2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)最新文献

Android Forensic and Security Assessment for Hospital and Stock-and-Trade Applications in Thailand 泰国医院和股票交易应用的安卓取证和安全评估

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-11 DOI: 10.1109/JCSSE.2018.8457347

Noppanat Phumkaew, V. Visoottiviseth

引用次数: 3

Traffic State Prediction Using Convolutional Neural Network 基于卷积神经网络的交通状态预测

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-11 DOI: 10.1109/JCSSE.2018.8457359

Ratchanon Toncharoen, M. Piantanakulchai

引用次数: 12

Safety Property Analysis of Service-Oriented IoT Based on Interval Timed Coloured Petri Nets 基于间隔时间彩色Petri网的服务型物联网安全特性分析

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-01 DOI: 10.1109/JCSSE.2018.8457326

V. Phartchayanusit, S. Rongviriyapanish

引用次数: 3

JCSSE 2018 Title Page JCSSE 2018标题页

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-01 DOI: 10.1109/jcsse.2018.8457357

引用次数: 0

The Clinical Decision Support System for the Snake Envenomation in Thailand 泰国蛇中毒的临床决策支持系统

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-01 DOI: 10.1109/JCSSE.2018.8457182

Apirak Hoonlor, Varodom Charoensawan, S. Srisuma

{"title":"The Clinical Decision Support System for the Snake Envenomation in Thailand","authors":"Apirak Hoonlor, Varodom Charoensawan, S. Srisuma","doi":"10.1109/JCSSE.2018.8457182","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457182","url":null,"abstract":"With the rises of the AI technology in Healthcare, researchers have been using the technology to develop a computational system to aid diagnosis, commonly known as 'Clinical Decision Support Systems (CDSSs)'. The CDSS applications currently available are usually neither free, nor optimized for treating Thai patients. In this work, we propose a new CDSS platform intended as an open platform for the CDSS application in Thailand. As a prototype and proof of concept, we developed the Mahidol Snake Envenomation Support System (MSESS), as the first C DSS a pplication u sing o ur n ew p latform. MSESS was designed to help its user formulate a treatment plan for the patient with snake bite found in Thailand, particularly in rural areas, and guide the user through the treatment flow. The treatments suggested by MSESS strictly follows the Snake Envenomation guideline provided by the Ramathibodi Poison Center. The targeted user is the medical personnel such as general practitioner seeking a medical advice from specialists. The medical personnel will first e nter t he p atient information to the CDSS. The system will then retrieve the information, submit it to the inference engine unit hosted at our central computing facilities, and display the suggested actions to the medical personnel via our application. We discuss our lesson learn from the development of MSESS for the future development of CDSS applications on our platform.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130564189","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

Logging mechanism for Internet of Things: A Case Study of Patient Monitoring System 物联网日志机制:以病人监护系统为例

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-01 DOI: 10.1109/JCSSE.2018.8457390

Piyawat Maneenual, S. Vasupongayya

引用次数: 2

A Deep Learning Methodology for Automatic Assessment of Portrait Image Aesthetic Quality 一种用于人像图像美学质量自动评估的深度学习方法

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-01 DOI: 10.1109/JCSSE.2018.8457381

Poom Wettayakorn, Siripong Traivijitkhun, Ponpat Phetchai, Suppawong Tuarob

{"title":"A Deep Learning Methodology for Automatic Assessment of Portrait Image Aesthetic Quality","authors":"Poom Wettayakorn, Siripong Traivijitkhun, Ponpat Phetchai, Suppawong Tuarob","doi":"10.1109/JCSSE.2018.8457381","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457381","url":null,"abstract":"Generally, a traditional methodology to assess the aesthetics (appreciating beauty) of a photograph involves a number of professional photographers rating the photo based on given criteria and providing ensemble feedback minimize bias. Such a traditional photo assessment method, however, is not applicable to massive users, especially in real-time. To mitigate such an issue, recent studies have devoted on developing algorithms to automatically provide feedback to photo takers. Most of such algorithms train variants of neural networks using ground-truth photos assessed by professional photographers. Regardless, most existing photo assessment algorithms provide the aesthetic score as a single number. From our observation, users typically use multiple criteria to justify the beautifulness of a photo, and hence a single rating score may not be informative. In this paper, we propose a novel Fine-tuned Inception with Fully Connected and Regression Layers model which gives five attribute scores: vivid colour, colour harmony, lighting, balance of elements, and depth of field. T his s olution i ncorporates t he p re-trained inception model which is the state-of-the-art model for processing images. Our proposed algorithm enhances the existing state-of-the-art by fine-tuning the parameters, introducing fully connected layers, and attaching the regression layers to compute the numeric score for each focus attribute. The experimental results show that our model helps to decrease the mean absolute error (MAE) to 0.211, benchmarking on the aesthetics and attributes datasets provided in the previous studies.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122098429","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

Temporal Topic Correlation and Evolution 时间主题关联与演化

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-01 DOI: 10.1109/JCSSE.2018.8457380

A. Prayote, Kallaya Songklang

{"title":"Temporal Topic Correlation and Evolution","authors":"A. Prayote, Kallaya Songklang","doi":"10.1109/JCSSE.2018.8457380","DOIUrl":"https://doi.org/10.1109/JCSSE.2018.8457380","url":null,"abstract":"This paper presents a technique to alternatively discover the temporal research topics correlation by using a topic model, Latent Dirichlet Allocation (LDA). LDA model assumes the documents as a mixture of topics that group the co-occurrence words with the certain probabilities. Hence the model is popularly used to extract the latent topics from document collections. However, LDA gives an independence assumption between topics and is unable to model the correlation between the topics. Motivated by above limitation, this study introduces a method for improving the topic correlation. The correlation of two topics from different time periods can occur when there exists a publication tagged by the two topics and these two topics are said to be co-occurred by this publication. LDA weights of these co-occurred topics are used in our model to calculate gross-correlation values. The number of publications in a topic co-occurrence is also used in the model. Therefore, we split dataset into groups with some common sub-dataset ordered by temporal timestamp of published year. The experiment results show the correlation between topics in different time periods and results can further support the research collaboration in future.","PeriodicalId":338973,"journal":{"name":"2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122380502","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

JCSSE 2018 Author Index JCSSE 2018作者索引

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-01 DOI: 10.1109/jcsse.2018.8457387

引用次数: 0

Crowd Estimation Using Region-Specific HOG With SVM 基于区域HOG和SVM的人群估计

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2018-07-01 DOI: 10.1109/JCSSE.2018.8457384

J. Ilao, M. Cordel

引用次数: 6