Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00016
Yunyun Liu, Wenrong Jiang
As a necessary protective equipment for workers to enter and exit the construction environment, safety helmets are of great significance to ensure the safe operation of workers. However, there are still some workers who lack safety awareness and do not wear safety helmets from time to time, and there are great safety hazards. This paper is based on the target detection algorithm of YOLOv4, focusing on the real construction site, and real-time detection of workers' helmet wearing in complex scenes. In order to solve the common phenomenon that only one type of helmet is detected, the helmet that is standing on the table or held in the hand is also recognized as a worker wearing a helmet. This article adds a human body model based on the helmet training. Training makes the detected helmet and the human body have a one-to-one correspondence. Experimental results show that the model achieves 93% accuracy on 9986 hard hat data sets. At the same time, the model has been deployed to the actual construction site to meet the daily detection of workers' hard hats, which verifies the effectiveness of the algorithm.
{"title":"Detection of wearing safety helmet for workers based on YOLOv4","authors":"Yunyun Liu, Wenrong Jiang","doi":"10.1109/ICCEAI52939.2021.00016","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00016","url":null,"abstract":"As a necessary protective equipment for workers to enter and exit the construction environment, safety helmets are of great significance to ensure the safe operation of workers. However, there are still some workers who lack safety awareness and do not wear safety helmets from time to time, and there are great safety hazards. This paper is based on the target detection algorithm of YOLOv4, focusing on the real construction site, and real-time detection of workers' helmet wearing in complex scenes. In order to solve the common phenomenon that only one type of helmet is detected, the helmet that is standing on the table or held in the hand is also recognized as a worker wearing a helmet. This article adds a human body model based on the helmet training. Training makes the detected helmet and the human body have a one-to-one correspondence. Experimental results show that the model achieves 93% accuracy on 9986 hard hat data sets. At the same time, the model has been deployed to the actual construction site to meet the daily detection of workers' hard hats, which verifies the effectiveness of the algorithm.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132689075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00039
Cheng Chen, L. Fan
Deep learning is the current advanced solution for remote sensing segmentation. Massive high-quality training datasets are the basic inputs to deep learning networks for solving the segmentation problems. Most of the existing remotely sensed image datasets have low segmentation accuracy due to their coarse spatial resolution and the susceptibility to image noise. Image augmentation is a technical means of effectively solving deep learning trainings in small and/or low-quality training datasets, which has continuously accompanied the development of deep learning and machine vision. Many augmentation techniques and methods have been proposed to enrich and augment the training datasets and to improve the generalization ability of neural networks. Common image augmentation methods are based mainly on image transformations, such as photometric changes, flips, rotations, dithering and blurring. In this paper, the segmentation task of multispectral remote sensing data is validated by augmentation methods. The segmentation accuracy was found to be 96.10%, which is higher than that (92.36%) of the corresponding un-augmented data.
{"title":"Scene segmentation of remotely sensed images with data augmentation using U-net++","authors":"Cheng Chen, L. Fan","doi":"10.1109/ICCEAI52939.2021.00039","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00039","url":null,"abstract":"Deep learning is the current advanced solution for remote sensing segmentation. Massive high-quality training datasets are the basic inputs to deep learning networks for solving the segmentation problems. Most of the existing remotely sensed image datasets have low segmentation accuracy due to their coarse spatial resolution and the susceptibility to image noise. Image augmentation is a technical means of effectively solving deep learning trainings in small and/or low-quality training datasets, which has continuously accompanied the development of deep learning and machine vision. Many augmentation techniques and methods have been proposed to enrich and augment the training datasets and to improve the generalization ability of neural networks. Common image augmentation methods are based mainly on image transformations, such as photometric changes, flips, rotations, dithering and blurring. In this paper, the segmentation task of multispectral remote sensing data is validated by augmentation methods. The segmentation accuracy was found to be 96.10%, which is higher than that (92.36%) of the corresponding un-augmented data.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116051747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00073
Yongsheng Xie, C. Feng, Chenhao Gai, Changgang Li
Emergency load shedding (ELS) is an essential measure to prevent power system accidents from expanding. Economy and security need to be optimized comprehensively for ELS. In this paper, an ELS optimization model is established, which takes the minimum load shedding amount as the objective function and the transient angle security, transient voltage deviation acceptability, transient frequency deviation acceptability, maximum controllable load as constraints. The social learning-based particle swarm optimization (SL-PSO) algorithm is proposed to solve the ELS optimization problem, which adopts adaptive parameters. The portable and open-source power system dynamic simulation toolkit (STEPS) is used for numerical simulation to check the feasibility of the solution. Finally, the efficiency of the solution is improved by parallel computing. The proposed model and algorithm are validated with the IEEE 39 bus test system.
{"title":"Optimization of Emergency Load Shedding Employing Social Learning-Based PSO","authors":"Yongsheng Xie, C. Feng, Chenhao Gai, Changgang Li","doi":"10.1109/ICCEAI52939.2021.00073","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00073","url":null,"abstract":"Emergency load shedding (ELS) is an essential measure to prevent power system accidents from expanding. Economy and security need to be optimized comprehensively for ELS. In this paper, an ELS optimization model is established, which takes the minimum load shedding amount as the objective function and the transient angle security, transient voltage deviation acceptability, transient frequency deviation acceptability, maximum controllable load as constraints. The social learning-based particle swarm optimization (SL-PSO) algorithm is proposed to solve the ELS optimization problem, which adopts adaptive parameters. The portable and open-source power system dynamic simulation toolkit (STEPS) is used for numerical simulation to check the feasibility of the solution. Finally, the efficiency of the solution is improved by parallel computing. The proposed model and algorithm are validated with the IEEE 39 bus test system.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124172984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00076
Hao He, Yong Yang
Intrinsically disordered proteins (IDPs) possess flexible 3-D structures, which make them play an important role in a variety of biological functions. We develop a method to predict intrinsically disordered proteins based on feature selection and convolutional neural networks (CNN). The combination of structural, physicochemical and evolutionary properties is used to describe the differences between disordered and ordered regions. Especially, to highlight the correlation between the target residue and adjacent residues, multiple windows are selected to preprocess the selected properties. After that, these calculated properties are combined into the feature matrix to predict IDPs through the constructed CNN. Our method is training as well as testing based on the DisProt database. The simulation results show that the proposed method can predict intrinsically disordered proteins effectively, and the performance is competitive in comparison with IsUnstruct and ESpritz.
{"title":"Prediction of Intrinsically Disordered Proteins with Convolutional Neural Networks based on Feature Selection","authors":"Hao He, Yong Yang","doi":"10.1109/ICCEAI52939.2021.00076","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00076","url":null,"abstract":"Intrinsically disordered proteins (IDPs) possess flexible 3-D structures, which make them play an important role in a variety of biological functions. We develop a method to predict intrinsically disordered proteins based on feature selection and convolutional neural networks (CNN). The combination of structural, physicochemical and evolutionary properties is used to describe the differences between disordered and ordered regions. Especially, to highlight the correlation between the target residue and adjacent residues, multiple windows are selected to preprocess the selected properties. After that, these calculated properties are combined into the feature matrix to predict IDPs through the constructed CNN. Our method is training as well as testing based on the DisProt database. The simulation results show that the proposed method can predict intrinsically disordered proteins effectively, and the performance is competitive in comparison with IsUnstruct and ESpritz.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122170621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00040
Yu Chen, Jun Zhang, Zhicheng Wang, Gejian Liao, Shu Liu, Hai Tan, Guowei Yang, Ying Fang, Shuai Wang, Zhaoqun Sun
Massive small files access is the main challenge for the Hadoop Distributed File System. To solve these problems, we present a new Algorithm of archive file, A Faster Read and Less Storage Algorithm for Small Files on Hadoop. A new logical file name is used to identify the file which generated by the pair in the name node. Our experiments show that the algorithm is around 76.6% faster than original HDFS in the time of file storing, and around 31.9.6% faster than original HDFS in the time of file reading, around 73.9% less than original HDFS in the memory consumption of namenode.
{"title":"A Faster Read and Less Storage Algorithm for Small Files on Hadoop","authors":"Yu Chen, Jun Zhang, Zhicheng Wang, Gejian Liao, Shu Liu, Hai Tan, Guowei Yang, Ying Fang, Shuai Wang, Zhaoqun Sun","doi":"10.1109/ICCEAI52939.2021.00040","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00040","url":null,"abstract":"Massive small files access is the main challenge for the Hadoop Distributed File System. To solve these problems, we present a new Algorithm of archive file, A Faster Read and Less Storage Algorithm for Small Files on Hadoop. A new logical file name is used to identify the file which generated by the pair in the name node. Our experiments show that the algorithm is around 76.6% faster than original HDFS in the time of file storing, and around 31.9.6% faster than original HDFS in the time of file reading, around 73.9% less than original HDFS in the memory consumption of namenode.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115730555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00038
Yan Zhimei, Pan Ping
The risk is uncertain. It is impossible to determine the influence of a risk by default risk, but only to cognize the influence degree of its existence. In an open behavior system, according to the quantum theory, the risk is reflected in the wave function of the behavioral system, and at risk, the dynamic evolution of the system is determined by the environmental Hamiltonian in system Hamiltonian. When the environment is perfectly correlated to the eigenvalue of the behavior subject, it does not affect its evolution, and the risk is controllable. Otherwise, the risk will harm the evolution of the behavior subject. Meanwhile, the corresponding control strategy is proposed through the dynamic analysis of the evolution of quantum wave function.
{"title":"Risk Analysis Based on Quantum Theory","authors":"Yan Zhimei, Pan Ping","doi":"10.1109/ICCEAI52939.2021.00038","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00038","url":null,"abstract":"The risk is uncertain. It is impossible to determine the influence of a risk by default risk, but only to cognize the influence degree of its existence. In an open behavior system, according to the quantum theory, the risk is reflected in the wave function of the behavioral system, and at risk, the dynamic evolution of the system is determined by the environmental Hamiltonian in system Hamiltonian. When the environment is perfectly correlated to the eigenvalue of the behavior subject, it does not affect its evolution, and the risk is controllable. Otherwise, the risk will harm the evolution of the behavior subject. Meanwhile, the corresponding control strategy is proposed through the dynamic analysis of the evolution of quantum wave function.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129597767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00020
Zhong Guoyun, Liu Jun, Hong Yang, Liu Meifeng, Sun Hongyang
A fine-grained image classification method based on generating adversarial networks with SIFT (Scale Invariant Feature Transform) texture input is proposed to improve the recognition ratio of fine-grained image classification by deep learning. For the phenomenon of data sets that require a large amount of labeled information for strong supervised learning, active learning capabilities of generative and adversarial networks and excellent image modeling capabilities for target classification images are used to achieve active learning of image features. Then the difficulty of data set construction and the computational complexity are reduced, and the disturbance to the network that may be caused by manually set labeled boxes is lessened. The input method of generating the adversarial network to is fixed to balance the authenticity and diversity of the generated samples. The idea of image restoration is considered. The random input method of the generative adversarial network that combines image feature points and random noise to is used to reduce the training difficulty of the generative and adversarial network. Experiments results show that our method outperformances the current deep learning methods in fine-grained image classification.
{"title":"Fine-grained image classification method based on generating adversarial networks with SIFT texture input","authors":"Zhong Guoyun, Liu Jun, Hong Yang, Liu Meifeng, Sun Hongyang","doi":"10.1109/ICCEAI52939.2021.00020","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00020","url":null,"abstract":"A fine-grained image classification method based on generating adversarial networks with SIFT (Scale Invariant Feature Transform) texture input is proposed to improve the recognition ratio of fine-grained image classification by deep learning. For the phenomenon of data sets that require a large amount of labeled information for strong supervised learning, active learning capabilities of generative and adversarial networks and excellent image modeling capabilities for target classification images are used to achieve active learning of image features. Then the difficulty of data set construction and the computational complexity are reduced, and the disturbance to the network that may be caused by manually set labeled boxes is lessened. The input method of generating the adversarial network to is fixed to balance the authenticity and diversity of the generated samples. The idea of image restoration is considered. The random input method of the generative adversarial network that combines image feature points and random noise to is used to reduce the training difficulty of the generative and adversarial network. Experiments results show that our method outperformances the current deep learning methods in fine-grained image classification.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129895098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00062
Zhuo Wang, Huan Li, Bin Nie, Jianqiang Du, Yuwen Du, Yufeng Chen
Aiming at the dimensional disaster and over-fitting problems in data analysis, this paper proposes a feature selection method using hybrid integration of difference models and random forests (Integrate-RF), firstly, Integrate-RF use CART, CHAID, SVM, BN, NN, K-Means, Kohonen to evaluate the importance of features, and then, for the above seven sorts, Integrate-RF use the arithmetic average method to calculate the importance of the features; secondly, Integrate-RF select the most important features from the remaining features into features subset, and use random forest classification to get the corresponding out-of-bag(OOB) data classification error rate; finally, the optimal features subset can be selected based on the OOB data classification error rate. Experiments show that feature selection methods proposed in this paper effectively reduces the data dimension, selects features better and more adaptable.
{"title":"Feature selection using different evaluate strategy and random forests","authors":"Zhuo Wang, Huan Li, Bin Nie, Jianqiang Du, Yuwen Du, Yufeng Chen","doi":"10.1109/ICCEAI52939.2021.00062","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00062","url":null,"abstract":"Aiming at the dimensional disaster and over-fitting problems in data analysis, this paper proposes a feature selection method using hybrid integration of difference models and random forests (Integrate-RF), firstly, Integrate-RF use CART, CHAID, SVM, BN, NN, K-Means, Kohonen to evaluate the importance of features, and then, for the above seven sorts, Integrate-RF use the arithmetic average method to calculate the importance of the features; secondly, Integrate-RF select the most important features from the remaining features into features subset, and use random forest classification to get the corresponding out-of-bag(OOB) data classification error rate; finally, the optimal features subset can be selected based on the OOB data classification error rate. Experiments show that feature selection methods proposed in this paper effectively reduces the data dimension, selects features better and more adaptable.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116141887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00100
Chaolin Peng, Lixiang Zhong
China is promoting the action of “using numbers to enrich wisdom” and cultivating new economy. As a typical small and medium-sized manufacturing enterprise, Dongguan Taiwei Electronics constructs intelligent lean system through intelligent factory construction, including intelligent lean central control room, station intelligent management center, automatic line intelligent management center, team intelligent management center, application equipment management system, combining lean production with intelligent manufacturing concept, using intelligent lean tools with deep knowledge precipitation, directly solve pain point problem. China's small and medium-sized manufacturing enterprises can promote intelligent lean through the path of “standardization, lean, digital, intelligent”, follow the basic strategy of “overall planning, step by step implementation, pull forward”, expand from the field of production to the field of research and development, marketing, and gradually achieve the goal of intelligence.
{"title":"Intelligent Transformation of Small and Medium-sized Manufacturing Enterprise in China - Case Study of Dongguan Taiwei Electronics","authors":"Chaolin Peng, Lixiang Zhong","doi":"10.1109/ICCEAI52939.2021.00100","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00100","url":null,"abstract":"China is promoting the action of “using numbers to enrich wisdom” and cultivating new economy. As a typical small and medium-sized manufacturing enterprise, Dongguan Taiwei Electronics constructs intelligent lean system through intelligent factory construction, including intelligent lean central control room, station intelligent management center, automatic line intelligent management center, team intelligent management center, application equipment management system, combining lean production with intelligent manufacturing concept, using intelligent lean tools with deep knowledge precipitation, directly solve pain point problem. China's small and medium-sized manufacturing enterprises can promote intelligent lean through the path of “standardization, lean, digital, intelligent”, follow the basic strategy of “overall planning, step by step implementation, pull forward”, expand from the field of production to the field of research and development, marketing, and gradually achieve the goal of intelligence.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133059754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective to explore the nursing intervention points of postoperative hypoglycemia in elderly patients with endometrial cancer and diabetes mellitus. Method: 50 elderly patients with hypoglycemia after endometrial cancer and diabetes were selected. During the nursing process, the author made a detailed record and follow-up observation, and summarized the data. Result: After treatment, the condition of some patients were under control, including 44 cases with obvious effect, 4 cases with general effect, 2 cases with no effect, and the effective rate was 98%. Conclusion: In the clinical nursing of endometrial cancer patients with diabetes mellitus, effective nursing can greatly improve the recovery effect of patients, and can effectively control the deterioration of the disease, so efficient nursing methods can be popularized in clinical.
{"title":"Nursing intervention of postoperative hypoglycemia in elderly patients with endometrial cancer and diabetes mellitus","authors":"Ting Sun, Huiqing Hua, Lijuan Gao, Fengju Chen, Lingling Wu","doi":"10.1109/ICCEAI52939.2021.00102","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00102","url":null,"abstract":"Objective to explore the nursing intervention points of postoperative hypoglycemia in elderly patients with endometrial cancer and diabetes mellitus. Method: 50 elderly patients with hypoglycemia after endometrial cancer and diabetes were selected. During the nursing process, the author made a detailed record and follow-up observation, and summarized the data. Result: After treatment, the condition of some patients were under control, including 44 cases with obvious effect, 4 cases with general effect, 2 cases with no effect, and the effective rate was 98%. Conclusion: In the clinical nursing of endometrial cancer patients with diabetes mellitus, effective nursing can greatly improve the recovery effect of patients, and can effectively control the deterioration of the disease, so efficient nursing methods can be popularized in clinical.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114078674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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