AIMC Topic: Ensemble Learning

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A deep ensemble learning framework for brain tumor classification using data balancing and fine-tuning.

Scientific reports
Brain tumors are a critical medical challenge, requiring accurate and timely diagnosis to improve patient outcomes. Misclassification can significantly reduce life expectancy, emphasizing the need for precise diagnostic methods. Manual analysis of ex...

Discovering periodontitis biomarkers and therapeutic targets through bioinformatics and ensemble learning analysis.

Scientific reports
Periodontitis, a prevalent inflammatory disease, leads to the progressive destruction of periodontal tissues and poses significant systemic health risks. Despite its widespread impact, the molecular mechanisms driving periodontitis remain poorly unde...

Advanced MRI based Alzheimer's diagnosis through ensemble learning techniques.

Scientific reports
Alzheimer's Disease is a condition that affects the brain and causes changes in behavior and memory loss while making it hard to carry out tasks properly. It's vital to spot the illness early, for effective treatment. MRI technology has advanced in d...

iBitter-Stack: A multi-representation ensemble learning model for accurate bitter peptide identification.

Journal of molecular biology
The identification of bitter peptides is crucial in various domains, including food science, drug discovery, and biochemical research. These peptides not only contribute to the undesirable taste of hydrolyzed proteins but also play key roles in physi...

Integrating snapshot ensemble learning into masked autoencoders for efficient self-supervised pretraining in medical imaging.

Scientific reports
Self-supervised learning (SSL) has gained significant attention in medical imaging for its ability to leverage large amounts of unlabeled data for effective model pretraining. Among SSL methods, the masked autoencoder (MAE) has proven robust in learn...

Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics.

Scientific reports
The prompt diagnosis of pulmonary infections with unknown etiology in patients in severe condition remains a challenge due to the lack of rapid and effective diagnostic methods. While metatranscriptomic sequencing offers a powerful approach, its clin...

Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification.

Scientific reports
Eye diseases can affect vision and well-being, so early, accurate diagnosis is crucial to prevent serious impairment. Deep learning models have shown promise for automating the diagnosis of eye diseases from images. However, current methods mostly us...

Beyond unimodal analysis: Multimodal ensemble learning for enhanced assessment of atherosclerotic disease progression.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Atherosclerosis is a leading cardiovascular disease typified by fatty streaks accumulating within arterial walls, culminating in potential plaque ruptures and subsequent strokes. Existing clinical risk scores, such as systematic coronary risk estimat...

Enhancing IoT cybersecurity through lean-based hybrid feature selection and ensemble learning: A visual analytics approach to intrusion detection.

PloS one
The dynamical growth of cyber threats in IoT setting requires smart and scalable intrusion detection systems. In this paper, a Lean-based hybrid Intrusion Detection framework using Particle Swarm Optimization and Genetic Algorithm (PSO-GA) to select ...

Ensemble learning for microbiome-based caries diagnosis: multi-group modeling and biological interpretation from salivary and plaque metagenomic data.

BMC oral health
BACKGROUND: Oral microbiota is a major etiological factor in the development of dental caries. Next-generation sequencing techniques have been widely used, generating vast amounts of data which is underexplored. The advancement of artificial intellig...