AIMC Topic: Machine Learning

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Synthesis and perceptual scaling of high-resolution naturalistic images using Stable Diffusion.

Behavior research methods
Naturalistic scenes are of key interest for visual perception, but controlling their perceptual and semantic properties is challenging. Previous work on naturalistic scenes has frequently focused on collections of discrete images with considerable ph...

Flash droughts threaten global managed forests.

Nature communications
Flash droughts, characterized by rapid onset and increasing frequency, pose significant threats to ecosystem stability and function. However, there remains no global consensus regarding forest responses to flash droughts. Here, using a reconstructed ...

Development and Validation of a Web-Based Machine Learning Model for Predicting Early Neurological Deterioration Following Stroke Thrombolysis: Multicenter Study.

Journal of medical Internet research
BACKGROUND: Early neurological deterioration (END) significantly worsens outcomes in patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis, yet clinicians lack reliable tools to identify high-risk patients who need intensified ...

Predicting adsorption capacities of pharmaceutical pollutants using chemoinformatics and machine learning techniques.

Environmental geochemistry and health
Pharmaceutical pollutants are increasingly recognized as emerging contaminants in aquatic environments. Their persistence, bioactivity, and resistance to conventional treatment processes raise ecological and human health concerns, including the sprea...

Dynamic reward-augmented ensemble learning for EEG signal classification in major depressive disorder.

Biomedical physics & engineering express
Major Depressive Disorder (MDD) diagnosis through Electroencephalography (EEG) is hindered by the non-stationary characteristics of neural oscillations and the limited adaptability of conventional classification frameworks. Static ensemble models, wh...

Phenotype-driven leaf deep metabolomics framework depicts key metabolisms and metabolites associated with yield traits in rice.

Planta
This study links rice leaf metabolome to yield traits, identifying 13 key metabolites through computational metabolomics. These enable early prediction of high-yield varieties, enhancing screening strategies in crop breeding. Metabolites serve as dyn...

Improved predictive formulae for wave overtopping at sloped breakwaters using interpretable machine learning models.

PloS one
Accurate prediction of mean wave overtopping discharge is essential for the safe and cost-effective design of coastal defence structures. While traditional empirical, physical, and numerical models remain important, Machine Learning (ML) has recently...

Development of machine learning models for prediction of current and future dementia.

PloS one
Dementia is among the most distressing and burdensome health challenges in aging populations. Treatment efficacy is limited; however, early diagnosis can delay or prevent disease progression. Previous machine learning-based prediction models have lim...

Hybrid quantum neural network models for fruit quality assessment.

PloS one
This study investigates hybrid quantum neural networks for fruit quality assessment, with a focus on the impact of the entangling gate choice. Two architectures were developed: NNQEv1, utilizing controlled-NOT (CNOT) gates, and NNQEv2, employing cont...

Nanopore Fingerprinting of Structurally Diverse Amino Acid-Conjugated Bile Acids.

Nano letters
Bile acids are key signaling molecules implicated in various hepatobiliary and metabolic diseases. Synthesized via distinct pathways and undergoing diverse modifications, bile acids exhibit significant structural variation. The discovery of novel ami...