Documentos donde el Tema es "Materias > Ingeniería"

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Número de registros en este nivel: 83.

2026

Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.

Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision.

Introduction: Jackfruit cultivation is highly affected by leaf diseases that reduce yield, fruit quality, and farmer income. Early diagnosis remains challenging due to the limitations of manual inspection and the lack of automated and scalable disease detection systems. Existing deep-learning approaches often suffer from limited generalization and high computational cost, restricting real-time field deployment. Methods: This study proposes CNNAttLSTM, a hybrid deep-learning architecture integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) units, and an attention mechanism for multi-class classification of algal leaf spot, black spot, and healthy jackfruit leaves. Each image is divided into ordered 56×56 spatial patches, treated as pseudo-temporal sequences to enable the LSTM to capture contextual dependencies across different leaf regions. Spatial features are extracted via Conv2D, MaxPooling, and GlobalAveragePooling layers; temporal modeling is performed by LSTM units; and an attention mechanism assigns adaptive weights to emphasize disease-relevant regions. Experiments were conducted on a publicly available Kaggle dataset comprising 38,019 images, using predefined training, validation, and testing splits. Results: The proposed CNNAttLSTM model achieved 99% classification accuracy, outperforming the baseline CNN (86%) and CNN–LSTM (98%) models. It required only 3.7 million parameters, trained in 45 minutes on an NVIDIA Tesla T4 GPU, and achieved an inference time of 22 milliseconds per image, demonstrating high computational efficiency. The patch-based pseudo-temporal approach improved spatial–temporal feature representation, enabling the model to distinguish subtle differences between visually similar disease classes. Discussion: Results show that combining spatial feature extraction with temporal modeling and attention significantly enhances robustness and classification performance in plant disease detection. The lightweight design enables real-time and edge-device deployment, addressing a major limitation of existing deep-learning techniques. The findings highlight the potential of CNNAttLSTM for scalable, efficient, and accurate agricultural disease monitoring and broader precision agriculture applications.

CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification.

Correction: Enhancing fault detection in new energy vehicles via novel ensemble approach.

Introduction: Weather classification plays a crucial role in applications such as environmental monitoring, disaster management, and smart city infrastructure. Accurate and efficient classification of weather conditions from images remains a challenging task due to variations in illumination, texture, and atmospheric conditions.Methods: This study proposes an efficient deep learning framework for multi-class weather classification by integrating the Xception architecture with Squeeze-and-Excitation (SE) blocks and a spatial attention mechanism. Transfer learning with pre-trained ImageNet weights was employed, and a comparative analysis was conducted using EfficientNet-B3, ResNet152V2, and Xception architectures. The proposed enhanced Xception model incorporates channel-wise recalibration and spatial feature refinement to improve representational capability. The model was trained and evaluated on the Multi-Class Weather Dataset (MWD), which consists of 1,125 images categorized into four classes: sunshine, cloudy, rain, and sunrise. To ensure robustness and generalization, 5-fold cross-validation, statistical significance testing, calibration analysis, and robustness evaluation under image perturbations were performed.Results: The proposed model achieved a classification accuracy of 99.06% on the test set. Additionally, it attained a macro precision of 98.3%, macro recall of 97.7%, and macro F1-score of 98.0%. The model demonstrated strong generalization capability and robustness under varying perturbation conditions, with only moderate computational overhead.Discussion: The integration of SE blocks and spatial attention significantly enhances feature representation by emphasizing informative channels and spatial regions. Compared to baseline architectures, the proposed framework shows superior performance in terms of accuracy and robustness. These results indicate that the model is well-suited for real-world weather classification applications, particularly in intelligent environmental monitoring systems.

Enhanced weather classification using xception with SENet and attention mechanisms.

Enhancing fault detection in new energy vehicles via novel ensemble approach.

Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems.

Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models.

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

A novel approach for disease and pests detection in potato production system based on deep learning.

2025

Advancing fake news combating using machine learning: a hybrid model approach.

Deep image features sensing with multilevel fusion for complex convolution neural networks & cross domain benchmarks.

Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis.

Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2.

Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization.

Ensemble stacked model for enhanced identification of sentiments from IMDB reviews.

Fundus image classification using feature concatenation for early diagnosis of retinal disease.

Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.

Scalable Comprehensive Automatic Inspection, Cleaning, and Evaluation Mechanism for Large‐Diameter Pipes.

Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence.

2024

A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces (APIs). A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus. Furthermore, an algorithm is developed to annotate the data into three depression classes: ‘Mild,’ ‘Moderate,’ and ‘Severe,’ based on International Classification of Diseases-10 (ICD-10) depression diagnostic criteria. Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus. Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model, which significantly increases the depression classification performance to an 84% F1 score and 90% accuracy compared to baselines. Finally, a FastText-based weighted soft voting ensemble (WSVE) is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances. The proposed WSVE outperformed all baselines as well as FastText alone, with an F1 of 89%, 5% higher than FastText alone, and an accuracy of 93%, 3% higher than FastText alone. The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble.

Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.

Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network.

DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network.

Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.

Novel model to authenticate role-based medical users for blockchain-based IoMT devices.

Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model.

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.

A deep learning approach for Named Entity Recognition in Urdu language.

2023

Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.

An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.

An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation.

Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach.

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning.

Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems.

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.

Contextual Urdu Lemmatization Using Recurrent Neural Network Models.

Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection.

Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance.

Deep learning model for detection of brown spot rice leaf disease with smart agriculture.

DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents.

Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning.

Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification.

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas.

Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets.

Image Watermarking Using Least Significant Bit and Canny Edge Detection.

Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance.

IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System.

Mitigating Low-Frequency Oscillations and Enhancing the Dynamic Stability of Power System Using Optimal Coordination of Power System Stabilizer and Unified Power Flow Controller.

Navigating SMEs in the tourism sector through crisis (T-CRISIS-NAV).

PRUS: Product Recommender System Based on User Specifications and Customers Reviews.

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.

Real Word Spelling Error Detection and Correction for Urdu Language.

Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective.

Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety.

A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis.

Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19.

Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis.

Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches.

A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.

2022

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives.

Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix.

An Analytical Framework for Innovation Determinants and Their Impact on Business Performance.

Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.

Patient care and convenience remain the concern of medical professionals and caregivers alike. An unconscious patient confined to a bed may develop fluid accumulation and pressure sores due to inactivity and deficiency of oxygen flow. Moreover, weight monitoring is crucial for an effective treatment plan, which is difficult to measure for bedridden patients. This paper presents the design and development of a smart and cost-effective independent system for lateral rotation, movement, weight measurement, and transporting immobile patients. Optimal dimensions and practical design specifications are determined by a survey across various hospitals. Subsequently, the proposed hoist-based weighing and turning mechanism is CAD-modeled and simulated. Later, the structural analysis is carried out to select suitable metallurgy for various sub-assemblies to ensure design reliability. After fabrication, optimization, integration, and testing procedures, the base frame is designed to mount a hydraulic motor for the actuator, a DC power source for self-sustenance, and lockable wheels for portability. The installation of a weighing scale and a hydraulic actuator is ensured to lift the patient for weight measuring up to 600 pounds or lateral turning of 80 degrees both ways. The developed system offers simple operating characteristics, allows for keeping patient weight records, and assists nurses in changing patients’ lateral positions both ways, comfortably massage patients’ backs, and transport them from one bed to another. Additionally, being lightweight offers reduced contact with the patient to increase the healthcare staff’s safety in pandemics; it is also height adjustable and portable, allowing for use with multiple-sized beds and easy transportation across the medical facility. The feedback from paramedics is encouraging regarding reducing labor-intensive nursing tasks, alleviating the discomfort of long-term bed-ridden patients, and allowing medical practitioners to suggest better treatment plans

Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics.

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.

Emotion Detection Using Facial Expression Involving Occlusions and Tilt.

Environmental Sciences and Practices.

Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean.

IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition.

Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring.

La línea de actividad científico-técnica que se propone se titula “Observatorio 5G“ y está orientada a generar conocimiento en el ámbito de las nuevas redes de telecomunicaciones y servicios asociados al estándar tecnológico de quinta generación para redes móviles de banda ancha (5G). El despliegue de la quinta generación de tecnologías de telefonía móvil, conocida como 5G, está protagonizado por la necesidad de conseguir que las diferentes compañías fabricantes consigan implantar sus estándares a nivel internacional. A diferencia de las tecnologías de 3G y 4G donde era necesario un despliegue masivo para dar servicio a cuanto mayor número posible de población, la tecnología 5G se basa en el concepto de despliegues particulares, con soluciones críticas mediante soluciones ad-hoc. Por ello, es importante tanto la creación de un potente ecosistema 5G así como que el mismo contemple a los emprendedores y pequeñas empresas que será quienes creen los servicios que solucionen los problemas concretos de las industrias sobre esta nueva tecnología. La tecnología 5G será una realidad en breve. Por ello, se requiere realizar acciones que permitan que los países lideren su implantación de una manera sólida, ordenada y consensuada permitiendo una ventaja competitiva tanto a nivel gubernamental como industrial para desarrollar un ecosistema adecuado del despliegue de 5G. Para poder dar soluciones en tres ámbitos de actuación (Coordinación de Proyectos; Regulación y Legislación; e Innovación, Emprendimiento y Estandarización) se propone analizar la creación de un Observatorio 5G. El objetivo general del presente proyecto es elaborar un estudio que permita analizar la factibilidad de la creación de un Observatorio 5G. Para ello, será necesario identificar las grandes líneas maestras que deben ser comunes a un observatorio según las singularidades de cada territorio. En particular, nuestro interés será identificar oportunidades alrededor de lo que denominábamos “innovación y ecosistema 5G”, es decir, oportunidades que se puedan abrir especialmente: - Para la creación de un ecosistema científico-técnico que comparta la capacidad de Innovación mediante la tecnología 5G (Universidades, Centros Tecnológicos, Centros de I+D de las empresas, etc.). - Para la generación de conocimiento con el mundo científico y académico que permita adaptar la formación del talento para tener en cuenta las necesidades futuras en base a la tecnología. - Crear sinergias desde el ecosistema de innovación con el ecosistema de emprendimiento que favorezca la creación de nuevas empresas y productos para liderar el mercado. - Generar capacitaciones y formación continua.

Observatorio 5G.

Prediction β-Thalassemia carriers using complete blood count features.

Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT.

Threatening URDU Language Detection from Tweets Using Machine Learning.

2021

DIGI: Desarrollo de un prototipo digital para la gestión de recursos audiovisuales.

Desarrollo experimental de una arquitectura de microservicios aplicada a la gestión académica.

El proyecto de investigación que se pretende llevar a cabo se refiere a la “Formación práctica mediante la aplicación de tecnologías basadas en entornos virtuales, aumentados e inmersivos“, y está orientado a una investigación que nos permita aplicar tecnologías de la información para simular entornos reales que son útiles en el ámbito de la educación y en concreto pretendemos innovar en los sistemas de evaluación que permitan a los docentes emplear estos entornos digitales. Las plataformas y medios digitales están cada vez más presentes en la sociedad y por ende en las organizaciones empresariales. Los profesionales de la educación no son ajenos a esta situación y se aprovechan de estas tecnologías y a la vez se enfrentan al reto de adaptarse de manera constante al avance tecnológico y a las repercusiones que tiene en su desempeño. En este ámbito, el desarrollo de las plataformas digitales para aprendizaje se ha visto impulsado por la confluencia de múltiples factores entre los cuales se destaca el avance tecnológico, la disponibilidad de dispositivos, las nuevas generaciones de nativos digitales. La formación e-learning es un ejemplo del auge de estas plataformas digitales pero todavía nos encontramos tecnologías más avanzadas como la realidad virtual, tecnologías inmersivas, Internet de las Cosas, etc. que también tienen o tendrán cabida en el entorno educativo. Nuestro proyecto nace con el objetivo de aportar valor a este escenario alrededor de los conocidos como entornos virtuales. Desde el sector educativo universitario, se ha sabido ver la oportunidad de la aplicación de estas técnicas a los procesos formativos del alumnado, inicialmente desde las ramas de la ingeniería que se dedicaban al propio desarrollo de estas tecnologías, y posteriormente desde las disciplinas más afines al aprendizaje cognitivo humano como pueden ser la Psicología o la Pedagogía que buscan evaluar estas técnicas respecto a otras metodologías más clásicas presentes en la Educación. Sin embargo, como se puede extraer de diversos artículos científicos que aplican estas modalidades para la educación, persisten carencias para que los docentes de cualquier área/disciplina dispongan de herramientas lo suficientemente intuitivas para crear los entornos virtuales para simular los entornos profesionales de su especialidad. El diseño de herramientas para docentes (T. Budai, 2019), ayudaría a evitar estas barreras de entrada para extender su uso. Por otro lado, aunque las publicaciones que aplican este tipo de tecnologías a la enseñanza (N. Pellas, 2020), la formación profesional (H. B. Andersson, 2020), o incluso a aprendizajes cognitivos (E. Rho, 2020), consideran que son muy positivas desde el punto de vista pedagógico (H. Ardiny, 2018), se reclama una necesidad en cuanto a establecer unas métricas y metodologías de evaluación apropiadas al proceso de enseñanza-aprendizaje (A. Dengel, 2018), (A. Christopoulos, 2019). En algunos casos se habla la gran asignatura pendiente, que es el tema de la evaluación. Cuando los docentes intentan implementar instrumentos de evaluación basados en entornos digitales, encuentran dificultades para hallar el equilibrio entre la evaluación, la metodología y el uso de los nuevos medios. Ante este escenario, el proyecto pretende diseñar y desarrollar un entorno virtual experimental para la educación práctica universitaria con énfasis en el sistema de evaluación del proceso de aprendizaje y el control de calidad.

IMMERSIVE TECH: Formación práctica mediante la aplicación de tecnologías basadas en entornos virtuales, aumentados e inmersivos.

2020

1- Gestionar online el proceso de revisión de contenidos recibidos y gestionarlo a distancia, contando con usuarios que se conectan al sistema de forma online y aportan sus valoraciones a través de la misma plataforma. En este caso se trata de facilitar un flujo de trabajo entre los diferentes participantes en el proceso (director de revista, editor en jefe, editor y revisor), de forma que puedan optimizar su productividad y trabajar de forma asincrónica sobre unos mismos contenidos editoriales y siguiendo un proceso homogéneo de acuerdo a nuestros procedimientos. 2- Automatizar determinados procesos de revisión de contenidos. En concreto, habíamos considerado de interés mejorar el proceso de revisión del formato de los artículos recibidos gracias a un software basado en inteligencia artificial. Teniendo en cuenta que los artículos científicos tienen una estructura y contenidos normalizados, pensamos que era posible automatizar algunos elementos de la revisión preliminar de contenidos. 3- Disponer de una solución para la fidelización de autores-revisores generando automáticamente certificados de participación como revisores de artículos científicos. Teniendo en cuenta la dificultad de lograr la participación de revisores científicos, y como parte del sistema de fidelización, se propuso una innovación en la plataforma, que permite generar de forma automática un auto-certificado para los revisores. 4- Estudiar la aplicación de los metadatos, las plataformas multilingües y las de e-commerce para distribución de contenidos. En este caso, lo que se hizo fue solicitar unos estudios de vigilancia tecnológica relacionados con: - Estándares internacionales para la creación de metadatos que nos permitan indexar de la mejor manera posible nuestros contenidos. - Estándares para plataformas multilingües que nos fueran de aplicación para crear un sistema de gestión de contenidos multi-idioma enlazado con los procesos de traducción. - Plataformas de e-commerce adaptadas a la distribución de contenidos electrónicos que nos permitiesen monetizar determinados contenidos y venderlos en Internet.

Estudios de vigilancia tecnológica y proyecto piloto para revista electrónica.

2019

Project Design and Management.

SOSeas: An assessment tool for predicting the dynamic risk of drowning on beaches.

2017

TICartículo: Tecnologías de intercambio de datos de artículos científicos.

2016

Nuevos mecanismos para conocer el riesgo de lesión en el deporte en diferentes tramos de la temporada deportiva. R&P (Recovery and Performance).

PREVENT-SOS: Desarrollo de tecnologías para la identificación de riesgos en espacios acuáticos naturales.

SMART ACTIVE LIFE: Desarrollo de tecnologías inteligentes para la promoción de la vida activa y segura.

Este listado fue generado el Mon Jul 13 01:38:34 2026 UTC.