Statistical learning can be used for spectrum prediction, significantly reducing the large computational resources required by simulation software. However, traditional statistical learning methods ...

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Pooled effect sizes (r) were calculated using random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman correction, and subgroup and meta-regression analyses were conducted to explore potential ...

Background: Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting ...

Statistical learning (SL) is a fundamental cognitive ability enabling individuals to detect and exploit regularities in environmental input. It plays a crucial role in language acquisition, perceptual ...

Abstract: Emotion classification in social media texts has several challenges, such as the characteristics of social media texts that tend to use informal language, unbalanced data distribution, ...

ABSTRACT: This paper presents a comprehensive machine learning approach for credit score classification, addressing key challenges in financial risk assessment. We propose an optimized CatBoost-based ...

ABSTRACT: This review focuses on the recent advancements in neuroimaging enabled by deep learning techniques, specifically highlighting their applications in brain disorder detection and diagnosis.