2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)最新文献

筛选
英文 中文
Predicting the incidence of dengue in Costa Rica using a decision tree model based on climatic and socioeconomic variables 利用基于气候和社会经济变量的决策树模型预测哥斯达黎加登革热发病率
2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI) Pub Date : 2021-10-25 DOI: 10.1109/jocici54528.2021.9794345
A. Murillo, Alvaro Soto B
{"title":"Predicting the incidence of dengue in Costa Rica using a decision tree model based on climatic and socioeconomic variables","authors":"A. Murillo, Alvaro Soto B","doi":"10.1109/jocici54528.2021.9794345","DOIUrl":"https://doi.org/10.1109/jocici54528.2021.9794345","url":null,"abstract":"Problem: Since 1993, Costa Rica has faced the re-emergence of the disease caused by the dengue virus, and despite the continuous efforts of local authorities to control the vector Aedes aegypti, dengue disease continues to be a problem for the Costa Rican population. Objective: To propose a decision tree model to predict the incidence of dengue in Costa Rica. Method: Quantitative analysis of the incidence of dengue, climatic and socioeconomic variables, by socioeconomic region of Costa Rica, from 2012 to 2018, to perform a predictive model regression decision trees to estimate the incidence of dengue disease per week; as well as its subsequent evaluation with the registered cases of dengue from week 1 to 46 of 2019. Results: The predictive model (RMSE: 5,348) yielded promising estimates for the evaluation period. Conclusions: The added value that predictive models could provide to the control of vector-borne diseases, such as dengue, is demonstrated.","PeriodicalId":339143,"journal":{"name":"2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124496720","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
Estimation for a student collaboration hours management system at the University of Costa Rica: a case study 哥斯达黎加大学学生协作时数管理系统的评估:一个案例研究
2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI) Pub Date : 2021-10-25 DOI: 10.1109/jocici54528.2021.9794343
Jose Daniel Sanchez Castillo, Marcelo Jenkins
{"title":"Estimation for a student collaboration hours management system at the University of Costa Rica: a case study","authors":"Jose Daniel Sanchez Castillo, Marcelo Jenkins","doi":"10.1109/jocici54528.2021.9794343","DOIUrl":"https://doi.org/10.1109/jocici54528.2021.9794343","url":null,"abstract":"Software estimation is a tool that seeks to provide organizations with a means to know the risks, costs and benefits that software development implies. The goal of this study is to estimate the effort required for a student collaboration hours management system in the context of the University of Costa Rica. For this, the IFPUG function point count standard and estimation models such as COCOMO II, the regression technique, the analogy technique and the comparison technique were used. The results show that the COCOMO II estimation method has the highest values of effort and duration and the analog technique the lowest values. For the regression and comparison techniques, the values were similar, located between the values obtained with COCOMO II and analogy, therefore, it was considered as a good estimation option for the system.","PeriodicalId":339143,"journal":{"name":"2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122349612","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
Detection of Solid Waste Deposits in Urban Areas using Artificial Intelligence and Image Processing: a Literature Review 利用人工智能和图像处理技术检测城市固体废弃物:文献综述
2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI) Pub Date : 2021-10-25 DOI: 10.1109/jocici54528.2021.9794347
Esteban Segura-Benavides, Gabriela Marín-Raventós
{"title":"Detection of Solid Waste Deposits in Urban Areas using Artificial Intelligence and Image Processing: a Literature Review","authors":"Esteban Segura-Benavides, Gabriela Marín-Raventós","doi":"10.1109/jocici54528.2021.9794347","DOIUrl":"https://doi.org/10.1109/jocici54528.2021.9794347","url":null,"abstract":"Abstract One of the main problems that governments around the world face is the prevalent presence of solid wastes in their countries. Scattered solid wastes in rural and urban areas cause serious problems to people and the environment. In the last years, different solutions for solid waste management have been developed using technology and artificial intelligence. Computer vision is one of these areas in constant development, with improvements in techniques and algorithms to detect and classify objects in images and videos. By doing a literature review in different databases, we found 17 studies from IEEE, ACM, Science Direct, Springer and IOPScience that address the use of artificial intelligence techniques to detect and classify solid waste deposits using computer images. We analyzed information about the object detection techniques and the dataset used for algorithm training in these studies. We also depicted the metrics used to evaluate the performance, accuracy, and precision to detect garbage on images. Deep learning is the main technique used for image processing. YOLO, Deep CNN and Faster R-CNN are the principal techniques used for classification and detection of solid waste due to their speed and accuracy. These results may be very useful to induce and to guide the development of tools to detect solid waste in our country.","PeriodicalId":339143,"journal":{"name":"2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133909045","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
JoCICI 2021 Cover Page JoCICI 2021封面
2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI) Pub Date : 2021-10-25 DOI: 10.1109/jocici54528.2021.9794348
{"title":"JoCICI 2021 Cover Page","authors":"","doi":"10.1109/jocici54528.2021.9794348","DOIUrl":"https://doi.org/10.1109/jocici54528.2021.9794348","url":null,"abstract":"","PeriodicalId":339143,"journal":{"name":"2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122781433","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信