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A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks (vol 14, 6961, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Berchez-Moreno, Francisco Duran-Rosal, Antonio M. Martinez, Cesar Hervas Gutierrez, Pedro A. Fernandez, Juan C. Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Maimonides Biomedical Research Institute of Córdoba IMIBIC University of Córdoba 14071 Córdoba Spain Department of Quantitative Methods Universidad Loyola Andalucía Córdoba Spain

Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance.

关键词： Artificial neural networks Classification Coral reef optimisation algorithm Local search Neuroevolution Robust estimators

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Vision-based Situational Graphs Exploiting Fiducial Markers for the Integration of Semantic Entities

arXiv

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arXiv 2023年

作者： Tourani, Ali Bavle, Hriday Sanchez-Lopez, Jose Luis Avşar, Deniz Işınsu Salinas, Rafael Muñoz Voos, Holger University of Luxembourg Luxembourg Institute for Advanced Studies University of Luxembourg Luxembourg Department of Physics & Materials Science University of Luxembourg Luxembourg Department of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus Spain

Situational Graphs (S-Graphs) merge geometric models of the environment generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D scene graphs into a multi-layered jointly optimizable factor graph. As an advantage, S-Graphs not only offer a more comprehensive robotic situational awareness by combining geometric maps with diverse hierarchically organized semantic entities and their topological relationships within one graph, but they also lead to improved performance of localization and mapping on the SLAM level by exploiting semantic information. In this paper, we introduce a vision-based version of S-Graphs where a conventional Visual SLAM (VSLAM) system is used for low-level feature tracking and mapping. In addition, the framework exploits the potential of fiducial markers (both visible as well as our recently introduced transparent or fully invisible markers) to encode comprehensive information about environments and the objects within them. The markers aid in identifying and mapping structural-level semantic entities, including walls and doors in the environment, with reliable poses in the global reference, subsequently establishing meaningful associations with higher-level entities, including corridors and rooms. However, in addition to including semantic entities, the semantic and geometric constraints imposed by the fiducial markers are also utilized to improve the reconstructed map’s quality and reduce localization errors. Experimental results on a real-world dataset collected using legged robots show that our framework excels in crafting a richer, multi-layered hierarchical map and enhances robot pose accuracy at the same time. © 2023, CC BY.

关键词： Mapping

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Survival of Listeria Monocytogenes Biofilms and Planktonic Cells on Cooked Ham in a Contamination-Storage-Digestion Process

SSRN

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SSRN 2025年

作者： Serrano-Heredia, Salud María Sánchez-Martín, Javier López Carmona, José Luis Sánchez-Gutiérrez, Mónica Valero Díaz, Antonio Jiménez, Elena Carrasco CeiA3 Universidad de Córdoba Campus Rabanales Córdoba14014 Spain KDISLab Department of Computer Science and Numerical Analysis University of Córdoba Spain Rabanales Campus Córdoba14071 Spain

In 2023, listeriosis experienced a significant increase in Europe, with 2,952 invasive cases and 19.70% mortality rate, being the ready-to-eat foods category identified as the mostly involved. This study focused on the survival of L. monocytogenes through a process of food contamination-storage-digestion. For this, it was assayed the transfer of biofilm and inoculation of planktonic cells to cooked ham, and the subsequent survival of the pathogen (planktonic or biofilm) after in vitro digestion with or without storage of cooked ham vacuum-packed and stored at 7°C for 7 days. Transfer results to cooked ham after 90 seconds of contact revealed inter-strain variability, with a global transfer rate of 14.07%. From biofilm, initial concentration of L. monocytogenes in the food, 3.53 log CFU/cm2 on average, grew ≈ 0.5 log after storage. Digestion, both immediate after contamination or after food storage, resulted in a reduction of ≈ 0.5 log. However, in the case of planktonic cells, although reduction after immediate digestion (≈ -2.1 log) and after storage (≈ - 0.6 log) were observed as expected (given their less resistance than biofilm cells), an unexpectedly high concentration of the pathogen was found after food storage and digestion (even with a slight increase of ≈ 0.25 log). It is plausible that a biofilm was formed during storage of the contaminated food with planktonic cells. In all cases, high variability in numbers was observed after storage and digestion, being L196 the most resistant strain. More research in omics sciences is needed to clarify L. monocytogenes behaviour. © 2025, The Authors. All rights reserved.

关键词： Food storage

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Validation of artificial neural networks to model the acoustic behaviour of induction motors

arXiv

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arXiv 2024年

作者： Jiménez-Romero, F.J. Guijo-Rubio, D. Lara-Raya, F.R. Ruiz-González, A. Hervás-Martínez, C. Department of Electrical Engineering Universidad de Córdoba Córdoba Spain Department of Computer Science and Numerical Analysis Universidad de Córdoba Córdoba Spain Department of Electrical Engineering University of Málaga EII Málaga Spain

In the last decade, the sound quality of electric induction motors is a hot topic in the research field. Specially, due to its high number of applications, the population is exposed to physical and psychological discomfort caused by the noise emission. Therefore, it is necessary to minimise its psychological impact on the population. In this way, the main goal of this work is to evaluate the use of multitask artificial neural networks as a modelling technique for simultaneously predicting psychoacoustic parameters of induction motors. Several inputs are used, such as, the electrical magnitudes of the motor power signal and the number of poles, instead of separating the noise of the electric motor from the environmental noise. Two different kind of artificial neural networks are proposed to evaluate the acoustic quality of induction motors, by using the equivalent sound pressure, the loudness, the roughness and the sharpness as outputs. Concretely, two different topologies have been considered: simple models and more complex models. The former are more interpretable, while the later lead to higher accuracy at the cost of hiding the cause-effect relationship. Focusing on the simple interpretable models, product unit neural networks achieved the best results: 38:77 for MSE and 13:11 for SEP. The main benefit of this product unit model is its simplicity, since only 10 inputs variables are used, outlining the effective transfer mechanism of multitask artificial neural networks to extract common features of multiple tasks. Finally, a deep analysis of the acoustic quality of induction motors in done using the best product unit neural networks. © 2024, CC BY-NC-ND.

关键词： Induction motors

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A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data

arXiv

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arXiv 2023年

作者： Vargas, Víctor Manuel Rosati, Riccardo Hervás-Martínez, César Mancini, Adriano Romeo, Luca Gutiérrez, Pedro Antonio Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Department of Information Engineering Marche Polytechnic University Ancona Italy Department of Economics and Law University of Macerata Macerata Italy

Predictive Maintenance (PdM) methods aim to facilitate the scheduling of maintenance work before equipment failure. In this context, detecting early faults in automated teller machines (ATMs) has become increasingly important since these machines are susceptible to various types of unpredictable failures. ATMs track execution status by generating massive event-log data that collect system messages unrelated to the failure event. Predicting machine failure based on event logs poses additional challenges, mainly in extracting features that might represent sequences of events indicating impending failures. Accordingly, feature learning approaches are currently being used in PdM, where informative features are learned automatically from minimally processed sensor data. However, a gap remains to be seen on how these approaches can be exploited for deriving relevant features from event-log-based data. To fill this gap, we present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from the original event-log data and a linear classifier to classify the sample based on the learned features. The proposed methodology is applied to a significant real-world collected dataset. Experimental results demonstrated how one of the proposed convolutional kernels (i.e. HYDRA) exhibited the best classification performance (accuracy of 0.759 and AUC of 0.693). In addition, statistical analysis revealed that the HYDRA and MiniROCKET models significantly overcome one of the established state-of-the-art approaches in time series classification (InceptionTime), and three non-temporal ML methods from the literature. The predictive model was integrated into a container-based decision support system to support operators in the timely maintenance of ATMs. © 2023, CC BY-NC-ND.

关键词： Decision support systems

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Sequential solution strategies for the Cahn-Hilliard-Biot model

arXiv

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arXiv 2024年

作者： Storvik, Erlend Riethmüller, Cedric Both, Jakub W. Radu, Florin A. Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied sciences Norway Institute of Applied Analysis and Numerical Simulation University of Stuttgart Germany Centre for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Norway

This paper presents a study of solution strategies for the Cahn-Hilliard-Biot equations, a complex mathematical model for understanding flow in deformable porous media with changing solid phases. Solving the Cahn-Hilliard-Biot system poses significant challenges due to its coupled, nonlinear and non-convex nature. We explore various solution algorithms, comparing monolithic and splitting strategies, focusing on both their computational efficiency and robustness. © 2024, CC BY.

关键词： Computational efficiency

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Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction

arXiv

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arXiv 2025年

作者： Gijón, Alfonso Eiraudo, Simone Manjavacas, Antonio Schiera, Daniele Salvatore Molina-Solana, Miguel Gómez-Romero, Juan Department of Computer Science and Artificial Intelligence Universidad de Granada Granada18071 Spain Energy Center Lab Politecnico di Torino Torino10138 Italy Deparment of Informatics and Numerical Analysis Universidad de Córdoba Córdoba14014 Spain

The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations and ensure effective maintenance through early fault detection systems. While traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed, they often fail to capture the complex, non-linear relationships between other input variables and the resulting power output. Data-driven machine learning methods present a promising avenue for improving wind turbine modeling by leveraging large datasets, enhancing prediction accuracy but often at the cost of interpretability. In this study, we propose a hybrid semi-parametric model that combines the strengths of both approaches, applied to a dataset from a wind farm with four turbines. The model integrates a physics-inspired submodel, providing a reasonable approximation of power generation, with a non-parametric submodel that predicts the residuals. This non-parametric submodel is trained on a broader range of variables to account for phenomena not captured by the physics-based component. The hybrid model achieves a 37% improvement in prediction accuracy over the physics-based model. To enhance interpretability, SHAP values are used to analyze the influence of input features on the residual submodel’s output. Additionally, prediction uncertainties are quantified using a conformalized quantile regression method. The combination of these techniques, alongside the physics grounding of the parametric submodel, provides a flexible, accurate, and reliable framework. Ultimately, this study opens the door for evaluating the impact of unmodeled variables on wind turbine power generation, offering a basis for potential optimization. © 2025, CC BY.

关键词： Windmill

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Evolving machine learning workflows through interactive AutoML

arXiv

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arXiv 2024年

作者： Barbudo, Rafael Ramírez, Aurora Romero, José Raúl Department of Computer Science and Numerical Analysis University of Córdoba Córdoba14071 Spain Departamento de Lenguajes y Ciencias de la Computación University of Málaga Málaga29071 Spain Córdoba Spain

Automatic workflow composition (AWC) is a relevant problem in automated machine learning (AutoML) that allows finding suitable sequences of preprocessing and prediction models together with their optimal hyperparameters. This problem can be solved using evolutionary algorithms and, in particular, grammar-guided genetic programming (G3P). Current G3P approaches to AWC define a fixed grammar that formally specifies how workflow elements can be combined and which algorithms can be included. In this paper we present iEvoFlow, an interactive G3P algorithm that allows users to dynamically modify the grammar to prune the search space and focus on their regions of interest. Our proposal is the first to combine the advantages of a G3P method with ideas from interactive optimisation and human-guided machine learning, an area little explored in the context of AutoML. To evaluate our approach, we present an experimental study in which 20 participants interact with iEvoFlow to evolve workflows according to their preferences. Our results confirm that the collaboration between iEvoFlow and humans allows us to find high-performance workflows in terms of accuracy that require less tuning time than those found without human *** Codes 68T05 © 2024, CC BY.

关键词： Machine learning

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Marker-Based Visual SLAM Leveraging Hierarchical Representations

Marker-Based Visual SLAM Leveraging Hierarchical Representat...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ali Tourani Hriday Bavle Jose Luis Sanchez-Lopez Rafael Muñoz Salinas Holger Voos Automation and Robotics Research Group Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Department of Computer Science and Numerical Analysis Rabanales Campus University of Cordoba Spain Faculty of Science Technology and Medicine University of Luxembourg Luxembourg

Fiducial markers can encode rich information about the environment and aid Visual SLAM (VSLAM) approaches in reconstructing maps with practical semantic information. Current marker-based VSLAM approaches mainly utilize markers for improving feature detections in low-feature environments and/or incorporating loop closure constraints, generating only low-level geometric maps of the environment prone to inaccuracies in complex environments. To bridge this gap, this paper presents a VSLAM approach utilizing a monocular camera along with fiducial markers to generate hierarchical representations of the environment while improving the camera pose estimate. The proposed approach detects semantic entities from the surroundings, including walls, corridors, and rooms encoded within markers, and appropriately adds topological constraints among them. Experimental results on a real-world dataset collected with a robot demonstrate that the proposed approach outperforms a marker-based VSLAM baseline in terms of accuracy, given the addition of new constraints while creating enhanced map representations. Furthermore, it shows satisfactory results when comparing the reconstructed map quality to the one rebuilt using a LiDAR SLAM approach.

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Rational Gaussian wavelets and corresponding model driven neural networks

arXiv

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arXiv 2025年

作者： Ámon, Attila Miklós Fenech, Kristian Kovács, Péter Dózsa, Tamás Eötvös Loránd University Faculty of Informatics Department of Numerical Analysis Hungary Eötvös Loránd University Faculty of Informatics Department of Artificial Intelligence Hungary HUN-REN Institute for Computer Science and Control Systems and Control Laboratory Hungary University of Győr Hungary

In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a biomedical application, namely, the detection of ventricular ectopic beats (VEBs) in real ECG *** Codes 65D15 © 2025, CC BY.

关键词： Electrocardiography

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