AIMC Topic: Algorithms

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Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms.

Medicina (Kaunas, Lithuania)
: Polycystic ovary syndrome (PCOS) is a complex disorder that can negatively impact the obstetrical outcomes. The aim of this study was to determine the predictive performance of four machine learning (ML)-based algorithms for the prediction of adver...

Comparative analysis of feature selection techniques for COVID-19 dataset.

Scientific reports
In the context of early disease detection, machine learning (ML) has emerged as a vital tool. Feature selection (FS) algorithms play a crucial role in ensuring the accuracy of predictive models by identifying the most influential variables. This stud...

Effective descriptor extraction strategies for correspondence matching in coronary angiography images.

Scientific reports
The importance of 3D reconstruction of coronary arteries using multiple coronary angiography (CAG) images has been increasingly recognized in the field of cardiovascular disease management. This process relies on the camera matrix's optimization, nee...

Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis.

Medical & biological engineering & computing
There is no effective fall risk screening tool for the elderly that can be integrated into clinical practice. Developing a system that can be easily used in primary care services is a current need. Current studies focus on the use of multiple sensors...

Randomized algorithms for large-scale dictionary learning.

Neural networks : the official journal of the International Neural Network Society
Dictionary learning is an important sparse representation algorithm which has been widely used in machine learning and artificial intelligence. However, for massive data in the big data era, classical dictionary learning algorithms are computationall...

Data-free knowledge distillation via generator-free data generation for Non-IID federated learning.

Neural networks : the official journal of the International Neural Network Society
Data heterogeneity (Non-IID) on Federated Learning (FL) is currently a widely publicized problem, which leads to local model drift and performance degradation. Because of the advantage of knowledge distillation, it has been explored in some recent wo...

Improving accuracy and efficiency of the machined PEEK denture based on NSGA-II integrated GABP neural network.

Dental materials : official publication of the Academy of Dental Materials
OBJECTIVES: The polymer polyetheretherketone (PEEK) is gradually being used in dental restorations because of its excellent mechanical properties, chemical resistance, fatigue resistance, thermal stability, radiation translucency and good biocompatib...

Automated Method for Growing Rod Length Measurement on Ultrasound Images in Children With Early Onset Scoliosis.

Ultrasound in medicine & biology
OBJECTIVE: To develop and validate machine learning algorithms to automatically extract the rod length of the magnetically controlled growing rod from ultrasound images (US) in a pilot study.

Diversity matters: Cross-head mutual mean-teaching for semi-supervised medical image segmentation.

Medical image analysis
Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Nevertheless, existing state-of-the-art (SOTA) methods encounter challenges in accurately predic...

Joint use of population pharmacokinetics and machine learning for prediction of valproic acid plasma concentration in elderly epileptic patients.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
BACKGROUND: Valproic acid (VPA) is a commonly used broad-spectrum antiepileptic drug. For elderly epileptic patients, VPA plasma concentrations have a considerable variation. We aim to establish a prediction model via a combination of machine learnin...