2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)最新文献_第2页

Classification of Diagnosis-Related Groups using Computational Intelligence Techniques. 使用计算智能技术对诊断相关组进行分类。

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247889

Angelower Santana-Velásquez, M. John Freddy Duitama, J. D. Arias-Londoño

{"title":"Classification of Diagnosis-Related Groups using Computational Intelligence Techniques.","authors":"Angelower Santana-Velásquez, M. John Freddy Duitama, J. D. Arias-Londoño","doi":"10.1109/ColCACI50549.2020.9247889","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247889","url":null,"abstract":"The optimization of the resources used in clinics and hospitals is a key problem in hospital management. In particular, how to improve the efficiency in procedures and treatments for patients, reducing cost, but without deteriorating the quality of the patient’s stay is one of the greatest challenges faced by health providers. In this sense, the development of tools that can help health care providers to ensure that inpatients can be discharged at the times indicated by international standards according to their pathological condition is of great interest for the optimization of resources, especially in developing countries. There are different standards for grouping patients according to their diagnoses and procedures information, this work focuses on the Diagnosis-Related Groups (DRGs) patient classification system. Typically DRGs are obtained after patients’ discharge, only for billing and payment purposes, which reduce the ability of health providers to take corrective actions when the health care attention deviates from the standard attention of specific patients’ conditions.This work focuses in the use of Machine Learning (ML) techniques as an alternative to DRGs regular classification methods. The main aim is to evaluate whether ML methods are able to classify patients according to the DRGs standard, using the information available at the patient’s discharge. This results would be the base line for further analysis focused on the prediction of DRGs in early stages of the patient’s hospitalization. The results show that DRGs classification using Artificial Neural Networks and Ensemble methods can achieve up to 96% of accuracy in a real database of more than 82.910 health records.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126315392","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

Efficient Transfer Learning for Spectral Image Reconstruction from RGB Images 基于RGB图像的光谱图像重建的高效迁移学习

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247895

Emmanuel Martinez, S. Castro, Jorge Bacca, H. Arguello

{"title":"Efficient Transfer Learning for Spectral Image Reconstruction from RGB Images","authors":"Emmanuel Martinez, S. Castro, Jorge Bacca, H. Arguello","doi":"10.1109/ColCACI50549.2020.9247895","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247895","url":null,"abstract":"Spectral image reconstruction from RGB images has emerged as a hot topic in the computer vision community due to easy-access and low-cost acquisition of the latter. The goal is to learn a non-linear mapping from 3-RGB bands to L spectral bands. With the growth of the available spectral datasets, this mapping has been learned using deep convolutional representations. However, these methods demand a large number of spectral images to train the net to obtain a good recovery. In contrast, the proposed process consists of a pre-training step where the weights of a convolutional neural network fit with a large amount of available RGB datasets without spectral mapping, taking into account the RGB system acquisition as a layer. Then, some layers of this pre-trained network are frozen to retrain it with the available spectral dataset to generate a spectral image with L bands. The proposed training scheme can be used with any pre-existing deep network that maps RGB to spectral images and it is here evaluated with a “U-net” architecture, and the RGB sensing is based on the Bayer filter pattern. The simulated and experimental data demonstrate the effectiveness of the proposed approach compared to training without transfer learning, showing a gain of up to 4 dB, with less spectral data.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116578220","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

Spatio-Spectral Enhancement of Single-Pixel Reconstruction by a Fully Convolutional Neural Network 基于全卷积神经网络的单像素重建的空间光谱增强

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247903

Kevin Lozano, L. Galvis, H. Arguello

{"title":"Spatio-Spectral Enhancement of Single-Pixel Reconstruction by a Fully Convolutional Neural Network","authors":"Kevin Lozano, L. Galvis, H. Arguello","doi":"10.1109/ColCACI50549.2020.9247903","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247903","url":null,"abstract":"Spectral images (SI) contain a large amount of information, which increases the cost of their collection, storage, and processing. Compressive spectral imaging (CSI) methods allow the reconstruction of that information from a small set of random projections. However, compressed sensing with high spatial-spectral resolution requires expensive sensors. The single-pixel camera is an architecture that provides a low-cost solution for compressive SI acquisition, but with a significant limitation in image resolution. This work proposes a deep learning approach by means of a fully convolutional neural network (FCNN) with the ability to retrieve the spatio-spectral information from a single-pixel reconstruction in order to produce a spatio-spectral enhancement, suitable for applications requiring higher quality and low-cost acquisition systems. Simulations and experimental results show that the trained FCNN with a limited data set improves the quality of the single-pixel reconstruction in up to 20 dB, without requiring additional measurements. The performance of the proposed approach is compared against the traditional method of the single-pixel reconstruction.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133933136","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

Electoral Manifestos and Online Campaign Analysis: Case Study - The 2019 Ecuadorian Sectional Elections 选举宣言和在线竞选分析:案例研究- 2019年厄瓜多尔地方选举

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9248720

Daniel Riofrío, Pamela Almeida, José Dávalos, Ricardo Flores Moyano, Noel Pérez, D. Benítez, Pablo Medina-Pérez

{"title":"Electoral Manifestos and Online Campaign Analysis: Case Study - The 2019 Ecuadorian Sectional Elections","authors":"Daniel Riofrío, Pamela Almeida, José Dávalos, Ricardo Flores Moyano, Noel Pérez, D. Benítez, Pablo Medina-Pérez","doi":"10.1109/ColCACI50549.2020.9248720","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9248720","url":null,"abstract":"Social media is an important information outlet and a new political landscape for politicians. In fact, politicians use social media to promote their candidacies while running for office. In this paper, we discuss about an application prototype built to measure the closeness of a candidate electoral manifesto to hers/his online campaign. In particular, we show our results tracking the 2019 Ecuadorian Sectional Elections based on data collected from candidates’ timelines on Twitter during the campaign and their official campaign manifestos. We configured our application to gather information from Major candidates in the city of Quito during the 2019 Ecuadorian Sectional Elections. This prototype collected Tweets into a relational database based on each candidate’s Twitter account. For this campaign, 18 candidates run for office. From these, we gathered 17 electoral manifestos and fed them to our application. Both, tweets and manifestos were preprocessed in order to produce a high dimensional word vector describing the collected timelines of each candidate and his/her manifesto. Later, the cosine similarity was used to compare a candidate political plan against hers or his digital campaign. Our results suggest that candidates drift from their electoral manifestos during social media campaigns. We discuss possible reasons for our results and pave the path for future research.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121816181","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

FCM Algorithm: Analysis of the Membership Function Influence and Its consequences for fuzzy clustering FCM算法:隶属函数对模糊聚类的影响及其后果分析

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247944

Luis Mantilla, Yessenia Yari

引用次数: 1

Transfer Learning with Convolutional Neural Network for Gastrointestinal Diseases Detection using Endoscopic Images 基于卷积神经网络的胃肠疾病内镜图像检测迁移学习

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247847

Jessica Escobar, N. Gomez, Karen Sanchez, H. Arguello

引用次数: 6

Rule-based Control and Fuzzy Control for Power Management Strategies for Hybrid Vehicles 混合动力汽车动力管理策略的规则控制与模糊控制

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247872

F. C. Corrêa, J. Eckert, Ludmila C. A. Silva, M. Martins, V. Baroncini, F. M. Santiciolli, C. Gonçalves, F. Dedini

引用次数: 3

On the Use of a Low-Cost Embedded System for Face Detection and Recognition 一种低成本的嵌入式人脸检测与识别系统

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247856

R. Sandoval, Vanessa Camino, Ricardo Flores Moyano, Daniel Riofrío, Noel Pérez, D. Benítez

引用次数: 2

Categorizing Volcanic Seismic Events with Unsupervised Learning 用无监督学习对火山地震事件进行分类

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247854

Adrián Duque, K. González, Noel Pérez, D. Benítez

引用次数: 0

Morphological Neural Networks for Parkinson Detection through Speech Signals 基于语音信号的帕金森检测形态学神经网络

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247918

Luis David Gutierrez-Loaiza, W. Alfonso-Morales

引用次数: 2