2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)最新文献_第9页

GA-XGBoost Driving Distraction State Classification Model Based on Non-Driving Related Tasks 基于非驾驶相关任务的GA-XGBoost驾驶分心状态分类模型

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00038

Yajie Bai, Liang Hong, Dan Wang, Zhenzhen Bi, Yi'er Lin

{"title":"GA-XGBoost Driving Distraction State Classification Model Based on Non-Driving Related Tasks","authors":"Yajie Bai, Liang Hong, Dan Wang, Zhenzhen Bi, Yi'er Lin","doi":"10.1109/ISAIEE57420.2022.00038","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00038","url":null,"abstract":"At present, the man-machine control switch does not consider that the driver in different driving behavior states and lead to certain differences in the takeover efficiency, in order to study the relationship between the physiological rhythm signal and the driving behavior state when the driver is in different driving behavior state, this paper proposes to establish a classification model of the driving behavior state through the physiological rhythm signal. Firstly, based on PreScan, the simulated driving environment is established, and the physiological instrument is used to monitor the physiological signals of the driver during the test, the physiological rhythm signal data set under different driving states is established, and the corresponding labels are assigned to different driving behavior states, the physiological data is used as the input of the model, and the driving behavior state is used as the output, and then the parameters of the single XGBoost model are optimized by genetic algorithm to generate a GA-XGBoost driving behavior state classification model, and the results show that The optimized model accuracy increased by 12.5%. Therefore, the classification model proposed in this paper can effectively judge the driving behavior state through the physiological rhythm signals of the human body, and use the changes of some physiological indicators of the driver to achieve real-time monitoring of driving distraction.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127019878","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}

引用次数: 0

Research on Optimization of cell Nucleus Image Segmentation based on U-Net 基于U-Net的细胞核图像分割优化研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00049

Jiayi Lin, Zishan Shu, Zihao Wu

{"title":"Research on Optimization of cell Nucleus Image Segmentation based on U-Net","authors":"Jiayi Lin, Zishan Shu, Zihao Wu","doi":"10.1109/ISAIEE57420.2022.00049","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00049","url":null,"abstract":"To solve the problem of low precision of nucleus segmentation in cell microscopic images, a method of nucleus segmentation based on deep learning is proposed. This paper presents a new network architecture Res18 U-Net++, which is based on the network framework of U-Net++ and introduces the residual module ResNet-18 and spatial attention mechanism. The network structure can make better use of the relationship between feature maps, and use set modules to enhance the reuse of feature information, thus improving the network performance. This work uses a large number of training data sets to train the network. Numerical calculation and experimental results show that this method can quickly and accurately achieve the segmentation of cell nucleus images, and achieve the effect of data enhancement. On this basis, the parameters of the model such as optimizer, loss function, learning rate, iteration value, and batch_size are modified and debugged, and images are preprocessed to try to increase image pixels to achieve better training results. In addition, the network framework proposed in this paper is compared with many network structures in the U-Net family, and the higher IoU value and smaller loss function show the advantages of the network framework proposed in this paper. Finally, validation nuclei segmentation is performed using the generative adversarial network trained on the validation dataset, demonstrating the feasibility of the method. The U-Net cell nucleus image segmentation method based on the convolutional neural network proposed in this paper is helpful for cell biology research and medical image cell nucleus processing.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127058226","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}

引用次数: 0

Research on classification and recognition method of plant leaves based on deep learning 基于深度学习的植物叶片分类识别方法研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00039

Limei Chang, Xue-wen Ding

{"title":"Research on classification and recognition method of plant leaves based on deep learning","authors":"Limei Chang, Xue-wen Ding","doi":"10.1109/ISAIEE57420.2022.00039","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00039","url":null,"abstract":"With the development of global science technology and economy, the coordination of social relations and ecological relations has become an important problem that needs to be solved urgently in contemporary development. Tree resources are the foundation of the ecosystem. In order to make better use of tree resources and implement strategies tailored to local conditions, it is necessary to identify the types of trees. Deep learning is an important method to distinguish tree species by analyzing leaves. Deep learning provides incomparable advantages of traditional object detection methods for accurately extracting the deep features and classification of leaf images. In order to achieve accurate and rapid classification and identification of plant leaves, this paper uses two network models, YOLOv5 network and image recognition ResNet-50 based on faster regions with convolutional (Faster-RCNN), to make a comparative study on the constructed 10 types of common plant leaf datasets. The experimental results show that yolov5 network can quickly and accurately identify plant leaves, and its lightweight model can be easily deployed on mobile terminals. Faster-RCNN network model can accurately extract the multi-layer feature images of plant leaves to obtain a average accuracy that is 1.1% higher than that of the YOLOv5, but the recognition speed is slower. It can be used for fine classification and leaf identification in subsequent plant experiments.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125607621","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}

引用次数: 0

Construction of Smart Rural Tourism Big Data Platform Based on Kano Model 基于Kano模型的智慧乡村旅游大数据平台建设

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00134

Huarong Gan

{"title":"Construction of Smart Rural Tourism Big Data Platform Based on Kano Model","authors":"Huarong Gan","doi":"10.1109/ISAIEE57420.2022.00134","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00134","url":null,"abstract":"With the rapid development of society and economy, the pace of life of urban residents has accelerated, and the pressure of work has increased. In order to get rid of the shackles of urban steel and concrete, people go to the suburbs or surrounding villages to experience the comfortable life rhythm, healthy ecological environment, and rich rural environment. The psychological appeal of local culture is increasing day by day; although rural tourism has achieved remarkable results in the country in recent years, rural tourism is still in its infancy in general, the level of rural tourism planning and construction is not high, the supporting facilities are not perfect, and the service level is low. This paper aims to study the construction of the smart rural tourism big data platform based on the KANO model. This paper discusses the background and importance of both theory and practice, and summarizes the research concept, research method and research process of this paper through the construction of smart travel platforms at home and abroad. Then, it briefly introduces big data, smart travel, smart travel service platform, related big data technology and science, and summarizes the relationship between smart travel and big data. This paper expounds the development status of rural tourism, constructs KANO model data, and analyzes the position that various attributes should occupy in rural tourism. Experiments have shown that the effectiveness of the system functions in this article is more than 95%, but the user satisfaction in the souvenir mall needs to be improved. The rural tourism data using this article's smart service platform has doubled compared to before use, and the peak season is throughout the year.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128007664","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}

引用次数: 0

Vehicle Recognition Based on Improved Faster R-CNN 基于改进更快R-CNN的车辆识别

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00025

Feixiang Du, Ling Xu, Kunwei Tang, Anqi Wang, Yucheng Wan, Xiaoling Zeng

引用次数: 0

Performance Analysis of Utilizing Reed Solomon Code in Redundant Array of Independent Disk 独立磁盘冗余阵列中Reed Solomon码的性能分析

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00022

Zixuan Jiang

{"title":"Performance Analysis of Utilizing Reed Solomon Code in Redundant Array of Independent Disk","authors":"Zixuan Jiang","doi":"10.1109/ISAIEE57420.2022.00022","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00022","url":null,"abstract":"The Redundant array of independent disks (RAID) is a method of storing the same data on multiple hard disks in different places. By placing data on multiple hard disks, I/O operations can overlap in a balanced way, improving performance. RAID utilizes multiple disks to achieve higher data reliability at the cost of reduced storage capacity. RAID6 has become a popular option for RAID disk array due to its capability to recover two disk failures in a setup with two parity disks. A RAID6 setup usually includes several data disks and two parity disks. Although the general setup is similar, in practice there are a variety of encoding scheme to achieve similar effect. Two commonly used coding schemes are Reed-Solomon (RS) code and EVENODD code. This article will discuss the effectiveness of RS code in theory, by calculating the data I/O operation needed to read, write and rebuild disk in both coding schemes, and compare the result against that of EVENODD coding. Simulation of RAID6 structure will also be performed, both under ideal condition and non-ideal condition, to consider the influence of real factor such as degrading disk has on the effectiveness of such RAID structures.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133230105","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}

引用次数: 0

Research on RBF Neural Network Model Based on Particle Swarm Optimization 基于粒子群优化的RBF神经网络模型研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00062

Yue Meng, Yuedong Zhang

引用次数: 0

Iterative XGBoost Algorithm and Application on Modeling the Cognitive Laws of Chinese Characters Based on Psychological Cognitive Feature Extraction 基于心理认知特征提取的迭代XGBoost算法及其在汉字认知规律建模中的应用

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00124

Yihan Wang, Xiaopeng Ren

{"title":"Iterative XGBoost Algorithm and Application on Modeling the Cognitive Laws of Chinese Characters Based on Psychological Cognitive Feature Extraction","authors":"Yihan Wang, Xiaopeng Ren","doi":"10.1109/ISAIEE57420.2022.00124","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00124","url":null,"abstract":"Chinese character learning is an important subject in primary school teaching. The survey shows that students have different degrees of difficulty in recognizing different Chinese characters. Firstly, according to the laws of pedagogy and students' cognitive psychology, this paper extracted some characteristics of Chinese characters as the independent variables of the study. Secondly, students were tested for Chinese character cognition, and the average score of each Chinese character was calculated to identify the difficulty of Chinese characters, which was used as the dependent variable of the study. Then, the cognitive difficulty classification problem of Chinese characters was modeled as a binary classification problem in machine learning, and an iterative XGBoost algorithm is proposed to complete the modeling task. When the classification model was applied to the test data set, the accuracy rate, recall rate and F1 score are 84.5%, 90.0% and 87.2% respectively. The model has good classification accuracy, and has certain reference significance for teachers to carry out personalized teaching.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133408718","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}

引用次数: 0

A Fast and Effective Classification Method for Missing Data 一种快速有效的缺失数据分类方法

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00052

Y. Liu, Chaoya Wang, Wenxin Sun

引用次数: 0

Study on the Learning Effect of Intelligent Foreign Language Learning System based on Data Mining Technology 基于数据挖掘技术的智能外语学习系统的学习效果研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00092

Pin Li

{"title":"Study on the Learning Effect of Intelligent Foreign Language Learning System based on Data Mining Technology","authors":"Pin Li","doi":"10.1109/ISAIEE57420.2022.00092","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00092","url":null,"abstract":"The intelligent foreign language learning system has become an indispensable learning tool for college students. The rich learning resources in the system can help students improve their foreign language viewing, listening, speaking, reading, writing and translation. This study collected the learning behavior data generated by some students of China University of Geosciences (Wuhan) in the intelligent Foreign Language learning system, and selected the total learning time, the average length of stay in each online learning, the daily homework scores, and the number of discussions and exchanges as the feature data. After data preprocessing, K-means clustering algorithm is used for cluster analysis. Setting the number of clusters to 4 and the number of iterations to 20 can obtain better analysis results. The results of cluster analysis show that the learning behavior of students in the foreign language intelligent learning system is closely related to the learning effect, and the students with excellent learning behavior tend to have a better learning effect. Teachers can use the data mining method provided by this research to conduct a regular cluster analysis of students' learning effects, and timely give warning of learning and teaching intervention according to the analysis results.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115759950","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}

引用次数: 0