AIMC Topic: Algorithms

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Improved predictive formulae for wave overtopping at sloped breakwaters using interpretable machine learning models.

PloS one
Accurate prediction of mean wave overtopping discharge is essential for the safe and cost-effective design of coastal defence structures. While traditional empirical, physical, and numerical models remain important, Machine Learning (ML) has recently...

Development of machine learning models for prediction of current and future dementia.

PloS one
Dementia is among the most distressing and burdensome health challenges in aging populations. Treatment efficacy is limited; however, early diagnosis can delay or prevent disease progression. Previous machine learning-based prediction models have lim...

Hybrid quantum neural network models for fruit quality assessment.

PloS one
This study investigates hybrid quantum neural networks for fruit quality assessment, with a focus on the impact of the entangling gate choice. Two architectures were developed: NNQEv1, utilizing controlled-NOT (CNOT) gates, and NNQEv2, employing cont...

DDU-Net: learning complex vascular topologies with KAN-Swin transformers and double dynamic upsampler.

Biomedical physics & engineering express
To segment complex vascular topologies in Optical Coherence Tomography Angiography (OCTA), we introduce DDU-Net. This work addresses the theoretical limitations of standard Swin Transformers, whose internal Multi-Layer Perceptron (MLP) blocks use fix...

Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage.

PloS one
BACKGROUND: Malocclusion is a common anomaly and is frequently observed in children and adults. Early detection and treatment of malocclusion is necessary to prevent and minimize complications. Therefore, developing a tool to check dentition at an ea...

Optimization of two-passenger ride-pooling orders based on ST-GNN and path optimization.

PloS one
Urban dynamic ride-pooling faces significant challenges in achieving efficient real-time order matching and path planning, primarily due to the complex spatio-temporal coupling of passenger demand and traffic conditions. Traditional algorithms often ...

Identifying influential determinants of women's empowerment in Bangladesh using machine learning algorithms.

PloS one
BACKGROUND AND OBJECTIVES: Women's empowerment is a vital issue in lower-middle-income developing countries like Bangladesh, where it plays a pivotal role in advancing development across the nation. Thus, this study aimed to identify the influential ...

scSemiPLC: a semi-supervised learning framework for annotating single-cell RNA-Seq data by generating pseudo-labels through clustering.

mSystems
UNLABELLED: Single-cell RNA sequencing (scRNA-seq) technology enables researchers to explore heterogeneity of diverse cell types within complex tissues at the single-cell resolution. Cell annotation, as a crucial step in scRNA-seq data analysis, prov...

ZNGEA: ZINB-NMF Integrated Graph Embedding Autoencoder for Metabolite-Disease Association Identification.

Analytical chemistry
Metabolism, a series of complex and orderly chemical reactions within a biological organism, has a significant impact on sustaining life activities. Disease development is often linked to alterations in the types or levels of metabolites; however, tr...

A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease.

Scientific reports
Retinitis pigmentosa (RP) and Stargardt Disease (STGD) are inherited retinal diseases that can seriously affect vision. In this study, we present a novel, two-phase self-supervised learning method that addresses the challenge of limited labeled data ...