Brazilian Conference on Intelligent Systems最新文献_第9页

Decoding machine learning benchmarks 解码机器学习基准

Brazilian Conference on Intelligent Systems Pub Date : 2020-07-29 DOI: 10.1007/978-3-030-61380-8_28

Lucas F. F. Cardoso, Vitor Santos, R. S. K. Francês, R. Prudêncio, Ronnie Alves

引用次数: 5

Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes 从巴西-葡萄牙临床记录预测多个ICD-10代码

Brazilian Conference on Intelligent Systems Pub Date : 2020-07-29 DOI: 10.1007/978-3-030-61377-8_39

A. D. Reys, Danilo Silva, Daniel de Souza Severo, S. Pedro, Marcia M. de Souza e S'a, Guilherme A. C. Salgado

引用次数: 9

Deep learning models for representing out-of-vocabulary words 表示词汇表外单词的深度学习模型

Brazilian Conference on Intelligent Systems Pub Date : 2020-07-14 DOI: 10.1007/978-3-030-61377-8_29

Johannes V. Lochter, R. M. Silva, Tiago A. Almeida

引用次数: 7

Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models 语音电话:一种新的高效的说话人识别模型训练方法

Brazilian Conference on Intelligent Systems Pub Date : 2020-02-25 DOI: 10.1007/978-3-030-91699-2_39

Edresson Casanova, Arnaldo Cândido Júnior, C. Shulby, F. S. Oliveira, L. Gris, Hamilton Pereira da Silva, S. Aluísio, M. Ponti

引用次数: 0

An Improved Heuristic Based Genetic Algorithm for Bin Packing Problem 一种改进的启发式遗传算法求解装箱问题

Brazilian Conference on Intelligent Systems Pub Date : 2019-10-01 DOI: 10.1109/BRACIS.2019.00020

Aluísio Cardoso Silva, C. Borges

{"title":"An Improved Heuristic Based Genetic Algorithm for Bin Packing Problem","authors":"Aluísio Cardoso Silva, C. Borges","doi":"10.1109/BRACIS.2019.00020","DOIUrl":"https://doi.org/10.1109/BRACIS.2019.00020","url":null,"abstract":"The NP-complete bin packing problem is a widely studied grouping problem that serves to model several useful and practical problems, e.g., batch-processing machine scheduling, industrial and transportation logistics, etc. Due to the complexity involved to solve this class of problems, usually two main strategies are adopted: sub-optimal building heuristics and optimization models using metaheuristics algorithms. The building heuristics are computationally efficient, however, usually obtaining non-optimal solutions or local minima. Otherwise, adapted metaheuristics to handle this problem allows an effective global search which augments the chance to obtain optimal or quasi-optimal solutions, however with a high computational cost. This work develops a heuristic based genetic algorithm aiming to obtain a hybrid approach constructed to explore the best features of each strategy. Special encoding handling and specific operators are included additionally to the final model to enhance the behavior and performance of the hybrid model. Numerical experimental using well-established benchmarks for one-dimensional bin packing problem are carried out to compare the versions of the presented hybrid methods with high-quality methods presented in the literature. The results indicate the potential for the presented strategy to solve the one-dimensional bin packing problems.","PeriodicalId":335206,"journal":{"name":"Brazilian Conference on Intelligent Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124709017","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

Meta-Data Construction for Selection of Breast Tissue Biopsy Slides Image Classifier to Identify Ductal Carcinoma 选择乳腺组织活检切片图像分类器识别导管癌的元数据构建

Brazilian Conference on Intelligent Systems Pub Date : 2019-10-01 DOI: 10.1109/BRACIS.2019.00131

Luis Fernando Marin Sepulveda, A. Silva, J. O. Diniz

引用次数: 0

Development of Criminal Ontologies to Enhance Situation Assessment 发展犯罪本体以加强态势评估

Brazilian Conference on Intelligent Systems Pub Date : 2019-10-01 DOI: 10.1109/BRACIS.2019.00122

J. F. Saran, L. C. Botega

{"title":"Development of Criminal Ontologies to Enhance Situation Assessment","authors":"J. F. Saran, L. C. Botega","doi":"10.1109/BRACIS.2019.00122","DOIUrl":"https://doi.org/10.1109/BRACIS.2019.00122","url":null,"abstract":"Situation Awareness (SAW) refers to the level of consciousness that an individual or team holds about a situation. In the field of risk management and criminal data analysis, SAW failures may led human operators to errors in the decision-making process and jeopardize human life, heritage and environment. In this scenario, critical situation assessment processes, which usually involve methods as mining, fusion and others, present opportunities to deliver better information for human reasoning and to assist in the development of SAW. However, on attempting to characterize complex scenarios can lead to poor information representation and expressiveness, which can induce the misinterpretation of data, mainly due to their quality, producing uncertainties. The state-of-the-art on information representation of risk situations and related areas presents approaches with limited usage of the quality of information. In addition, the solutions are limited to syntactic mechanisms for characterizing relations between the information, negatively limiting the assertiveness of the results. Thus, this work aims to present the development of a new approach of semantic information representation of crime situations, more specifically by modeling domain ontologies, instantiated with qualified criminal data. In a case study, real crime information is processed, represented by the new semantic model and consumed by computational inference methods. Results validate the applicability of the produced ontologies on characterizing and inferring robbery and theft situations.","PeriodicalId":335206,"journal":{"name":"Brazilian Conference on Intelligent Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121854144","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

Active Learning for Evolutionary Constrained Clustering 进化约束聚类的主动学习

Brazilian Conference on Intelligent Systems Pub Date : 2019-10-01 DOI: 10.1109/BRACIS.2019.00037

Matheus Campos Fernandes, T. Covões, André Luiz Vizine Pereira

{"title":"Active Learning for Evolutionary Constrained Clustering","authors":"Matheus Campos Fernandes, T. Covões, André Luiz Vizine Pereira","doi":"10.1109/BRACIS.2019.00037","DOIUrl":"https://doi.org/10.1109/BRACIS.2019.00037","url":null,"abstract":"The high cost of labeling data for analysis has increased interest in semi-supervised learning. One of its most common types is constrained clustering, which is a type of learning that does not rely on class labels for a group of objects. Instead, there is only information if some pairs of objects must be in the same cluster or in different clusters. In some applications, identifying such constraints involves reduced cost since it is less information than a class label. At the same time, Active Learning (AL) aims to minimize the cost of creating labeled datasets, trying to identify which unlabeled data are more relevant for using during the learning process, considering the labels that are already available. This paper proposes three AL strategies to an evolutionary constrained clustering algorithm (FIECE-EM) based on Gaussian Mixture Models (GMM). Experiments were executed on 10 well-known datasets, as a way to measure the impacts of each strategy. We compare the results with baseline supervised algorithms as well as COBRAS, a state-of-the-art Active Learning algorithm for constrained clustering. Two of the proposed strategies obtained significantly better results than COBRAS in our empirical evaluation. Thus, the combination of FIECE-EM with these strategies can be considered viable alternatives for AL in a constrained clustering setting.","PeriodicalId":335206,"journal":{"name":"Brazilian Conference on Intelligent Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123855954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Random Forest Classifier for Prokaryotes Gene Prediction 原核生物基因预测的随机森林分类器

Brazilian Conference on Intelligent Systems Pub Date : 2019-10-01 DOI: 10.1109/BRACIS.2019.00101

Raíssa Silva, K. Souza, F. Góes, Ronnie Alves

引用次数: 1

An Efficient Kick Strategy for Agents in the 2D Simulation League 2D模拟联赛中agent的有效踢脚策略

Brazilian Conference on Intelligent Systems Pub Date : 2019-10-01 DOI: 10.1109/BRACIS.2019.00087

João Pedro Figueirôa Nascimento, R. Neto, Lourinaldo Júnior Macário Amorim

引用次数: 1