2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)最新文献_第5页

Performance comparison of machine learning models for streamflow forecasting 流量预测机器学习模型的性能比较

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769829

José Fernando de Toledo, Patrícia Teixeira Leite Assano, H. Siqueira, R. Sacchi

引用次数: 1

Probabilistic Multistep Time Series Forecasting Using Conditional Generative Adversarial Networks 基于条件生成对抗网络的概率多步时间序列预测

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769836

Gerardo Zúñiga, G. Acuña

{"title":"Probabilistic Multistep Time Series Forecasting Using Conditional Generative Adversarial Networks","authors":"Gerardo Zúñiga, G. Acuña","doi":"10.1109/LA-CCI48322.2021.9769836","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769836","url":null,"abstract":"Time series forecasting is a problem that has been studied for many years due to the impact it can have on the world economy and well-being. Predicting multiple future values is an especially complex problem due to the increasing error. This is why there is a need to design and evaluate more and better methods for this forecasting problem. The adversarial generative networks seem to have an excellent performance generating time series indistinguishable from real series. It has been shown that a probabilistic prediction of time series called ForGAN adversary generative network has been successfully used for one-step-ahead predictions. In this work, a modified architecture of ForGAN with multiple outputs is proposed in order to perform multiple-step-ahead predictions. We show by means of experiments using a real dataset that statistically significant improvement of multiple-step-ahead predictions with the proposed modified architecture of ForGAN compared with the use of the original ForGAN network is achieved, decreasing RMSE by 17.6% and CRPS by 17.3% when predicting 5 steps ahead.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"122 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":"132053107","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 Systematic Mapping of Smart Farming and Image Recognition in Agriculture 智能农业和农业图像识别的系统测绘

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769828

Bruno Gutierrez Ríos, G. C. Saldías

{"title":"A Systematic Mapping of Smart Farming and Image Recognition in Agriculture","authors":"Bruno Gutierrez Ríos, G. C. Saldías","doi":"10.1109/LA-CCI48322.2021.9769828","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769828","url":null,"abstract":"Automation, Internet 4.0, the use of production models and Computational Intelligence techniques have been strongly related with agriculture in recent years. Considering agriculture as a fundamental human activity and also a key industry that is being strongly affected by the climate crisis, knowing how to propose solutions to its various problems through information technologies is of utmost importance. Consequently, this article presents a systematic mapping in order to identify the different applications of machine learning in agriculture, paying special attention to the tools used to acquire relevant information, such as drones and sensors, and to the models used to create solutions, such as Support Vector Machines and Artificial Neural Networks. The paper focuses on the use of image classifying models, evaluating the applications of artificial vision in agriculture, especially in detecting diseases. A comparative study carried out with different deep learning tools in order to identify plant diseases will be presented. The study shows the power of deep learning tools using transfer learning, evidencing that, in networks, these tools learn within few iterations, maintaining excellent levels of generalization, as shown by the validation results.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"57 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":"130970752","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 protocol for Brain-Computer Interfaces based on Musical Notes Imagery 基于音符意象的脑机接口协议

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769845

Anna Montevilla, Guillermo Sahonero-Alvarez

{"title":"A protocol for Brain-Computer Interfaces based on Musical Notes Imagery","authors":"Anna Montevilla, Guillermo Sahonero-Alvarez","doi":"10.1109/LA-CCI48322.2021.9769845","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769845","url":null,"abstract":"The application of Brain-Computer Interfaces is expected to become a matter of daily life. For this purpose, several efforts are being developed to ensure that users can employ this technology without difficulties. A large amount of studies consider motor imagery, which implies the usage of sensorimotor rhythms produced when imaging motor actions. However, previous works have shown that from a sample of population, a portion of users (15~30%) is unable to efficiently control a BCI based on such paradigm. The roots of this issue have been partially located to different factors related to the training protocol that users follow to learn how to use the system. Thus, in order to extend the applicability of BCIs, training procedures must consider different approaches. Musical imagery is another mental task that may be used to control BCIs and requires users to have music related thoughts or imagine specific notes and even songs. In this work, we propose a protocol to explore the properties of Musical Imagery based training procedures. For this, we developed both offline and online experiments, where the last one consisted of 4 sessions. The data-processing steps include filtering the data using a FIR filter to later extract features using PCA, and classify such features with a multi-class SVM. Our results show that the offline classification is comparable to motor imagery based BCIs as the accuracy is between 80% to 95%. Moreover, we found that the online setup results point to up to 64% of accuracy for the third session with feedback.","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":"121316986","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

Reducing the effort of Evolutionary Calibrator Using Opposite Information 利用相反信息减少进化校准器的工作量

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-11-02 DOI: 10.1109/LA-CCI48322.2021.9769793

Nicolás Rojas-Morales, M. Riff

引用次数: 1

Detection of blue whale vocalisations using a temporal-domain convolutional neural network 使用时域卷积神经网络检测蓝鲸发声

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-10-05 DOI: 10.1109/LA-CCI48322.2021.9769846

Bryan Sagredo, Sonia Espanol-Jim'enez, Felipe A. Tobar

引用次数: 0

Bayesian autoregressive spectral estimation 贝叶斯自回归谱估计

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-10-05 DOI: 10.1109/LA-CCI48322.2021.9769834

Alejandro Cuevas, Sebasti'an D. L'opez, D. Mandic, Felipe A. Tobar

引用次数: 0

High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series 基于嵌入非平稳模糊时间序列的物联网应用高维多元时间序列预测

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-07-20 DOI: 10.1109/LA-CCI48322.2021.9769792

H. V. Bitencourt, F. Guimarães

引用次数: 3

Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games 计算机游戏中实时情绪识别的自适应高斯模糊分类器

2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2021-03-05 DOI: 10.1109/LA-CCI48322.2021.9769842

D. Leite, Volnei Frigeri, Rodrigo Medeiros

{"title":"Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games","authors":"D. Leite, Volnei Frigeri, Rodrigo Medeiros","doi":"10.1109/LA-CCI48322.2021.9769842","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769842","url":null,"abstract":"Emotion recognition has become a need for more realistic and interactive machines and computer systems. The greatest challenge is the availability of high-performance algorithms to effectively manage individual differences and nonstationarities in physiological data, i.e., algorithms that customize models to users with no subject-specific calibration data. We describe an evolving Gaussian Fuzzy Classifier (eGFC), which is supported by a semi-supervised learning algorithm to recognize emotion patterns from electroencephalogram (EEG) data streams. We extract features from the Fourier spectrum of EEG data. The data are provided by 28 individuals playing the games ‘Train Sim World’, ‘Unravel’, ‘Slender The Arrival’, and ‘Goat Simulator’ – a public dataset. Different emotions prevail, namely, boredom, calmness, horror and joy. We analyze individual electrodes, time window lengths, and frequency bands on the accuracy of user-independent eGFCs. We conclude that both brain hemispheres may assist classification, especially electrodes on the frontal (Af3-Af4), occipital (O1-O2), and temporal (T7-T8) areas. We observe that patterns may be eventually found in any frequency band; however, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, are more correlated with the emotion classes. eGFC has shown to be effective for real-time learning of EEG data. It reaches a 72.2% accuracy using a variable rule base, 10-second windows, and 1.8ms/sample processing time in a highly-stochastic time-varying 4-class classification problem.","PeriodicalId":431041,"journal":{"name":"2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"843 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116423598","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