2013 International Computer Science and Engineering Conference (ICSEC)最新文献_第8页

Erlang C model for evaluate incoming call uncertainty in automotive call centers 基于Erlang C的汽车呼叫中心呼叫不确定性评估模型

2013 International Computer Science and Engineering Conference (ICSEC) Pub Date : 2013-09-01 DOI: 10.1109/ICSEC.2013.6694762

Laksamon Archawaporn, W. Wongseree

引用次数: 2

Outlier detection score based on ordered distance difference 基于有序距离差的离群点检测评分

2013 International Computer Science and Engineering Conference (ICSEC) Pub Date : 2013-09-01 DOI: 10.1109/ICSEC.2013.6694771

Nattorn Buthong, Arthorn Luangsodsai, K. Sinapiromsaran

引用次数: 17

Farthest boundary clustering algorithm: Half-orbital extreme pole 最远边界聚类算法:半轨道极

2013 International Computer Science and Engineering Conference (ICSEC) Pub Date : 2013-09-01 DOI: 10.1109/ICSEC.2013.6694773

Benjapun Kaveelerdpotjana, K. Sinapiromsaran, Boonyarit Intiyot

{"title":"Farthest boundary clustering algorithm: Half-orbital extreme pole","authors":"Benjapun Kaveelerdpotjana, K. Sinapiromsaran, Boonyarit Intiyot","doi":"10.1109/ICSEC.2013.6694773","DOIUrl":"https://doi.org/10.1109/ICSEC.2013.6694773","url":null,"abstract":"Clustering analysis is a process of splitting a dataset into various groups of smaller datasets such that instances in a particular group are more similar to one another than instances from other groups. In this paper, we propose a novel boundary approach to perform a clustering analysis. Our algorithm starts from identifying two instances that have the largest distance within the dataset, called extreme poles. The two farthest pairs of instances can either be two far ends of the same cluster group or two far ends of two different groups. Then a vector core is generated using these two poles. Various pre-determined distances from one of these two poles will split data into various layers. If the extreme poles lie within one group, then the number of instances within the layers must be distributed appropriately. Otherwise, the dataset needs to be split. Our algorithm will recursively perform on these smaller datasets until the stopping criteria are met. To demonstrate the effectiveness of our method, we compare our algorithm with the K-means clustering algorithm using the value of K from our algorithm. The results show that the total variance from our algorithm is not larger than that from the K-means algorithm.","PeriodicalId":191620,"journal":{"name":"2013 International Computer Science and Engineering Conference (ICSEC)","volume":"44 53","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113993587","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: 3

Independent component analysis based assessment of linked gray and white matter in the initial stages of Alzheimer's disease using structural MRI phase images 基于独立成分分析的评估，在阿尔茨海默病的初始阶段，使用结构MRI相位图像的相关灰质和白质

2013 International Computer Science and Engineering Conference (ICSEC) Pub Date : 2013-09-01 DOI: 10.1109/ICSEC.2013.6694804

Ahsan Bin Tufail, Syed Abdul Rehman Rizvi, A. M. Siddiqui, M. S. Younis, A. Abidi

{"title":"Independent component analysis based assessment of linked gray and white matter in the initial stages of Alzheimer's disease using structural MRI phase images","authors":"Ahsan Bin Tufail, Syed Abdul Rehman Rizvi, A. M. Siddiqui, M. S. Younis, A. Abidi","doi":"10.1109/ICSEC.2013.6694804","DOIUrl":"https://doi.org/10.1109/ICSEC.2013.6694804","url":null,"abstract":"Alzheimer's disease (AD) is a common form of dementia that is affecting the elderly population worldwide. We present here a novel approach based on independent component analysis (ICA) method to get useful features that are representative of the interrelationship among the structural magnetic resonance imaging (sMRI) brain voxels. ICA effectively considers the information inherent in the sMRI scans and provides information about the independent sources of brain that are affected during the course of progression of AD. Phase images summarize the complex relationship between gray and white matter in the brain. The results presented depicts interesting differences among the healthy elderly controls and elder patients belonging to early categories of AD with clinical dementia rating (CDR) of 0.5 and 1 for parahippocampus and other areas. The effects of socioeconomic factors on ICA features also shows the usefulness of sources that are preserved by ICA features. These interesting findings show the usefulness of ICA for feature extraction and analysis in AD research. In addition, the use of phase images for feature extraction have a clear advantage over other approaches that consider the relationship among gray and white matter intermittently.","PeriodicalId":191620,"journal":{"name":"2013 International Computer Science and Engineering Conference (ICSEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122260171","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: 1