AIMC Topic: Algorithms

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Classifying complex multimorbidity using latent class analysis and machine learning to generate insights into clustering of mental and cardiometabolic conditions.

PloS one
Machine learning techniques earn higher accuracy and robustness in multimorbidity prediction at this moment in time. Among various forms of multimorbidity, complex multimorbidity, especially the intersection of cardiometabolic disorders and mental he...

Fast machine learning image reconstruction of radially undersampled k-space data for low-latency real-time MRI.

PloS one
Fast data acquisition and fast image reconstruction are essential to enable low-latency real-time magnetic resonance (MR) imaging applications with high temporal resolution such as interstitial percutaneous needle interventions or MR-guided radiother...

Enhancing museum collection images with fuzzy set guided convolutional neural network: A novel approach leveraging fuzzy set theory.

PloS one
Museum collection images are invaluable for preserving cultural heritage and studying history. However, these images often lack quality and clarity. This study introduces a novel museum collection image enhancement technique based on fuzzy set theory...

Multi-marker discovery for mild cognitive impairment in metabolomics using machine learning with a global surrogate model via partial least squares.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Dementia can be prevented through early intervention; hence, there is an urgent need for biomarkers to help diagnose mild cognitive impairment (MCI).

The application of artificial intelligence-based algorithms in predicting the progression of keratoconus: a systematic review.

International ophthalmology
PURPOSE: To conduct a systematic review of studies examining the use of artificial intelligence (AI) algorithms in predicting the progression of keratoconus (KCN).

Deep Fuzzy-NN modeling for the prediction of Zn(II) adsorption in columns using alkaline modified biochar: Integrated experimental and computational insights.

Environmental research
The precise prediction of adsorption process is significant in the optimization of pollutant removal systems. In this research, deep fuzzy neural network (DFNN) model was developed for the prediction of Zn(II) removal efficiency using alkaline activa...

How public involvement can improve the science of AI.

Proceedings of the National Academy of Sciences of the United States of America
As AI systems from decision-making algorithms to generative AI are deployed more widely, computer scientists and social scientists alike are being called on to provide trustworthy quantitative evaluations of AI safety and reliability. These calls hav...

Development of a consensus molecular classifier for pancreatic ductal adenocarcinoma.

Genome medicine
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) presents a significant challenge, with a 5-year survival rate of approximately 10%. Tumor heterogeneity contributes to the limited effectiveness of treatments. Several tumor and stroma molecular cla...

Artificial intelligence-driven kidney organ allocation: systematic review of clinical outcome prediction, ethical frameworks, and decision-making algorithms.

BMC nephrology
Kidney transplantation remains the optimal treatment for end-stage renal disease, yet persistent organ shortages and inequitable allocation necessitate innovative solutions. Artificial intelligence (AI) and machine learning (ML) have emerged as promi...

LiteBoost: a lightweight and explainable boosting model for predicting polymer density from SMILES data.

Journal of computer-aided molecular design
Accurately predicting polymer density from SMILES strings remains challenging due to the small size, high noise, and chemically diversity of typical datasets. We introduce LiteBoost, a deliberately minimalist gradient boosting model that employs shal...