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A prioritization system for failures of a vital signs monitor 生命体征监测仪故障优先排序系统
Latin American Conference on Computational Intelligence Pub Date : 2018-11-01 DOI: 10.1109/LA-CCI.2018.8625238
Karen-Jazmín Mendoza-Bautista, M. Ramírez-Sotelo, A. Cabrera-Llanos
{"title":"A prioritization system for failures of a vital signs monitor","authors":"Karen-Jazmín Mendoza-Bautista, M. Ramírez-Sotelo, A. Cabrera-Llanos","doi":"10.1109/LA-CCI.2018.8625238","DOIUrl":"https://doi.org/10.1109/LA-CCI.2018.8625238","url":null,"abstract":"This work presents, the development of a MISO (Multi Input Single Output) system of fuzzy logic (diffuse), used for the prioritization of possible failures in vital signs monitor whose physiological variables are: electrocardiogram (ECG), heart rate (HR), respiratory rate (FR), temperature (T) and the partial oxygen saturation (SPO2). For the MISO system we classify the possible failures of the monitor in physiological failures and technical failures, developing two MISO systems, one for each type of failure. For the physiological failures system are taken as inputs the variables of HR, RR, SPO2 and T taking their membership functions as the ranges that these are normal and are considered uncommon in an adult, while for the techniques failures system are considered as input the failures in the sensors monitor and its membership functions vary according to the operational status of the same, delivering both systems a fuzzy output of prioritization whose labels are: normal, mild, moderate, and critical. Subsequently to be designed fuzzy systems miso, tests were carried out the method Montecarlo by checking the functionality of these. Due to the results achieved, the fuzzy systems described above can be applied in the automation of alarms of vital signs monitors that have the characteristics mentioned above or can serve as a reference for the development of future fuzzy systems applied to different types of monitors the vital signs.","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114628344","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
Welcome page 欢迎页面
Latin American Conference on Computational Intelligence Pub Date : 2016-11-01 DOI: 10.1109/la-cci.2016.7885694
A. Orjuela-Cañón
{"title":"Welcome page","authors":"A. Orjuela-Cañón","doi":"10.1109/la-cci.2016.7885694","DOIUrl":"https://doi.org/10.1109/la-cci.2016.7885694","url":null,"abstract":"LA-CCI is an acronym that refers to the Latin American Conference on Computational Intelligence (CI) that, in this year, is sponsored by the IEEE Colombia Section and IEEE Computational Intelligence Society. Currently, countries as Argentina, Bolivia, Brazil, Chile, Ecuador, Colombia, Mexico, Peru and Venezuela contribute to promote the CI, exchanging experiences/personnel and activities as present conference. At same time, it is a structuring endeavor from LA-CIS (Latin American Society of Computational Intelligence) that is encouraging research groups of these participating countries to teams up around the exciting topic of Computational Intelligence.","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130541713","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
Passive Sonar Classification Using Time-Domain Information and Recurrent Neural Networks 基于时域信息和递归神经网络的被动声纳分类
Latin American Conference on Computational Intelligence Pub Date : 1900-01-01 DOI: 10.1109/LA-CCI54402.2022.9981792
Marlon Jovenil de Souza, Natanael Nunes de Moura Junior, J. Seixas
{"title":"Passive Sonar Classification Using Time-Domain Information and Recurrent Neural Networks","authors":"Marlon Jovenil de Souza, Natanael Nunes de Moura Junior, J. Seixas","doi":"10.1109/LA-CCI54402.2022.9981792","DOIUrl":"https://doi.org/10.1109/LA-CCI54402.2022.9981792","url":null,"abstract":"Sonar systems have widely been used in both military and civilian applications. In particular, passive sonar systems play an important role in submarine operations in any nation’s Navy. Usually, passive sonar signal processing is performed in frequency domain for target detection and identification. Alternatively, in this work, a classifier based on recurrent neural networks and fed from the time-domain information is proposed. The proposed model employs Long Short-Term Memory (LSTM) networks aiming at classifying signals coming from 24 classes of military ships, which were organized into 4 super-classes based on expert knowledge. The model achieved an accuracy of 86.03%±3.08% outperforming a multilayer perceptron network (MLP) baseline model that was fed from frequency-domain data and obtained from Short-Time Fourier transformation.","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121060018","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
Automatic Feature Engineering Using Self-Organizing Maps 使用自组织地图的自动特征工程
Latin American Conference on Computational Intelligence Pub Date : 1900-01-01 DOI: 10.1109/LA-CCI48322.2021.9769788
Ericks da Silva Rodrigues, D. Martins, F. B. L. Neto
{"title":"Automatic Feature Engineering Using Self-Organizing Maps","authors":"Ericks da Silva Rodrigues, D. Martins, F. B. L. Neto","doi":"10.1109/LA-CCI48322.2021.9769788","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769788","url":null,"abstract":"Feature Engineering (FE) consists of generating new, better features to improve the results obtained by Machine Learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by data scientists. Moreover, FE requires data-specific and domain knowledge, both rarely easy to acquire. To alleviate these problems, we propose an automatic FE approach based on Self-Organizing Maps (SOM) in which new features are generated via pattern recognition. The use of the SOM algorithm in variable generation tasks can identify data elements that help Machine Learning models to obtain better results and points out to a broad direction for future researches.","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124645171","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
Performing Deep Recurrent Double Q-Learning for Atari Games 对Atari游戏进行深度循环双q学习
Latin American Conference on Computational Intelligence Pub Date : 1900-01-01 DOI: 10.1109/LA-CCI47412.2019.9036763
F. Vera
{"title":"Performing Deep Recurrent Double Q-Learning for Atari Games","authors":"F. Vera","doi":"10.1109/LA-CCI47412.2019.9036763","DOIUrl":"https://doi.org/10.1109/LA-CCI47412.2019.9036763","url":null,"abstract":"Currently, many applications in Machine Learning are based on defining new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, we proposed deep recurrent double Q-learning that is an improvement of the algorithms Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121189630","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: 14
Parameter selection for swarm intelligence algorithms - Case study on parallel implementation of FSS 群智能算法的参数选择——FSS并行实现的案例研究
Latin American Conference on Computational Intelligence Pub Date : 1900-01-01 DOI: 10.1109/LA-CCI.2017.8285694
B. Menezes, Fabian Wrede, H. Kuchen, Fernando Buarque de Lima-Neto
{"title":"Parameter selection for swarm intelligence algorithms - Case study on parallel implementation of FSS","authors":"B. Menezes, Fabian Wrede, H. Kuchen, Fernando Buarque de Lima-Neto","doi":"10.1109/LA-CCI.2017.8285694","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285694","url":null,"abstract":"","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122161138","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 Transfer Learning Approach for the Tattoo Classification Problem 纹身分类问题的迁移学习方法
Latin American Conference on Computational Intelligence Pub Date : 1900-01-01 DOI: 10.1109/LA-CCI54402.2022.9981650
Rodrigo Barbosa da Silva, Heitor Silvério Lopes
{"title":"A Transfer Learning Approach for the Tattoo Classification Problem","authors":"Rodrigo Barbosa da Silva, Heitor Silvério Lopes","doi":"10.1109/LA-CCI54402.2022.9981650","DOIUrl":"https://doi.org/10.1109/LA-CCI54402.2022.9981650","url":null,"abstract":"Object classification is a widely studied topic in computational vision, with several areas of application. However, when it comes to images that contain tattoos, current models do not fit properly and, in general, they cannot correctly classify the tattoo elements. Thus, we present a deep learning model based on transfer learning for the tattoo classification problem. Furthermore, given that datasets in this area are very rare, a new dataset with 40 tattoo classes was created and presented for the proposed study. We also used data augmentation to improve the diversity of the training sets to achieve better classification accuracy. As a result, the proposed model demonstrated robustness in classifying tattoo images with an accuracy of 85.24% and, even in tests with images of unknown classes, the model proved to be comprehensive enough to classify these images as the semantically similar classes known by the model.","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124167204","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 Model for Selecting Relevant Topics in Documents Aimed at Compliance Processes 针对法规遵循过程的文件中相关主题的选择模型
Latin American Conference on Computational Intelligence Pub Date : 1900-01-01 DOI: 10.1109/LA-CCI48322.2021.9769786
João Alberto Arantes do Amaral, Fernando Buarque de Lima-Neto
{"title":"A Model for Selecting Relevant Topics in Documents Aimed at Compliance Processes","authors":"João Alberto Arantes do Amaral, Fernando Buarque de Lima-Neto","doi":"10.1109/LA-CCI48322.2021.9769786","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769786","url":null,"abstract":"This paper proposes a semantic Natural Language Processing (NLP) approach used to assist in the automated characterization of information relevant to compliance activities. In this context, the proposed model combines two topic modeling techniques: Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA), the first used to assist in the dimensionality reduction process, while the second, used to identify the number of relevant topics addressed in the processed data. Interesting results were achieved when three large databases tested the model, namely: Database of European Laws (period 1952 to 1990), Database of Audit reports issued by the State General Secretariat of Management of Pernambuco (period 2010 to 2019), and Database of Appellate Decisions issued by the Brazilian Federal Accountability Office (year of 2019). We compared the performance of three machine learning methods: K-means, LSA and LDA. In our experiments, we observed that (i) pre-processing techniques have a marked influence on the result of topic extraction; that (ii) Silhouette techniques and topic coherence produced the best value for the quantitative topics in all databases ; that LSA associated with LDA presented the best performance in all three databases, regarding the identification of relevant themes (topics) identified. Overall, the best results were obtained using the database in English (European Union Laws).","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122692793","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
Predicting material backorders in inventory management using machine learning 利用机器学习预测库存管理中的物料缺货
Latin American Conference on Computational Intelligence Pub Date : 1900-01-01 DOI: 10.1109/LA-CCI.2017.8285684
Rodrigo Barbosa de Santis, E. Aguiar, L. G. Fonseca
{"title":"Predicting material backorders in inventory management using machine learning","authors":"Rodrigo Barbosa de Santis, E. Aguiar, L. G. Fonseca","doi":"10.1109/LA-CCI.2017.8285684","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285684","url":null,"abstract":"Material backorder is a common supply chain problem, impacting an inventory system service level and effectiveness. Identifying parts with the highest chances of shortage prior its occurrence can present a high opportunity to improve an overall company's performance. In this paper, machine learning classifiers are investigated in order to propose a predictive model for this imbalanced class problem, where the relative frequency of items that goes into backorder is rare when compared to items that do not. Specific metrics such as area under the Receiver Operator Characteristic and precision-recall curves, sampling techniques and ensemble learning are employed in this particular task.","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130309451","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: 20
MapView: Exploring Datasets via Unsupervised View Recommendation MapView:通过无监督视图推荐探索数据集
Latin American Conference on Computational Intelligence Pub Date : 1900-01-01 DOI: 10.1109/LA-CCI48322.2021.9769785
Buenos Aires de Carvalho, D. Martins, Fernando Buarque de Lima-Neto
{"title":"MapView: Exploring Datasets via Unsupervised View Recommendation","authors":"Buenos Aires de Carvalho, D. Martins, Fernando Buarque de Lima-Neto","doi":"10.1109/LA-CCI48322.2021.9769785","DOIUrl":"https://doi.org/10.1109/LA-CCI48322.2021.9769785","url":null,"abstract":"Exploring large datasets in search for valuable insights requires time and sufficient technical knowledge. In order to alleviate this task, we propose and implemented a prototype of a data exploration tool. It is based on Self-Organizing Maps (SOM) and helps non-technical users with limited technical expertise and time. Our proposed approach employs SOM as a clustering mechanism to group and recommend exploratory data views to the user. This recommendation process can also be personalized to meet user’s intention in an interactive manner. Experimental results show that the reported prototype is effective in recommending valuable views, hence, being of aid in data exploration tasks.","PeriodicalId":152596,"journal":{"name":"Latin American Conference on Computational Intelligence","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116644291","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
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