2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)最新文献_第2页

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2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/icawst.2019.8923262

引用次数: 0

Identification of DNA Methylation Signatures for Diagnosis of Lung Adenocarcinoma DNA甲基化特征在肺腺癌诊断中的鉴定

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923469

Yongjun Piao, Kwang-Ho Park, K. Ryu, R. Xiang

{"title":"Identification of DNA Methylation Signatures for Diagnosis of Lung Adenocarcinoma","authors":"Yongjun Piao, Kwang-Ho Park, K. Ryu, R. Xiang","doi":"10.1109/ICAwST.2019.8923469","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923469","url":null,"abstract":"Lung adenocarcinoma is the leading cause of death among men and women with cancer worldwide. Here, we performed an analysis of Illumina HumanMethylation450K data from TCGA to identify DNA methylation markers for lung adenocarcinoma diagnosis. We examined the DNA methylation landscape of lung adenocarcinoma and investigated the relationship between DNA methylation and clinical features. We then extracted differentially methylated cytosines in CpG island promoter regions, and then adopted machine learning techniques to determine the final methylation markers. As a result, we identified three methylation subtypes of lung adenocarcinoma, and found that the methylation status was not significantly related with the prognosis of lung adenocarcinoma. We finally identified two novel lung adenocarcinoma methylation markers including cg14823851 (TBX4) and cg07792478 (MIR124-2) with the AUCs of 100%, 100%, 98.3%, and 100% on support vector machine, logistic regression, decision tree, and random forest, respectively. Overall, our study demonstrates the potential use of methylation markers in lung adenocarcinoma diagnosis and may boost the development of new epigenetic therapies.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129541174","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

Data Acquisition Framework for Cloud Robotics 云机器人的数据采集框架

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923436

Y. Watanobe, Y. Yaguchi, T. Miyaji, R. Yamada, K. Naruse

引用次数: 1

Awareness of Privacy and Intellectual Property Rights under the Economic Partnership Agreement between EU and Japan 欧盟与日本经济伙伴关系协定下的私隐及知识产权意识

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923131

Peter Burgstaller

{"title":"Awareness of Privacy and Intellectual Property Rights under the Economic Partnership Agreement between EU and Japan","authors":"Peter Burgstaller","doi":"10.1109/ICAwST.2019.8923131","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923131","url":null,"abstract":"Only as recent as on February 1st, 2019 the Economic Partnership between the EU and Japan entered into force. The new agreement will give consumers greater choice and cheaper price. It will not only protect property rights for great European and Japanese products, both in Japan and EU, such as the Austrian \"Tiroler Speck\" or \"Kobe Beef\", but also allow personal data to flow freely and safely protected between the two partners. The agreement moreover defines that the parties of the agreement are obliged to grant and ensure intellectual property rights such as copyrights, trademarks, designs or patents and provide proceedings against infringer of such rights, including counterfeiting and piracy. This, of course, extends to IT and software goods and services. Awareness of the details of such an agreement is a necessity for both creators and users of such products because awareness of such legal frameworks is a precondition for legal compliance. This work tries to promote an awareness of the intricate nuances of such agreement for software practitioners who do not have any legal background.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127173108","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

A Survey about WSN and IoT Based Health Care Applications and ADPLL Contribution for Health Care Systems 基于WSN和物联网的医疗保健应用及ADPLL对医疗保健系统的贡献

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923167

R. Dinesh, R. Marimuthu

引用次数: 2

Acoustic-to-Articulatory Inversion of a Three-dimensional Theoretical Vocal Tract Model Using Deep Learning-based Model 基于深度学习的三维理论声道模型的声学-发音反转

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923588

Thanat Lapthawan, S. Prom-on

{"title":"Acoustic-to-Articulatory Inversion of a Three-dimensional Theoretical Vocal Tract Model Using Deep Learning-based Model","authors":"Thanat Lapthawan, S. Prom-on","doi":"10.1109/ICAwST.2019.8923588","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923588","url":null,"abstract":"This paper presents an acoustic-to-articulatory mapping of a three-dimensional theoretical vocal tract model using deep learning methods. Prominent deep learning-based network structures are explored and evaluated for their suitability in capturing the relationship between acoustic and articulatory-oriented vocal tract parameters. The dataset was synthesized from VocalTractLab, a three-dimensional theoretical articulatory synthesizer, in forms of the pairs of acoustic, represented by Mel-frequency cepstral coefficients (MFCCs), and articulatory signals, represented by 23 vocal tract parameters. The sentence structure used in the dataset generation were both monosyllabic and disyllabic vowel articulations. Models were evaluated using the root-mean-square error (RMSE) and R-squared (R2). The deep artificial neural network architecture (DNN), regulating using batch normalization, achieves the best performance for both inversion tasks, RMSE of 0.015 and R2 of 0.970 for monosyllabic vowels and RMSE of 0.015and R2 of 0.975 for disyllabic vowels. The comparison, between a formant of a sound from inverted articulatory parameters and the original synthesized sound, demonstrates that there is no statistically different between original and estimated parameters. The results indicate that the deep learning-based model is effectively estimated articulatory parameters in a three-dimensional space of a vocal tract model.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125257001","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

Modeling and Prediction of Time-Series-A Case Study with Forex Data 时间序列的建模与预测——以外汇数据为例

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923188

Y. Shiao, G. Chakraborty, Shin-Fu Chen, Li-Hua Li, R. Chen

引用次数: 7

A Study on the Tourism Features Extraction from Photos in a Tourism Website by Image Analysis 基于图像分析的旅游网站照片旅游特征提取研究

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923581

Shuang Li, Jun Sasaki

引用次数: 0

Time Series Electricity Consumption Analysis using Non-negative Matrix Factorization 基于非负矩阵分解的时间序列用电量分析

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923311

Akira Kusaba, T. Kuboyama, T. Hashimoto

引用次数: 0

Sleep Stage Classification Based on EEG, EOG, and CNN-GRU Deep Learning Model 基于EEG、EOG和CNN-GRU深度学习模型的睡眠阶段分类

2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) Pub Date : 2019-10-01 DOI: 10.1109/ICAwST.2019.8923359

M. IsuruNiroshanaS., Xin Zhu, Ying Chen, Wenxi Chen

{"title":"Sleep Stage Classification Based on EEG, EOG, and CNN-GRU Deep Learning Model","authors":"M. IsuruNiroshanaS., Xin Zhu, Ying Chen, Wenxi Chen","doi":"10.1109/ICAwST.2019.8923359","DOIUrl":"https://doi.org/10.1109/ICAwST.2019.8923359","url":null,"abstract":"This paper presents a CNN-GRU deep learning model for classifying sleep stages. The Conventional sleep stage scoring method is a visual classification process, based on a set of biomedical signals such as Electroencephalogram (EEG) and Electrooculogram (EOG), where high human intervention is required. In this study, we proposed a deep neural network involving convolutional neural networks and gated recurrent units, to automatically extract the most appropriate features and sequence trends of PSG signals, without utilizing hand crafted features for scoring sleep stages. The proposed model, which uses multiple PSG channels, was evaluated using two data sets collected from 184 patients and 70 healthy subjects. The proposed multi-channel model showed 91.9 % of overall accuracy, while recall, precision, and f1 measures were approximately 92 % for patients. For healthy subjects, the multi-channel model showed 89.3 % overall classification accuracy. Recall, precision, and f1 measures showed approximately 89 %. The main model was adapted to utilize with a single EEG channel configuration, which yields 4 single-channel models for each data set. Therefore, the proposed model is capable of performing sleep stage classification using a single EEG channel without altering the model architecture.","PeriodicalId":156538,"journal":{"name":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130863083","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: 10