Computación Y Sistemas最新文献

Diet Recommendation according to Kilocalories and People’s Tastes 根据卡路里和人们的口味推荐饮食

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-3983

Flor C. Cárdenas-Mariño, Hugo D. Calderon-Vilca, Vladimiro Quispe Ibañez, Hesmeralda Rojas

{"title":"Diet Recommendation according to Kilocalories and People’s Tastes","authors":"Flor C. Cárdenas-Mariño, Hugo D. Calderon-Vilca, Vladimiro Quispe Ibañez, Hesmeralda Rojas","doi":"10.13053/cys-27-3-3983","DOIUrl":"https://doi.org/10.13053/cys-27-3-3983","url":null,"abstract":"Malnutrition and eating disorders are a latent problem in our society which are generated by an inadequate combination of foods either by lack of time, money, knowledge or a specialist who can help to properly manage food with the macronutrients necessary for good nutrition. In this research we present an architecture of diet recommendation using fuzzy logic and first-order logic, the research is divided into three phases: first, people’s data such as age, weight, height, physical activity level and gender were taken into account to obtain the required daily kilocalories using fuzzy logic; second, we considered as a knowledge base the menu plan for breakfast, mid-morning snack, lunch, mid-afternoon snack and dinner according to the tastes of the person for the first order logic; third, using a selection algorithm, a daily menu plan according to its kilocalories and the list of menus obtained with the first order logic are recommended. To validate the proposed architecture, Kaggle’s Cardiovascular Disease Detection dataset has been taken from which 500 people data have been taken for the research, the preferences of each person have been added to the dataset, finally the prototype recommends the diet for the 500 people according to the required kilocalories, the average kilocalories required are 1776 and the average kilocalories of the recommended menus are 1864, being the difference of 88 kilocalories, we conclude that our prototype based on the proposed architecture performs a proper recommendation.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297619","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

trACE - Anomaly Correlation Engine for Tracing the Root Cause on a Cloud based Microservice Architecture trACE——用于在基于云的微服务架构上跟踪根本原因的异常关联引擎

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-4498

Anukampa Behera, Chhabi Rani Panigrahi, Sitesh Behera, Rohit Patel, Saurav Bera

{"title":"trACE - Anomaly Correlation Engine for Tracing the Root Cause on a Cloud based Microservice Architecture","authors":"Anukampa Behera, Chhabi Rani Panigrahi, Sitesh Behera, Rohit Patel, Saurav Bera","doi":"10.13053/cys-27-3-4498","DOIUrl":"https://doi.org/10.13053/cys-27-3-4498","url":null,"abstract":"The introduction of cloud based microservices architectures has made the process of designing applications more complex. Such designs include numerous degrees of dependencies - starting with hardware and ending with the distribution of pods, a fundamental component of a service. Though microservice based architectures function independently and provides a lot of flexibility in terms of scalability, maintenance and debugging, in case of any failure, a large number of anomalies are detected due to complex and interdependent microservices, raising alerts across numerous operational teams. Tracing down the root cause and finally closing down the anomalies via correlating them is quite challenging and time taking for the present industry ecosystem. The proposed model - trACE discusses how to correlate alerts or anomalies from all the subsystems and trace down to the true root cause in a systematic manner, thereby improving the Mean Time to Resolve (MTTR) parameter. This facilitates the effectiveness and systematic functioning of different operation teams, allowing them to respond to the anomalies faster and thus bringing up the performance and uptime of such subsystems. On experimentation, it was found that trACE achieved an average cost of (in terms of time) 1.18 seconds on prepared dataset and 4.47 seconds when applied on end-to-end real time environment. When tested on a microservice benchmark running on Amazon Web Services (AWS) with Kubernetes cluster, trACE achieved a Mean Average Precision (MAP) of 98% which is an improvement of 1% to 34% over the state of the art as well as other baseline methods.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135296634","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

gTravel: Weather-Aware POI Recommendation Engine for a Group of Tourists gTravel:为一群游客提供天气感知POI推荐引擎

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-4550

Rajani Trivedi, Bibudhendu Pati, Subhendu Kumar Rath

引用次数: 0

Machine Learning for the Prediction of Anemia in Children Under 5 Years of Age by Analyzing their Nutritional Status Using Data Mining 利用数据挖掘分析5岁以下儿童营养状况，预测其贫血的机器学习

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-4315

Alexander J. Marcos Valdez, Eduardo G. Navarro Ortiz, Rodrigo E. Quinteros Peralta, Juan J. Tirado Julca, David F. Valentin Ricaldi, Hugo D. Calderon Vilca

{"title":"Machine Learning for the Prediction of Anemia in Children Under 5 Years of Age by Analyzing their Nutritional Status Using Data Mining","authors":"Alexander J. Marcos Valdez, Eduardo G. Navarro Ortiz, Rodrigo E. Quinteros Peralta, Juan J. Tirado Julca, David F. Valentin Ricaldi, Hugo D. Calderon Vilca","doi":"10.13053/cys-27-3-4315","DOIUrl":"https://doi.org/10.13053/cys-27-3-4315","url":null,"abstract":"One of the main public health problems is child malnutrition, since it negatively affects the individual throughout his life, limits the development of society and makes it difficult to eradicate poverty. The first objective of this research is to apply data mining techniques for preprocessing, cleaning, reduction and transformation to a data lake that has allowed analyzing anemia in children under 5 years of age, the second objective is to apply Machine Learning algorithms to obtain the best model to predict anemia in children under 5 years of age. The data set was extracted from the open data platform of the government of Peru that corresponds to South Lima, North Lima, East Lima, Central Lima and rural Lima, which collected a total of 138,369 instances and 36 variables of which 30 are categorical and 6 numeric, being an unbalanced data set. In order to obtain the best predictor variables, the Anova F-test and Chi Square filters were used, and it was possible to reduce them to 10 variables, cases were also carried out without considering one of the filters and both filters.To find the best prediction model, the algorithms have been tested: decision tree, logistic regression, K nearest neighbors, random forest and naive bayes. As a result, we show that the best algorithm to predict anemia in children under 5 years of age is the Naive Bayes algorithm with the highest recall of 74%, precision of 43% and accuracy of 70%.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"494 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297611","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

Influencing Factors on Individual Economic Decision Making: A Computational Mobile Model 个体经济决策的影响因素:一个计算移动模型

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-3992

Carlos Hiram Moreno Montiel, Marisol Sandoval Rios, Benjamin Moreno Montiel, Miriam Noemi Moreno Montiel, José Luis Bernal López, Ezequiel Alpuche de la Cruz

引用次数: 0

Traditional Machine Learning based on Atmospheric Conditions for Prediction of Dengue Presence 基于大气条件预测登革热存在的传统机器学习

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-4383

Brenda Sofía Sánchez López, Daniela Candioti Nolberto, José Antonio Taquía Gutiérrez, Yvan García López

引用次数: 0

Development of Membranes for Use as the Seismic Mass in a Fiber Optic-Accelerometer 光纤加速度计地震质量用膜的研制

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-4607

Salvador Israel Avilés-López, Miguel Ángel Basurto-Pensado, Omar Palillero-Sandoval, Francisco Antonio Castillo-Velásquez

引用次数: 0

Carbon/Nitrogen Ratio Estimation for Urban Organic Waste Using Convolutional Neural Networks 基于卷积神经网络的城市有机废弃物碳氮比估算

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-4301

Andrea De Anda-Trasviña, Alejandra Nieto-Garibay, Fernando D. Von Borstel, Enrique Troyo-Diéguez, José Luis García-Hernández, Joaquin Gutierrez

{"title":"Carbon/Nitrogen Ratio Estimation for Urban Organic Waste Using Convolutional Neural Networks","authors":"Andrea De Anda-Trasviña, Alejandra Nieto-Garibay, Fernando D. Von Borstel, Enrique Troyo-Diéguez, José Luis García-Hernández, Joaquin Gutierrez","doi":"10.13053/cys-27-3-4301","DOIUrl":"https://doi.org/10.13053/cys-27-3-4301","url":null,"abstract":". In this paper, the Carbon/Nitrogen ratio was estimated by classifying the urban organic waste (UOW) based on qualitative (color and maturity) and quantitative (weight) characteristics via convolutional neural networks (CNN) and image processing. The reuse of UOW is a suitable process in waste management, preventing its disposition in landfills and reducing the effects on the environment and human health. Ambient conditions affect the UOW characteristics over time. Knowing these changes is essential to reuse them appropriately, mainly both carbon and nitrogen content. A categorization associated with the decomposition stage of the UOW was proposed, which becomes the corresponding UOW classes. Three convolutional neural network models were trained with UOW images. Two pre-trained CNN (MobileNet and VGG16) were trained by transfer learning technique, and one proposed model (UOWNet) was trained from scratch. The UOWNet model presented a good agreement for the classification task. The results show that this preprocess is a practical tool for assessing the Carbon/Nitrogen ratio of UOW from its qualitative and quantitative features through image analysis. It is a preliminary framework aimed to support household organic waste recycling and community sustainability.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297623","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

Logic Abstraction Operations and their Algorithmic Implementation 逻辑抽象操作及其算法实现

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-4411

Pavel Zheltov

{"title":"Logic Abstraction Operations and their Algorithmic Implementation","authors":"Pavel Zheltov","doi":"10.13053/cys-27-3-4411","DOIUrl":"https://doi.org/10.13053/cys-27-3-4411","url":null,"abstract":"The article deals with logic abstraction operations, such as isolation, identification and generalization and their algorithmic implementation using the meta-programming language Sympl that is being developed by the author. As part of the implemented logic operations, new data types such as “identification set\", \"concept\", “notion” and \"category\" were implemented. The data type “identification set” represents sets, the elements of which all have either common properties or relations and are the result of the application of identification operation to logical objects. The data type “concept” is used for representation of concepts that are results of application of identification and generalization operations and is represented by two daughter data types (subtypes): “notion” and “category”. The “notion” data type represents the result of application of abstraction of generalization to an identification set. The application of abstraction of generalization two (or more times) results in a “category” data type - an extremely broad notion. The developed algorithms can be applied in text analysis when words are presented as logical objects: for finding synonyms, functionally similar personages or objects by their description and activities and so on.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297609","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

Comparison of Neural Networks for Emotion Detection 情感检测的神经网络比较

Computación Y Sistemas Pub Date : 2023-09-29 DOI: 10.13053/cys-27-3-4515

Jose Angel Martinez-Navarro, Elsa Rubio-Espino, Juan Humberto Sossa-Azuela, Victor Hugo Ponce-Ponce, Heron Molina-Lozano, Luis Martin Garcia-Sebastian

引用次数: 0