Bagging is a popular method used to increase the accuracy of classification, by training a set of classifiers on slightly different datasets and aggregating their output by voting. Usually, the majority voting is used for this purpose, or the plurality voting, when the problem has multiple class values. In this study, we analyze the influence of several voting methods on the performance of two classification algorithms used for datasets with different levels of difficulty. The results reveal that the single transferable vote can be a good alternative to plurality voting, although it has the drawback of a higher computational cost related to the calculation of preference ordering.

This paper proposes a classification methodology aimed at identifying correlations between job ad requirements and transversal skill sets, with a focus on predicting the necessary skills for individual job descriptions using a deep learning model. The approach involves data collection, preprocessing, and labeling using ESCO (European Skills, Competences, and Occupations) taxonomy. Hierarchical classification and multi-label strategies are used for skill identification, while augmentation techniques address data imbalance, enhancing model robustness. A comparison between results obtained with English-specific and multi-language sentence embedding models reveals close accuracy. The experimental case studies detail neural network configurations, hyperparameters, and cross-validation results, highlighting the efficacy of the hierarchical approach and the suitability of the multi-language model for the diverse European job market. Thus, a new approach is proposed for the hierarchical classification of transversal skills from job ads.

This paper addresses the issue of the two-terminal reliability evaluation of medium-to-large networks and proposes a new method to solve the problem of `sum of disjoint products' (SDP) algebraically. This method uses a `multiple variables inversion' (MVI) technique for transforming a structure function into a sum of disjoint products which has a one-to-one correspondence with the reliability expression. The results of our method for several network models are compared with those obtained by means of other well-known MVI techniques. For large-scale networks, our method offers a better solution.

Neural networks have demonstrated their usefulness for solving complex regression problems in circumstances where alternative methods do not provide satisfactory results. Finding a good neural network model is a time-consuming task that involves searching through a complex multidimensional hyperparameter and weight space in order to find the values that provide optimal convergence. We propose a novel neural network optimizer that leverages the advantages of both an improved evolutionary competitive algorithm and gradient-based backpropagation. The method consists of a modified, hybrid variant of the Imperialist Competitive Algorithm (ICA). We analyze multiple strategies for initialization, assimilation, revolution, and competition, in order to find the combination of ICA steps that provides optimal convergence and enhance the algorithm by incorporating a backpropagation step in the ICA loop, which, together with a self-adaptive hyperparameter adjustment strategy, significantly improves on the original algorithm. The resulting hybrid method is used to optimize a neural network to solve a complex problem in the field of chemical engineering: the synthesis and swelling behavior of the semi- and interpenetrated multicomponent crosslinked structures of hydrogels, with the goal of predicting the yield in a crosslinked polymer and the swelling degree based on several reaction-related input parameters. We show that our approach has better performance than other biologically inspired optimization algorithms and generates regression models capable of making predictions that are better correlated with the desired outputs.

In the brick manufacturing industry, there is a growing concern among researchers to find solutions to reduce energy consumption. An industrial process for obtaining bricks was approached, with the manufacturing mix modified via the introduction of sunflower seed husks and sawdust. The process was analyzed with artificial intelligence tools, with the goal of minimizing the exhaust emissions of CO and CH₄. Optimization algorithms inspired by human and virus behaviors were applied in this approach, which were associated with neural network models. A series of feed-forward neural networks have been developed, with 6 inputs corresponding to the working conditions, one or two intermediate layers and one output (CO or CH₄, respectively). The results for ten biologically inspired algorithms and a search grid method were compared successfully within a single objective optimization procedure. It was established that by introducing 1.9% sunflower seed husks and 0.8% sawdust in the brick manufacturing mix, a minimum quantity of CH₄ emissions was obtained, while 0% sunflower seed husks and 0.5% sawdust were the minimum quantities for CO emissions.

Social networks currently belong to a vast area of research as information spreads at a remarkable speed due to technology, and social connections have become easily accessible in the online environment. Social networks are dynamic entities, which new individuals can join, or other links can be lost because members no longer interact with one-another. Dynamic analysis of social networks is important in topology changes of the network and also in information diffusion. Some information that spreads through the social network may be untrue, hence in this paper we propose a protocol based on evidence theory with Dempster-Shafer and Yager’s rules in which the network becomes more immune to false information. We also analyze the impact of topology change for an initial network by adding new connections in the information diffusion process. We show information diffusion by coloring the nodes of the network and also illustrate the time evolution of messages for a better accuracy in our comparisons. The experimental results confirm that the proposed model fits the behavior of inhibiting false information.