2016 8th International Conference on Knowledge and Smart Technology (KST)最新文献_第2页

Experiences with empirical modelling tools in schools in Greece and Thailand 在希腊和泰国的学校使用实证建模工具的经验

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440536

A. Harfield

引用次数: 0

Improved EM method for internet traffic classification 改进的EM网络流量分类方法

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440488

Songyin Liu, Jing Hu, Shengnan Hao, Tiecheng Song

引用次数: 7

General trust is correlated with attentional orientation triggered by gaze direction 一般信任与凝视方向引发的注意定向相关

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440515

Saki Takao, Atsunori Ariga

引用次数: 0

Optimizing HBase table scheme for marketing strategy suggestion 优化HBase表方案，提出营销策略建议

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440532

Seungkyun Hong, Min-Hee Cho, Sungho Shin, Jung-Ho Um, Choong-Nyoung Seon, Sa-kwang Song

引用次数: 3

Investigating ultraharmonic modeling from ultrasound echo signal with SISO volterra filter 用SISO伏特拉滤波器研究超声回波信号的超谐波建模

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440498

C. Samakee

引用次数: 1

Flexible interval representation system in negative binary base 负二进制进制中的柔性区间表示系统

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440506

Pipop Thienprapasith, A. Surarerks

引用次数: 1

Efficiency of data mining models to predict academic performance and a cooperative learning model 高效预测学习成绩的数据挖掘模型和合作学习模型

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440483

Pensri Amornsinlaphachai

{"title":"Efficiency of data mining models to predict academic performance and a cooperative learning model","authors":"Pensri Amornsinlaphachai","doi":"10.1109/KST.2016.7440483","DOIUrl":"https://doi.org/10.1109/KST.2016.7440483","url":null,"abstract":"Two purposes of this study are 1) to select a data mining model to predict learners' academic performance in computer programming subject to group learners for cooperative learning by comparing the efficiency of the models created from data mining with classification technique and 2) to develop a model for cooperative learning via web using the selected data mining model to group learners. The efficiency of seven models created from data mining with classification technique by using seven algorithms that are Artificial Neural Network, K-Nearest Neighbor, Naive Bayes, Bayesian Belief Network, JRIP, ID3 and C4.5 is compared and it was found that the models created from C4.5 has the best efficiency. The accuracy of the model created from C4.5 is about 74.8945% and the accuracy tests show that this model is reliable. Therefore this model is selected to group learners with STAD technique for cooperative learning through web. The result also shows that ID3 is inappropriate to predict learners' performance. The data mining model created from C4.5 shows that math's GPA has the most influential for academic performance in computer programming subject. The model for the cooperative learning model via web using C4.5 to group learners consists of 5 components that are data management module, prediction and grouping module, learning resources, cooperative community and quiz module. The results also show that in the case of using the selected model to group learners and in the case of grouping learners by the lecturers, the learning progressive-score in the first case is higher.","PeriodicalId":350687,"journal":{"name":"2016 8th International Conference on Knowledge and Smart Technology (KST)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125106887","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: 25

Measuring force of game information in the brain: Linking game refinement theory and neuroscience 游戏信息在大脑中的测量力:将游戏精炼理论与神经科学联系起来

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440518

Sakshi Agarwal, R. Ram, H. Iida

引用次数: 5

Energy efficient density-based clustering technique for wireless sensor network 基于能量高效密度的无线传感器网络聚类技术

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440509

Walaa Abd-Ellatief, O. Younes, Hatem Ahmed, M. M. Hadhoud

引用次数: 13

From sensors and data to data mining for e-Health 从传感器和数据到电子健康的数据挖掘

2016 8th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2016-03-24 DOI: 10.1109/KST.2016.7440471

P. Lenca, J. Soulas, S. Berrouiguet

{"title":"From sensors and data to data mining for e-Health","authors":"P. Lenca, J. Soulas, S. Berrouiguet","doi":"10.1109/KST.2016.7440471","DOIUrl":"https://doi.org/10.1109/KST.2016.7440471","url":null,"abstract":"Summary form only given. Nowadays many users can be involved willingly and monitored in a more or less intrusive manner with data collected in their homes. This data can be collected from mobile and wearable devices (such as smart phone and smart watch), from sensors disseminated in the home (such as motion detectors and contact switches) and from self-reported information systems (using paper based or web-based ecological momentary assessment techniques). This data can then be useful to characterize the activity, the health and the well-being of the involved person. Enabling people suffering (such as elderly people, people with physical disabilities and people with mental-health condition) to stay in their home as long as possible in good condition is an important challenge for many countries. Firstly, most of people would rather to continue to live in their own home rather than move to a nursing-home or an hospital, secondly the solutions enabling staying at home are also usually cheaper for the society. As a consequence Smart Home and Ambient Assisted Living (SHAAL) systems gain more and more attention. SHAAL systems use information and communication technologies in a person's daily living environment to enable them to stay active longer, remain socially connected and live independently. SHAAL's research covers a wide range of topics. This talk will review the main aspects of SHAAL systems from the data mining point of view. Several case studies will be illustrated. In particular it will emphasize the key role of activity learning and behavior understanding.","PeriodicalId":350687,"journal":{"name":"2016 8th International Conference on Knowledge and Smart Technology (KST)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114440607","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