Repositorio Universidad Internacional Iberoamericana
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Modelo de gestión de responsabilidad social en universidades. Caso: Universidad Tecnológica de Panamá
No full textLa responsabilidad social universitaria es un tema de alta resonancia en los foros internacionales, pues las universidades son las responsables de formar a las generaciones de relevo, y es menester que, los formen con conciencia social y responsabilidad ambiental. Por ello, para la UTP es fundamental que se establezca un modelo de gestión de RSU que promueva una mayor conciencia social y ambiental, así como mayores vínculos con la comunidad y se realcen verdaderos impactos en la sociedad, mediante la ejecución de proyectos de gran envergadura. En consecuencia, se plateó una investigación bajo el paradigma positivista, con un enfoque cuantitativo y nivel descriptivo con diseño no experimental de campo, mediante la aplicación de dos instrumentos sobre una muestra de 314 y 378, profesores y estudiantes, respectivamente. Los principales hallazgos de la investigación fueron que el modelo teórico de gestión de RSU que mejor se adapta a las necesidades y estructura de la UTP es el propuesto por URSULA. También, se pudo saber que profesores y estudiantes ubican a la UTP en un nivel medio de ejecución de RSU porque hay poca participación de la comunidad en la decisión de los proyectos, los estudiantes no están motivados a participar y los docentes no incorporan suficientes contenidos sociales y/o ambientales en las asignaturas; tampoco se observan sinergias entre las distintas funciones universitarias, ni desarrollo de investigaciones conjuntas entre distintas disciplinas. Finalmente, se propone un modelo teórico de gestión de RSU basado en el modelo URSULA, pero adaptado a las características de la UTP. Asimismo, se propone una matriz de indicadores fácilmente asimilable por la universidad para iniciar la implementación del modelo propuesto
La Evaluación de la Cultura Organizacional en Universidades Cubanas
No full textLa investigación aborda como problema científico: ¿Cómo evaluar integralmente la cultura organizacional en universidades cubanas? Su contribución a la teoría se concreta en un modelo de evaluación de la cultura organizacional en universidades cubanas, diseñado con una estructura y funcionamiento sistémico que garantiza la integralidad de dicho proceso de evaluación y su mejora continua, lo que delimita a su vez la novedad científica de la investigación y explica dicho proceso a partir del subsistema orientador, ejecutor y dinamizador, y de las relaciones y cualidades devenidas de su interacción. El aporte práctico es un procedimiento que, mediante sus etapas, fases y acciones, posibilita la concreción del proceso evaluativo al ofrecer una guía para su implementación organizada y dinámica. En este procedimiento se revela además la relación esencial inherente al proceso modelado, que se manifiesta entre la intencionalidad evaluativa, la pertinencia evaluativa y la continuidad evaluativa de la cultura organizacional, de la que resulta la integralidad de la evaluación de la cultura organizacional en las universidades. La estrategia metodológica utilizada para corroborar el valor científico delas propuestas, se apoyó en el método de criterio de expertos y el taller de socialización, lo que demostró la pertinencia del modelo y el procedimiento
Prediction of leukemia peptides using convolutional neural network and protein compositions
No full textLeukemia is a type of blood cell cancer that is in the bone marrow’s blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy
Federated Learning on Internet of Things: Extensive and Systematic Review
No full textThe proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles. A search was performed in the IEEE, Elsevier, Arxiv, ACM, and WOS databases and 92 articles were finally examined. Inclusion measures were published in English and with the keywords “FL” and “IoT”. The methodology begins with an overview of recent advances in FL and the IoT, followed by a discussion of how these two technologies can be integrated. To be more specific, we examine and evaluate the capabilities of FL by talking about communication protocols, frameworks and architecture. We then present a comprehensive analysis of the use of FL in a number of key IoT applications, including smart healthcare, smart transportation, smart cities, smart industry, smart finance, and smart agriculture. The key findings from this analysis of FL IoT services and applications are also presented. Finally, we performed a comparative analysis with FL IID (independent and identical data) and non-ID, traditional centralized deep learning (DL) approaches. We concluded that FL has better performance, especially in terms of privacy protection and resource utilization. FL is excellent for preserving privacy because model training takes place on individual devices or edge nodes, eliminating the need for centralized data aggregation, which poses significant privacy risks. To facilitate development in this rapidly evolving field, the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT
Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing
No full textWith the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health. Continuous patient monitoring is a hectic practice in hospitals with limited staff; in a pandemic situation like COVID-19, it becomes much more difficult practice when hospitals are working at full capacity and there is still a risk of medical workers being infected. In this study, we propose an Internet of Things (IoT)-based patient health monitoring system that collects real-time data on important health indicators such as pulse rate, blood oxygen saturation, and body temperature but can be expanded to include more parameters. Our system is comprised of a hardware component that collects and transmits data from sensors to a cloud-based storage system, where it can be accessed and analyzed by healthcare specialists. The ESP-32 microcontroller interfaces with the multiple sensors and wirelessly transmits the collected data to the cloud storage system. A pulse oximeter is utilized in our system to measure blood oxygen saturation and body temperature, as well as a heart rate monitor to measure pulse rate. A web-based interface is also implemented, allowing healthcare practitioners to access and visualize the collected data in real-time, making remote patient monitoring easier. Overall, our IoT-based patient health monitoring system represents a significant advancement in remote patient monitoring, allowing healthcare practitioners to access real-time data on important health metrics and detect potential health issues before they escalate
Efficacy and classification of Sesamum indicum linn seeds with Rosa damascena mill oil in uncomplicated pelvic inflammatory disease using machine learning
No full textBackground and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine are presently being studied with an eye of great curiosity and hope. Hence, complementary and alternative treatments for uncomplicated pelvic inflammatory disease (uPID) are explored for their efficacy. Therefore, this study determined the therapeutic efficacy and safety of Sesamum indicum Linn seeds with Rosa damascena Mill Oil in uPID with standard control. Additionally, we analyzed the data with machine learning.
Materials and methods: We included 60 participants in a double-blind, double-dummy, randomized standard-controlled study. Participants in the Sesame and Rose oil group (SR group) (n = 30) received 14 days course of black sesame powder (5 gm) mixed with rose oil (10 mL) per vaginum at bedtime once daily plus placebo capsules orally. The standard group (SC), received doxycycline 100 mg twice and metronidazole 400 mg thrice orally plus placebo per vaginum for the same duration. The primary outcome was a clinical cure at post-intervention for visual analogue scale (VAS) for lower abdominal pain (LAP), and McCormack pain scale (McPS) for abdominal-pelvic tenderness. The secondary outcome included white blood cells (WBC) cells in the vaginal wet mount test, safety profile, and health-related quality of life assessed by SF-12. In addition, we used AdaBoost (AB), Naïve Bayes (NB), and Decision Tree (DT) classifiers in this study to analyze the experimental data.
Results: The clinical cure for LAP and McPS in the SR vs SC group was 82.85% vs 81.48% and 83.85% vs 81.60% on Day 15 respectively. On Day 15, pus cells less than 10 in the SR vs SC group were 86.6% vs 76.6% respectively. No adverse effects were reported in both groups. The improvement in total SF-12 score on Day 30 for the SR vs SC group was 82.79% vs 80.04% respectively. In addition, our Naive Bayes classifier based on the leave-one-out model achieved the maximum accuracy (68.30%) for the classification of both groups of uPID.
Conclusion: We concluded that the SR group is cost-effective, safer, and efficacious for curing uPID. Proposed alternative treatment (test drug) could be a substitute of standard drug used for Female genital tract infections
Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning
No full textThe perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models
Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization
No full textWith the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce. This study aims to address the HSD task on Twitter using Roman Urdu text. The contribution of this research is the development of a hybrid model for Roman Urdu HSD, which has not been previously explored. The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. To further enhance model performance, we employ several hyperparameter optimization (HPO) techniques, including Grid Search (GS), Randomized Search (RS), and Bayesian Optimization with Gaussian Processes (BOGP). Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. On the HS-RU-20 corpus, this model attained an accuracy of 0.93 and an F1 score of 0.95 for the Neutral-Hostile classification task, and an accuracy of 0.89 with an F1 score of 0.88 for the Hate Speech-Offensive task. On the RUHSOLD corpus, the same model achieved an accuracy of 0.95 and an F1 score of 0.94 for the Coarse-grained task, alongside an accuracy of 0.87 and an F1 score of 0.84 for the Fine-grained task. These results demonstrate the effectiveness of our hybrid approach for Roman Urdu hate speech detection
Una estrategia de inclusión a través del efecto Pigmalión para fortalecer las condiciones para el proceso de enseñanza y aprendizaje en nivel VIII de inglés en una universidad privada en Colombia
No full textEl objetivo de esta investigación fue proponer oportunidades para mejorar el desempeño académico de los estudiantes de nivel VIII de inglés de la Universidad a través de estrategias socio-emocionales al mismo tiempo que se puede lograr la inclusión de todos los estudiantes del grupo teniendo en cuenta la contingencia generada por el COVID-19. Las relaciones afectivas tienen impacto e importancia al momento de enfrentar dificultades de aprendizaje de inglés y lograr que haya inclusión. En el contexto de la pandemia dada por el COVID-19 se agravaron las condiciones de aprendizaje al migrar a la virtualidad. La enseñanza remota no resolvió los problemas de aprendizaje y enseñanza que en este contexto generó. De esta manera la afectividad fue la mejor herramienta para una enseñanza inclusiva que permitió fortalecer las condiciones de aprendizaje y enseñanza en las clases de inglés para que puedan aprender en entornos virtuales y en otros entornos. Esta investigación tuvo un enfoque mixto. Se enmarcó dentro del diseño no experimental transeccional/transversal descriptivo una de cuyas características es su “enfoque en el estudio de la realidad en su dinámica natural” (Hernández-Sampieri, 2018). Se pretendió con este tipo de diseño de investigación “describir, explicar y predecir la realidad, desde una aproximación a su dinámica natural” (ídem). Se creía que la barrera más importante está en la formación del docente cuya trayectoria se basa en pedagogías tradicionales y que no han sido fácilmente adaptables a los entornos digitales creando en el profesor un grado alto de frustración que se ve reflejado en el bajo desempeño académico de los estudiantes de inglés. Se logró identificar cuáles son las variables que intervienen en el desempeño académico de los estudiantes en la clase de inglés. Además, se identificó la incidencia motivacional que tiene el profesor en el éxito o fracaso en el aprendizaje de lenguas extranjeras en una universidad privada en Colombia
A Detectability Analysis of Retinitis Pigmetosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images
No full textRetinitis pigmentosa (RP) is a group of genetic retinal disorders characterized by progressive vision loss, culminating in blindness. Identifying pigment signs (PS) linked with RP is crucial for monitoring and possibly slowing the disease’s degenerative course. However, the segmentation and detection of PS are challenging due to the difficulty of distinguishing between PS and blood vessels and the variability in size, shape, and color of PS. Recently, advances in deep learning techniques have shown impressive results in medical image analysis, especially in ophthalmology. This study presents an approach for classifying pigment marks in color fundus images of RP using a modified squeeze-and-excitation ResNet (SE-ResNet) architecture. This variant synergizes the efficiency of residual skip connections with the robust attention mechanism of the SE block to amplify feature representation. The SE-ResNet model was fine-tuned to determine the optimal layer configuration that balances performance metrics and computational costs. We trained the proposed model on the RIPS dataset, which comprises images from patients diagnosed at various RP stages. Experimental results confirm the efficacy of the proposed model in classifying different types of pigment signs associated with RP. The model yielded performance metrics, such as accuracy, sensitivity, specificity, and f-measure of 99.16%, 97.70%, 96.93%, 90.47%, 99.37%, 97.80%, 97.44%, and 90.60% on the testing set, based on GT1 & GT2 respectively. Given its performance, this model is an excellent candidate for integration into computer-aided diagnostic systems for RP, aiming to enhance patient care and vision-related healthcare services