2016 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第10页

Mapping spatio-temporally encoded patterns by reward-modulated STDP in Spiking neurons 通过奖励调制的STDP在尖峰神经元中映射时空编码模式

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850248

Ibrahim Ozturk, D. Halliday

引用次数: 2

Region-of-interest extraction of fMRI data using genetic algorithms 利用遗传算法提取功能磁共振成像数据的兴趣区域

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850135

S. Hiwa, Yuuki Kohri, Keisuke Hachisuka, T. Hiroyasu

{"title":"Region-of-interest extraction of fMRI data using genetic algorithms","authors":"S. Hiwa, Yuuki Kohri, Keisuke Hachisuka, T. Hiroyasu","doi":"10.1109/SSCI.2016.7850135","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850135","url":null,"abstract":"Functional connectivity, which is indicated by time-course correlations of brain activities among different brain regions, is one of the most useful metrics to represent human brain states. In functional connectivity analysis (FCA), the whole brain is parcellated into a certain number of regions based on anatomical atlases, and the mean time series of brain activities are calculated. Then, the correlation between mean signals of two regions is repeatedly calculated for all combinations of regions, and finally, we obtain the correlation matrix of the whole brain. FCA allows us to understand which regions activate cooperatively during specific stimulus or tasks. In this study, we attempt to represent human brain states using functional connectivity as feature vectors. As there are a number of brain regions, it is difficult to determine which regions are prominent to represent the brain state. Therefore, we proposed an automatic region-of-interest (ROI) extraction method to classify human brain states. Time-series brain activities were measured by functional magnetic resonance imaging (fMRI), and FCA was performed. Each element of the correlation matrix was used as a feature vector for brain state classification, and element characteristics were learned using supervised learning methods. The elements used as feature vectors, i.e., ROIs, were determined automatically using a genetic algorithm to maximize the classification accuracy of brain states. fMRI data measured during two emotional conditions, i.e., pleasant and unpleasant emotions, were used to show the effectiveness of the proposed method. Numerical experiments revealed that the proposed method could extract the superior frontal gyrus, orbitofrontal cortex, cuneus, cerebellum, and cerebellar vermis as ROIs associated with pleasant and unpleasant emotions.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125709756","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

Advanced parallel copula based EDA 基于并行耦合的高级EDA

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850202

Martin Hyrs, J. Schwarz

引用次数: 0

Towards the evolution of indirect communication for social robots 面向社会机器人的间接通信进化

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850183

B. Mocialov, P. A. Vargas, M. Couceiro

引用次数: 4

Towards automated cyber decision support: A case study on network segmentation for security 迈向自动化网络决策支持:网络安全分割案例研究

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849908

Neal Wagner, C. Sahin, M. Winterrose, J. Riordan, Jaime Peña, D. Hanson, W. Streilein

引用次数: 30

A new hybrid global optimization approach for selecting clinical and biological features that are relevant to the effective diagnosis of ovarian cancer 一种新的混合全局优化方法，用于选择与卵巢癌有效诊断相关的临床和生物学特征

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849954

Abeer Alzubaidi, David J. Brown, G. Cosma, A. Pockley

{"title":"A new hybrid global optimization approach for selecting clinical and biological features that are relevant to the effective diagnosis of ovarian cancer","authors":"Abeer Alzubaidi, David J. Brown, G. Cosma, A. Pockley","doi":"10.1109/SSCI.2016.7849954","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849954","url":null,"abstract":"Reducing the number of features whilst maintaining an acceptable classification accuracy is a fundamental step in the process of constructing cancer predictive models. In this work, we introduce a novel hybrid (MI-LDA) feature selection approach for the diagnosis of ovarian cancer. This hybrid approach is embedded within a global optimization framework and offers a promising improvement on feature selection and classification accuracy processes. Global Mutual Information (MI) based feature selection optimizes the search process of finding best feature subsets in order to select the highly correlated predictors for ovarian cancer diagnosis. The maximal discriminative cancer predictors are then passed to a Linear Discriminant Analysis (LDA) classifier, and a Genetic Algorithm (GA) is applied to optimise the search process with respect to the estimated error rate of the LDA classifier (MI-LDA). Experiments were performed using an ovarian cancer dataset obtained from the FDA-NCI Clinical Proteomics Program Databank. The performance of the hybrid feature selection approach was evaluated using the Support Vector Machine (SVM) classifier and the LDA classifier. A comparison of the results revealed that the proposed (MI-LDA)-LDA model outperformed the (MI-LDA)-SVM model on selecting the maximal discriminative feature subset and achieved the highest predictive accuracy. The proposed system can therefore be used as an efficient tool for finding predictors and patterns in serum (blood)-derived proteomic data for the detection of ovarian cancer.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"473 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127008676","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

Finding Trendsetters on Yelp Dataset 在Yelp数据集上寻找趋势引领者

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849866

Pierfrancesco Cervellini, A. G. Menezes, Vijay Mago

引用次数: 9

Forgotten effects and heavy moving averages in exchange rate forecasting 汇率预测中的遗忘效应和重移动平均线

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850015

Ezequiel Avilés-Ochoa, Ernesto León-Castro, J. M. Lindahl, A. M. G. Lafuente

引用次数: 13

Data analytics on network traffic flows for botnet behaviour detection 针对僵尸网络行为检测的网络流量数据分析

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850078

Duc C. Le, A. N. Zincir-Heywood, M. Heywood

引用次数: 31

An investigation into the effect of unlabeled neurons on Self-Organizing Maps 未标记神经元对自组织图影响的研究

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849938

Willem S. van Heerden, A. Engelbrecht

引用次数: 0