2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)最新文献

Detection of weapons using Efficient Net and Yolo v3 使用高效网和Yolo v3探测武器

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769779

Anthony Ortiz Ramon, L. Barba-Guaman

引用次数: 0

Covid-19 Automatic Test through Human Breathing 通过人体呼吸自动检测Covid-19

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769822

Rui Faria, E. J. S. Pires, Argentina Leite, Tatiana Saraiva

引用次数: 0

Classification of cardiovascular signals 心血管信号的分类

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769782

Tatiana Saraiva, A. Leite, E. J. S. Pires, Rui Faria

引用次数: 0

Time Series Forecasting using NARX and NARMAX models with shallow and deep neural networks 基于浅层和深层神经网络的NARX和NARMAX模型的时间序列预测

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769832

Francisco Muñoz, G. Acuña

引用次数: 1

Fire Detection based on a Two-Dimensional Convolutional Neural Network and Temporal Analysis 基于二维卷积神经网络和时域分析的火灾探测

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769824

P. A. Venâncio, T. M. Rezende, A. C. Lisboa, A. V. Barbosa

{"title":"Fire Detection based on a Two-Dimensional Convolutional Neural Network and Temporal Analysis","authors":"P. A. Venâncio, T. M. Rezende, A. C. Lisboa, A. V. Barbosa","doi":"10.1109/LA-CCI48322.2021.9769824","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769824","url":null,"abstract":"In the last few years there has been a substantial increase in the success of deep learning, especially with regard to convolutional neural networks for computer vision tasks. These architectures are being widely used in emergency situations, where a fast and accurate response is needed. In environmental monitoring, several works have focused on fire detection, since fires have been increasingly associated with negative consequences such as respiratory diseases, economical losses and the destruction of natural resources. The automatic detection of smoke and fire, however, poses a particularly difficult challenge to computer vision systems, since the variability in the shape, color and texture of these objects makes the process of learning how to detect them much more complicated than for other ordinary objects. As a consequence, the number of false positives may grow high, which is especially problematic for a real-time application that mobilizes human efforts to fight fire. This work presents a robust fire detection tool based on a 2D deep convolutional network capable of suppressing false alarms from clouds, fogs, car lights and other objects that are easily confused with fire and smoke. Our approach integrates an object detector with an object tracker; this makes it possible to analyze the temporal behavior of the object and use that information in the decision process. We also present D-Fire, a public and labeled dataset containing more than 21,000 images, which is used to train and test the proposed system. The experimental results show that the detector reached an mAP@0.50 = 75.91% and that the incorporation of the temporal context resulted in a 60% reduction in the false positive rate at the cost of a 2.86% reduction in true positive rate. In addition, the computational cost added by the proposed approach to the fire detector is negligible, so that real-time detection is still completely feasible.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130581340","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

Towards a Multi-Output Kaizen Programming Algorithm 一种多输出改进规划算法

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769841

J. Ferreira, A. I. Torres, M. Pedemonte

{"title":"Towards a Multi-Output Kaizen Programming Algorithm","authors":"J. Ferreira, A. I. Torres, M. Pedemonte","doi":"10.1109/LA-CCI48322.2021.9769841","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769841","url":null,"abstract":"A model obtained from solving a symbolic regression problem is a surrogate model that represent a system with high accuracy. In the area of process system engineering, surrogate models substitute rigorous models in optimization and design process problems. As chemical processes have several outputs with a common physical-chemical phenomena, it is expected that the surrogate models generated for the outputs share terms or function basis. Kaizen Programming (KP) is a novel technique to solve symbolic regression problems, which do not assume any supposition of the form of the model in advance. This technique has shown a better performance than Genetic Programming on benchmarking functions. In this work, we propose an extension of Kaizen Programming, Multi-Output KP (MO-KP), to construct multi-output models in a single execution.The experimental evaluation was conducted on an extension of three classical benchmarking functions to multi-output scenarios, considering three different schemes of function basis sharing. The experimental results shown that MO-KP builds well fitted models, and it is even able to construct better models than single-output KP in some scenarios. The results also confirm that MO-KP favors the sharing of terms between the generated models. Finally, we found that the median execution time of MO-KP is in general shorter than the equivalent executions of single-output KP, but with larger variability in the distribution of the runtimes.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114778149","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

A hybrid sequential system for inflation forecasting 用于通货膨胀预测的混合顺序系统

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769835

André L. S. Xavier, Bruno José Torres Fernandes, J. F. D. de Oliveira

{"title":"A hybrid sequential system for inflation forecasting","authors":"André L. S. Xavier, Bruno José Torres Fernandes, J. F. D. de Oliveira","doi":"10.1109/LA-CCI48322.2021.9769835","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769835","url":null,"abstract":"Forecasting price indexes of the economy has received an attention from scholars and policy makers due to its significant effect on various sectors and markets. The stability of economy is at risk if inflation is not properly checked through models and macroeconomic studies, therefore, forecasting inflation is an important task for the formulation of policies in governments and companies. In this sense, this research work attempts to model inflation rate to Brazil, United State of America and Japan. The literature, in the field of time series, indicates the combination of linear and non-linear models to model inflation. In this paper, a hybrid ARIMA-MLP system has been proposed to map linear and nonlinear patterns. This is explored using a hybrid evolutionary system consisting of a simple exponential filter, linear ARIMA and autoregressive (AR) models and a Multilayer Perceptron model. In addition, it was also implemented exponential smoothing models (ETS), Qunatile Regression (QR) and Support Vector Machines. The experimental results show that the hybrid evolutionary system presented promising results in the prediction domain.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133958127","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

Industrial case study of causal modeling of continuous casting and lamination of steel tubes 钢管连铸与层压因果建模的工业实例研究

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769827

D. Silva, T. T. Salis, A.P. Braga

引用次数: 1

Use of Fitness Sharing in the Local Rule-Based Explanations Method 适应度共享在局部规则解释方法中的应用

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769789

Daniel A. Santos, J. A. Baranauskas, R. Tinós

{"title":"Use of Fitness Sharing in the Local Rule-Based Explanations Method","authors":"Daniel A. Santos, J. A. Baranauskas, R. Tinós","doi":"10.1109/LA-CCI48322.2021.9769789","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769789","url":null,"abstract":"Recent machine learning algorithms present remarkable results in many problems. However, the decisions made by some of these algorithms are very often difficult for human experts to interpret. Some recent works in the literature try to minimize this disadvantage, proposing algorithms that explain the decisions taken by any black-box model. One of these models is the Local Rule Based Explanations (LORE), that generates local explanations by using a Decision Tree (DT) that locally reproduces the decision boundaries of the black-box model. In LORE, the DT is trained by using an artificial dataset generated by a standard Genetic Algorithm (GA). In this paper, we show that the GA employed in LORE does not necessarily preserve the diversity of solutions in the final population. The diversity of the population is important to generate DTs that can more accurately reproduce the decision boundaries of the black-box model close to the instance to be explained. We then propose the use of fitness sharing in the GA in order to preserve the diversity of the population and generate local decision boundaries of the DT more similar to those of the black-box model. Experimental results show that LORE with fitness sharing generally produces better local explanations.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"342 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127818195","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

A Product Recommendation System for e-Shopping 面向电子购物的产品推荐系统

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769830

Muhammad Yasir Imam, Z. Usmani, Arsalan Khan, Osama Usmani

引用次数: 2