Proceedings of the 2020 12th International Conference on Computer and Automation Engineering最新文献_第2页

About Secure Intergration of Video Consultation In IOT Based Health Care Monitoring System 基于物联网的医疗监控系统中视频会诊的安全集成

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384652

Md. Nazmus Saadat, Nur Liyana Rosli

引用次数: 0

Determination of Methanol, Ethanol, and Ethylene Glycol Content in Coconut Wine using PCA and SIMCA 用PCA和SIMCA法测定椰子酒中甲醇、乙醇和乙二醇的含量

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384638

Velorie D. Baquiran, Jelani Anne M. Bate, M. Sembrano, J. Villaverde, G. Magwili

引用次数: 2

mFAST: Automatic Stoke Evaluation System for Time-Critical Treatment with Multimodal Feature Collection and Machine Learning Classification mFAST:基于多模态特征收集和机器学习分类的时间临界处理的自动脑卒中评估系统

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384653

Eunjeong Park, Taehwa Han, H. Nam

{"title":"mFAST: Automatic Stoke Evaluation System for Time-Critical Treatment with Multimodal Feature Collection and Machine Learning Classification","authors":"Eunjeong Park, Taehwa Han, H. Nam","doi":"10.1145/3384613.3384653","DOIUrl":"https://doi.org/10.1145/3384613.3384653","url":null,"abstract":"In this paper, we introduce an automatic evaluation system, mFAST, for the examination of neurological deficits and personalized stroke prediction. The proposed system enables mobile monitoring of stroke patients utilizing sensors and machine learning techniques. This research is composed of objective and rapid measurement of neurological deficits of stroke patients; development of stroke-triage prediction; and data collection of monitored neurological deficits and personalized stroke prediction system to rapidly recognize the symptoms and enhance the treatment in hospitals. The research proposes to implement the mobile services for FAST (Face, Arm, Speech, Time) stroke activation to measure face palsy, arm weakness, speech disturbance and to analyze the automatically collected data. The measured FAST features can be used in the prediction of stroke scores including NIHSS (National Institute of Health for Stroke Scale), CPSS (Cincinnati Pre-hospital Stroke Scale), MRC (Medial Research Council) to enable stroke patients to be treated in a restricted time frame.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124184019","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: 2

Study on a New Method of Rare Pattern Mining Based on QAR Data 基于QAR数据的稀有模式挖掘新方法研究

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384640

G. Cheng, Shu Gao, Fan Luo

引用次数: 0

Eye Tracking Solutions in Cognitive CAPTCHA Authentication 认知CAPTCHA认证中的眼动追踪解决方案

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384655

M. Ogiela, L. Ogiela

引用次数: 1

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering 2020年第12届计算机与自动化工程国际会议论文集

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613

引用次数: 0

A Review of Techniques for Image Classification to Enhance Online Animal Adoption Speed 提高在线动物收养速度的图像分类技术综述

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384656

P. Jeyaraj, A. Aponso

{"title":"A Review of Techniques for Image Classification to Enhance Online Animal Adoption Speed","authors":"P. Jeyaraj, A. Aponso","doi":"10.1145/3384613.3384656","DOIUrl":"https://doi.org/10.1145/3384613.3384656","url":null,"abstract":"Overpopulation of stray dogs and cats is a serious threat to human community since they transmit a dangerous viral infection called rabies. Therefore overpopulation of strays needs to be controlled. Strategies to control overpopulation include sterilizing, euthanizing and adopting the strays. The ethical and popular method of acquisition in controlling strays is adoption. Adoptions are mostly carried out by animal shelters. The increase in usage of internet has provided platforms for shelters to advertise adoptions online by sharing pictures. Although there have been past researches on analyzing photo traits affecting the adoption speed, yet simpler improvements are required to guide shelters in posting influential photos. Hence it is essential to use an automated approach to guide shelters in posting influential photos to increase adoption speed. This paper presents the analysis of photo traits affecting adoption speed and computerized automated approaches of image classification. The classification approaches will be reviewed and used in designing an application of the ongoing research to predict the adoption speed of an input animal's image.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131393186","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

A Natural Language Processing (NLP) Framework for Embedded Systems to Automatically Extract Verification Aspects from Textual Design Requirements 嵌入式系统从文本设计需求中自动提取验证方面的自然语言处理框架

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384619

Muhammad Waseem Anwar, Imran Ahsan, F. Azam, Wasi Haider Butt, M. Rashid

{"title":"A Natural Language Processing (NLP) Framework for Embedded Systems to Automatically Extract Verification Aspects from Textual Design Requirements","authors":"Muhammad Waseem Anwar, Imran Ahsan, F. Azam, Wasi Haider Butt, M. Rashid","doi":"10.1145/3384613.3384619","DOIUrl":"https://doi.org/10.1145/3384613.3384619","url":null,"abstract":"Embedded systems requirements are significantly different with respect to general purpose systems due to the safety-critical nature and the presence of temporal aspects. Particularly, the design requirements of embedded systems, comprise several temporal conditions, are first identified. Subsequently, a test engineer / system engineer analyzes the design requirements manually to identify the verification characteristics and develops the verification assertions / constraints accordingly. However, the manual analysis of design requirements for verification is time consuming task. Furthermore, high level of domain expertise is required to develop the correct and complete verification assertions from the design requirements. This article presents a novel Natural Language Processing (NLP) framework for embedded systems to analyze and automatically extract verification aspects from the textual design requirements. This leads to considerably simplify and accelerate the development of verification assertions. As a part of research, a complete AR2AA (Automated Requirements 2 Assertions Analyzer) tool is developed in C# by utilizing the SharpNLP and regular expression libraries. The usefulness of proposed framework is demonstrated through Car Collision and Avoidance System (CCAS) case study. The initial results prove that the proposed framework is highly effective for the analysis and development of verification assertions from the textual design requirements.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125053939","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

Underwater Target Detection Based on ANSYS Finite Element Analysis and SVM 基于ANSYS有限元分析和支持向量机的水下目标检测

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384646

Yuwu Wang, Panxiang Rong, Guobing Sun, Yongsheng Qiu, Xiaodan Che

引用次数: 0

Continuous QoE Prediction Based on WaveNet 基于WaveNet的连续QoE预测

Proceedings of the 2020 12th International Conference on Computer and Automation Engineering Pub Date : 2020-02-14 DOI: 10.1145/3384613.3384633

Tan Phan-Xuan, Tho Nguyen Duc, Chanh Minh Tran, E. Kamioka

{"title":"Continuous QoE Prediction Based on WaveNet","authors":"Tan Phan-Xuan, Tho Nguyen Duc, Chanh Minh Tran, E. Kamioka","doi":"10.1145/3384613.3384633","DOIUrl":"https://doi.org/10.1145/3384613.3384633","url":null,"abstract":"Continuous QoE prediction is crucial in the purpose of maximizing viewer satisfaction, by which video service providers could improve the revenue. Continuously predicting QoE is challenging since it requires QoE models that are capable of capturing the complex dependencies among QoE influence factors. The existing approaches that utilize Long-Short-Term-Memory (LSTM) network successfully model such long-term dependencies, providing the superior QoE prediction performance. However, the inherent drawback of sequential computing of LSTM will result in high computational cost in training and prediction tasks. Recently, WaveNet, a deep neural network for generating raw audio waveform, has been introduced. Immediately, it gains a great attention since it successfully leverages the characteristic of parallel computing of causal convolution and dilated convolution to deal with time-series data (e.g., audio signal). Being inspired by the success of WaveNet, in this paper, we propose WaveNet-based QoE model for continuous QoE prediction in video streaming services. The model is trained and tested upon on two publicly available databases, namely, LFOVIA Video QoE and LIVE Mobile Stall Video II. The experimental results demonstrate that the proposed model outperforms the baselines models in terms of processing time, while maintaining sufficient accuracy.","PeriodicalId":214098,"journal":{"name":"Proceedings of the 2020 12th International Conference on Computer and Automation Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129228074","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: 0