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Critical Regimes of Systemic Risk: Flow Network Cascades in the U.S. Banking System
(MDPI AG, 2026)
Montañez Jacquez, Samuel
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Quezada Téllez, Luis Alberto
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Morales Mendoza, Rodrigo
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Moya-Albor, Ernesto
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Fernández Anaya, Guillermo
Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models systemic risk as a capacity-constrained loss-diffusion process governed by flow conservation, contractual seniority, and interbank topology. Using regulatory balance sheet data for four major U.S. banks across six quarters of the 2007–2008 financial crisis, we simulate millions of unit-consistent cascade scenarios to characterize the distribution of bank failures and aggregate losses. Despite severe macro-financial stress, the system remains in a subcritical contagion regime, exhibiting frequent single-bank failures, virtually no multi-bank cascades, and quasi-stationary aggregate losses concentrated around USD 420–430B.We extend the model to a stochastic setting in which the initial shock magnitude is randomized while propagation mechanics remain deterministic. The resulting loss distribution remains tightly concentrated and scales approximately linearly with shock size, suggesting that uncertainty in shock realizations does not induce nonlinear cascade amplification. Applying an efficient network benchmark, we estimate that 10–23% of expected systemic loss is attributable to suboptimal network architecture, implying potential gains from structural policy intervention. A comparison with SRISK reveals early divergence and convergence only at peak stress, highlighting the complementary roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow network data fails to reproduce threshold-driven cascade dynamics, underscoring the importance of considering network structures vis-à-vis data-driven approaches. © The authors © MDPI.
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A Deep-Learning and Bio-inspired Vision Model-Based Approach for Automatic Coronary Arteries Segmentation
(IEEE, 2025)
López-Figueroa, Alberto
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Brieva, Jorge
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Moya-Albor, Ernesto
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Gomez-Coronel, Sandra L.
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Renza, Diego
The accurate segmentation of coronary arteries from X-ray angiograms is critical for the diagnosis and treatment of cardiovascular diseases. Yet, it remains a challenging task due to low image contrast and complex vessel structures. This paper introduces a novel hybrid methodology that combines a bio-inspired vision model, the Steered Hermite Transform (SHT), with a deep learning architecture for robust vessel segmentation. We leverage the SHT to decompose each angiogram into a rich, multi-resolution set of 15 feature maps that capture local image structures at different scales and orientations. These Hermite coefficients, along with the original image, form a 16-channel input tensor used to train a U-Net. This approach enables the network to learn from an enhanced feature space that explicitly represents vessel-like patterns. Evaluated on a public dataset of 134 coronary angiograms, our model demonstrates outstanding performance, achieving an Area Under the Curve (AUC) of 0.9872. The results confirm that enriching the input of a deep neural network with SHT coefficients significantly improves its ability to identify and segment complex vascular networks accurately. ©The authors ©IEEE.
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Image Facial Expression Recognition based on Active Muscles and their Notable Triangle Points
(IEEE, 2025)
Aguilera-Hernández, Edgar I.
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Cruz-Aceves, Ivan
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Hernández-Aguirre, Arturo
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Moya-Albor, Ernesto
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Brieva, Jorge
During emotion experience originated in psychological changes, the effect in face muscles results in a characteristic set of contractions associated to specific emotions. This paper propose an intuitive representation of these interactions with the objective of facial expression recognition through geometric features. In medical research, it has generated insights regarding emotional state, cognitive function, and pain level during clinical procedures leading to an effective patient treatment, assisting diagnosis and monitoring disease progression mainly in neurological conditions. Starting from a facial muscle modeling using triangles, it utilizes an initial 68 landmarks fitting algorithm, and later the computation of triangle notable points to work as anchors of specific muscles. Secondly, the optimization process through stochastic techniques is applied to set the point type combination so that the F1-Score is maximized. Experimental results were performed with conventional classifiers and no fine tuning, accomplishing an accuracy, precision, recall and F1-score of 0.88 for KDEF dataset, while 0.84, 0.86, 0.84, and 0.84 respectively for the JAFFE dataset, proving to be a reliable technique in the expression recognition problem. ©The authors ©IEEE.
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Complexity-Driven Adversarial Validation for Corrupted Medical Imaging Data
(MDPI AG, 2026)
Renza, Diego
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Brieva, Jorge
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Moya-Albor, Ernesto
Distribution shifts commonly arise in real-world machine learning scenarios in which the fundamental assumption that training and test data are drawn from independent and identically distributed samples is violated. In the case of medical data, such distribution shifts often occur during data acquisition and pose a significant challenge to the robustness and reliability of artificial intelligence systems in clinical practice. Additionally, quantifying these shifts without training a model remains a key open problem. This paper proposes a comprehensive methodological framework for evaluating the impact of such shifts on medical image datasets under artificial transformations that simulate acquisition variations, leveraging the Cumulative Spectral Gradient (CSG) score as a measure of multiclass classification complexity induced by distributional changes. Building on prior work, the proposed approach is meaningfully extended to twelve 2D medical imaging benchmarks from the MedMNIST collection, covering both binary and multiclass tasks, as well as grayscale and RGB modalities. We evaluate the metric analyzing its robustness to clinically inspired distribution shifts that are systematically simulated through motion blur, additive noise, brightness and contrast variation, and sharpness variation, each applied at three severity levels. This results in a large-scale benchmark that enables a detailed analysis of how dataset characteristics, transformation types, and distortion severity influence distribution shifts. Thus, the findings show that while the metric remains generally stable under noise and focus distortions, it is highly sensitive to variations in brightness and contrast. On the other hand, the proposed methodology is compared against Cleanlab’s widely used Non-IID score on the RetinaMNIST dataset using a pre-trained ResNet-50 model, including both class-wise analysis and correlation assessment between metrics. Finally, interpretability is incorporated through class activation map analysis on BloodMNIST and its corrupted variants to support and contextualize the quantitative findings. ©The authors ©MDPI.
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Machine Learning Methods in Biomedical Field : Computer-Aided Diagnostics, Healthcare and Biology Applications
(Springer Nature Switzerland, 2026)
Moya-Albor, Ernesto
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Ponce, Hiram
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Brieva, Jorge
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Gomez-Coronel, Sandra L.
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Renza Torres, Diego
This book provides an in-depth exploration of machine learning techniques and their biomedical applications, particularly in developing intelligent computer-aided diagnostic systems, creating groundbreaking healthcare technologies, uncovering novel biological applications, and fostering sustainable health solutions. Integrating artificial intelligence, mathematical modeling, and emergent systems, this book highlights the profound impact of these advanced tools in not only enhancing problem-solving within the biomedical field but also in catalyzing the development of novel solutions. This book is a valuable resource for readers interested in understanding the revolutionary impact of novel machine learning methodologies on the biomedical landscape. Furthermore, it offers a blend of theory and practical applications for those interested in biomedical education and training, biology, medicine, and sustainable health development. ©The authors ©Springer.
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Orthosis Control Based on Electromyographic Signals and Machine Learning
(Springer Nature Switzerland, 2025)
Escobedo-Gordillo, Andrés
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Díaz, Fernando
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Villa, Jesús
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Sepúlveda, Miguel
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García-Casas, Sebastián
The human hand is indispensable for daily activities, and those who suffer from dysfunction due to strokes or accidents often require therapy to improve their condition. This study has developed a hand orthosis that uses surface Electromyographic (sEMG) signals and machine learning to address therapeutic needs and improve the quality of life for individuals with reduced motor skills in their hands and/or wrists. While current orthoses meet therapy requirements, they do not incorporate machine learning (ML) or sEMG sensors to optimize performance and accessibility. This chapter describes a remote-controlled, electro-mechanical orthosis that can replicate six basic movements of the human hand using three sEMG channels and ML. Our dataset of 14,400 samples, each labeled with a hand gesture, was generated by eight participants. The orthosis is comfortable and customizable for different users, as shown in prototype testing. The convolutional neural network (CNN) used achieves an accuracy of 90.38% with an inference time of 1.515 ms. Therefore, this orthosis system has significant potential for further development and practical application in patients who require such intervention. ©The authors ©Springer.
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Challenges and Advances in Digital Processing of Fetal Phonocardiography Signal: A Review
(Springer Nature Switzerland, 2025)
Moya-Albor, Ernesto
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Brieva, Jorge
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Gomez-Coronel, Sandra L.
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Renza, Diego
This chapter presents a state-of-the-art review of different investigations focused on Fetal Phonocardiography (fPCG). fPCG signals allow the identification of the fetus’s cardiac alterations during pregnancy through a noninvasive and secure approach. However, fPCG signals present some challenges, for example: very weak signal sources, high levels of noise, source mixing, and significant signal attenuation. This work provides a review of available fPCG datasets and the methods proposed for source separation, extraction, and filtering of fPCG signals, as well as the methods for estimating fetal heart rate (fHR) and detecting fetal Heart Sounds (fHS). Additionally, since it is sometimes necessary to transmit or store fPCG signals, the chapter also discusses signal compression approaches and applications involving fPCG signals. ©The authors ©Springer.
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Edge-Enhanced Knowledge Distillation System for Diabetic Retinopathy Lesions Computer-Aided Diagnosis
(Springer Nature Switzerland, 2025)
Lopez-Figueroa, Alberto
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Jacome-Herrera, Sebastian
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Moya-Albor, Ernesto
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Renza, Diego
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Brieva, Jorge
This work addresses the challenge of deploying computationally intensive Deep Learning (DL) models for Diabetic Retinopathy (DR) lesion detection in clinical settings, particularly on resource-constrained edge devices. DR is a significant global health issue and a leading cause of preventable blindness, making early and accessible detection crucial. We propose a proof-of-concept system utilizing Knowledge Distillation (KD) to create a tiny, efficient DL model for DR lesion detection, specifically designed for embedding into retinal scanners via the NVIDIA Jetson Nano platform. Our novel approach employs a KD framework where a pre-trained Inception-v3 model acts as the ‘teacher,’ fine-tuned on fundus image data. This teacher model distills its knowledge into a compact ‘student’ model based on the MobileNet-v2 architecture, which is trained on a small, synthetically generated dataset optimized through an iterative distillation process using a custom loss function combining Kullback-Leibler divergence and Categorical Cross-Entropy. This method significantly reduces model size and computational requirements while maintaining high diagnostic accuracy, comparable to larger, state-of-the-art models. By enabling real-time, on-device analysis, this embedded AI solution enhances data privacy, ensures consistent performance, and improves the accessibility of advanced DR screening, particularly in remote or underserved healthcare environments. This makes AI-assisted DR detection feasible for widespread clinical adoption directly within scanning devices. ©The authors ©Springer.
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Metaheuristic Strategy in Automatic Robotics Navigation for Patient Care in Hospitals
(Springer Nature Switzerland, 2025)
Monroy-Rueda, Irvine J.
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Moya-Albor, Ernesto
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Brieva, Jorge
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Ponce, Hiram
This chapter presents a novel approach for developing a global automatic navigation strategy for a mobile robot for patient care into health centers and hospitals. The proposal uses a metaheuristic strategy through the Grey Wolf Optimizer algorithm to find the shortest route between the robot’s starting and patient destination points, while also integrating knowledge of the environment acquired through vision. The global navigation tracking is achieved through the use of Fuzzy Logic Rules to control the robot’s wheel velocity according to its current position and orientation. The proposed strategy was implemented in a virtual robotics environment demonstrating that the approach successfully generated optimal paths for various environments with a minimal number of control points and in a relatively short amount of time. In addition, a simulated hospital room with common furniture and tasks was used to evaluate the performance of the global navigation strategy, demonstrating that the robot could successfully generate a global route and navigate within this environment. ©The authors ©Springer.
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Automatic Robotics Medication Delivery System: The ANDIS Case Study
(Springer Nature Switzerland, 2025)
Carbajal, Pablo
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Cobb, Ethan
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Hernández, César
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Mejía, Alfredo
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Menchaca, Lucía
Hospitals face challenges such as nurse burnout and unproductive time utilization. Interviews with nurses and physicians revealed that 75% of their workday is dedicated to patient care and the remaining 25% could be considered unproductive due to other tasks such as manual record-keeping, canceled surgeries and postponed procedures. Streamlining tasks can free up nurses’ time for better patient care. One of these tasks is medication delivery, which can be automated using autonomous delivery robots. In this regard, we propose the design of a small autonomous robot that delivers drugs inside hospitals to reduce the medical staff’s workload. The proposed robotics system uses ultrasonic sensors and fuzzy logic control for the avoidance of obstacle tasks. In addition, an AI camera and color indicators are used to identify the room and follow the designed trajectory to deliver the medication. Results showed that the robot navigates without colliding, and the final distance between the robot and the indicators was considered appropriate for medication delivery tasks. Furthermore, its maintenance is straightforward due to its uncomplicated mechanical design. Finally, the non-invasive nature of the colored indicators minimizes the visual impact on hospital environments. ©The authors © Spring.

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