2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)最新文献

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Class VR: Learning Class Environment for Special Educational Needs using Virtual Reality Games 虚拟现实课堂:使用虚拟现实游戏为特殊教育需要学习课堂环境

2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2019-11-01 DOI: 10.1109/CENIM48368.2019.8973353

Arik Kurniawati, Ari Kusumaningsih, Imam Hasan

引用次数: 10

Immersive Hand Gesture for Virtual Museum using Leap Motion Sensor Based on K-Nearest Neighbor 基于k近邻的Leap运动传感器虚拟博物馆沉浸式手势

2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2019-11-01 DOI: 10.1109/CENIM48368.2019.8973273

S. Sumpeno, I. G. A. Dharmayasa, S. M. S. Nugroho, D. Purwitasari

{"title":"Immersive Hand Gesture for Virtual Museum using Leap Motion Sensor Based on K-Nearest Neighbor","authors":"S. Sumpeno, I. G. A. Dharmayasa, S. M. S. Nugroho, D. Purwitasari","doi":"10.1109/CENIM48368.2019.8973273","DOIUrl":"https://doi.org/10.1109/CENIM48368.2019.8973273","url":null,"abstract":"Virtual museum is a place where users can explore museum collection freely. In this study, we are discussing the 3D interactions presented in a virtual museum application using hand-sensing sensor named Leap Motion. In making 3D interaction, some hand gestures are needed to functions any interact in virtual world. To prevent miss-occurring in 3D interaction, it is necessary to do a hand pattern classification to improve accuracy and make it more precision so as not to reduce the quality of immersion in the virtual world. The classification method used in this study is K-Nearest Neighbor (KNN) classification methods. KNN is a method that is quite popular and simple. The first step is data acquisition processing that is used as training data using Leap Motion Controller which takes vector value data (x, y, z) from each fingertip. Then the data normalization process is carried out to facilitate the next process which is feature extraction process. Features are being extracted including angle value between fingers, angle value between fingertips, angle between fingertips and palms, distance vector between fingertips and palms, and elevation value between fingertips and palms. After that, extracted data are being trained and classified using K-Nearest Neighbor (KNN).","PeriodicalId":106778,"journal":{"name":"2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131039482","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: 7

Comparison of simple and stratified random sampling on porn videos recognition using CNN 简单抽样与分层随机抽样在CNN色情视频识别中的比较

2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) Pub Date : 2019-11-01 DOI: 10.1109/CENIM48368.2019.8973305

I Wayan Agus Arimbawa, I. S. Wijaya, Ilham Bintang

{"title":"Comparison of simple and stratified random sampling on porn videos recognition using CNN","authors":"I Wayan Agus Arimbawa, I. S. Wijaya, Ilham Bintang","doi":"10.1109/CENIM48368.2019.8973305","DOIUrl":"https://doi.org/10.1109/CENIM48368.2019.8973305","url":null,"abstract":"Video classification is challenging because the video consists of many frames. In the videos recognition system, the proper sampling method affects the classification process because it uses image recognition model on each frame to recognize the video. This study focuses on comparing the sampling methods used in pornographic video recognition systems. Porn videos have high heterogeneity so that a sophisticated approach is needed to analyze the provided data for learning the data patterns. The Convolutional Neural Network method is employed because it can automatically detect features or similarity from the given training data. Besides that, this method could recognize the image quickly because it just fit test data into the final weights and biases from training. In this research, the stratified random sampling method gives 80% of accuracy while the simple random sampling method is the fastest method, which recognizes the video in 94 seconds. Additionally, all porn videos provided can be identified entirely so that the recall value for all test video is 100% while specificity average is 55.2%.","PeriodicalId":106778,"journal":{"name":"2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125583792","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

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