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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Zhang, Chuanlin Cao, Kai Huang, Mengge Deng, Tao School of Mathematics and Computer Science Northwest Minzu University Lanzhou China Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou China Key Laboratory of Streaming Data Computing Technologies and Application School of Mathematics and Computer Science Northwest Minzu University Lanzhou China

ISBN: (纸本)9780738110837

Convolutional long and short time memory network is a kind of fusion model, which inherits the excellent spatial feature extraction ability of convolutional neural network, and can effectively complete the processing and classification of time series data by using the memory ability of long and short time memory network to historical data and the unique gating mechanism. This paper uses the human behavior data set collected by the Wireless data Mining laboratory (WISDM) of Fordham University to predict and classify the six daily human behaviors: walking, jogging, going upstairs, going downstairs, sitting and standing. By comparing with long and short time memory network, convolutional neural network and other deep learning models, the experimental results show that the convolutional long and short time memory network has the best performance among them, which the accuracy reaches 97.43% and has a great improvement in real-time and accuracy. © 2021 IEEE.

关键词： Convolutional neural networks

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Creating Knowledge-Based Diagnostic Models by Mining Textual Diagnostic Reports of SPECT Scans

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Journal of Computer and Communications 2021年第5期9卷 10-19页

作者： Chuangui Cao Chengcheng Han Qiang Lin School of Mathematics and Computer Science Northwest Minzu University Lanzhou China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou China

Mining rich semantic information hidden in heterogeneous information network is one of the important tasks of data mining. Generally, a nuclear medicine text consists of the description of disease (i.e., lesions) and diagnostic results. However, how to construct a computer-aided diagnostic model with a large number of medical texts is a challenging task. To automatically diagnose diseases with SPECT imaging, in this work, we create a knowledge-based diagnostic model by exploring the association between a disease and its properties. Firstly, an overview of nuclear medicine and data mining is presented. Second, the method of preprocessing textual nuclear medicine diagnostic reports is proposed. Last, the created diagnostic modes based on random forest and SVM are proposed. Experimental evaluation conducted real-world data of diagnostic reports of SPECT imaging demonstrates that our diagnostic models are workable and effective to automatically identify diseases with textual diagnostic reports.

关键词： Text Classification Nuclear Medicine SPECT Imaging Diagnostic Model Random Forest SVM

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Collaborative filtering recommendation algorithm integrating user similarity and trust 4

Collaborative filtering recommendation algorithm integrating...

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2021 4th International Conference on Computer Information Science and application Technology, CISAT 2021

作者： Huang, Mengge Cao, Kai Zhang, Jingyi Zhang, Chuanlin Deng, Tao Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China

The rapid development of the Internet has brought convenience to people and has also produced the problem of "information overload". In view of the traditional collaborative filtering algorithm facing some bottlenecks to be solved, this study proposes a collaborative filtering algorithm that combines similarity and trust. First of all, in view of the large deviation of traditional similarity calculation and prediction of user ratings, this study proposes an optimized Pearson correlation coefficient calculation method;secondly, the trust relationship is established based on the user's rating of the common project, and the trust relationship between users who do not have a direct trust relationship is established through the transfer of trust;then find the nearest neighbor set of the target user through the fusion of user similarity and trust;finally, the item is scored and predicted to generate a recommendation list. Experimental results show that the algorithm proposed in this study can effectively improve the accuracy of recommendation. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Collaborative filtering

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Bone metastasis segmentation based on Improved U-NET algorithm 4

Bone metastasis segmentation based on Improved U-NET algorit...

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2021 4th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Zhang, Jingyi Huang, Mengge Deng, Tao Cao, Yongchun Lin, Qiang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China

Whole body bone scan image analysis is widely used in nuclear medicine to assist nuclear medicine physicians in the detection of bone metastases. At present, the analysis of whole-body bone scan images mainly relies on the manual reading of nuclear medicine doctors. The doctors, based on personal knowledge and experience, look for abnormal lesion locations and diagnose them by examining the whole-body bone scan images. However, this method is prone to misdiagnosis and missed diagnosis. To solve the above problems, this study proposes an image segmentation method based on deep learning, which can automatically identify the location of bone metastases, so that doctors can make more accurate diagnosis. The Methods Attention mechanism was added to the jump connection of the original U-NET network to enhance the image feature selection. Experiments show that the algorithm in this study teaches traditional U-Net to show better results on the three indicators of MIoU Dice and MAP. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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Deep learning SPECT lung perfusion image classification method based on attention mechanism 5

Deep learning SPECT lung perfusion image classification meth...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Zeng, Sitao Cao, Yongchun Lin, Qiang Man, Zhengxing Deng, Tao Wang, Rong Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou Gansu730030 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou Gansu730030 China Department of Nuclear Medicine Gansu Provincial Hospital Lanzhou Gansu730030 China

SPECT lung perfusion is an important functional imaging technology. It can capture the functional lesions of the lung in a non-invasive manner and has become an important clinical detection method for diseases such as pulmonary embolism. In order to realize the automatic detection of the degree of pulmonary embolism, this paper studies and constructs a deep classification model based on the attention mechanism. First, the normalization technique is used to convert the original lung perfusion file into a SPECT image;secondly, in view of the over-fitting phenomenon of the deep learning model caused by the small amount of medical image data and the unbalanced data, the image translation and rotation techniques are used to perform effective expansion;then, in order to improve the model's feature extraction ability, the attention mechanism is combined with the depth classification model to build a SPECT lung perfusion image classification model;finally, a set of real SPECT lung perfusion images were used to carry out comparative experiments on various depth classification models. The experimental results show that the model proposed in this paper can effectively detect the extent of lung disease lesions, and the classification accuracy rate exceeds 88%, which verifies the effectiveness and reliability of the classification model. © Published under licence by IOP Publishing Ltd.

关键词： Image classification

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SPECT bone imaging thyroid lesion segmentation

SPECT bone imaging thyroid lesion segmentation

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2021 International Conference on Big data and Intelligent Algorithms, BDIA 2021

作者： Mingyang-Luo Qiang-Lin Tongtong-Li Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing and Application Northwest Minzu University Lanzhou730124 China

Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83. © Published under licence by IOP Publishing Ltd.

关键词： Nuclear medicine

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Adjacent vertex distinguishing incidence coloring of semi-strong product of paths

Adjacent vertex distinguishing incidence coloring of semi-st...

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2020 International Conference on Computer Science and Management Technology, ICCSMT 2020

作者： Peng, Lizhen Tian, Shuangliang Lu, Qiqi Northwest Minzu University School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Lanzhou China

ISBN: (纸本)9781728186689

Adjacent vertex distinguishing incidence coloring of graph G is an incidence coloring that satisfies that adjacent vertices have different color sets, and the minimum color number is called the adjacent vertex distinguishing incidence coloring number of graph G, which is denoted as χai (G). In this paper, we study the adjacent vertex distinguishing incidence coloring of semi-strong products and strong products of paths of different orders, and obtain the exact values of the corresponding coloring numbers. © 2020 IEEE.

关键词： Coloring

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The acyclic coloring of the Corona product and Edge corona product of special graphs

The acyclic coloring of the Corona product and Edge corona p...

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2020 International Conference on Computer Science and Management Technology, ICCSMT 2020

作者： Lu, Qiqi Tian, Shuangliang Peng, Lizhen Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University School of Mathematics and Computer Science Lanzhou China

ISBN: (纸本)9781728186689

An acyclic coloring of a graph is a proper vertex coloring such that there are no bichromatic cycles. The acyclic chromatic number of G, denoted a(G), is the minimum number of colors required for acyclic coloring of a graph G. We study the acyclic coloring of the corona product, edge corona product of paths or circles and any simple graph, and the corresponding acyclic chromatic number are determined. © 2020 IEEE.

关键词： Coloring

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Existence of periodic and S-asymptotically periodic solutions to fractional diffusion equations with analytic semigroups

Existence of periodic and S-asymptotically periodic solution...

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作者： Mu, Jia Nan, Jiecuo Zhou, Yong Key Laboratory of China's Ethnic Languages and Information Technology of Ministry Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730000 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730000 China School of Mathematics and Computer Science Xiangtan University Hunan411105 China Nonlinear Analysis and Applied Mathematics Research Group Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

In this paper, we study periodic and S-asymptotically periodic solutions for fractional diffusion equations (FDE). As we all know, there is no exact periodic solution to differential equations with Caputo or Riemann-Liouville fractional derivatives. Even so, in this paper, periodic (S-asymptotically periodic) mild or classical solutions for FDE with Weyl-Liouville fractional derivatives could be obtained in various fractional power spaces. In addition, a numerical simulation example and a specific example of fractional diffusion equation are given to verify the main theoretical results. © 2019 John Wiley & Sons, Ltd.

关键词： Diffusion

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Segmentation of bone metastases based on attention mechanism

Segmentation of bone metastases based on attention mechanism

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Power Electronics, Computer applications (ICPECA), IEEE International Conference on

作者： Guoyi Che Yongchun Cao Ao Zhu Qiang Lin Zhengxing Man Haijun Wang Key Laborator of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou China Gansu Provincial Hospital Lanzhou China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

SPECT bone imaging is an important means to assist doctors in diagnosing diseases. The traditional processing method is that radiologists diagnose images. Manual diagnosis is not only cumbersome and time-consuming, but also different diagnosis results will be caused by the different diagnosis experience of doctors. In view of the above problems, this paper uses U-Net network as the basic model, and at the same time conducts model performance optimization research. Based on the U-Net network, the attention mechanism is integrated to segment the bone metastases in the pelvic area. Introducing the attention mechanism into the U-Net network can help improve the correlation of the pelvic region and reduce the interference caused by problems such as uneven brightness and low contrast to the model. Through multiple sets of experimental demonstrations, the U-Net network integrated with the attention mechanism can better segment bone metastases in the pelvic region based on SPECT images, and the model indicators have been significantly improved.

关键词： Image segmentation Medical services Interference Bones Single photon emission computed tomography Optimization Diseases

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