AIMC Topic: Machine Learning

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Predictive modeling of tax compliance risks: A comparative study of machine learning approaches.

PloS one
Modern enterprises grapple with complex financial data and multidimensional risk interdependencies in their operations. Machine learning offers transformative potential for tax risk assessment and smart auditing solutions. This research analyzes 3,23...

Application of machine learning models in predicting physical literacy in 4-6-year-old children: A comprehensive analysis of individual and family factors.

PloS one
Physical literacy in children has become a significant research topic in both education and psychology. Recently, machine learning, as a cutting-edge AI technology, has started to play a crucial role in these fields. This study aimed to apply machine...

Graph-Based Machine Learning Framework for Predicting Hydrogen Storage Capacity in Metal-Organic Frameworks.

Journal of chemical information and modeling
Hydrogen is a clean and high-energy fuel, yet its safe and efficient storage remains a key obstacle to widespread adoption. Metal-organic frameworks (MOFs), with their high surface area and tunable porosity, have emerged as promising candidates for s...

Machine-Learning-Assisted CRISPR/Cas12a Biosensors for Monitoring Organophosphorus Pesticide Degradation.

Analytical chemistry
Owing to the severe environmental and health issues posed by organophosphorus pesticides (OPs), a dual-enzyme cascade biosensing platform based on manganese dioxide (MnO) and CRISPR/Cas12a was developed in this study. Smartphones were innovatively in...

Next-generation COVID-19 detection using a metasurface biosensor with machine learning-enhanced refractive index sensing.

Scientific reports
This work introduces a high-performance graphene-silver hybrid metasurface biosensor for the fast and precise detection of COVID-19. Through parametric optimization with COMSOL Multiphysics, the sensor achieves a sensitivity of 400 GHz/RIU, a figure ...

Integrated multi omics and machine learning reveal mitochondrial immunometabolic networks in sepsis associated encephalopathy.

Scientific reports
Sepsis-associated encephalopathy (SAE) is a major complication in intensive care units, characterized by diffuse brain dysfunction due to systemic inflammation. Despite advances in critical care medicine, SAE remains a key factor in poor patient outc...

An infection prediction model developed from inpatient data can predict out-of-hospital COVID-19 infections from wearable data when controlled for dataset shift.

Scientific reports
The COVID-19 pandemic highlighted the importance of early detection of illness and the need for health monitoring solutions outside of the hospital setting. We have previously demonstrated a real-time system to identify COVID-19 infection before diag...

Dual Attention-Based recurrent neural network and Two-Tier optimization algorithm for human activity recognition in individuals with disabilities.

Scientific reports
Human activity recognition (HAR) has been one of the active research areas for the past two years for its vast applications in several fields like remote monitoring, gaming, health, security and surveillance, and human-computer interaction. Activity ...

Revealing gait as a murine biomarker of injury, disease, and age with multivariate statistics and machine learning.

Scientific reports
Hundreds of rodent gait studies have been published over the past two decades, according to a PubMed search. Treadmill gait data, for example from the DigiGait system, generates over 30 + spatial and temporal measures. Despite this multi-dimensional ...

An enhancement of machine learning model performance in disease prediction with synthetic data generation.

Scientific reports
The challenges of handling imbalanced datasets in machine learning significantly affect the model performance and predictive accuracy. Classifiers tend to favor the majority class, leading to biased training and poor generalization of minority classe...