AIMC Topic: Algorithms

Clear Filters Showing 4551 to 4560 of 28713 articles

Toward automated small bowel capsule endoscopy reporting using a summarizing machine learning algorithm: The SUM UP study.

Clinics and research in hepatology and gastroenterology
BACKGROUND AND OBJECTIVES: Deep learning (DL) algorithms demonstrate excellent diagnostic performance for the detection of vascular lesions via small bowel (SB) capsule endoscopy (CE), including vascular abnormalities with high (P2), intermediate (P1...

Biomedical document-level relation extraction with thematic capture and localized entity pooling.

Journal of biomedical informatics
In contrast to sentence-level relational extraction, document-level relation extraction poses greater challenges as a document typically contains multiple entities, and one entity may be associated with multiple other entities. Existing methods often...

Spatial prediction of human brucellosis susceptibility using an explainable optimized adaptive neuro fuzzy inference system.

Acta tropica
Brucellosis, a zoonotic disease caused by Brucella bacteria, poses significant risks to human, livestock, and wildlife health, alongside economic losses from livestock morbidity and mortality. This study improves Human Brucellosis Susceptibility Mapp...

Assessing the performance of machine learning algorithms for analyzing land use changes in the Hyrcanian forests of Iran.

Environmental science and pollution research international
Land use changes are of critical importance in understanding and managing environmental sustainability and resource utilization. Machine learning algorithms (MLAs) have emerged as powerful tools for analyzing and predicting land use changes, offering...

Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition.

Sensors (Basel, Switzerland)
Gait recognition is a behavioral biometric technique that identifies individuals based on their unique walking patterns, enabling long-distance identification. Traditional gait recognition methods rely on appearance-based approaches that utilize back...

A unique unsupervised enhanced intuitionistic fuzzy C-means for MR brain tissue segmentation.

Scientific reports
The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brai...

Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.

Medical image analysis
Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning method...

An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.

Medical & biological engineering & computing
Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and sel...

MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways.

Medical & biological engineering & computing
The precise segmentation and three-dimensional reconstruction of the nasopharyngeal airway are crucial for the diagnosis and treatment of adenoid hypertrophy in children. However, traditional methods face challenges such as information loss and low c...

CorLabelNet: a comprehensive framework for multi-label chest X-ray image classification with correlation guided discriminant feature learning and oversampling.

Medical & biological engineering & computing
Recent advancements in deep learning techniques have significantly improved multi-label chest X-ray (CXR) image classification for clinical diagnosis. However, most previous studies neither effectively learn label correlations nor take full advantage...