AIMC Topic: Machine Learning

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Machine Learning-Based prediction models for postoperative delirium: a systematic review and Meta-Analysis.

BMC psychiatry
BACKGROUND: The number of risk prediction models for postoperative delirium(POD) has increased yearly, but their quality and applicability in clinical practice and future research remain unclear.

Estimation of woody vegetation biomass in Australia based on multi-source remote sensing data and stacking models.

Scientific reports
Vegetation serves as the most critical carbon reservoir within terrestrial ecosystems and plays a vital role in mitigating global climate change. Australia features a vast and diverse landscape, ranging from dense eucalyptus forests to sparse woodlan...

Age estimation of children and adolescents from mandibles using machine learning.

Scientific reports
Age estimation is a crucial step in forensic identification, particularly in scenarios where dental structures may be absent. This study aimed to develop and evaluate supervised machine learning models to predict chronological age based on mandibular...

Analytical and machine learning approaches identify a sea star steroid with promising activity for COVID-19 therapeutic development.

Scientific reports
The pressing demand for safe and efficient COVID-19 treatments has intensified interest in Natural products, especially those derived from marine organisms. In this study, a bioactive steroidal compound, 5α-cholesta-9(11)-en-3β,20β-diol, was successf...

SHAP-driven insights into multimodal data: behavior phase prediction for industrial safety applications.

Scientific reports
Unsafe behaviors among coal miners are a primary factor contributing to accidents, posing significant challenges for safety management. This study develops a behavior state prediction framework using artificial intelligence and machine learning (ML) ...

Rapid reagent free COVID19 detection using MEMS based FTIR spectroscopy and machine learning in NIR and MIR regions.

Scientific reports
This study presents rapid, reagent-free detection of COVID-19 using miniaturized MEMS-based Fourier-transform infrared (FTIR) spectrometers integrated with machine learning models. Two portable spectrometers analyze 363 nasopharyngeal swab samples st...

Survival analysis of electric vehicle charging behavior and the temporal evolution of feature effects.

Scientific reports
This study proposes a survival-based modeling framework that combines behavioral features with interpretable machine learning to understand and predict user churn in electric vehicle charging services. Using a dataset of 1,074 users and 107,531 charg...

Predicting the co-invasion of two Asteraceae plant genera in post-mining landscapes using satellite remote sensing and airborne LiDAR.

Scientific reports
The Asteraceae plant family includes the most widespread weedy invaders in Europe, which may jointly inhibit natural succession in degraded land under restoration. The complex local drivers of co-invasions hinder remote sensing (RS) monitoring effort...

Optimizing imbalanced learning with genetic algorithm.

Scientific reports
Training AI models on imbalanced datasets with skewed class distributions poses a significant challenge, as it leads to model bias towards the majority class while neglecting the minority class. Various methods, such as Synthetic Minority Over Sampli...

Machine learning approaches overcome imbalanced clinical data for intraoral free flap monitoring.

Scientific reports
Free flap reconstruction is essential for treating intraoral defects; however, failure can lead to complex and prolonged complications. While various monitoring methods have been employed to prevent such situations, they are qualitative and sometimes...