2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)最新文献_第10页

Research of CDIO “Project-based” Education Quality Evaluation System CDIO“项目化”教育质量评价体系研究

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687253

Jing Wang, Xumei Yuan

引用次数: 0

Deep Convolutional Neural Networks with Transfer Learning for Neonatal Pain Expression Recognition 基于迁移学习的深度卷积神经网络用于新生儿疼痛表情识别

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687129

G. Lu, Qiang Hao, Kaiting Kong, Jingjie Yan, Haibo Li, Xiaonan Li

{"title":"Deep Convolutional Neural Networks with Transfer Learning for Neonatal Pain Expression Recognition","authors":"G. Lu, Qiang Hao, Kaiting Kong, Jingjie Yan, Haibo Li, Xiaonan Li","doi":"10.1109/FSKD.2018.8687129","DOIUrl":"https://doi.org/10.1109/FSKD.2018.8687129","url":null,"abstract":"For the neonatal pain expression recognition task, the recognition precision of the algorithms based on traditional machine learning isn't robust to the illumination and pose variations. The recognition algorithms based on deep learning usually rely on large-scale labeled training datasets, the recognition performance of these algorithms will be low when the labeled neonatal pain expression image dataset is small. To overcome these drawbacks, we present a neonatal pain expression recognition approach based on pre-trained deep convolutional neural network (DCNN) model with transfer learning. In this work, the introduction of transfer learning technology avoids the occurrence of over-fitting and accelerate the training procedure. Firstly, some typical DCNNs which have been trained on the ImageNet dataset, such as AlexNet, VGG-16, Inception- V3,ResNet-50 and Xception, are selected as the basic models to extract the general features of images. Then, in order to enhance the generalization ability of the DCNNs, the pre-trained DCNNs are fine-tuned by using the neonatal pain expression image dataset, and so that the feature transfer from the general image to the neonatal expression image is realized. Finally, we use different transfer learning methods to test the fine-tuned DCNN models. The experiment results show that the fine-tuned VGG-16 model achieved the best recognition accuracy (78.3 %) on the small neonatal pain expression image dataset, which indicates that the fine-tuning method can effectively obtain a DCNN model with good performance, and the transfer learning is an effective method for training DCNN when the available labeled training dataset is small. The effectiveness of DCNN and transfer learning for neonatal pain expression recognition shows promising application for clinical diagnosis.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"418 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133618972","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: 5

Inner Product Based Particle Swarm Optimization 基于内积的粒子群优化

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8686896

Xinchao Zhao, Jia Liu, Jiaqi Chen, Min Chen, Sai Guo, X. Zuo

{"title":"Inner Product Based Particle Swarm Optimization","authors":"Xinchao Zhao, Jia Liu, Jiaqi Chen, Min Chen, Sai Guo, X. Zuo","doi":"10.1109/FSKD.2018.8686896","DOIUrl":"https://doi.org/10.1109/FSKD.2018.8686896","url":null,"abstract":"Standard Particle Swarm Optimization (SPSO)is a well-known and very competitive swarm optimization approach, which is designed by Particle Swarm Central. In all PSO variants, the relative position relation between the individual and the global optimal position has important influences on the performance of algorithms. In this paper, an alternative Standard Particle Swarm Optimization (SPSO 2007)is proposed, which is based on the inner product of difference vectors. One particle will confuse which solution it should learn from when the global best and the personal best positions have comparable attractions to different directions during its velocity updating process. Even the oscillation phenomenon will appear that the global best solution draws the particle close to it at one generation and the personal best solution draws the particle back to it at next generation. In order to overcome this phenomenon particle adopts different velocity update strategies when the angle between difference vectors is either acute or obtuse of two directions in this paper. Two difference vectors refer to the current particle to the global and the personal best solutions. The vector level and the component level inner product based PSOs are proposed, denoted as IPSPSO2007V and IPSPSO2007C respectively. They are analyzed firstly and then compared with SPSO2007 with IEEE CEC2015 benchmarks, which indicate that two inner product based PSOs show promising performance.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133428258","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

Sharpening the WBSI Imagery of Tiangong-II: Gram-Schmidt and Principal Components Transform in Comparison 天宫二号WBSI图像锐化:Gram-Schmidt与主成分变换的比较

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687270

Qingsheng Liu

{"title":"Sharpening the WBSI Imagery of Tiangong-II: Gram-Schmidt and Principal Components Transform in Comparison","authors":"Qingsheng Liu","doi":"10.1109/FSKD.2018.8687270","DOIUrl":"https://doi.org/10.1109/FSKD.2018.8687270","url":null,"abstract":"With an increasing number of remotely sensed sensors acquired multi-spectral images over several separate wavelength ranges at various spectral resolutions, many sharpening techniques were developed to improve the lower spatial resolution imagery to become a same high spatial resolution multispectral dataset for meeting the demands of numerous applications. In this work, two well known sharpening techniques namely Gram-schmidt (GS) and Principal Components Transform (PC) were used to sharpen two shortwave infrared (SWIR) bands with the visible and near-infrared (VNIR) spectral bands of the Wide Band Spectral Imager (WBSI) on board the Tiangong-II space lab. It had been proved that the vegetation proportion of the image affected the CC value between the resized and the sharpened SWIR images when the different VNIR bands were used as the high spatial resolution band during the GS procedure. When the VNIR Band 1 was used as the high spatial resolution band, the quality of the sharpened SWIR images from the GS and PC sharpening method was similar. From the visual comparison of the sharpened results, the necessity of compromise between spatial resolution enhancement and spectral similarity was accepted, and for the different applications, the different sharpening techniques should be used.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133566316","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

Auto-Detection of Alzheimer's Disease Using Deep Convolutional Neural Networks 利用深度卷积神经网络自动检测阿尔茨海默病

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687207

Lu Yue, Xiaoliang Gong, Kaibo Chen, Mingze Mao, Jie Li, A. Nandi, Maozhen Li

{"title":"Auto-Detection of Alzheimer's Disease Using Deep Convolutional Neural Networks","authors":"Lu Yue, Xiaoliang Gong, Kaibo Chen, Mingze Mao, Jie Li, A. Nandi, Maozhen Li","doi":"10.1109/FSKD.2018.8687207","DOIUrl":"https://doi.org/10.1109/FSKD.2018.8687207","url":null,"abstract":"Alzheimer's disease(AD) is a kind of progressive neurodegenerative disease. One who is diagnosed as an Alzheimer's disease patient may has many symptoms, such as deterioration of memory and language. Once those symptoms was noticed, they usually can survive 4 to 20 years. So far, Alzheimer's disease has become the sixth leading cause of death, and it has become a worldwide health and social challenge. Traditional methods of diagnosing AD and mild cognitive impairment(MCI), mostly depend on capturing features from variable modalities of brain image data. It is a big challenge to pick out the MCI from normal controller (NC) and AD, especially for those who are lacking experience. In this article, we employ deep convolutional neural network (DCNN) to extract the most useful features of the structural magnetic resonance imaging (MRI). Firstly, the structural MRls are pre-processed in a strict pipeline. Then, instead of parcellating regions of interest, we re-slice each volume, and put the resliced images into a DCNN directly. Finally, four stages of Alzheimer's are identified, and the average accuracy is 94.5% for NC versus LMCI, 96.9% for NC versus AD, 97.2% for LMCI and AD, 97.81 % for EMCI versus AD, 94.8% for LMCI versus EMCI. The results show that the DCNN outperforms existing methods.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130041396","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: 34

Attribute Reducts and Decision Rules in Real Valued Information Systems with Fuzzy Decision 具有模糊决策的实值信息系统属性约简与决策规则

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687227

Yanling Wang, Yanyong Guan, Junli Zhou, Hongkai Wang

引用次数: 0

Discussing Alternative Login Methods and Their Advantages and Disadvantages 讨论可选的登录方法及其优缺点

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687163

Chandler Mitchell, Chen-chi Shing

引用次数: 3

Automatic Classification of Chinese Herbal Based on Deep Learning Method 基于深度学习方法的中药自动分类

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687165

Shupeng Liu, Weiyang Chen, Xiangjun Dong

{"title":"Automatic Classification of Chinese Herbal Based on Deep Learning Method","authors":"Shupeng Liu, Weiyang Chen, Xiangjun Dong","doi":"10.1109/FSKD.2018.8687165","DOIUrl":"https://doi.org/10.1109/FSKD.2018.8687165","url":null,"abstract":"In today's society, people's living standards are getting better and better. At the same time, many problems have also appeared in the diet, which has led to an increase in the incidence of diseases. Chinese herbal medicine has been widely used in the treatment of many diseases. But it is a problem for the collection and classification of Chinese herbal medicines. There are a wide variety of Chinese herbal medicine plants, and there are also some Chinese herbal medicine plants that look very similar. Even a taxonomist can hardly distinguish every herbal medicine, let alone for beginners. So we designs a method to automatically identify and classify Chinese herbal medicines by processing images and deep learning method, which can greatly reduce the workload, and improve the efficiency of work. The technology of Chinese herbal medicine recognition and identification based on image processing and deep learning method can effectively overcome the shortcomings of manual recognition that require rich experience. At present, deep learning is more and more popular, especially for image classification, so we use GoogLeNet to classify 50 kinds of Chinese herbal medicine by their images under natural conditions with complex backgrounds. And the method achieved good performance. TOP-1 achieved an accuracy of 62.8%, and TOP-5 achieved an accuracy of 89.4%.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133494487","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: 10

The Interference Cancellation Scheme for AF Successive Relay Networks Based on Postmultiplying Precoding 基于后乘预编码的AF连续中继网络干扰消除方案

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687198

Ran Deng, Gaoqi Dou, Jun Gao, Qingbo Wang

引用次数: 0

Learning From Fails in Crisis Management: Case of Stress Impact 从危机管理的失败中学习:压力影响的案例

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687304

Teffali Sammy Abdelghani, M. Nada, C. Eric

引用次数: 1