Repositorio Universidad Europea del Atlántico
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Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review
Full text linkAccurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids
Deep learning-assisted 3D model for the detection and classification of knee arthritis
Full text linkOsteoarthritis (OA) affects nearly 240 million people worldwide. It is a common degenerative illness that typically affects the knee joint OA causes pain, and functional disability, especially in older adults is a common disease. One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis involves observing X-ray images of the knee area and classifying it into different five grades. This requires the physician's expertise, suitable experience, and a lot of time, and even after that, the diagnosis can be prone to errors. Therefore, researchers in the machine learning (ML) and deep learning (DL) domains have employed the capabilities of deep neural network (DNN) models to identify and classify medical images in an automated, faster, and more accurate manner. Combining multiple imaging modalities or utilizing three-dimensional reconstructions can enhance the accuracy and completeness of 2D Images in diagnostic information. Hence to overcome the drawbacks of 2D imaging, the reconstruction of 3D models using 2D images is the main theme of our research. In this paper, we propose a deep learning-based model for the detection and classification of the early diagnosis of arthritis. It is a four-step procedure starting with data collection followed by data conversion. In this step, our proposed model deforms the target's convex hull to produce a 3D model. Herein, a series of 2D photos is utilized, along with surface rendering methods, to create a 3D model. In the third step, the feature extraction is performed followed by mesh refinement. The chamfer loss is optimized based on the rotational shape of the leg bones, and subsequently, the weight of the loss function can be allocated to the target's geometric properties. We have used a modified Gray Level Co-occurrence Matrix (GLCM) for feature extraction. In the fourth step, the image classification is performed and the suggested optimization strategy raises the model's accuracy. A comparison of results with current 3D reconstruction techniques proves that the suggested method can consistently produce a waterproof model with a greater reconstruction accuracy. The deep-seated intricacies and distinct patterns across arthritic phases are estimated through the extraction of complicated statistical variables combined with power spectral density. The high-dimensional data is divided into separate, easily observable groups using the Lion Optimization Algorithm and proposed distance metric. The F1 Score and Jaccard Metric showed an average of 0.85 and 0.23, indicating effective differentiation across clusters
Cidadania digital e EJA: análise qualitativa da ausência de educação midiática em contextos periféricos
Full text linkAnalisamos o consumo digital de estudantes da EJA e sua relação com a circulação de desinformação em contextos periféricos, como o distrito do Itaim Paulista. Empregamos metodologia qualitativa com observação participante e grupos focais. Verificamos falta generalizada de habilidades críticas para navegar no ciberespaço e alta exposição à desinformação: 57,14% dos participantes relataram contato diário com notícias falsas e 81% nunca tiveram qualificação formal em Alfabetização Midiática e Informacional. Conclui-se que programas de educação midiática podem mitigar o consumo de desinformação intencional, mal-entendidos involuntários e fomentar a participação cívica em comunidades periféricas
Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis
Full text linkIn the world of remote sensing, hyperspectral imaging has emerged as a powerful tool that captures incredibly detailed information about our environment. These images contain hundreds of spectral bands that reveal what the human eye cannot see, making them invaluable for applications ranging from precision agriculture to environmental monitoring. However, extracting insights from complex data requires sophisticated analytical approaches. Our research dives into the performance comparison of two popular machine learning approaches: the support vector machine (SVM) and the more recent deep learning-based stacked autoencoder (SAE). We wanted to understand which approach works better under different real-world conditions that researchers and practitioners face. Through extensive experiments across five diverse public hyperspectral datasets, we discovered that the choice between these models is not straightforward, it depends significantly on your specific circumstances. When labeled data are scarce, which is a common challenge in remote sensing, SVM proves more reliable and efficient. Conversely, when abundant training data are available, SAE demonstrates impressive capabilities in learning complex patterns. One interesting finding was how active learning as a technique that intelligently selects the most informative samples for labeling, improved SAE’s performance on medium-sized datasets, potentially offering a practical solution to the data scarcity problem. The proposed approaches showed vulnerability to noise, highlighting the importance of preprocessing steps in real-world applications. Although SVM generally requires less computational resources, SAE’s potential to handle large and complex datasets makes it an attractive option when the appropriate computing infrastructure is available. The model training also achieved high accuracy, compared to other models published in the literature. The results achieved provide a practical path for researchers and practitione..
Análisis comparativo de dos propuestas de entrenamiento concurrente en la condición física en mujeres adultas con sobrepeso
Full text linkEl entrenamiento concurrente (EC) es una herramienta eficaz para mejorar la condición física (CF) y composición corporal (CC) en personas con sobrepeso. El objetivo principal de esta investigación fue comparar si existían diferencias significativas entre dos grupos de EC en diferentes variables relacionadas con la CF. La muestra estuvo compuesta por 11 mujeres adultas con sobrepeso y un nivel de actividad física moderado medido a través del International Physical Activity Questionnaire (IPAQ). A pesar de no encontrarse diferencias significativas al comparar ambos grupos en ninguna de las variables analizadas, se observó una mejora en todas las variables respecto al inicio de la intervención en ambos grupos. El grupo de EC intra-sesión obtuvo mayores cambios respecto al inicio en todas las variables, a excepción del índice de masa corporal (IMC), fuerza máxima en press de pecho con mancuernas y flexibilidad del tren superior, donde se obtuvieron mayores beneficios en el grupo de EC intra-microciclo. Se concluye que ambos programas de entrenamiento podrían mejorar la CF de mujeres con sobrepeso y un nivel moderado de AF tras un periodo de 6 semanas de intervención con esta metodología de entrenamiento, pudiendo resultar más beneficioso en esta población el EC intra-sesión si el objetivo es la mejora general de la CF
Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images
Full text linkMango 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
An improved hybrid image steganography method using AES algorithm
Full text linkImage steganography is the process of hiding information, which can be text, image, or video inside a cover image. Recent steganography literature hasn’t addressed the problem of loss of secret information during extraction and reliability. Hence, to reduce information loss and provide reliability between in the basic criteria, Herein, we proposed a hybrid method that utilizes the least significant bit (LSB) substitution, transppsition, magic matrix, key and Advance Encrytion Standard (AES) algorithm. The LSB method decreases embedding errors by implementing a new value difference algorithm. In addition, to improves the reliability between the basic criterion for image steganography we used transposition, magic matrix, key and AES. The proposed method ensures a high-quality image format in the RGB color model to conceal the hidden message within the cover image which is jpeg. The proposed hybrid method performed several experiments and these are mainly based on quality assessment metrics such as PSNR, SSIM, RMSE, NCC, etc. which showed better results. The proposed method also analyzed with different perspectives in terms of different dimensions of images and different sizes of message text which showed better results. In addition, the performance of the proposed method showed better results based on (regular and singular) steganalysis, noise, and cropping attacks. The security analyses such as key space, differential, and statistical attacks show that the proposed scheme is secure and robust against channel noise and JPEG compression
On the correlation between Google Play Store application icons and downloads
No full textIcons are the first visual element users encounter when searching for applications in online store. Icons with eye-catching features can make an app stand out in user searches, playing a crucial role in attracting user attention and influencing selection. This increases the likelihood of downloads, which can expand the user base, improve revenue, and enhance engagement, contributing to the application’s overall success. However, the majority of research focused on evaluating appeal of apps through application icons is empirical in nature and may lack comprehensive data analytical approaches. While empirical research holds its significance, it may still be limited by the size of the dataset analyzed and could also be subjective. This proposed research presents a novel data-analytical methodology to analyze a large dataset of application icons from Google Play to determine their influence on downloads. It clusters the icons using three different techniques:
-means clustering with two distinct feature vectors and agglomerative clustering, extracting various visual features from the clusters that are strongly correlated with application installs. Subsequently, validation of results has revealed that factors of varied colors, the dominance of white or black colors, text, and exposure in the icons can be linked to downloads
La pose como acto social: tecnología y representación en el retrato foto-gráfico femenino, de la solemnidad al "selfie"
No full textSe realiza un recorrido por la evolución de la pose corporal en el retrato
fotográfico popular desde los primeros años de la fotografía, a mediados del
siglo XIX, hasta las primeras décadas del siglo XXI, con el objetivo de analizar si cada hito tecnológico ha establecido nuevos estilos o cánones que
han influido en la representación del individuo en la imagen. El estudio se
centra en retratos tipo selfie protagonizados por mujeres, con el fin de examinar cómo estos procesos inciden en su exposición, idealización e identidad
visual. Se adopta un enfoque cualitativo basado en el análisis documental de
fuentes teóricas entre ellas Fontcuberta, Sontag, Eco, Goffman, Berger y
Freund para reflexionar sobre el papel de la pose fotográfica en la representación del individuo y su relación con los contextos culturales, visuales y
técnicos. Se identifican momentos clave, como el daguerrotipo, las cámaras
de 35 mm la digitalización y la incorporación de la cámara al móvil, transformado cómo las personas se muestran ante la cámara. La investigación
concluye que la elección de la pose no es un acto aislado, sino que responde
a un proceso de imitación y adaptación a modelos visuales predominantes
en la sociedad
Association of planetary health diet indices with diet composition, nutritional quality and environmental impacts in Italian adults
No full textBackground and aims
Sustainable diets are increasingly recognized as a key strategy to promote human health while reducing environmental impacts. The Planetary Health Diet (PHD) provides a global framework for sustainable and healthy eating patterns, but evidence on its adherence and implications in specific populations is still limited. The aim of this study was to test the level of adherence, the environmental impact, and the nutritional quality of several scores assessing the level of adherence to the PHD in a cohort of Italian individuals.
Methods and results
Dietary habits were assessed through validated food frequency questionnaires while various scores have been applied to evaluate the level of adherence to PHD (ELD-I, EAT, PHDI-Cacau, NB-EAT, PHDI-Bui) in 1936 Italian adults, using the Mediterranean diet (MEDI-LITE) as reference. The environmental impact was quantified as carbon and water footprints (CF and WF) using the SU-EATABLE LIFE database. Higher adherence to PHD-related indices generally corresponded to healthier nutrient profiles, higher fiber intake, and better concordance with Italian dietary recommendations, although some indices predicted lower intake of certain nutrients (e.g., vitamin B12, calcium). The MEDI-LITE index consistently predicted higher adequacy across dietary and nutrient recommendations. Absolute CF and WF showed mixed trends across indices, while energy-standardized values (per 1000 kcal) indicated lower impacts for all PHD-related scores, apart from the ELD-I. Adherence to the Mediterranean diet was also associated with favorable energy-adjusted environmental outcomes.
Conclusion
These findings reinforce the existing alignment between the intrinsic characteristics of the Mediterranean diet with both nutrition and sustainability objectives