AIMC Topic: Machine Learning

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Enhancing classification in correlative microscopy using multiple classifier systems with dynamic selection.

Ultramicroscopy
Correlative microscopy combines data from different microscopical techniques to gain unique insights about specimens. A key requirement to unlocking the full potential is an advanced classification method that can combine the various analytical signa...

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications.

Environmental science & technology
Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computati...

A machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands.

International journal of health geographics
BACKGROUND: Local policymakers require information about public health, housing and well-being at small geographical areas. A municipality can for example use this information to organize targeted activities with the aim of improving the well-being o...

Analysis of Traditional Cultural Acceptance Based on Deep Learning.

Computational intelligence and neuroscience
Technological development has resulted in the utilisation of advanced technology search using deep learning technology in all industries. Artificial intelligence is set to be filled with machines that can perform tasks that need human intelligence. T...

Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images.

Analytical and bioanalytical chemistry
Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited cryst...

Intracerebral hemorrhage detection on computed tomography images using a residual neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Intracerebral hemorrhage (ICH) is a high mortality rate, critical medical injury, produced by the rupture of a blood vessel of the vascular system inside the skull. ICH can lead to paralysis and even death. Therefore, it is considered a clinically da...

Forecasting induced seismicity in Oklahoma using machine learning methods.

Scientific reports
Oklahoma earthquakes in the past decade have been mostly associated with wastewater injection. Here we use a machine learning technique-the Random Forest to forecast induced seismicity rate in Oklahoma based on injection-related parameters. We split ...

An efficient model of residual based convolutional neural network with Bayesian optimization for the classification of malarial cell images.

Computers in biology and medicine
BACKGROUND: Malaria is a disease caused by the Plasmodium parasite, which results in millions of deaths in the human population worldwide each year. It is therefore considered a major global health issue with a massive disease burden. Accurate and ra...

MGLNN: Semi-supervised learning via Multiple Graph Cooperative Learning Neural Networks.

Neural networks : the official journal of the International Neural Network Society
In many machine learning applications, data are coming with multiple graphs, which is known as the multiple graph learning problem. The problem of multiple graph learning is to learn consistent representation by exploiting the complementary informati...

Sparse group selection and analysis of function-related residue for protein-state recognition.

Journal of computational chemistry
Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical...