AIMC Topic: Algorithms

Clear Filters Showing 2041 to 2050 of 28713 articles

Efficient urban flood control and drainage management framework based on digital twin technology and optimization scheduling algorithm.

Water research
Urban flood control and drainage systems often face significant challenges in coordinating municipal drainage with river-lake flood prevention during flood seasons. Rising river levels can create backwater effects, which substantially increase urban ...

Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method.

Neural networks : the official journal of the International Neural Network Society
Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-comp...

Time series compression using quaternion valued neural networks and quaternion backpropagation.

Neural networks : the official journal of the International Neural Network Society
We propose a novel quaternionic time series compression methodology where we divide a long time series into segments of data, extract the min, max, mean and standard deviation of these chunks as representative features and encapsulate them in a quate...

Symmetry discovery for different data types.

Neural networks : the official journal of the International Neural Network Society
Equivariant neural networks incorporate symmetries into their architecture, achieving higher generalization performance. However, constructing equivariant neural networks typically requires prior knowledge of data types and symmetries, which is diffi...

Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity.

Molecular diversity
Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of drug resistance highlights the urgent need for new antimalarial agents. This study focuses on predictive modeling of 104 Plasmodium falciparum protein...

Digital image processing combined with machine learning: A novel approach for bee pollen classification.

Food research international (Ottawa, Ont.)
The classification of bee pollen is crucial for ensuring product authenticity, quality control, and fraud prevention, particularly given the high commercial value of stingless bee pot-pollen. Although traditional pollen analysis methods are available...

SynMSE: A multimodal similarity evaluator for complex distribution discrepancy in unsupervised deformable multimodal medical image registration.

Medical image analysis
Unsupervised deformable multimodal medical image registration often confronts complex scenarios, which include intermodality domain gaps, multi-organ anatomical heterogeneity, and physiological motion variability. These factors introduce substantial ...

FADE: Forecasting for anomaly detection on ECG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multip...

Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.

Annals of the Academy of Medicine, Singapore
INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers a...

Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.

BMC medical informatics and decision making
OBJECTIVE: To compare the performance of predictive models for cardiovascular event (CVE) in patients undergoing peritoneal dialysis (PD) based on machine learning algorithm and Cox proportional hazard regression.