2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)最新文献

A Novel Algorithm for Fast Speckled Beacon Flicker Objects Detection in Complicated Environment 复杂环境下斑点信标闪烁目标快速检测新算法

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497905

Haiying Liu, Xingyu Mu, Lixia Deng, Yang Zhao

引用次数: 0

Applicable model of liver disease detection based on the improved CART-AdaBoost algorithm 基于改进CART-AdaBoost算法的肝病检测适用模型

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498046

Xutao Li, Xian Chen, Zhihang Yuan

{"title":"Applicable model of liver disease detection based on the improved CART-AdaBoost algorithm","authors":"Xutao Li, Xian Chen, Zhihang Yuan","doi":"10.1109/ICAICA52286.2021.9498046","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498046","url":null,"abstract":"Traditional diagnosis technology on earlier detection of some deadly liver diseases has many disadvantages. These shortcomings are due mainly to inadequate accuracy, which usually leads to failing to give liver patients timely treatment. In order to solve this problem, this paper used Classification and Regression Tree (CART) as a weak classifier of the AdaBoost framework to propose a Classification and Regression Tree-Adaptive Boosting (CART-AdaBoost) model. Moreover, the authors trained and verified the model basing on the Indian Liver Patient Dataset (ILPD) of UCI. The results showed that the model's accuracy was 83.06%, and its precision was 84.31%. Besides, F1-score could reach 80.75%, and the recall metric was 77.48%. All the former three indicators were higher than those produced by single models or combination models (weak classifier + AdaBoost) listed in this paper. Besides, it is worth noting that the prediction accuracy and precision of the CART-AdaBoost model were improved by a maximum value of 18.60% and 23.84%, respectively. Therefore, the suggested model is of great benefit in enhancing the early detection effect of liver diseases.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"486 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125120140","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

Optimization Model of Airport Taxi Riding Point Based on Neural Network and Genetic Algorithm 基于神经网络和遗传算法的机场出租车乘车点优化模型

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498107

Jinghang Li, Juanli Bai

引用次数: 0

Automatic generation of glass insulator formulations based on time-scale uniformity 基于时间尺度均匀性的玻璃绝缘子配方自动生成

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498134

Yongjian Fan, Fengyu Yang, Qing Du

{"title":"Automatic generation of glass insulator formulations based on time-scale uniformity","authors":"Yongjian Fan, Fengyu Yang, Qing Du","doi":"10.1109/ICAICA52286.2021.9498134","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9498134","url":null,"abstract":"The glass insulator formulation is a major factor affecting the production yield of glass insulators. In actual production, there are multiple production stages, such as incoming, laboratory and manufacturing, and there are inconsistencies in the corresponding time scales of the raw materials in the formula at different production stages. During the production process, the inconsistency in the time scale of each production stage causes the ratio of raw materials in the formulation to change frequently, which has a significant impact on the quality of the product. To solve the current problem that the generation of glass insulator recipes can only be achieved manually, which is time-consuming and labourintensive, and is prone to errors due to the inconsistent time scales of each production stage. we propose a method for automatic generation of glass insulator recipes based on uniform time scales in combination with machine learning, and evaluate the results of the method using MAPE and RMSE metrics. It is concluded that the time-scale-uniform glass insulator recipe generation method is more effective than the method without time-scale uniformity.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126627625","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-Task Network and Optimization for Face Detection and Attribute Analysis 人脸检测与属性分析的多任务网络与优化

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497937

Yunhao Lin, Zhibin Gao, Shenmin Zhang, Lizhong Li, Lianfeng Huang

引用次数: 0

Research on bearing fault diagnosis method based on deep convolutional neural network 基于深度卷积神经网络的轴承故障诊断方法研究

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497921

Yi-Ting Wei, Ronghao Li

{"title":"Research on bearing fault diagnosis method based on deep convolutional neural network","authors":"Yi-Ting Wei, Ronghao Li","doi":"10.1109/ICAICA52286.2021.9497921","DOIUrl":"https://doi.org/10.1109/ICAICA52286.2021.9497921","url":null,"abstract":"With the increasing complexity of the modern engineering environment, diagnosis-bearing fault under the changeable engineering condition is of great significance to managing the equipment’s health state. Therefore, to solve the traditional method that is difficult to extract bearing fault features and lower diagnostic accuracy accurately, this paper presents a bearing fault diagnosis method based on a deep convolutional neural network. Firstly, the original data are pre-processed by data enhancement. The bearing fault features are extracted by alternately superimposed convolution layer and pooling layer, which enhances the nonlinear expression ability of the model and enlarges the range of high and low-frequency features captured by the model. Finally, based on fault feature extraction, bearing fault types are classified by using the softmax function. The validity of the method is verified by the Case Western Reserve University experimental platform’s fault data. The experimental results show that the proposed method’s classification accuracy in the standard bearing fault diagnosis data set of CWRU is over 99.6%, which is better than that of the Long Short-Term Memory(LSTM) neural network and other traditional classifiers.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122056655","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

Computer Artificial Intelligence Test System in the Internet Information Age 互联网信息时代的计算机人工智能测试系统

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498184

Donglai Wu, Shilei Tang

引用次数: 0

Depression Tendency Detection for Microblog Users Based on SVM 基于SVM的微博用户抑郁倾向检测

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498003

Sicheng Liu, Jian Shu, Yunchun Liao

引用次数: 4

Depression tendency detection model for Weibo users based on Bi-LSTM 基于Bi-LSTM的微博用户抑郁倾向检测模型

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9497931

Xing Hu, Jian Shu, Zhaoyu Jin

引用次数: 3

Real Time Tunnel Structure Monitoring System Based on Edge Computing 基于边缘计算的隧道结构实时监测系统

2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) Pub Date : 2021-06-28 DOI: 10.1109/ICAICA52286.2021.9498000

DongDong Zhang, Li Liang, Wei Wang, Xiaogang Song, Xiaochun Ren

引用次数: 0