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Número de documentos: 5.

2025

Otro Materias > Alimentación Universidad Europea del Atlántico > Investigación > Herramientas TIC
Fundación Universitaria Internacional de Colombia > Investigación > Herramientas TIC
Universidad Internacional Iberoamericana México > Investigación > Herramientas TIC
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Herramientas TIC
Universidad Internacional do Cuanza > Investigación > Herramientas TIC
Universidad de La Romana > Investigación > Herramientas TIC
Abierto Inglés, Español, Portugués Se trata de una plataforma que integra cinco bots diferentes disponibles en cinco idiomas. El bot enseña al estudiante de nutrición y dietética a realizar un proceso de exploración clínica de forma online/interactiva. Estos bots proporcionan los siguientes casos: Gastroenterología, Diabetes mellitus tipo 1, enfermedades cardiovasculares y diabetes, obesidad y enfermedades renales. Cada bot dispone de un cuestionario relacionado con el ámbito de la nutrición, y una encuesta final para conocer la experiencia del usuario. Desarrollada en el marco del proyecto E+DIETing_LAB metadata SIN ESPECIFICAR mail SIN ESPECIFICAR (2025) Virtual Patient (E+DIETing_LAB). Repositorio de la Universidad.

Otro Materias > Alimentación Universidad Europea del Atlántico > Investigación > Herramientas TIC
Fundación Universitaria Internacional de Colombia > Investigación > Herramientas TIC
Universidad Internacional Iberoamericana México > Investigación > Herramientas TIC
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Herramientas TIC
Universidad Internacional do Cuanza > Investigación > Herramientas TIC
Universidad de La Romana > Investigación > Herramientas TIC
Abierto Inglés, Español, Portugués Una herramienta que ofrece una formación centrada en el Proceso de Atención Nutricional (PAN) y el servicio a la comunidad. Mediante videollamada las personas interesadas pueden recibir consejo dietético gratuito y unas recomendaciones de cómo mejorar su alimentación, bajo la supervisión de un profesor. Desarrollada en el marco del proyecto E+DIETing_LAB metadata SIN ESPECIFICAR mail SIN ESPECIFICAR (2025) Virtual nutritional clinic (E+DIETing_LAB). Repositorio de la Universidad.

2023

Artículo Materias > Ciencias Sociales
Materias > Educación
Universidad Internacional do Cuanza > Investigación > Artículos y libros Abierto Portugués O quadro legal angolano para o subsistema de ensino superior cresceu significativamente desde 2009, um crescimento que tem estado a visar o aumento da transparência e da qualidade dos processos educacionais nas instituições de ensino superior (IES) angolanas. Entretanto, a qualidade do ensino superior em Angola não sofreu melhorias significativas por não se estar a cumprir escrupulosamente com o quadro legal de forma sistemática, o que tem resultado em encerramentos de cursos e instituições do ensino superior. Este artigo tem como objetivo principal desenvolver um instrumento de auto- monitorização da conformidade legal que pode ajudar as IES angolanas a tirarem mais proveito do quadro legal do ensino superior. Por intermédio de um levantamento bibliográfico das leis relevantes ao ensino superior em Angola, a identificação de obrigações legais nestas e o desenvolvimento de uma série de tabelas de verificação de conformidade, este estudo apresenta uma checklist de auto verificação da conformidade entre o funcionamento das instituições do ensino superior e o quadro legal relevante ao ensino superior em Angola. Pela utilização deste instrumento, foi possível dissecar as obrigações legais em requisitos ou critérios. Foi também possível estabelecer três graus de conformidade legal, nomeadamente: total, parcial e nenhuma. Notou-se, de igual forma, a existência de um total de 83 obrigações legais das instituições do ensino superior em Angola, sendo os regulamentos e as normas as fontes do maior número de obrigações. Destes, existem entre cinco a quinze requisitos legais por obrigação, perfazendo um volume enorme de requisitos legais com os quais as IES em Angola devem mostrar conformidade legal. A aplicação da checklist permite a gestão desse leque diverso e numeroso de requisitos específicos legais. São sugeridas várias medidas complementares ao quadro legal que devem ser implementadas em Angola com o intuito de se criar uma cultura de conformidade legal no ensino superior, promovendo-se, deste modo, a sua qualidade. metadata da Costa Canoquena, João Manuel; Castro Rodríguez, María Elena y Moreira Cabrera, Yanisleidy mail joao.canoquena@unic.co.ao, maria.rodriguez@unic.co.ao, yanisleidy.cabrera@unic.co.ao (2023) Conformidade legal no ensino superior em Angola: criação de um instrumento de gestão das obrigações legais. Sapientiae, 8 (2). pp. 203-226. ISSN 2183-5063

2022

Otro Materias > Alimentación Universidad Europea del Atlántico > Investigación > Herramientas TIC
Fundación Universitaria Internacional de Colombia > Investigación > Herramientas TIC
Universidad Internacional Iberoamericana México > Investigación > Herramientas TIC
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Herramientas TIC
Universidad Internacional do Cuanza > Investigación > Herramientas TIC
Universidad de La Romana > Investigación > Herramientas TIC
Abierto Inglés, Español, Italiano, Portugués Composición Nutricional es un espacio creado para proporcionar una serie de servicios de valor añadido, ofreciendo herramientas, recursos e informaciones sobre programas de formación e investigación para profesionales e interesados en el ámbito de la nutrición y salud. metadata SIN ESPECIFICAR mail SIN ESPECIFICAR (2022) Composición Nutricional. Repositorio de la Universidad.

Artículo Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Artículos y libros
Abierto Portugués O presente trabalho foi desenvolvido no âmbito de um projeto de pesquisa, para validar o uso do portfólio no desenvolvimento da aprendizagem reflexiva em alunos de cursos à distância e assim, evidenciar as vantagens do uso desta ferramenta para a reflexão no aprendizado. Foram avaliados os estilos de aprendizagem promovidos pela ferramenta em 6 diferentes países em 2 cursos de mestrado. Evidenciou-se que o portfólio digital, implementado como recurso para a aprendizagem e não apenas para avaliação, promoveu estilos relacionados à competência reflexiva, resultando útil para o desenvolvimento de currículos nos programas de formação de professores. metadata Sartor-Harada, Andresa; Ulloa Guerra, Oscar; Cordovés Santiesteban, Alexander Armando y Cordero, Yoanky mail andresa.sartor@uneatlantico.es, SIN ESPECIFICAR, alexander.cordoves@unini.edu.mx, SIN ESPECIFICAR (2022) Portfólio digital docente para o desenvolvimento do aprendizado reflexivo. Profesorado, Revista de Currículum y Formación del Profesorado, 26 (3). pp. 311-338. ISSN 1138-414X

Este listado fue generado el Wed Apr 2 23:50:05 2025 UTC.

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Tensiomyography, functional movement screen and counter movement jump for the assessment of injury risk in sport: a systematic review of original studies of diagnostic tests

Background: Scientific research should be carried out to prevent sports injuries. For this purpose, new assessment technologies must be used to analyze and identify the risk factors for injury. The main objective of this systematic review was to compile, synthesize and integrate international research published in different scientific databases on Countermovement Jump (CMJ), Functional Movement Screen (FMS) and Tensiomyography (TMG) tests and technologies for the assessment of injury risk in sport. This way, this review determines the current state of the knowledge about this topic and allows a better understanding of the existing problems, making easier the development of future lines of research. Methodology: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and the PICOS model until November 30, 2024, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus and Scopus databases. The risk of bias was assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 510 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 40 articles. These studies maintained a high standard of quality. This revealed the effects of the CMJ, FMS and TMG methods for sports injury assessment, indicating the sample population, sport modality, assessment methods, type of research design, study variables, main findings and intervention effects. Conclusions: The CMJ vertical jump allows us to evaluate the power capacity of the lower extremities, both unilaterally and bilaterally, detect neuromuscular asymmetries and evaluate fatigue. Likewise, FMS could be used to assess an athlete's basic movement patterns, mobility and postural stability. Finally, TMG is a non-invasive method to assess the contractile properties of superficial muscles, monitor the effects of training, detect muscle asymmetries, symmetries, provide information on muscle tone and evaluate fatigue. Therefore, they should be considered as assessment tests and technologies to individualize training programs and identify injury risk factors.

Producción Científica

Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Antonio Bores-Cerezal mail antonio.bores@uneatlantico.es, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Julio Calleja-González mail ,

Velarde-Sotres

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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices

In the rapidly evolving landscape of artificial intelligence (AI) and the Internet of Things (IoT), the significance of device diagnostics and prognostics is paramount for guaranteeing the dependable operation and upkeep of intricate systems. The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. However, extracting valuable insights and making well-timed decisions from this extensive data reservoir remains a formidable challenge. This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. The proposed methodology utilizes rule-based inference to detect real-time anomalies and predict potential future failures, providing a dual-layered approach for diagnostics and prognostics. The results show that the diagnostics engine using forward chaining detects real-time issues like “High Temperature” and “Low Pressure,” while the prognostics engine with backward chaining predicts potential future occurrences of these issues, enabling proactive prevention measures. The experimental results demonstrate that adopting this approach could offer valuable assistance to authorities and stakeholders. Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost.

Producción Científica

Muhammad Shoaib Farooq mail , Rizwan Pervez Mir mail , Atif Alvi mail , Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Fadwa Alrowais mail , Hanen Karamti mail , Imran Ashraf mail ,

Farooq

<a class="ep_document_link" href="/17140/1/s41598-025-90616-w.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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

The process of image formulation uses semantic analysis to extract influential vectors from image components. The proposed approach integrates DenseNet with ResNet-50, VGG-19, and GoogLeNet using an innovative bonding process that establishes algorithmic channeling between these models. The goal targets compact efficient image feature vectors that process data in parallel regardless of input color or grayscale consistency and work across different datasets and semantic categories. Image patching techniques with corner straddling and isolated responses help detect peaks and junctions while addressing anisotropic noise through curvature-based computations and auto-correlation calculations. An integrated channeled algorithm processes the refined features by uniting local-global features with primitive-parameterized features and regioned feature vectors. Using K-nearest neighbor indexing methods analyze and retrieve images from the harmonized signature collection effectively. Extensive experimentation is performed on the state-of-the-art datasets including Caltech-101, Cifar-10, Caltech-256, Cifar-100, Corel-10000, 17-Flowers, COIL-100, FTVL Tropical Fruits, Corel-1000, and Zubud. This contribution finally endorses its standing at the peak of deep and complex image sensing analysis. A state-of-the-art deep image sensing analysis method delivers optimal channeling accuracy together with robust dataset harmonization performance.

Producción Científica

Khadija Kanwal mail , Khawaja Tehseen Ahmad mail , Aiza Shabir mail , Li Jing mail , Helena Garay mail helena.garay@uneatlantico.es, Luis Eduardo Prado González mail uis.prado@uneatlantico.es, Hanen Karamti mail , Imran Ashraf mail ,

Kanwal

<a class="ep_document_link" href="/17392/1/journal.pone.0317863.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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

Efficient image retrieval from a variety of datasets is crucial in today's digital world. Visual properties are represented using primitive image signatures in Content Based Image Retrieval (CBIR). Feature vectors are employed to classify images into predefined categories. This research presents a unique feature identification technique based on suppression to locate interest points by computing productive sum of pixel derivatives by computing the differentials for corner scores. Scale space interpolation is applied to define interest points by combining color features from spatially ordered L2 normalized coefficients with shape and object information. Object based feature vectors are formed using high variance coefficients to reduce the complexity and are converted into bag-of-visual-words (BoVW) for effective retrieval and ranking. The presented method encompass feature vectors for information synthesis and improves the discriminating strength of the retrieval system by extracting deep image features including primitive, spatial, and overlayed using multilayer fusion of Convolutional Neural Networks(CNNs). Extensive experimentation is performed on standard image datasets benchmarks, including ALOT, Cifar-10, Corel-10k, Tropical Fruits, and Zubud. These datasets cover wide range of categories including shape, color, texture, spatial, and complicated objects. Experimental results demonstrate considerable improvements in precision and recall rates, average retrieval precision and recall, and mean average precision and recall rates across various image semantic groups within versatile datasets. The integration of traditional feature extraction methods fusion with multilevel CNN advances image sensing and retrieval systems, promising more accurate and efficient image retrieval solutions.

Producción Científica

Jyotismita Chaki mail , Aiza Shabir mail , Khawaja Tehseen Ahmed mail , Arif Mahmood mail , Helena Garay mail helena.garay@uneatlantico.es, Luis Eduardo Prado González mail uis.prado@uneatlantico.es, Imran Ashraf mail ,

Chaki

<a class="ep_document_link" href="/17450/1/ejaz-et-al-2025-fundus-image-classification-using-feature-concatenation-for-early-diagnosis-of-retinal-disease.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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

Background Deep learning models assist ophthalmologists in early detection of diseases from retinal images and timely treatment. Aim Owing to robust and accurate results from deep learning models, we aim to use convolutional neural network (CNN) to provide a non-invasive method for early detection of eye diseases. Methodology We used a hybridized CNN with deep learning (DL) based on two separate CNN blocks, to identify multiple Optic Disc Cupping, Diabetic Retinopathy, Media Haze, and Healthy images. We used the RFMiD dataset, which contains various categories of fundus images representing different eye diseases. Data augmenting, resizing, coping, and one-hot encoding are used among other preprocessing techniques to improve the performance of the proposed model. Color fundus images have been analyzed by CNNs to extract relevant features. Two CCN models that extract deep features are trained in parallel. To obtain more noticeable features, the gathered features are further fused utilizing the Canonical Correlation Analysis fusion approach. To assess the effectiveness, we employed eight classification algorithms: Gradient boosting, support vector machines, voting ensemble, medium KNN, Naive Bayes, COARSE- KNN, random forest, and fine KNN. Results With the greatest accuracy of 93.39%, the ensemble learning performed better than the other algorithms. Conclusion The accuracy rates suggest that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. It contributes to the field of eye disease detection through the analysis of color fundus images by providing a reliable and efficient diagnostic system.

Producción Científica

Sara Ejaz mail , Hafiz U Zia mail , Fiaz Majeed mail , Umair Shafique mail , Stefanía Carvajal-Altamiranda mail stefania.carvajal@uneatlantico.es, Vivian Lipari mail vivian.lipari@uneatlantico.es, Imran Ashraf mail ,

Ejaz