Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824721

Honglin Ji, Zijian Cui

引用次数: 0

Design of water quality monitoring system based on NB-IoT technolog 基于NB-IoT技术的水质监测系统设计

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824243

Yanbing Wang, Fang Lv

引用次数: 0

A Highly Accurate Attention-Based Convolutional Neural Network for Classification of Brain Tumors 用于脑肿瘤分类的高度精确的基于注意力的卷积神经网络

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825036

Xinyu Zhang

{"title":"A Highly Accurate Attention-Based Convolutional Neural Network for Classification of Brain Tumors","authors":"Xinyu Zhang","doi":"10.1109/cvidliccea56201.2022.9825036","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825036","url":null,"abstract":"Brain tumors have always been one of the common tumors threatening human life safety. At present, there are still relatively few computer-aided diagnostic systems in China specifically for detection of specific conditions of brain tumor, as well as related studies. This study collected a certain number of publicly available datasets of brain magnetic resonance imaging (MRI) images and data preprocessing such as normalization was conducted on it. According to the characteristics of medical image complexity of brain MRI, this study proposed an approach of incorporating attention mechanism with Convolutional Neural Network (CNN) to reduce the influence caused by irrelevant background information features in images. The experiment results based on the proposed method were compared with self-defined classic models such as VGGNet and MobileNet. Through testing on the dataset, the results show that the CNN model's accuracy after adding an attention mechanism improves significantly compared to the other three models, demonstrating that the attention mechanism in the model can reduce the impact of context irrelevant information to the classification outcome to some extent and performed well on the brain tumor recognition classification task. Finally, this paper deploys the trained analysis model on the web page, the interface is simple and friendly, and convenient for medical staff to operate.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"5 1","pages":"124-128"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80941682","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: 1

Design and implementation of question recommendation system based on deep knowledge tracing 基于深度知识追踪的问题推荐系统的设计与实现

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9823984

Shuai Guo

{"title":"Design and implementation of question recommendation system based on deep knowledge tracing","authors":"Shuai Guo","doi":"10.1109/cvidliccea56201.2022.9823984","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9823984","url":null,"abstract":"Online education has developed rapidly since 2020, and the completion of after-school exercises is a part of online education, which plays an important role in improving students’ knowledge. However, the existing question recommendation systems mainly have two problems: (1) The question recommendation is completely based on the parametric theoretical model. The parametric theoretical model parameterizes the questions and the students’ ability to answer the questions, so it cannot provide a personalized question recommendation strategy. (2) The question recommendation strategy depends on the teacher’s formulation, and the efficiency is not high. In order to solve the above two problems, this paper is based on deep knowledge tracing and uses a strategy for recommending questions for students’ weak knowledge points. This method first uses the deep knowledge tracing model to model students’ personal knowledge level, and then finds out students’ weak knowledge points. Recommend questions for students’ weak knowledge points. Under the real experimental data set, this method can recommend personalized questions for students without the participation of experts.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"20 1","pages":"1041-1046"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80994085","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

An improved multi-resolution 2D/3D registration method 一种改进的多分辨率2D/3D配准方法

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824315

Yipei Cao, Fei He, Feng Qu, Tiejun Wang, Chen Yang, Weili Shi, Zhengang Jiang

引用次数: 0

Mobile Crowdsensing Task Allocation Model Based on Discrete Particle Swarm Optimization 基于离散粒子群优化的移动众感知任务分配模型

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824089

S. Lou, Gang Liu, Zhiyu Chen, Jianwei Guo, Peng Liu

引用次数: 0

Research and implementation of real-time transmission technology for industrial interconnection 工业互联实时传输技术的研究与实现

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824933

Zhen He, Liang Ye