Publications

@article{riego_del_castillo_estimation_2022,

title = {Estimation of lamb weight using transfer learning and regression},

author = {Virginia Riego del Castillo and Lidia Sánchez-González and Laura Fernández-Robles and Manuel Castejón-Limas and Rubén Rebollar},

url = {https://link.springer.com/chapter/10.1007/978-3-031-18050-7_3},

year  = {2022},

date = {2022-01-01},

journal = {International Workshop on Soft Computing Models in Industrial and Environmental Applications},

pages = {23–30},

abstract = {This paper proposes an automatic, non-invasive, and cost-effective method to estimate the weight of live lambs using a camera, such as those found in mobile phones. The approach employs a pre-trained Convolutional Neural Network (Xception) with transfer learning to estimate lamb weight based on an image, the lamb's sex, and the height from which the image is taken. The method achieved a mean absolute error (MAE) of 0.58 kg and an R² value of 0.96, offering improved accuracy over traditional methods, and providing a practical solution to estimate lamb weight with minimal input and error.},

note = {Publisher: Springer Nature Switzerland Cham},

keywords = {convolutional neural network, Lamb Weight Estimation, Non-invasive Measurement, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}

@article{blanco-medina_detecting_2021,

title = {Detecting vulnerabilities in critical infrastructures by classifying exposed industrial control systems using deep learning},

author = {Pablo Blanco-Medina and Eduardo Fidalgo and Enrique Alegre and Andrés Carofilis-Vasco and Francisco Jáñez-Martino and Víctor Fidalgo-Villar},

url = {https://www.mdpi.com/2076-3417/11/1/367},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Applied Sciences},

volume = {11},

number = {1},

pages = {367},

abstract = {This paper presents a deep learning pipeline to classify industrial control panel screenshots into three categories: internet technologies, operation technologies, and others. Using the CRINF-300 dataset, the authors compared CNN architectures and found that Inception-ResNet-V2 and VGG16 performed best, while MobileNet-V1 was recommended for time-sensitive systems with GPU availability.},

note = {Publisher: MDPI},

keywords = {deep learning, Fine-tuning, Image classification, Industrial Control System, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}

@article{blanco-medina_classification_2020,

title = {Classification of Industrial Control Systems screenshots using Transfer Learning},

author = {Pablo Blanco-Medina and Eduardo Fidalgo and Enrique Alegre and Francisco Jáñez-Martino and Andrés Carofilis-Vasco and Víctor Fidalgo-Villar},

year  = {2020},

date = {2020-01-01},

journal = {arXiv e-prints},

pages = {arXiv–2005},

abstract = {This study evaluates CNN-based transfer learning for classifying Industrial Control System screenshots. Five pre-trained architectures are tested, with MobileNetV1 achieving the best balance of accuracy (97.95% F1-score) and CPU speed (0.47s). For GPU-dependent, time-critical tasks, VGG16 is faster (0.04s) but less accurate (87.67%).},

keywords = {Image classification, Industrial Control System, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}

@article{mazo_transfer_2018,

title = {Transfer learning for classification of cardiovascular tissues in histological images},

author = {Claudia Mazo and Jose Bernal and María Trujillo and Enrique Alegre},

url = {https://www.sciencedirect.com/science/article/pii/S0169260718305297},

year  = {2018},

date = {2018-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {165},

pages = {69–76},

abstract = {This paper proposes an automatic method for classifying healthy tissues and organs from histology images using Convolutional Neural Networks (CNNs). The approach aims to address the challenges in automated tissue and organ recognition, which is crucial for educational and medical purposes. By leveraging the powerful capabilities of deep learning, particularly CNNs, the method seeks to improve classification accuracy and efficiency, building on prior advances in image processing and supervised learning.},

note = {Publisher: Elsevier},

keywords = {cardiovascular system, Fundamental Tissues, Histological Images, Organs, SVM, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}

@article{carofilis-vasco_classifying_nodate,

title = {Classifying Screenshots of Industrial Control System Using Transfer Learning and Fine-Tuning},

author = {Andrés Carofilis-Vasco and Pablo Blanco-Medina and Francisco Jáñez-Martino and Guru Swaroop Bennabhaktula and Eduardo Fidalgo and Alejandro Prieto-Castro and Víctor Fidalgo-Villar},

url = {https://buleria.unileon.es/handle/10612/20274},

abstract = {This paper proposes a deep learning pipeline to classify industrial control panel screenshots into IT, OT, and other categories. Using transfer learning on nine pre-trained CNNs, the model is tested on the CRINF-300 dataset. Inception-ResNet-V2 achieves the best F1-score (98.32%), while MobileNet-V1 offers the best speed-performance balance.},

keywords = {deep learning, Fine-tuning, Image classification, Industrial Control Systems, Transfer Learning},

pubstate = {published},

tppubtype = {article}

}