ABSTRACT: Detecting behavioural signatures of depression from everyday digital traces is a central challenge in computational psychiatry. Real-world datasets from smartphones and wearables often ...
A QuPath extension that brings XGBoost gradient-boosted tree classification to object classification workflows. Train a model from point or area annotations across multiple project images, then run ...
Abstract: Decision trees in machine learning achieved satisfactory performance in classification. Decision trees offer the advantage of handling high-dimensional and complexly correlated data through ...
Abstract: Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction ...
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell ...
This study introduces the XGBoost Multimodal Autism Predictor (XMAP), an interpretable machine learning framework designed to improve ASD classification by integrating publicly available behavioral ...
You’re all geared up to watch the 2024 Paralympic Games. But as you flick on an event, you can’t help but wonder: Why are there 16 different men’s 100-meter races on the track and seven different ...
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