Molecular epidemiology, transmission and clinical features of 2022‐mpox outbreak: A systematic review

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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.

Producción Científica

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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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.

Producción Científica

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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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.

Producción Científica

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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Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis

The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.

Producción Científica

Hafiz Muhammad Raza Ur Rehman mail , Mahpara Saleem mail , Muhammad Zeeshan Jhandir mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Helena Garay mail helena.garay@uneatlantico.es, Imran Ashraf mail ,

Raza Ur Rehman

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Ensemble stacked model for enhanced identification of sentiments from IMDB reviews

The 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.

Producción Científica

Komal Azim mail , Alishba Tahir mail , Mobeen Shahroz mail , Hanen Karamti mail , Annia A. Vázquez mail annia.almeyda@uneatlantico.es, Angel Olider Rojas Vistorte mail angel.rojas@uneatlantico.es, Imran Ashraf mail ,

Azim