Repositorio Universidad Internacional Iberoamericana
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Gestão de Projetos como Estratégia de Sustentabilidade para o Terceiro Setor na Região Norte do Brasil
No full textConhecer as razões e os percentuais de execuções de projetos como estratégia de sustentabilidade, nas organizações do Terceiro Setor na Região Norte do Brasil e destacar a relevância do assunto para as organizações sem fins lucrativos atuantes nas áreas da Educação, Saúde e Assistência Social. A fundamentação teórica, os procedimentos metodológicos e a pesquisa de campo são os pilares que nortearam a análise dos dados coletados. O instrumento para coleta de dados foi um questionário eletrônico, elaborado num aplicativo específico denominado “pesquisa online”, composto por vinte perguntas e foi aplicado em instituições do Terceiro Setor localizadas nos sete Estados que compõem a Região Norte do Brasil. A amostragem está composta por vinte e uma instituições do Terceiro Setor, sendo dez instituições da área da Educação, dez da área da Saúde e uma da área de Assistência Social. Os dados coletados foram organizados estatisticamente em forma de gráficos e tabelas. O objetivo da pesquisa foi apresentar a importância e viabilidade da utilização de projetos como estratégia de sustentabilidade para transformar desafios ou situações conflitivas em soluções, direcionado para as instituições do Terceiro Setor nas três áreas de atuação. E quando oportuno, divulgar a pesquisa com o intuito de motivar e despertar interesse dos gestores para captar recursos através de projetos e potencializar cada instituição em sua especificidade. Os projetos proporcionam oportunidades para a instituição qualificar os serviços e ambiência; modernizar e avançar no sentido de a viabilidade institucional assegurar os valores e manter a tradição em sua missão sem ser tradicional nas ações. A captação de recursos através de projetos é inerente a capacitação de pessoal para desenvolver tal serviço. Incluindo a responsabilidade no gerenciamento e transparência na prestação de contas, assegurando credibilidade institucional. Enfatizar a importância de usar os recursos tecnológicos em benefício da organização, capacitação de pessoal, obtenção de projetos e divulgação dos efeitos benéficos para a instituição e sociedade
A systematic review of deep learning methods for community detection in social networks
No full textIntroduction: The rapid expansion of generated data through social networks has introduced significant challenges, which underscores the need for advanced methods to analyze and interpret these complex systems. Deep learning has emerged as an effective approach, offering robust capabilities to process large datasets, and uncover intricate relationships and patterns.
Methods: In this systematic literature review, we explore research conducted over the past decade, focusing on the use of deep learning techniques for community detection in social networks. A total of 19 studies were carefully selected from reputable databases, including the ACM Library, Springer Link, Scopus, Science Direct, and IEEE Xplore. This review investigates the employed methodologies, evaluates their effectiveness, and discusses the challenges identified in these works.
Results: Our review shows that models like graph neural networks (GNNs), autoencoders, and convolutional neural networks (CNNs) are some of the most commonly used approaches for community detection. It also examines the variety of social networks, datasets, evaluation metrics, and employed frameworks in these studies.
Discussion: However, the analysis highlights several challenges, such as scalability, understanding how the models work (interpretability), and the need for solutions that can adapt to different types of networks. These issues stand out as important areas that need further attention and deeper research. This review provides meaningful insights for researchers working in social network analysis. It offers a detailed summary of recent developments, showcases the most impactful deep learning methods, and identifies key challenges that remain to be explored
Molecular mechanisms underlying the neuroprotective effects of polyphenols: implications for cognitive function
No full textPolyphenols are naturally occurring compounds that can be found in plant-based foods, including fruits, vegetables, nuts, seeds, herbs, spices, and beverages, the use of which has been linked to enhanced brain health and cognitive function. These natural molecules are broadly classified into two main groups: flavonoids and non-flavonoid polyphenols, the latter including phenolic acids, stilbenes, and tannins. Flavonoids are primarily known for their potent antioxidant properties, which help neutralize harmful reactive oxygen species (ROS) in the brain, thereby reducing oxidative stress, a key contributor to neurodegenerative diseases. In addition to their antioxidant effects, flavonoids have been shown to modulate inflammation, enhance neuronal survival, and support neurogenesis, all of which are critical for maintaining cognitive function. Phenolic acids possess strong antioxidant properties and are believed to protect brain cells from oxidative damage. Neuroprotective effects of these molecules can also depend on their ability to modulate signaling pathways associated with inflammation and neuronal apoptosis. Among polyphenols, hydroxycinnamic acids such as caffeic acid have been shown to enhance blood-brain barrier permeability, which may increase the delivery of other protective compounds to the brain. Another compound of interest is represented by resveratrol, a stilbene extensively studied for its potential neuroprotective properties related to its ability to activate the sirtuin pathway, a molecular signaling pathway involved in cellular stress response and aging. Lignans, on the other hand, have shown promise in reducing neuroinflammation and oxidative stress, which could help slow the progression of neurodegenerative diseases and cognitive decline. Polyphenols belonging to different subclasses, such as flavonoids, phenolic acids, stilbenes, and lignans, exert neuroprotective effects by regulating microglial activation, suppressing pro-inflammatory cytokines, and mitigating oxidative stress. These compounds act through multiple signaling pathways, including NF-κB, MAPK, and Nrf2, and they may also influence genetic regulation of inflammation and immune responses at brain level. Despite their potential for brain health and cognitive function, polyphenols are often characterized by low bioavailability, something that deserves attention when considering their therapeutic potential. Future translational studies are needed to better understand the right dosage, the overall diet, the correct target population, as well as ideal formulations allowing to overcome bioavailability limitations
Detection and classification of brain tumor using a hybrid learning model in CT scan images
No full textAccurate diagnosis of brain tumors is critical in understanding the prognosis in terms of the type, growth rate, location, removal strategy, and overall well-being of the patients. Among different modalities used for the detection and classification of brain tumors, a computed tomography (CT) scan is often performed as an early-stage procedure for minor symptoms like headaches. Automated procedures based on artificial intelligence (AI) and machine learning (ML) methods are used to detect and classify brain tumors in Computed Tomography (CT) scan images. However, the key challenges in achieving the desired outcome are associated with the model’s complexity and generalization. To address these issues, we propose a hybrid model that extracts features from CT images using classical machine learning. Additionally, although MRI is a common modality for brain tumor diagnosis, its high cost and longer acquisition time make CT scans a more practical choice for early-stage screening and widespread clinical use. The proposed framework has different stages, including image acquisition, pre-processing, feature extraction, feature selection, and classification. The hybrid architecture combines features from ResNet50, AlexNet, LBP, HOG, and median intensity, classified using a multilayer perceptron. The selection of the relevant features in our proposed hybrid model was extracted using the SelectKBest algorithm. Thus, it optimizes the proposed model performance. In addition, the proposed model incorporates data augmentation to handle the imbalanced datasets. We employed a scoring function to extract the features. The Classification is ensured using a multilayer perceptron neural network (MLP). Unlike most existing hybrid approaches, which primarily target MRI-based brain tumor classification, our method is specifically designed for CT scan images, addressing their unique noise patterns and lower soft-tissue contrast. To the best of our knowledge, this is the first work to integrate LBP, HOG, median intensity, and deep features from both ResNet50 and AlexNet in a structured fusion pipeline for CT brain tumor classification. The proposed hybrid model is tested on data from numerous sources and achieved an accuracy of 94.82%, precision of 94.52%, specificity of 98.35%, and sensitivity of 94.76% compared to state-of-the-art models. While MRI-based models often report higher accuracies, the proposed model achieves 94.82% on CT scans, within 3–4% of leading MRI-based approaches, demonstrating strong generalization despite the modality difference. The proposed hybrid model, combining hand-crafted and deep learning features, effectively improves brain tumor detection and classification accuracy in CT scans. It has the potential for clinical application, aiding in early and accurate diagnosis. Unlike MRI, which is often time-intensive and costly, CT scans are more accessible and faster to acquire, making them suitable for early-stage screening and emergency diagnostics. This reinforces the practical and clinical value of the proposed model in real-world healthcare settings
Botnet detection in internet of things using stacked ensemble learning model
No full textBotnets are used for malicious activities such as cyber-attacks, spamming, and data theft and have become a significant threat to cyber security. Despite existing approaches for cyber attack detection, botnets prove to be a particularly difficult problem that calls for more advanced detection methods. In this research, a stacking classifier is proposed based on K-nearest neighbor, support vector machine, decision tree, random forest, and multilayer perceptron, called KSDRM, for botnet detection. Logistic regression acts as the meta-learner to combine the predictions from the base classifiers into the final prediction with the aim of increasing the overall accuracy and predictive performance of the ensemble. The UNSW-NB15 dataset is used to train machine learning models and evaluate their effectiveness in detecting cyber-attacks on IoT networks. The categorical features are transformed into numerical values using label encoding. Machine learning techniques are adopted to recognize botnet attacks to enhance cyber security measures. The KSDRM model successfully captures the complex patterns and traits of botnet attacks and obtains 99.99% training accuracy. The KSDRM model also performs well during testing by achieving an accuracy of 97.94%. Based on 3, 5, 7, and 10 folds, the k-fold cross-validation results show that the proposed method’s average accuracy is 99.89%, 99.88%, 99.89%, and 99.87%, respectively. Further, the demonstration of experiments and results shows the KSDRM model is an effective method to identify botnet-based cyber attacks. The findings of this study have the potential to improve cyber security controls and strengthen networks against changing threats
Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence
No full textGender classification plays a vital role in various applications, particularly in security and healthcare. While several biometric methods such as facial recognition, voice analysis, activity monitoring, and gait recognition are commonly used, their accuracy and reliability often suffer due to challenges like body part occlusion, high computational costs, and recognition errors. This study investigates gender classification using gait data captured by Ultra-Wideband radar, offering a non-intrusive and occlusion-resilient alternative to traditional biometric methods. A dataset comprising 163 participants was collected, and the radar signals underwent preprocessing, including clutter suppression and peak detection, to isolate meaningful gait cycles. Spectral features extracted from these cycles were transformed using a novel integration of Feedforward Artificial Neural Networks and Random Forests , enhancing discriminative power. Among the models evaluated, the Random Forest classifier demonstrated superior performance, achieving 94.68% accuracy and a cross-validation score of 0.93. The study highlights the effectiveness of Ultra-wideband radar and the proposed transformation framework in advancing robust gender classification
Ensemble stacked model for enhanced identification of sentiments from IMDB reviews
No full textThe emergence of social media platforms led to the sharing of ideas, thoughts, events, and reviews. The shared views and comments contain people’s sentiments and analysis of these sentiments has emerged as one of the most popular fields of study. Sentiment analysis in the Urdu language is an important research problem similar to other languages, however, it is not investigated very well. On social media platforms like X (Twitter), billions of native Urdu speakers use the Urdu script which makes sentiment analysis in the Urdu language important. In this regard, an ensemble model RRLS is proposed that stacks random forest, recurrent neural network, logistic regression (LR), and support vector machine (SVM). The Internet Movie Database (IMDB) movie reviews and Urdu tweets are examined in this study using Urdu sentiment analysis. The Urdu hack library was used to preprocess the Urdu data, which includes preprocessing operations including normalizing individual letters, merging them, including spaces, etc. concerning punctuation. The problem of accurately encoding Urdu characters and replacing Arabic letters with their Urdu equivalents is fixed by the normalization module. Several models are adopted in this study for extensive evaluation of their accuracy for Urdu sentiment analysis. While the results promising, among machine learning models, the SVM and LR attained an accuracy of 87%, according to performance criteria such as F-measure, accuracy, recall, and precision. The accuracy of the long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) was 84%. The suggested ensemble RRLS model performs better than other learning algorithms and achieves a 90% accuracy rate, outperforming current methods. The use of the synthetic minority oversampling technique (SMOTE) is observed to improve the performance and lead to 92.77% accuracy
Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images
No full textMango is one of the most beloved fruits and plays an indispensable role in the agricultural economies of many tropical countries like Pakistan, India, and other Southeast Asian countries. Similar to other fruits, mango cultivation is also threatened by various diseases, including Anthracnose and Red Rust. Although farmers try to mitigate such situations on time, early and accurate detection of mango diseases remains challenging due to multiple factors, such as limited understanding of disease diversity, similarity in symptoms, and frequent misclassification. To avoid such instances, this study proposes a multimodal deep learning framework that leverages both leaf and fruit images to improve classification performance and generalization. Individual CNN-based pre-trained models, including ResNet-50, MobileNetV2, EfficientNet-B0, and ConvNeXt, were trained separately on curated datasets of mango leaf and fruit diseases. A novel Modality Attention Fusion (MAF) mechanism was introduced to dynamically weight and combine predictions from both modalities based on their discriminative strength, as some diseases are more prominent on leaves than on fruits, and vice versa. To address overfitting and improve generalization, a class-aware augmentation pipeline was integrated, which performs augmentation according to the specific characteristics of each class. The proposed attention-based fusion strategy significantly outperformed individual models and static fusion approaches, achieving a test accuracy of 99.08%, an F1 score of 99.03%, and a perfect ROC-AUC of 99.96% using EfficientNet-B0 as the base. To evaluate the model’s real-world applicability, an interactive web application was developed using the Django framework and evaluated through out-of-distribution (OOD) testing on diverse mango samples collected from public sources. These findings underline the importance of combining visual cues from multiple organs of plants and adapting model attention to contextual features for real-world agricultural diagnostics
Factores determinantes en el desempeño de los emprendimientos empresariales a escala local en Costa Rica. Herramienta metodológica para su identificación, evaluación y diseño de estrategias para su potenciación
No full textLa presente tesis doctoral tiene como objetivo desarrollar una herramienta metodológica para la identificación y evaluación de los factores determinantes del desempeño en los emprendimientos empresariales y la elaboración de estrategias para su potenciación o atenuación a escala local en Costa Rica, tomando como objeto de estudio la provincia de Guanacaste. Se planteó un estudio descriptivo-explicativo, de tipo no experimental, transeccional correlacional, basado en el enfoque cuantitativo de investigación. Los datos fueron recopilados mediante encuestas con preguntas de tipo escala Likert. En donde participaran 468 emprendimientos, seleccionados con una muestra probabilística estratificada. Además del uso de la técnica Delphi a un grupo de 150 expertos. El análisis de datos se realizó con la ayuda de los sistemas informáticos Microsoft Excel, Software IBM SPSS Statistic, AHP Decision y SmartPLS para establecer valores y resultados más exactos y precisos considerando un modelo ecuaciones estructurales PLS-SEM para validar hipótesis. Los resultados de la investigación permitieron la identificación de 47 factores determinantes agrupados en 4 dimensiones: las dimensiones de factores de capital humano, psicológicos, endógenos y exógenos. Asimismo, los factores como: perfil socioeconómico, la ética laboral, la autoestima, las relaciones interpersonales, la orientación al cliente, el conocimiento de la tecnología, microeconómicos y comerciales presentan un factor de ponderación promedio de 30,75 siendo de los factores con mayor impacto en el desempeño, por lo que aquellos emprendimientos que desarrollen sus estrategias a partir de estos determinantes estarán más cerca de lograr el éxito, de tal manera que la herramienta metodológica propuesta contribuye a la toma de decisiones de política pública costarricense. La herramienta metodológica desarrollada en esta investigación contribuye a garantizar el éxito de los emprendimientos empresariales a escala local en Costa Rica permitiendo tener un punto de referencia para la toma de decisiones, asesoramiento, realimentación, evaluación, planeación y control de las personas emprendedoras y de todas aquellas instituciones públicas o privadas que deseen involucrarse con el tema de los negocios emprendedores
Virtual Patient (E+DIETing_LAB)
No full textSe 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_LA