Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18284
B. M. A. Matsui, D. Goya
Práticas de DevOps têm sido cada vez mais utilizadas por equipes de engenharia de software com o intuito de aprimorar as etapas de desenvolvimento. Em processos que envolvem machine learning (ML), DevOps também pode ser aplicado a fim de implantar modelos de aprendizado de máquina em produção – prática também conhecida como MLOps. Neste mapeamento sistemático objetiva-se entender como DevOps tem sido aplicado a processos de machine learning e quais são os desafios enfrentados. Foram selecionados 15 artigos e observou-se que a maioria faz uso de práticas de CI/CD e propõe arquiteturas para a implantação de modelos de ML. Como maiores desafios, têm-se as características inerentes aos modelos de ML e resistência à mudança.
{"title":"Applying DevOps to Machine Learning Processes: A Systematic Mapping","authors":"B. M. A. Matsui, D. Goya","doi":"10.5753/eniac.2021.18284","DOIUrl":"https://doi.org/10.5753/eniac.2021.18284","url":null,"abstract":"Práticas de DevOps têm sido cada vez mais utilizadas por equipes de engenharia de software com o intuito de aprimorar as etapas de desenvolvimento. Em processos que envolvem machine learning (ML), DevOps também pode ser aplicado a fim de implantar modelos de aprendizado de máquina em produção – prática também conhecida como MLOps. Neste mapeamento sistemático objetiva-se entender como DevOps tem sido aplicado a processos de machine learning e quais são os desafios enfrentados. Foram selecionados 15 artigos e observou-se que a maioria faz uso de práticas de CI/CD e propõe arquiteturas para a implantação de modelos de ML. Como maiores desafios, têm-se as características inerentes aos modelos de ML e resistência à mudança.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122457020","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}
Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18243
Renato O. Miyaji, Pedro L. P. Corrêa
Uma das ferramentas mais utilizadas para o monitoramento da biodiversidade é a modelagem de distribuição de espécies. Para a sua aplicação, é necessário possuir uma grande base de dados confiáveis a respeito da ocorrência de espécies. Entretanto, essa condição não é satisfeita quando existem poucos registros de ocorrência. Nesse contexto, podem ser aplicadas técnicas de tratamento de incertezas. Assim, este trabalho buscou utilizar a abordagem Bayesiana para permitir a modelagem de distribuição de espécies na região da Bacia Amazônica próxima a Manaus (AM), com base em dados coletados pelo projeto GoAmazon 2014/15. Os resultados foram comparados com os resultantes de técnicas clássicas, obtendo desempenhos semelhantes.
{"title":"Handling uncertainty through Bayesian inference for Species Distribution Modelling in the Amazon Basin region","authors":"Renato O. Miyaji, Pedro L. P. Corrêa","doi":"10.5753/eniac.2021.18243","DOIUrl":"https://doi.org/10.5753/eniac.2021.18243","url":null,"abstract":"Uma das ferramentas mais utilizadas para o monitoramento da biodiversidade é a modelagem de distribuição de espécies. Para a sua aplicação, é necessário possuir uma grande base de dados confiáveis a respeito da ocorrência de espécies. Entretanto, essa condição não é satisfeita quando existem poucos registros de ocorrência. Nesse contexto, podem ser aplicadas técnicas de tratamento de incertezas. Assim, este trabalho buscou utilizar a abordagem Bayesiana para permitir a modelagem de distribuição de espécies na região da Bacia Amazônica próxima a Manaus (AM), com base em dados coletados pelo projeto GoAmazon 2014/15. Os resultados foram comparados com os resultantes de técnicas clássicas, obtendo desempenhos semelhantes.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116196717","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}
Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18299
M. F. Oliveira, M. Delgado, R. Lüders
Collaborative Filtering (CF) can be understood as the process of predicting the preferences of users and deriving useful patterns by studying their activities. In the survey context, it can be used to predict answers to questions as combinations of other available answers. In this paper, we aim to test five CF-based algorithms (item-item, iterative matrix factorization, neural collaborative filtering, logistic matrix factorization, and an ensemble of them) to estimate scores in four survey applications (checkpoints) composed of 700,000 employee's ratings. These data have been collected from 2019 to 2020 by a large Brazilian tech company with more than 10,000 employees. The results show that collaborative filtering approaches provide relevant alternatives to score questions of surveys. They provided good quality estimates. This result can be further explored to eventually reduce the size of questionnaires, avoiding burden phenomena faced by respondents when dealing with large surveys.
{"title":"Comparative Analysis of Collaborative Filtering-Based Predictors of Scores in Surveys of a Large Company","authors":"M. F. Oliveira, M. Delgado, R. Lüders","doi":"10.5753/eniac.2021.18299","DOIUrl":"https://doi.org/10.5753/eniac.2021.18299","url":null,"abstract":"Collaborative Filtering (CF) can be understood as the process of predicting the preferences of users and deriving useful patterns by studying their activities. In the survey context, it can be used to predict answers to questions as combinations of other available answers. In this paper, we aim to test five CF-based algorithms (item-item, iterative matrix factorization, neural collaborative filtering, logistic matrix factorization, and an ensemble of them) to estimate scores in four survey applications (checkpoints) composed of 700,000 employee's ratings. These data have been collected from 2019 to 2020 by a large Brazilian tech company with more than 10,000 employees. The results show that collaborative filtering approaches provide relevant alternatives to score questions of surveys. They provided good quality estimates. This result can be further explored to eventually reduce the size of questionnaires, avoiding burden phenomena faced by respondents when dealing with large surveys.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114781071","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}
Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18246
T. Lima, André C. A. Nascimento, P. Miranda, R. F. Mello
In Brazil, several minority languages suffer a serious risk of extinction. The appropriate documentation of such languages is a fundamental step to avoid that. However, for some of those languages, only a small amount of text corpora is digitally accessible. Meanwhile there are many issues related to the identification of indigenous languages, which may help to identify key similarities among them, as well as to connect related languages and dialects. Therefore, this paper proposes to study and automatically classify 26 neglected Brazilian native languages, considering a small amount of training data, under a supervised and unsupervised setting. Our findings indicate that the use of machine learning models to the analysis of Brazilian Indigenous corpora is very promising, and we hope this work encourage more research on this topic in the next years.
{"title":"Analysis of a Brazilian Indigenous corpus using machine learning methods","authors":"T. Lima, André C. A. Nascimento, P. Miranda, R. F. Mello","doi":"10.5753/eniac.2021.18246","DOIUrl":"https://doi.org/10.5753/eniac.2021.18246","url":null,"abstract":"In Brazil, several minority languages suffer a serious risk of extinction. The appropriate documentation of such languages is a fundamental step to avoid that. However, for some of those languages, only a small amount of text corpora is digitally accessible. Meanwhile there are many issues related to the identification of indigenous languages, which may help to identify key similarities among them, as well as to connect related languages and dialects. Therefore, this paper proposes to study and automatically classify 26 neglected Brazilian native languages, considering a small amount of training data, under a supervised and unsupervised setting. Our findings indicate that the use of machine learning models to the analysis of Brazilian Indigenous corpora is very promising, and we hope this work encourage more research on this topic in the next years.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129477550","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}
Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18248
Guilherme Souza, J. Santos, Gabriel SantClair, Janaína Gomide, Luan Santos
The Sustainable Development Goals (SDGs) are part of a global effort to reduce the impacts of climate change, promoting social justice and economic growth. The United Nations provides a database with hundreds of indicators to track the SDGs since 2016 for a total of 302 regions. This work aims to assess which countries are in a similar situation regarding sustainable development. Principal Component Analysis was used to reduce the dimension of the dataset and k-means algorithm was used to cluster countries according to their SDGs indicators. For the years of 2016, 2017 and 2018 were obtained 11, 13 and 11 groups, respectively. This paper also analyses clusters changes throughout the years.
{"title":"A Comparative Analysis of Countries' Performance According to SDG Indicators based on Machine Learning","authors":"Guilherme Souza, J. Santos, Gabriel SantClair, Janaína Gomide, Luan Santos","doi":"10.5753/eniac.2021.18248","DOIUrl":"https://doi.org/10.5753/eniac.2021.18248","url":null,"abstract":"The Sustainable Development Goals (SDGs) are part of a global effort to reduce the impacts of climate change, promoting social justice and economic growth. The United Nations provides a database with hundreds of indicators to track the SDGs since 2016 for a total of 302 regions. This work aims to assess which countries are in a similar situation regarding sustainable development. Principal Component Analysis was used to reduce the dimension of the dataset and k-means algorithm was used to cluster countries according to their SDGs indicators. For the years of 2016, 2017 and 2018 were obtained 11, 13 and 11 groups, respectively. This paper also analyses clusters changes throughout the years.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"117 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130131814","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}
Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18425
A. C. Lorena, F. Verri, Tiago A. Almeida
Este artigo editorial descreve a Competição Brasileira de Descoberta de Conhecimento em Bancos de Dados (KDD-BR 2021) e resume as contribuições das três melhores soluções obtidas em sua quinta edição. A competição de 2021 envolveu a resolução de instâncias do Problema do Caixeiro Viajante, de diferentes tamanhos, usando uma abordagem de previsão de arestas.
{"title":"The 5th Brazilian Competition on Knowledge Discovery in Databases (KDD-BR 2021)","authors":"A. C. Lorena, F. Verri, Tiago A. Almeida","doi":"10.5753/eniac.2021.18425","DOIUrl":"https://doi.org/10.5753/eniac.2021.18425","url":null,"abstract":"Este artigo editorial descreve a Competição Brasileira de Descoberta de Conhecimento em Bancos de Dados (KDD-BR 2021) e resume as contribuições das três melhores soluções obtidas em sua quinta edição. A competição de 2021 envolveu a resolução de instâncias do Problema do Caixeiro Viajante, de diferentes tamanhos, usando uma abordagem de previsão de arestas.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126777355","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}
Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18278
Anísio Pereira Batista Filho, Débora da Conceição Araújo, Maverick Andre Dionisio Ferreira, P. M. Mattos Neto
A disseminação de notícias falsas tem sido um problema notado em diversos setores da sociedade, e vem dificultando o combate à pandemia causada pelo novo coronavírus (Sars-Cov-2). Combater desinformação sobre o Sars-Cov-2, principalmente nas redes sociais, é de fundamental importância para o controle da propagação do vírus e, consequentemente, da pandemia. Diante disso, nesse trabalho são construídos modelos de aprendizado supervisionado focados na identificação de notícias falsas sobre o novo coronavírus. Como resultados, foram construídos e avaliados 18 modelos, os quais chegaram a alcançar 0.62%, 0.82% e 0.47% de f-score para as classes consideradas (news, opinion e fake).
{"title":"Fake News Detection about Covid-19 in the Portuguese Language","authors":"Anísio Pereira Batista Filho, Débora da Conceição Araújo, Maverick Andre Dionisio Ferreira, P. M. Mattos Neto","doi":"10.5753/eniac.2021.18278","DOIUrl":"https://doi.org/10.5753/eniac.2021.18278","url":null,"abstract":"A disseminação de notícias falsas tem sido um problema notado em diversos setores da sociedade, e vem dificultando o combate à pandemia causada pelo novo coronavírus (Sars-Cov-2). Combater desinformação sobre o Sars-Cov-2, principalmente nas redes sociais, é de fundamental importância para o controle da propagação do vírus e, consequentemente, da pandemia. Diante disso, nesse trabalho são construídos modelos de aprendizado supervisionado focados na identificação de notícias falsas sobre o novo coronavírus. Como resultados, foram construídos e avaliados 18 modelos, os quais chegaram a alcançar 0.62%, 0.82% e 0.47% de f-score para as classes consideradas (news, opinion e fake).","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121158026","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}
Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18268
Thiago Abdo, Fabiano Silva
The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.
{"title":"Iterative machine learning applied to annotation of text datasets","authors":"Thiago Abdo, Fabiano Silva","doi":"10.5753/eniac.2021.18268","DOIUrl":"https://doi.org/10.5753/eniac.2021.18268","url":null,"abstract":"The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124914794","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}
Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18274
Mateus Alex dos Santos Luna, Andre Paulino de Lima, T. Neubauer, M. Fantinato, S. M. Peres
Process mining explores event logs to offer valuable insights to business process managers. Some types of business processes are hard to mine, including unstructured and knowledge-intensive processes. Then, trace clustering is usually applied to event logs aiming to break it into sublogs, making it more amenable to the typical process mining task. However, applying clustering algorithms involves decisions, such as how traces are represented, that can lead to better results. In this paper, we compare four vector space models for trace clustering, using them with an agglomerative clustering algorithm in synthetic and real-world event logs. Our analyses suggest the embeddings-based vector space model can properly handle trace clustering in unstructured processes.
{"title":"Vector space models for trace clustering: a comparative study","authors":"Mateus Alex dos Santos Luna, Andre Paulino de Lima, T. Neubauer, M. Fantinato, S. M. Peres","doi":"10.5753/eniac.2021.18274","DOIUrl":"https://doi.org/10.5753/eniac.2021.18274","url":null,"abstract":"Process mining explores event logs to offer valuable insights to business process managers. Some types of business processes are hard to mine, including unstructured and knowledge-intensive processes. Then, trace clustering is usually applied to event logs aiming to break it into sublogs, making it more amenable to the typical process mining task. However, applying clustering algorithms involves decisions, such as how traces are represented, that can lead to better results. In this paper, we compare four vector space models for trace clustering, using them with an agglomerative clustering algorithm in synthetic and real-world event logs. Our analyses suggest the embeddings-based vector space model can properly handle trace clustering in unstructured processes.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132637698","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}
Pub Date : 2021-11-29DOI: 10.5753/eniac.2021.18279
André Branco, Janaína Gomide
A taxa de perfuração (ROP) de um poço de petróleo é uma métrica muito importante de se controlar, por influenciar na produtividade do poço, no desgaste da broca e na segurança do poço e operação. Este trabalho avalia a utilização do algoritmo de floresta aleatória para previsão da ROP em poços de petróleo offshore como alternativa para modelos tradicionalmente utilizados pela indústria. Modelos de floresta aleatória foram avaliados com diferentes hiper-parâmetros para 4 poços offshore no pré-sal da Bacia de Santos. De forma geral, os modelos de floresta aleatória apresentaram resultados superiores para cada poço, mas não houve uma combinação de hiper-parâmetros ótima comum entre os poços.
穿孔率(ROP)油田上的是一个非常重要的指标,如果控制在fl影响生产率的水泵,在钻头的磨损和井的安全操作。这工作评估的使用fl算法的随机森林预测罗普在近海油井作为产业替代传统的模型。fl模型随机森林近海油井将有四个不同的超级-parâmetros pre -桑托斯盆地。一般来说,fl模式随机森林的结果更大了每口井,但没有一个很好的结合了高级-parâmetros火热的常见问题。
{"title":"Previsão de Taxa de Perfuração em Poços de Petróleo Offshore Utilizando Aprendizado de Máquina","authors":"André Branco, Janaína Gomide","doi":"10.5753/eniac.2021.18279","DOIUrl":"https://doi.org/10.5753/eniac.2021.18279","url":null,"abstract":"A taxa de perfuração (ROP) de um poço de petróleo é uma métrica muito importante de se controlar, por influenciar na produtividade do poço, no desgaste da broca e na segurança do poço e operação. Este trabalho avalia a utilização do algoritmo de floresta aleatória para previsão da ROP em poços de petróleo offshore como alternativa para modelos tradicionalmente utilizados pela indústria. Modelos de floresta aleatória foram avaliados com diferentes hiper-parâmetros para 4 poços offshore no pré-sal da Bacia de Santos. De forma geral, os modelos de floresta aleatória apresentaram resultados superiores para cada poço, mas não houve uma combinação de hiper-parâmetros ótima comum entre os poços.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124610665","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}
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