Pub Date : 2023-01-30DOI: 10.22456/2175-2745.125825
Wagner da Silva Maciel Sodré, J. C. Duarte
A chatbot or conversational agent is a software that can interact or ``chat'' with a human user using a natural language, like English, for instance. Since the first chatbot developed, many have been created but most of their problems still persist, like providing the right answer to the user and user acceptance itself. Considering such facts, in this work, we present a chatbot-building framework that considers the use of sentiment analysis and tree timelines to provide a better chatbot answer. For instance, as presented in our experiments, the user can be addressed to a human attendant when its sentiment is very negative, or even try another branch of the tree timeline, as an alternative answer, whenever the user sentiment is less negative.
{"title":"Chatbot Optimization using Sentiment Analysis and Timeline Navigation","authors":"Wagner da Silva Maciel Sodré, J. C. Duarte","doi":"10.22456/2175-2745.125825","DOIUrl":"https://doi.org/10.22456/2175-2745.125825","url":null,"abstract":"A chatbot or conversational agent is a software that can interact or ``chat'' with a human user using a natural language, like English, for instance. Since the first chatbot developed, many have been created but most of their problems still persist, like providing the right answer to the user and user acceptance itself. Considering such facts, in this work, we present a chatbot-building framework that considers the use of sentiment analysis and tree timelines to provide a better chatbot answer. For instance, as presented in our experiments, the user can be addressed to a human attendant when its sentiment is very negative, or even try another branch of the tree timeline, as an alternative answer, whenever the user sentiment is less negative.","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43735684","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 : 2023-01-30DOI: 10.22456/2175-2745.127207
Vitor M. T. Aleluia, V. Soares, João M. L. P. Caldeira, P. Gaspar
This paper presents the proposal, implementation and validation of a low cost fault-tolerant functional prototype for livestock monitoring. This prototype uses IoT devices, ESP8266 and ESP32, creating a mesh network, managed by the painlessMesh library, with WiFi and LoRa technologies. It allows, for instance, the collection of vital signs from animals. In comparison with the traditional method of livestock examination, this cost-efficient approach reduces manual labor and saves working time. It also improves animal health, increases profits and decreases the environmental footprint.
{"title":"Livestock Monitoring Prototype Implementation and Validation","authors":"Vitor M. T. Aleluia, V. Soares, João M. L. P. Caldeira, P. Gaspar","doi":"10.22456/2175-2745.127207","DOIUrl":"https://doi.org/10.22456/2175-2745.127207","url":null,"abstract":"This paper presents the proposal, implementation and validation of a low cost fault-tolerant functional prototype for livestock monitoring. This prototype uses IoT devices, ESP8266 and ESP32, creating a mesh network, managed by the painlessMesh library, with WiFi and LoRa technologies. It allows, for instance, the collection of vital signs from animals. In comparison with the traditional method of livestock examination, this cost-efficient approach reduces manual labor and saves working time. It also improves animal health, increases profits and decreases the environmental footprint.","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43338169","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 : 2023-01-30DOI: 10.22456/2175-2745.126016
Lucas Augusto Müller de Souza, H. Bernardino, Jairo Francisco de Souza, Alex Vieira
The World Health Organization (WHO) has declared the novel coronavirus (COVID-19) outbreak a global pandemic in March 2020. Through a lot of cooperation and the effort of scientists, several vaccines have been created. However, there is no guarantee that the virus will shortly disappear, even if a large part of the population is vaccinated. Therefore, non-invasive methods, with low cost and real-time results, are important to detect infected individuals and enable earlier adequate treatment, in addition to preventing the spread of the virus. An alternative is using forced cough sounds and medical information to distinguish a healthy person from those infected with COVID-19 via artificial intelligence. An additional challenge is the unbalancing of these data, as there are more samples of healthy individuals than contaminated ones. We propose here a Deep Neural Network model to classify people as healthy or sick concerning COVID-19. We used here a model composed by an Convolutional Neural Network and two other Neural Networks with two full-connected layers, each one trained with different data from the same individual. To evaluate the performance of the proposed method, we combined two datasets from the literature: COUGHVID and Coswara. That dataset contains clinical information regarding previous respiratory conditions, symptoms (fever or muscle pain), and a cough record. The results show that our model is simpler (with fewer parameters) than those from the literature and generalizes better the prediction of infected individuals. The proposal presents an average Area Under the ROC Curve (AUC) equal to 0.885 with a confidence interval (0.881 - 0.888), while the literature reports 0.771 with (0.752 - 0.783).
{"title":"COVID-19 Detection Using Forced Cough Sounds and Medical Information","authors":"Lucas Augusto Müller de Souza, H. Bernardino, Jairo Francisco de Souza, Alex Vieira","doi":"10.22456/2175-2745.126016","DOIUrl":"https://doi.org/10.22456/2175-2745.126016","url":null,"abstract":"The World Health Organization (WHO) has declared the novel coronavirus (COVID-19) outbreak a global pandemic in March 2020. Through a lot of cooperation and the effort of scientists, several vaccines have been created. However, there is no guarantee that the virus will shortly disappear, even if a large part of the population is vaccinated. Therefore, non-invasive methods, with low cost and real-time results, are important to detect infected individuals and enable earlier adequate treatment, in addition to preventing the spread of the virus. An alternative is using forced cough sounds and medical information to distinguish a healthy person from those infected with COVID-19 via artificial intelligence. An additional challenge is the unbalancing of these data, as there are more samples of healthy individuals than contaminated ones. We propose here a Deep Neural Network model to classify people as healthy or sick concerning COVID-19. We used here a model composed by an Convolutional Neural Network and two other Neural Networks with two full-connected layers, each one trained with different data from the same individual. To evaluate the performance of the proposed method, we combined two datasets from the literature: COUGHVID and Coswara. That dataset contains clinical information regarding previous respiratory conditions, symptoms (fever or muscle pain), and a cough record. The results show that our model is simpler (with fewer parameters) than those from the literature and generalizes better the prediction of infected individuals. The proposal presents an average Area Under the ROC Curve (AUC) equal to 0.885 with a confidence interval (0.881 - 0.888), while the literature reports 0.771 with (0.752 - 0.783).","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46442474","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 : 2023-01-30DOI: 10.22456/2175-2745.125567
Tiago Costa, Luís Santos, João M. L. P. Caldeira, V. Soares, P. Gaspar
This paper presents the performance evaluation of a wireless sensor mesh network, for monitoring temperature and humidity in horticultural products when transported in truck galleys. For this purpose a software solution was proposed using ESP8266 devices powered by batteries. The mesh network was managed by the painlessMesh library. The proposed solution aims to minimize the energy consumption of the sensor nodes. The validation of the solution was performed in an area simulating a galley of a truck, where five sensor nodes and a root node were distributed. The tests were developed considering four different models involving variations in messages delivery confirmation, number of attempts until successful delivery and duty cycle duration of the nodes. The performance evaluation of the solution aimed to determine, connectivity rate, sending rate after connection and delivery rates of the first and second attempts. The results obtained show that the message delivery confirmation does not bring added value to the solution, contributing only to increase energy consumption. The use of synchronous duty cycles also showed worse results than the asynchronous use. These results allow the creation of a knowledge base for the use of this solution in a real context.
{"title":"Performance Assessment of a Wireless Mesh Network for Post-harvest Food Quality Traceability of Fruit Products: A Case Study","authors":"Tiago Costa, Luís Santos, João M. L. P. Caldeira, V. Soares, P. Gaspar","doi":"10.22456/2175-2745.125567","DOIUrl":"https://doi.org/10.22456/2175-2745.125567","url":null,"abstract":"This paper presents the performance evaluation of a wireless sensor mesh network, for monitoring temperature and humidity in horticultural products when transported in truck galleys. For this purpose a software solution was proposed using ESP8266 devices powered by batteries. The mesh network was managed by the painlessMesh library. The proposed solution aims to minimize the energy consumption of the sensor nodes. The validation of the solution was performed in an area simulating a galley of a truck, where five sensor nodes and a root node were distributed. The tests were developed considering four different models involving variations in messages delivery confirmation, number of attempts until successful delivery and duty cycle duration of the nodes. The performance evaluation of the solution aimed to determine, connectivity rate, sending rate after connection and delivery rates of the first and second attempts. The results obtained show that the message delivery confirmation does not bring added value to the solution, contributing only to increase energy consumption. The use of synchronous duty cycles also showed worse results than the asynchronous use. These results allow the creation of a knowledge base for the use of this solution in a real context.","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42118789","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 : 2023-01-20DOI: 10.33936/isrtic.v6i2.5286
Andres Fernando Zambrano Caicedo, Karen Ivonne Briones Giler, Victor Joel Pinargote Bravo, Alfonso Tomás Loor Vera
El presente artículo plantea una propuesta de los conceptos teóricos básicos para el funcionamiento de un prototipo de un robot agrícola inteligente capaz de sembrar en pendientes menores a 45° aplicando las nuevas tecnologías de la Industria 4.0 como lo son la inteligencia artificial, la agricultura de precisión, los robots (agentes inteligentes) y los sensores, realizando una investigación exploratoria para realizar una primera aproximación al objeto de estudio, partiendo de una revisión bibliográfica que brinda una perspectiva más amplia del tema a investigar para elaborar un árbol de problema para así esquematizar los componentes de Hardware y Software necesarios para el prototipo. Una vez que ha sido aplicada la metodología escogida se obtiene como resultado el árbol de problema que muestra las causas y efectos del problema, una matriz REAS que describe los componentes de hardware del prototipo, una lista inicial de algoritmos necesarios para el entrenamiento y posterior funcionamiento del robot, y un modelo en 3D del robot realizado en SketchUp.
{"title":"Investigación Exploratoria de Robots Agrícolas","authors":"Andres Fernando Zambrano Caicedo, Karen Ivonne Briones Giler, Victor Joel Pinargote Bravo, Alfonso Tomás Loor Vera","doi":"10.33936/isrtic.v6i2.5286","DOIUrl":"https://doi.org/10.33936/isrtic.v6i2.5286","url":null,"abstract":"El presente artículo plantea una propuesta de los conceptos teóricos básicos para el funcionamiento de un prototipo de un robot agrícola inteligente capaz de sembrar en pendientes menores a 45° aplicando las nuevas tecnologías de la Industria 4.0 como lo son la inteligencia artificial, la agricultura de precisión, los robots (agentes inteligentes) y los sensores, realizando una investigación exploratoria para realizar una primera aproximación al objeto de estudio, partiendo de una revisión bibliográfica que brinda una perspectiva más amplia del tema a investigar para elaborar un árbol de problema para así esquematizar los componentes de Hardware y Software necesarios para el prototipo. Una vez que ha sido aplicada la metodología escogida se obtiene como resultado el árbol de problema que muestra las causas y efectos del problema, una matriz REAS que describe los componentes de hardware del prototipo, una lista inicial de algoritmos necesarios para el entrenamiento y posterior funcionamiento del robot, y un modelo en 3D del robot realizado en SketchUp.","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86080792","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 : 2022-12-28DOI: 10.22456/2175-2745.121425
Daniel W. S. Rocha, Eduardo Cardoso Melo, B. A. Oliveira
Beekeeping is one of the most important activities for humans. Since ancient times, honey has been used in the treatment of several diseases and is an extremely powerful antioxidant. The process of visual analysis of the apiary requires trained specialists who try to obtain relevant information to make a decision about what to do with the honeycomb. Since the process is performed manually, given the complexity of the task, opportunities arise for the application of automated systems that can assist the beekeeper's decision making. Thus, this paper presents the development of the application textit{SegBee}, a computational tool that performs the segmentation in the apiary plates, where there is the presence of honey, in an accessible, fast and practical way. To do this, the OpenCV library was used for the digital image processing part, and the Kivy library was used to develop the interface of the mobile application. The tests performed showed that the images were adequately segmented by textit{SegBee}, indicating where the honey is located on each analyzed plate. A visual comparison was made between results obtained by textit{SegBee} and another commercial application, demonstrating the effectiveness of the developed tool. The proposed solution contributes to the improvement of the beekeeping professionals' work, once the application is simple to use and fast to process, being able to help in the honey identification task in apiaries plates.
{"title":"SEGBEE: Mobile Application for Honey Segmentation in Apiary Boards","authors":"Daniel W. S. Rocha, Eduardo Cardoso Melo, B. A. Oliveira","doi":"10.22456/2175-2745.121425","DOIUrl":"https://doi.org/10.22456/2175-2745.121425","url":null,"abstract":"Beekeeping is one of the most important activities for humans. Since ancient times, honey has been used in the treatment of several diseases and is an extremely powerful antioxidant. The process of visual analysis of the apiary requires trained specialists who try to obtain relevant information to make a decision about what to do with the honeycomb. Since the process is performed manually, given the complexity of the task, opportunities arise for the application of automated systems that can assist the beekeeper's decision making. Thus, this paper presents the development of the application textit{SegBee}, a computational tool that performs the segmentation in the apiary plates, where there is the presence of honey, in an accessible, fast and practical way. To do this, the OpenCV library was used for the digital image processing part, and the Kivy library was used to develop the interface of the mobile application. The tests performed showed that the images were adequately segmented by textit{SegBee}, indicating where the honey is located on each analyzed plate. A visual comparison was made between results obtained by textit{SegBee} and another commercial application, demonstrating the effectiveness of the developed tool. The proposed solution contributes to the improvement of the beekeeping professionals' work, once the application is simple to use and fast to process, being able to help in the honey identification task in apiaries plates.","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45831073","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 : 2022-12-28DOI: 10.22456/2175-2745.126309
Arthur Alexandre Artoni, C. Barbosa, Marcelo Morandini
Autism Spectrum Disorder (ASD) is a common but complex disorder to diagnose since there are no imaging or blood tests that can detect ASD. Several techniques can be used, such as diagnostic scales that contain specific questionnaires formulated by specialists that serve as a guide in the diagnostic process. In this paper, Machine Learning (ML) was applied on three public databases containing AQ-10 test results for adults, adolescents, and children; as well as other characteristics that could influence the diagnosis of ASD. Experiments were carried out on the databases to list which attributes would be truly relevant for the diagnosis of ASD using ML, which could be of great value for medical students or residents, and for physicians who are not specialists in ASD. The experiments have shown that it is possible to reduce the number of attributes to only 5 while maintaining an Accuracy above 0.9. In the other Database to maintain the same level of Accuracy, the fewer attribute numbers were 7. The Support Vector Machine stood out from the others algorithms used in this paper, obtaining superior results in all scenarios.
{"title":"Autism Spectrum Disorder Diagnosis Assistance using Machine Learning","authors":"Arthur Alexandre Artoni, C. Barbosa, Marcelo Morandini","doi":"10.22456/2175-2745.126309","DOIUrl":"https://doi.org/10.22456/2175-2745.126309","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a common but complex disorder to diagnose since there are no imaging or blood tests that can detect ASD. Several techniques can be used, such as diagnostic scales that contain specific questionnaires formulated by specialists that serve as a guide in the diagnostic process. In this paper, Machine Learning (ML) was applied on three public databases containing AQ-10 test results for adults, adolescents, and children; as well as other characteristics that could influence the diagnosis of ASD. Experiments were carried out on the databases to list which attributes would be truly relevant for the diagnosis of ASD using ML, which could be of great value for medical students or residents, and for physicians who are not specialists in ASD. The experiments have shown that it is possible to reduce the number of attributes to only 5 while maintaining an Accuracy above 0.9. In the other Database to maintain the same level of Accuracy, the fewer attribute numbers were 7. The Support Vector Machine stood out from the others algorithms used in this paper, obtaining superior results in all scenarios.","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48522879","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 : 2022-12-28DOI: 10.22456/2175-2745.121388
Joelson Miller Bezerra De Souza, Patricia H. Moraes Rego, Guilherme Bonfim De Sousa, Janes Valdo Rodrigues Lima
The development of an optimal controller for stabilization of a quadrotor system using an adaptive critic structure based on policy iteration schemes is proposed in this paper. This approach is inserted in the context of Approximate Dynamic Programming and it is used to solve optimal decision problems on-line, without requiring complete knowledge of the system dynamics model to be controlled. The main feature of the adaptive critic design method that allows for on-line implementation is that it solves the Bellman optimality equation in a forward-in-time fashion, whereas traditional dynamic programming requires a backward-in-time procedure. This feedback control design technique is able to tune the controller parameters on-line in the presence of variations in plant dynamics and external disturbances using data measured along the system trajectories. Computational simulation results based on a quadrotor model demonstrate the effectiveness of the proposed control scheme.
{"title":"Optimal Control and Adaptive Learning for Stabilization of a Quadrotor-type Unmanned Aerial Vehicle via Approximate Dynamic Programming","authors":"Joelson Miller Bezerra De Souza, Patricia H. Moraes Rego, Guilherme Bonfim De Sousa, Janes Valdo Rodrigues Lima","doi":"10.22456/2175-2745.121388","DOIUrl":"https://doi.org/10.22456/2175-2745.121388","url":null,"abstract":"The development of an optimal controller for stabilization of a quadrotor system using an adaptive critic structure based on policy iteration schemes is proposed in this paper. This approach is inserted in the context of Approximate Dynamic Programming and it is used to solve optimal decision problems on-line, without requiring complete knowledge of the system dynamics model to be controlled. The main feature of the adaptive critic design method that allows for on-line implementation is that it solves the Bellman optimality equation in a forward-in-time fashion, whereas traditional dynamic programming requires a backward-in-time procedure. This feedback control design technique is able to tune the controller parameters on-line in the presence of variations in plant dynamics and external disturbances using data measured along the system trajectories. Computational simulation results based on a quadrotor model demonstrate the effectiveness of the proposed control scheme.","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47550038","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 : 2022-12-28DOI: 10.22456/2175-2745.125396
C. K. D. S. Rodrigues
This article comparatively analyzes two platforms based on Blockchain, aiming at the management of electronic health records in the public healthcare system of Brazil. The difference between the platforms primarily lies in the deployed consensus algorithm. Efficiency, availability, integrity, and confidentiality requirements are evaluated through analytical models and theoretical discussions. Among the obtained results, we highlight the following: (i) the platform with a voting-based consensus algorithm yields a more efficient system, but is more prone to service unavailability, than that of the platform deploying an intensive-compute consensus algorithm; (ii) integrity and confidentiality requirements may be satisfactorily met regardless of the consensus type. As the main contribution, this article provides valuable experimental results and theoretical subsidies, which together complement previous research and help to lay the groundwork for the fruitful development of real projects. Finally, conclusions and future work conclude this article.
{"title":"Comparative Analysis of Blockchain-Based Platforms for Managing Electronic Health Records in the Public Healthcare System of Brazil","authors":"C. K. D. S. Rodrigues","doi":"10.22456/2175-2745.125396","DOIUrl":"https://doi.org/10.22456/2175-2745.125396","url":null,"abstract":"This article comparatively analyzes two platforms based on Blockchain, aiming at the management of electronic health records in the public healthcare system of Brazil. The difference between the platforms primarily lies in the deployed consensus algorithm. Efficiency, availability, integrity, and confidentiality requirements are evaluated through analytical models and theoretical discussions. Among the obtained results, we highlight the following: (i) the platform with a voting-based consensus algorithm yields a more efficient system, but is more prone to service unavailability, than that of the platform deploying an intensive-compute consensus algorithm; (ii) integrity and confidentiality requirements may be satisfactorily met regardless of the consensus type. As the main contribution, this article provides valuable experimental results and theoretical subsidies, which together complement previous research and help to lay the groundwork for the fruitful development of real projects. Finally, conclusions and future work conclude this article.","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42387176","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 : 2022-12-28DOI: 10.22456/2175-2745.120399
Allan Matheus Marques Dos Santos, R. Pinto, J. C. Duarte, B. Schulze
Today, cloud environments are widely used as execution platforms for most applications. In these environments, virtualized applications often share computing resources. Although this increases hardware utilization, resources competition can cause performance degradation, and knowing which applications can run on the same host without causing too much interference is key to a better scheduling and performance. Therefore, it is important to predict the resource consumption profile of applications in their subsequent iterations. This work evaluates the use of machine learning techniques to predict the increase or decrease in computational resources consumption. The prediction models are evaluated through experiments using real and benchmark applications. Finally, we conclude that some models offer significantly better performance when compared to the current trend of resource usage. These models averaged up to 94% on the F1 metric for this task.
{"title":"Application of Profile Prediction for Proactive Scheduling","authors":"Allan Matheus Marques Dos Santos, R. Pinto, J. C. Duarte, B. Schulze","doi":"10.22456/2175-2745.120399","DOIUrl":"https://doi.org/10.22456/2175-2745.120399","url":null,"abstract":"Today, cloud environments are widely used as execution platforms for most applications. In these environments, virtualized applications often share computing resources. Although this increases hardware utilization, resources competition can cause performance degradation, and knowing which applications can run on the same host without causing too much interference is key to a better scheduling and performance. Therefore, it is important to predict the resource consumption profile of applications in their subsequent iterations. This work evaluates the use of machine learning techniques to predict the increase or decrease in computational resources consumption. The prediction models are evaluated through experiments using real and benchmark applications. Finally, we conclude that some models offer significantly better performance when compared to the current trend of resource usage. These models averaged up to 94% on the F1 metric for this task.","PeriodicalId":53421,"journal":{"name":"Revista de Informatica Teorica e Aplicada","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48231516","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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