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{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-Gutiérrez and Rocío Alaíz-Rodríguez and Víctor González-Castro},

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

year  = {2024},

date = {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{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{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 Aláiz 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{al-nabki_classifying_2024,

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

author = {Mhd 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},

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_applying_2023,

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

author = {Alicia Martínez Mendoza and Manuel Sánchez Paniagua and Roberto Andrés Carofilis Vasco and Francisco Jañez Martino and Eduardo Fidalgo Fernández 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{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}

}

@article{al-nabki_author_2023,

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

author = {MHD Wesam Al-Nabki and Eduardo Fidalgo and Enrique Alegre and Rocio Alaiz-Rodriguez},

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

year  = {2023},

date = {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{sanchez-paniagua_phishing_2022-1,

title = {Phishing URL detection: A real-case scenario through login URLs},

author = {Manuel Sánchez-Paniagua and Eduardo Fidalgo and Enrique Alegre and Wesam Al-Nabki and Victor Gonzalez-Castro},

url = {https://ieeexplore.ieee.org/abstract/document/9759382},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Access},

volume = {10},

pages = {42949–42960},

abstract = {This paper compares machine learning and deep learning techniques to detect phishing websites through URL analysis. Unlike most current methods, which use only homepages, this study includes URLs from login pages for both legitimate and phishing websites, providing a more realistic scenario. It also demonstrates that existing techniques have high false-positive rates when tested on URLs from legitimate login pages. The authors create a new dataset, Phishing Index Login URL (PILU-90K), and show how older models decrease in accuracy over time. A Logistic Regression model with TF-IDF feature extraction achieves 96.50% accuracy on the login URL dataset.},

note = {Publisher: IEEE},

keywords = {Dataset Creation, machine learning, phishing detection, URL analysis},

pubstate = {published},

tppubtype = {article}

}

@article{fidalgo_state_2021,

title = {State of the Art: Content-based and Hybrid Phishing},

author = {Luis Felipe Castaño Eduardo Fidalgo and Enrique Alegre and Daisy Chaves and Manuel Sánchez-Paniagua},

year  = {2021},

date = {2021-01-01},

abstract = {Phishing attacks have evolved and increased over time and, for this reason, the task of distinguishing between a legitimate site and a phishing site is more and more difficult, fooling even the most expert users. The main proposals focused on addressing this problem can be divided into four approaches: List-based, URL based, content-based, and hybrid. In this state of the art, the most recent techniques using web content-based and hybrid approaches for Phishing Detection are reviewed and compared.},

keywords = {Content-based Features, Cybersecurity, deep learning, Hybrid Features, Hybrid Phishing, machine learning, phishing detection},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_ct_2020,

title = {CT radiomics in colorectal cancer: Detection of KRAS mutation using texture analysis and machine learning},

author = {Víctor González-Castro and Eva Cernadas and Emilio Huelga and Manuel Fernández-Delgado and Jacobo Porto and José Ramón Antunez and Miguel Souto-Bayarri},

url = {https://www.mdpi.com/2076-3417/10/18/6214},

year  = {2020},

date = {2020-01-01},

journal = {Applied Sciences},

volume = {10},

number = {18},

pages = {6214},

abstract = {This study analyzes CT image textures to classify colorectal cancer patients as KRAS+ or KRAS- using machine learning classifiers (SVM, GBM, NNET, RF). Texture analysis quantifies tumor heterogeneity, supporting the use of radiomics to predict KRAS mutations. A retrospective study with 47 patients achieved the highest accuracy (83%) and kappa (64.7%) using NNET with wavelet transform and Haralick features. This non-invasive approach could eliminate the need for biopsies, reducing risks and enabling more personalized treatment.},

note = {Publisher: MDPI},

keywords = {colorectal cancer, KRAS Mutation, machine learning, Non-invasive Diagnosis, Radiomics},

pubstate = {published},

tppubtype = {article}

}

@article{janez-martino_classification_2020,

title = {Classification of spam emails through hierarchical clustering and supervised learning},

author = {Francisco Jánez-Martino and Eduardo Fidalgo and Santiago González-Martínez and Javier Velasco-Mata},

url = {https://arxiv.org/abs/2005.08773},

year  = {2020},

date = {2020-01-01},

journal = {arXiv preprint arXiv:2005.08773},

abstract = {This work introduces SPEMC-11K, the first multi-class spam email dataset, categorizing spam into Health and Technology, Personal Scams, and Sexual Content. Using TF-IDF and BOW with Naïve Bayes, Decision Trees, and SVM, the best accuracy (95.39% F1-score) is achieved with TF-IDF and SVM, while TF-IDF and NB offer the fastest classification (2.13ms per email).},

keywords = {Cybersecurity, machine learning, Spam Classification, Text Processing, TF-IDF & BOW},

pubstate = {published},

tppubtype = {article}

}

@article{chaves_automated_2020,

title = {Automated inspection of char morphologies in colombian coals using image analysis},

author = {Deisy Chaves and Maria Trujillo and Edward Garcia and Juan Barraza and Edward Lester and Maribel Barajas and Billy Rodriguez and Manuel Romero and Laura Fernández Robles},

url = {https://d1wqtxts1xzle7.cloudfront.net/84400929/pdf-libre.pdf?1650296199=&response-content-disposition=inline%3B+filename%3DAutomated_Inspection_of_Char_Morphologie.pdf&Expires=1739453001&Signature=WRmkYL0vVspGUAWK2T6VzJ7LlDmM3124W~OVgR50dihJQFxgHvpXDGOjN8HhZa3-1MVnKi0FAOlZOlYD3Uv49praFJTy-WokdxMEcJ6DLPl7hJosZQahVgkjY-mVWHZJ~tq6FxhHV471iEpDts1G8MhynylHeFPJRbpmDRxSsNujdBUhj6j9s1a97oZGsQV8gpI8fJegGdr3sysuw46eWo8vF5wlVv7sSz40QP53B0hzipH9k-JTds2WE59sWOu2NxhORsVBYTRfAeXE2XJHLG3F0W44aKpsVc3c3MI4nkDlWqFMbMarR4VIQxm5q1S9LD8qMzzx7F4MVGjYTF4YcA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},

year  = {2020},

date = {2020-01-01},

journal = {Intelligent Automation & Soft Computing},

volume = {26},

number = {3},

pages = {397–405},

abstract = {This paper proposes machine learning algorithms to automate char morphology classification during coal combustion, improving industrial control efficiency. The approach outperforms the traditional ICCP method by evaluating various morphological features, including the unfused material feature. Results confirm the model’s accuracy in identifying and classifying char particles automatically.},

keywords = {automated classification, char morphology, coal combustion, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{velasco-mata_botnet_2019,

title = {Botnet detection on TCP traffic using supervised machine learning},

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

url = {https://link.springer.com/chapter/10.1007/978-3-030-29859-3_38},

year  = {2019},

date = {2019-01-01},

journal = {Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, León, Spain, September 4–6, 2019, Proceedings 14},

pages = {444–455},

abstract = {The rise of botnets on the Internet requires detecting their activity. Two datasets (TCP-Int and TCP-Sink) were created to evaluate traffic classifiers. Four Machine Learning models were tested, with Decision Tree achieving the best performance: 0.99 F1 score on TCP-Int and 0.99 AUC score on TCP-Sink.},

note = {Publisher: Springer International Publishing},

keywords = {Botnets, Classifiers, Cybersecurity, Datasets, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{dominguez_application_2019,

title = {Application of extractive text summarization algorithms to speech-to-text media},

author = {Víctor Domínguez and Eduardo Fidalgo and Rubel Biswas and Enrique Alegre and Laura Fernández Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-030-29859-3_46},

year  = {2019},

date = {2019-01-01},

journal = {Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, León, Spain, September 4–6, 2019, Proceedings 14},

pages = {540–550},

abstract = {This paper evaluates six extractive text summarization algorithms for speech-to-text summarization. The study assesses Luhn, TextRank, LexRank, LSA, SumBasic, and KLSum using ROUGE metrics on two datasets (DUC2001 and OWIDSum). Additionally, five speech documents from the ISCI Corpus were transcribed using Google Cloud Speech API and summarized. Results indicate that Luhn and TextRank perform best for extractive speech-to-text summarization.},

note = {Publisher: Springer International Publishing},

keywords = {AI, machine learning, natural languaje processing, speech to text, Text summarization},

pubstate = {published},

tppubtype = {article}

}

@article{cueto_lopez_assessing_2018,

title = {Assessing feature selection techniques for a colorectal cancer prediction model},

author = {Nahúm Cueto López and Rocío Alaiz Rodríguez and María Teresa García Ordás and Carmen González Donquiles and Vicente Martín},

url = {https://link.springer.com/chapter/10.1007/978-3-319-67180-2_46},

year  = {2018},

date = {2018-01-01},

journal = {International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding 12},

pages = {471–481},

abstract = {Risk prediction models for colorectal cancer help identify high-risk individuals and key risk factors. This study evaluates feature ranking algorithms in terms of stability and performance. Results show that Random Forest (RF) is the most stable but not the best-performing model, while SVM-wrapper and Pearson correlation achieve a balance between stability and predictive accuracy.},

note = {Publisher: Springer International Publishing},

keywords = {colorectal cancer, feature selection, healthcare analytics, machine learning, risk prediction},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_reliability_2017,

title = {Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance},

author = {Victor Gonzalez-Castro and María del C Valdés-Hernández and Francesca M Chappell and Paul A Armitage and Stephen Makin and Joanna M Wardlaw},

url = {https://portlandpress.com/clinsci/article/131/13/1465/71656/Reliability-of-an-automatic-classifier-for-brain},

year  = {2017},

date = {2017-01-01},

journal = {Clinical Science},

volume = {131},

number = {13},

pages = {1465–1481},

abstract = {Enlarged perivascular spaces (PVS) in the brain are associated with small vessel disease, poor cognition, and hypertension. This study proposes a fully automated method using a support vector machine (SVM) to classify PVS burden in the basal ganglia (BG) as low or high from T2-weighted MRI images. Three feature extraction techniques were evaluated, with the bag of visual words (BoW) approach achieving the highest accuracy (81.16%). The classifier's performance was comparable to that of trained human observers, and its predictions were clinically meaningful, as indicated by high AUC values (0.90–0.93). These findings suggest that automated PVS burden assessment could serve as a valuable clinical tool.},

note = {Publisher: Portland Press Ltd.},

keywords = {Bag of Visual Words, brain MRI, Discrete Wavelet Transform, Local Binary Patterns, machine learning, perivascular spaces, small vessel disease, support vector machine},

pubstate = {published},

tppubtype = {article}

}

@article{garcia_ordas_computer_2017,

title = {A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques},

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

url = {https://link.springer.com/article/10.1007/s00170-016-9541-0},

year  = {2017},

date = {2017-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {90},

pages = {1947–1961},

abstract = {In this paper, we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORCHIZ, a novel shape-based descriptor computed from the wear region image. A new Insert dataset with 212 images of tool wear has been created to evaluate our approach. It contains two subsets: one with images of the main cutting edge and the other one with the edges that converge to it (called Insert-C and Insert-I, respectively). The experiments were conducted trying to discriminate between two (low-high) and three (low-medium-high) different wear levels, and the classification stage was carried out using a support vector machine (SVM). Results show that B-ORCHIZ outperforms other shape descriptors (aZIBO and ZMEG) achieving accuracy values between 80.24 and 88.46 % in the different scenarios evaluated. Moreover, a hierarchical cluster analysis was performed, offering prototype images for wear levels, which may help researchers and technicians to understand how the wear process evolves. These results show a very promising opportunity for wear monitoring automation in edge profile milling processes.},

note = {Publisher: Springer London},

keywords = {Computer vision, cutting tool wear, machine learning, shape descriptors, wear monitoring automation},

pubstate = {published},

tppubtype = {article}

}

@article{al_nabki_classifying_2017,

title = {Classifying illegal activities on tor network based on web textual contents},

author = {Mhd Wesam Al Nabki and Eduardo Fidalgo and Enrique Alegre and Ivan De Paz},

url = {https://aclanthology.org/E17-1004/},

year  = {2017},

date = {2017-01-01},

journal = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},

pages = {35–43},

abstract = {This paper introduces DUTA, a publicly available dataset of Darknet domains labeled into 26 classes. Using DUTA, a classification study was conducted with TF-IDF and supervised classifiers. Logistic Regression with TF-IDF achieved 96.6% accuracy and a 93.7% F1-score in detecting illegal activities, aiding potential law enforcement tools.},

keywords = {Cybersecurity, Darknet Analysis, Logistic Regression, machine learning, Text classification, TF-IDF},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-ordas_computer_2017,

title = {A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques},

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

url = {https://link.springer.com/article/10.1007/s00170-016-9541-0},

year  = {2017},

date = {2017-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {90},

pages = {1947–1961},

abstract = {In this paper, we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORCHIZ, a novel shape-based descriptor computed from the wear region image. A new Insert dataset with 212 images of tool wear has been created to evaluate our approach. It contains two subsets: one with images of the main cutting edge and the other one with the edges that converge to it (called Insert-C and Insert-I, respectively). The experiments were conducted trying to discriminate between two (low-high) and three (low-medium-high) different wear levels, and the classification stage was carried out using a support vector machine (SVM). Results show that B-ORCHIZ outperforms other shape descriptors (aZIBO and ZMEG) achieving accuracy values between 80.24 and 88.46 % in the different scenarios evaluated. Moreover, a hierarchical cluster analysis was performed, offering prototype images for wear levels, which may help researchers and technicians to understand how the wear process evolves. These results show a very promising opportunity for wear monitoring automation in edge profile milling processes.},

note = {Publisher: Springer London},

keywords = {Computer vision, cutting tool wear, machine learning, shape descriptors, wear monitoring automation},

pubstate = {published},

tppubtype = {article}

}

@article{fernandez_robles_object_2016,

title = {Object recognition techniques in real applications},

author = {Laura Fernández Robles},

url = {https://research.rug.nl/en/publications/object-recognition-techniques-in-real-applications},

year  = {2016},

date = {2016-01-01},

abstract = {This doctoral thesis presents object description and retrieval techniques applied to three different fields: boar spermatozoa classification based on acrosome integrity, tool wear monitoring in machining processes, and specific object detection in images to combat child sexual exploitation. The research develops new methods and descriptors, highlighting the creation of the colour COSFIRE filter, which enhances color description and object discrimination while maintaining background invariance.},

keywords = {Computer vision, image processing, machine learning, object recognition},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_automatic_2016,

title = {Automatic rating of perivascular spaces in brain MRI using bag of visual words},

author = {Víctor González-Castro and María del C Valdés Hernández and Paul A Armitage and Joanna M Wardlaw},

url = {https://link.springer.com/chapter/10.1007/978-3-319-41501-7_72},

year  = {2016},

date = {2016-01-01},

journal = {Image Analysis and Recognition: 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016, Proceedings 13},

pages = {642–649},

abstract = {This paper presents a fully automatic method for assessing perivascular space (PVS) burden in the basal ganglia using structural MRI. A Support Vector Machine classifier, combined with a Bag of Visual Words (BoW) model, describes the region using two local descriptor approaches: SIFT and textons. The method achieves an accuracy of 82.34% with SIFT and 79.61% with textons, aiding in the study of neurological conditions linked to enlarged PVS.},

note = {Publisher: Springer International Publishing},

keywords = {machine learning, MRI, neurological disorders, perivascular spaces},

pubstate = {published},

tppubtype = {article}

}

@article{mazo_automatic_2016,

title = {Automatic recognition of fundamental tissues on histology images of the human cardiovascular system},

author = {Claudia Mazo and Maria Trujillo and Enrique Alegre and Liliana Salazar},

url = {https://www.sciencedirect.com/science/article/pii/S0968432816301573},

year  = {2016},

date = {2016-01-01},

journal = {Micron},

volume = {89},

pages = {1–8},

abstract = {This paper presents an automatic method for classifying fundamental tissues in histological images using k-means clustering. It identifies epithelial, connective, and muscle tissues with high sensitivity (0.79–0.91) and expert validation (4.82–4.85/5), supporting medical diagnosis and education.},

note = {Publisher: Pergamon},

keywords = {histology, machine learning, medical imaging, tissue classification},

pubstate = {published},

tppubtype = {article}

}

@article{garcia_ordas_azibo_2015,

title = {aZIBO shape descriptor for monitoring tool wear in milling},

author = {María Teresa García Ordás and Enrique Alegre and Víctor González Castro and O García Olalla and Joaquín Barreiro and Ana Isabel Fernández Abia},

url = {https://www.sciencedirect.com/science/article/pii/S1877705815044951},

year  = {2015},

date = {2015-01-01},

journal = {Procedia Engineering},

volume = {132},

pages = {958–965},

abstract = {This paper proposes an automated method for estimating insert wear in metal machining to optimize tool replacement. The aZIBO shape descriptor (absolute Zernike moments with Invariant Boundary Orientation) is used for wear characterization. A dataset of 577 wear regions was classified into two (Low-High) and three (Low-Medium-High) classes using kNN and SVM classifiers. aZIBO outperformed traditional shape descriptors, achieving success rates of 91.33% for two-class and 90.12% for three-class classification.},

note = {Publisher: No longer published by Elsevier},

keywords = {image processing, machine learning, metal machining, shape descriptors, tool wear detection},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez_castro_automatic_2015,

title = {Automatic classification of skin lesions using color mathematical morphology-based texture descriptors},

author = {Victor Gonzalez Castro and Johan Debayle and Yanal Wazaefi and Mehdi Rahim and Caroline Gaudy Marqueste and Jean-Jacques Grob and Bernard Fertil},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9534/953409/Automatic-classification-of-skin-lesions-using-color-mathematical-morphology-based/10.1117/12.2182592.short},

year  = {2015},

date = {2015-01-01},

journal = {Twelfth International Conference on Quality Control by Artificial Vision 2015},

volume = {9534},

pages = {53–59},

abstract = {This paper presents an automatic method for classifying skin lesions in dermoscopic images using color texture analysis. It combines mathematical morphology for local pixel descriptors with Kohonen Self-Organizing Maps (SOM) for clustering and global texture description, eliminating the need for segmentation. Two approaches—classical and adaptive morphology—achieve similar AUC scores (0.854 and 0.859), surpassing dermatologist predictions (0.792).},

note = {Publisher: SPIE},

keywords = {dermoscopic imaging, machine learning, skin lesion classification, texture analysis},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez_castro_adaptive_2014,

title = {Adaptive texture description and estimation of the class prior probabilities for seminal quality control},

author = {Víctor González Castro},

url = {https://www.raco.cat/index.php/ELCVIA/article/view/281622},

year  = {2014},

date = {2014-01-01},

journal = {ELCVIA: electronic letters on computer vision and image analysis},

volume = {13},

number = {2},

pages = {19–21},

abstract = {Semen quality assessment is essential in artificial insemination for both humans and animals. In livestock farming, high-quality semen samples are crucial for successful fertilization, requiring strict quality control. Currently, sperm vitality and acrosome integrity are assessed manually, which is costly and prone to human errors. This research proposes an automated system based on image processing and machine learning to estimate the proportion of dead spermatozoa and damaged acrosomes using an affordable phase contrast microscope. New intelligent segmentation techniques and adaptive texture description methods have been developed and evaluated to improve automatic boar semen quality estimation.},

keywords = {artificial insemination, image processing, machine learning, semen quality},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-ordas_automatic_2013,

title = {Automatic tampering detection in spliced images with different compression levels},

author = {Diego García-Ordás and Laura Fernández-Robles and Enrique Alegre and María Teresa García-Ordás and Oscar García-Olalla},

url = {https://link.springer.com/chapter/10.1007/978-3-642-38628-2_49},

year  = {2013},

date = {2013-01-01},

journal = {Pattern Recognition and Image Analysis: 6th Iberian Conference, IbPRIA 2013, Funchal, Madeira, Portugal, June 5-7, 2013. Proceedings 6},

pages = {416–423},

abstract = {This paper presents a blind tampering detection method using JPEG ghosts to identify spliced regions with different compression levels. A Support Vector Machine classifier is trained on recompressed images. The method achieved over 97% accuracy on the Columbia dataset and 98.71% on the CASIA1 dataset, outperforming previous techniques, especially for small tampered regions.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {image forensics, JPEG ghosts, machine learning, tampering detection},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_assessment_2013,

title = {Assessment of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ},

author = {Enrique Alegre and Michael Biehl and Nicolai Petkov and Lidia Sanchez},

url = {https://www.sciencedirect.com/science/article/pii/S0169260713001478},

year  = {2013},

date = {2013-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {111},

number = {3},

pages = {525–536},

abstract = {This paper presents a digital image processing method to assess the acrosome state of boar spermatozoa heads. Using grayscale images labeled with fluorescent data, the sperm heads are segmented, and multiple inner contours are generated using a logarithmic distance function. Local texture features are computed for these contours, and classification performance is evaluated using Relevance Learning Vector Quantization, class conditional means, and KNN with cross-validation. The best results are achieved with gradient magnitude data, yielding a test error of only 1%, outperforming previous methods and demonstrating the potential for automated veterinary applications.},

note = {Publisher: Elsevier},

keywords = {digital image processing, machine learning, Sperm Analysis, veterinary science},

pubstate = {published},

tppubtype = {article}

}

@article{moreno_torres_unifying_2012,

title = {A unifying view on dataset shift in classification},

author = {Jose G Moreno Torres and Troy Raeder and Rocío Alaiz Rodríguez and Nitesh V Chawla and Francisco Herrera},

url = {https://www.sciencedirect.com/science/article/pii/S0031320311002901},

year  = {2012},

date = {2012-01-01},

journal = {Pattern recognition},

volume = {45},

number = {1},

pages = {521–530},

abstract = {The field of dataset shift has received a growing amount of interest in the last few years. The fact that most real-world applications have to cope with some form of shift makes its study highly relevant. The literature on the topic is mostly scattered, and different authors use different names to refer to the same concepts, or use the same name for different concepts. With this work, we attempt to present a unifying framework through the review and comparison of some of the most important works in the literature.},

note = {Publisher: Pergamon},

keywords = {data distribution, data science, dataset shift, machine learning, research framework},

pubstate = {published},

tppubtype = {article}

}

@article{morala_arguello_application_2009,

title = {Application of textural descriptors for the evaluation of surface roughness class in the machining of metals},

author = {Patricia Morala Argüello and Joaquín Barreiro García and Enrique Alegre and Víctor González Castro and others},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=opCbArQAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=opCbArQAAAAJ:UebtZRa9Y70C},

year  = {2009},

date = {2009-01-01},

abstract = {Surface roughness measurement has been a key topic in metal machining research for decades. Traditional methods rely on tactile devices providing 2D profiles, but advances in computer vision now enable 3D surface characterization. This paper proposes a computer vision-based method to evaluate machined part quality using five feature vectors: Hu, Flusser, Taubin, Zernike, and Legendre moments. Images were classified into low and high roughness using k-NN and neural networks. Results show that Zernike and Legendre descriptors perform best, achieving a 6.5% error rate with k-NN classification.},

keywords = {Computer vision, machine learning, machining, quality control, surface roughness},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_automatic_2008,

title = {Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ},

author = {Enrique Alegre and Michael Biehl and Nicolai Petkov and Lidia Sánchez},

url = {https://www.sciencedirect.com/science/article/pii/S0010482508000103},

year  = {2008},

date = {2008-01-01},

journal = {Computers in Biology and Medicine},

volume = {38},

number = {4},

pages = {461–468},

abstract = {This paper presents an automatic method for classifying boar sperm cells as acrosome-intact or acrosome-damaged using phase-contrast microscopy images. Sperm heads are segmented, and feature vectors based on gradient magnitude along the contour are extracted. Learning Vector Quantization (LVQ) is applied to classify 320 labeled sperm heads, achieving a 6.8% test error, which is sufficient for semen quality control in artificial insemination.},

note = {Publisher: Pergamon},

keywords = {artificial insemination, machine learning, phase-contrast microscopy, sperm classification},

pubstate = {published},

tppubtype = {article}

}

@article{alaiz-rodriguez_demo_2008,

title = {Demo Papers-A Visualization-Based Exploratory Technique for Classifier Comparison with Respect to Multiple Metrics and Multiple Domains},

author = {Rocio Alaiz-Rodriguez and Nathalie Japkowicz and Peter Tischer},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=2gj1UNYAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=2gj1UNYAAAAJ:3fE2CSJIrl8C},

year  = {2008},

date = {2008-01-01},

journal = {Lecture Notes in Computer Science},

volume = {5212},

pages = {660},

abstract = {Demo Papers-A Visualization-Based Exploratory Technique for Classifier Comparison with Respect to Multiple Metrics and Multiple Domains},

keywords = {Classifier Comparison, machine learning, Papers-A, Visualization-Based},

pubstate = {published},

tppubtype = {article}

}

@article{alaiz_rodriguez_visualization-based_2008,

title = {A visualization-based exploratory technique for classifier comparison with respect to multiple metrics and multiple domains},

author = {Rocío Alaiz Rodríguez and Nathalie Japkowicz and Peter Tischer},

url = {https://link.springer.com/chapter/10.1007/978-3-540-87481-2_43},

year  = {2008},

date = {2008-01-01},

journal = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},

pages = {660–665},

abstract = {Classifier performance evaluation typically gives rise to a multitude of results that are difficult to interpret. On the one hand, a variety of different performance metrics can be applied, each adding a little bit more information about the classifiers than the others; and on the other hand, evaluation must be conducted on multiple domains to get a clear view of the classifier’s general behaviour.},

note = {Publisher: Springer Berlin Heidelberg Berlin, Heidelberg},

keywords = {classifier evaluation, machine learning, model testing, performance metrics},

pubstate = {published},

tppubtype = {article}

}

@article{alaiz_rodriguez_assessing_2008,

title = {Assessing the impact of changing environments on classifier performance},

author = {Rocío Alaiz Rodríguez and Nathalie Japkowicz},

url = {https://link.springer.com/chapter/10.1007/978-3-540-68825-9_2},

year  = {2008},

date = {2008-01-01},

journal = {Advances in Artificial Intelligence: 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008 Windsor, Canada, May 28-30, 2008 Proceedings 21},

pages = {13–24},

abstract = {This paper tests the hypothesis that simple classifiers are more robust to environmental changes than complex ones. The authors develop a strategy to generate artificial but realistic domains, allowing controlled testing of various scenarios. Their results show that evaluating classifiers in changing environments is challenging, as shifts can make a domain either simpler or more complex. They introduce a metric to address this issue and use it to assess classifier performance. The findings indicate that simple classifiers degrade more in mild population drifts, while in severe cases, all classifiers suffer equally. Ultimately, complex classifiers remain more accurate, refuting the initial hypothesis.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {changing environments, classifier robustness, machine learning, performance evaluation},

pubstate = {published},

tppubtype = {article}

}

@article{sanchez_classification_2005,

title = {Classification of boar spermatozoid head images using a model intracellular density distribution},

author = {Lidia Sánchez and Nicolai Petkov and Enrique Alegre},

url = {https://link.springer.com/chapter/10.1007/11578079_17},

year  = {2005},

date = {2005-01-01},

journal = {Progress in Pattern Recognition, Image Analysis and Applications: 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, November 15-18, 2005. Proceedings 10},

pages = {154–160},

abstract = {A novel method is proposed to classify boar spermatozoid heads based on intracellular intensity distribution. A model distribution is created from normal samples, and deviations are used for classification. The decision criterion minimizes classification errors, achieving a global error of 20.40%.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {Biomedical Imaging, Image classification, machine learning, Pattern Recognition, Sperm Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{marcos_provecho_autoguiado_2004,

title = {Autoguiado de robots móviles mediante redes neuronales},

author = {Mª Concepción Marcos Provecho and Roberto Guzmán Martínez and Rocío Alaiz Rodríguez},

year  = {2004},

date = {2004-01-01},

journal = {XXV Jornadas de Automática: Ciudad Real, 8, 9, y 10 de septiembre de 2004},

pages = {56},

abstract = {Este trabajo implementa una estrategia de autoguiado para un robot móvil en entornos desconocidos mediante una red neuronal. Se desarrolló un entorno en Matlab para la generación de trayectorias, el entrenamiento y la simulación. Las pruebas en el microbot PICBOT3 demostraron la capacidad de la red para navegar sin colisionar con obstáculos.},

note = {Publisher: JA Somolinos},

keywords = {autonomous navigation, machine learning, mobile robotics, neural networks, redes neuronales, robótica móvil},

pubstate = {published},

tppubtype = {article}

}