Pub Date : 2018-10-01DOI: 10.1109/BRACIS.2018.00030
Cyntia Eico Hayama Nishida, Anna Helena Reali Costa, R. Bianchi
Basin of attraction contains biological functions and channels cell behavior, so when a gene network is in an unhealthy basin it may cause diseases. Control techniques can support the design of therapies that promote the transition of a biological system from diseased to healthier basins. Most control methods first infer a gene network and then derive a control strategy to avoid diseased states. However, this approach is limited to few genes and may cause other diseases, as the biological side of the problem is not considered. While changing between basins may change a diseased biological function for a healthier one, state avoidance can change functions in an unexpected way. We propose to extend a batch reinforcement learning method FQI-Sarsa, to change basin of attractions in a partial observable network. Using a batch reinforcement learning technique avoids the most time consuming phases that are the inference and control of the gene network. Results demonstrate that our method, BOAFQI-Sarsa, is more effective than previous studies that do not consider basins in their computations.
{"title":"Control of Gene Regulatory Networks Basin of Attractions with Batch Reinforcement Learning","authors":"Cyntia Eico Hayama Nishida, Anna Helena Reali Costa, R. Bianchi","doi":"10.1109/BRACIS.2018.00030","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00030","url":null,"abstract":"Basin of attraction contains biological functions and channels cell behavior, so when a gene network is in an unhealthy basin it may cause diseases. Control techniques can support the design of therapies that promote the transition of a biological system from diseased to healthier basins. Most control methods first infer a gene network and then derive a control strategy to avoid diseased states. However, this approach is limited to few genes and may cause other diseases, as the biological side of the problem is not considered. While changing between basins may change a diseased biological function for a healthier one, state avoidance can change functions in an unexpected way. We propose to extend a batch reinforcement learning method FQI-Sarsa, to change basin of attractions in a partial observable network. Using a batch reinforcement learning technique avoids the most time consuming phases that are the inference and control of the gene network. Results demonstrate that our method, BOAFQI-Sarsa, is more effective than previous studies that do not consider basins in their computations.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120972818","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 : 2018-10-01DOI: 10.1109/BRACIS.2018.00104
Xuejian Wang, M. Fairhurst, A. Canuto
Facial expressions can be seen as a form of non-verbal communication as well as a primary means of conveying social information among humans.Automatic facial expression recognition (FER) can be applied to a wide range of scenarios in human-computer interaction, facial animation, entertainment, and psychology studies. For feature representation in a FER system, various texture descriptors have been employed to derive an effective solution for this system. However, these individual texture descriptor-based FER systems have often failed to achieve effective performance in the recognition of facial expressions. In this sense, it is necessary to further improve the general performance of a facial expression recognition system, evaluating different feature representations. In this paper, a novel local descriptor for a facial expression recognition system is proposed, designated the level of difference descriptor (LOD). The main goal is to use this descriptor as a supplement to state-of-the-art local descriptors to further improve the performance of a FER system in terms of classification accuracy. Furthermore, the fusion of various texture features for devising a robust feature representation for multi-view facial expression recognition is presented.
{"title":"Fusion of Local Descriptors for Multi-view Facial Expression Recognition","authors":"Xuejian Wang, M. Fairhurst, A. Canuto","doi":"10.1109/BRACIS.2018.00104","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00104","url":null,"abstract":"Facial expressions can be seen as a form of non-verbal communication as well as a primary means of conveying social information among humans.Automatic facial expression recognition (FER) can be applied to a wide range of scenarios in human-computer interaction, facial animation, entertainment, and psychology studies. For feature representation in a FER system, various texture descriptors have been employed to derive an effective solution for this system. However, these individual texture descriptor-based FER systems have often failed to achieve effective performance in the recognition of facial expressions. In this sense, it is necessary to further improve the general performance of a facial expression recognition system, evaluating different feature representations. In this paper, a novel local descriptor for a facial expression recognition system is proposed, designated the level of difference descriptor (LOD). The main goal is to use this descriptor as a supplement to state-of-the-art local descriptors to further improve the performance of a FER system in terms of classification accuracy. Furthermore, the fusion of various texture features for devising a robust feature representation for multi-view facial expression recognition is presented.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116703874","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 : 2018-10-01DOI: 10.1109/BRACIS.2018.00100
V. A. Padilha, A. Carvalho
Biclustering algorithms have become one of the main tools for the analysis of gene expression data. They allow the identification of local patterns defined by subsets of genes and subsets of samples, which cannot be detected by traditional clustering algorithms. However, although useful, biclustering is a NP-hard problem. Therefore, the majority of biclustering algorithms look for biclusters optimizing a pre-established coherence measure. In the last 20 years, several heuristics and measures have been published for biclustering. However, most of these publications do not provide an extensive comparison of bicluster coherence measures on practical scenarios. To deal with this problem, this paper analyze the behavior of 15 bicluster coherence measures and external evaluation regarding 9 algorithms from the literature on gene expression datasets. According to the experimental results, there is no clear relation between these measures and assessment using information from gene ontology.
{"title":"A Study of Biclustering Coherence Measures for Gene Expression Data","authors":"V. A. Padilha, A. Carvalho","doi":"10.1109/BRACIS.2018.00100","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00100","url":null,"abstract":"Biclustering algorithms have become one of the main tools for the analysis of gene expression data. They allow the identification of local patterns defined by subsets of genes and subsets of samples, which cannot be detected by traditional clustering algorithms. However, although useful, biclustering is a NP-hard problem. Therefore, the majority of biclustering algorithms look for biclusters optimizing a pre-established coherence measure. In the last 20 years, several heuristics and measures have been published for biclustering. However, most of these publications do not provide an extensive comparison of bicluster coherence measures on practical scenarios. To deal with this problem, this paper analyze the behavior of 15 bicluster coherence measures and external evaluation regarding 9 algorithms from the literature on gene expression datasets. According to the experimental results, there is no clear relation between these measures and assessment using information from gene ontology.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125195472","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 : 2018-10-01DOI: 10.1109/BRACIS.2018.00062
Edesio Alcobaça, R. G. Mantovani, A. L. Rossi, A. Carvalho
Given the increase in data generation, as many algorithms have become available in recent years, the algorithm recommendation problem has attracted increasing attention in Machine Learning. This problem has been addressed in the Machine Learning community as a learning task at the meta-level where the most suitable algorithm has to be recommended for a specific dataset. Since it is not trivial to define which characteristics are the most useful for a specific domain, several meta-features have been proposed and used, increasing the meta-data meta-feature dimension. This study investigates the influence of dimensionality reduction techniques on the quality of the algorithm recommendation process. Experiments were carried out with 15 algorithm recommendation problems from the Aslib library, 4 meta-learners, and 3 dimensionality reduction techniques. The experimental results showed that linear aggregation techniques, such as PCA and LDA, can be used in algorithm recommendation problems to reduce the number of meta-features and computational cost without losing predictive performance.
{"title":"Dimensionality Reduction for the Algorithm Recommendation Problem","authors":"Edesio Alcobaça, R. G. Mantovani, A. L. Rossi, A. Carvalho","doi":"10.1109/BRACIS.2018.00062","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00062","url":null,"abstract":"Given the increase in data generation, as many algorithms have become available in recent years, the algorithm recommendation problem has attracted increasing attention in Machine Learning. This problem has been addressed in the Machine Learning community as a learning task at the meta-level where the most suitable algorithm has to be recommended for a specific dataset. Since it is not trivial to define which characteristics are the most useful for a specific domain, several meta-features have been proposed and used, increasing the meta-data meta-feature dimension. This study investigates the influence of dimensionality reduction techniques on the quality of the algorithm recommendation process. Experiments were carried out with 15 algorithm recommendation problems from the Aslib library, 4 meta-learners, and 3 dimensionality reduction techniques. The experimental results showed that linear aggregation techniques, such as PCA and LDA, can be used in algorithm recommendation problems to reduce the number of meta-features and computational cost without losing predictive performance.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121742678","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 : 2018-10-01DOI: 10.1109/bracis.2018.00022
P. P. Rebouças Filho, Navar de Medeiros Mendonça e Nascimento, Shara Shami Araújo Alves, Samuel Luz Gomes, Cláudio Marques de Sá Medeiros
Wind energy is an excellent source of alternative energy to complement the Brazilian energy matrix. However, one of the significant challenges lies in managing this resource, due to its intermittent behavior. This study addresses the estimation of the electric power production of the wind park, so its management could be more efficient. A real data from one-year records of wind speed and power from a wind park installed in a wind farm in Ceará State, Brazil, is used. At first, we provide a study of Logistic versus Least Squares regression to model the wind turbine power curve. Then, a novel variant of the Least Square Support Regression is used to forecast wind speed on the site. The Logistic regression demonstrated to be more suitable for the task of regression, and the wind speed forecasting with three steps ahead provided lower error rates. Our approach represents a system based on data from both wind turbine power and speed to serve as a tool for helping energy selling issues and scheduling turbine maintenance on periods of time with low energy production in the wind park.
{"title":"Estimation of the Energy Production in a Wind Farm Using Regression Methods and Wind Speed Forecast","authors":"P. P. Rebouças Filho, Navar de Medeiros Mendonça e Nascimento, Shara Shami Araújo Alves, Samuel Luz Gomes, Cláudio Marques de Sá Medeiros","doi":"10.1109/bracis.2018.00022","DOIUrl":"https://doi.org/10.1109/bracis.2018.00022","url":null,"abstract":"Wind energy is an excellent source of alternative energy to complement the Brazilian energy matrix. However, one of the significant challenges lies in managing this resource, due to its intermittent behavior. This study addresses the estimation of the electric power production of the wind park, so its management could be more efficient. A real data from one-year records of wind speed and power from a wind park installed in a wind farm in Ceará State, Brazil, is used. At first, we provide a study of Logistic versus Least Squares regression to model the wind turbine power curve. Then, a novel variant of the Least Square Support Regression is used to forecast wind speed on the site. The Logistic regression demonstrated to be more suitable for the task of regression, and the wind speed forecasting with three steps ahead provided lower error rates. Our approach represents a system based on data from both wind turbine power and speed to serve as a tool for helping energy selling issues and scheduling turbine maintenance on periods of time with low energy production in the wind park.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115183348","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 : 2018-10-01DOI: 10.1109/BRACIS.2018.00045
S. Chevtchenko, Rafaella F. Vale, F. Cordeiro, V. Macário
Convolutional architectures have in recent years become state-of-the-art for several object detection tasks. However, these detectors have not yet been evaluated for detection and monitoring of beach areas. As some of these areas need to be continually monitored for dangerous situations, such as shark attacks, an automated system would be an effective risk control measure. The most significant and specific challenges for this problem are variable scene illumination, partial occlusion and distant camera position. In this work we present a study on three recent convolutional architectures for the task of people detection in beach scenarios. Our dataset is composed of images taken in the Boa Viagem beach, in Brazil, and is used to evaluate Faster R-CNN, R-FCN and SSD in terms of quality and speed of detection. The detectors are pretrained on a dataset containing 91 classes of objects, including people with different levels of scale and occlusion. The results suggest that the Faster R-CNN meta-architecture with the Resnet 101 feature extractor generates significantly better detections in terms of F-measure, while performing at 5.6 fps on a GTX 1080 Ti GPU.
卷积架构近年来已经成为一些目标检测任务的最先进的技术。然而，这些探测器在探测和监测海滩地区方面尚未得到评价。由于其中一些区域需要持续监测危险情况，例如鲨鱼袭击，自动化系统将是一种有效的风险控制措施。这个问题最重要和最具体的挑战是可变的场景照明，部分遮挡和远距离摄像机位置。在这项工作中，我们对海滩场景中人员检测任务的三种最新卷积架构进行了研究。我们的数据集由在巴西Boa Viagem海滩拍摄的图像组成，并用于评估Faster R-CNN, R-FCN和SSD在检测质量和速度方面的性能。检测器在包含91类对象的数据集上进行预训练，包括具有不同规模和遮挡水平的人。结果表明，更快的R-CNN元架构与Resnet 101特征提取器在F-measure方面产生了显着更好的检测，而在GTX 1080 Ti GPU上以5.6 fps的速度执行。
{"title":"Deep Learning for People Detection on Beach Images","authors":"S. Chevtchenko, Rafaella F. Vale, F. Cordeiro, V. Macário","doi":"10.1109/BRACIS.2018.00045","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00045","url":null,"abstract":"Convolutional architectures have in recent years become state-of-the-art for several object detection tasks. However, these detectors have not yet been evaluated for detection and monitoring of beach areas. As some of these areas need to be continually monitored for dangerous situations, such as shark attacks, an automated system would be an effective risk control measure. The most significant and specific challenges for this problem are variable scene illumination, partial occlusion and distant camera position. In this work we present a study on three recent convolutional architectures for the task of people detection in beach scenarios. Our dataset is composed of images taken in the Boa Viagem beach, in Brazil, and is used to evaluate Faster R-CNN, R-FCN and SSD in terms of quality and speed of detection. The detectors are pretrained on a dataset containing 91 classes of objects, including people with different levels of scale and occlusion. The results suggest that the Faster R-CNN meta-architecture with the Resnet 101 feature extractor generates significantly better detections in terms of F-measure, while performing at 5.6 fps on a GTX 1080 Ti GPU.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128039211","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 : 2018-10-01DOI: 10.1109/bracis.2018.00075
Saulo Martiello Mastelini, Everton José Santana, Victor Guilherme Turrisi da Costa, Sylvio Barbon Junior
Machine learning methods for multi-target regression (MTR) rely on the hypothesis that an inter-target correlation can improve predictive performance. In the last years, many MTR methods were developed, but there are still questions about how their performances are influenced by the datasets characteristics such as linearity, number of targets, and inter-correlation complexity. Aiming at contributing to the understanding of the relationship between the dataset properties and MTR methods, we generated 33 synthetic datasets with controlled characteristics and tested their performance with single-target and six MTR methods. The results showed that MTR methods were able to improve performance even in datasets whose targets were not linearly correlated among them, but the predictive improvement differed among the combinations of method/regressor according to the dataset composition.
{"title":"Benchmarking Multi-target Regression Methods","authors":"Saulo Martiello Mastelini, Everton José Santana, Victor Guilherme Turrisi da Costa, Sylvio Barbon Junior","doi":"10.1109/bracis.2018.00075","DOIUrl":"https://doi.org/10.1109/bracis.2018.00075","url":null,"abstract":"Machine learning methods for multi-target regression (MTR) rely on the hypothesis that an inter-target correlation can improve predictive performance. In the last years, many MTR methods were developed, but there are still questions about how their performances are influenced by the datasets characteristics such as linearity, number of targets, and inter-correlation complexity. Aiming at contributing to the understanding of the relationship between the dataset properties and MTR methods, we generated 33 synthetic datasets with controlled characteristics and tested their performance with single-target and six MTR methods. The results showed that MTR methods were able to improve performance even in datasets whose targets were not linearly correlated among them, but the predictive improvement differed among the combinations of method/regressor according to the dataset composition.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122760121","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 : 2018-10-01DOI: 10.1109/BRACIS.2018.00089
V. G. T. D. Costa, S. M. Mastelini, A. Carvalho, Sylvio Barbon Junior
Recently, several classification algorithms capable of dealing with potentially infinite data streams have been proposed. One of the main challenges of this task is to continuously update predictive models to address concept drifts without compromise their predictive performance. Moreover, the classification algorithm used must be able to efficiently deal with processing time and memory limitations. In the data stream mining literature, ensemble-based classification algorithms are a good alternative to satisfy the previous requirements. These algorithms combine multiple weak learner algorithms, e.g., the Very Fast Decision Tree (VFDT), to create a model with higher predictive performance. However, the memory costs of each weak learner are stacked in an ensemble, compromising the limited space requirements. To manage the trade-off between accuracy, memory space, and processing time, this paper proposes to use the Strict VFDT (SVFDT) algorithm as an alternative weak learner for ensemble solutions which is capable of reducing memory consumption without harming the predictive performance. This paper experimentally compares two traditional and three state-of-the-art ensembles using as weak learners the VFDT and SVFDT across thirteen benchmark datasets. According to the experimental results, the proposed algorithm can obtain a similar predictive performance with a significant economy of memory space.
{"title":"Making Data Stream Classification Tree-Based Ensembles Lighter","authors":"V. G. T. D. Costa, S. M. Mastelini, A. Carvalho, Sylvio Barbon Junior","doi":"10.1109/BRACIS.2018.00089","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00089","url":null,"abstract":"Recently, several classification algorithms capable of dealing with potentially infinite data streams have been proposed. One of the main challenges of this task is to continuously update predictive models to address concept drifts without compromise their predictive performance. Moreover, the classification algorithm used must be able to efficiently deal with processing time and memory limitations. In the data stream mining literature, ensemble-based classification algorithms are a good alternative to satisfy the previous requirements. These algorithms combine multiple weak learner algorithms, e.g., the Very Fast Decision Tree (VFDT), to create a model with higher predictive performance. However, the memory costs of each weak learner are stacked in an ensemble, compromising the limited space requirements. To manage the trade-off between accuracy, memory space, and processing time, this paper proposes to use the Strict VFDT (SVFDT) algorithm as an alternative weak learner for ensemble solutions which is capable of reducing memory consumption without harming the predictive performance. This paper experimentally compares two traditional and three state-of-the-art ensembles using as weak learners the VFDT and SVFDT across thirteen benchmark datasets. According to the experimental results, the proposed algorithm can obtain a similar predictive performance with a significant economy of memory space.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"71 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120968808","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 : 2018-10-01DOI: 10.1109/BRACIS.2018.00076
G. E. Rodrigues, Wilson Estécio Marcílio Júnior, D. M. Eler
Dimensionality Reduction is a commonly used method to reduce the number of dimensions of data. In this work, we verified its influence in classification process using combinations of projection techniques as dimensionality reduction algorithms. We also used Naïve Bayes and SMO as classifiers.
{"title":"Data Classification: Dimensionality Reduction Using Combined and Non-combined Multidimensional Projection Techniques","authors":"G. E. Rodrigues, Wilson Estécio Marcílio Júnior, D. M. Eler","doi":"10.1109/BRACIS.2018.00076","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00076","url":null,"abstract":"Dimensionality Reduction is a commonly used method to reduce the number of dimensions of data. In this work, we verified its influence in classification process using combinations of projection techniques as dimensionality reduction algorithms. We also used Naïve Bayes and SMO as classifiers.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121376821","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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