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2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)最新文献

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A Graph Convolutional Network-Based Approach for Community Detection in Attributed Networks 基于图卷积网络的属性网络社区检测方法
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00068
Zaisheng Wang, Xiaofeng Wang, Guodong Shen, Zengjie Zhang, Daying Quan, Jianhua Li
{"title":"A Graph Convolutional Network-Based Approach for Community Detection in Attributed Networks","authors":"Zaisheng Wang, Xiaofeng Wang, Guodong Shen, Zengjie Zhang, Daying Quan, Jianhua Li","doi":"10.1109/ICAICE54393.2021.00068","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00068","url":null,"abstract":"Community detection has attracted widespread attention since it helps reveal geometric structures and latent functions of complex networks. Recently community detection has been revisited with the development of network representation learning, many approaches have been presented, including graph convolutional network (GCN) based methods. Existing GCN-based community detection methods usually rely on a considerable number of prior labels to infer unknown nodes. To address this problem, we propose a new GCN-based method for community detection in attributed networks without any label information. Based on the local self-organization characteristics of the communities, we integrate a label sampling model and the shallow GCN architecture into an unsupervised learning framework, the former helps construct a balanced training set via a local expansion strategy to train GCN. Moreover, we reveal the underlying community structures by fusing topology and attribute information. Experimental results on several real-world networks indicate our method is effective compared with the state-of-the-art community detection algorithms.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128390407","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
On Design and Implementation of Health Reminder System Based on Apple iOS Platform 基于Apple iOS平台的健康提醒系统设计与实现
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00118
Shi Ge, Huichao Huang, Jing-Chiou Liou
{"title":"On Design and Implementation of Health Reminder System Based on Apple iOS Platform","authors":"Shi Ge, Huichao Huang, Jing-Chiou Liou","doi":"10.1109/icaice54393.2021.00118","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00118","url":null,"abstract":"This study provides a preliminary understanding of the effectiveness of wearable devices used for health information monitoring and alerting on health hazards. The article designs a health reminder system tracking client based on platform of Apple's iOS system, and it downloads scientific exercise prescriptions from health cloud via iOS system-based intelligent mobile terminal device. These suggestions on scientific exercise are proposed by comprehensively calculating personal information of users, and through wearable devices, physiological data of users in obeying exercise prescriptions can be monitored. In accordance with build-in coprocessor of Apple mobile devices, basic exercise data of users is obtained, which facilitates tracking and evaluating execution intensity and progress of exercise prescriptions, so as to achieve scientific and effective health reminders.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134517411","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
Sleep staged method based on 2D CNN-MEMM model 基于二维CNN-MEMM模型的睡眠分段方法
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00092
Gang Tao, Hongqiong Huang
{"title":"Sleep staged method based on 2D CNN-MEMM model","authors":"Gang Tao, Hongqiong Huang","doi":"10.1109/ICAICE54393.2021.00092","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00092","url":null,"abstract":"Sleep staged is an important process to detect sleep quality and diagnose sleep disorders. The traditional sleep staged method has the disadvantage of insufficient accuracy and the problem of the upper limit of classification accuracy, so a method of automatic sleep stageding used jointly by 2D CNN and MEMM is proposed. Sampled from about 95 hours of sleep EEG test data from 4 subjects, the epoch-wish classification was first performed using 2D CNN and Subject-wish classification was used by MEMM. The main idea of this model is to use 2D CNN to automatically extract features from the original EEG signal, classify them by softmax, and then use the MEMM model to convert the sleep phase of the adjacent EEG cycle into a priori message, so as to improve the S2 classification performance, thereby improving the classification performance of 2D CNN. Experimental studies show that the overall accuracy of the model on the Sleep-EDF Database Expanded data set is 90.3%, and it is proved that the model can provide a way to evaluate sleep quality.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134536049","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
Scattering Speckle Information Extraction Network for Aircraft Detection in SAR Images SAR图像中飞机检测的散射散斑信息提取网络
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00137
Sizhe Lin, Xiaohong Huang, Mingwu Li
{"title":"Scattering Speckle Information Extraction Network for Aircraft Detection in SAR Images","authors":"Sizhe Lin, Xiaohong Huang, Mingwu Li","doi":"10.1109/icaice54393.2021.00137","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00137","url":null,"abstract":"Accurate detection of aircrafts in synthetic aperture radar (SAR) images has an important role in both military and civilian areas. However, aircrafts in SAR images are composed of several scattering speckles whose intensity and distribution are sensitive to the environmental factors and imaging conditions. Existing detection methods have difficulties capturing variable information of these scattering speckles. To alleviate this problem, a scattering speckle information extraction network (SSIEN) based on deep learning is proposed, which is capable of adaptively extracting speckle information of the targets for aircraft detection in various scenarios. In SSIEN, an information enhancement and extraction module (IEEM) is proposed. IEEM integrates two key components, weakly supervised deformable convolution module (WSDCM) and convolution block attention module (CBAM). The former is proposed to locate the speckles and extract the information, while the latter is employed to highlight valuable information and suppress interference. The experimental results on Gaofen-3 SAR images demonstrate the excellent performance of the proposed network for SAR image aircraft detection.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132579686","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
3D Reconstruction Using a Linear Laser Scanner and A Camera 使用线性激光扫描仪和相机的三维重建
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00131
Rui Wang
{"title":"3D Reconstruction Using a Linear Laser Scanner and A Camera","authors":"Rui Wang","doi":"10.1109/ICAICE54393.2021.00131","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00131","url":null,"abstract":"With the rapid development of computer graphics and vision, several three-dimensional (3D) reconstruction techniques have been proposed and used to obtain the 3D representation of objects in the form of point cloud models, mesh models, and geometric models. The cost of 3D reconstruction is declining due to the maturing of this technology, however, the inexpensive 3D reconstruction scanners on the market may not be able to generate a clear point cloud model as expected. This study systematically reviews some basic types of 3D reconstruction technology and introduces an easy implementation using a linear laser scanner, a camera, and a turntable. The implementation is based on the monovision with laser and has tested several objects like wiki and mug. The accuracy and resolution of the point cloud result are quite satisfying. It turns out everyone can build such a 3D reconstruction system with appropriate procedures.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114508321","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: 2
IMAGE DETECTION OF DENTAL DISEASES BASED ON DEEP TRANSFER LEARNING 基于深度迁移学习的牙齿疾病图像检测
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00151
Jiakai Zhang, Xiaodong Li, Zhigang Gao, Jing Chen
{"title":"IMAGE DETECTION OF DENTAL DISEASES BASED ON DEEP TRANSFER LEARNING","authors":"Jiakai Zhang, Xiaodong Li, Zhigang Gao, Jing Chen","doi":"10.1109/ICAICE54393.2021.00151","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00151","url":null,"abstract":"Traditional dental disease detection is done by doctors using naked eyes directly, which contains many uncertain factors for misdiagnosis and missed diagnosis. In order to improve the accuracy and efficiency of the detection of dental diseases, a dental disease image detection assistance system based on deep transfer learning is designed, which can autonomously recognize the photos obtained from the camera that assists the doctor in the detection. Performing transfer training on the trained model on the tooth data set, retain all pretrained convolutional layer parameters, and fine-tune the model to be more suitable for tooth image recognition. At the same time, AlexNet, GoogLeNet, and VGG models will be used for traditional deep learning training and the results obtained will be compared and analyzed with the results obtained by deep transfer learning in terms of accuracy and timeliness.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122099386","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: 2
EEG-based Confusion Recognition Using Different Machine Learning Methods 使用不同机器学习方法的基于脑电图的混淆识别
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00160
S. He, Yanran Xu, Lanyi Zhong
{"title":"EEG-based Confusion Recognition Using Different Machine Learning Methods","authors":"S. He, Yanran Xu, Lanyi Zhong","doi":"10.1109/ICAICE54393.2021.00160","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00160","url":null,"abstract":"Massive Open Online Course (MOOC) has emerged as a key trend. As a way of teaching online, the main shortcoming of MOOC is lacking feedback because there is a distance in both time and space between teachers and students. This study proposes the confusion recognition system based on Electroencephalography(EEG). We apply machine learning methods, including Naive Bayes, KNN, Random Forest, XGBoost, and also a deep learning method, LSTM, on the EEG data set respectively to detect whether a student feel confused. We find that LSTM shows better performance than any machine learning methods we use. The average accuracy of LSTM classifier is 78.1%. This study shows the significance of detecting confusion through EEG and helping students in improving learning efficiency.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121968759","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: 2
Hurricane Damage Detection using Convolutional Neural Network and Customized KNN 基于卷积神经网络和自定义KNN的飓风损伤检测
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00099
Bohan Zhang
{"title":"Hurricane Damage Detection using Convolutional Neural Network and Customized KNN","authors":"Bohan Zhang","doi":"10.1109/icaice54393.2021.00099","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00099","url":null,"abstract":"Hurricane hits have great harm to people's lives, and it is very important to provide help to the affected area in time after the hit. This paper proposes a model to predict whether a hurricane damages a house through satellite images. I apply a logistic regression model and two convolutional neural network models and find the AlexNet's best performance. To use the location information of the images, I make certain modifications to the KNN model and combine it with AlexNet for hurricane damage detection and classification. I find that the new model has the best classification result, with an accuracy rate of 95.39% and an F1 value of 0.9739. The model-based method can better help relevant government departments and provide timely and accurate assistance to the disaster-stricken areas after the hurricane hits.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129286354","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
Efficiency for Face Mask Detection in Neural Network 基于神经网络的人脸检测效率研究
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00079
Yi-Chun Fang
{"title":"Efficiency for Face Mask Detection in Neural Network","authors":"Yi-Chun Fang","doi":"10.1109/icaice54393.2021.00079","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00079","url":null,"abstract":"Because of COVID-19, wearing a face mask has become the most efficient and convenient way to spread this virus. Face mask detection can fulfill the function of warning those people who do not wear a face mask. Using the Convolutional neural network, the Feedforward Neural Network and the MobileNet V2, a high recognition rate for the face mask detecting system can be achieved. This study compares the accuracy, the loss and the training time for these models and concludes that CNN is the best model based on its high accuracy of 100%. The result that comes out from our study can improve the efficiency of the face mask detecting system. In general, the identification model in our study can be changed easily to apply in other areas, such as medical image classification and geographic image classification.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121439270","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
Multi-Rotor UAV Autonomous Tracking and Obstacle Avoidance Based on Improved DDPG 基于改进DDPG的多旋翼无人机自主跟踪与避障
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00059
Wen Chao, D. Han, Xiewu Jie
{"title":"Multi-Rotor UAV Autonomous Tracking and Obstacle Avoidance Based on Improved DDPG","authors":"Wen Chao, D. Han, Xiewu Jie","doi":"10.1109/icaice54393.2021.00059","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00059","url":null,"abstract":"To solve the problem of multi-rotor UAV autonomous tracking dynamic ground targets in obstacles environment, we used Markov decision process (MDP) to establish an autonomous maneuvering model of multi-rotor. Considering the obstacle avoidance requirements of UAV during the tracking process, we integrated the Long Short-Term Memory (LSTM) neural network with memory unit and time series data processing characteristics into the Deep Deterministic Policy Gradient (DDPG) algorithm framework, so that the Actor network can fully refer to the prior state information when making decisions. Finally, the performance test was implemented on the UAV 3D simulation platform based on Robot Operating System (ROS). The results show that the method proposed in this paper can enable the UAV to complete the whole process of autonomous tracking of the ground dynamic target. Compared with the traditional DDPG algorithm, the DDPG algorithm combined with LSTM has stronger accuracy and real-time performance, and can better meet the tracking and obstacle avoidance mission requirements of the multi-rotor UAV.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124333894","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
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