AIMC Topic: Machine Learning

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Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas.

International journal of environmental research and public health
Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying a...

Construction of a machine learning-based artificial neural network for discriminating PANoptosis related subgroups to predict prognosis in low-grade gliomas.

Scientific reports
The poor prognosis of gliomas necessitates the search for biomarkers for predicting clinical outcomes. Recent studies have shown that PANoptosis play an important role in tumor progression. However, the role of PANoptosis in in gliomas has not been f...

Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging.

Nature communications
Structured Illumination Microscopy, SIM, is one of the most powerful optical imaging methods available to visualize biological environments at subcellular resolution. Its limitations stem from a difficulty of imaging in multiple color channels at onc...

Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models.

Radiology
Background Radiomics is the extraction of predefined mathematic features from medical images for the prediction of variables of clinical interest. While some studies report superlative accuracy of radiomic machine learning (ML) models, the published ...

Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability.

Environmental science and pollution research international
Gas breakthrough pressure is a significant parameter for the gas exploration and safety evaluation of engineering barrier systems in the carbon dioxide storage, remediation of contaminated sites, and deep geological repository for disposal of high-le...

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis.

IEEE transactions on visualization and computer graphics
Reference-based cell-type annotation can significantly reduce time and effort in single-cell analysis by transferring labels from a previously-annotated dataset to a new dataset. However, label transfer by end-to-end computational methods is challeng...

Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups.

Sensors (Basel, Switzerland)
Deep learning pervades heavy data-driven disciplines in research and development. The Internet of Things and sensor systems, which enable smart environments and services, are settings where deep learning can provide invaluable utility. However, the d...

Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures.

Sensors (Basel, Switzerland)
(1) Background: The success of physiotherapy depends on the regular and correct unsupervised performance of movement exercises. A system that automatically evaluates these exercises could increase effectiveness and reduce risk of injury in home based...

Machine learning prediction of academic collaboration networks.

Scientific reports
We investigate the different roles played by nodes' network and non-network attributes in explaining the formation of European university collaborations from 2011 to 2016, in three European Research Council (ERC) domains: Social Sciences and Humaniti...

Neural network and decision tree-based machine learning tools to analyse the anion-responsive behaviours of emissive Ru(II)-terpyridine complexes.

Dalton transactions (Cambridge, England : 2003)
We implemented both neural network and decision tree-based machine learning tools to analyse the anion-responsive behaviours of two heteroleptic Ru(II) complexes based on two tridentate ligands, 2,6-bis(benzimidazole-2-yl)pyridine (Hpbbzim) and subst...