2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)最新文献_第3页

Mobile Applications vs. Chat-based Applications : A Comparative Study based on Academic Library Domain 移动应用与聊天应用:基于学术图书馆领域的比较研究

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493834

Watanee Jearanaiwongkul, Chutiporn Anutariya, Kurapati Tejaswini Reddy

引用次数: 0

Combination Ultrasound and Mammography for Breast Cancer Classification using Deep Learning 基于深度学习的超声和乳房x线摄影联合用于乳腺癌分类

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493840

Orawan Chunhapran, Tongjai Yampaka

引用次数: 1

Classification of Astronomical Objects in the Galaxy M81 using Machine Learning Techniques II. An Application of Clustering in Data Pre-processing M81星系中天体的机器学习分类II。聚类技术在数据预处理中的应用

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493825

Tapanapong Chuntama, C. Suwannajak, P. Techa-angkoon, Benjamas Panyangam, N. Tanakul

{"title":"Classification of Astronomical Objects in the Galaxy M81 using Machine Learning Techniques II. An Application of Clustering in Data Pre-processing","authors":"Tapanapong Chuntama, C. Suwannajak, P. Techa-angkoon, Benjamas Panyangam, N. Tanakul","doi":"10.1109/JCSSE53117.2021.9493825","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493825","url":null,"abstract":"Identifying objects with a certain class in the current data in astronomy are challenging. In this study, we explored the methods to identify globular cluster candidates from a pool of astronomical objects in the galaxy M81. First, we developed a method to automatically cross-match the data. This process was done by manually overlayed the imaging data in the previous study. The process also eliminated the data points that only appear in only one or two filters, which indicates that they are artifacts. Next, we used the Expectation Maximization (EM) clustering technique to label the training dataset with classes and to reduce the use of humans in the preprocessing process. Our results show that the data can be clustered into 12 clusters, which can be grouped into 6 groups of astronomical objects with similar morphological structures. When using these 6 groups of data to build classification models, we found that the prediction accuracies have improved significantly. In the case of Random Forest, the accuracy has improved from 79.9% to 90.57% and from 67.1% to 91.59% for Multilayer Perceptron. Moreover, when using the model built from those data to analyze the unseen dataset, the results also show that the model can categorize the objects into classes with characteristics close to those in astronomy. However, this model still cannot fully separate globular clusters from foreground stars and background galaxies due to the similarities in their photometric properties.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127514362","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

SEPBO: Trash separator bot VR game SEPBO:垃圾分离器机器人VR游戏

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493830

Thirada Theethum, Sopoat Iamcharoen, Attawut Arpornrat, Sirion Vittayakorn

引用次数: 1

Detecting Anomaly and Replacement Prediction for Rainfall Open Data in Thailand 泰国降雨开放数据异常检测与替代预测

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493814

Intouch Prakaisak, E. Phaisangittisagul, M. Maleewong, Kanoksri Sarinnapakorn, C. Phongpensri

{"title":"Detecting Anomaly and Replacement Prediction for Rainfall Open Data in Thailand","authors":"Intouch Prakaisak, E. Phaisangittisagul, M. Maleewong, Kanoksri Sarinnapakorn, C. Phongpensri","doi":"10.1109/JCSSE53117.2021.9493814","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493814","url":null,"abstract":"The rainfall data set usually contains missing values due to easily broken sensors. In Thailand, many public agencies collect rainfall values, including National Hydro Informatics (HII), Thai Meteorological Department, etc., since the data are valuable in terms of rainfall prediction, which is important for an agricultural country like Thailand. The rainfall data is normally collected hourly, and because there are many sensor locations, it is hard to maintain these sensors. The sensor data can be lost transiently and/or may yield anomaly values. Since there is a lot of data flowing to the server every day, it is hard to inspect manually or even semi-manually. This project collaborates with HII to develop a system that automates the rainfall data quality improvement process. The machine learning algorithms are used as tools for data cleansing. The derived data can be exposed as an open data set for many developers to explore new innovations. We explore data set characteristics and adopt both statistical and machine learning methods. The results show that the approach used both statistical and machine learning resulting in higher accuracy than using only statistical or machine learning approaches. We also develop a web application to visualize rainfall data results after cleansing and be connected to the models for the automatic cleansing pipelines.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134618336","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

Performance Analysis in UAV-enabled Relay with NOMA under Nakagami-m Fading Considering Adaptive Power Splitting 考虑自适应功率分割的中川米衰落下带NOMA的无人机中继性能分析

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493850

Anh-Nhat Nguyen, Van Nhan Vo, C. So-In, Dac-Binh Ha, Van-Truong Truong

{"title":"Performance Analysis in UAV-enabled Relay with NOMA under Nakagami-m Fading Considering Adaptive Power Splitting","authors":"Anh-Nhat Nguyen, Van Nhan Vo, C. So-In, Dac-Binh Ha, Van-Truong Truong","doi":"10.1109/JCSSE53117.2021.9493850","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493850","url":null,"abstract":"This paper investigates the system performance of an energy harvesting (EH) unmanned aerial vehicle (UAV)-enabled relay (UR) in the Internet of Things (IoT) under Nakagami-m fading, where the UR applied time switching (TS) and adaptive power splitting (APS) (U-TSAPS). To increase throughput, all links (i.e., from the base station (BS) to the UR and from the UR to the IoT device (Id) clusters) are transmitted using the nonorthogonal multiple access (NOMA) technique. The U-TSAPS protocol is divided into two stages. In the first stage, the BS chooses the best antenna for transmitting the signal to the UR. The UR then divides the received signal into two streams, one for information processing and the other for the EH. In the second stage, the UR uses the decode-and-forward (DF) scheme to send the obtained signal to the best far device (BFD) in the far cluster and the best near device (BND) in the near cluster. Under imperfect channel state information (ICSI) details, we derive closed-form expressions for the outage probability (OP) of BFD and BND with the APS ratio to evaluate device efficiency. These derivations are also used to evaluate the throughput of the system under consideration. Monte Carlo simulations are used to validate our system.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132605671","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: 4

Text Summarization for Thai Food Reviews using Simplified Sentiment Analysis 使用简化情感分析的泰国食物评论文本摘要

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493839

P. Porntrakoon, Chayapol Moemeng, P. Santiprabhob

引用次数: 1

Towards robust Machine Learning models for grape ripeness assessment 用于葡萄成熟度评估的鲁棒机器学习模型

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493822

Véronique M. Gomes, P. Melo-Pinto

引用次数: 2

[Copyright notice] (版权)

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/jcsse53117.2021.9493838

引用次数: 0

Intelligent Clinical Training during the COVID-19 Pandemic COVID-19大流行期间的智能临床培训

2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2021-06-30 DOI: 10.1109/JCSSE53117.2021.9493841

S. Suebnukarn

{"title":"Intelligent Clinical Training during the COVID-19 Pandemic","authors":"S. Suebnukarn","doi":"10.1109/JCSSE53117.2021.9493841","DOIUrl":"https://doi.org/10.1109/JCSSE53117.2021.9493841","url":null,"abstract":"Clinical training is one of the most challenging areas for education especially during the COVID-19 pandemic. There are limited access to apprenticeship training in the complex scenarios with corresponding difficulty training in a time-effective manner. Our work on intelligent clinical training systems provides one effective solution to this problem by introducing intelligent clinical training systems that can supplement tutoring sessions by expert clinical instructors. The Bayesian representation techniques and algorithms for generating tutoring feedback in medical problem-based learning group problem solving made important contributions to the field of Intelligent Tutoring Systems. In particular, it was one of the first systems for tutoring groups of students and the first intelligent tutoring systems for medical problem-based learning. The virtual reality simulator we developed is one of the most sophisticated dental simulators. It stands out as the first dental simulator to integrate sophisticated analysis of the surgical procedure. Particularly noteworthy is also the creative way to understand important issues such as differences in expert and novice performance, the effectiveness of virtual pre-operative practice, and the teaching effectiveness of the simulator. The systems have been implemented in undergrad pre-clinical training and postgrad pre-surgical training with strong scientific evidence of their effectiveness.","PeriodicalId":437534,"journal":{"name":"2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123327868","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