Negation of probability distributions (PD) was initially introduced by Yager as a transformation of probability distributions representing linguistic terms like High Price, into probability distributions representing linguistic terms like Not High Price. Further, different negations of PD and formal definitions of negations of probability distributions have been proposed, and several classes of such negations have been studied. Here we give a new look at negators dependent and not dependent on probability distributions. We consider different parametric representations of linear negators and analyze relationships between the parameters of these representations. We introduce a new parametric negation of probability distributions based on the involutive negation of PD. Recently it was proposed to consider probability distributions as fuzzy distribution sets, which paved the way for the extension of many concepts and operations of fuzzy sets on probability distributions. From such a point of view, Yager’s negation of probability distributions is an extension of the standard negation of fuzzy logic, also known as Zadeh’s negation. In this paper, using this approach, we extend the parametric Yager’s and Sugeno’s negation of fuzzy logic on probability distributions and study their properties. Considered parametric negations of probability distributions can be used in the models of probabilistic reasoning.
{"title":"Parametric Negations of Probability Distributions and Fuzzy Distribution Sets","authors":"Ildar Batyrshin, Imre Rudas, Nailya Kubysheva","doi":"10.13053/cys-27-3-4709","DOIUrl":"https://doi.org/10.13053/cys-27-3-4709","url":null,"abstract":"Negation of probability distributions (PD) was initially introduced by Yager as a transformation of probability distributions representing linguistic terms like High Price, into probability distributions representing linguistic terms like Not High Price. Further, different negations of PD and formal definitions of negations of probability distributions have been proposed, and several classes of such negations have been studied. Here we give a new look at negators dependent and not dependent on probability distributions. We consider different parametric representations of linear negators and analyze relationships between the parameters of these representations. We introduce a new parametric negation of probability distributions based on the involutive negation of PD. Recently it was proposed to consider probability distributions as fuzzy distribution sets, which paved the way for the extension of many concepts and operations of fuzzy sets on probability distributions. From such a point of view, Yager’s negation of probability distributions is an extension of the standard negation of fuzzy logic, also known as Zadeh’s negation. In this paper, using this approach, we extend the parametric Yager’s and Sugeno’s negation of fuzzy logic on probability distributions and study their properties. Considered parametric negations of probability distributions can be used in the models of probabilistic reasoning.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"3 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":"135296633","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}
Lourdes P. Aquino-Martinez, Beatriz Ortega Guerrero, Arturo I. Quintanar, Carlos A. Ochoa Moya, Ricardo Barrón-Fernández
This study explores the performance of the Weather Research and Forecasting System Model (WRF v.4.0) for a winter case under stable meteorological conditions in the Mexico Basin. To evaluate the sensitivity to spatial resolution and parameterization configurations, a suite of different numerical experiments is designed to test five Planetary Boundary Layer (PBL) schemes coupled to a Surface Layer parameterization (SL) and a cloud microphysics (MP) parameterization to find an optimal configuration in terms of closeness to physical reality and computational efficiency. The WRF atmospheric dynamics core and its ancillary physics routines constitute a massively parallel FORTRAN code that runs on the Tlaloc cluster at the ICAyCC-UNAM with optimized MPICH software. Two model performance metrics are used: 1) Taylor statistics to measure the distance between simulations and observed meteorological fields (near-surface and upper-level temperature and winds), and 2) CPU execution time. Results show that the Mellor-Yamada-Janjic (M) scheme performs best near the surface at 2.0 km horizontal resolution. However, the Yonsei University (Y) PBL scheme outperforms the M scheme when looking at temperature vertical profiles at the exact horizontal resolution. Both PBL schemes show negligible CPU execution time differences.
{"title":"High-Performance Computing with the Weather Research and Forecasting System Model: A Case Study under Stable Conditions over Mexico Basin","authors":"Lourdes P. Aquino-Martinez, Beatriz Ortega Guerrero, Arturo I. Quintanar, Carlos A. Ochoa Moya, Ricardo Barrón-Fernández","doi":"10.13053/cys-27-3-4035","DOIUrl":"https://doi.org/10.13053/cys-27-3-4035","url":null,"abstract":"This study explores the performance of the Weather Research and Forecasting System Model (WRF v.4.0) for a winter case under stable meteorological conditions in the Mexico Basin. To evaluate the sensitivity to spatial resolution and parameterization configurations, a suite of different numerical experiments is designed to test five Planetary Boundary Layer (PBL) schemes coupled to a Surface Layer parameterization (SL) and a cloud microphysics (MP) parameterization to find an optimal configuration in terms of closeness to physical reality and computational efficiency. The WRF atmospheric dynamics core and its ancillary physics routines constitute a massively parallel FORTRAN code that runs on the Tlaloc cluster at the ICAyCC-UNAM with optimized MPICH software. Two model performance metrics are used: 1) Taylor statistics to measure the distance between simulations and observed meteorological fields (near-surface and upper-level temperature and winds), and 2) CPU execution time. Results show that the Mellor-Yamada-Janjic (M) scheme performs best near the surface at 2.0 km horizontal resolution. However, the Yonsei University (Y) PBL scheme outperforms the M scheme when looking at temperature vertical profiles at the exact horizontal resolution. Both PBL schemes show negligible CPU execution time differences.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"47 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":"135297618","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}
Iván A. Juárez Trujillo, Jonny P. Zavala de Paz, Omar Palillero Sandoval, Francisco A. Castillo Velásquez
A methodology for multispectral camera calibration using convolutional neural networks is presented. RGB images were captured from the multispectral camera for each of the standards, the samples are taken under the same lighting conditions and with the same capture angle. The images are fragmented into small matrix sizes added to a specific class, and saved with a special label to distinguish it from the entire class database, the same process takes the remaining 7 Lucideon Std color tiles. One of the tiles will correspond to a particular class with an equal dimension for all classes. Finally, based on the presented methodology, it is possible to calibrate the camera with respect to the references.
{"title":"Multispectral Camera Calibration Using Convolutional Neural Networks","authors":"Iván A. Juárez Trujillo, Jonny P. Zavala de Paz, Omar Palillero Sandoval, Francisco A. Castillo Velásquez","doi":"10.13053/cys-27-3-4605","DOIUrl":"https://doi.org/10.13053/cys-27-3-4605","url":null,"abstract":"A methodology for multispectral camera calibration using convolutional neural networks is presented. RGB images were captured from the multispectral camera for each of the standards, the samples are taken under the same lighting conditions and with the same capture angle. The images are fragmented into small matrix sizes added to a specific class, and saved with a special label to distinguish it from the entire class database, the same process takes the remaining 7 Lucideon Std color tiles. One of the tiles will correspond to a particular class with an equal dimension for all classes. Finally, based on the presented methodology, it is possible to calibrate the camera with respect to the references.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"30 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":"135296631","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}
It is said that prevention is better than cure. Hence the idea of preventing crime from occurring is the best for public safety. This can only be achieved if the law enforcement agencies have a prior knowledge of where and when a crime will occur. A crime is an act that is criminal under the law. It is detrimental to society to comprehend crime in order to prevent criminal action. In order to prevent and solve crime, data-driven research is beneficial. Bandit crime has been on the rise in Nigeria, thereby causing public disorder. In this study, from the perspective of artificial intelligence, a novel hybrid deep learning model for crime prediction is proposed. Bandits’ crime datasets are obtained online through news archives which are less expensive. Spatial crime analysis was carried out on the novel bandit crime dataset obtained and prediction were made using the newly proposed DECrimeXGBoost model. A comparative analysis was performed with respect to precision, recall, f-measure, and accuracy with other crime predictions algorithms and the proposed model outperformed the other algorithms with accuracy of 99.9999%.
{"title":"Spatiotemporal Bandits Crime Prediction from Web News Archives Analysis","authors":"Angbera Ature, Huah Yong Chan","doi":"10.13053/cys-27-3-4110","DOIUrl":"https://doi.org/10.13053/cys-27-3-4110","url":null,"abstract":"It is said that prevention is better than cure. Hence the idea of preventing crime from occurring is the best for public safety. This can only be achieved if the law enforcement agencies have a prior knowledge of where and when a crime will occur. A crime is an act that is criminal under the law. It is detrimental to society to comprehend crime in order to prevent criminal action. In order to prevent and solve crime, data-driven research is beneficial. Bandit crime has been on the rise in Nigeria, thereby causing public disorder. In this study, from the perspective of artificial intelligence, a novel hybrid deep learning model for crime prediction is proposed. Bandits’ crime datasets are obtained online through news archives which are less expensive. Spatial crime analysis was carried out on the novel bandit crime dataset obtained and prediction were made using the newly proposed DECrimeXGBoost model. A comparative analysis was performed with respect to precision, recall, f-measure, and accuracy with other crime predictions algorithms and the proposed model outperformed the other algorithms with accuracy of 99.9999%.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"21 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":"135297607","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}
Laura Cleofas-Sánchez, Juan Pablo Francisco Posadas-Durán, Pedro Martínez-Ortiz, Gilberto Loyo-Desiderio, Eduardo Alberto Ruvalcaba-Hernández, Omar González Brito
This paper presents a prototype of an automobile driver assistance system based on YOLOv3. The system detects car types, traffic signs, and traffic lights in real-time and warns the driver accordingly. In the learning phase of the YOLO algorithm, the standard weights are learned first, followed by transfer learning to the objects of interest. The retraining phase uses 2,800 images obtained from the Internet of three countries of the real-life, and the testing phase uses real-time videos of Mexico City roads. In the validation phase, the proposed system achieves 95%, 37%, and 40% performance on the compiled dataset for the detection of road elements. The results obtained are comparable and in some cases better than those reported in previous works. Using a Raspberry Pi 4, the prototype was tested in real-life, generating visual and audible warnings for the driver, with an object recognition rate of 0.4 fps. A mean average precision (mAP) of 53% was reached by the proposed system. The experiments showed that the prototype achieved a poor recognition rate and required high computational processing for object recognition. However, YOLO is a model that can have good performance on low-resource hardware.
{"title":"Automatic Detection of Vehicular Traffic Elements based on Deep Learning for Advanced Driving Assistance Systems","authors":"Laura Cleofas-Sánchez, Juan Pablo Francisco Posadas-Durán, Pedro Martínez-Ortiz, Gilberto Loyo-Desiderio, Eduardo Alberto Ruvalcaba-Hernández, Omar González Brito","doi":"10.13053/cys-27-3-4508","DOIUrl":"https://doi.org/10.13053/cys-27-3-4508","url":null,"abstract":"This paper presents a prototype of an automobile driver assistance system based on YOLOv3. The system detects car types, traffic signs, and traffic lights in real-time and warns the driver accordingly. In the learning phase of the YOLO algorithm, the standard weights are learned first, followed by transfer learning to the objects of interest. The retraining phase uses 2,800 images obtained from the Internet of three countries of the real-life, and the testing phase uses real-time videos of Mexico City roads. In the validation phase, the proposed system achieves 95%, 37%, and 40% performance on the compiled dataset for the detection of road elements. The results obtained are comparable and in some cases better than those reported in previous works. Using a Raspberry Pi 4, the prototype was tested in real-life, generating visual and audible warnings for the driver, with an object recognition rate of 0.4 fps. A mean average precision (mAP) of 53% was reached by the proposed system. The experiments showed that the prototype achieved a poor recognition rate and required high computational processing for object recognition. However, YOLO is a model that can have good performance on low-resource hardware.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"66 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":"135297612","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}
Claudia Elena Durango Vanegas, Juan Camilo Giraldo Mejía, Fabio Alberto Vargas Agudelo, Dario Enrique Soto Duran
CRoss Industry Standard Process for Data Mining (CRISP-DM) is a data mining project development methodology that establishes tasks and levels of abstraction, hierarchically structured to facilitate its implementation through a set of actions that help in making decisions. Essence is a theory that helps identify best practices and essential, common, and universal elements to all endeavor in the software development cycle. In the literature, there are different models of representation of the CRISP-DM methodology, such as verbal model, conceptual model, process understanding model, and ontology. However, it considered that these representation models lack the incorporation of some elements, such as, activities, work products, and roles of the CRISP-DM methodology. In this paper we propose a representation based on Essence of the CRISP-DM methodology, incorporating the essential elements that we believe are missing from existing representations. With the representation in Essence that is proposed, the aim is to improve the understanding of best practices and the essential, common, and universal elements of the CRISP-DM methodology for future implementations in data mining projects. In addition, it seeks to validate that Essence can be used in different of data mining projects.
{"title":"A Representation Based on Essence for the CRISP-DM Methodology","authors":"Claudia Elena Durango Vanegas, Juan Camilo Giraldo Mejía, Fabio Alberto Vargas Agudelo, Dario Enrique Soto Duran","doi":"10.13053/cys-27-3-3446","DOIUrl":"https://doi.org/10.13053/cys-27-3-3446","url":null,"abstract":"CRoss Industry Standard Process for Data Mining (CRISP-DM) is a data mining project development methodology that establishes tasks and levels of abstraction, hierarchically structured to facilitate its implementation through a set of actions that help in making decisions. Essence is a theory that helps identify best practices and essential, common, and universal elements to all endeavor in the software development cycle. In the literature, there are different models of representation of the CRISP-DM methodology, such as verbal model, conceptual model, process understanding model, and ontology. However, it considered that these representation models lack the incorporation of some elements, such as, activities, work products, and roles of the CRISP-DM methodology. In this paper we propose a representation based on Essence of the CRISP-DM methodology, incorporating the essential elements that we believe are missing from existing representations. With the representation in Essence that is proposed, the aim is to improve the understanding of best practices and the essential, common, and universal elements of the CRISP-DM methodology for future implementations in data mining projects. In addition, it seeks to validate that Essence can be used in different of data mining projects.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"43 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":"135297621","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}
A recommender system is a type of information filtering system that predicts and recommends items or products to users based on their preferences and past behaviors. It is commonly used in e-commerce and social media to suggest items that a user may be interested in purchasing, reading, watching, or listening to. Sentiment analysis is an area of natural language processing that has emerged as a popular way for organizations to detect and categorize opinions about a product, idea or service. In recent years, many attempts have been made to apply sentiment analysis in designing recommender systems, in order to recommend various items, such as hotels. It is thought that providing a quality hotel suggestion based on the requirements and preferences of users is a challenge and, naturally, alluring effort for tourism applications. In this paper, the quality of decision making for hotel recommender system based on sentiment analysis, deep learning and data balancing techniques has been improve. Multiple approaches are used with our proposed system to provide high-quality hotel recommendations. To achieve this goal, first, the existing dataset is balanced, using the translating and text paraphrasing policy by a transformer-based model called T5. Afterwards, an integrated method, including the transformer-based XLM-RoBERTa model is used along with the attention mechanism for sentiment analysis. The result of the comparison of our proposed model with the four best non-transformer-based models; RNN, GRU, LSTM, Bi-LST, and the most recent transformer-based model, En-RFBERT, on the TripAdvisor dataset showed the superiority of our proposed method. Our propose system beats En-RFBERT by 3%, 7%, and 5% in Macro Precision, Recall, and F1-score, respectively and performs better than En-RFBERT when it comes to responsiveness time.
{"title":"Improving Sentiment Classification for Hotel Recommender System through Deep Learning and Data Balancing","authors":"Reza Nouralizadeh Ganji, Chitra Dadkhah, Nasim Tohidi","doi":"10.13053/cys-27-3-4655","DOIUrl":"https://doi.org/10.13053/cys-27-3-4655","url":null,"abstract":"A recommender system is a type of information filtering system that predicts and recommends items or products to users based on their preferences and past behaviors. It is commonly used in e-commerce and social media to suggest items that a user may be interested in purchasing, reading, watching, or listening to. Sentiment analysis is an area of natural language processing that has emerged as a popular way for organizations to detect and categorize opinions about a product, idea or service. In recent years, many attempts have been made to apply sentiment analysis in designing recommender systems, in order to recommend various items, such as hotels. It is thought that providing a quality hotel suggestion based on the requirements and preferences of users is a challenge and, naturally, alluring effort for tourism applications. In this paper, the quality of decision making for hotel recommender system based on sentiment analysis, deep learning and data balancing techniques has been improve. Multiple approaches are used with our proposed system to provide high-quality hotel recommendations. To achieve this goal, first, the existing dataset is balanced, using the translating and text paraphrasing policy by a transformer-based model called T5. Afterwards, an integrated method, including the transformer-based XLM-RoBERTa model is used along with the attention mechanism for sentiment analysis. The result of the comparison of our proposed model with the four best non-transformer-based models; RNN, GRU, LSTM, Bi-LST, and the most recent transformer-based model, En-RFBERT, on the TripAdvisor dataset showed the superiority of our proposed method. Our propose system beats En-RFBERT by 3%, 7%, and 5% in Macro Precision, Recall, and F1-score, respectively and performs better than En-RFBERT when it comes to responsiveness time.","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":"135297617","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 simulation of systems with random variables of a discrete or continuous distribution is a very useful technique in many situations in which it is necessary to analyze the behavior of complex systems in which some variables have a probability distribution and behave randomly. This technique is used to model uncertainty and analyze how it affects the behavior of the system, evaluate different scenarios and make decisions based on data, reduce risks and costs associated with the implementation of system changes, identify problems and bottlenecks, improve performance with more efficient and productive results in one system. The simulation of systems with random variables of probabilistic distribution is a very useful technique to model complex systems in which some variables have a discrete or continuous distribution and behave randomly. This technique allows users to evaluate different scenarios, reduce risks, identify problems, improve performance, and make data-driven decisions.
{"title":"Simulation of Systems with Random Variables for Making Strategic Decisions","authors":"María del Consuelo Argüelles Arellano","doi":"10.13053/cys-27-3-4708","DOIUrl":"https://doi.org/10.13053/cys-27-3-4708","url":null,"abstract":"The simulation of systems with random variables of a discrete or continuous distribution is a very useful technique in many situations in which it is necessary to analyze the behavior of complex systems in which some variables have a probability distribution and behave randomly. This technique is used to model uncertainty and analyze how it affects the behavior of the system, evaluate different scenarios and make decisions based on data, reduce risks and costs associated with the implementation of system changes, identify problems and bottlenecks, improve performance with more efficient and productive results in one system. The simulation of systems with random variables of probabilistic distribution is a very useful technique to model complex systems in which some variables have a discrete or continuous distribution and behave randomly. This technique allows users to evaluate different scenarios, reduce risks, identify problems, improve performance, and make data-driven decisions.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"132 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":"135296632","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}
Juan C. Olivares-Rojas, Enrique Reyes-Archundia, José A. Gutiérrez-Gnecchi
Cybersecurity incidents are becoming more frequent due to the high degree of penetration that information and communication technologies have in our daily lives. One of the critical infrastructures that has benefited the most in recent years from the broad integration of technologies has been the smart grid. Smart metering systems allow, among other things, the monitoring of energy consumption and production readings that are translated into monetary transactions. The tampering and manipulation of the smart meter readings are reflected in economic losses for the utilities and loss of confidence in the end-users. This work presents a cybersecurity architecture based on a multitier blockchain capable of adapting to smart metering systems' architecture through an edge-fog-cloud distributed computing scheme. The proposed architecture is highly scalable to the various components of smart metering systems and improves the performance of blockchains in aspects such as storage and processing. This blockchain uses its own consensus algorithm proof-of-efficiency, which allows benefiting end-users through more efficient use of their energy consumption considering the power quality, the forecast of the demand, and the support for detecting theft and energy fraud. The consensus algorithm uses the same architecture proposed to determine users' rewards through data analytics and machine learning techniques. All of this lays the foundation for a more intelligent, more transactional, and cybersecure metering system. The architecture developed was tested to guarantee the cybersecurity of the transactions carried out in the smart metering systems. The results obtained suggest that using a blockchain architecture allows improving the cybersecurity of smart metering systems and giving end-users greater confidence in their energy transactions, being able to receive better economic incentives by making more efficient use of their energy consumption.
{"title":"A Cybersecurity Transaction Energy System Using Multi-Tier Blockchain","authors":"Juan C. Olivares-Rojas, Enrique Reyes-Archundia, José A. Gutiérrez-Gnecchi","doi":"10.13053/cys-27-3-4071","DOIUrl":"https://doi.org/10.13053/cys-27-3-4071","url":null,"abstract":"Cybersecurity incidents are becoming more frequent due to the high degree of penetration that information and communication technologies have in our daily lives. One of the critical infrastructures that has benefited the most in recent years from the broad integration of technologies has been the smart grid. Smart metering systems allow, among other things, the monitoring of energy consumption and production readings that are translated into monetary transactions. The tampering and manipulation of the smart meter readings are reflected in economic losses for the utilities and loss of confidence in the end-users. This work presents a cybersecurity architecture based on a multitier blockchain capable of adapting to smart metering systems' architecture through an edge-fog-cloud distributed computing scheme. The proposed architecture is highly scalable to the various components of smart metering systems and improves the performance of blockchains in aspects such as storage and processing. This blockchain uses its own consensus algorithm proof-of-efficiency, which allows benefiting end-users through more efficient use of their energy consumption considering the power quality, the forecast of the demand, and the support for detecting theft and energy fraud. The consensus algorithm uses the same architecture proposed to determine users' rewards through data analytics and machine learning techniques. All of this lays the foundation for a more intelligent, more transactional, and cybersecure metering system. The architecture developed was tested to guarantee the cybersecurity of the transactions carried out in the smart metering systems. The results obtained suggest that using a blockchain architecture allows improving the cybersecurity of smart metering systems and giving end-users greater confidence in their energy transactions, being able to receive better economic incentives by making more efficient use of their energy consumption.","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"62 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":"135297616","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}
{"title":"Impact of Bayesian Approach to Demand Management in Supply Chains for the Consumption of Dynamic Products","authors":"José Antonio Taquía Gutiérrez","doi":"10.13053/cys-27-2-4382","DOIUrl":"https://doi.org/10.13053/cys-27-2-4382","url":null,"abstract":"","PeriodicalId":333706,"journal":{"name":"Computación Y Sistemas","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123994025","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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