AIMC Topic: Machine Learning

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Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders.

Journal of chemical theory and computation
Discovering meaningful collective variables for enhancing sampling, via applied biasing potentials or tailored MC move sets, remains a major challenge within molecular simulation. While recent studies identifying collective variables with variational...

An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer.

Sensors (Basel, Switzerland)
Skin Cancer (SC) is considered the deadliest disease in the world, killing thousands of people every year. Early SC detection can increase the survival rate for patients up to 70%, hence it is highly recommended that regular head-to-toe skin examinat...

Predicting the quality of soybean seeds stored in different environments and packaging using machine learning.

Scientific reports
The monitoring and evaluating the physical and physiological quality of seeds throughout storage requires technical and financial resources and is subject to sampling and laboratory errors. Therefore, machine learning (ML) techniques could help optim...

A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data.

Scientific reports
Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI ...

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.

Applied clinical informatics
OBJECTIVE: As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text dat...

An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images.

Computational intelligence and neuroscience
The lungs are COVID-19's most important focus, as it induces inflammatory changes in the lungs that can lead to respiratory insufficiency. Reducing the supply of oxygen to human cells negatively impacts humans, and multiorgan failure with a high mort...

A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial.

Medical decision making : an international journal of the Society for Medical Decision Making
This article examines a causal machine-learning approach, causal forests (CF), for exploring the heterogeneity of treatment effects, without prespecifying a specific functional form.The CF approach is considered in the reanalysis of the 65 Trial and ...

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration.

Annual review of biomedical data science
Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by elec...

Permutation-Invariant-Polynomial Neural-Network-Based Δ-Machine Learning Approach: A Case for the HO Self-Reaction and Its Dynamics Study.

The journal of physical chemistry letters
Δ-machine learning, or the hierarchical construction scheme, is a highly cost-effective method, as only a small number of high-level energies are required to improve a potential energy surface (PES) fit to a large number of low-level points. However...