Inteligencia Artif.最新文献

{"title":"Ethics in Artificial Intelligence: an Approach to Cybersecurity","authors":"Ariel López González, Mailyn Moreno, Ariadna C. Moreno Román, Yahima Hadfeg Fernández, Nayma Cepero-Pérez","doi":"10.4114/intartif.vol27iss73pp38-54","DOIUrl":"https://doi.org/10.4114/intartif.vol27iss73pp38-54","url":null,"abstract":"In the paper, an analysis is conducted on the intricate relationship between ethics, artificial intelligence, and cybersecurity. The ethical principles that govern the advancement of AI are examined, alongside the security issues that arise from its implementation. The ethical utilization of artificial intelligence in the realms of cybersecurity and hacking is explored. Emphasis is placed on the significance of AI ethics, particularly in terms of transparency, accountability, and fairness. Additionally, the paper delves into the security challenges that emerge as AI is adopted, such as safeguarding user privacy and ensuring equitable access to the technology.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"4 12","pages":"38-54"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139536116","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":"De nuevo sobre la persona robótica","authors":"Moises Barrio","doi":"10.4114/intartif.vol27iss73pp65-79","DOIUrl":"https://doi.org/10.4114/intartif.vol27iss73pp65-79","url":null,"abstract":"La entrada de la robótica y de la inteligencia artificial está alcanzando al Derecho. Surgen nuevos conceptos, hay replanteamiento de los viejos paradigmas jurídicos y es imprescindible actualizar la regulación jurídica existente. En este estudio se aborda una de las cuestiones nucleares que afectan al Derecho en su conjunto: la noción de persona electrónica robótica y las consecuencias jurídicas derivadas de su actuación, su estatuto jurídico en el ordenamiento jurídico, sus atributos y las diferencias con respecto a la persona física.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"17 3","pages":"65-79"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139536128","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":"Improved BAT Algorithm Using Density-Based Clustering","authors":"S. Al‐Asadi, S. Al-Mamory","doi":"10.4114/intartif.vol26iss72pp102-123","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp102-123","url":null,"abstract":"BAT algorithm is a nature-inspired metaheuristic algorithm that depends on the principle of the echolocation behavior of bats. However, the algorithm suffers from being stuck in the local optima early due to its poor exploration. An improved BAT algorithm based on the density-based clustering technique is proposed to enhance the algorithm’s performance. \u0000In this paper, the initial population is improved by generating two populations, randomly and depending on the clusters’ center information, and by getting the fittest individuals from these two populations, the initial improved one is generated. The random walk function is improved using chaotic maps instead of the fixed-size movement, and so the local search is improved as well as the global search abilities by diversifying the solutions. Another improvement is to deal with stagnation by partitioning the search space into two parts depending on the generated clusters’ information to obtain the newly generated solution and comparing their quality with the previously generated solution and choosing the best. \u0000The performance of the proposed improved BAT algorithm is evaluated by comparing it with the original BAT algorithm over ten benchmark optimization test functions. Depending on the results, the improved BAT outperforms the original BAT by obtaining the optimal global solutions for most of the benchmark test functions.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132586112","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":"Exploring the Generality of Norms in Multi-Agent Systems","authors":"Jhonatan Alves, J. F. Hübner, Jerusa Marchi","doi":"10.4114/intartif.vol26iss72pp60-80","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp60-80","url":null,"abstract":"Norms are useful tools to regulate autonomous agents, and their generality is the focus of this paper. The generality of norms refers to the extent of behaviors the norms are capable of regulating. While very specific norms tend to be inefficient to avoid undesirable behaviors (since they are rarely activated), very general norms tend to limit excessively the options of the agents (since they are activated too often) hindering them to achieve the system goal. Therefore, a norm that efficiently regulates the agents should have a balanced generality, being neither too specific nor too general. Therefore, we consider that exploring the generality of norms is a fundamental key to obtaining efficient norms. However, the evaluation of their generality usually considers every behavior they regulate. Since it is likely an unfeasible task, in this paper, we investigate alternatives to estimate the norms generality from their syntactic characteristics. Based on these characteristics, we obtain different sequences of norms that vary, approximately, from the most specific to the most general. We assume thus that norms with a balanced generality are more easily found considering these orderings. Therefore, it is relevant to understand the impact of the syntactical characteristics in ordering the norms. In this context, we found out how different alternatives organize the norms space. This result is particularly useful for the development of algorithms for searching efficient norms that, through different strategies, may exploit how norms space is arranged and may be pruned.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121442735","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":"New Exploration/Exploitation Improvements of GWO for Robust Control of a Nonlinear Inverted Pendulum","authors":"S. M. Djaouti, M. Khelfi, M. Malki, S. Mohammed","doi":"10.4114/intartif.vol26iss72pp30-43","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp30-43","url":null,"abstract":"Tuning a nonlinear inverted pendulum is a complex and uncertain optimization problem. In this paper, we develop two new GWO variants by introducing a DLH (Dimension Learning-based Hunting) module and new formulas to enhance the exploitation/exploration ratio aiming to avoid local minima. A statistical analysis is carried out to compare the two proposed approaches with five GWO variants. After that, they are used to tune a PID and FSMC controller. The obtained results are promising even when compared to other approaches","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129243871","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":"An Optimized Clustering Approach using Tree Seed Algorithm for the Brain MRI Images Segmentation","authors":"Ghazi Boumediene ghaouti, Boudjelal Meftah","doi":"10.4114/intartif.vol26iss72pp44-59","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp44-59","url":null,"abstract":"Clustering algorithms are widely used to segment medical images. However, these techniques are difficult to perform, especially in brain magnetic resonance images (MRI), given the complexity of the anatomical structure of brain tissue, the in-homogeneity of pixel intensity in these images, and partial volume and noise effects. This will cause the algorithm to fall into the local minima problem; for this reason, it is recommended to improve such clustering algorithms using optimization techniques to obtain better results. In this study, we have proposed a developed clustering algorithm and we optimized it using a tree seed algorithm (TSA) to segment brain MRI image. Algorithms are tested on real brain image datasets. The experimental results on simulated and real brain MRI datasets show that our proposed method has satisfactory results regarding the Davies-Bouldin index (DBI) compared to the fuzzy c-mean (FCM) algorithm.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131675642","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":"Semi-supervised learning models for document classification: A systematic review and meta-analysis","authors":"Alex Cevallos-Culqui, C. Pons, Gustavo Rodríguez","doi":"10.4114/intartif.vol26iss72pp81-111","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp81-111","url":null,"abstract":"The continuous increase of digital documents on the web creates the need to search for information patterns that allow the categorization of organizational documents to generate knowledge in an institution. An Artificial Intelligence technique for this purpose is text classification, it for its application uses labels (previously categorized documents) with supervised (with labels) or unsupervised (without labels) training models. Both traditional models with their advantages and disadvantages have been joined into semi-supervised models that extract the best qualities of each one, however, the labeling process involves resources and time that try to be optimized to improve classification accuracy. \u0000An analysis of the different semi-supervised models would show us the advantages of their training and the way how the structure of each of them affects the accuracy of their classification. In the present study, a classification structure of the semi-supervised models in the classification of documents is proposed to analyze their qualities and categorization process, through an SLR (Revision of systematic literature) that extracts performance metrics from the identified studies to perform a meta-analysis through forest plots. \u0000To define the search strategy for studies, the PICOC (Population, Intervention, Comparison, Outputs, Context) method has been used, it is supported by the research question defines a search string, which has allowed the collection of 228 research, these are filtered with the PRISMA declaration method and the determination of exclusion criteria, in this way 35 researches are selected for the present study. \u0000The analysis of the selected studies identifies a structure for the different semi-supervised learning models, and a scheme of their work process is obtained, it has been used to extract advantages, disadvantages, and performance metrics. Through a meta-analysis with forest diagrams, the classification accuracy performance of the researches in each learning model is evaluated, determining as results that regardless of the characteristics of its process, active learning (0.89) and assembled learning (0.83) present the best performance levels.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132577978","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":"PB3C-CNN: An integrated PB3C and CNN based approach for plant leaf classification","authors":"Sukanta Ghosh, Ashutosh Kumar Singh, Shakti Kumar","doi":"10.4114/intartif.vol26iss72pp15-29","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss72pp15-29","url":null,"abstract":"Plant identification and classification are critical to understand, protect, and conserve biodiversity. Traditional plant classification requires years of intensive training and experience, making it difficult for others to classify plants. Plant leaf classification is a challenging issue as similar features appears in different species of plant. With the development of automated image-based classification, machine learning (ML) is becoming very popular. Deep learning (DL) methods have significantly improved plant image identification and classification. In the last decade, convolutional neural networks (CNN) have entirely dominated the field of computer vision, showing outstanding feature extraction capabilities and significant identification and classification performance. The capability of CNN lies in its network. The primary strategy to continue this trend in the literature relies on further scaling networks in size. However, costs increase rapidly, while performance improvements may be marginal when the number of net-works increases. Hence, there is a need to optimize the CNN network to get the best possible result with the minimum number of networks and other parameters such as the number of epochs, number of layers, batch size and number of neurons. The paper aims to evolve the optimal architecture of CNN using PB3C algorithm for plant leaf classification. For this, we use the nature-inspired computing technique parallel big bang–big crunch to evolve a CNN's optimal architecture automatically. Current study validated the proposed approach for plant leaf classification and compared it with 11 other machine learning-based approaches. From the results obtained it was found that the proposed approach was able to outperforms all 11 existing state-of-the-art techniques. ","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126792855","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":"Prediction of Research Project Execution using Data Augmentation and Deep Learning","authors":"Anibal Flores, Hugo Tito-Chura, Lissethe Zea-Rospigliosi","doi":"10.4114/intartif.vol26iss71pp46-58","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss71pp46-58","url":null,"abstract":"This paper presents the results of seven deep learning models for prediction of research project execution in graduates from a public university in Peru. The deep learning models implemented are non-hybrid: Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN) and, hybrid: CNN+GRU, CNN+ LSTM and LSTM+GRU. Since most of the dataset prediction features are of the nominal type (true false), this paper proposes a simple novel data augmentation technique for this type of features. Taking as inspiration the input data type of a neural network, the proposal data augmentation technique considers nominal features as numeric, and obtain random values close to them to generate synthetic records. The results show that most of deep learning models with data augmentation significantly outperform models without data augmentation in terms of accuracy, precision, f1-score and specificity, being the main improvements of 17.39%, 66.67%, 25.00% and 25.00% respectively.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126249296","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":"Identifying Acoustic Features to Distinguish Highly and Moderately Altered Soundscapes in Colombia","authors":"F. M. Tabares, M. Orozco-Alzate","doi":"10.4114/intartif.vol26iss71pp34-45","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss71pp34-45","url":null,"abstract":"Numerous acoustic features have been proposed as useful measures to characterize natural soundscapes, which can be employed to examine the impact of land transformation on the audible properties of a location. The extensive collection of available features demands an examination to identify the most informative and discriminative ones for a given problem. In this study, we conduct an empirical investigation into the selection of acoustic features for discriminating between highly and moderately transformed versions of four Colombian soundscapes: Moorlands, coffee plantations, dry tropical forests, and pastures. We employ classical supervised feature selection techniques along with exploratory tools such as correlation matrices and scatter plots. Our results indicate that a few acoustic features are sufficient to differentiate between the classes. Specifically, those features that estimate acoustic complexity via intrinsic variability of sound intensities or biodiversity through species richness or abundance in specific frequency bands are the most discriminative ones. These findings suggest that the selection of acoustic features can assist in analyzing and distinguishing between different soundscapes.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128052344","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}