AIMC Topic: Machine Learning

Clear Filters Showing 31101 to 31110 of 34417 articles

Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations.

Mathematical biosciences and engineering : MBE
Physics-informed neural networks (PINN) have lately become a research hotspot in the interdisciplinary field of machine learning and computational mathematics thanks to the flexibility in tackling forward and inverse problems. In this work, we explor...

Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.

Bioinformatics (Oxford, England)
MOTIVATION: Existing computational models can predict single- and double-mutant fitness but they do have limitations. First, they are often tested via evaluation metrics that are inappropriate for imbalanced datasets. Second, all of them only predict...

Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.

Bioinformatics (Oxford, England)
MOTIVATION: In this article, we consider how to evaluate survival distribution predictions with measures of discrimination. This is non-trivial as discrimination measures are the most commonly used in survival analysis and yet there is no clear metho...

Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction.

Mathematical biosciences and engineering : MBE
As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD) concentration, which is a stream pollutant with an extreme circumstance of...

Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.

Advances in chronic kidney disease
Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as ...

Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics.

Diagnostic and interventional radiology (Ankara, Turkey)
Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology research to deal with large and complex imaging data sets. Nowadays, ML tools have become easily accessible to anyone. Such a low threshold to accessibility mig...

Research on multi-parameter fusion non-invasive blood glucose detection method based on machine learning.

European review for medical and pharmacological sciences
OBJECTIVE: Traditional blood glucose testing methods have several disadvantages, such as high pain and poor acquisition continuity. In response to these shortcomings, we propose a multi-parameter fusion non-invasive blood glucose detection method tha...

Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies.

Schizophrenia bulletin
OBJECTIVES: Machine learning (ML) and natural language processing have great potential to improve efficiency and accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers...