2014 International Conference on Advanced Computer Science and Information System最新文献

Nonlinearly weighted multiple kernel learning for time series forecasting 时间序列预测的非线性加权多核学习

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065860

Agus Widodo, I. Budi, B. Widjaja

引用次数: 0

An extension of Petri network for multi-agent system representation Petri网络在多智能体系统表示中的扩展

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065829

P. Sauvage, A. Courtin, P.A Bonneau, K. Chauffeur, V. Claisse

引用次数: 0

Learning content personalization based on triple-factor learning type approach in e-learning 基于三因素学习型方法的网络学习内容个性化

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065884

M. Suryani, H. Santoso, Z. Hasibuan

{"title":"Learning content personalization based on triple-factor learning type approach in e-learning","authors":"M. Suryani, H. Santoso, Z. Hasibuan","doi":"10.1109/ICACSIS.2014.7065884","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065884","url":null,"abstract":"One of the emerging issue in e-learning is to create adaptive learning based on learner's perspective. Adaptive learning can be realized through personalization of e-learning. Personalized learning help learners to use their best performance in order to reach learning goals based on their needs, preferences, and characteristics. To accomodate different characteristics of the learners, learning content personalization system based on triple-factor learning type was developed. The characteristics of 36 triple-factor learning type were used as input for learning content personalization algorithm to produce learning content that suitable for the learners's learning type. The algorithm implemented into a system which called SCELE-Personalization Dynamic E-learning. The system was used by 118 learners in Science Writing course at the Faculty of Computer Science, Universitas Indonesia as experimental group. In order to find the best learning performance, the exam score from experiment group were compared with the exam score from control group. The result shows learning performance of experimental group that used personalized learning feature is better than learning performance of control group who used non-personalized learning feature. It can be seen from significant value (p<;0,05) and the different mean score of the experimental group that reach 13,68.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125944344","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

Identification of single nucleotide polymorphism using support vector machine on imbalanced data 支持向量机在不平衡数据上的单核苷酸多态性识别

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065854

L. S. Hasibuan, W. Kusuma, Willy Bayuardi Suwamo

引用次数: 3

Pareto frontier optimization in soccer simulation using normalized normal constraint 基于归一化正态约束的足球模拟Pareto边界优化

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065890

Darius Andana Haris

引用次数: 0

Particle swarm optimation based 2-dimensional randomized hough transform for fetal head biometry detection and approximation in ultrasound imaging 基于粒子群优化的二维随机霍夫变换在胎儿头部超声成像中的检测与逼近

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065898

I. P. Satwika, I. Habibie, M. A. Ma'sum, A. Febrian, E. Budianto

{"title":"Particle swarm optimation based 2-dimensional randomized hough transform for fetal head biometry detection and approximation in ultrasound imaging","authors":"I. P. Satwika, I. Habibie, M. A. Ma'sum, A. Febrian, E. Budianto","doi":"10.1109/ICACSIS.2014.7065898","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065898","url":null,"abstract":"One of the most profound use of ultrasound imaging is to generate the image of fetal during pregnancy. This paper will describe an ellipse detection approach to automatically detect and approximate the head size of the fetal. The method was developed using the Hough Transform techniques that have been modified and optimized by Particle Swarm Optimization (PSO). Experiments of the method are tested on synthetic and real ellipse image dataset. For real images, the detection was applied on 2D ultrasonography images to perform fetal head measurement to approximate the Head Circumference (HC) and Biparietal Diameter (BPD). Experiment result showed that the proposed method can perform ellipse detection in synthetic dataset with satisfactory result for noisy images with noise density up to 0.4 and able to perform the fetal head detection for real images with an averate hit rate of 0.654. This proposed method can also perform detection on images that have high degree of noise or incomplete ellipse images generated from the fetal objects.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125578470","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: 14

Learning to rank for determining relevant document in Indonesian-English cross language information retrieval using BM25 学习用BM25在印尼语-英语跨语言信息检索中排序确定相关文档

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065896

Syandra Sari, M. Adriani

引用次数: 11

Interaction between users and buildings: Results of a multicreteria analysis 用户与建筑物之间的交互:多条件分析的结果

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065825

Audrey Bona, Jean-Marc Salotti

引用次数: 0

3D virtual pet game "Moar" with augmented reality to simulate pet raising scenario on mobile device 3D虚拟宠物游戏“Moar”，通过增强现实技术在移动设备上模拟宠物饲养场景

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065880

Cliffen Allen, Jeanny Pragantha, Darius Andana Haris

引用次数: 6

Performance of robust two-dimensional principal component for classification 鲁棒二维主成分分类性能

2014 International Conference on Advanced Computer Science and Information System Pub Date : 2014-10-01 DOI: 10.1109/ICACSIS.2014.7065889

D. Herwindiati, S. M. Isa, J. Hendryli

{"title":"Performance of robust two-dimensional principal component for classification","authors":"D. Herwindiati, S. M. Isa, J. Hendryli","doi":"10.1109/ICACSIS.2014.7065889","DOIUrl":"https://doi.org/10.1109/ICACSIS.2014.7065889","url":null,"abstract":"The robust dimension reduction for classification of two dimensional data is discussed in this paper. The classification process is done with reference of original data. The classifying of class membership is not easy when more than one variable are loaded with the same information, and they can be written as a near linear combination of other variables. The standard approach to overcome this problem is dimension reduction. One of the most common forms of dimensionality reduction is the principal component analysis (PCA). The two-dimensional principal component (2DPCA) is often called a variant of principal component. The image matrices were directly treated as 2D matrices; the covariance matrix of image can be constructed directly using the original image matrices. The presence of outliers in the data has been proved to pose a serious problem in dimension reduction. The first component consisting of the greatest variation is often pushed toward the anomalous observations. The robust minimizing vector variance (MW) combined with two dimensional projection approach is used for solving the problem. The computation experiment shows the robust method has the good performances for matrix data classification.","PeriodicalId":443250,"journal":{"name":"2014 International Conference on Advanced Computer Science and Information System","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115780183","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