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Novel method for modeling 2d and 3D shapes analytically with biomedical applications

Multimedia Tools and Applications 2025年 1-43页

作者： Abu-Qasmieh, Isam Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid Jordan

A novel, fast and simple approach to model full-analytical two-dimensional (2D) and three-dimensional (3D) spatial shapes is presented. The concept of this study is inspired from the behavior of the Butterworth Low-pass filter, used in signal and image processing, at different orders, where it behaves like ideal filter as the filter’s order goes to infinity and close to Gaussian (normal) distribution for very small orders. Different 2D polygons are modeled such as Rectangle, Parallelogram, Trapezoid, Triangle, Rhombus-Diamond, Hexagon and Octagon. Also, different 3D polygons are modeled such as Cuboid, Parallelepiped, Cylinder, Prism, Pyramid, Cone, Hexagonal-Prism, Octagon-Prism, Dough-Rolling-Pin and others. Those 2D and 3D shapes are modeled by contributing the Butterworth filter formula in the semi-axes of the ellipse and ellipsoid mathematical equations respectively. The 2D and 3D polygons can be modeled with sharp or smooth edges by changing the Butterworth order. By modifying some of the 3D shapes mathematical formula other biomedical 3D shapes can be modeled such as Bacillus-Bacteria, Bacteriophage and Sickle red blood cell 3D shapes. The 2D and 3D shapes can be selectively shifted or/and rotated with different angles using the 2D and 3D rotation matrix respectively without changing the relative Euclidean distances between the shape’s pixels. The 2D or 3D generated shapes can be detected using a powerful tool, for graphic element extraction from images, such as Hough transform for recognition of the scale, center coordinates and rotation angles for specific parametric shape in 2D, or 3D space respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Butterworth filters

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Digital Media Content for Mentoring Programs Targeted at Women in informatics 27th

Digital Media Content for Mentoring Programs Targeted at Wom...

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27th International Conference on Interactive Collaborative Learning, ICL 2024

作者： Sousa, Francisca Silva, Paula Alexandra Centre for Informatics and Systems Department of Informatics Engineering University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031835193

Gender disparities persist in informatics, posing challenges for women interested in the field. Mentoring programs are an effective strategy for supporting women in such contexts. This paper presents the findings of a focus group aimed at understanding the challenges women experience to determine possible solutions and the potential of a mentoring program and its digital media content in supporting women. The focus group involved eight female participants, current and former students of an informatics department, and found that mentoring is perceived as potentially beneficial. Participants suggested several key contents for the mentoring program's digital media platforms, such as testimonials from former students and graduates, workshops and networking events, and instructions and support for completing their studies. This study contributes to the understanding of the needs of women in informatics and how mentoring programs can better address those needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： informatics

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Shaping the Digital Content of Mentoring Programs for Women in informatics: Insights from an Exploratory Study 17

Shaping the Digital Content of Mentoring Programs for Women ...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Sousa, Francisca Tejo, Leonor Miranda, Sunny Silva, Paula Alexandra Department of Informatics Engineering University of Coimbra Portugal Department of Informatics Engineering University of Coimbra CISUC/LASI - Centre for Informatics and Systems of the University of Coimbra Portugal

ISBN: (纸本)9789897587467

Gender disparities in informatics persist as a significant issue, with women facing barriers to entry, retention, and advancement. Mentoring programs hold the potential to improve this issue. Because change demands a collective response, this study explores how to shape the digital content of mentoring programs to support the students, women and men, in an informatics department. This work builds upon the findings of a focus group study, complementing them by applying a questionnaire to gain insight into students' academic experiences, perceptions of the benefits of a mentoring program, and preferences concerning digital communication platforms. Findings indicate that the digital content of mentoring programs can help in three keyways: providing insights into the job market, shedding light on career and recruitment processes, and offering real-life content. These findings are valuable to departments and mentoring programs that wish to support women in informatics through the digital content of their websites and social media platforms. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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Improving Fraud Detection with 1D-Convolutional Spiking Neural Networks Through Bayesian Optimization 23rd

Improving Fraud Detection with 1D-Convolutional Spiking Neu...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Perdigão, Dylan Antunes, Francisco Silva, Catarina Ribeiro, Bernardete Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031735028

The digitalization of the banking sector has enabled an increasing number of fraudulent activities in the past years. The development of new practical solutions for fraud detection is particularly challenging since the industry needs to respect a 5% threshold of false detection of fraud, avoiding monetary losses. The usage of traditional machine learning algorithms faces other challenges, such as classification discrimination and high energy consumption. Spiking Neural Networks, designed to mimic the brain’s natural processes, present a compelling alternative. Particularly on neuromorphic hardware, they exhibit remarkable energy efficiency, paving the way for the advancement of sustainable Artificial Intelligence. Our approach consists of applying 1D-Convolutional Spiking Neural Networks to improve fraud detection on the Bank Account Fraud dataset with the Bayesian optimization of hyperparameters. As a result, we show that these architectures can solve the problem of biased data while maintaining algorithm performance when compared against more traditional approaches such as Gradient Boosting models. Moreover, we lay out the Bayesian optimization process for the hyperparameters, thereby enhancing the performance of these algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Exploring Neural Joint Activity in Spiking Neural Networks for Fraud Detection 27th

Exploring Neural Joint Activity in Spiking Neural Networks ...

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27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2024

作者： Perdigão, Dylan Antunes, Francisco Silva, Catarina Ribeiro, Bernardete Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031766039

Spiking Neural Networks (SNNs), inspired by the real brain’s behavior, offer an energy-efficient alternative to traditional artificial neural networks coupled with their neural joint activity, also referred to as population coding. This population coding is replicated in SNNs by attributing more than one neuron to each class in the output layer. This study leverages SNNs for fraud detection through real-world datasets, namely the Bank Account Fraud dataset suite, addressing the fairness and bias issues inherent in conventional machine learning algorithms. Different configurations of time steps and population sizes were compared within a 1D-Convolutional Spiking Neural Network, whose hyperparameters were optimized through a Bayesian optimization process. Our proposed SNN approach with neural joint activity enables the classification of fraudulent opening of bank accounts more accurately and fairly than standard SNNs. The results highlight the potential of SNNs to surpass non-population coding baselines by achieving an average of 47.08% of recall at a business constraint of 5% of false positive rate, offering a robust solution for fraud detection. Moreover, the proposed approach attains comparable results to gradient-boosting machine models while maintaining predictive equality towards sensitive attributes above 90%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A Multidimensional Taxonomy for Recent Trends in Explainable Artificial Intelligence 23rd

A Multidimensional Taxonomy for Recent Trends in Explainab...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Carvalho, Isabel Gonçalo Oliveira, Hugo Silva, Catarina Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering University of Coimbra Coimbra Portugal Coimbra Portugal

ISBN: (纸本)9783031734991

With the pervasive integration of Artificial Intelligence (AI) into various facets of society, concerns regarding its transparency and interpretability have gained prominence, particularly in critical or citizen-facing applications. The field of Explainable Artificial Intelligence (XAI) has witnessed rapid growth in response to these concerns, with recent research endeavors focusing on elucidating the inner workings of AI systems. We present a bibliometric study encompassing recent developments in XAI since 2020. We identify seven distinct areas of application where XAI methodologies have been applied. Furthermore, we propose a multidimensional taxonomy that categorizes these approaches and applications, aiming to contribute to the ongoing efforts towards the standardization and uniformization of XAI practices. By shedding light on the current landscape of research and offering a structured taxonomy for analysis, we expose under and over-explored techniques, and encourage the employment and development of diversified approaches for XAI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Self-organising Approach to Anomaly Mitigation in the Cloud-to-Edge Continuum 30th

Self-organising Approach to Anomaly Mitigation in the Clo...

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30th International Conference on Cooperative Information systems, CoopIS 2024

作者： Faria, Bruno Abreu, David Perez Velasquez, Karima Curado, Marília Laboratory for Informatics and Systems Pedro Nunes Institute Coimbra Portugal CISUC Department of Informatics Engineering University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031813740

The cloud-to-edge continuum paradigm has permeated various application domains, including critical urban-city safety systems. In these contexts, anomalies can compromise public safety, for example, by disrupting the communication between smart city infrastructure and vehicles, which aims to prevent accidents at pedestrian crossings. Given these environments’ heterogeneous and large-scale nature, manual recovery from anomalies is not feasible. Machine Learning techniques have emerged as an alternative, supporting a zero-touch approach that enables self-organising and self-healing solutions for anomaly prediction, detection, and mitigation. This paper proposes an Artificial Intelligence-driven, self-organising approach for anomaly management in the cloud-to-edge continuum, integrating both reactive and proactive mechanisms. We evaluate different Machine Learning models, including Random Forest Classifiers, Neural Networks, and Convolutional Neural Networks, to predict node performance anomalies. The simulation results obtained using the COSCO framework showcase the effectiveness of our method. It achieves an F1 score of 73% for multiclass classification, predicting different levels of anomaly severity, and 87% for binary classification, distinguishing between normal and abnormal states. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anomaly management Cloud-to-edge continuum Machine learning Self-X Time-series classification Zero-touch

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Observer-based Implementation of Discrete Gabor Transform

Periodica Polytechnica Electrical Engineering and Computer S...

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Periodica Polytechnica Electrical engineering and Computer Science 2025年第1期69卷 79-88页

作者： Ország, Bence Sujbert, László Department of Artificial Intelligence and Systems Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

The most commonly used technique in time-frequency analysis is the short-time Fourier transform. It can be used to determine the spectral components as they change over time by computing the Fourier transform of a windowed segment of the signal. A fundamental constraint of this method is that the frequency resolution of the representation in the time-frequency domain will be linear by design. The frequency adaptive nonstationary discrete Gabor transform offers an alternative that does not have this limitation. A Luenberger observer is capable of the implementation of the short-time Fourier transform, and its numerical advantages are already established based on the work of Hostetter and Péceli. Here, we introduce the family of discrete Gabor transforms and its properties along with a constructive method to define such transforms. Furthermore, we show that they are realizable by Luenberger observers which are capable of the error-free reconstruction of the observed signal in finite steps. The dead-beat property is derived for the state variables as well which estimate the transform of the windowed signal. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Frequency domain analysis

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Investigating the correlation between smoking and blood pressure via photoplethysmography

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BioMedical engineering Online 2025年第1期24卷 1-22页

作者： Qananwah, Q. Quran, H. Dagamseh, A. Blazek, V. Leonhardt, S. Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid Jordan Department of Electronics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid Jordan Helmholtz-Institute for Biomedical Engineering RWTH Aachen University Aachen Germany

Smoking has been widely identified for its detrimental effects on human health, particularly on the cardiovascular health. The prediction of these effects can be anticipated by monitoring the dynamic changes in vital signs and other physiological signals or parameters such as heart rate, blood pressure (BP), Electrocardiogram (ECG), and Photoplethysmogram (PPG), which subtly encode smoking-related effects. We investigated the influence of different smoking habits—normal cigarettes (NC), electronic cigarettes (EC), and shisha (SH)—on BP through analysis of ECG and PPG signals. The measurements of these physiological signals were taken across three distinct smoking phases: "before", "during", and "after" smoking. The study assessed changes in heart rate, as well as morphological and statistical characteristics of ECG and PPG signals, induced by smoking. A machine learning (ML) model was developed to predict BP values with different smoking habits and smoking phases, while also evaluating the temporal effects of smoking phases. Results show that smoking markedly alters PPG features in such it significantly affects systolic time, heart rate, peak pulse interval variability, and augmentation index. BP variations were evident across all smoking habits and phases. The ML model demonstrated strong accuracy in estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) during and post-smoking, with a mean error of 0.01 ± 0.29 mmHg and a root mean square error (RMSE) of 0.2924 mmHg for DBP and RMSE of 0.0082 mmHg for SBP. Such a study underscores the pronounced effect of smoking on BP and its potential role in cardiovascular system alterations, offering insights into the development of related diseases. © The Author(s) 2025.

关键词： Plasma (human)

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Virtual Reality Visions from Science Fiction Movies 23rd

Virtual Reality Visions from Science Fiction Movies

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23rd IFIP TC 14 International Conference on Entertainment Computing, ICEC 2024

作者： Cardoso, Jorge C. S. Center for Informatics and Systems of the University of Coimbra Department of Informatics Engineering University of Coimbra Pinhal de Marrocos Coimbra3030-290 Portugal

ISBN: (纸本)9783031743528

Science fiction movies have been a source of entertainment for many decades, but they have also been an inspiration for many people in the science and technology domains. Science fiction is not just about entertainment: it has been recently adopted also as a speculative design approach useful for designing and reflecting on the possibilities of future technologies. In this paper, we take existing science fiction works (movies) as speculative designs to understand and reflect on the possibilities of virtual reality. We have created a database of science fiction movies, TV shows, and episodes and analysed it along the dimensions of technology, form factor, user’s body position, and application domain. The result is a structured analysis of how existing science fiction movies have imagined the possibilities for virtual reality. This analysis may serve as further inspiration or for identifying research and development paths related to virtual reality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Virtual environments

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