AIMC Topic: Machine Learning

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One-pot synthesized multifunctional Zn-MOF/HOF heterostructure sensor array assisted by machine learning for efficient capture, target discrimination and optosmart sensing of doxycycline analogs.

Journal of hazardous materials
The ideal multifunctional platform that combines the capabilities of effective capture, sensitive detection, and accurate identification of doxycycline analogs (DCs) remains a serious challenge for ensuring the environment and food security. This wor...

Machine learning driven prediction of drug efficacy in lung cancer: based on protein biomarkers and clinical features.

Life sciences
Currently, chemotherapy drugs are the first-line treatment for lung cancer patients, and evaluating their efficacy is of utmost significance. However, assessing the clinical efficacy of chemotherapy drugs remains a challenging task. In recent years, ...

Advancing wetland groundwater pollution zoning: A novel integration of Monte Carlo health risk modeling and machine learning.

Journal of hazardous materials
Wetlands serve as crucial water reservoirs, providing essential water resources for the surrounding regions. However, elevated ion concentrations in wetland groundwater may pose health risks to local populations. This study focused on Judian Lake and...

Demystifying food flavor: Flavor data interpretation through machine learning.

Food chemistry
Flavor data obtained from analytical techniques are vast and complex, which increases the difficulty of multi-factorial analysis. This study aims to provide a machine learning (ML)-based framework to interpret flavor data, exploiting four widely used...

Fluorescent sensor array for rapid bacterial identification using antimicrobial peptide-functionalized gold nanoclusters and machine learning.

Talanta
Bacterial infectious diseases pose significant challenges to public health, emphasizing the need for rapid and accurate diagnostic tools. Here, we introduced a multichannel fluorescent sensor array based on antimicrobial peptide-functionalized gold n...

Machine learning methods for sex estimation of sub-adults using cranial computed tomography images.

Anthropologischer Anzeiger; Bericht uber die biologisch-anthropologische Literatur
This research aimed to compare the classification accuracy of three machine learning (ML) methods (random forest (RF), support vector machines (SVM), linear discriminant analysis (LDA)) for sex estimation of sub-adults using cranial computed tomograp...

[Development of a machine learning-based diagnostic model for T-shaped uterus using transvaginal 3D ultrasound quantitative parameters].

Zhonghua yi xue za zhi
To develop a machine learning diagnostic model for T-shaped uterus based on quantitative parameters from 3D transvaginal ultrasound. A retrospective cross-sectional study was conducted, recruiting 304 patients who visited the hysteroscopy centre of...

Improving the Ecotoxicological Hazard Assessment of Chemicals by Pairwise Learning.

Environmental science & technology
This study demonstrates how machine learning techniques can bridge data gaps in the ecotoxicological hazard assessment of chemical pollutants and illustrates how the results can be used in practice. The innovation herein consists of the prediction of...

Applied Machine Learning for Prediction of Energy-Efficient CO Desorption on Solid Acid Catalysts.

Environmental science & technology
The development of solid acid catalysts (SACs) for energy-efficient CO desorption and amine regeneration is critical to carbon capture commercialization. To avoid the time-consuming and ineffective screening process, a predictive model correlating th...

ML-STIM: Machine Learning for SubThalamic nucleus Intraoperative Mapping.

Journal of neural engineering
Deep Brain Stimulation (DBS) of the SubThalamic Nucleus (STN) is effective in alleviating motor symptoms in medication-refractory patients with Parkinson's Disease (PD). Intraoperative identification of the STN relies on MicroElectrode Recordings (ME...