AIMC Topic: Machine Learning

Clear Filters Showing 16501 to 16510 of 34417 articles

Self-consistent determination of long-range electrostatics in neural network potentials.

Nature communications
Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-...

[Artificial Intelligence: Challenges and Applications in Intensive Care Medicine].

Anasthesiologie, Intensivmedizin, Notfallmedizin, Schmerztherapie : AINS
The high workload in intensive care medicine arises from the exponential growth of medical knowledge, the flood of data generated by the permanent and intensive monitoring of intensive care patients, and the documentation burden. Artificial intellige...

Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network.

Journal of healthcare engineering
One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine l...

Early identification of ICU patients at risk of complications: Regularization based on robustness and stability of explanations.

Artificial intelligence in medicine
The aim of this study is to build machine learning models to predict severe complications using administrative and clinical elements that are collected immediately after patient admission to the intensive care unit (ICU). Risk models are of increasin...

Low-precision feature selection on microarray data: an information theoretic approach.

Medical & biological engineering & computing
The number of interconnected devices, such as personal wearables, cars, and smart-homes, surrounding us every day has recently increased. The Internet of Things devices monitor many processes, and have the capacity of using machine learning models fo...

A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning.

Sensors (Basel, Switzerland)
As an emerging artificial intelligence technology, federated learning plays a significant role in privacy preservation in machine learning, although its main objective is to prevent peers from peeping data. However, attackers from the outside can ste...

Data-driven discovery of Green's functions with human-understandable deep learning.

Scientific reports
There is an opportunity for deep learning to revolutionize science and technology by revealing its findings in a human interpretable manner. To do this, we develop a novel data-driven approach for creating a human-machine partnership to accelerate sc...

Mind the gap: Performance metric evaluation in brain-age prediction.

Human brain mapping
Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristic...

BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction.

Journal of molecular biology
Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating ...

Image quality assessment for machine learning tasks using meta-reinforcement learning.

Medical image analysis
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-bas...