2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)最新文献_第3页

A practical Deep Learning approach to assist COVID-19 detection based on Chest X-ray images 基于胸部x射线图像辅助COVID-19检测的实用深度学习方法

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769790

Thiago Camargo, P. V. Santos, Sthefanie Premebida, G. Ribeiro, Mayler Olombrada, Vinicios Soares, R. Barbosa, Wesley Calixto Pacheco, Cleomar Rocha, Cristhiane Gonçalves, Fernanda Cristina Correa, V. Baroncini, M. Martins

{"title":"A practical Deep Learning approach to assist COVID-19 detection based on Chest X-ray images","authors":"Thiago Camargo, P. V. Santos, Sthefanie Premebida, G. Ribeiro, Mayler Olombrada, Vinicios Soares, R. Barbosa, Wesley Calixto Pacheco, Cleomar Rocha, Cristhiane Gonçalves, Fernanda Cristina Correa, V. Baroncini, M. Martins","doi":"10.1109/LA-CCI48322.2021.9769790","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769790","url":null,"abstract":"Given the large number of COVID-19 cases around the world, a practical solution to decrease and relieve the queue of patients in the hospitals and in the health care systems is welcome. Fast and reliable diagnosis based on technological tools can support medical professionals to manage this bottleneck situation, such as the diagnostic based on image techniques, which allows non-intrusive procedures. In this paper, we propose a practical methodology using deep learning to detect and classify lungs affected by COVID-19 using Chest X-ray radiography. RetinaNet architecture is considered here. This architecture is an one-stage object detection using focal loss often applied with dense, small and imbalance objects. We consider a dataset with 2500 images for model training and 1000 images to validate the model. Besides, a set of 1000 images from two different datasets are applied to test the pipeline approach. The obtained results show a specificity score of 0.54, precision of 0.68, recall of 0.994, and mAP of 0.913. The high recall score explains that a patient with COVID-19 will be classified correctly.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"14 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":"132013382","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

Inverse Impulsive Optimal Neural Control for Complex Networks Applied to Epidemic Influenza Type A Model 复杂网络逆脉冲最优神经控制在甲型流感模型中的应用

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769820

Nancy F. Ramirez, A. Alanis, E. Hernández-Vargas, Daniel Ríos-Rivera

引用次数: 1

PM2.5 prediction with Recurrent Neural Networks and Data Augmentation 基于递归神经网络和数据增强的PM2.5预测

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769784

Anibal Flores, José Valeriano-Zapana, Victor Yana-Mamani, Hugo Tito-Chura

引用次数: 0

Semi-supervised learning for MS MALDI-TOF data MS MALDI-TOF数据的半监督学习

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769825

Xaviera A. López-Cortés, C. Astudillo, Camila González, S. Maldonado

{"title":"Semi-supervised learning for MS MALDI-TOF data","authors":"Xaviera A. López-Cortés, C. Astudillo, Camila González, S. Maldonado","doi":"10.1109/LA-CCI48322.2021.9769825","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769825","url":null,"abstract":"MALDI-TOF mass spectrometry (laser desorption/ionization assisted by a flight time mass detection matrix) is a promising strategy for identifying patterns in data, establishing a relevant methodology for rapid and accurate identification of microorganisms. However, this type of data is difficult to analyze due to its complexity, and sometimes it is impossible to make a correct labeling. To address this problem, advanced data analysis techniques such as machine learning (ML) methods can be applied. This research proposes a methodology to classify mass spectrometry (MS) data applying a semi-supervised learning (SSL) approach called self-training. This type of learning uses labeled and unlabeled data simultaneously in the training process to alleviate the scarcity of data labels. To demonstrate the efficiency of this proposal, MS data of healthy salmon infected with the pathogen Piscirickettsia salmonis was analyzed. Experimental results showed that self-training with random forest performs appropriately, achieving an accuracy of 0.9. Furthermore, feature selection allows the identification of seven potential biomarkers that define healthy and sick salmon profiles accurately.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"163 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":"122655988","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

A Comparison of Convolutional Neural Networks for RR Lyrae Light Curve Classification 卷积神经网络在天琴座RR光曲线分类中的比较

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769795

A. Morales, Javier Rojas, P. Huijse, R. C. Ramos

引用次数: 0

A Survey on Security Concerns and Their Actual Solutions for using FPGAs in Cloud Computing 在云计算中使用fpga的安全问题及其实际解决方案调查

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769794

P. Rosero-Montalvo

引用次数: 2

Identifying Process Graphs Properties with Network Science Metrics 用网络科学指标识别过程图属性

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769833

L. Verçosa, Renato Cirne, C. B. Filho, B. Bezerra

{"title":"Identifying Process Graphs Properties with Network Science Metrics","authors":"L. Verçosa, Renato Cirne, C. B. Filho, B. Bezerra","doi":"10.1109/LA-CCI48322.2021.9769833","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769833","url":null,"abstract":"Process Mining graphs are models that represent business processes. These graphs have been used in some contexts as social networks and process concept drift. However, they have scarcely been studied in the context of network science as graphs with particular properties. In this work, we used network science metrics and machine learning models to distinguish process graphs from diverse non-process graphs belonging to social and random models. We performed our experiments with a real dataset containing multiple process logs from a Brazilian justice system. We generated non-process graphs with Barabási, Duplication-Divergence, Erdõs-Rényi, Gaussian Random Partition, and Newman Watts Strogatz generators. Our results suggest that the metrics used are highly efficient to distinguish among the analysed graphs. The process graphs presented particular characteristics such as higher clustering coefficient and lower assortativity than non-process graphs. These findings may encourage the usage of network science metrics and machine learning models for process mining challenges in big data logs.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"78 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":"125927338","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

Machine Learning Techniques to Determine Mutation Impact in Proteins Associated to Neurofibromatosis 确定神经纤维瘤病相关蛋白突变影响的机器学习技术

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769823

Haider Rodríguez Pinto, Tatiana Tellez Silva, A. Orjuela-Cañón

引用次数: 0

LSTM network for the detection of P and S waves in seismic signals from the Nevados de Chillán volcano (Chile) 智利内华达Chillán火山地震信号中P、S波的LSTM网络探测

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769852

Macarena Garay, Millaray Curilem, F. Huenupán, César San-Martín, M. Castilla

{"title":"LSTM network for the detection of P and S waves in seismic signals from the Nevados de Chillán volcano (Chile)","authors":"Macarena Garay, Millaray Curilem, F. Huenupán, César San-Martín, M. Castilla","doi":"10.1109/LA-CCI48322.2021.9769852","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769852","url":null,"abstract":"This work presents the design and evaluation of an architecture based on LSTM recurrent neural networks to create P and S wave identification models in volcanic earthquakes. The detection of these waves is a challenge in volcanic signals because, unlike tectonic seismicity, the distances between the seismic sources and the sensors are short. Nevertheless, it is an important stage for vulcanological monitoring because it can locate the origin of the seismic event and obtain physical parameters crucial to forecast the state of a volcano’s activity. In general, this process is done manually by analysts in volcano observatories; however, due to the large number of volcanos monitored by the Observatorio Vulcanológico de los Andes Sur (OVDAS) in Chile, it must be automated. The article applies a methodology proposed in the literature to a currently active volcano in southern Chile, the Nevados de Chillán, achieving promising results, especially for the detection of S waves, which are more difficult to detect than P waves.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"25 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":"131909529","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

Time Series Imputation by Nature and by Decomposition 时间序列自然归算与分解归算

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769791

S. Ribeiro, C. L. D. Castro

引用次数: 0