2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)最新文献

Space syntax and time distance based analysis on the influences of the subways to the pubic traffic accessibility in Nanchang city 基于空间句法和时间距离的南昌市地铁对公共交通可达性影响分析

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-11-08 DOI: 10.1109/FSKD.2017.8393169

Handan Zhang, B. Hu

引用次数: 1

Designing fuzzy apparatus to model dyslexic individual symptoms for clinical use 设计用于临床的模糊模型来模拟阅读困难的个体症状

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-30 DOI: 10.1109/FSKD.2017.8393410

Tereza Parilová, E. Hladká, P. Říha

{"title":"Designing fuzzy apparatus to model dyslexic individual symptoms for clinical use","authors":"Tereza Parilová, E. Hladká, P. Říha","doi":"10.1109/FSKD.2017.8393410","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393410","url":null,"abstract":"Along with the discovery of new facts and the development of new technologies and methodologies, more and more definitions and specifications emerge. The quantity of these emergences, however, can lead to paradoxical contradictions, which obscure borders. Often, we see this phenomenon of vagueness within natural language or non-exact topics. Fuzzy principles are therefore applied in wide range of (not only) scientific areas. In applied technical science, a user model based on interface and human computer cooperation meets such fuzzy borders. So, why not use it in assistive technology models? Fuzzy deals from its nature with linguistic variables and such variables are being transformed from numbers to expressions on predefined scales. Dyslexia is a neurobiological cognitive based disorder and ideal for (neuro) fuzzy computational modeling for many reasons. This paper describes the idea and process of using the fuzzy approach for obtaining information about individual problems of dyslexic users and differentiating between the type(s) of dyslexic user model he or she may belong to. Such information may serve for further clinical and psychological studies of dyslexia and linguistic based problems.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132136605","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

Face recognition with improved deep belief networks 改进深度信念网络的人脸识别

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393043

Rong Fan, Wenxin Hu

引用次数: 2

Exploiting semantic knowledge base for patent retrieval 利用语义知识库进行专利检索

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393111

Feng Wang, Lanfen Lin

引用次数: 4

A self-organizing community detection algorithm for complex networks 复杂网络自组织社区检测算法

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393291

Dongming Chen, Zhaoliang Song, Cenyi Luo, Xinyu Huang

引用次数: 0

Analyst intuition based Hidden Markov Model on high speed, temporal cyber security big data 基于分析师直觉的隐马尔可夫模型在高速、瞬时网络安全大数据中的应用

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393092

Teoh Teik Toe, Y. Nguwi, Y. Elovici, Ngai-Man Cheung, W. Ng

{"title":"Analyst intuition based Hidden Markov Model on high speed, temporal cyber security big data","authors":"Teoh Teik Toe, Y. Nguwi, Y. Elovici, Ngai-Man Cheung, W. Ng","doi":"10.1109/FSKD.2017.8393092","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393092","url":null,"abstract":"Hidden Markov Models (HMM) are probabilistic models that can be used for forecasting time series data. It has seen success in various domains like finance [1-5], bioinformatics [6-8], healthcare [9-11], agriculture [12-14], artificial intelligence[15-17]. However, the use of HMM in cyber security found to date is numbered. We believe the properties of HMM being predictive, probabilistic, and its ability to model different naturally occurring states form a good basis to model cyber security data. It is hence the motivation of this work to provide the initial results of our attempts to predict security attacks using HMM. A large network datasets representing cyber security attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides the weight of each attribute and forms a scoring system by annotating the log history. We applied HMM to distinguish between a cyber security attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally use HMM state-based approach. By doing so, our results are very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127007125","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

EEG signal analysis of patients with obstructive sleep apnea syndrome (OSAS) using power spectrum and fuzzy entropy 基于功率谱和模糊熵的阻塞性睡眠呼吸暂停综合征(OSAS)脑电信号分析

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393366

Szu-Yu Lin, Yu-Te Wu, W. Mao, Po-Shan Wang

{"title":"EEG signal analysis of patients with obstructive sleep apnea syndrome (OSAS) using power spectrum and fuzzy entropy","authors":"Szu-Yu Lin, Yu-Te Wu, W. Mao, Po-Shan Wang","doi":"10.1109/FSKD.2017.8393366","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393366","url":null,"abstract":"Sleep is important for the restoration and renewal of the human body. Obstructive sleep apnea syndrome (OSAS), which is caused by repetitive episodes of partial or complete upper airway obstruction during sleep, is the most common type of sleep apnea. The sleep electroencephalogram (EEG) analysis has been an important tool to investigate brain activity. In this study, we used the spectral analysis and fuzzy entropy to analyze the EEG signals collected from the OSAS patients and normal control. Results obtained from the EEG power spectrum and fuzzy entropy with and without principal component analysis (PCA) process were used as the features and fed into four different classifiers, namely, linear Support Vector Machines (SVM), Liner Discriminant Analysis (LDA), subspace k-nearest neighbor (k-NN) and subspace discriminant analysis, to differentiate these two groups. Our results demonstrated that the feature resulted from power spectrum with PCA process and subspace discriminate method using 5-fold cross-validation produces the superior classification rate which is 89 ± 3.74%.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123395874","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

Infrared image change detection of substation equipment in power system using random forest 基于随机森林的电力系统变电设备红外图像变化检测

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393030

Hua Yang, Jipu Gao, Changbao Xu, Zheng Long, Weigang Feng, S. Xiong, Shuaiwei Liu, Shan Tan

{"title":"Infrared image change detection of substation equipment in power system using random forest","authors":"Hua Yang, Jipu Gao, Changbao Xu, Zheng Long, Weigang Feng, S. Xiong, Shuaiwei Liu, Shan Tan","doi":"10.1109/FSKD.2017.8393030","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393030","url":null,"abstract":"Early detection of equipment faults plays a crucial role in power system, and automatic change detection of working status of an equipment is an efficient tool for this purpose. In this study, we proposed a novel method to automatically detect temperature change in local region of a substation equipment in power system using bi-temporal infrared images. We considered the change detection as two-class classification problem, and a supervised machine learning algorithm — Random Forest (RF) — was used for forecasting change trend. Various features were extracted from two temporal images for change detection. The features we extracted include gray-level, weighted intensity mean, RGB, LBP, gray-level histogram, and texture originating from the grayscale images and color images of the bi-temporal infrared images of the substation equipment. Cross validation was used to evaluate the robustness of these extracted features. Due to the existence of environmental noise, there are isolated detection points in the change detection results. In order to remove these isolated noise points and improve detection accuracy, we performed a morphological filtering on the detection results. Evaluation indexes such as Dice Similarity Index (DSI), kappa coefficient were used to evaluate the detection performance. Four classical change detection methods i.e. Image Differencing, Image Ratioing, Change vector analysis (CVA) and Principal Component Analysis (PCA) were tested for comparison purpose. Experimental results demonstrated that the proposed algorithm outperformed significantly these classical methods.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121230077","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: 4

Binary variational genetic programming for the problem of synthesis of control system 控制系统综合问题的二元变分遗传规划

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393051

A. Diveev, G. Balandina, S. Konstantinov

引用次数: 5

Convolutional neural networks for class attendance 卷积神经网络的课堂出勤率

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393379

Zhao Pei, Hai-Dong Shang, Yi Su, Miao Ma, Yali Peng

引用次数: 6