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An Analytical Framework for Innovation Determinants and Their Impact on Business Performance.
Betalains: The main bioactive compounds of Opuntia spp and their possible health benefits in the Mediterranean diet.
Blockchain based transparent and reliable framework for wheat crop supply chain.
Botnet detection in internet of things using stacked ensemble learning model.
Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems.
Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network.
DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network.
Ensemble stacked model for enhanced identification of sentiments from IMDB reviews.
Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance.
Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria.
IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System.
Methodology and content for the design of basketball coach education programs: a systematic review.
Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring.
A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization.
Novel model to authenticate role-based medical users for blockchain-based IoMT devices.
Novel transfer learning approach for hand drawn mathematical geometric shapes classification.
Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model.
Prickly pear fruits from "Opuntia ficus-indica" varieties as a source of potential bioactive compounds in the Mediterranean diet.
Real Word Spelling Error Detection and Correction for Urdu Language.
Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.
A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking Prediction.
Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.
Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis.
A ferramenta SWOT na gestão escolar.
A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.
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Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach.
Detecting Cyberattacks to Federated Learning on Software-Defined Networks.
Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets.
Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective.
Natural Language Processing-Based Software Testing: A Systematic Literature Review.
Relación entre funcionalidad motriz y factores antropométricos de riesgo cardio metabólico en bomberos de la región de Valparaíso, Chile.
SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations.
Use of Fuzzy Approach Methodology and Consensus in Creating a Hierarchy of Satisfaction for Measurement Criteria: Application to Online Training Actions Directed at Classification by Key Competency Profiles in Sales Supervision (SPV) within the Automotive.
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Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model.
Can alpha‐linolenic acid be a modulator of “cytokine storm,” oxidative stress and immune response in SARS‐CoV‐2 infection?
Can the phenolic compounds of Manuka honey chemosensitize colon cancer stem cells? A deep insight into the effect on chemoresistance and self-renewal.
Human‐based new approach methodologies to accelerate advances in nutrition research.
Inequalities and Asymmetries in the Development of Angola’s Provinces: The Impact of Colonialism and Civil War.
A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.
Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba.
PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making.
Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques.
Underwater Wireless Sensor Networks: Enabling Technologies for Node Deployment and Data Collection Challenges.
An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks.
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Deep learning model for detection of brown spot rice leaf disease with smart agriculture.
NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange.
Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.
Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.
População infantil e adolescente nas ruas e estudantes estrangeiros: impactos interculturais no desenvolvimento e no acesso às escolas.
Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study.
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Advancing Nutritional Science: Contemporary Perspectives on Diet’s Role in Metabolic Health and Disease Prevention.
A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.
Emotional Management in Journalism and Communication Studies.
Risk Factors for Eating Disorders in University Students: The RUNEAT Study.
The Scope of Technostress and Care of The Self on Journalists During the Pandemic.
Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band.
A systematic review of deep learning methods for community detection in social networks.
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Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives.
Client engagement solution for post implementation issues in software industry using blockchain.
Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.
Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection.
Detection of cotton crops diseases using customized deep learning model.
DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents.
Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices.
Image Watermarking Using Least Significant Bit and Canny Edge Detection.
IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition.
Modelo geoespacial para priorizar los factores de riesgo ambiental de las comunidades sin alcantarillado sanitario en la cuenca del Río Grande de Loíza en Puerto Rico.
Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms.
Sustentabilidade em sistemas de segurança do trabalho na construção civil.
Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.
An enhanced approach for predicting air pollution using quantum support vector machine.
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An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring.
Anthocyanins: what do we know until now?
Development Agencies and Local Governments—Coexistence within the Same Territory.
Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center.
Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean.
Flavan-3-ols and Vascular Health: Clinical Evidence and Mechanisms of Action.
Instrumentalization of a Model for the Evaluation of the Level of Satisfaction of Graduates under an E-Learning Methodology: A Case Analysis Oriented to Postgraduate Studies in the Environmental Field.
Integration of Sustainable Criteria in the Development of a Proposal for an Online Postgraduate Program in the Projects Area.
Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence.
Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults.
Towards Security Mechanism in D2D Wireless Communication: A 5G Network Approach.
Underrated aspects of a true Mediterranean diet: understanding traditional features for worldwide application of a “Planeterranean” diet.
A violência doméstica no município da Cela, província do Cuanza-Sul –Angola: um fenómeno que tem preocupado o governo e a sociedade.
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Contextual Urdu Lemmatization Using Recurrent Neural Network Models.
PRUS: Product Recommender System Based on User Specifications and Customers Reviews.
Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models.
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Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction.
Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.
Transformer-based ECG classification for early detection of cardiac arrhythmias.
Virtual histopathology methods in medical imaging - a systematic review.
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Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos.
Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential.
Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification.
A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization.
An Optimized Open Pit Mine Application for Limestone Quarry Production Scheduling to Maximize Net Present Value.
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Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants.
Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment.
Correction: Prediction of leukemia peptides using convolutional neural network and protein compositions.
Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data.
Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review.
An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field.
Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support.
Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements.
An Optimized Intelligent Computational Security Model for Interconnected Blockchain-IoT System & Cities.
Prediction of leukemia peptides using convolutional neural network and protein compositions.
A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions.
A deep learning approach for Named Entity Recognition in Urdu language.
A real-time air-writing model to recognize Bengali characters.
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Association between blood cortisol levels and numerical rating scale in prehospital pain assessment.
Association between blood cortisol levels and numerical rating scale in prehospital pain assessment.
Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.
Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.
FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things.
Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?
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Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix.
Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.
Efficient deep learning-based approach for malaria detection using red blood cell smears.
Implementation of photovoltaic energy, sustainability, economic and social development in a Higher Education Institution in Brazil.
Mitigating 5G security challenges for next-gen industry using quantum computing.
Prehospital qSOFA, mSOFA, and NEWS2 performance for sepsis prediction: A prospective, multi-center, cohort study.
Pupilometer efficacy in monitoring anxiety in undergraduate medical students during high-fidelity clinical simulation.
Threatening URDU Language Detection from Tweets Using Machine Learning.
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Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.
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Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos.
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Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease.
Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT.
The Use of Neuroimaging to Assess Associations Among Diet, Nutrients, Metabolic Syndrome, and Alzheimer’s Disease.
Wastewater Treatment with Technical Intervention Inclination towards Smart Cities.
A internacionalização em casa na pós-graduação na América Latina e Caribe na modalidade a distância.
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Emotion Detection Using Facial Expression Involving Occlusions and Tilt.
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Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach.
An Artificial Neural Network Model for Water Quality and Water Consumption Prediction.
Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble.
A Detectability Analysis of Retinitis Pigmetosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images.
Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm.
Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model.
Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.
Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data.
Funcionalidad motriz, estado nutricional e índices antropométricos de riesgo cardiometabólico en adolescentes chilenos de 12 a 15 años.
Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities.
Lifestyle Factors Associated with Children’s and Adolescents’ Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project.
Prediction β-Thalassemia carriers using complete blood count features.
Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.
Software Cost and Effort Estimation: Current Approaches and Future Trends.
White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.
An improved deep convolutional neural network-based YouTube video classification using textual features.
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The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.
An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.
An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation.
Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning.
Carotenoids Intake and Cardiovascular Prevention: A Systematic Review.
A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.
Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance.
Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics.
Diagnosing Training Needs in European Tourism SMEs: The TC-NAV Project for Managing and Overcoming Virulent Crises.
Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city.
An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.
Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.
Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning.
Estudio comparativo entre metodología de aula invertida y metodología tradicional en clases de español, inglés y matemáticas.
Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review.
Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.
Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model.
La comunidad docente y las competencias digitales: la formación a lo largo de la vida.
Molecular epidemiology, transmission and clinical features of 2022‐mpox outbreak: A systematic review.
Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis.
Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh.
Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety.
Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape.
Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19.
Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence.
Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches.
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Current- and Voltage-Actuated Transmission Line Protection Scheme Using a Hybrid Combination of Signal Processing Techniques.
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Perseguindo o inédito viável: a pedagogia freiriana, a necesssidade da linguagem inclusiva e a denúncia à neolíngua do generismo queer.
StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides.
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Efecto de un entrenamiento propioceptivo para prevenir el riesgo de caída en adultos mayores.
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.
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Hybridizing Artificial Intelligence Algorithms for Forecasting of Sediment Load with Multi-Objective Optimization.
Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models.
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Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective.
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Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence
Gender 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.
Adil Ali Saleem mail , Hafeez Ur Rehman Siddiqui mail , Muhammad Amjad Raza mail , Sandra Dudley mail , Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Isabel de la Torre Díez mail ,
Saleem
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Background: Physical activity in children and adolescents represents one of the most important lifestyle factors to determine current and future health. Aim: The aim of the study is to assess the lifestyle and dietary factors linked to physical activity in younger populations across five countries in the Mediterranean region. Design: A total of 2,011 parents of children and adolescents (age range 6–17 years) participating to a preliminary survey of the DELICIOUS project were investigated to determine children's adequate physical activity level (identified using the short form of the international physical activity questionnaire) as well as diet quality parameters [measured as Youth-Healthy Eating Index (Y-HEI)] and eating and lifestyle factors (i.e., meal habits, sleep duration, screen time, etc.). Logistic regression analyses were performed to assess the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between variables of interest. Results: Younger children of younger parents currently working had higher rates and probability to have adequate physical activity. Multivariate analysis showed that children and adolescents who had breakfast (OR = 1.88, 95% CI: 1.38, 2.56) and often ate with their family (OR = 1.80, 95% CI: 0.90, 3.61) were more likely to have an adequate level of physical activity. Children and adolescents who reported a sleep duration (8–10 h) closest to the recommended one were significantly more likely to achieve adequate levels of physical activity (OR = 1.88, 95% CI: 1.38, 2.56). Conversely, those with more than 4 h of daily screen time were less likely to engage in adequate physical activity (OR = 0.77, 95% CI: 0.54, 1.10). Furthermore, children and adolescents in the highest tertile of YEHI scores showed a 60% greater likelihood of engaging in adequate physical activity (OR = 1.60, 95% CI: 1.27, 2.01). Conclusion: These results emphasize the importance of promoting healthy diet and lifestyle habits, including structured and high quality shared meals, sufficient sleep, and screen time moderation, as key strategies to support active behaviors in younger populations. Future interventions should focus on reinforcing these behaviors through parental guidance and community-based initiatives to foster lifelong healthy habits.
Alice Rosi mail , Francesca Scazzina mail , Maria Antonieta Touriz Bonifaz mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Achraf Ammar mail , Khaled Trabelsi mail , Osama Abdelkarim mail , Mohamed Aly mail , Evelyn Frias-Toral mail , Juancho Pons mail , Laura Vázquez-Araújo mail , Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Lorenzo Monasta mail , Nunzia Decembrino mail , Ana Mata mail , Adrián Chacón mail , Pablo Busó mail , Giuseppe Grosso mail ,
Rosi
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A systematic review of deep learning methods for community detection in social networks
Introduction: 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.
Mohamed El-Moussaoui mail , Mohamed Hanine mail , Ali Kartit mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Helena Garay mail helena.garay@uneatlantico.es, Isabel de la Torre Díez mail ,
El-Moussaoui
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Transformer-based ECG classification for early detection of cardiac arrhythmias
Electrocardiogram (ECG) classification plays a critical role in early detection and trocardiogram (ECG) classification plays a critical role in early detection and monitoring cardiovascular diseases. This study presents a Transformer-based deep learning framework for automated ECG classification, integrating advanced preprocessing, feature selection, and dimensionality reduction techniques to improve model performance. The pipeline begins with signal preprocessing, where raw ECG data are denoised, normalized, and relabeled for compatibility with attention-based architectures. Principal component analysis (PCA), correlation analysis, and feature engineering is applied to retain the most informative features. To assess the discriminative quality of the selected features, t-distributed stochastic neighbor embedding (t-SNE) is used for visualization, revealing clear class separability in the transformed feature space. The refined dataset is then input to a Transformer- based model trained with optimized loss functions, regularization strategies, and hyperparameter tuning. The proposed model demonstrates strong performance on the MIT-BIH benchmark dataset, showing results consistent with or exceeding prior studies. However, due to differences in datasets and evaluation protocols, these comparisons are indicative rather than conclusive. The model effectively classifies ECG signals into categories such as Normal, atrial premature contraction (APC), ventricular premature contraction (VPC), and Fusion beats. These results underscore the effectiveness of Transformer-based models in biomedical signal processing and suggest potential for scalable, automated ECG diagnostics. However, deployment in real-time or resource-constrained settings will require further optimization and validation.
Sunnia Ikram mail , Amna Ikram mail , Harvinder Singh mail , Malik Daler Ali Awan mail , Sajid Naveed mail , Isabel De la Torre Díez mail , Henry Fabian Gongora mail henry.gongora@uneatlantico.es, Thania Chio Montero mail ,
Ikram
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Association between blood cortisol levels and numerical rating scale in prehospital pain assessment
Background Nowadays, there is no correlation between levels of cortisol and pain in the prehospital setting. The aim of this work was to determine the ability of prehospital cortisol levels to correlate to pain. Cortisol levels were compared with those of the numerical rating scale (NRS). Methods This is a prospective observational study looking at adult patients with acute disease managed by Emergency Medical Services (EMS) and transferred to the emergency department of two tertiary care hospitals. Epidemiological variables, vital signs, and prehospital blood analysis data were collected. A total of 1516 patients were included, the median age was 67 years (IQR: 51–79; range: 18–103) with 42.7% of females. The primary outcome was pain evaluation by NRS, which was categorized as pain-free (0 points), mild (1–3), moderate (4–6), or severe (≥7). Analysis of variance, correlation, and classification capacity in the form area under the curve of the receiver operating characteristic (AUC) curve were used to prospectively evaluate the association of cortisol with NRS. Results The median NRS and cortisol level are 1 point (IQR: 0–4) and 282 nmol/L (IQR: 143–433). There are 584 pain-free patients (38.5%), 525 mild (34.6%), 244 moderate (16.1%), and 163 severe pain (10.8%). Cortisol levels in each NRS category result in p < 0.001. The correlation coefficient between the cortisol level and NRS is 0.87 (p < 0.001). The AUC of cortisol to classify patients into each NRS category is 0.882 (95% CI: 0.853–0.910), 0.496 (95% CI: 0.446–0.545), 0.837 (95% CI: 0.803–0.872), and 0.981 (95% CI: 0.970–0.991) for the pain-free, mild, moderate, and severe categories, respectively. Conclusions Cortisol levels show similar pain evaluation as NRS, with high-correlation for NRS pain categories, except for mild-pain. Therefore, cortisol evaluation via the EMS could provide information regarding pain status.
Raúl López-Izquierdo mail , Elisa A. Ingelmo-Astorga mail , Carlos del Pozo Vegas mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Ancor Sanz-García mail , Francisco Martín-Rodríguez mail ,
López-Izquierdo