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Apr 17, 2025Open Access
Migraines are a prevalent and debilitating neurological disorder, affecting millions worldwide. Characterized by symptoms such as nausea, photophobia, phonophobia, and visual disturbances, diagnosing and classifying migraines remains a challenge due to their heterogeneous nature. This study leverages machine learning techniques to analyze a dataset comprising 400 patient records, identifying key factors that contribute to migraine classification. Using statistical analysis, correlation matrices,...
Apr 11, 2025Open Access
Human Metapneumovirus (HMPV) is a prominent respiratory pathogen, particularly affecting children, the elderly, and immunocompromised populations. Early detection of HMPV is critical for timely intervention and improved patient outcomes; however, traditional diagnostic methods are often hindered by overlapping symptoms with other respiratory illnesses. This research explores the application of machine learning models for HMPV detection using synthetic clinical data designed to replicate real-wor...
Mar 24, 2025Open Access
Bipolar fuzzy graphs use positive and negative membership functions to characterize the uncertainty of structured fuzzy data. This article extends the concept of network distance to bipolar fuzzy graphs, provides corresponding definitions, and obtains several remarks.
Mar 01, 2025Open Access
Face recognition is rapidly becoming one of the most popular biometric authentication methods. Most face recognition systems are focused on extracting features and enhancing their verification and identification capabilities. The detection of security vulnerabilities of different types of attacks has been given attention only in recent years. These attacks can include, but are not limited to: Obfuscation Spoofing and morphing; for example, a hacker can masquerade as a target to gain access to th...
Mar 01, 2025Open Access
Bipolar depression with comorbid obsessive-compulsive disorder (OCD) presents a significant clinical challenge due to its complex symptomatology, unpredictable treatment responses, and high relapse rates. Traditional ap-proaches to treatment planning lack reliable tools for predicting pa-tient-specific outcomes, leaving clinicians with limited options for personal-izing care. This study leverages advanced machine learning (ML), specifical-ly XGBoost, to develop a predictive framework capable of ...
Feb 27, 2025Open Access
The growing need for sustainable energy solutions has driven the integration of Artificial Intelligence (AI) into renewable energy systems, enabling the optimization of resource utilization, efficiency, and environmental impact. This study explores the transformative role of AI in addressing challenges such as intermittency, grid integration, and real-time decision-making in renewable energy sources, including solar, wind, and wave power. AI-driven innovations, such as predictive algorithms, rei...
Feb 19, 2025Open Access
Accurate prediction of treatment response in bipolar disorder patients with comorbid obsessive-compulsive disorder (OCD) is essential to improving clinical outcomes and minimizing ineffective interventions. The complex interplay between bipolar disorder and OCD often complicates pharmacological treatment, leading to inconsistent results. This study aims to leverage machine learning (ML) techniques to develop predictive models that enhance the precision of quetiapine monotherapy outcomes. The pri...
Feb 17, 2025Open Access
The treatment of major depressive disorder (MDD) often involves antidepressants, yet non-response to initial therapies remains a significant clinical and economic burden. This research aims to evaluate the comparative efficacy and cost-efficiency of 13 commonly prescribed antidepressants, spanning four major drug classes: SSRIs, SNRIs, NaSSAs, and TCAs. By employing machine learning and simulated patient data, we model non-response rates over two years, highlighting each drug’s cumulative risk t...
Feb 17, 2025Open Access
Depressive disorders are complex, multifactorial conditions that exhibit significant variability in treatment response, often influenced by gender differences. This study leverages advanced machine learning (ML) techniques to predict antidepressant response to sertraline and imipramine, addressing the pressing need for personalized treatment strategies. By employing the Synthetic Minority Oversampling Technique (SMOTE), the research overcomes class imbalance—a common limitation in clinical datas...
Feb 17, 2025Open Access
Pneumonia remains a significant cause of morbidity and mortality worldwide, particularly in vulnerable populations such as children and the elderly. Early detection through chest X-ray analysis plays a crucial role in timely treatment; however, reliance on radiologists can lead to variability, delays, and diagnostic errors. This paper presents a convolutional neural network (CNN) designed to automate pneumonia diagnosis from chest X-ray images, addressing the need for faster and more consistent ...
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