https://hdl.handle.net/10259/7109 2026-04-18T02:40:45Z 2026-04-18T02:40:45Z Redondo Guevara, Raquel Santos González, Pedro Bustillo Iglesias, Andrés Sedano, Javier Villar, José R. Correa, Maritza Alique, José Ramón Corchado, Emilio https://hdl.handle.net/10259/11485 2026-03-21T01:05:42Z 2009-01-01T00:00:00Z

A Soft Computing System to Perform Face Milling Operations Redondo Guevara, Raquel; Santos González, Pedro; Bustillo Iglesias, Andrés; Sedano, Javier; Villar, José R.; Correa, Maritza; Alique, José Ramón; Corchado, Emilio In this paper we present a soft computing system developed to optimize the face milling operation under High Speed conditions in the manufacture of steel components like molds with deep cavities. This applied research presents a multidisciplinary study based on the application of neural projection models in conjunction with identification systems, in order to find the optimal operating conditions in this industrial issue. Sensors on a milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures industrial tools. The presented model is based on a two-phase application. The first phase uses a neural projection model capable of determine if the data collected is informative enough. The second phase is focus on identifying a model for the face milling process based on low-order models such as Black Box ones. The whole system is capable of approximating the optimal form of the model. Finally, it is shown that the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples, is the most appropriate model to control such industrial task for the case of steel tools. Comunicación presentada en: 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II

2009-01-01T00:00:00Z Marco Detchart, Cédric Curiel Herrera, Leticia Elena Urda Muñoz, Daniel Rincón Arango, Jaime Andrés https://hdl.handle.net/10259/11162 2025-12-19T01:05:40Z 2025-11-01T00:00:00Z

Towards Intelligent and Immersive Healthcare: Edge-AI, IoMT, and Digital Twins in the Metaverse Marco Detchart, Cédric; Curiel Herrera, Leticia Elena; Urda Muñoz, Daniel; Rincón Arango, Jaime Andrés The integration of emerging technologies such as Virtual Reality (VR), Artificial Intelligence (AI), and the Internet of Medical Things (IoMT) is transforming personalized healthcare. This work presents an innovative system that leverages the concept of digital health twins, IoMT devices, and immersive VR environments to enable real-time remote monitoring of biomedical signals (ECG, PPG, blood pressure, oxygen saturation, among others). Captured data is transmitted to centralized servers where neural networks analyze health parameters, allowing early anomaly detection and dynamic adjustment of treatment plans. The system also incorporates a cloud-connected assistant based on embedded hardware, enhancing continuous patient monitoring and interaction within both physical and virtual environments. To safeguard sensitive medical data, robust cybersecurity measures are integrated, including encrypted communications, authentication protocols, and local storage failover in case of connectivity loss. This multi-layered approach ensures patient confidentiality and system resilience. By improving diagnostic accuracy, optimizing patient experiences, and extending healthcare access to remote areas, this solution contributes to reducing overcrowding in medical facilities and enabling more equitable healthcare delivery. Trabajo presentado en: International Conference on Intelligent Data Engineering and Automated Learning 2025, realizado del 13 al 15 de noviembre de 2025, en Jaén (España)

2025-11-01T00:00:00Z Cabeza-Lopez, Eduardo Manuel Ruiz González, Rubén Merino Gómez, Alejandro Curiel Herrera, Leticia Elena Rincón Arango, Jaime Andrés https://hdl.handle.net/10259/9761 2025-11-16T23:42:18Z 2024-11-01T00:00:00Z

A Comparison of AI-Enabled Techniques for the Detection of Attacks in IoT Devices Cabeza-Lopez, Eduardo Manuel; Ruiz González, Rubén; Merino Gómez, Alejandro; Curiel Herrera, Leticia Elena; Rincón Arango, Jaime Andrés The purpose of this research is to enhance the security of Internet of Things devices. The deployment of these gadgets has increased exponentially, nowadays they can be found in every sector from industrial environments to applications in residential homes. This technology is important for enterprises due to the ability to manage different kind of data and control critical operations in an efficiently way. Consequently, these devices have become frequent targets of cyberattacks, highlighting the necessity of robust detection methods. However it is a complicated task to find a uniform security solution due to the variety of devices, with different capabilities and requirements. This study evaluates some artificial intelligence supervised learning algorithms such as XGBoost and Random Forest using the TON_IoT dataset, which are new generations of Internet of Things and Industrial datasets for evaluating the fidelity and the efficiency of different cybersecurity applications based on Artificial Intelligence. The analysis shows both algorithms are effective in detecting cyberattacks, achieving accuracies close to 98%, with a minimal variation in terms of precision and recall. These algorithms outperform the results obtained in previous works with the same dataset, then they can provide an additional security layer, allowing accurate identification of potential attacks. This research shows the importance of artificial intelligence algorithms in cybersecurity and their potential to improve the protection of this kind of devices. Comunicación presentada en: 17th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2024) and 15th International Conference on European Transnational Education (ICEUTE 2024), 9-11 October 2024, Salamanca (Spain)

2024-11-01T00:00:00Z Curiel Herrera, Leticia Elena Herrero Cosío, Álvaro Corchado, Emilio Bravo Díez, Pedro Miguel https://hdl.handle.net/10259/9394 2024-07-17T00:05:20Z 2005-01-01T00:00:00Z

Collaborative agents for drilling optimisation tasks using an unsupervised connectionist model Curiel Herrera, Leticia Elena; Herrero Cosío, Álvaro; Corchado, Emilio; Bravo Díez, Pedro Miguel The purpose of this study is the optimization of drilling tasks in the construction of big auto-carrier storage warehouses. This is carried out by applying different Artificial Intelligence (AI) techniques: a cooperative unsupervised connectionist model (focused on the detection of some optimal drilling conditions) and software agents. These agents can collaborate to save drilling time and waste by interchanging information about the conditions of drill bits and the kind of material to be drilled. Trabajo presentado en: 4th International Workshop on Practical Applications of Agents and Multiagent Systems (IWPAAMS), realizado el 20 y 21 de octubre de 2005, en León

2005-01-01T00:00:00Z