Flor C. Cárdenas-Mariño, Hugo D. Calderon-Vilca, Vladimiro Quispe Ibañez, Hesmeralda Rojas
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.
{"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}
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.
基于云的微服务架构的引入使得设计应用程序的过程变得更加复杂。这样的设计包含了多种程度的依赖关系——从硬件开始，到pod(服务的基本组件)的分发。尽管基于微服务的架构独立运行，并在可扩展性、维护和调试方面提供了很大的灵活性，但在任何故障的情况下，由于复杂和相互依赖的微服务，会检测到大量异常，从而在众多运营团队中发出警报。对于目前的行业生态系统来说，通过关联找出根本原因并最终消除异常现象是相当具有挑战性和耗时的。提出的模型- trACE讨论了如何关联来自所有子系统的警报或异常，并以系统的方式跟踪到真正的根本原因，从而改进了平均解决时间(MTTR)参数。这促进了不同操作团队的有效性和系统功能，使他们能够更快地响应异常，从而提高这些子系统的性能和正常运行时间。在实验中，我们发现trACE在准备好的数据集上的平均成本为1.18秒，在端到端实时环境中应用时的平均成本为4.47秒。在使用Kubernetes集群的Amazon Web Services (AWS)上运行的微服务基准测试中，trACE实现了98%的平均精度(MAP)，比目前的技术水平和其他基准方法提高了1%到34%。
{"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}
Weather is a big factor in tourist decisions, andcertain places or events aren’t even recommendedduring dangerously bad weather. It is difficult to providea better recommendation to a group of tourists in thesecircumstances. We propose gTravel, a weather assistantframework that predicts weather in specified pointsof interest for a group of tourists. We demonstratehow prior knowledge of climatic patterns at a POI,as well as prior insights into how visitors rank theirdestinations in a variety of weather conditions, can helpimprove choice reliability. According to our findings, therecommendations are significantly more valid, and therecommended remedy is more comfortable.
{"title":"gTravel: Weather-Aware POI Recommendation Engine for a Group of Tourists","authors":"Rajani Trivedi, Bibudhendu Pati, Subhendu Kumar Rath","doi":"10.13053/cys-27-3-4550","DOIUrl":"https://doi.org/10.13053/cys-27-3-4550","url":null,"abstract":"Weather is a big factor in tourist decisions, andcertain places or events aren’t even recommendedduring dangerously bad weather. It is difficult to providea better recommendation to a group of tourists in thesecircumstances. We propose gTravel, a weather assistantframework that predicts weather in specified pointsof interest for a group of tourists. We demonstratehow prior knowledge of climatic patterns at a POI,as well as prior insights into how visitors rank theirdestinations in a variety of weather conditions, can helpimprove choice reliability. According to our findings, therecommendations are significantly more valid, and therecommended remedy is more comfortable.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"10 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":"135297614","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}
Alexander J. Marcos Valdez, Eduardo G. Navarro Ortiz, Rodrigo E. Quinteros Peralta, Juan J. Tirado Julca, David F. Valentin Ricaldi, Hugo D. Calderon Vilca
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%.
{"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}
Carlos Hiram Moreno Montiel, Marisol Sandoval Rios, Benjamin Moreno Montiel, Miriam Noemi Moreno Montiel, José Luis Bernal López, Ezequiel Alpuche de la Cruz
The objective of this paper is to analyze the key factors which influence individual decision making in economics. To this purpose, a computational mobile modelling is used for human behavior which is treated as a complex system. Decision tables is used for such modelling to determine the similarities of the responses obtained by users for analysis. The results show that emotional aspects are the ones that most influence economic decision-making in people when they fear success. the visceral aspects are detrimental to making economic decisions in the individuals of the work.
{"title":"Influencing Factors on Individual Economic Decision Making: A Computational Mobile Model","authors":"Carlos Hiram Moreno Montiel, Marisol Sandoval Rios, Benjamin Moreno Montiel, Miriam Noemi Moreno Montiel, José Luis Bernal López, Ezequiel Alpuche de la Cruz","doi":"10.13053/cys-27-3-3992","DOIUrl":"https://doi.org/10.13053/cys-27-3-3992","url":null,"abstract":"The objective of this paper is to analyze the key factors which influence individual decision making in economics. To this purpose, a computational mobile modelling is used for human behavior which is treated as a complex system. Decision tables is used for such modelling to determine the similarities of the responses obtained by users for analysis. The results show that emotional aspects are the ones that most influence economic decision-making in people when they fear success. the visceral aspects are detrimental to making economic decisions in the individuals of the work.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"1 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":"135297620","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}
Brenda Sofía Sánchez López, Daniela Candioti Nolberto, José Antonio Taquía Gutiérrez, Yvan García López
{"title":"Traditional Machine Learning based on Atmospheric Conditions for Prediction of Dengue Presence","authors":"Brenda Sofía Sánchez López, Daniela Candioti Nolberto, José Antonio Taquía Gutiérrez, Yvan García López","doi":"10.13053/cys-27-3-4383","DOIUrl":"https://doi.org/10.13053/cys-27-3-4383","url":null,"abstract":"","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"17 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":"135297622","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}
Salvador Israel Avilés-López, Miguel Ángel Basurto-Pensado, Omar Palillero-Sandoval, Francisco Antonio Castillo-Velásquez
This work presents the design and test of a fiber optic-based one-axes accelerometer. This device is a type of reflexive-optical accelerometer and implements a membrane for the seismic mass. For this, a set of membranes have been developed with both different geometries and materials.
{"title":"Development of Membranes for Use as the Seismic Mass in a Fiber Optic-Accelerometer","authors":"Salvador Israel Avilés-López, Miguel Ángel Basurto-Pensado, Omar Palillero-Sandoval, Francisco Antonio Castillo-Velásquez","doi":"10.13053/cys-27-3-4607","DOIUrl":"https://doi.org/10.13053/cys-27-3-4607","url":null,"abstract":"This work presents the design and test of a fiber optic-based one-axes accelerometer. This device is a type of reflexive-optical accelerometer and implements a membrane for the seismic mass. For this, a set of membranes have been developed with both different geometries and materials.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"19 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":"135297608","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}
Andrea De Anda-Trasviña, Alejandra Nieto-Garibay, Fernando D. Von Borstel, Enrique Troyo-Diéguez, José Luis García-Hernández, Joaquin Gutierrez
. 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.
{"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}
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.
{"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}
Jose Angel Martinez-Navarro, Elsa Rubio-Espino, Juan Humberto Sossa-Azuela, Victor Hugo Ponce-Ponce, Heron Molina-Lozano, Luis Martin Garcia-Sebastian
This article presents the findings of a bio-inspired audio emotion-detection system and compares its performance with various neural network approaches, namely spiking neural networks, convolutional neural networks, and multilayer perceptrons. The simulation results demonstrate the effectiveness of the proposed approach in accurately detecting audio emotions. Additionally, the detection task can achieve even higher levels of precision by improving the training methods. The research utilizes the EmoDB, SAVEE, and RAVDESS databases.
{"title":"Comparison of Neural Networks for Emotion Detection","authors":"Jose Angel Martinez-Navarro, Elsa Rubio-Espino, Juan Humberto Sossa-Azuela, Victor Hugo Ponce-Ponce, Heron Molina-Lozano, Luis Martin Garcia-Sebastian","doi":"10.13053/cys-27-3-4515","DOIUrl":"https://doi.org/10.13053/cys-27-3-4515","url":null,"abstract":"This article presents the findings of a bio-inspired audio emotion-detection system and compares its performance with various neural network approaches, namely spiking neural networks, convolutional neural networks, and multilayer perceptrons. The simulation results demonstrate the effectiveness of the proposed approach in accurately detecting audio emotions. Additionally, the detection task can achieve even higher levels of precision by improving the training methods. The research utilizes the EmoDB, SAVEE, and RAVDESS databases.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"46 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":"135297613","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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