sequences spatiotemporal sequences
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| GB/T 7714 | Liu, Bo , Yan, Shuo , Li, Jianqiang et al. A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2021 , 8 (3) : 578-588 . |
| MLA | Liu, Bo et al. "A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 8 . 3 (2021) : 578-588 . |
| APA | Liu, Bo , Yan, Shuo , Li, Jianqiang , Li, Yong , Lang, Jianlei , Qu, Guangzhi . A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2021 , 8 (3) , 578-588 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明涉及一种眼底图像血管的识别方法、装置、电子设备及存储介质，该方法包括：获取待测眼底图像；基于检测算子，提取待测眼底图像的第一特征图像及第二特征图像；基于语义分割模型，提取待测眼底图像的空间形状特征图像；根据第一特征图像、第二特征图像及空间形状特征图像，重建待测眼底图像；将重建后的待测眼底图像输入血管分割模型，得到待测眼底图像的血管分割图像；其中，语义分割模型为根据眼底图像训练集训练得到的；血管分割模型为根据重建的眼底图像训练集训练得到的。本发明通过重建待测眼底图像，提升了图像清晰度，使图形特征更加明显，通过将重建后的眼底图像输入血管分割模型进行血管识别，得到了分割精度更高的血管分割图像。
引用：
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| GB/T 7714 | 吕思锐 , 李鹏智 , 杨鑫 et al. 一种眼底图像血管的识别方法、装置、电子设备及存储介质 : CN202110344357.1[P]. | 2021-03-30 . |
| MLA | 吕思锐 et al. "一种眼底图像血管的识别方法、装置、电子设备及存储介质" : CN202110344357.1. | 2021-03-30 . |
| APA | 吕思锐 , 李鹏智 , 杨鑫 , 李建强 . 一种眼底图像血管的识别方法、装置、电子设备及存储介质 : CN202110344357.1. | 2021-03-30 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
基于混合域注意力机制和残差网络的特纳综合征分类研究
关键词 ：
残差网络 残差网络 特纳综合征 特纳综合征 空间域注意力机制 空间域注意力机制 通道域注意力机制 通道域注意力机制
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| GB/T 7714 | 刘璐 , 李建强 , 陈适 et al. 基于混合域注意力机制和残差网络的特纳综合征分类研究 [J]. | 刘璐 , 2021 , 16 (2) : 16-20 . |
| MLA | 刘璐 et al. "基于混合域注意力机制和残差网络的特纳综合征分类研究" . | 刘璐 16 . 2 (2021) : 16-20 . |
| APA | 刘璐 , 李建强 , 陈适 , 中国数字医学 . 基于混合域注意力机制和残差网络的特纳综合征分类研究 . | 刘璐 , 2021 , 16 (2) , 16-20 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明提供一种药物推荐方法、装置、电子设备及存储介质。该方法包括：获取目标对象的相关信息；对所述相关信息进行隐私保护预处理；基于已进行隐私保护预处理的所述相关信息以及基于梯度提升决策树算法的模型来生成针对所述目标对象的药物推荐信息。本发明的药物推荐方法在准确、可靠的给患者推荐药物的同时，能够有效地保护患者的隐私；能够对不同类型的数据进行合适的隐私保护处理；推荐算法的鲁棒性较强；在不损失太多精度的情况下，更有效地保护患者的隐私。
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| GB/T 7714 | 李建强 , 李媛 , 王延安 . 药物推荐方法、装置、电子设备及存储介质 : CN202110022884.0[P]. | 2021-01-08 . |
| MLA | 李建强 et al. "药物推荐方法、装置、电子设备及存储介质" : CN202110022884.0. | 2021-01-08 . |
| APA | 李建强 , 李媛 , 王延安 . 药物推荐方法、装置、电子设备及存储介质 : CN202110022884.0. | 2021-01-08 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Several developments in computational image processing methods assist the radiologist in detecting abnormal breast tissue in recent years. Consequently, deep learning-based models have become crucial for early screening and interpretation of mammographic images for breast masses diagnosis, helping for successful treatment. Breast masses and calcification is an essential parameter for the prognosis of breast cancer. However, the mammographic image's mass detection needs a deeper investigation due to the breast masses' heterogeneity and anomalies' characteristics that are easily confused with other objects present in the image. Hence, this study proposed a deep learning-based convolutional neural network (ConvNet) that will incorporate both mammography and clinical variables to predict and classify breast masses to assist the expert's decision-making processes. We trained our proposed model with 322 scanned digital mammographic images of the MIAS (Mammogram Image Analysis Society) dataset and 580 images of the private dataset to evaluate the performance, which is highly imbalanced. This study aimed to perform an automatic and comprehensive characterization of breast masses using appropriate layers deep ConvNet model with high accuracy true-positive rate, decreased error rate and applying data-augmentation techniques. We obtained a classification accuracy of 97% applying the filtered deep features, which is the best performance from the existing approaches.
关键词 ：
breast masses classification breast masses classification computer aid diagnosis computer aid diagnosis data-augmentation data-augmentation deep convolutional neural network deep convolutional neural network image classification image classification
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| GB/T 7714 | Mahmood, Tariq , Li, Jianqiang , Pei, Yan et al. Breast Mass Detection and Classification Using Deep Convolutional Neural Networks for Radiologist Diagnosis Assistance [C] . 2021 : 1918-1923 . |
| MLA | Mahmood, Tariq et al. "Breast Mass Detection and Classification Using Deep Convolutional Neural Networks for Radiologist Diagnosis Assistance" . (2021) : 1918-1923 . |
| APA | Mahmood, Tariq , Li, Jianqiang , Pei, Yan , Akhtar, Faheem , Jia, Yanhe , Khand, Zahid Hussain . Breast Mass Detection and Classification Using Deep Convolutional Neural Networks for Radiologist Diagnosis Assistance . (2021) : 1918-1923 . |
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