AIMC Topic: Machine Learning

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Machine learning strategy secures urban smart drinking water treatment plant through incremental advances.

Water research
The integration of machine learning into urban drinking water treatment plants (DWTPs) offers a transformative pathway to ensure drinking water safety while promoting the development of smart, low-carbon cities. However, the effectiveness of these sy...

A hybrid bioelectronic retina-probe interface for object recognition.

Biosensors & bioelectronics
Retina converts light stimuli into spike firings, encoding abundant visual information critical for both fundamental studies of the visual system and therapies for visual diseases. However, probing these spikes directly from the retina is hindered by...

Reimagining the Kendall plot: using N and O of nitrate and advanced machine learning to improve N pollutant source classification.

Isotopes in environmental and health studies
Nitrate () pollution is a serious water quality issue in many countries due to contamination of lakes, rivers, and aquifers by intensive agriculture practices and inadequate wastewater management. Nitrate pollution and associated cultural eutrophicat...

Automated ADHD detection using dual-modal sensory data and machine learning.

Medical engineering & physics
This study explores using dual-modal sensory data and machine learning to objectively identify Attention-Deficit/Hyperactivity Disorder (ADHD), a neurodevelopmental disorder traditionally diagnosed through subjective clinical evaluations. Six machine...

Application of explainable machine learning in the production of pullulan by Aureobasidium pullulans CGMCCNO.7055.

International journal of biological macromolecules
The application of machine learning in pullulan biofermentation has demonstrated significant potential. Explainable machine learning enhances model transparency and interpretability by revealing the relationships between variables. In this study, we ...

Eye movement detection using electrooculography and machine learning in cardiac arrest patients.

Resuscitation
AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. ...

Leveraging Transfer Learning for Predicting Protein-Small-Molecule Interaction Predictions.

Journal of chemical information and modeling
A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: ∼10 proteins in a typical human cell offer ...

Mechanistic Study of Protein Interaction with Natto Inhibitory Peptides Targeting Xanthine Oxidase: Insights from Machine Learning and Molecular Dynamics Simulations.

Journal of chemical information and modeling
Bioactive peptides from food sources offer a safe and biocompatible approach to enzyme inhibition, with potential applications in managing metabolic disorders such as hyperuricemia and gout, conditions linked to excessive xanthine oxidase activity. U...

Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland.

Journal of environmental management
For efficient decision-making and optimal land management trajectories, information on soil properties in relation to safety guidelines should be processed from point inventories to surface predictive maps. For large-scale predictive mapping, very fe...

Machine Learning-Based VO Estimation Using a Wearable Multiwavelength Photoplethysmography Device.

Biosensors
The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO is a powerf...