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- Universidad Internacional Iberoamericana México (1751)
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Ponencia/Presentación en Jornada, Congreso Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Congresos Cerrado Inglés According to Unicef, in 2019, 33 million children were international migrants. This situation has been intensified due to Covid-19 pandemic. Among the reasons to leave a country, we can find poverty, climate change, economic reasons or the hope of having a better life. Migrant children in school age can face many challenges: language barriers, lack of understanding of social norms, limited resources from the school, and psychosocial issues. These challenges can produce long-lasting psychological and physical effects leading to a halt on the developing of their full potential along their life. So, an early intervention is crucial to boost migrant children’s educational language acquisition and understanding of culture and social norms to their educational achievement. This paper discusses the advantages of mlearning to foster language learning and facilitate a cultural integration by migrant children with the support of translanguaging strategies and intercultural approach. The role of mlearning to foster language learning has been discussed by Azevedo-Gomes & Sartor-Harada (2020) with a mlearning model with four guidelines: the construction of meaning, the interaction between peers, a focus on previous experiences, and formative feedback. Mlearning seeks to integrate learning theories, especially constructivist and behavioral theories to also create collaborative working environments (Crompton, Burke & Gregory, 2017). Despite the fact the design is focused to improve a minority language, the concepts about psycholinguistic factors are similar to migrant children's needs. Furthermore, mlearning allows to involve parents in language instruction and provide flexible education pathways, both considered good policy practices by OECD (2021) to support the lifelong integration of immigrant children. The report examines the role of an intercultural approach with the support of translanguaging strategies. The first one considers the child’s heritage and could help to expand awareness towards both cultures in gamified activities. Plus, translanguaging strategies “leverages the fluid language of learners in ways that deepen their engagement and comprehension of complex content and texts” (García & Vogel, 2017, p.2) and could help children to transfer language competencies to a new language, speeding up their target language learning and fostering their self-esteem by valuing their previous knowledge. The authors base their assumptions on the thesis that the formula translanguaging and intercultural approach can contribute to a positive mixed identity construction. Finally, the authors present their strategy for gamified activities with mlearning support including translanguaging strategies and intercultural approach in order to ease integration and a full educational achievement of migrant children. metadata Azevedo-Gomes, Juliana; Sartor-Harada, Andresa; Cordovés Santiesteban, Alexander Armando y Cordero Gómez, Yoanky mail SIN ESPECIFICAR, SIN ESPECIFICAR, alexander.cordoves@unini.edu.mx, SIN ESPECIFICAR (2021) Translanguaging and intercultural approach: a mlearning proposal to ease inmigrant children's integration. In: 14th annual International Conference of Education, Research and Innovation.
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Artículo Materias > Psicología Universidad Internacional Iberoamericana México > Investigación > Congresos Abierto Inglés SIN ESPECIFICAR metadata Duñabeitia, Jon A.; Griffin, Kim L.; Martín, Juan L.; Oliva, Mireia; Sámano, María L. y Ivaz, Lela mail SIN ESPECIFICAR, kim.griffin@uneatlantico.es, juan.martin@uneatlantico.es, mireia.oliva@uneatlantico.es, marialuisa.samano@uneatlantico.es, SIN ESPECIFICAR (2016) The Spanish General Knowledge Norms. Front. Psychol., 7. p. 1888. ISSN 1664-1078
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Ponencia/Presentación en Jornada, Congreso Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Congresos Cerrado Inglés Conventional IP networks connect places at great distances and meet the connectivity needs of their users. To perform each of these operations, each packet must pass through various network devices, which make individual routing decisions that make centralized network management difficult. These networks have been growing both in size and complexity, each day at a higher rate, which has generated a series of difficulties in personalization, integration, security, and optimization of these. As a solution, the Software-Defined Networking (SDN) architecture [1] was created, which promises to be a dynamic, manageable, profitable and adaptable architecture, thus becoming an ideal tool to handle large bandwidths and the development and implementation of customized applications, for different types of needs on communication networks. This document shows a performance analysis between SDN and a conventional IP network configured with the EIGRP and BGP routing protocols, establishing a configuration scenario with physical network equipment and with an SDN emulator called Mininet. The research methodology is based on the guidelines of the Cisco PPDIOO methodology and is developed in the following phases: 1. Elaboration of physical network topology with Cisco equipment, performing experiments with IPv4 and IPv6, measuring variables such as Jitter, Delay and Throughput. 2. Carrying out the same experiments and tests with SDN, in a network topology with similar characteristics to those already mentioned, but with OpenFlow switches. 3. Analysis of results, for which the behavior of jitter, delay and throughput variations of both scenarios is examined to make a series of comparisons (made with statistical analysis) concerning protocol, addressing, packet size among others. Finally, it was obtained as a result that SDN has a lower delay and jitter than the conventional IP network in some cases, as well as a more favorable throughput. metadata Hernandez, Leonel; Jimenez, Genett; Pranolo, Andri y Uc-Rios, Carlos mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.uc@unini.edu.mx (2019) Comparative Performance Analysis Between Software-Defined Networks and Conventional IP Networks. In: 2019 5th International Conference on Science in Information Technology (ICSITech), 24-24 otubre de 2019, Yogyakarta, Indonesia.
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Ponencia/Presentación en Jornada, Congreso Materias > Ciencias Sociales Universidad Internacional Iberoamericana México > Investigación > Congresos Abierto Español El sistema propuesto permite hacer un análisis comparativo de los diferentes proyectos participantes en eventos de invención, innovación y creatividad, basados en sus características de calidad en uso, funcionalidad y usabilidad, mediante un plan de métricas externas y de calidad en uso. El modelo está basado en normas internacionales (ISO/IEC 9126, 14598, IEEE 1061) y modelos mexicanos (MECHDAV), y software propuesto, es desarrollado en un ambiente visual WEB, para dispositivos móviles (tabletas), permiten evaluar genéricamente la calidad de los proyectos-productos-servicios que participan en los concursos mencionados; este sistema proporciona un soporte a las personas evaluadoras (jurados) para emitir dictámenes imparciales con mayor precisión cuantitativa. Este sistema está dirigido a organizaciones, empresas y usuarios finales que necesiten seleccionar, fácilmente, los proyectos desarrollados con más calidad, para ser los ganadores en estos concursos. Se proporciona una guía para la instrumentación concreta de la evaluación, así como sus rangos, la presentación, procedimientos y documentación. Palabras clave: modelo de calidad, evaluación técnica de proyectos-productos; concurso de creatividad, calidad en uso, métricas externas. metadata Uc-Rios, Carlos; Varga Pérez, Laura Silvia; Gutiérrez Tornés, Agustín Francisco; Felipe Riverón, Edgardo Manuel; Soto Hernández, Ana Maria; Peralta Escobar, Jorge y Vargas, Vanesa mail carlos.uc@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2017) Calidad en uso: Fundamental en la evaluación de proyectos para la formación ingenieril de líderes de su entorno. In: Global Partnerships for Development and Engineering Education: Proceedings of the 15th LACCEI International Multi-Conference for Engineering, Education and Technology, 19-21-julio 2017, Boca Raton, Forida..
<a href="/10290/1/Influence%20of%20E-learning%20training%20on%20the%20acquisition%20of%20competences%20in%20basketball%20coaches%20in%20Cantabria.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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The main aim of this study was to analyse the influence of e-learning training on the acquisition of competences in basketball coaches in Cantabria. The current landscape of basketball coach training shows an increasing demand for innovative training models and emerging pedagogies, including e-learning-based methodologies. The study sample consisted of fifty students from these courses, all above 16 years of age (36 males, 14 females). Among them, 16% resided outside the autonomous community of Cantabria, 10% resided more than 50 km from the city of Santander, 36% between 10 and 50 km, 14% less than 10 km, and 24% resided within Santander city. Data were collected through a Google Forms survey distributed by the Cantabrian Basketball Federation to training course students. Participation was voluntary and anonymous. The survey, consisting of 56 questions, was validated by two sports and health doctors and two senior basketball coaches. The collected data were processed and analysed using Microsoft® Excel version 16.74, and the results were expressed in percentages. The analysis revealed that 24.60% of the students trained through the e-learning methodology considered themselves fully qualified as basketball coaches, contrasting with 10.98% of those trained via traditional face-to-face methodology. The results of the study provide insights into important characteristics that can be adjusted and improved within the investigated educational process. Moreover, the study concludes that e-learning training effectively qualifies basketball coaches in Cantabria.
Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Javier Jorge mail , Kamil Giglio mail ,
Alemany Iturriaga
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Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.
Hafeez Ur Rehman Siddiqui mail , Ambreen Akmal mail , Muhammad Iqbal mail , Adil Ali Saleem mail , Muhammad Amjad Raza mail , Kainat Zafar mail , Aqsa Zaib mail , Sandra Dudley mail , Jon Arambarri mail jon.arambarri@uneatlantico.es, Ángel Gabriel Kuc Castilla mail , Furqan Rustam mail ,
Siddiqui
<a href="/12749/1/fnut-11-1083759.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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In the last decades, the world population and demand for any kind of product have grown exponentially. The rhythm of production to satisfy the request of the population has become unsustainable and the concept of the linear economy, introduced after the Industrial Revolution, has been replaced by a new economic approach, the circular economy. In this new economic model, the concept of “the end of life” is substituted by the concept of restoration, providing a new life to many industrial wastes. Leaves are a by-product of several agricultural cultivations. In recent years, the scientific interest regarding leaf biochemical composition grew, recording that plant leaves may be considered an alternative source of bioactive substances. Plant leaves’ main bioactive compounds are similar to those in fruits, i.e., phenolic acids and esters, flavonols, anthocyanins, and procyanidins. Bioactive compounds can positively influence human health; in fact, it is no coincidence that the leaves were used by our ancestors as a natural remedy for various pathological conditions. Therefore, leaves can be exploited to manufacture many products in food (e.g., being incorporated in food formulations as natural antioxidants, or used to create edible coatings or films for food packaging), cosmetic and pharmaceutical industries (e.g., promising ingredients in anti-aging cosmetics such as oils, serums, dermatological creams, bath gels, and other products). This review focuses on the leaves’ main bioactive compounds and their beneficial health effects, indicating their applications until today to enhance them as a harvesting by-product and highlight their possible reuse for new potential healthy products.
Lucia Regolo mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Yasmany Armas Diaz mail , Bruno Mezzetti mail , Maria Elexpuru Zabaleta mail maria.elexpuru@uneatlantico.es, Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Luca Mazzoni mail ,
Regolo
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Efficient deep learning-based approach for malaria detection using red blood cell smears
Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.
Muhammad Mujahid mail , Furqan Rustam mail , Rahman Shafique mail , Elizabeth Caro Montero mail elizabeth.caro@uneatlantico.es, Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Isabel de la Torre Diez mail , Imran Ashraf mail ,
Mujahid
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In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.
Tumpa Rani Shaha mail , Momotaz Begum mail , Jia Uddin mail , Vanessa Yélamos Torres mail vanessa.yelamos@funiber.org, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Imran Ashraf mail , Md. Abdus Samad mail ,
Shaha