Automated behavioral measurement using machine learning is gaining ground in psychological research. Automated approaches have the potential to reduce the labor and time associated with manual behavioral coding, and to enhance measurement objectivity...
Traditional fish classification systems suffer from limited training data and imbalanced datasets, particularly for rare or morphologically complex species. This paper presents a novel Generative Adversarial Network architecture that integrates adapt...
Infertility is a growing concern in today's technologically driven and mechanized world, with male related factors contributing to nearly half of all cases yet often remaining under diagnosed due to societal misconceptions and stigma. Prolonged seden...
The clinical challenges in monitoring high-incidence complications in patients with colostomy after colorectal cancer surgery have led to the development of an intelligent monitoring system based on deep learning and augmented reality technology in t...
Developing poly(lactic-co-glycolic) acid (PLGA) nanoparticles with optimized drug encapsulation and loading is crucial for effective drug delivery. However, controlling the physicochemical properties of these nanoparticles remains challenging. In thi...
Degenerative cervical spondylosis, a chronic and progressive condition, has a considerable impact on global health. Spinal cord injury, a severe sequela of this disease, can result from this disease. Machine learning (ML) has emerged as a valuable to...
Hate speech detection is a challenging task due to complexities such as language ambiguity, limited context, cultural nuances, and situational factors. This challenge is further amplified in low-resource languages, i.e. Urdu. Most research on hate sp...
Species Distribution Models (SDMs) are widely used in ecology to analyze historical and future patterns of marine species distributions. Given the growing impact of climate change, predicting potential shifts in species ranges has become a key challe...
OBJECTIVES: The aim of this study was to identify high-risk dental extractions in patients taking antiplatelet (AP) medication or anticoagulants (ACs) and to compare an experienced surgeon's decisions with machine learning (ML) algorithms.
In computed tomography (CT), non-uniform detector responses often lead to ring artifacts in reconstructed images. For conventional energy-integrating detectors, such artifacts can be effectively addressed through dead-pixel correction and flat-dark f...
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