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2025
Association between blood cortisol levels and numerical rating scale in prehospital pain assessment.
Botnet detection in internet of things using stacked ensemble learning model.
Chronotype and Cancer: Emerging Relation Between Chrononutrition and Oncology from Human Studies.
Client engagement solution for post implementation issues in software industry using blockchain.
Clinical epidemiology of dengue and COVID-19 co-infection among the residents in Dhaka, Bangladesh, 2021-2023: A cross-sectional study.
Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis.
Detection of cotton crops diseases using customized deep learning model.
Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization.
Ensemble stacked model for enhanced identification of sentiments from IMDB reviews.
Fundus image classification using feature concatenation for early diagnosis of retinal disease.
Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices.
Incorporating soil information with machine learning for crop recommendation to improve agricultural output.
Mediterranean Diet and Quality of Life in Adults: A Systematic Review.
Metaheuristic-based optimal energy assessment of hybrid multi-effect evaporator with synergy of solar and wind energy sources.
Methodology and content for the design of basketball coach education programs: a systematic review.
Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.
Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.
Novel transfer learning approach for hand drawn mathematical geometric shapes classification.
Novel transfer learning based bone fracture detection using radiographic images.
Novel transfer learning based bone fracture detection using radiographic images.
Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults.
Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors.
Pupilometer efficacy in monitoring anxiety in undergraduate medical students during high-fidelity clinical simulation.
Strawberry as a health promoter: an evidence-based review. Where are we 10 years later?
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.
Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence.
A systematic review of deep learning methods for community detection in social networks.
2024
Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment.
Advancement in medical report generation: current practices, challenges, and future directions.
Carotenoids Intake and Cardiovascular Prevention: A Systematic Review.
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.
A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.
Correction: Prediction of leukemia peptides using convolutional neural network and protein compositions.
Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential.
Deep transfer learning-based bird species classification using mel spectrogram images.
DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network.
Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data.
Efficient deep learning-based approach for malaria detection using red blood cell smears.
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.
Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence.
Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review.
Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects.
Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.
Flavan-3-ols and Vascular Health: Clinical Evidence and Mechanisms of Action.
Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model.
Investigation of structural frustration in symmetric diblock copolymers confined in polar discs through cell dynamic simulation.
Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?
Lifestyle Factors Associated with Children’s and Adolescents’ Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project.
Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence.
Natural Language Processing-Based Software Testing: A Systematic Literature Review.
Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba.
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.
Prediction of leukemia peptides using convolutional neural network and protein compositions.
PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making.
Resveratrol and vascular health: evidence from clinical studies and mechanisms of actions related to its metabolites produced by gut microbiota.
Risk Factors for Eating Disorders in University Students: The RUNEAT Study.
Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.
Side effects associated with homogenous and heterogenous doses of Oxford–AstraZeneca vaccine among adults in Bangladesh: an observational study.
Side effects associated with homogenous and heterogenous doses of Oxford–AstraZeneca vaccine among adults in Bangladesh: an observational study.
Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models.
Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.
StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides.
Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.
Underwater Thermal Energy Harvesting: Frameworks, Challenges, Applications, and Future Investigation.
Virtual histopathology methods in medical imaging - a systematic review.
Youth Healthy Eating Index (YHEI) and Diet Adequacy in Relation to Country-Specific National Dietary Recommendations in Children and Adolescents in Five Mediterranean Countries from the DELICIOUS Project.
A deep learning approach to optimize remaining useful life prediction for Li-ion batteries.
An improved deep convolutional neural network-based YouTube video classification using textual features.
2023
Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field.
Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study.
Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis.
Software Cost and Effort Estimation: Current Approaches and Future Trends.
A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective.
A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis.
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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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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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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
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Botnet detection in internet of things using stacked ensemble learning model
Botnets 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.
Mudasir Ali mail , Muhammad Faheem Mushtaq mail , Urooj Akram mail , Daniel Gavilanes Aray mail daniel.gavilanes@uneatlantico.es, Manuel Masías Vergara mail manuel.masias@uneatlantico.es, Hanen Karamti mail , Imran Ashraf mail ,
Ali
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In the original publication [1], there was a mistake in Table 1 as published. In Table 1, the row labelled “Dose 1” appears twice; once at the top and once again at the bottom (after Dose 7). This repeated entry was unintentional and should be removed. The correct table should end at Dose 7, and the repeated Dose 1 row at the bottom is redundant and may cause confusion. The corrected Table 1 appears below. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.
Malaz Yousef mail , Jaime A. Yáñez mail jaime.yanez@unini.edu.mx, Raimar Löbenberg mail , Neal M. Davies mail ,
Yousef