Publications

@article{janez-martino_spam_2025,

title = {Spam email classification based on cybersecurity potential risk using natural language processing},

author = {Francisco Jáñez-Martino and Rocío Alaiz-Rodríguez and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},

url = {https://www.sciencedirect.com/science/article/pii/S0950705124015739},

year  = {2025},

date = {2025-01-01},

journal = {Knowledge-Based Systems},

volume = {310},

pages = {112939},

abstract = {This study focuses on detecting spam emails, a key vector for cyberattacks. It introduces 56 features based on NLP techniques, grouped into five categories: Headers, Text, Attachments, URLs, and Protocols. A new dataset, SERC, was created for spam risk classification. Using binary classification and regression, the Random Forest classifier achieved the best performance (F1-Score of 0.914), and Random Forest Regressor had the lowest Mean Square Error (0.781). Features from the Headers and Text groups were found to be the most important.},

note = {Publisher: Elsevier},

keywords = {Cybersecurity, Email Classification, machine learning, Natural Language Processing, Spam detection},

pubstate = {published},

tppubtype = {article}

}

@article{janez-martino_persuasion_2025,

title = {On persuasion in spam email: A multi-granularity text analysis},

author = {Francisco Jáñez-Martino and Alberto Barrón-Cedeño and Rocío Alaiz-Rodríguez and Víctor González-Castro and Arianna Muti},

url = {https://www.sciencedirect.com/science/article/pii/S0957417424026344},

year  = {2025},

date = {2025-01-01},

journal = {Expert Systems with Applications},

volume = {265},

pages = {125767},

abstract = {This paper explores the use of supervised machine learning models to detect persuasion techniques in spam emails, addressing both binary classification (presence/absence of persuasion) and multilabel classification (identifying specific persuasion techniques). The research utilizes natural language processing and adapts propaganda detection methods from news articles, analyzing emails at full-text, sentence, and snippet levels. The study includes the development of a custom spam dataset and fine-tuning of RoBERTa-based models, ultimately aiming to enhance cybersecurity through better understanding of persuasion tactics in malicious emails.},

note = {Publisher: Pergamon},

keywords = {Cybersecurity, machine learning, Natural Language Processing, Spam detection},

pubstate = {published},

tppubtype = {article}

}

@article{medina-martinez_repositorio_2024,

title = {Repositorio Git para aprendizaje basado en resolución de problemas. Asignatura de Dirección de Proyectos},

author = {Gabriel Medina and Laura Fernández-Robles and Manuel Castejón-Limas},

url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9318963},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Innovación docente en la Universidad de León},

pages = {317–324},

abstract = {Esta experiencia propone la implementación de un sistema de aprendizaje autónomo y guiado basado en la resolución de problemas específicos de gestión de plazos, costes y recursos. A través de un entorno de simulación, los estudiantes exploran los efectos de sus decisiones organizativas en proyectos. El sistema emplea un servidor Git para almacenar casos de estudio y un servidor de máquinas virtuales para ejecutar simulaciones personalizadas. Aplicado en la asignatura de Dirección de Proyectos en ingenierías, ha demostrado mejorar significativamente la calidad del aprendizaje, aumentando el interés estudiantil y los resultados académicos. Se planea expandir el repositorio en el futuro.},

note = {Publisher: Servicio de Publicaciones},

keywords = {Aprendizaje Basado en Problemas, Dirección de Proyectos, Git, Simulación de Proyectos, Virtualización},

pubstate = {published},

tppubtype = {article}

}

@article{al-nabki_classifying_2024,

title = {Classifying the content of online notepad services using active learning},

author = {Wesam Al-Nabki and Eduardo Fidalgo and Enrique Alegre and Sarah Jane Delany and Francisco Jáñez-Martino},

url = {https://link.springer.com/article/10.1007/s10844-024-00902-8},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Intelligent Information Systems},

pages = {1–27},

abstract = {This paper proposes a cascading classification system with Active Learning to identify suspicious activities on Pastebin. The model classifies texts into code snippets, readability, and suspicious or illegal activities. It introduces the INSPECT-3.8M dataset, containing 3.8 million labeled samples. This approach helps law enforcement agencies detect and block illegal content on Pastebin before it spreads.},

note = {Publisher: Springer US},

keywords = {Cybersecurity, Illegal Activities, machine learning, Pastebin, Text classification},

pubstate = {published},

tppubtype = {article}

}

@article{martinez-mendoza_towards_2024,

title = {Towards Multi-Class Smishing Detection: A Novel Feature Vector Approach and the Smishing-4C Dataset},

author = {Alicia Martínez-Mendoza and Francisco Jáñez-Martino and Andrés Carofilis-Vasco and Laura Fernández-Robles and Enrique Alegre and Eduardo Fidalgo},

url = {https://besaya.infor.uva.es/sepln24/paper06.pdf},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

keywords = {Multiclass Classification, Smishing Classification, Smishing-4C Dataset, SMS, Text classification},

pubstate = {published},

tppubtype = {article}

}

@article{apap_explainable_2024,

title = {Explainable multi-layer COSFIRE filters robust to corruptions and boundary attack with application to retina and palmprint biometrics},

author = {Adrian Apap and Amey Bhole and Laura Fernández-Robles and Manuel Castejón-Limas and George Azzopardi},

url = {https://link.springer.com/article/10.1007/s00521-024-10164-8},

year  = {2024},

date = {2024-01-01},

journal = {Neural Computing and Applications},

volume = {36},

number = {30},

pages = {19231–19245},

abstract = {This work introduces a unified and explainable biometric recognition method using hierarchical COSFIRE filters for retina and palmprint identification. The approach leverages COSFIRE’s one-shot trainable nature to capture spatial arrangements of keypoints in biometric images. It delivers state-of-the-art accuracy, including perfect classification on retina datasets and 97.54% on a palmprint dataset. The system remains robust against partial data and most adversarial attacks, while also offering clear, interpretable decision processes suitable for broader applications.},

note = {Publisher: Springer London London},

keywords = {biometrics, COSFIRE Filters, Explanaible AI},

pubstate = {published},

tppubtype = {article}

}

@article{castejon-limas_formula_2024,

title = {The formula for the completion time of project networks},

author = {Manuel Castejón-Limas and Gabriel Medina and Virginia Riego del Castillo and Laura Fernández-Robles},

url = {https://arxiv.org/abs/2410.10252},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {arXiv preprint arXiv:2410.10252},

abstract = {This paper models project completion time using linear transformations and singular value decomposition (SVD) to analyze the topological relevance of paths and activities. It introduces spectral networks to capture the project's fundamental structure and proposes a project stress metric for comparing alternatives. The Moore-Penrose inverse is used to determine activity durations for a given path duration. A systematic review supports the novelty of these concepts.},

keywords = {CMP, Duration, Matrix, Path, PERT, Project Networks, Project Scheduling},

pubstate = {published},

tppubtype = {article}

}

@article{janez-martino_analysis_2024,

title = {Analysis and classification of spam email using Artificial Intelligence to identify cyberthreats},

author = {Francisco Jáñez-Martino},

url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6586},

year  = {2024},

date = {2024-01-01},

journal = {Procesamiento del Lenguaje Natural},

volume = {72},

pages = {155–158},

abstract = {Summary of the Ph.D. thesis written by Francisco Jáñez Martino and supervised by Prof. Dra. Rocío Alaiz Rodríguez and Dr. Víctor González Castro at Universidad de León. The defense of the thesis was in León (Spain) in 21st of December 2023 by a committee formed by Dr. Arturo Montejo Ráez (Universidad de Jaén, Spain), Dr. Petr Motlicek (Idiap Research Institute, Switzerland), and Dra. Laura Fernández Robles (Universidad de León, Spain). An international mention was garnered following a six-month tenure at the Universitá di Bologna under the supervision of Dr. Alberto Barrón Cedeño. This Ph.D. thesis was awarded an outstanding Cum Laude grade.},

keywords = {Academic Excellence, International Research, Ph.D. Thesis, Universidad de León},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_familiarity_2024,

title = {Familiarity Analysis and Phishing Website Detection using PhiKitA Dataset},

author = {Enrique Alegre and Eduardo Fidalgo and Rocío Alaiz-Rodríguez and Felipe Castaño and Alicia Martínez-Mendoza},

url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9633478},

year  = {2024},

date = {2024-01-01},

journal = {IX Jornadas Nacionales de Investigación En Ciberseguridad},

pages = {442–443},

abstract = {La formación es clave para el desarrollo de la ciberseguridad ante la escasez de profesionales altamente calificados. Esta conferencia analiza las distintas opciones de capacitación, incluyendo cursos, entrenamientos, certificaciones y programas de educación superior, con énfasis en pregrado y posgrado. También se examinan los retos y perspectivas para satisfacer la creciente demanda en el sector.},

note = {Publisher: Antonia M. Reina Quintero},

keywords = {Certificaciones, Ciberseguridad, Jornads Nacionales de Investigación},

pubstate = {published},

tppubtype = {article}

}

@article{castano_automating_2024,

title = {Automating cybersecurity TTP classification based on nnstructured attack descriptions},

author = {Felipe Castaño and Amaia Gil-Lerchundi and Raúl Orduna-Urrutia and Eduardo Fidalgo and Rocío Alaiz-Rodríguez},

url = {https://idus.us.es/items/1566b428-106f-4ace-8d17-0835566c60bf},

year  = {2024},

date = {2024-01-01},

journal = {Jornadas Nacionales de Investigación en Ciberseguridad (JNIC)(9ª. 2024. Sevilla)(2024), pp. 46-50.},

abstract = {This paper introduces WAVE-27K, a large dataset of unstructured CTI descriptions covering 27 MITRE techniques and 7 tactics. It contains 22,539 single-technique samples and 5,262 multi-technique samples, making it the largest dataset in its category. A BERT-based model trained on WAVE-27K achieved a 97% micro F1-score, demonstrating its quality for machine learning applications in cybersecurity.},

note = {Publisher: Universidad de Sevilla. Escuela Técnica Superior de Ingeniería Informática},

keywords = {BERT, CTI, cyber threat intelligence, machile learning, SOC operations},

pubstate = {published},

tppubtype = {article}

}

@article{janez-martino_comparative_2024,

title = {Comparative Analysis of Natural Language Processing Models for Malware Spam Email Identification},

author = {Francisco Jáñez-Martino and Eduardo Fidalgo and Rocío Alaiz-Rodríguez and Andrés Carofilis-Vasco and Alicia Martínez-Mendoza},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=yATJZvcAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=yATJZvcAAAAJ:z_wVstp3MssC},

year  = {2024},

date = {2024-01-01},

journal = {International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security (NLPAICS 2024)},

abstract = {International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security (NLPAICS 2024)},

keywords = {Artificial Intelligence, Cybersecurity, Natural Language Processing},

pubstate = {published},

tppubtype = {article}

}

@article{diaz_spamclus_2024,

title = {SpamClus: An Agglomerative Clustering Algorithm for Spam Email Campaigns Detection},

author = {Daniel Díaz and Wesam Al-Nabki and Laura Fernández-Robles and Enrique Alegre and Eduardo Fidalgo and Alicia Martínez-Mendoza},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=yATJZvcAAAAJ&cstart=100&pagesize=100&sortby=title&citation_for_view=yATJZvcAAAAJ:t7zJ5fGR-2UC},

year  = {2024},

date = {2024-01-01},

journal = {International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security (NLPAICS 2024)},

abstract = {International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security (NLPAICS 2024)},

keywords = {Agglomerative Clustering, Cybersecurity, Email Classification, machine learning, Spam detection, SpamClus},

pubstate = {published},

tppubtype = {article}

}

@article{delgado_cecilia_2024,

title = {CECILIA: Enhancing CSIRT Effectiveness with Transformer-Based Cyber Incident Classification},

author = {Juan José Delgado and Eduardo Fidalgo and Enrique Alegre and Andrés Carofilis-Vasco and Alicia Martínez-Mendoza},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=yATJZvcAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=yATJZvcAAAAJ:XD-gHx7UXLsC},

year  = {2024},

date = {2024-01-01},

journal = {International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security (NLPAICS 2024)},

abstract = {International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security (NLPAICS 2024)},

keywords = {CECILIA, Cyber Incident Classification, Cybersecurity},

pubstate = {published},

tppubtype = {article}

}

@article{gangwar_deephsar_2024,

title = {DeepHSAR: Semi-supervised fine-grained learning for multi-label human sexual activity recognition},

author = {Abhishek Gangwar and Víctor González-Castro and Enrique Alegre and Eduardo Fidalgo and Alicia Martínez-Mendoza},

url = {https://www.sciencedirect.com/science/article/pii/S0306457324001596},

year  = {2024},

date = {2024-01-01},

journal = {Information Processing & Management},

volume = {61},

number = {5},

pages = {103800},

abstract = {This paper presents DeepHSAR, a deep learning framework for semi-supervised Human Sexual Activity Recognition (HSAR), using the SexualActs-150k dataset with 150k images. It employs two classification streams for global and fine-grained recognition, achieving an F1-score of 79.29%. The method outperforms previous approaches and achieves 99.85% accuracy on the NPDI Pornography-2k dataset.},

note = {Publisher: Pergamon},

keywords = {Fine-grained Classification, Multi-label Classification, pornography detection, Semi-supervised Classification, Sexual Activity Detection},

pubstate = {published},

tppubtype = {article}

}

@article{blanco-medina_clasificacion_2024,

title = {Clasificación de capturas de smishing con aprendizaje profundo e IRIS},

author = {Pablo Blanco-Medina and Andrés Carofilis-Vasco and Eduardo Fidalgo and Enrique Alegre},

url = {https://revistas.udc.gal/index.php/JA_CEA/article/view/10904},

year  = {2024},

date = {2024-01-01},

journal = {Jornadas de Automática},

number = {45},

abstract = {El Smishing es una variante del Phishing que usa mensajes de texto y smartphones para realizar actividades maliciosas. Los equipos de respuesta ante emergencias informáticas pueden beneficiarse de una herramienta para clasificar capturas de pantalla de smishing antes de extraer su contenido. Se compararon Redes Neuronales Convolucionales y Vision Transformers para clasificar estas capturas en dos categorías: texto dividido en líneas y texto unido. El conjunto de datos IRIS-244, con 244 capturas de smishing, se utilizó en el estudio, encontrando que la arquitectura Xception logró el mejor rendimiento con una precisión del 78.36%.},

keywords = {Apoyo a Operadores Humanos, Aprendizaje Profundo, Automatización para la Ayuda Internacional, Redes Sociales, Seguridad},

pubstate = {published},

tppubtype = {article}

}

@article{blanco-medina_preprocesado_2024,

title = {Preprocesado de imagen y OCR para mejorar deteccion de smishing},

author = {Pablo Blanco-Medina and Andrés Carofilis-Vasco and Eduardo Fidalgo and Enrique Alegre},

url = {https://revistas.udc.gal/index.php/JA_CEA/article/view/10955},

year  = {2024},

date = {2024-01-01},

journal = {Jornadas de Automática},

number = {45},

abstract = {La globalización de las tecnologías de comunicación ha facilitado el aumento de las estafas por phishing, en particular a través de SMS fraudulentos conocidos como Smishing, que suplantan a compañías para robar datos o realizar acciones no autorizadas. Este trabajo propone una estrategia para extraer automáticamente URLs de capturas de pantalla de Smishing, combinando técnicas de visión artificial y mecanismos de detección de URL. Evaluado en 117 capturas de pantalla con 121 URLs, se obtuvo una precisión del 61.16% en la extracción de URLs sospechosas.},

keywords = {Apoyo a Operadores Humanos, Aprendizaje Profundo, Redes Sociales, Seguridad, Sistemas de Control y Automatización para la Ayuda Internacional},

pubstate = {published},

tppubtype = {article}

}

@article{blanco-medina_mejoras_2024,

title = {Mejoras en extracción de URLs en smishing mediante text spotting},

author = {Pablo Blanco-Medina and Rubel Biswas and Victor González-Castro and Rocío Alaiz-Rodríguez and Eduardo Fidalgo and Enrique Alegre},

url = {https://revistas.udc.es/index.php/JA_CEA/article/view/10954},

year  = {2024},

date = {2024-01-01},

journal = {Jornadas de Automática},

number = {45},

abstract = {Este trabajo propone un proceso para extraer URLs de Smishing de capturas de pantalla, utilizando técnicas de Text Spotting y una reconstrucción personalizada de URLs. Al aplicar esta metodología en 244 capturas y 262 URLs, se mejora la precisión del reconocimiento de un 3,05% a un 22,90%, optimizando el procesamiento de texto en Smishing.},

keywords = {CERT, OCR, Smishing, text spotting},

pubstate = {published},

tppubtype = {article}

}

@article{swaroop_bennabhaktula_pushpull-net_2024,

title = {PushPull-Net: Inhibition-driven ResNet robust to image corruptions},

author = {Guru Swaroop Bennabhaktula and Enrique Alegre and Nicola Strisciuglio and George Azzopardi},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=opCbArQAAAAJ&cstart=100&pagesize=100&sortby=title&citation_for_view=opCbArQAAAAJ:NXb4pA-qfm4C},

year  = {2024},

date = {2024-01-01},

journal = {International Conference on Pattern Recognition},

pages = {391–408},

abstract = {This paper introduces a new computational unit called PushPull-Conv, applied in the first layer of a ResNet architecture. Inspired by anti-phase inhibition in the visual cortex, this unit uses a pair of complementary filters: a push kernel and a pull kernel. The push kernel learns to respond to specific stimuli, while the pull kernel reacts to opposite contrasts. This design enhances stimulus selectivity and improves robustness by inhibiting responses in regions without preferred stimuli. Integrating PushPull-Conv into ResNets improves their resilience to image corruption.},

note = {Publisher: Springer Nature Switzerland Cham},

keywords = {Convolutional Neural Networks, Image Corruption, ResNet, Visual Cortex Simulation},

pubstate = {published},

tppubtype = {article}

}

@article{castano_familiarity_2024,

title = {Familiarity Analysis and Phishing Website Detection using PhiKitA Dataset [Póster]},

author = {Felipe Castaño and Alicia Martínez-Mendoza and Eduardo Fidalgo and Rocío Alaiz-Rodríguez and Enrique Alegre},

url = {https://idus.us.es/items/04850276-e785-4039-977b-0c43806ac349},

year  = {2024},

date = {2024-01-01},

abstract = {Phishing kits enable attackers to launch phishing campaigns more efficiently. This paper introduces PhiKitA, a dataset of phishing kits and the websites they generate. Three experiments were conducted: familiarity analysis, phishing website detection, and phishing kit classification, using MD5 hashes, fingerprints, and graph-based DOM representation. Results show that phishing website detection achieved 92.50% accuracy, while phishing kit classification proved less effective due to insufficient extracted information.},

note = {Publisher: Universidad de Sevilla. Escuela Técnica Superior de Ingeniería Informática},

keywords = {Cybersecurity, machine learning, PhinKitA Dataset, phishing detection},

pubstate = {published},

tppubtype = {article}

}

@article{janez-martino_spam_2024-1,

title = {Spam hierarchical clustering for campaigns spotting and topic-based classification [Póster]},

author = {Francisco Jáñez-Martino and Andrés Carofilis-Vasco and Rocío Alaiz-Rodríguez and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},

url = {https://idus.us.es/items/9828eae7-9cec-4574-8863-99e9020e1770},

year  = {2024},

date = {2024-01-01},

abstract = {This article develops spam email multiclassification systems for cybersecurity, using two datasets: SPEMC-15K-E (English) and SPEMC-15K-S (Spanish). The datasets are classified into eleven categories. The best results for English (F1-score: 0.953, 94.6% accuracy) were achieved with TF-IDF and Logistic Regression, while for Spanish, TF-IDF and Naïve Bayes achieved an F1-score of 0.945 and 98.5% accuracy. TF-IDF with Logistic Regression also had the fastest processing time (2ms per email for English and 2.2ms for Spanish).},

note = {Publisher: Universidad de Sevilla. Escuela Técnica Superior de Ingeniería Informática},

keywords = {Cybersecurity, Logistic Regression, Multi-classification, Spam detection},

pubstate = {published},

tppubtype = {article}

}

@article{al_nabki_review_2024,

title = {A review of Spotting Child Sexual Exploitation Material using File Names and their Path},

author = {Wesam Al-Nabki and Francisco Jáñez-Martino and Eduardo Fidalgo and Enrique Alegre and Rocío Alaiz-Rodríguez},

url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9633501},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IX Jornadas Nacionales de Investigación En Ciberseguridad},

pages = {502–503},

abstract = {Cybersecurity training is a global priority due to the current shortage of highly skilled professionals. This conference aims to address various training options available, such as courses, workshops, certifications, and undergraduate and postgraduate education programs, with a particular focus on the latter. The discussion will also cover the challenges and future perspectives needed to meet the growing demand for cybersecurity professionals.},

note = {Publisher: Antonia M. Reina Quintero},

keywords = {certifications, Cybersecurity, higher education, training},

pubstate = {published},

tppubtype = {article}

}

@article{janez-martino_spam_2024,

title = {Spam hierarchical clustering for campaigns spotting and topic-based classification},

author = {Francisco Jañez-Martino and Andrés Carofilis-Vasco and Rocío Alaiz-Rodríguez and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},

url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9633481},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IX Jornadas Nacionales de Investigación En Ciberseguridad},

pages = {490–491},

abstract = {IX Jornadas Nacionales de Investigación En Ciberseguridad, 2024},

note = {Publisher: Antonia M. Reina Quintero},

keywords = {Campaigns Spotting, Ciberseguridad, Jornadas Nacionales, Spam Hierarchical Clustering, Topic-Based Classification},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-ordas_tool_2024,

title = {Tool wear monitoring using an online, automatic and low cost system based on local texture},

author = {María Teresa García-Ordás and Enrique Alegre and Rocío Alaiz-Rodríguez and Víctor González-Castro},

url = {https://arxiv.org/abs/2402.05977},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {arXiv preprint arXiv:2402.05977},

abstract = {This work presents a fast and cost-effective method using computer vision and machine learning to assess cutting tool wear in edge profile milling. A new dataset of 577 images was created, including functional and disposable cutting edges. The method divides the edges into regions (Wear Patches) and classifies them using texture descriptors (LBP). A Support Vector Machine (SVM) achieved 90.26% accuracy in detecting worn tools, demonstrating strong potential for automatic wear monitoring in milling.},

keywords = {Computer vision, machine learning, Milling, Tool wear},

pubstate = {published},

tppubtype = {article}

}

@article{nejad_sert_2024,

title = {SERT: A transformer based model for multivariate temporal sensor data with missing values for environmental monitoring},

author = {Amin Shoari Nejad and Rocío Alaiz-Rodríguez and Gerard D McCarthy and Brian Kelleher and Anthony Grey and Andrew Parnell},

url = {https://www.sciencedirect.com/science/article/pii/S0098300424000840},

year  = {2024},

date = {2024-01-01},

journal = {Computers & Geosciences},

volume = {188},

pages = {105601},

abstract = {This research focuses on environmental monitoring and introduces two models for spatio-temporal forecasting that can handle missing values in sensor data without the need for imputation. The first model, SERT (Spatio-temporal Encoder Representations from Transformers), utilizes a transformer-based approach. The second, SST-ANN (Sparse Spatio-Temporal Artificial Neural Network), is a simpler and more interpretable model. Extensive experiments show that these models perform competitively or better than existing state-of-the-art models for multivariate spatio-temporal forecasting.},

note = {Publisher: Pergamon},

keywords = {Environmental Monitoring, Missing Data, Spatio-Temporal Forecasting, Transformer Models},

pubstate = {published},

tppubtype = {article}

}

@article{medina-martinez_herramienta_2023,

title = {UNA HERRAMIENTA HACIA LA DESCARBONIZACIÓN DE LOS PROYECTOS DE INGENIERÍA},

author = {Gabriel Medina and Laura Fernández-Robles and Manuel Castejón-Limas},

url = {http://dspace.aeipro.com/xmlui/handle/123456789/3446},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

abstract = {La industria de la construcción enfrenta el desafío de alcanzar la neutralidad climática, lo cual requiere una planificación coordinada entre los actores involucrados en un proyecto. La sostenibilidad de los proyectos de ingeniería es clave para alcanzar el objetivo 55 de la Unión Europea, que busca reducir las emisiones de CO2 en un 55% para 2030. En este contexto, debido a la falta de alternativas reales a los materiales basados en cemento, es crucial desarrollar estrategias para descarbonizar los proyectos. Los materiales de cemento son responsables de un 30% de las emisiones globales de CO2 durante la vida útil de las infraestructuras, por lo que la búsqueda de cementos con menor huella de carbono podría aumentar la sostenibilidad de los proyectos. Este estudio evalúa la reducción de emisiones de CO2 en un proyecto que utiliza cementos convencionales en comparación con aquellos que incorporan nuevos cementos de bajo carbono.},

keywords = {Descarbonización, Estrategia, Materiales, Proyectos, Sostenibilidad},

pubstate = {published},

tppubtype = {article}

}

@article{joshi_deepsumm_2023,

title = {DeepSumm: Exploiting topic models and sequence to sequence networks for extractive text summarization},

author = {Akanksha Joshi and Eduardo Fidalgo and Enrique Alegre and Laura Fernández-Robles},

url = {https://www.sciencedirect.com/science/article/pii/S0957417422015391},

year  = {2023},

date = {2023-01-01},

journal = {Expert Systems with Applications},

volume = {211},

pages = {118442},

abstract = {This paper introduces DeepSumm, a method for extractive text summarization that combines topic modeling and word embeddings to improve summary quality. DeepSumm uses topic vectors and sequence networks to capture both local and global semantics in a document. It calculates scores for each sentence using Sentence Topic Score (STS), Sentence Content Score (SCS), Sentence Novelty Score (SNS), and Sentence Position Score (SPS), and combines them into a Final Sentence Score (FSS). The method outperforms existing approaches on the DUC 2002 and CNN/DailyMail datasets with improved ROUGE scores.},

note = {Publisher: Pergamon},

keywords = {deep learning, Extractive Summarization, Topic Modeling, Word embedding},

pubstate = {published},

tppubtype = {article}

}

@article{carofilis_mewehv_2023,

title = {MeWEHV: Mel and Wave Embeddings for Human Voice Tasks},

author = {Andrés Carofilis-Vasco and Laura Fernández-Robles and Enrique Alegre and Eduardo Fidalgo},

url = {https://ieeexplore.ieee.org/abstract/document/10198451},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {IEEE Access},

abstract = {This paper introduces MeWEHV, a model that generates robust embeddings for speech processing by combining two types of embeddings: one generated by a pre-trained raw audio waveform encoder and another extracted from Mel Frequency Cepstral Coefficients (MFCCs) using Convolutional Neural Networks (CNNs). We evaluate the performance of MeWEHV on three tasks: speaker identification, language identification, and accent identification. Various datasets were used, including VoxCeleb1 and VBHIR for speaker identification, VoxForge and LRE17 for language identification, and LASC and Common Voice for accent identification. The results show a significant performance increase in state-of-the-art embedding generation models with a low additional computational cost.},

note = {Publisher: IEEE},

keywords = {Accent Recognition, Embeddings, Speaker Identification, Speech Processing},

pubstate = {published},

tppubtype = {article}

}

@article{gangwar_triple-biggan_2023,

title = {Triple-BigGAN: Semi-supervised generative adversarial networks for image synthesis and classification on sexual facial expression recognition},

author = {Abhishek Gangwar and Víctor González-Castro and Enrique Alegre and Eduardo Fidalgo},

url = {https://www.sciencedirect.com/science/article/pii/S0925231223000346},

year  = {2023},

date = {2023-01-01},

journal = {Neurocomputing},

volume = {528},

pages = {200–216},

abstract = {This paper introduces a method for automatically recognizing facial expressions that reflect erotic activity, called Sexual Facial Expression Recognition (SFER). The authors present a new dataset, SEA-Faces-30k, consisting of 30,000 manually labeled images categorized as erotic, suggestive-erotic, or non-erotic. Given the difficulty of obtaining large annotated datasets for training deep learning models, they propose a semi-supervised GAN framework, Triple-BigGAN, which learns both a generative model and a classifier simultaneously using unlabeled or partially labeled data. The Triple-BigGAN framework achieves strong performance on the SFER task and performs well on several benchmark datasets. The study also demonstrates that synthetic erotic face images generated by Triple-BigGAN can enhance the performance of deep learning classifiers.},

note = {Publisher: Elsevier},

keywords = {deep learning, Emotion Detection, Facial Expressions, Not Safe For Work, NSFW, Obscene Image Retrieval, Pornography},

pubstate = {published},

tppubtype = {article}

}

@article{velasco-mata_real-time_2023,

title = {Real-time botnet detection on large network bandwidths using machine learning},

author = {Javier Velasco-Mata and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},

url = {https://www.nature.com/articles/s41598-023-31260-0},

year  = {2023},

date = {2023-01-01},

journal = {Scientific Reports},

volume = {13},

number = {1},

pages = {4282},

abstract = {An ultra-fast approach for botnet detection is developed to process network traffic in one-second windows without significant loss in performance. The model outperforms existing methods, achieving an F1 score of 0.926 with a processing time of 0.007 ms per sample. It remains robust under network saturation and performs well on various bandwidths with minimal CPU core requirements.},

note = {Publisher: Nature Publishing Group UK London},

keywords = {Botnet Detection, F1 score, Network Traffic, Performance Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{al_nabki_supervised_2023,

title = {Supervised ranking approach to identify infLuential websites in the darknet},

author = {Wesam Al-Nabki and Eduardo Fidalgo and Enrique Alegre and Deisy Chaves},

url = {https://link.springer.com/article/10.1007/s10489-023-04671-9},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Applied Intelligence},

volume = {53},

number = {19},

pages = {22952–22968},

abstract = {This paper introduces a supervised ranking framework to identify the most influential domains in the Tor network, focusing on criminal activities. It uses 40 features from various sources to train a learning-to-rank model, achieving an NDCG of 0.93 for top-10 drug-related domains. The framework outperforms link-based methods and demonstrates that user-visible text is key for effective ranking, aiding law enforcement in detecting suspicious Tor domains.},

note = {Publisher: Springer US New York},

keywords = {Criminal Detection, Domain Ranking, law enforcement, Learning-to Rank, TOR Network},

pubstate = {published},

tppubtype = {article}

}

@article{al-nabki_author_2023,

title = {Author Correction: Short text classification approach to identify child sexual exploitation material},

author = {Wesam Al-Nabki and Eduardo Fidalgo and Enrique Alegre and Rocío Alaiz-Rodríguez},

url = {https://www.nature.com/articles/s41598-023-45265-2},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Scientific Reports},

volume = {13},

number = {1},

pages = {17840},

abstract = {This paper presents a method to identify Child Sexual Exploitation Material (CSEM) files by analyzing file names and paths instead of content, aiding law enforcement in time-sensitive investigations. The approach tackles obfuscation using character n-grams, binary, and orthographic features. Two classification strategies are proposed: one combining separate file name and path classifiers, and another iterating over the path. Six machine learning and deep learning models were tested, with the best achieving an F1 score of 0.988, making it a promising tool for law enforcement agencies.},

note = {Publisher: Nature Publishing Group UK London},

keywords = {CSEM detection, law enforcement, machine learning, test classification},

pubstate = {published},

tppubtype = {article}

}

@article{martinez-mendoza_survey_2023,

title = {A survey on spam detection, spammer strategies and the dataset shift problem},

author = {Alicia Martínez-Mendoza and Francisco Jáñez-Martino and Rocío Aláiz-Rodríguez and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},

url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9044942},

year  = {2023},

date = {2023-01-01},

journal = {Actas de las VIII Jornadas Nacionales de Investigación en Ciberseguridad: Vigo, 21 a 23 de junio de 2023},

pages = {485–486},

abstract = {Actas de las VIII Jornadas Nacionales de Investigación en Ciberseguridad: Vigo, 21 a 23 de junio de 2023},

note = {Publisher: Universidade de Vigo},

keywords = {dataset shift, Spam detection, spammer strategies},

pubstate = {published},

tppubtype = {article}

}

@article{alaiz-rodriguez_differences_2023,

title = {Differences in clinical outcomes, health care resource utilization and costs in heart failure patients according to left ventricular ejection fraction},

author = {Rocío Alaiz-Rodríguez and Juan Carlos López-Azor and Juan Francisco Delgado and Jorge Vélez and Jorge Solís and Manuel Del Oro and Carmen Ortega and Rafael Salguero-Bodes and Beatriz Palacios and Lourdes Vicent },

url = {https://www.sciencedirect.com/science/article/pii/S1885585723001639},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Revista Española de Cardiología (English Edition)},

volume = {76},

number = {11},

pages = {862–871},

abstract = {The impact of left ventricular ejection fraction (LVEF) on health care resource utilization (HCRU) and cost in heart failure (HF) patients is not well known. We aimed to compare outcomes, HCRUs and costs according to LVEF groups.},

note = {Publisher: Elsevier Doyma},

keywords = {Cost Analysis, Ejection Fraction, Health Care Resources, Heart Failure, Hospitalization, Mortality},

pubstate = {published},

tppubtype = {article}

}

@article{shoari_nejad_sert_2023,

title = {SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring},

author = {Amin Shoari Nejad and Rocío Alaiz-Rodríguez and Gerard D McCarthy and Brian Kelleher and Anthony Grey and Andrew Parnell},

url = {https://arxiv.org/abs/2306.03042},

year  = {2023},

date = {2023-01-01},

journal = {arXiv e-prints},

pages = {arXiv–2306},

abstract = {This research focuses on environmental monitoring and introduces two models for spatio-temporal forecasting that can handle missing values in sensor data without the need for imputation. The first model, SERT (Spatio-temporal Encoder Representations from Transformers), utilizes a transformer-based approach. The second, SST-ANN (Sparse Spatio-Temporal Artificial Neural Network), is a simpler and more interpretable model. Extensive experiments show that these models perform competitively or better than existing state-of-the-art models for multivariate spatio-temporal forecasting.},

keywords = {Artificial Neural Networks, Missing Data Handling, Spatio-Temporal Forecasting, Trasformer Models},

pubstate = {published},

tppubtype = {article}

}

@article{nejad_sert_2023,

title = {SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring},

author = {Amin Shoari Nejad and Rocío Alaiz-Rodríguez and Gerard D McCarthy and Brian Kelleher and Anthony Grey and Andrew Parnell},

url = {https://ui.adsabs.harvard.edu/abs/2024CG....18805601N/abstract},

year  = {2023},

date = {2023-01-01},

journal = {arXiv preprint arXiv:2306.03042},

abstract = {This work introduces two models for spatio-temporal forecasting that effectively handle missing values in multivariate time series data without imputation. The first model, SERT (Spatio-temporal Encoder Representations from Transformers), is transformer-based, while the second, SST-ANN (Sparse Spatio-Temporal Artificial Neural Network), is a simpler and interpretable approach. Extensive experiments on two datasets show that both models outperform or match state-of-the-art performance in handling missing data for multivariate spatio-temporal forecasting.},

keywords = {Artificial Neural Networks, Missing Data Handling, Spatio-Temporal Forecasting, Trasformer Models},

pubstate = {published},

tppubtype = {article}

}

@article{castano_phikita_2023,

title = {PhiKitA: Phishing Kit Attacks Dataset for Phishing Websites Identification},

author = {Felipe Castaño and Eduardo Fidalgo and Rocío Alaiz-Rodríguez and Enrique Alegre},

url = {https://ieeexplore.ieee.org/abstract/document/10103863},

year  = {2023},

date = {2023-01-01},

journal = {IEEE Access},

volume = {11},

pages = {40779–40789},

abstract = {This paper introduces PhiKitA, a novel dataset containing phishing kits and phishing websites generated from these kits. The dataset is used to investigate phishing kit detection, phishing website identification, and the source of phishing websites. The study applied MD5 hashes, fingerprints, and graph representation DOM algorithms to analyze the dataset. The results show that the graph representation algorithm achieved an accuracy of 92.50% for phishing detection, while MD5 hash representation achieved a 39.54% F1 score, indicating its limited effectiveness in distinguishing phishing sources.},

note = {Publisher: IEEE},

keywords = {Cybersecurity, Dataset, phishing detection},

pubstate = {published},

tppubtype = {article}

}

@article{janez-martino_classifying_2023,

title = {Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach},

author = {Francisco Jáñez-Martino and Rocío Alaiz-Rodríguez and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},

url = {https://www.sciencedirect.com/science/article/pii/S1568494623002442},

year  = {2023},

date = {2023-01-01},

journal = {Applied Soft Computing},

volume = {139},

pages = {110226},

abstract = {This paper introduces two novel datasets, SPEMC-15K-E and SPEMC-15K-S, containing 15K spam emails each in English and Spanish. The emails are categorized into 11 classes using hierarchical clustering. Evaluation of 16 classification pipelines reveals that TF-IDF with Logistic Regression achieves the highest performance for the English dataset (F1 score of 0.953, accuracy of 94.6%), while TF-IDF with Naïve Bayes performs best for Spanish (F1 score of 0.945, accuracy of 98.5%). TF-IDF with LR is also the fastest for both languages.},

note = {Publisher: Elsevier},

keywords = {Hidden text, Image-based spam, Multi-classification, Spam detection, Term frequency, Text classification, Word embedding},

pubstate = {published},

tppubtype = {article}

}

@article{mateo_lipid_2023,

title = {Lipid characteristics of the muscle and perirenal fat in young tudanca bulls fed on different levels of grass silage},

author = {Javier Mateo and Irma Caro and Sergio Soto and Sonia Andrés and Rocío Aláiz-Rodríguez and Emma Serrano},

url = {https://www.mdpi.com/2076-2615/13/2/261},

year  = {2023},

date = {2023-01-01},

journal = {Animals},

volume = {13},

number = {2},

pages = {261},

abstract = {Grass silage is commonly used as a forage source in winter for finishing bull in European regions under oceanic climates, such as the Cantabrian cornice, where the local Tudanca breed is produced. Grass silage, compared with barley straw in the bull’s diet showed an effect on beef quality, i.e., increased saturated fatty acids, n-3 fatty acids, and β-carotene and decreased monounsaturated and polyunsaturated fatty acids. Beef from silage-fed bulls and straw-fed bulls could be differentiated by fatty acid percentages, especially 18:0, t-18:1, and c9-18:1, b-carotene content, b* colour value, and carotenoid colour index.},

note = {Publisher: MDPI},

keywords = {Authentication, Beef, Fat-soluble Vitamins, Fatty Acids, Feeding System, Volatile Compounds},

pubstate = {published},

tppubtype = {article}

}

@article{martinez-mendoza_applying_2023,

title = {Applying Machine Learning to login URLs for phishing detection},

author = {Alicia Martínez-Mendoza and Manuel Sánchez-Paniagua and Andrés Carofilis-Vasco and Francisco Jáñez-Martino and Eduardo Fidalgo and Enrique Alegre},

url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9044941},

year  = {2023},

date = {2023-01-01},

journal = {Actas de las VIII Jornadas Nacionales de Investigación en Ciberseguridad: Vigo, 21 a 23 de junio de 2023},

pages = {487–488},

abstract = {This paper explores the application of machine learning for phishing detection using login URLs. By analyzing URL patterns and features, the study aims to differentiate between legitimate and phishing websites. Various machine learning models are evaluated to enhance detection accuracy, providing a proactive approach to cybersecurity threats.},

note = {Publisher: Universidade de Vigo},

keywords = {AI, Cybersecurity, machine learning, phishing detection, URL analysis},

pubstate = {published},

tppubtype = {article}

}

@article{chaves_automatic_2023,

title = {Automatic classification of pores in aluminum castings using machine learning},

author = {Deisy Chaves and Eduardo Fidalgo and Pablo Rodríguez González and Ana Isabel Fernández-Abia and Enrique Alegre and Joaquín Barreiro},

url = {https://ruc.udc.es/dspace/handle/2183/33692},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {XLIV Jornadas de Automática},

pages = {849–854},

abstract = {This paper proposes automating the classification of porosity defects in aluminum parts manufactured by casting. Images of parts produced by traditional sand molding and the Binder Jetting (BJ) additive technique are analyzed. The method uses SIFT descriptors and BoVW features to train two SVM classifiers: one for detecting pores and another for classifying the type of porosity (gas-related or shrinkage-related). This automated approach improves inspection efficiency and accuracy compared to traditional manual methods.},

note = {Publisher: Universidade da Coruña. Servizo de Publicacións},

keywords = {casting manufacturing, Image classification, porosity detection, SVM classifiers},

pubstate = {published},

tppubtype = {article}

}

@article{carofilis-vasco_improvement_2023,

title = {Improvement of accent classification models through Grad-Transfer from Spectrograms and Gradient-weighted Class Activation Mapping},

author = {Andrés Carofilis-Vasco and Enrique Alegre and Eduardo Fidalgo and Laura Fernández-Robles},

url = {https://ieeexplore.ieee.org/abstract/document/10190103},

year  = {2023},

date = {2023-01-01},

journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},

abstract = {This article introduces a new method for accent classification using a descriptor called Grad-Transfer, which is extracted using Gradient-weighted Class Activation Mapping (Grad-CAM) based on convolutional neural network (CNN) interpretability. The proposed methodology transfers the knowledge gained by CNNs to classical machine learning algorithms. The study shows that Grad-CAM highlights key regions of spectrograms important for accent prediction, and the generated Grad-Transfer descriptors effectively distinguish different accents. Experiments on the Voice Cloning Toolkit dataset demonstrate an improvement in accent classification accuracy and recall when using Grad-Transfer, outperforming models trained directly on spectrograms.},

note = {Publisher: IEEE},

keywords = {Accent Classification, deep learning, Grad-Transfer, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{chaves_data_2023,

title = {A Data Augmentation Strategy for Improving Age Estimation to Support CSEM Detection},

author = {Deisy Chaves and N. Agarwal and Eduardo Fidalgo and Enrique Alegre},

url = {https://www.scitepress.org/PublishedPapers/2023/117197/117197.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},

volume = {5},

number = {ISBN 978-989-758-634-7, ISSN 2184-4321},

pages = {692–699},

abstract = {Leveraging image-based age estimation in preventing Child Sexual Exploitation Material (CSEM) content over the internet is not investigated thoroughly in the research community. While deep learning methods are considered state-of-the-art for general age estimation, they perform poorly in predicting the age group of minors and older adults due to the few examples of these age groups in the existing datasets. In this work, we present a data augmentation strategy to improve the performance of age estimators trained on imbalanced data based on synthetic image generation and artificial facial occlusion. Facial occlusion is focused on modelling as CSEM criminals tend to cover certain parts of the victim, such as the eyes, to hide their identity. The proposed strategy is evaluated using the Soft Stagewise Regression Network (SSR-Net), a compact size age estimator and three publicly available datasets composed mainly of non-occluded images. Therefore, we create the Synthetic Augmented with Occluded Faces (SAOF-15K) dataset to assess the performance of eye and mouthoccluded images. Results show that our strategy improves the performance of the evaluated age estimator.},

note = {Publisher: 10.5220/0011719700003417},

keywords = {age stimation, CSEM, data augmentation, facial occlusion, prevention, synthetic datasets},

pubstate = {published},

tppubtype = {article}

}

@article{martinez-mendoza_novel_2023,

title = {Novel benchmark dataset and features to detect phishing on web pages},

author = {Alicia Martínez-Mendoza and Manuel Sánchez-Paniagua and Francisco Jañez-Martino and Rocío Aláiz-Rodríguez and Eduardo Fidalgo and Enrique Alegre},

url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9057251},

year  = {2023},

date = {2023-01-01},

journal = {Actas de las VIII Jornadas Nacionales de Investigación en Ciberseguridad: Vigo, 21 a 23 de junio de 2023},

pages = {599–600},

abstract = {Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023},

note = {Publisher: Universidade de Vigo},

keywords = {Phishing, web pages},

pubstate = {published},

tppubtype = {article}

}

@article{janez-martino_malware_2023,

title = {Malware detection in spam emails using natural language processing},

author = {Francisco Jáñez-Martino and Luis Ángel Redondo-Gutiérrez and Alicia Martínez-Mendoza and Laura Fernández-Robles and Eduardo Fidalgo and Enrique Alegre},

url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9055756},

year  = {2023},

date = {2023-01-01},

journal = {Actas de las VIII Jornadas Nacionales de Investigación en Ciberseguridad: Vigo, 21 a 23 de junio de 2023},

pages = {529–530},

abstract = {Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023},

note = {Publisher: Universidade de Vigo},

keywords = {Actas, Ciberseguridad, Investigación, Jornadas Nacionales},

pubstate = {published},

tppubtype = {article}

}

@article{diaz-ocampo_authorship_2023,

title = {Authorship identification in text documents using BERT and POS features},

author = {Daniel Díaz and Laura Fernández-Robles and Wesam Al-Nabki and Alicia Martínez-Mendoza and Eduardo Fidalgo and Enrique Alegre and Andrés Carofilis-Vasco},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=yATJZvcAAAAJ&sortby=title&citation_for_view=yATJZvcAAAAJ:yB1At4FlUx8C},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {5th International Conference on Applications of Intelligent Systems (Las Palmas de Gran Canaria, España)},

abstract = {This paper enhances authorship identification by combining BERT embeddings with POS features, improving classification accuracy.},

keywords = {authorship identification, BERT, POS features, Text classification},

pubstate = {published},

tppubtype = {article}

}

@article{carofilis-vasco_impact_2023,

title = {Impact of facial occlusions in age estimation algorithms for forensic applications},

author = {Andrés Carofilis-Vasco and Deisy Chaves and Alicia Martínez-Mendoza and Eduardo Fidalgo and Víctor González-Castro and Enrique Alegre},

url = {https://buleria.unileon.es/handle/10612/20673},

year  = {2023},

date = {2023-01-01},

journal = {Actas de las VIII Jornadas Nacionales de Investigación en Ciberseguridad: Vigo, 21 a 23 de junio de 2023},

pages = {497–498},

abstract = {This study examines age estimation in Child Sexual Exploitation Materials (CSEM) using deep learning. It evaluates six age estimators on facial images with and without occlusion, focusing on how facial occlusion impacts accuracy. Results show that eye occlusion significantly affects age prediction, especially for minors and older adults. The study offers insights for age estimation in forensic applications related to CSEM.},

note = {Publisher: Universidade de Vigo},

keywords = {Age Estimation, CSEM, deep learning, facial occlusion},

pubstate = {published},

tppubtype = {article}

}

@article{porto-alvarez_ct-based_2023,

title = {CT-based radiomics to predict KRAS mutation in CRC patients using a machine learning algorithm: a retrospective study},

author = {Jacobo Porto-Álvarez and Eva Cernadas and Rebeca Aldaz-Martínez and Manuel Fernández-Delgado and Emilio Huelga and Víctor González-Castro and Sandra Baleato-González and Roberto García-Figueiras and José Ramón Antúnez-López and Miguel Souto-Bayarri},

url = {https://www.mdpi.com/2227-9059/11/8/2144},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Biomedicines},

volume = {11},

number = {8},

pages = {2144},

abstract = {This study examines the use of CT-based radiomics to predict KRAS mutations in colorectal cancer (CRC) patients. Several classifiers were tested, with AdaBoost on clinical data achieving the highest accuracy (76.8%). Texture descriptors also showed a correlation with KRAS mutations. Radiomics could reduce the need for invasive diagnostic methods for CRC in the future.},

note = {Publisher: MDPI},

keywords = {colorectal cancer, KRAS Mutation, Radiogenomics, Radiomics, texture analysis},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_smishing_2023,

title = {Smishing Detection: Recognition of URLs from SMS Screenshots},

author = {Enrique Alegre and Eduardo Fidalgo and Laura Fernandez-Robles and Alicia Martínez-Mendoza and Rubel Biswas},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=yATJZvcAAAAJ&cstart=100&pagesize=100&sortby=title&citation_for_view=yATJZvcAAAAJ:0KyAp5RtaNEC},

year  = {2023},

date = {2023-01-01},

journal = {5th International Conference on Applications of Intelligent Systems (Las Palmas de Gran Canaria, España)},

abstract = {5th International Conference on Applications of Intelligent Systems (Las Palmas de Gran Canaria, España)},

keywords = {Intelligent Systems, Recognition of URLs, Smishing Detection, SMS Screenshots},

pubstate = {published},

tppubtype = {article}

}

@article{janez-martino_review_2023,

title = {A review of spam email detection: analysis of spammer strategies and the dataset shift problem},

author = {Francisco Jáñez-Martino and Rocío Alaiz-Rodríguez and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},

url = {https://link.springer.com/article/10.1007/s10462-022-10195-4},

year  = {2023},

date = {2023-01-01},

journal = {Artificial Intelligence Review},

volume = {56},

number = {2},

pages = {1145–1173},

abstract = {Spam emails, which once were mainly an annoyance, now increasingly contain scams, malware, and phishing attempts. Despite high-performing spam filters based on machine learning, users continue to report rising incidents of fraud and attacks via spam. This paper highlights two key challenges in spam email detection: the dynamic nature of the environment, leading to dataset shift, and the presence of adversarial actors (spammers). The review focuses on the impact of these challenges and examines various spammer strategies and state-of-the-art techniques for developing robust filters. Experimental results show that ignoring dataset shift can severely degrade the performance of spam filters, leading to high error rates.},

note = {Publisher: Springer Netherlands Dordrecht},

keywords = {Cybersecurity, dataset shift, machine learning, Spam detection},

pubstate = {published},

tppubtype = {article}

}