AIMC Topic: Machine Learning

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Predicting the clinical evolution of septic patients from routinely collected data and vital signs variability using machine learning.

Physiological measurement
The existing literature lacks a comprehensive analysis of the clinical evolution of septic patients, which is highly heterogeneous and patient-dependent. The aim of this study is to develop machine learning models capable of predicting the clinical e...

Synthesis and characterization of lignin-copper nanohybrids for colorimetric acetaminophen detection: a combined physical chemistry and machine learning study.

Physical chemistry chemical physics : PCCP
Acetaminophen ranks among the most widely used pharmaceutical and personal care products today. Following consumption, the drug and its metabolites are excreted into sewage systems, wastewater treatment plants, and various aquatic environments, leadi...

Machine learning approaches for predicting the link of the global trade network of liquefied natural gas.

PloS one
With the rising geopolitical tensions, predicting future trade partners has become a critical topic for the global community. Liquefied natural gas (LNG), recognized as the cleanest burning hydrocarbon, plays a significant role in the transition to a...

Features extraction based on Naive Bayes algorithm and TF-IDF for news classification.

PloS one
The rapid proliferation of online news demands robust automated classification systems to enhance information organization and personalized recommendation. Although traditional methods like TF-IDF with Naive Bayes provide foundational solutions, thei...

A new strategy for skeletal muscle wound age estimation using machine learning and ATR-FTIR spectroscopy: Eliminating early postmortem interference.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Accurate wound age estimation is of great significance in forensic practice. However, postmortem changes often obscure or even obliterate the biological information of skeletal muscle injuries, making it extremely challenging to accurately estimate t...

Machine learning-enhanced SERS detection of melamine and its analogues in non-pretreated milk via filter-pressing assembled polytetrafluoroethylene-AgNPs substrate.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Melamine contamination from illegal additives, packaging contaminants, and pesticide residues threatens dairy product safety, demanding rapid detection. Traditional methods such as chromatography or mass spectrometry are precise but lack field applic...

Integrating machine learning for rapid and accurate multiplex identification of the allelic variants in single nucleotide polymorphisms by lateral flow genotyping assays.

Biosensors & bioelectronics
Single nucleotide polymorphisms (SNPs) are widely used in precision medicine, disease predisposition assessment, nutrigenetics and authenticity testing of agricultural and food products. SNP genotyping is much more challenging than detecting longer D...

Integrating machine learning for enhanced spatial prediction and risk assessment of soil heavy metal(loid)s.

Environmental pollution (Barking, Essex : 1987)
Accurately predicting the concentrations and spatial distribution of soil heavy metal(loid)s is crucial for effective environmental management and human health risk assessment. However, existing studies are often limited by poor model accuracy, featu...

Knee osteoarthritis prediction from gait kinematics: Exploring the potential of deep neural networks and transfer learning methods for time series classification.

Journal of biomechanics
Recent advances in artificial intelligence methods have allowed improved disease diagnosis using fast and low-cost protocols. The present study explored the potential of different deep neural networks (DNNs) and transfer learning methods to detect kn...

Explainable multimodal hematology analysis for white blood cell classification and attribute prediction.

Computers in biology and medicine
White blood cell (WBC) classification and morphological attribute prediction are critical for automated hematological analyses. To provide detailed and interpretable predictions, this paper proposes a multimodal visual-language embedding learning app...