2020 5th International Conference on Computational Intelligence and Applications (ICCIA)最新文献_第2页

Research on improved RFM customer segmentation model based on K-Means algorithm 基于K-Means算法的改进RFM客户细分模型研究

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00012

Yong Huang, Mingzhen Zhang, Yue He

引用次数: 9

A Natural Language-based Flight Searching System 基于自然语言的航班搜索系统

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00040

Xinfeng Ye, Mu Zhang, Zhaobin Liu

引用次数: 4

Research on Movie Recommendation Model Based on LSTM and CNN 基于LSTM和CNN的电影推荐模型研究

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00013

Wentao Wang, Chengxu Ye, Ping Yang, Zhikun Miao

引用次数: 12

A Small Sample Image Recognition Method Based on ResNet and Transfer Learning 基于ResNet和迁移学习的小样本图像识别方法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00022

Xiaozhen Han, Ran Jin

{"title":"A Small Sample Image Recognition Method Based on ResNet and Transfer Learning","authors":"Xiaozhen Han, Ran Jin","doi":"10.1109/ICCIA49625.2020.00022","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00022","url":null,"abstract":"With the vigorous development of artificial intelligence big data era and the advent of 5G era, the amount of network information shows a blowout-like growth. As a result, the accurate query of information is faced with unprecedented challenges. The image as a material reproduction of visual perception, has a large number of retrieval requests all the time, but the traditional image target recognition annotation is mainly based on pixel-level supervised learning. In the face of massive high-quality image recognition, it is very difficult for users to query the target content accurately and quickly. Therefore, this paper studies the animal classification model based on Convolutional Neural Network (CNN), using transfer learning to pre-train the characteristics of the network and combined with the hybrid classification model of CNN. In the experiment, CATS/DOGS were used as the data set, and PyTorch was used to train the network model. Experimental research shows that the accuracy of 96.43% is achieved by using CNN+ transfer learning algorithm, which is significantly higher than that of traditional methods. For small-scale data sets, it effectively solves the non-transferability of manual feature extraction, and improves the accuracy and robustness.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"3 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128777259","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: 9

ICCIA 2020 TOC

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/iccia49625.2020.00004

Iccia, Zhikun Miao, Yongbin Wang

引用次数: 0

ICCIA 2020 List Reviewer Page ICCIA 2020名单审查页面

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/iccia49625.2020.00007

引用次数: 0

Solving Nurikabe with Monte-Carlo Tree Serach 用蒙特卡罗树搜索求解Nurikabe

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00014

Ehsan Futuhi, Shayan Karimi

引用次数: 0

[Half-title page] (一半标题页)

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/iccia49625.2020.00001

引用次数: 0

Does ensemble really work when facing the twitter semantic classification? 当面对twitter语义分类时，集成真的有效吗?

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00015

Wenqiang Luo, Sheng Yi, Jiaxin Chen, Shuqing Weng, Zengwen Dong

引用次数: 2

Facial Complexion Recognition of Traditional Chinese Medicine Based on Computer Vision 基于计算机视觉的中药面部肤色识别

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00029

Yi Lin, Bin Wang

{"title":"Facial Complexion Recognition of Traditional Chinese Medicine Based on Computer Vision","authors":"Yi Lin, Bin Wang","doi":"10.1109/ICCIA49625.2020.00029","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00029","url":null,"abstract":"This paper makes an attempt to develop an automated facial complexion recognition method for objective and quantitative facial diagnosis. In TCM diagnosis, some regions of the face like Ting, Jia and Mingtang, can provide the most valuable information, so we use deep learning technique to determine the 68 landmarks of face and use their location to segment the regions of interest (ROI). The statistical characteristics of color histograms in multiple color space and texture features, lip color features are then introduced to describe the facial complexion. Finally, several machine learning methods including KNN, SVM and BPNN are used for classification. To verify the validity of our method, we collected a dataset of 575 face images from professional TCM medical institutions. The results show that the process of ROIs’ segmentation can improve the accuracy efficiently, higher than unsegmented image. The proposed method by fusing all three features achieves an accuracy of 91.03% which is higher than the existing methods and proves the effectiveness of the proposed method for facial complexion recognition. We confirm that extracting the complexion features particularly from the regions of interest of the face image achieves higher classification accuracy than characterizing the overall complexion directly from the unsegmented images. We show that the facial color features provide the most important clues for complexion classification among all the used features, which is consistent with the TCM diagnosis. Finally, we prove that the facial texture feature and lip color feature can be used as complementary clues and fused with the facial color features for further improving the complexion classification accuracy.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132260929","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