2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)最新文献_第4页

Big Data Mining Algorithm of Internet of Things Based on Artificial Intelligence Technology 基于人工智能技术的物联网大数据挖掘算法

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00032

W. Li

引用次数: 1

Application of quantum algorithms in secret keys 量子算法在密钥中的应用

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00030

Jiantang Zhang

引用次数: 0

Research on Multi-level Student Achievement Analysis Method Based on Cluster Analysis 基于聚类分析的多层次学生成绩分析方法研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00078

Na Wang, Minghai Yao, Jinsong Li

引用次数: 0

The Development of VR Technology and Computer Technology Integration Application 虚拟现实技术的发展与计算机技术的集成应用

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00105

Li Zhu, Yan Li, Lu Bai, Qingdi Yu, Mengqi Li

{"title":"The Development of VR Technology and Computer Technology Integration Application","authors":"Li Zhu, Yan Li, Lu Bai, Qingdi Yu, Mengqi Li","doi":"10.1109/ISAIEE57420.2022.00105","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00105","url":null,"abstract":"With the progress and development of science and technology, virtual reality(VR) technology has been widely used in military, medical, aerospace, architectural design, entertainment and other fields. VR technology is a comprehensive computer graphics technology, which is composed of multimedia technology, human It is a technology developed by a variety of science and technology such as computer interaction technology, network technology, stereoscopic display technology and simulation technology. This paper starts with the product characteristics of VR, studies and summarizes the characteristics that should be possessed at all levels in the integration of VR technology and computer, including composition characteristics, experience characteristics, technical characteristics and application characteristics, etc. The technical characteristics of VR products, and the research on VR interactive input methods. This paper systematically summarizes the interactive input characteristics of VR technology and computer technology, and then combines the traditional user interface design theories and compares the differences and commonalities between VR interfaces and traditional interfaces, and proposes information display strategies with VR characteristics. Interface presentation strategy and visual design strategy. The final results of the study show that, comparing the scores of the experimental group and the control group, the scores of the experimental group are higher than those of the control group. Therefore, VR technology can help to improve teaching results in classroom teaching.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"36 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":"121549503","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

Breast Cancer Classification Based on Various CNNs and Classifiers 基于各种cnn和分类器的乳腺癌分类

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00011

Yuchen Ge, Kejia Liu, Longxin Wang, Qianyi Xue

{"title":"Breast Cancer Classification Based on Various CNNs and Classifiers","authors":"Yuchen Ge, Kejia Liu, Longxin Wang, Qianyi Xue","doi":"10.1109/ISAIEE57420.2022.00011","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00011","url":null,"abstract":"Breast cancer is the second leading cause of death from cancer in women around the world. The CAD system utilizing machine learning and deep learning techniques facilitates the early detection of breast cancers. However, few recent studies focused on utilizing multiple feature extractors to compare and analyze the performances of various architectures. This paper analyzes the performances of architectures which are combinations of different feature extractors and classifiers in breast cancer diagnosis. Firstly, we collected histopathological breast cancer images from the BreakHis dataset. Secondly, the normalized data were converted to one-hot encoding for training, validating, and testing. Thirdly, we used VGG-16, VGG-19, Xception, ResNet50, Inception-V3, and Inception-Resnet-V2 to extract features. Next, fully connected layer (FCL), logistic regression (LR), and SVM were employed to classify breast cancers on the BreaKHis dataset. The experimental result shows that with the cyclical learning rate (CLR) policy, the ResNet50-SVM model obtained the optimal accuracy rate of 93.9% on eight-classification. The result shows that our proposed method could diagnose breast cancer with high accuracy.","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":"125403440","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

Substation Safety Control Robot System Based on 3D Virtual Simulation Technology 基于三维虚拟仿真技术的变电站安全控制机器人系统

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00035

Xinyu Liang, Yifan Hu, Haosen Wang, Yibing Wu, Jun Geng

引用次数: 0

Generation of Western Piano Music Based on Deep Learning 基于深度学习的西方钢琴音乐生成

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00113

Jiandong Tang, Lanqing Yin, Jinming Yu

引用次数: 0

Repairing Triple Data Erasure with Extending Row Diagonal Parity 用扩展行对角奇偶校验修复三重数据擦除

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00017

Xiaohe Liu

引用次数: 0

Behavior recognition research based on reinforcement learning for dynamic key feature selection 基于强化学习的动态关键特征选择行为识别研究

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00054

Zhang Tao, Chunmei Ma, Huazhi Sun, Yan Liang, Bo Wang, Yige Fang

{"title":"Behavior recognition research based on reinforcement learning for dynamic key feature selection","authors":"Zhang Tao, Chunmei Ma, Huazhi Sun, Yan Liang, Bo Wang, Yige Fang","doi":"10.1109/ISAIEE57420.2022.00054","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00054","url":null,"abstract":"In the task of behavior recognition based on time-series sequential data, there are often some features that are interference redundancies after feature extraction of the original data by the depth model, and these redundancies will not be beneficial to recognition but will have interference effects. Therefore, it is important to accurately select the features that are beneficial for recognition in behavior recognition tasks. To address the above issues, We propose a reinforcement learning framework, called Dynamic Key Feature Selection Network(DKFSN), aiming to achieve accuracy improvement by continuously exploring the advantages and disadvantages of distinguishing features, eliminating the redundant features that interfere with recognition, and retaining the features rich in quality information. First, feature extraction of the original data using a baseline network to capture depth features and prediction results. Using the depth features as input to a dynamic feature selection network to predict which features are retained and then making a determination to retain key features. Finally, behavior prediction by retained key features and feedback on the selection behavior using a reward function are used for the training of the DKFSN. We validated the validity of DKFSN on two public benchmark datasets.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"6 5 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":"122558810","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 Method of Training Neural Networks to Extract Wind-formed Sand Ripples 一种训练神经网络提取风形成的沙纹的方法

2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) Pub Date : 2022-12-01 DOI: 10.1109/ISAIEE57420.2022.00066

Chang-Beom An, Xiao-hong Dang, Z. Meng

引用次数: 0