AIMC Topic: Ensemble Learning

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Predicting the composition of aroma components in Baijiu using hyperspectral imaging combined with a replication allocation strategy-enhanced stacked ensemble learning model.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Nov 15, 2025
Ester and acid aroma compounds are crucial components affecting the fragrance of Baijiu, and their composition can endow the Baijiu with a fruity, acidic, floral, or roasted aroma. This study aims to quantitatively detect the ester and acid content i...
Machine Learning Ensemble Learning Odorants Acids Esters Hyperspectral Imaging Soy Foods
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`Probabilistic ensemble learning for prediction of stroke thrombectomy outcomes from the NeuroVascular Quality Initiative-Quality Outcomes Database (NVQI-QOD) Acute Ischemic Stroke Registry.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Sep 1, 2025
INTRODUCTION: Mechanical Thrombectomy (MT) is the standard of care in the interventional management of Acute Ischemic Stroke (AIS). The NVQI-QOD registry records detailed patient characteristics, pre-operative imaging, procedure metrics, and post-ope...
Time Factors Treatment Outcome Recovery of Function Predictive Value of Tests Aged, 80 and over Male Middle Aged Registries Stroke Machine Learning Thrombectomy Humans Decision Support Techniques Ensemble Learning Neural Networks, Computer Databases, Factual Risk Factors Ischemic Stroke Clinical Decision-Making Aged Disability Evaluation Female Functional Status
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Two-stage ensemble learning framework for automated classification of keratoconus severity.

Computers in biology and medicine Sep 1, 2025
BACKGROUND AND OBJECTIVE: Accurate staging of keratoconus (KC) is crucial for timely intervention and improving patient quality of life. Unlike prior studies that relied on traditional base machine learning (ML) models, this paper proposes a more adv...
Machine Learning Female Adult Male Humans Ensemble Learning Severity of Illness Index Support Vector Machine Keratoconus
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Algal bloom forecasting leveraging signal processing: A novel perspective from ensemble learning.

Water research Sep 1, 2025
Accurate forecasting of algal blooms is essential for implementing timely control measures. However, given their inherent complex time-frequency characteristics, capturing the dynamics of algal blooms remains an ongoing challenge in standalone models...
Machine Learning Algorithms Forecasting Environmental Monitoring Ensemble Learning Eutrophication Lakes
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Ensemble learning approach for detecting breast invasive ductal carcinoma from histopathological images.

Pathology, research and practice Aug 1, 2025
Invasive ductal carcinoma is a type of breast cancer that is one of the most frequent and aggressive forms of breast malignancy, necessitating accurate and timely diagnosis for effective treatment. Though considered the gold standard, traditional his...
Female Breast Neoplasms Deep Learning Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Algorithms Carcinoma, Ductal, Breast Ensemble Learning
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Predicting single-cell protein production from food-processing wastewater in sequencing batch reactors using ensemble learning.

Bioresource technology Aug 1, 2025
Producing single-cell protein (SCP) from food-processing wastewater offers a sustainable approach to resource recovery, animal feed production, and wastewater treatment. Decision-makers need accurate system performance data under variable influent co...
Dietary Proteins Ensemble Learning Food-Processing Industry Batch Cell Culture Techniques Machine Learning Wastewater Biomass Algorithms Bioreactors
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Interpretable lung cancer risk prediction using ensemble learning and XAI based on lifestyle and demographic data.

Computational biology and chemistry Aug 1, 2025
Lung cancer is a leading cause of cancer-related death worldwide. The early and accurate detection of lung cancer is crucial for improving patient outcomes. Traditional predictive models often lack the accuracy and interpretability required in clinic...
Life Style Ensemble Learning Humans Male Machine Learning Risk Assessment Risk Factors Lung Neoplasms
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Neuroimaging pattern interactions for suicide risk in depression captured by ensemble learning over transcriptome-defined parcellation.

Progress in neuro-psychopharmacology & biological psychiatry Jun 20, 2025
BACKGROUND: For suicide in major depression disorder, it is urgent to seek for a reliable neuroimaging biomarker with interpretable links to molecular tissue signatures. Accordingly, we developed an ensemble learning scheme over transcriptome-defined...
Brain Female Humans Machine Learning Adult Neuroimaging Depressive Disorder, Major Male Suicide Ensemble Learning Transcriptome Middle Aged Magnetic Resonance Imaging
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Ensemble learning of deep CNN models and two stage level prediction of Cobb angle on surface topography in adolescents with idiopathic scoliosis.

Medical engineering & physics Jun 1, 2025
This study employs Convolutional Neural Networks (CNNs) as feature extractors with appended regression layers for the non-invasive prediction of Cobb Angle (CA) from Surface Topography (ST) scans in adolescents with Idiopathic Scoliosis (AIS). The ai...
Surface Properties Scoliosis Ensemble Learning Convolutional Neural Networks Humans Female Adolescent Male Neural Networks, Computer Image Processing, Computer-Assisted Deep Learning
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CT-DCENet: Deep EEG Denoising via CNN-Transformer-Based Dual-Stage Collaborative Ensemble Learning.

IEEE journal of biomedical and health informatics Jun 1, 2025
Electroencephalogram (EEG) artifact removal has been investigated for decades with the goal of reconstructing the clean signals for the subsequent EEG analysis. However, existing denoising methods still have limited capabilities to handle the highly ...
Humans Artifacts Deep Learning Adult Signal-To-Noise Ratio Male Electroencephalography Ensemble Learning Neural Networks, Computer Signal Processing, Computer-Assisted Female
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