https://journal.iberamia.org/index.php/intartif/issue/feed Inteligencia Artificial 2022-05-05T10:21:57+02:00 Editor editor@iberamia.org Open Journal Systems <p style="text-align: justify;"><span style="color: #000000;"><strong><em><a style="color: #003366; text-decoration: underline;" href="http://journal.iberamia.org/" target="_blank" rel="noopener">Inteligencia Artificial</a></em></strong><span id="result_box" class="" lang="en"> is an international open access journal promoted by <span class="">the Iberoamerican Society of</span> Artificial Intelligence (<a href="http://www.iberamia.org">IBERAMIA</a>). </span></span>Since 1997, the journal publishes high-quality original papers reporting theoretical or applied advances in all areas of Artificial Intelligence. <span style="color: rgba(0, 0, 0, 0.87); font-family: 'Noto Sans', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 14px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: justify; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">There are no fees for subscription, publication nor editing tasks<span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="es" data-phrase-index="0">.</span></span> <span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="es" data-phrase-index="0">Articles can be written in English, Spanish or Portuguese and <a href="https://journal.iberamia.org/index.php/intartif/about/submissions">will be subjected</a> to a double-blind peer review process.</span></span> <span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="es" data-phrase-index="0">The journal is abstracted and indexed in several <a href="http://journal.iberamia.org/index.php/intartif/metrics">data bases</a>.</span></span><br /></span></p> https://journal.iberamia.org/index.php/intartif/article/view/701 A Gene Expression Clustering Method to Extraction of Cell-to-Cell Biological Communication 2021-12-21T01:27:50+01:00 Hui Wang wanghui07401@163.com Yan Sha uestcsy2009@126.com Dan Wang ndskywd@126.com Hamed Nazari st_h.nazari@urmia.ac.ir <p><strong>Graph-based clustering identification is a practical method to detect the communication between nodes in complex networks that has obtained considerable comments. Since identifying different communities in large-scale data is a challenging task, by understanding the communication between the behaviors of the elements in a community (a cluster), the general characteristics of clusters can be predicted. Graph-based clustering methods have played an important role in clustering gene expression data because of their ability to show the relations between the data. In order to be able to identify genes that lead to the development of diseases, the communication between the cells must be established. The communication between different cells can be indicated by the expression of different genes within them. In this study, the problem of cell-to-cell communication is expressed as a graph and the communication are extracted by recognizing the communities. The FANTOM5 dataset is used to simulate and calculate the similarity between cells. After preprocessing and normalizing the data, to convert this data into graphs, the expression of genes in different cells was examined and by considering a threshold and Wilcoxon test, the communication between them were identified through using clustering.</strong></p> 2022-03-11T00:00:00+01:00 Copyright (c) 2022 Iberamia & The Authors https://journal.iberamia.org/index.php/intartif/article/view/702 Artificial Intelligence techniques based on the integration of symbolic logic and deep neural networks: A systematic review of the literature 2022-01-10T14:39:14+01:00 Pablo Negro pabloariel.negro@alumnos.uai.edu.ar Claudia Pons claudia.pons@uai.edu.ar <p>The need for neural-symbolic integration becomes apparent as more complex problems are tackled, and they go beyond limited domain tasks such as classification. In this sense, understanding the state of the art of hybrid technologies based on Deep Learning and augmented with logic based systems, is of utmost importance. As a consequence, we seek to understand and represent the current state of these technologies that are highly used in intelligent systems engineering.<br>This work aims to provide a comprehensive view of the solutions available in the literature, within the field of applied Artificial Intelligence (AI), using technologies based on AI techniques that integrate symbolic and non-symbolic logic (in particular artificial neural networks), making them the subject of a systematic literature review (SLR). <br>The resulting technologies are discussed and evaluated from both perspectives: symbolic and non-symbolic AI.<br>In this work, we use the PICOC &amp; Limits method to define the research questions and analyze the results.<br>Out of a total of 65 candidate studies found, 24 articles (37%) relevant to this study were selected. Each study also focuses on different application domains. Conclusion: Through the analysis of the selected works throughout this review, we have seen different combinations of logical systems with some form of neural network and, although we have not found a clear architectural pattern, efforts to find a model of general purpose combining both worlds drive trends and research efforts.</p> 2022-03-11T00:00:00+01:00 Copyright (c) 2022 Iberamia & The Authors https://journal.iberamia.org/index.php/intartif/article/view/707 Rough-Fuzzy Support Vector Clustering with OWA Operators 2021-12-17T12:25:13+01:00 Ramiro Saltos Atiencia ramiro.saltos@upacifico.edu.ec Richard Weber richard.weber@uchile.cl <p>Rough-Fuzzy Support Vector Clustering (RFSVC) is a novel soft computing derivative of the classical Support Vector Clustering (SVC) algorithm, which has been used already in many real-world applications. RFSVC’s strengths are its ability to handle arbitrary cluster shapes, identify the number of clusters, and e?ectively detect outliers by the means of membership degrees. However, its current version uses only the closest support vector of each cluster to calculate outliers’ membership degrees, neglecting important information that remaining support vectors can contribute. We present a novel approach based on the ordered weighted average (OWA) operator that aggregates information from all cluster representatives when computing ?nal membership degrees and at the same time allows a better interpretation of the cluster structures found. Particularly, we propose the induced OWA using weights determined by the employed kernel function. The computational experiments show that our approach outperforms the current version of RFSVC as well as alternative techniques ?xing the weights of the OWA operator while maintaining the level of interpretability of membership degrees for detecting outliers.</p> 2022-03-21T00:00:00+01:00 Copyright (c) 2022 Iberamia & The Authors https://journal.iberamia.org/index.php/intartif/article/view/758 Feature extractions and selection of bot detection on Twitter A systematic literature review 2022-02-25T02:32:50+01:00 Raad Al-azawi raad.alazawi@student.uobabylon.edu.iq Safaa O. AL-mamory salmamory@uoitc.edu.iq <p>Abstract <strong>Automated or semiautomated computer programs that imitate humans and/or human behavior in online social networks are known as social bots. Users can be attacked by social bots to achieve several hidden aims, such as spreading information or influencing targets. While researchers develop a variety of methods to detect social media bot accounts, attackers adapt their bots to avoid detection. This field necessitates ongoing growth, particularly in the areas of feature selection and extraction. The study's purpose is to provide an overview of bot attacks on Twitter, shedding light on issues in feature extraction and selection that have a significant impact on the accuracy of bot detection algorithms, and highlighting the weaknesses in training time and dimensionality reduction. To the best of our knowledge, this study is the first systematic literature review based on a preset search-strategy that encompasses literature published between 2018 and 2021 which are concerned with Twitter features (attributes). The key findings of this research are threefold. First, the paper provides an improved taxonomy of feature extraction and selection approaches. Second, it includes a comprehensive overview of approaches for detecting bots in the Twitter platform, particularly machine learning techniques. The percentage was calculated using the proposed taxonomy, with metadata, tweet text, and merging (meta and tweet text) accounting for 37%, 31%, and 32%, respectively. Third, some gaps are also highlighted for further research. The first is that public datasets are not precise or suitable in size. Second, the use of integrated systems and real-time detection is uncommon. Third, detecting each bots category identified separately is needed, rather than detecting all categories of bots using one generic model and the same features' values. Finally, extracting influential features that assist machine learning algorithms in detecting Twitter bots with high accuracy is critical, especially if the type of bot is pre-determined.</strong></p> 2022-04-13T00:00:00+02:00 Copyright (c) 2022 Iberamia & The Authors https://journal.iberamia.org/index.php/intartif/article/view/750 Web architecture for URL-based phishing detection based on Random Forest, Classification Trees, and Support Vector Machine 2022-03-01T22:57:50+01:00 Julio Lamas Piñeiro juliolamas4795@gmail.com Lenis Wong Portillo lwongp@unmsm.edu.pe <p>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%.</p> <p>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%.</p> 2022-05-09T00:00:00+02:00 Copyright (c) 2022 Iberamia & The Authors https://journal.iberamia.org/index.php/intartif/article/view/734 FRBF: A Fuzzy Rule Based Framework for Heart Disease Diagnosis 2022-05-05T10:21:57+02:00 Tanmay Kasbe Tanmay tanmay.kasbe@gmail.com <p>Heart disease is also known as cardiovascular disease. It is one of the most dangerous<br>and deadly disease in all over the globe. Cardiovascular disease was deemed as a<br>major illness in old and middle age, but recent trends shown that now cardiovascular<br>disease is also a deadly disease in young age group due to irregular habit. However,<br>Angiography is one of the way to diagnose heart disease, but it is very expensive and<br>also has major side effect. The aim of this research paper is to design a fuzzy rule<br>based framework to diagnosis of the risk level of the heart disease. Our proposed<br>framework used a Mamdani interface system and used UCI machine repository<br>dataset for heart disease diagnosis. In this proposed study, we have used 10 Input<br>attribute and one output attribute with 554 rules. Besides, a comparative table is also<br>presented, where proposed methodology is better than other methodology. According<br>to the proposed methodology results, that the performance is highly successful and it<br>is a promising tool for identification of a heart disease patient at an early stage. We<br>have achieved accuracy, sensitivity rates of 95.2% and 87.04 respectively, on the UCI<br>dataset.</p> 2022-05-16T00:00:00+02:00 Copyright (c) 2022 Iberamia & The Authors https://journal.iberamia.org/index.php/intartif/article/view/768 Mono-objective Evolutionary Model for Affective Algorithmic Composition 2022-04-28T23:20:18+02:00 Carla Sanches Nere dos Santos carla.nere@aluno.ufop.edu.br Alan Robert Resende de Freitas alandefreitas@gmail.com <p>The Affective Music Composition study indicates that musical features can be associated to emotions. Thus, Affective Algorithmic Composition systems implements such features in order to generate melodies that can express or induce emotions. These systems can be applied to different fields, like health and entertainment. However, the composition of melodies with unlimited duration time and diversity is still an open question. This work aims to identify strategies to perform multiple affective transformations in melodies. Therefore, emotional models and the most implemented musical features in the literature are presented, as well as a evolutionary mono-objective model to an affective transformative system.</p> 2022-06-05T00:00:00+02:00 Copyright (c) 2022 Iberamia & The Authors https://journal.iberamia.org/index.php/intartif/article/view/773 Fault Tolerance for Composite Cloud Services: A Novel Approach Based MAS 2022-04-23T21:52:32+02:00 Ouassila Hioual ouassila.hioual@gmail.com Ouided Hioual ouided.hioual@gmail.com Sofiane Mounine Hemam sofiane.hemam@gmail.com Rania Mordjane raniamordj@gmail.com Nessrine Bouhlala bouhlalanoussa@gmail.com <p>Several Cloud services may be composed in order to respond quickly to the needs of users. Unfortunately, when running such a service some faults may occur. The outcome of fault control is a big challenge. In this paper, the authors propose a new approach based back recovery and multi-agent planning methods. The proposed architecture based MAS (Multi-Agent System) is composed of two main types of Agents&nbsp;: a Composition Manager Agent (CMA) and a Supervisor Agent (SA). The role of the CMA is to create a set of plans as an oriented graph where the nodes are the Cloud services and the valued arcs represent the composition order of these services. This agent saves checkpoints (nodes) in a stable memory so that there are at least one possible path. However, the SA ensures that the running plan is working properly; otherwise, it informs the CMA to select another sub-plan. Experimental results show the performance and effectiveness of the proposed approach.</p> 2022-06-15T00:00:00+02:00 Copyright (c) 2022 Iberamia & The Authors https://journal.iberamia.org/index.php/intartif/article/view/796 Detection of Loanwords in Angolan Portuguese: A Text Mining Approach 2022-04-28T20:09:31+02:00 Timóteo Muhongo timuhongo@hotmail.com Pavel Brazdil pbrazdil@inesctec.pt Fátima Silva mhenri@letras.up.pt <p class="p1">Angola is characterized by many different languages and social, cultural and political realities, which had a marked effect on Angolan Portuguese (AP). Consequently, AP is characterized by diatopic variation. One of the marked effects is in the loanwords imported from other Angolan languages. Our objective is to analyze different Angolan texts, analyze the lexical forms used and conduct a comparative study with European Portuguese, whose aim is to identify the possible loanwords in Angolan. This process was automated, as well as the identification of cotexts of all loanwords. In addition, we determine the lexical class of each loanword and the Angolan language of origin. Most lexical loanwords come from the Kimbundu, although AP includes loanwords from some other Angolan languages, too. Our study serves as a basis for preparation of an Angolan regionalism dictionary. We note that more than 700 loanwords identified do not figure in the existing dictionaries.</p> 2022-06-22T00:00:00+02:00 Copyright (c) 2022 Iberamia & The Authors