Inteligencia Artificial-Iberoamerical Journal of Artificial Intelligence最新文献

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}