2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献_第2页

Neural networks for sentiment analysis on Twitter 推特情感分析的神经网络

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259397

B. Duncan, Yanqing Zhang

引用次数: 52

Formal properties and rules of concept algebra 概念代数的形式性质和规则

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259376

M. Valipour, Yingxu Wang

引用次数: 8

Support vector machines and Word2vec for text classification with semantic features 支持向量机和Word2vec用于语义特征的文本分类

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259377

Joseph Lilleberg, Yun Zhu, Yanqing Zhang

引用次数: 380

Recognizing the sentiments of web images using hand-designed features 使用手工设计的功能来识别网页图像的情感

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259380

Eunjeong Ko, Eun Yi Kim

引用次数: 6

Novel biometric features fusion method based on possibility theory 基于可能性理论的生物特征融合新方法

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259419

Hanêne Guesmi, H. Trichili, A. Alimi, B. Solaiman

引用次数: 5

Research of micro-blog diffusion effect based on analysis of retweet behavior 基于转发行为分析的微博扩散效应研究

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259394

Wei Hou, Yuan Huang, Kao Zhang

引用次数: 6

Analysis of speech imagery using brain connectivity estimators 使用脑连通性估计器分析语音图像

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259410

C. Sandhya, G. Srinidhi, R. Vaishali, M. Visali, A. Kavitha

{"title":"Analysis of speech imagery using brain connectivity estimators","authors":"C. Sandhya, G. Srinidhi, R. Vaishali, M. Visali, A. Kavitha","doi":"10.1109/ICCI-CC.2015.7259410","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259410","url":null,"abstract":"The estimation of brain connectivity allows description of the functional links established between different cortical areas during different forms of mental imagery. Speech imagery is a form of mental imagery, which refers to the activity of talking to oneself in silence. In this paper, coherence, an EEG synchronicity parameter is calculated to quantitatively analyze the concurrence of the different regions of the brain while performing speech imagery. Brain connectivity measures of speech imagery based on EEG were also investigated to understand brain function. In particular, Granger causality parameters such as Partial Directed Coherence (PDC) and Directed Transfer Function (DTF) measurements based on MVAR models are applied to multi-channel EEG data to find direction and strength of the connectivity patterns of the given speech imagery task. From the results obtained, it can be observed that there is a bilateral brain interaction of frontal and temporal brain regions andthe cross electrode coherence of the left frontal lobe was found to be high during speech production and that of the left temporal lobe was found to be high during speech imagery due to the proximity of the electrodes to the Broca's and Wernicke's area respectively. It can also be concluded that the direction of information flow from left hemisphere of the brain is more than right hemisphere of the brain using brain connectivity parameters based on MVAR models. Thus, the perceptibility of verbalizations in the brain, or in other words, speech imagery can be captured through EEG and the observations suggest that the proposed methodology is a promising non-invasive approach to study directional connectivity in the brain between mutually interconnected neural populations.","PeriodicalId":328695,"journal":{"name":"2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373694","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: 8

An analytical multiobjective optimization of joint spectrum sensing and power control in cognitive radio networks 认知无线电网络中频谱感知与功率控制的多目标分析优化

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259360

Hieu V. Dang, W. Kinsner

引用次数: 10

Online biomedical publication classification using Multi-Instance Multi-Label algorithms with feature reduction 基于特征约简的多实例多标签在线生物医学出版物分类

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259391

Dong Ren, Long Ma, Yanqing Zhang, Rajshekhar Sunderraman, P. Fox, A. Laird, J. Turner, Matthew D. Turner

{"title":"Online biomedical publication classification using Multi-Instance Multi-Label algorithms with feature reduction","authors":"Dong Ren, Long Ma, Yanqing Zhang, Rajshekhar Sunderraman, P. Fox, A. Laird, J. Turner, Matthew D. Turner","doi":"10.1109/ICCI-CC.2015.7259391","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259391","url":null,"abstract":"Text annotation, the assignment of metadata to documents, requires significant time and effort when performed by humans. A variety of text mining methods have been used to automate this process, many of them based on either keyword extraction or word counts. However, when using keywords as text classification features, it is common to find that (1) the number of training instances is much less than the number of features extracted. This complexity affects text classification performance. Another challenge is (2) the assignment of multiple, non-exclusive labels to the documents (multi-label classification). This problem makes text classification more complicated when compared with single label classification. We use, as an example, a set of expertly labeled documents from the human functional neuroimaging literature, and we apply a Multi-instance Multi-label (MIML) classification algorithm to the problem. To address (1), we apply a feature reduction approach to reduce the feature dimension. For (2) we use an MIML algorithm called MIMLfast to implement the multi-label classification.","PeriodicalId":328695,"journal":{"name":"2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134316629","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: 8

An information-theoretic feature selection method based on estimation of Markov blanket 一种基于马尔可夫包层估计的信息论特征选择方法

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI: 10.1109/ICCI-CC.2015.7259406

Hongzhi Liu, Zhonghai Wu, Xing Zhang, D. Hsu

引用次数: 3