Pub Date : 2018-10-01DOI: 10.1109/BRACIS.2018.00029
S. N. D. Dôres, Carlos Soares, D. Ruiz
Machine Learning (ML) has been successfully applied to a wide range of domains and applications. Since the number of ML applications is growing, there is a need for tools that boost the data scientist's productivity. Automated Machine Learning (AutoML) is the field of ML that aims to address these needs through the development of solutions which enable data science practitioners, experts and non-experts, to efficiently create fine-tuned predictive models with minimum intervention. In this paper, we present the application of the multi-armed bandit optimization algorithm Hyperband to address the AutoML problem of generating customized classification workflows, a combination of preprocessing methods and ML algorithms including hyperparameter optimization. Experimental results comparing the bandit-based approach against Auto ML Bayesian Optimization methods show that this new approach is superior to the state-of-the-art methods in the test evaluation and equivalent to them in a statistical analysis.
机器学习(ML)已经成功地应用于广泛的领域和应用。由于ML应用程序的数量正在增长，因此需要提高数据科学家生产力的工具。自动化机器学习(AutoML)是机器学习领域，旨在通过开发解决方案来满足这些需求，这些解决方案使数据科学从业者，专家和非专家能够以最小的干预有效地创建微调的预测模型。在本文中，我们提出了应用多臂强盗优化算法Hyperband来解决生成自定义分类工作流的AutoML问题，将预处理方法和ML算法(包括超参数优化)相结合。通过与Auto ML Bayesian Optimization方法的对比实验结果表明，该方法在测试评估方面优于现有方法，在统计分析方面与现有方法相当。
{"title":"Bandit-Based Automated Machine Learning","authors":"S. N. D. Dôres, Carlos Soares, D. Ruiz","doi":"10.1109/BRACIS.2018.00029","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00029","url":null,"abstract":"Machine Learning (ML) has been successfully applied to a wide range of domains and applications. Since the number of ML applications is growing, there is a need for tools that boost the data scientist's productivity. Automated Machine Learning (AutoML) is the field of ML that aims to address these needs through the development of solutions which enable data science practitioners, experts and non-experts, to efficiently create fine-tuned predictive models with minimum intervention. In this paper, we present the application of the multi-armed bandit optimization algorithm Hyperband to address the AutoML problem of generating customized classification workflows, a combination of preprocessing methods and ML algorithms including hyperparameter optimization. Experimental results comparing the bandit-based approach against Auto ML Bayesian Optimization methods show that this new approach is superior to the state-of-the-art methods in the test evaluation and equivalent to them in a statistical analysis.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"13 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":"123852665","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.2018.00102
Fernanda Eustáquio, T. Nogueira
Fuzzy clustering validation of high-dimensional data sets is only possible using a reliable cluster validity index. Therefore, the selection of an index is as important as choosing an appropriate clustering algorithm. A good validity index is that one that correctly recognize the data structure by choosing its correct number of clusters, and it is not sensitive to any parameter of the clustering algorithm or data property. However, some classical fuzzy validity indices as Partition Coefficient (PC), Partition Entropy (PE) and Fukuyama-Sugeno (FS) are sensitive to the fuzzification factor m and the number of clusters c, both parameters of the well-known Fuzzy c-Means (FCM) algorithm. They present the monotonic tendency in function of c even varying the values of m: the PC and FS values become smaller when c increases and the opposite occurs with PE. Although the literature presents extensive investigations about such tendency, they were conducted for low-dimensional data, in which such data property does not affect the clustering behavior. In order to investigate how such aspects affect the fuzzy clustering results of high-dimensional data, in this work we have clustered objects of ten real high-dimensional data sets, using FCM validated by PC, PE, FS and some proposed modifications of them to lead with the monotonic tendency. The results showed that the Modified Partition Coefficient (MPC) is the more reliable index to validate fuzzy clustering of high-dimensional data.
{"title":"On Monotonic Tendency of Some Fuzzy Cluster Validity Indices for High-Dimensional Data","authors":"Fernanda Eustáquio, T. Nogueira","doi":"10.1109/BRACIS.2018.00102","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00102","url":null,"abstract":"Fuzzy clustering validation of high-dimensional data sets is only possible using a reliable cluster validity index. Therefore, the selection of an index is as important as choosing an appropriate clustering algorithm. A good validity index is that one that correctly recognize the data structure by choosing its correct number of clusters, and it is not sensitive to any parameter of the clustering algorithm or data property. However, some classical fuzzy validity indices as Partition Coefficient (PC), Partition Entropy (PE) and Fukuyama-Sugeno (FS) are sensitive to the fuzzification factor m and the number of clusters c, both parameters of the well-known Fuzzy c-Means (FCM) algorithm. They present the monotonic tendency in function of c even varying the values of m: the PC and FS values become smaller when c increases and the opposite occurs with PE. Although the literature presents extensive investigations about such tendency, they were conducted for low-dimensional data, in which such data property does not affect the clustering behavior. In order to investigate how such aspects affect the fuzzy clustering results of high-dimensional data, in this work we have clustered objects of ten real high-dimensional data sets, using FCM validated by PC, PE, FS and some proposed modifications of them to lead with the monotonic tendency. The results showed that the Modified Partition Coefficient (MPC) is the more reliable index to validate fuzzy clustering of high-dimensional data.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"25 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":"121071180","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.2018.00067
Kleyton Pontes Cotta, Raul Sena Ferreira, Felipe M. G. França
Recommender systems generally are made to predict user preferences' for items. However, in high dimensional datasets this task demands high computational costs. Taking into account that data distribution changes through time, it is important that online recommender systems have a fast retraining process in order to keep the model updated, delivering accurate predictions. Therefore, we propose a new approach for recommender systems using a weightless neural network, denominated WiSARD. We show that our proposal increases training and prediction processing speed, without decreasing the quality of predictions. First results show that our proposal is 306% faster than the improved regularized singular value decomposition (IRSVD), a well-known state-of-the-art algorithm. Moreover, our proposal still had an improvement of 3.7% regarding the mean absolute error (MAE). We show how to apply the WiSARD algorithm for online recommender systems, its drawbacks, and insights for further research.
{"title":"Weightless Neural Network WiSARD Applied to Online Recommender Systems","authors":"Kleyton Pontes Cotta, Raul Sena Ferreira, Felipe M. G. França","doi":"10.1109/bracis.2018.00067","DOIUrl":"https://doi.org/10.1109/bracis.2018.00067","url":null,"abstract":"Recommender systems generally are made to predict user preferences' for items. However, in high dimensional datasets this task demands high computational costs. Taking into account that data distribution changes through time, it is important that online recommender systems have a fast retraining process in order to keep the model updated, delivering accurate predictions. Therefore, we propose a new approach for recommender systems using a weightless neural network, denominated WiSARD. We show that our proposal increases training and prediction processing speed, without decreasing the quality of predictions. First results show that our proposal is 306% faster than the improved regularized singular value decomposition (IRSVD), a well-known state-of-the-art algorithm. Moreover, our proposal still had an improvement of 3.7% regarding the mean absolute error (MAE). We show how to apply the WiSARD algorithm for online recommender systems, its drawbacks, and insights for further research.","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":"115987450","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.2018.00010
J. Kuk, Richard A. Gonçalves, C. Almeida, Sandra M. Venske, A. Pozo
As well as new algorithms are constantly proposed, new test functions for these algorithms are also designed. In this paper we explore 15 new benchmark functions proposed for CEC-2018 Multiobjective Evolutionary Algorithms (MOEA) Competition for many-objective optimization. The functions have diverse properties which cover a good representation of various real-world scenarios. We propose many-objective approaches that were designed considering three schemes to perform adaptive operator selection with NSGA-III algorithm: Thompson Sampling, Probability Matching and Adaptive Pursuit. They select from a pool of candidates composed by DE mutations and a Genetic Algorithm crossover. Thompson Sampling is a multi-armed bandit approach, i.e., it was designed to deal with the exploration versus exploitation dilemma intrinsic to the adaptive operator selection problem. Its use in a many objective evolutionary algorithm is innovative and constitutes the main contribution of this work. As the CEC-2018 is composed by complex, potentially nonlinear functions, we also perform the analysis of the effects of the insertion of a nonlinear operator within the candidate pool of operators. Statistical analysis of the experiments were performed with Mann-Whitney and Friedman tests. The IGD indicator was used to infer the quality of the solutions. The results indicate the use of Thompson Sampling as an adaptive operator selection is promising and increases the optimization performance of NSGA-III. They also indicate that the use of the nonlinear operator is capable of improving the results of all adaptive versions.
{"title":"A New Adaptive Operator Selection for NSGA-III Applied to CEC 2018 Many-Objective Benchmark","authors":"J. Kuk, Richard A. Gonçalves, C. Almeida, Sandra M. Venske, A. Pozo","doi":"10.1109/BRACIS.2018.00010","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00010","url":null,"abstract":"As well as new algorithms are constantly proposed, new test functions for these algorithms are also designed. In this paper we explore 15 new benchmark functions proposed for CEC-2018 Multiobjective Evolutionary Algorithms (MOEA) Competition for many-objective optimization. The functions have diverse properties which cover a good representation of various real-world scenarios. We propose many-objective approaches that were designed considering three schemes to perform adaptive operator selection with NSGA-III algorithm: Thompson Sampling, Probability Matching and Adaptive Pursuit. They select from a pool of candidates composed by DE mutations and a Genetic Algorithm crossover. Thompson Sampling is a multi-armed bandit approach, i.e., it was designed to deal with the exploration versus exploitation dilemma intrinsic to the adaptive operator selection problem. Its use in a many objective evolutionary algorithm is innovative and constitutes the main contribution of this work. As the CEC-2018 is composed by complex, potentially nonlinear functions, we also perform the analysis of the effects of the insertion of a nonlinear operator within the candidate pool of operators. Statistical analysis of the experiments were performed with Mann-Whitney and Friedman tests. The IGD indicator was used to infer the quality of the solutions. The results indicate the use of Thompson Sampling as an adaptive operator selection is promising and increases the optimization performance of NSGA-III. They also indicate that the use of the nonlinear operator is capable of improving the results of all adaptive versions.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"34 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":"126347022","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.2018.00055
Gustavo Bennemann de Moura, Valéria Delisandra Feltrim
The importance of identifying rhetorical categories in texts has been widely acknowledged in the literature, since information regarding text organization or structure can be applied in a variety of scenarios, including genre-specific writing support and evaluation, both manually and automatically. In this paper we present a Long Short-Term Memory (LSTM) encoder-decoder classifier for scientific abstracts. As a large corpus of annotated abstracts was required to train our classifier, we built a corpus using abstracts extracted from PUBMED/MEDLINE. Using the proposed classifier we achieved approximately 3% improvement in per-abstract accuracy over the baselines and 1% improvement for both per-sentence accuracy and f1-score.
{"title":"Using LSTM Encoder-Decoder for Rhetorical Structure Prediction","authors":"Gustavo Bennemann de Moura, Valéria Delisandra Feltrim","doi":"10.1109/bracis.2018.00055","DOIUrl":"https://doi.org/10.1109/bracis.2018.00055","url":null,"abstract":"The importance of identifying rhetorical categories in texts has been widely acknowledged in the literature, since information regarding text organization or structure can be applied in a variety of scenarios, including genre-specific writing support and evaluation, both manually and automatically. In this paper we present a Long Short-Term Memory (LSTM) encoder-decoder classifier for scientific abstracts. As a large corpus of annotated abstracts was required to train our classifier, we built a corpus using abstracts extracted from PUBMED/MEDLINE. Using the proposed classifier we achieved approximately 3% improvement in per-abstract accuracy over the baselines and 1% improvement for both per-sentence accuracy and f1-score.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"49 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":"121690251","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.2018.00032
C. Almeida, Richard A. Gonçalves, Sandra M. Venske, R. Lüders, M. Delgado
In this work, we propose MAB variants as selection mechanisms of a hyper-heuristic running on the multi-objective framework named MOEA/D-DRA to solve the Permutation Flow Shop Problem (PFSP). All the variants are designed to choose which of low-level heuristic components (for crossover and mutation operators) should be applied to each solution during execution. FRRMAB is the classical MAB, RMAB is restless and LinUCB is contextual (its context is based on side information). The proposed approaches are compared with each other and the best one, MOEA/D-LinUCB, is compared with MOEA/DDRA using the hypervolume indicator and nonparametric statistical tests. The results demonstrate the robustness of MAB-based approaches, especially the contextual-based one.
{"title":"Multi-armed Bandit Based Hyper-Heuristics for the Permutation Flow Shop Problem","authors":"C. Almeida, Richard A. Gonçalves, Sandra M. Venske, R. Lüders, M. Delgado","doi":"10.1109/BRACIS.2018.00032","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00032","url":null,"abstract":"In this work, we propose MAB variants as selection mechanisms of a hyper-heuristic running on the multi-objective framework named MOEA/D-DRA to solve the Permutation Flow Shop Problem (PFSP). All the variants are designed to choose which of low-level heuristic components (for crossover and mutation operators) should be applied to each solution during execution. FRRMAB is the classical MAB, RMAB is restless and LinUCB is contextual (its context is based on side information). The proposed approaches are compared with each other and the best one, MOEA/D-LinUCB, is compared with MOEA/DDRA using the hypervolume indicator and nonparametric statistical tests. The results demonstrate the robustness of MAB-based approaches, especially the contextual-based one.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"66 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":"121928388","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.2018.00037
Enrico S. Miranda, F. Fabris, Chrystian G. M. Nascimento, A. Freitas, A. Oliveira
It is of great interest to build recommendation systems capable of choosing the best solver for a particular problem of a combinatorial optimisation task given past runs of solvers in various problems of that optimisation task. In this paper, a meta-learning approach is proposed to predict which metaheuristic is the best solver for MaxSAT problems. The proposal includes the creation of new meta-features derived from graph descriptions of MaxSAT problems and an interpretation of the meta-model. Our approach successfully selected the best metaheuristic to solve each problem in 87% of the cases. Also, the new meta-features have shown to be as good as the state-of-the-art meta-features, and the meta-model interpretation found interesting problem-specific knowledge.
{"title":"Meta-Learning for Recommending Metaheuristics for the MaxSAT Problem","authors":"Enrico S. Miranda, F. Fabris, Chrystian G. M. Nascimento, A. Freitas, A. Oliveira","doi":"10.1109/BRACIS.2018.00037","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00037","url":null,"abstract":"It is of great interest to build recommendation systems capable of choosing the best solver for a particular problem of a combinatorial optimisation task given past runs of solvers in various problems of that optimisation task. In this paper, a meta-learning approach is proposed to predict which metaheuristic is the best solver for MaxSAT problems. The proposal includes the creation of new meta-features derived from graph descriptions of MaxSAT problems and an interpretation of the meta-model. Our approach successfully selected the best metaheuristic to solve each problem in 87% of the cases. Also, the new meta-features have shown to be as good as the state-of-the-art meta-features, and the meta-model interpretation found interesting problem-specific knowledge.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"147 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":"127511655","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.2018.00041
P. D. Santos, Ismael C. S. Araújo, Rodrigo S. Sousa, A. J. D. Silva
In this work, we propose a quantum-classical algorithm able to perform a k-fold cross-validation with linear speedup. The proposed method creates a quantum superposition with patterns from a dataset and a classifier can evaluate all patterns at once. We used a probabilistic quantum memory in order to conduct the performance evaluation. The proposed method was verified through a reduced experimental analysis conducted classically.
{"title":"Quantum Enhanced k-fold Cross-Validation","authors":"P. D. Santos, Ismael C. S. Araújo, Rodrigo S. Sousa, A. J. D. Silva","doi":"10.1109/BRACIS.2018.00041","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00041","url":null,"abstract":"In this work, we propose a quantum-classical algorithm able to perform a k-fold cross-validation with linear speedup. The proposed method creates a quantum superposition with patterns from a dataset and a classifier can evaluate all patterns at once. We used a probabilistic quantum memory in order to conduct the performance evaluation. The proposed method was verified through a reduced experimental analysis conducted classically.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"198 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":"134152169","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.2018.00046
Jonas Wacker, R. Ferreira, M. Ladeira
The Observatory of Public Spending (ODP, in Portuguese) is a special unit of Brazil's Ministry of Transparency and Office of the Comptroller-General (CGU, in Portuguese) responsible for gathering managerial and audit information to support the work of its auditors. One of the most important tasks of this unit is to monitor government suppliers who have won procurement processes. Image analysis of the location of many of these suppliers revealed suspicious scenes, such as rural areas, isolated places or slums. These scenes could be an indicator of fake suppliers with poor capacity of delivering public goods. However, checking thousands of images in order to find suspicious suppliers would be very expensive. Our objective is to automatically distinguish images of valid supplier locations from arbitrary buildings and landscapes. We extract deep features from a collection of Google Street View images using a pretrained convolutional neural network (Places CNN) to classify supplier locations and show that these features can be well applied to the context of identifying valid suppliers, independent of the image perspective that was collected.
{"title":"Detecting Fake Suppliers using Deep Image Features","authors":"Jonas Wacker, R. Ferreira, M. Ladeira","doi":"10.1109/BRACIS.2018.00046","DOIUrl":"https://doi.org/10.1109/BRACIS.2018.00046","url":null,"abstract":"The Observatory of Public Spending (ODP, in Portuguese) is a special unit of Brazil's Ministry of Transparency and Office of the Comptroller-General (CGU, in Portuguese) responsible for gathering managerial and audit information to support the work of its auditors. One of the most important tasks of this unit is to monitor government suppliers who have won procurement processes. Image analysis of the location of many of these suppliers revealed suspicious scenes, such as rural areas, isolated places or slums. These scenes could be an indicator of fake suppliers with poor capacity of delivering public goods. However, checking thousands of images in order to find suspicious suppliers would be very expensive. Our objective is to automatically distinguish images of valid supplier locations from arbitrary buildings and landscapes. We extract deep features from a collection of Google Street View images using a pretrained convolutional neural network (Places CNN) to classify supplier locations and show that these features can be well applied to the context of identifying valid suppliers, independent of the image perspective that was collected.","PeriodicalId":405190,"journal":{"name":"2018 7th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"77 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":"129990684","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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