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ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds.

Scientific reports
Mental workload is an interdisciplinary construct that significantly influences human performance, particularly in tasks requiring sustained attention and cognitive processing. Effective mental workload assessment is critical for preventing cognitive...

Artificial intelligence in student management systems to enhance academic performance monitoring and intervention.

Scientific reports
In recent years, the integration of artificial intelligence (AI) in student management systems (SMS) has gained significant attention, particularly for monitoring academic performance and predicting at-risk students. Traditional approaches often lack...

A machine learning approach for detection of claustrophobic brain activity in electroencephalography.

Scientific reports
Claustrophobia, a phobia with a specific unreasonable and excessive fear of enclosed spaces, can have a considerable impact on an individual's life. Electroencephalography (EEG) has been a tool with potential for studying neural processes in anxiety ...

Error-related potentials in EEG signals: feature-based detection for human-robot interaction.

Scientific reports
This study explores how to improve the detection of Error-Related Potentials (ErrPs), namely brain signals generated when a person perceives an unexpected action performed by an interacting agent. ErrPs are promising for improving interactions betwee...

Evaluating AI-Powered Applications for Enhancing Undergraduate Students' Metacognitive Strategies, Self-Determined Motivation, and Social Learning in English Language Education.

Scientific reports
Artificial Intelligence (AI) technologies are transforming educational settings by offering tools that enhance learning experiences. AI-powered applications, such as ChatGPT and Poe, provide real-time assistance, fostering learner autonomy and self-d...

Development and validation of a machine learning model integrating BUN/Cr ratio for mortality prediction in critically ill atrial fibrillation patients.

Scientific reports
Atrial fibrillation (AF), the most prevalent critical care arrhythmia, demonstrates substantial mortality associations where renal dysfunction management plays a pivotal therapeutic role. We examined the prognostic capacity of admission blood urea ni...

Machine learning reveals limited predictive value of clinical factors for asthma exacerbations.

Scientific reports
While predictors of asthma exacerbation risk are generally well established, predictors of exacerbation severity remain largely undefined. Identifying robust clinical predictors of exacerbation severity is essential to support tailored management str...

Invasive and non-invasive variables prediction models for cardiovascular disease-specific mortality between machine learning vs. traditional statistics.

Scientific reports
This study examined the predictive performance of cardiovascular disease (CVD)-specific mortality using traditional statistical and machine learning models with non-invasive indicators, and assessed whether adding blood lipid profiles improves predic...

The influence of higher education based on machine learning on subjective well-being.

Scientific reports
As higher education becomes increasingly prevalent and accessible in China, a growing number of residents are afforded the option to pursue advanced studies. Can higher education genuinely enhance residents' subjective well-being? The response to thi...

A robust artificial intelligence system for predicting EBV status in gastric cancer biopsy and resection specimens.

Scientific reports
Epstein-Barr virus (EBV) associated gastric cancer, accounting for ~ 9% of all gastric cancers, has unique pathologic, genomic, and clinical features and is linked to a better prognosis. Therefore, we aim to develop and validate a robust deep learnin...