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Enhanced generation of automatically labelled image segmentation datasets by advanced style interpreter deep architectures

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Pattern Recognition Letters 2025年 193卷 101-107页

作者： Pacheco dos Santos Lima, marcos Sergio López-Rubio, Ezequiel Ortiz-de-Lazcano-Lobato, Juan miguel Fernández-Rodríguez, José David Department of Computer Languages and Computer Science. University of Málaga Bulevar Louis Pasteur 35 Málaga29071 Spain C/ Doctor Miguel Díaz Recio 28 Málaga29010 Spain

Large image datasets with annotated pixel-level semantics are necessary to train and evaluate supervised deep-learning models. These datasets are very expensive in terms of the human effort required to build them. Still, recent developments such as DatasetGAN open the possibility of leveraging generative systems to automatically synthesise massive amounts of images along with pixel-level information. This work analyses DatasetGAN and proposes a novel architecture that utilises the semantic information of neighbouring pixels to achieve significantly better performance. Additionally, the overfitting observed in the original architecture is thoroughly investigated, and modifications are proposed to mitigate it. Furthermore, the implementation has been redesigned to greatly reduce the memory requirements of DatasetGAN, and a comprehensive study of the impact of the number of classes in the segmentation task is presented. © 2025 The Authors

关键词： Semantic Segmentation

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Enhancing financial time series forecasting through topological data analysis

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Neural Computing and Applications 2025年第9期37卷 6527-6545页

作者： de Jesus, Luiz Carlos Fernández-Navarro, Francisco Carbonero-Ruz, mariano Department of Quantitative Methods Universidad Loyola Andalucía Córdoba41704 Spain Department of Computer Languages and Computer Science Universidad de Málaga Málaga29010 Spain

Topological data analysis (TDA) is increasingly acknowledged within financial markets for its capacity to manage complexity and discern nuanced patterns and structures. It has been applied effectively to uncover intricate relationships and capture non-linear dependencies inherent in market data. This manuscript presents a groundbreaking study that delves into integrating features derived from TDA to improve the performance of forecasting models for univariate time series prediction. The research specifically examines whether incorporating features extracted from TDA-such as entropy, amplitude, and the number of points obtained from persistent diagrams can provide valuable supplementary information to the baseline forecasting model. Thus, the aim is to determine if these TDA-derived features can boost forecasting accuracy by offering additional insights that existing models might overlook. The N-BEATS model serves as the baseline forecasting model due to its robust generalization capabilities and flexibility in incorporating additional features into the model. The proposed methodology is compared against a univariate N-BEATS model without additional features and other strategies incorporating supplementary features such as temporal decomposition and time delay embeddings. The evaluation includes forecasting for six cryptocurrencies across four distinct time scenarios and four traditional financial instruments across two scenarios each, resulting in 32 datasets. The results obtained were promising, as the proposed method, N-BEATS+TDA, achieved the best results in mean performance and mean ranking for the three metrics considered (mAPE, mAE, and RmSE). Significant differences were observed with the rest of the proposed methods using a significance level of α=0.10, highlighting the effectiveness of integrating TDA features to enhance forecasting models. © The Author(s) 2024.

关键词： Prediction models

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Novel Positional Encoding methods for Neural Rendering 19th

Novel Positional Encoding Methods for Neural Rendering

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19th International Conference on Soft Computing models in Industrial and Environmental Applications, SOCO 2024

作者： molina-Pinel, Daniel García-González, Jorge Domínguez, Enrique López-Rubio, Ezequiel Department of Computer Languages and Computer Sciences University of Málaga Málaga Spain ITIS Software University of Málaga C/. Arquitecto Francisco Peñalosa 18 Málaga29010 Spain

ISBN: (纸本)9783031750090

This work focuses on proposing alternatives for the γφ function used to perform positional encoding in volumetric rendering methods based on radiance field optimization. The approach is to replace the standard sinusoidal function with other proposed alternatives having similar properties. Alternatives that combine several γφ functions are also tested. Experiments with quantitative and qualitative results are included to evaluate the results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Volume rendering

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Smart Packet Delivery in mobile Underwater Sensors Networks (m-CTSP)

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IEEE Access 2025年 13卷 38655-38670页

作者： Khan, Abdul moid Luque-Nieto, miguel-Angel Akbar Siddique, Ali University of Málaga E.T.S.I. Telecommunication Department Ingeniería de Comunicaciones Málaga29010 Spain Sir Syed University of Engineering and Technology Electronic Engineering Department Karachi75300 Pakistan Institute of Oceanic Engineering University of Málaga Málaga29010 Spain Iqra University Edc Campus Department of Computer Science Karachi75500 Pakistan

This research is focused on leveraging the Travelling Salesman Problem (TSP) to establish the optimal route of getting from a source node to a destination node in an underwater dynamic location. In this context, all nodes are mobile and slightly adjust their positions due to water currents. Clustering is introduced (CTSP) to identify the best possible route, where cluster heads are chosen using the known criterium of center of mass of the cluster as well as residual energy. Packets travel from source to destination (nodes) in a multiloop process, visiting cluster heads nodes, with minimum energy consumption. The intention behind using TSP in this approach is thus to efficiently handle the dynamic movement of underwater nodes by conserving energy and ensuring delivery of packets can be relied upon. Additionally, to the movement-related challenges, this method also enhances network lifetime by providing a systematic and energy-efficient way of transmitting information through cluster heads. Simulations using the novel m-CTSP into the routing algorithm yields to promising results the m-CTSP protocol enhances routing execution with the use of dynamic clustering and traveling salesman optimization. This results in a 40% rise in energy use reduction, 15% improvement in packet delivery with a 30% increase in network lifetime when compared to normal clustering-based protocols: LEACH and EECBP-FOA. Experimental results prove that m-CTSP contributes extensively in terms of node mobility packet delivery and energy conservation in dynamic UWSN environments. © 2013 IEEE.

关键词： Traveling salesman problem

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Advancing Photovoltaic Forecasting with Neural Networks: Integrating N-Beats and Sequential models with Fourier Analysis 18th

Advancing Photovoltaic Forecasting with Neural Networks: In...

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18th Ibero-American Conference on Artificial Intelligence, IBERAmIA 2024

作者： Surribas-Sayago, Gonzalo Fernández-Rodríguez, Jose David Dominguez, Enrique ITIS Software University of Málaga Malaga Spain Department of Computer Science University of Malaga Malaga29071 Spain

ISBN: (纸本)9783031803659

The rise of green hydrogen as a clean energy source underscores the need for advanced management of renewable energy inputs, particularly photovoltaic (PV) power generation, which is known for its variability and unpredictability. Effective integration and use of PV power is essential for grid stability and sustainable green hydrogen production, which heavily depends on the availability of excess renewable energy. This study introduces a hybrid deep learning model that combines the N-Beats architecture with a sequential model to enhance the accuracy of PV power generation forecasts. The N-Beats model, known for its ability to capture complex temporal relationships, is used alongside a sequential model that processes additional temporal dependencies. Additionally, this study applies Fourier Analysis to identify significant frequency components in the time variable, revealing key elements like yearly, monthly, and daily cycles. The performance of the hybrid model is compared against traditional algorithms like logistic regression, random forests, support vector machines (SVm), and XGBoost, using metrics such as accuracy, mean Bias Error (mBE), mean Absolute Percentage Error (mAPE), and Root mean Squared Error (RmSE). Our results suggest that the hybrid N-Beats model outperforms traditional algorithms in terms of accuracy and stability, making it a promising option for improving efficiency in green hydrogen production. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clean energy

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Investigating the Transferability of Evasion Attacks in Network Intrusion Detection Systems Considering Domain-Specific Constraints 25th

Investigating the Transferability of Evasion Attacks in N...

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25th International Conference on Information Security Applications, WISA 2024

作者： mbow, mariama Roman, Rodrigo Seal, Ayan Wang, Kevin I-Kai mohanty, Sraban Kumar Sakurai, Kouichi Department of Information Science and Technology Kyushu University Fukuoka Japan Lab University of Malaga Málaga Spain PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (纸本)9789819616237

Recently, adversarial attacks against machine learning-based network intrusion detection systems (NIDS) have gained significant attention in cybersecurity. This study investigates the transferability of these adversarial attacks in the context of NIDS. In addition, most existing studies in adversarial learning against NIDS adopted attack strategies designed originally for image classification without considering network traffic characteristics. However, this is impractical and will fail in the real world as network traffic features are constrained, and ignoring the functional behavior of the network traffic features leads to invalid network traffic flow or produces adversarial samples that do not retain their original functionality (malicious or benign). To address these issues, we propose a constrained momentum iterative fast gradient sign method (C-mIFGSm) to generate adversarial network flows that can successfully evade an mL-based IDS through transfer-based attacks while preserving the functional behavior of the network traffic. Our approach was validated using several target NIDS models built with the NSLKDD benchmark dataset. Experimental results demonstrate that even without knowledge of the target model and under feature constraints, it is possible to generate adversarial network traffic flows that achieve a high evasion attack success rate against NIDS built with deep learning and classical mL models. For example, the attack degraded the detection rate of the DoS traffic drops from 92.35% to 20.27% for the mLP model with an evasion increase rate of 78.04%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Prediction of Rare Channel Conditions using Bayesian Statistics and Extreme Value Theory

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IEEE Transactions on Communications 2025年

作者： Kallehauge, Tobias Kalor, Anders E. Ramirez-Espinosa, Pablo Biscio, Christophe Popovski, Petar Aalborg University Department of Electronic Systems Denmark Keio University Department of Information and Computer Science Japan Málaga29071 Spain Aalborg University Department of Mathematical Sciences Denmark

Estimating the probability of rare channel conditions is a central challenge in ultra-reliable wireless communication, where random events, such as deep fades, can cause sudden variations in the channel quality. This paper proposes a sample-efficient framework for predicting the statistics of such events by utilizing spatial dependency between channel measurements acquired from various locations. The proposed framework combines radio maps with non-parametric models and extreme value theory (EVT) to estimate rare-event channel statistics under a Bayesian formulation. The framework can be applied to a wide range of problems in wireless communication and is exemplified by rate selection in ultra-reliable communications. Notably, besides simulated data, the proposed framework is also validated with experimental measurements. The results in both cases show that the Bayesian formulation provides significantly better results in terms of throughput compared to baselines that do not leverage measurements from surrounding locations. It is also observed that the models based on EVT are generally more accurate in predicting rare-event statistics than non-parametric models, especially when only a limited number of channel samples are available. Overall, the proposed methods can significantly reduce the number of measurements required to predict rare channel conditions and guarantee reliability. © 2025 IEEE.

关键词： Reliability theory

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Enhanced multi-Label Ocular Disease Identification Using a Quantum Convolutional Neural Network Approach Based on Fundus Images

SSRN

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SSRN 2025年

作者： Alqassab, Ans Ibrahim mahameed Luque-Nieto, miguel-Angel mohammed, mazin Abed Telecommunications Engineering School University of Malaga Málaga29010 Spain Tikrit University Saladin Governorate Tikrit34000 Iraq Institute of Oceanic Engineering Research University of Malaga Málaga29010 Spain Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar31001 Iraq

Ocular diseases remain a leading cause of blindness or impaired vision, affecting millions of people worldwide, making early diagnosis critical for effective treatment. This work aims to develop an improved multi-label ocular disease diagnosis model, focusing on the clinical analysis of fundus images using a Quantum Convolutional Neural Network (QCNN) based on CNN architecture. To achieve this, we have integrated compact quantum convolutional pooling layers into traditional CNNs, leveraging the advantages of both for enhanced feature learning and classification. The system's performance was evaluated using the OIA-ODIR dataset, and its results were compared with models such as Fundus-DeepNet, Inception-v4, VGG16 + SGD, and ResNet-101. Key performance indicators, including Accuracy (Acc), Precision, Recall, and F1-score, were used to assess the developed models. The findings demonstrate that our proposed QCNN model offers a state-of-the-art solution, achieving a 94% classification accuracy across both on-site and off-site test sets, alongside notable improvements in precision, recall, and F1-score. These results confirm the superiority of the proposed model in diagnosing multiple ocular diseases from fundus images, particularly in addressing data imbalance issues through a comprehensive augmentation process. Thus, the proposed QCNN-based system enables the early detection of ocular diseases, and its application could be highly effective in clinical practice © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A NETWORK ANALYSIS OF THE 2010 FIFA WORLD CUP CHAmPION TEAm PLAY

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Journal of Systems science.& Complexity 2013年第1期26卷 21-42页

作者： COTTA Carlos mORA Antonio m mERELO Juan Julin mERELO-mOLINA Cecilia Department of Computer Science University of Mlaga Department of Architecture and Computer Technology University of Granada

This paper analyzes the network of passes among the players of the Spanish team during the last FIFA World Cup 2010,where they emerged as the champion,with the objective of explaining the results obtained from the behavior at the complex network *** team is considered a network with players as nodes and passes as(directed) edges.A temporal analysis of the resulting passes network is also done,looking at the number of passes,length of the chain of passes,and to network measures such as player centrality and clustering *** of the last three matches(the decisive ones) indicate that the clustering coefficient of the pass network remains high,indicating the elaborate style of the Spanish *** effectiveness of the opposing team in negating the Spanish game is reflected in the change of several network measures over time,most importantly in drops of the clustering coefficient and passing length/speed,as well as in their being able in removing the most talented players from the central positions of the ***'s ability to restore their combinative game and move the focus of the game to offensive positions and talented players is shown to tilt the balance in favor of the Spanish team.

关键词： Complex network complex systems FIFA World Cup football La Roja network analysis soccer Spanish team sports.

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Correction to: modelling the Covariance Structure in marginal multivariate Count models: Hunting in Bioko Island

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Journal of Agricultural, Biological and Environmental Statistics 2025年 1-2页

作者： Bonat, W. H. Olivero, J. Grande-Vega, m. Farfán, m. A. Fa, J. E. Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Department of Statistics Centro Politécnico Paraná Federal University Curitiba Brazil Grupo de Biogeografía Diversidad Y Conservación Departamento de Biologia Animal Facultad de Ciencias Universidad de Málaga Málaga Spain Research Group for Sustainable Management Silvanet Faculty of Forestry Technical University of Madrid (Universidad Politécnica de Madrid) Madrid Spain Asociación Ecotono Madrid Spain BioGea Consultores Málaga Spain Division of Biology and Conservation Ecology School of Science and the Environment Manchester Metropolitan University Manchester UK Center for International Forestry Research (CIFOR) CIFOR Headquarters Bogor Indonesia

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