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...
The rising prevalence of mental health disorders such as depression, anxiety, and bipolar disorder underscores the urgent need for effective tools to enable early detection and intervention. Social media platforms like Reddit offer a rich source of u...
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 ...
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...
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...
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...
Accurate and efficient classification of lung diseases from medical images remains a significant challenge in computer-aided diagnosis systems. This research presents a novel approach integrating transfer learning techniques with fuzzy decision suppo...
Obesity, currently the fifth leading cause of death worldwide, has seen a significant increase in prevalence over the past four decades. Timely identification of obesity risk facilitates proactive measures against associated factors. In this paper, w...
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...
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...
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