Inteligencia Artificial

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ISSN / EISSN : 1137-3601 / 1988-3064
Total articles ≅ 564
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Pablo Negro, Claudia Pons
Inteligencia Artificial, Volume 25, pp 13-41;

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.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). The resulting technologies are discussed and evaluated from both perspectives: symbolic and non-symbolic AI.In this work, we use the PICOC & Limits method to define the research questions and analyze the results.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.
Raad Al-Azawi, Safaa O. Al-Mamory
Inteligencia Artificial, Volume 25, pp 57-86;

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.
Ramiro Saltos Atiencia, Richard Weber
Inteligencia Artificial, Volume 25, pp 42-56;

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.
Tanmay Kasbe Tanmay
Inteligencia Artificial, Volume 25, pp 122-138;

Heart disease is also known as cardiovascular disease. It is one of the most dangerousand deadly disease in all over the globe. Cardiovascular disease was deemed as amajor illness in old and middle age, but recent trends shown that now cardiovasculardisease is also a deadly disease in young age group due to irregular habit. However,Angiography is one of the way to diagnose heart disease, but it is very expensive andalso has major side effect. The aim of this research paper is to design a fuzzy rulebased framework to diagnosis of the risk level of the heart disease. Our proposedframework used a Mamdani interface system and used UCI machine repositorydataset for heart disease diagnosis. In this proposed study, we have used 10 Inputattribute and one output attribute with 554 rules. Besides, a comparative table is alsopresented, where proposed methodology is better than other methodology. Accordingto the proposed methodology results, that the performance is highly successful and itis a promising tool for identification of a heart disease patient at an early stage. Wehave achieved accuracy, sensitivity rates of 95.2% and 87.04 respectively, on the UCIdataset.
Julio Lamas Piñeiro, Lenis Wong Portillo
Inteligencia Artificial, Volume 25, pp 107-121;

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%.
Hui Wang, Yan Sha, Dan Wang, Hamed Nazari
Inteligencia Artificial, Volume 25, pp 1-12;

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.
Sami Nasser Lauar,
Inteligencia Artificial, Volume 24, pp 123-137;

In this work, we present a metaheuristic based on the genetic and greedy algorithms to solve an application of the set covering problem (SCP), the data aggregator positioning in smart grids. The GGH (Greedy Genetic Hybrid) is structured as a genetic algorithm, but it has many modifications compared to the classic version. At the mutation step, only columns included in the solution can suffer mutation and be removed. At the recombination step, only columns from the parent’s solutions are available to generate the offspring. Moreover, the greedy algorithm generates the initial population, reconstructs solutions after mutation, and generates new solutions from the recombination step. Computational results using OR-Library problems showed that the GGH reached optimal solutions for 40 instances in a total of 75 and, in the other instances, obtained good and promising values, presenting a medium gap of 1,761%.
Moussa Demba
Inteligencia Artificial, Volume 24, pp 37-52;

In relational databases, it is essential to know all minimal keys since the concept of database normaliza-tion is based on keys and functional dependencies of a relation schema. Existing algorithms for determining keysor computing the closure of arbitrary sets of attributes are generally time-consuming. In this paper we present anefficient algorithm, called KeyFinder, for solving the key-finding problem. We also propose a more direct methodfor computing the closure of a set of attributes. KeyFinder is based on a powerful proof procedure for findingkeys called tableaux. Experimental results show that KeyFinder outperforms its predecessors in terms of searchspace and execution time.
Rupinder Kaur, Anurag Sharma
Inteligencia Artificial, Volume 24, pp 104-122;

Several studies have been reported the use of machine learning algorithms in the detection of Tuberculosis, but studies that discuss the detection of both types of TB, i.e., Pulmonary and Extra Pulmonary Tuberculosis, using machine learning algorithms are lacking. Therefore, an integrated system based on machine learning models has been proposed in this paper to assist doctors and radiologists in interpreting patients’ data to detect of PTB and EPTB. Three basic machine learning algorithms, Decision Tree, Naïve Bayes, SVM, have been used to predict and compare their performance. The clinical data and the image data are used as input to the models and these datasets have been collected from various hospitals of Jalandhar, Punjab, India. The dataset used to train the model comprises 200 patients’ data containing 90 PTB patients, 67 EPTB patients, and 43 patients having NO TB. The validation dataset contains 49 patients, which exhibited the best accuracy of 95% for classifying PTB and EPTB using Decision Tree, a machine learning algorithm.
Mohammad Al-Azawi
Inteligencia Artificial, Volume 24, pp 138-150;

Medical image processing, which includes many applications such as magnetic resonance image (MRI) processing, is one of the most significant fields of computer-aided diagnostic (CAD) systems. the detection and identification of abnormalities in the magnetic resonance imaging of the brain is one of the important applications that uses magnetic resonance imaging and digital image processing techniques. In this study, we present a method that relies on the symmetry and similarity between the two lobes of the brain to determine if there are any abnormalities in the brain because tumours cause deformations in the shape of one of the lobes, which affects this symmetry. The proposed approach overcomes the challenge arising from different shapes of brain images of different people, which poses an obstacle to some approaches that rely on comparing one person’s brain image with other people's brain images. In the proposed method the image of the brain is divided into two parts, one for the left lobe and the other for the right lobe. Some measures are extracted from the features of the image of each lobe separately and the distance between the corresponding metrics are calculated. These distances are used as the independent variables of the classification algorithm which determines the class to which the brain belongs. Metrics extracted from various features, such as colour and texture, were studied, discussed and used in the classification process. The proposed algorithm was applied to 366 images from standard datasets and four classifiers were tested namely Naïve Bayes (NB), random forest (RF), logistic regression (LR), and support vector machine (SVM). The obtained results from these classifiers have been discussed thoroughly and it was found that the best results were obtained from RF classifiers where the accuracy was 98.2%. Finally, The results obtained and the limitations were discussed and benchmarked with state-of-the-art approaches.
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