https://hdl.handle.net/10259/7409 Tue, 21 Apr 2026 09:26:04 GMT 2026-04-21T09:26:04Z https://hdl.handle.net/10259/11162 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) Sat, 01 Nov 2025 00:00:00 GMT https://hdl.handle.net/10259/11162 2025-11-01T00:00:00Z https://hdl.handle.net/10259/8580 Improving Energy Efficiency in Buildings Using Machine Intelligence Sedano, Javier; Villar, José Ramón; Curiel Herrera, Leticia Elena; Cal, Enrique de la; Corchado, Emilio Improving the detection of thermal insulation in buildings –which includes the development of models for heating and ventilation processes and fabric gain - could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon footprints of domestic heating systems. Thermal insulation standards are now contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those standards. Lighting, occupancy, set point temperature profiles, air conditioning and ventilation services all increase the complexity of measuring insulation efficiency. The identification of thermal insulation failure can help to reduce energy consumption in heating systems. Conventional methods can be greatly improved through the application of hybridized machine learning techniques to detect thermal insulation failures when a building is in operation. A three-step procedure is proposed in this paper that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modelled, for each building type and climate zone. Secondly, Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter zone C cities in Spain. Although a great deal of further research remains to be done in this field, the proposed system is expected to outperform conventional methods described in Spanish building codes that are used to calculate energetic profiles in domestic and residential buildings. Trabajo presentado en: International Conference on Intelligent Data Engineering and Automated Learning, 2009 Thu, 01 Jan 2009 00:00:00 GMT https://hdl.handle.net/10259/8580 2009-01-01T00:00:00Z https://hdl.handle.net/10259/8578 Modelling of Heat Flux in Building Using Soft-Computing Techniques Sedano, Javier; Villar, José Ramón; Curiel Herrera, Leticia Elena; Cal, Enrique de la; Corchado, Emilio Improving the detection of thermal insulation failures in buildings includes the development of models for heating process and fabric gain -heat flux through exterior walls in the building-. Thermal insulation standards are now contractual obligations in new buildings, the energy efficiency in the case of buildings constructed before the regulations adopted is still an open issue, and the assumption is that it will be based on heat flux and conductivity measurement. A three-step procedure is proposed in this study that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, a supervised neural model and identification techniques are applied, in order to detect the heat flux through exterior walls in the building. The reliability of the proposed method is validated for a winter zone, associated to several cities in Spain. Trabajo presentado en: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2010 Fri, 01 Jan 2010 00:00:00 GMT https://hdl.handle.net/10259/8578 2010-01-01T00:00:00Z https://hdl.handle.net/10259/8577 Complications Detection in Treatment for Bacterial Endocarditis Curiel Herrera, Leticia Elena; Baruque Zanón, Bruno; Dueñas, Carlos; Corchado, Emilio; Pérez, Cristina This study proposes the use of decision trees to detect possible complications in a critical disease called endocarditis. The endocarditis illness could produce heart failure, stroke, kidney failure, emboli, immunological disorders and death. The aim is to obtained a tree decision classifier based on the symptoms (attributes) of patients (the data instances) observed by doctors to predict the possible complications that can occur when a patient is in treatment of bacterial endocarditis and thus, help doctors to make an early diagnose so that they can treat more effectively the infection and aid to a patient faster recovery. The results obtained using a real data set, show that with the information extracted form each case in an early stage of the development of the patient a quite accurate idea of the complications that can arise can be extracted. Trabajo presentado en: International Symposium on Distributed Computing and Artificial Intelligence, 2011 Sat, 01 Jan 2011 00:00:00 GMT https://hdl.handle.net/10259/8577 2011-01-01T00:00:00Z