{"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}
{"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}
{"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}
{"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}