Pub Date : 2023-05-23DOI: 10.4114/intartif.vol26iss71pp142-152
S. Dhouib, Danijela Pezer
The Computer Numerical Control (CNC) machine represents a turning point in today's production which has high requirements for product accuracy. The CNC machine enables a high flexibility in work and time saving and also reduces the time required for product accuracy control. Moreover, the CNC machine are used for several activities, most often for turning, drilling and milling operations. Usually, the productivity of any CNC machine can be increased thanks to the minimization of the non-productive of tool movement. In this paper, the results of a new metaheuristic named Dhouib-Matrix-4 (DM4) with an application on the NP-hard problem based on the Travelling Salesman Problem are presented. DM4 is used for increasing the performance of the CNC Machine by optimizing a tool path length in the drilling process performed on the CNC milling machine. The proposed algorithm (DM4) achieves a solution closed to the optimum, compared with the results obtained with the Ant Colony Optimization algorithm and the results found with the manual programming in G code by using a control unit for the selected CNC milling machine.
{"title":"Increasing the Performance of Computer Numerical Control Machine via the Dhouib-Matrix-4 Metaheuristic","authors":"S. Dhouib, Danijela Pezer","doi":"10.4114/intartif.vol26iss71pp142-152","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss71pp142-152","url":null,"abstract":"The Computer Numerical Control (CNC) machine represents a turning point in today's production which has high requirements for product accuracy. The CNC machine enables a high flexibility in work and time saving and also reduces the time required for product accuracy control. Moreover, the CNC machine are used for several activities, most often for turning, drilling and milling operations. Usually, the productivity of any CNC machine can be increased thanks to the minimization of the non-productive of tool movement. In this paper, the results of a new metaheuristic named Dhouib-Matrix-4 (DM4) with an application on the NP-hard problem based on the Travelling Salesman Problem are presented. DM4 is used for increasing the performance of the CNC Machine by optimizing a tool path length in the drilling process performed on the CNC milling machine. The proposed algorithm (DM4) achieves a solution closed to the optimum, compared with the results obtained with the Ant Colony Optimization algorithm and the results found with the manual programming in G code by using a control unit for the selected CNC milling machine.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41925089","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 : 2023-05-12DOI: 10.4114/intartif.vol26iss71pp114-130
Fernando Cardoso Durier da Silva, Ana Cristina Bicharra Garcia, Sean Wolfgand Matsui Siqueira
Information sharing on the Web has also led to the rise and spread of fake news. Considering that fake information is generally written to trigger stronger feelings from the readers than simple facts, sentiment analysis has been widely used to detect fake news. Nevertheless, sarcasm, irony, and even jokes use similarwritten styles, making the distinction between fake and fact harder to catch automatically. We propose a new fake news Classifier that considers a set of language attributes and the gradient of sentiments contained in a message. Sentiment analysis approaches are based on labelling news with a unique value that shrinks the entire message to a single feeling. We take a broader view of a message’s sentiment representation, trying to unravel the gradient of sentiments a message may bring. We tested our approach using two datasets containing texts written in Portuguese: a public one and another we created with more up-to-date news scrapped from the Internet. Although we believe our approach is general, we tested for the Portuguese language. Our results show that the sentiment gradient positively impacts the fake news classification performance with statistical significance. The F-Measure reached 94 %, with our approach surpassing available ones (with a p-value less than 0.05 for our results).
{"title":"Sentiment Gradient - Improving Sentiment Analysis with Entropy Increase","authors":"Fernando Cardoso Durier da Silva, Ana Cristina Bicharra Garcia, Sean Wolfgand Matsui Siqueira","doi":"10.4114/intartif.vol26iss71pp114-130","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss71pp114-130","url":null,"abstract":"Information sharing on the Web has also led to the rise and spread of fake news. Considering that fake information is generally written to trigger stronger feelings from the readers than simple facts, sentiment analysis has been widely used to detect fake news. Nevertheless, sarcasm, irony, and even jokes use similarwritten styles, making the distinction between fake and fact harder to catch automatically. We propose a new fake news Classifier that considers a set of language attributes and the gradient of sentiments contained in a message. Sentiment analysis approaches are based on labelling news with a unique value that shrinks the entire message to a single feeling. We take a broader view of a message’s sentiment representation, trying to unravel the gradient of sentiments a message may bring. We tested our approach using two datasets containing texts written in Portuguese: a public one and another we created with more up-to-date news scrapped from the Internet. Although we believe our approach is general, we tested for the Portuguese language. Our results show that the sentiment gradient positively impacts the fake news classification performance with statistical significance. The F-Measure reached 94 %, with our approach surpassing available ones (with a p-value less than 0.05 for our results).","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44176627","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 : 2023-04-29DOI: 10.4114/intartif.vol26iss71pp75-113
David Zabala-Blanco, Diego Martinez-Pereira, Marco J. Flores-Calero, Jayanta Datta, Ali Dehghan Firoozabadi
The fingerprint comes to be the most popular and utilized biometric for identifying persons owing to its bio-invariant characteristic, precision, as well as easy acquisition. A sub-system of an identification system is the classification stage in order to diminish the penetration rate and computational complexity. Actually, there are many formal investigations regarding techniques by exploiting convolutional neural networks (CNN) together with fingerprint images, which have superior performance metrics at the cost of large training times even employing high-performance computing, which is not feasible in the standard world. In our manuscript, researches about identifying and classifying fingerprint databases by recurring to extreme learning machines (ELM) will be extensively reported and discussed for the first time. The diverse methodologies (ELM plus feature extractors) given by the authors will be studied and contrasted considering performance analysis. Consequently, academic papers with diverse versions of ELMs are developed to observe the pros and cons that they exhibit with each other and to probe how they may help for minimizing the penetration rate of fingerprint databases. In fact, this issue is very relevant because enhancing the penetration rate means shorting search times and computational complexity in fingerprints.
{"title":"A-Survey: Identification and Classification of Fingerprints via the Extreme Learning Machine Algorithm","authors":"David Zabala-Blanco, Diego Martinez-Pereira, Marco J. Flores-Calero, Jayanta Datta, Ali Dehghan Firoozabadi","doi":"10.4114/intartif.vol26iss71pp75-113","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss71pp75-113","url":null,"abstract":"The fingerprint comes to be the most popular and utilized biometric for identifying persons owing to its bio-invariant characteristic, precision, as well as easy acquisition. A sub-system of an identification system is the classification stage in order to diminish the penetration rate and computational complexity. Actually, there are many formal investigations regarding techniques by exploiting convolutional neural networks (CNN) together with fingerprint images, which have superior performance metrics at the cost of large training times even employing high-performance computing, which is not feasible in the standard world. In our manuscript, researches about identifying and classifying fingerprint databases by recurring to extreme learning machines (ELM) will be extensively reported and discussed for the first time. The diverse methodologies (ELM plus feature extractors) given by the authors will be studied and contrasted considering performance analysis. Consequently, academic papers with diverse versions of ELMs are developed to observe the pros and cons that they exhibit with each other and to probe how they may help for minimizing the penetration rate of fingerprint databases. In fact, this issue is very relevant because enhancing the penetration rate means shorting search times and computational complexity in fingerprints.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46838226","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 : 2023-04-29DOI: 10.4114/intartif.vol26iss71pp59-74
David Kuboñ, F. Mráz, Ivan Rychtera
One-dimensional (string) formal languages and their learning have been studied in considerable depth. However, the knowledge of their two-dimensional (picture) counterpart, which retains similar importance, is lacking. We investigate the problem of learning formal two-dimensional picture languages by applying learning methods for one-dimensional (string) languages. We formalize the transcription process from a two-dimensional input picture into a string and propose a few adaptations to it. These proposals are then tested in a series of experiments, and their outcomes are compared. Finally, these methods are applied to a practical problem and an automaton for recognizing a part of the MNIST dataset is learned. The obtained results show improvements in the topic and the potential to use the learning of automata in fitting problems.
{"title":"Learning Picture Languages Using Dimensional Reduction","authors":"David Kuboñ, F. Mráz, Ivan Rychtera","doi":"10.4114/intartif.vol26iss71pp59-74","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss71pp59-74","url":null,"abstract":"One-dimensional (string) formal languages and their learning have been studied in considerable depth. However, the knowledge of their two-dimensional (picture) counterpart, which retains similar importance, is lacking. We investigate the problem of learning formal two-dimensional picture languages by applying learning methods for one-dimensional (string) languages. We formalize the transcription process from a two-dimensional input picture into a string and propose a few adaptations to it. These proposals are then tested in a series of experiments, and their outcomes are compared. Finally, these methods are applied to a practical problem and an automaton for recognizing a part of the MNIST dataset is learned. The obtained results show improvements in the topic and the potential to use the learning of automata in fitting problems.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46965022","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 : 2023-01-01DOI: 10.4114/intartif.vol26iss72pp1-14
H. Sikkandar, S. Subbaraj, D. ShriDharshini, A. Nivetha
{"title":"TVN: Detect Deepfakes Images using Texture Variation Network","authors":"H. Sikkandar, S. Subbaraj, D. ShriDharshini, A. Nivetha","doi":"10.4114/intartif.vol26iss72pp1-14","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp1-14","url":null,"abstract":"","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":"26 1","pages":"1-14"},"PeriodicalIF":2.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70874820","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 : 2022-12-08DOI: 10.4114/intartif.vol25iss70pp95-109
Andrinandrasana David Rasamoelina, Ivan Cík, Peter Sincak, Marián Mach, Lukás Hruska
Activation functions play an important role in the convergence of learning algorithms based on neural networks. Theyprovide neural networks with nonlinear ability and the possibility to fit in any complex data. However, no deep study exists in theliterature on the comportment of activation functions in modern architecture. Therefore, in this research, we compare the 18 most used activation functions on multiple datasets (CIFAR-10, CIFAR-100, CALTECH-256) using 4 different models (EfficientNet,ResNet, a variation of ResNet using the bag of tricks, and MobileNet V3). Furthermore, we explore the shape of the losslandscape of those different architectures with various activation functions. Lastly, based on the result of our experimentation,we introduce a new locally quadratic activation function namely Hytana alongside one variation Parametric Hytana whichoutperforms common activation functions and address the dying ReLU problem.
{"title":"A Large-Scale Study of Activation Functions in Modern Deep Neural Network Architectures for Efficient Convergence","authors":"Andrinandrasana David Rasamoelina, Ivan Cík, Peter Sincak, Marián Mach, Lukás Hruska","doi":"10.4114/intartif.vol25iss70pp95-109","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss70pp95-109","url":null,"abstract":"Activation functions play an important role in the convergence of learning algorithms based on neural networks. Theyprovide neural networks with nonlinear ability and the possibility to fit in any complex data. However, no deep study exists in theliterature on the comportment of activation functions in modern architecture. Therefore, in this research, we compare the 18 most used activation functions on multiple datasets (CIFAR-10, CIFAR-100, CALTECH-256) using 4 different models (EfficientNet,ResNet, a variation of ResNet using the bag of tricks, and MobileNet V3). Furthermore, we explore the shape of the losslandscape of those different architectures with various activation functions. Lastly, based on the result of our experimentation,we introduce a new locally quadratic activation function namely Hytana alongside one variation Parametric Hytana whichoutperforms common activation functions and address the dying ReLU problem.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":"25 1","pages":"95-109"},"PeriodicalIF":2.3,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70874749","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 : 2022-09-25DOI: 10.4114/intartif.vol25iss70pp13-32
Afiq Raihan, Israt Sharmin, B. M. Khan, Md. Ismail Jabiullah, Md. Tarek Habib
Coastal fish is one of the prominent marine resources, which takes a necessary role in the economic growth of a country. Because of environmental issues along with other reasons, not only most of the marine resources are diminishing but also many coastal fishes are getting extinct gradually. As a result, the young peoples have insufficient knowledge of coastal fish. This issue can be solved with the use of vision-based technologies. To deal with this situation, a coastal fish recognition system based on machine vision is conceived, which can be approached by the images of coastal fish that are captured with a portable device and identify the fish to recognize fish. Numerous experimental analyses are executed to exhibit the benefit of this proposed expert system. In the beginning, conversion of a color image into a gray-scale image occurs and the gray-scale histogram is developed. Using the histogram-based method, image segmentation is conducted. After that, a set of thirteen features comprising of four classes is extracted to be fed to a classifier. For reducing the number of features, PCA is applied. To recognize coastal fish, three cutting-edge classifiers are performed, where k-NN provides a potential accuracy of up to 98.7%.
{"title":"A Machine Vision Approach for Recognizing Coastal Fish","authors":"Afiq Raihan, Israt Sharmin, B. M. Khan, Md. Ismail Jabiullah, Md. Tarek Habib","doi":"10.4114/intartif.vol25iss70pp13-32","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss70pp13-32","url":null,"abstract":"Coastal fish is one of the prominent marine resources, which takes a necessary role in the economic growth of a country. Because of environmental issues along with other reasons, not only most of the marine resources are diminishing but also many coastal fishes are getting extinct gradually. As a result, the young peoples have insufficient knowledge of coastal fish. This issue can be solved with the use of vision-based technologies. To deal with this situation, a coastal fish recognition system based on machine vision is conceived, which can be approached by the images of coastal fish that are captured with a portable device and identify the fish to recognize fish. Numerous experimental analyses are executed to exhibit the benefit of this proposed expert system. In the beginning, conversion of a color image into a gray-scale image occurs and the gray-scale histogram is developed. Using the histogram-based method, image segmentation is conducted. After that, a set of thirteen features comprising of four classes is extracted to be fed to a classifier. For reducing the number of features, PCA is applied. To recognize coastal fish, three cutting-edge classifiers are performed, where k-NN provides a potential accuracy of up to 98.7%.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":"25 1","pages":"13-32"},"PeriodicalIF":2.3,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70874662","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 : 2022-05-09DOI: 10.4114/intartif.vol25iss69pp107-121
Julio Lamas Piñeiro, Lenis Wong Portillo
Nowadays phishing is as serious a problem as any other, but it has intensified a lot in the current coronavirus pandemic, a time when more than ever we all use the Internet even to make payments daily. In this context, tools have been developed to detect phishing, there are quite complex tools in a computational calculation, and they are not so easy to use for any user. Therefore, in this work, we propose a web architecture based on 3 machine learning models to predict whether a web address has phishing or not based mainly on Random Forest, Classification Trees, and Support Vector Machine. Therefore, 3 different models are developed with each of the indicated techniques and 2 models based on the models, which are applied to web addresses previously processed by a feature retrieval module. All this is deployed in an API that is consumed by a Frontend so that any user can use it and choose which type of model he/she wants to predict with. The results reveal that the best performing model when predicting both results is the Classification Trees model obtaining precision and accuracy of 80%. En la actualidad el phishing es un problema tan serio como cualquier otro, pero se ha intensificado bastante en la actual pandemia del coronavirus, un momento en el que más que nunca todos utilizamos internet hasta para realizar pagos cotidianamente. En este contexto se han desarrollado herramientas para detectar phishing, existen herramientas bastante complejas en calculo computacional y que no son de tan sencilla utilización para cualquier usuario. Por ende, en este trabajo proponemos una arquitectura web basada en 3 modelos de aprendizaje automático para predecir si una dirección web tiene phishing o no basados principalmente en Random Forest, Classification Trees y Support Vector Machine. Por lo tanto, se desarrollan 3 modelos distintos con cada una de las técnicas indicadas y 2 modelos basados en los anteriormente mencionados modelos, los cuales son aplicados a direcciones web previamente procesadas por un módulo de obtención de características. Todo ello se despliega en un API la cual es consumida por un Frontend para que cualquier usuario lo pueda utilizar y escoger con qué tipo de modelo quiere predecir. Los resultados revelan que el modelo que mejor se comporta al momento de predecir ambos resultados es el modelo de Árboles de clasificación obteniendo una precisión y exactitud de 80%.
如今，网络钓鱼和其他任何问题一样严重，但在当前的冠状病毒大流行中，它已经加剧了很多，我们比以往任何时候都更多地使用互联网，甚至每天进行支付。在这种背景下，已经开发出了检测网络钓鱼的工具，在计算计算中有相当复杂的工具，并且它们对任何用户来说都不是那么容易使用。因此，在这项工作中，我们提出了一种基于3种机器学习模型的web架构，主要基于随机森林、分类树和支持向量机来预测网址是否存在网络钓鱼。因此，使用每种技术开发了3个不同的模型，并基于这些模型开发了2个模型，这些模型应用于先前由特征检索模块处理的web地址。所有这些都部署在由前端使用的API中，以便任何用户都可以使用它并选择他/她想要预测的模型类型。结果表明，在预测两种结果时，表现最好的模型是分类树模型，其精度和准确度均达到80%。在现实中，网络钓鱼是一个问题，而严重的网络钓鱼是一个问题，人们认为，在实际的冠状病毒大流行中，网络钓鱼的加剧是一个问题，在现实中，网络钓鱼的加剧是一个问题，在现实中，网络钓鱼的加剧是一个问题。在这种情况下，我们可以看到，在网络钓鱼检测中，存在的网络钓鱼检测是复杂的，在计算中，存在的网络钓鱼检测是复杂的，在网络钓鱼检测中，存在的网络钓鱼检测是复杂的，在网络钓鱼检测中存在的网络钓鱼检测是复杂的。基于随机森林、分类树和支持向量机的网络钓鱼的基本原理。基于随机森林、分类树和支持向量机的网络钓鱼的基本原理。在这里，我们将介绍3个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:1个模型的不同之处，例如:módulo de obtención de características。为了更好地描述API中所描述的特性，可以使用使用的特性，或者使用使用的特性，或者使用使用的特性，例如使用使用的特性，或者使用的特性。结果表明，该模型的精度为80%;结果表明，该模型的精度为80%;结果表明，该模型的精度为80%;
{"title":"Web architecture for URL-based phishing detection based on Random Forest, Classification Trees, and Support Vector Machine","authors":"Julio Lamas Piñeiro, Lenis Wong Portillo","doi":"10.4114/intartif.vol25iss69pp107-121","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp107-121","url":null,"abstract":"Nowadays phishing is as serious a problem as any other, but it has intensified a lot in the current coronavirus pandemic, a time when more than ever we all use the Internet even to make payments daily. In this context, tools have been developed to detect phishing, there are quite complex tools in a computational calculation, and they are not so easy to use for any user. Therefore, in this work, we propose a web architecture based on 3 machine learning models to predict whether a web address has phishing or not based mainly on Random Forest, Classification Trees, and Support Vector Machine. Therefore, 3 different models are developed with each of the indicated techniques and 2 models based on the models, which are applied to web addresses previously processed by a feature retrieval module. All this is deployed in an API that is consumed by a Frontend so that any user can use it and choose which type of model he/she wants to predict with. The results reveal that the best performing model when predicting both results is the Classification Trees model obtaining precision and accuracy of 80%. \u0000En la actualidad el phishing es un problema tan serio como cualquier otro, pero se ha intensificado bastante en la actual pandemia del coronavirus, un momento en el que más que nunca todos utilizamos internet hasta para realizar pagos cotidianamente. En este contexto se han desarrollado herramientas para detectar phishing, existen herramientas bastante complejas en calculo computacional y que no son de tan sencilla utilización para cualquier usuario. Por ende, en este trabajo proponemos una arquitectura web basada en 3 modelos de aprendizaje automático para predecir si una dirección web tiene phishing o no basados principalmente en Random Forest, Classification Trees y Support Vector Machine. Por lo tanto, se desarrollan 3 modelos distintos con cada una de las técnicas indicadas y 2 modelos basados en los anteriormente mencionados modelos, los cuales son aplicados a direcciones web previamente procesadas por un módulo de obtención de características. Todo ello se despliega en un API la cual es consumida por un Frontend para que cualquier usuario lo pueda utilizar y escoger con qué tipo de modelo quiere predecir. Los resultados revelan que el modelo que mejor se comporta al momento de predecir ambos resultados es el modelo de Árboles de clasificación obteniendo una precisión y exactitud de 80%.","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42654045","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 : 2022-01-01DOI: 10.4114/intartif.vol25iss69pp139-158
Carla Sanches Nere dos Santos, A. Freitas
{"title":"Mono-objective Evolutionary Model for Affective Algorithmic Composition","authors":"Carla Sanches Nere dos Santos, A. Freitas","doi":"10.4114/intartif.vol25iss69pp139-158","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp139-158","url":null,"abstract":"","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":"25 1","pages":"139-158"},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70874966","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 : 2022-01-01DOI: 10.4114/intartif.vol25iss69pp122-138
Tanmay Kasbe
{"title":"FRBF: A Fuzzy Rule Based Framework for Heart Disease Diagnosis","authors":"Tanmay Kasbe","doi":"10.4114/intartif.vol25iss69pp122-138","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss69pp122-138","url":null,"abstract":"","PeriodicalId":43470,"journal":{"name":"Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence","volume":"25 1","pages":"122-138"},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70875021","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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