AIMC Topic: Algorithms

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Applications of newly defined diamond Pythagorean fuzzy CODAS method via multi-criteria decision-making problems.

PloS one
The diverse decision values may fail to capture an accurate perspective when multiple decision-makers are part of the process. To address this challenge, this work introduces the diamond Pythagorean fuzzy set (Dia‑PyFS), an advancement over both the ...

A fault diagnosis method for rotating machinery components based on enhanced YOLO v8 and integrated attention mechanism.

PloS one
Accurate fault diagnosis of rotating machinery components is the key to ensuring the safe operation of the mechanical system. Aiming at problems such as inaccurate detection of small target fault features and loss of fault information in the process ...

Evaluating large language models in biomedical data science challenges through a classroom experiment.

Proceedings of the National Academy of Sciences of the United States of America
Large language models (LLMs) have shown remarkable capabilities in algorithm design, but their effectiveness in solving data science challenges in real-world settings remains poorly understood. We conducted a classroom experiment in which graduate st...

A hybrid CNN-transformer framework optimized by Grey Wolf Algorithm for accurate sign language recognition.

Scientific reports
This paper introduces the Gray Wolf Optimized Convolutional Transformer Network, a combined deep learning framework aimed at accurately and efficiently recognizing dynamic hand gestures, especially in American Sign Language (ASL). The model integrate...

Dynamic reward-augmented ensemble learning for EEG signal classification in major depressive disorder.

Biomedical physics & engineering express
Major Depressive Disorder (MDD) diagnosis through Electroencephalography (EEG) is hindered by the non-stationary characteristics of neural oscillations and the limited adaptability of conventional classification frameworks. Static ensemble models, wh...

Unsupervised discovery of ischemic stroke phenotypes from multimodal MRI radiomics.

Biomedical physics & engineering express
This study presents a fully unsupervised and label-independent radiomic pipeline designed to group different types of ischemic stroke lesions using multimodal Magnetic Resonance Imaging (MRI) . The aim is to address lesion heterogeneity and the absen...

MLGF-GAN: a multi-level local-global feature fusion GAN for OCT image super-resolution.

Biomedical physics & engineering express
Optical coherence tomography (OCT), a non-invasive imaging modality, holds significant clinical value in cardiology and ophthalmology. However, its imaging quality is often constrained by inherently limited resolution, thereby affecting diagnostic ut...

Assessing photoplethysmography signal quality for wearable devices during unrestricted daily activities.

Biomedical physics & engineering express
Photoplethysmography (PPG) is widely used in wearable health monitors for tracking fundamental physiological parameters (e.g., heart rate and blood oxygen saturation) and advancing applications requiring high-quality signals-such as blood pressure as...

Temporal social network modeling of mobile connectivity data with graph neural networks.

PloS one
Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analy...

Data-driven prediction of future purchase behavior in cross-border e-commerce using sequence modeling with PSO-tuned LSTM.

PloS one
With the rapid advancement of cross-border e-commerce, accurately predicting user purchase behavior has emerged as a critical challenge for enhancing platform operational efficiency and user experience. This study proposes a hybrid deep learning fram...