AIMC Topic: Machine Learning
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Software technical debt prediction based on complex software networks.
PloS one
Jun 9, 2025
Technical debt prediction (TDP) is crucial for the long-term maintainability of software. In the literature, many machine-learning based TDP models have been proposed; they used TD-related metrics as input features for machine-learning classifiers to...
Causal Discovery Analysis Reveals Insights into Psychosis Proneness, Brain Function, and Environmental Factors among Young Individuals.
Psychiatry research. Neuroimaging
Jun 8, 2025
Experiencing mild symptoms of psychosis, like delusions and hallucinations, occurs sometimes in general, nonclinical populations, often termed psychosis proneness (PP), potentially part of the psychosis continuum. Understanding the neural and environ...
Tailoring task arithmetic to address bias in models trained on multi-institutional datasets.
Journal of biomedical informatics
Jun 8, 2025
OBJECTIVE: Multi-institutional datasets are widely used for machine learning from clinical data, to increase dataset size and improve generalization. However, deep learning models in particular may learn to recognize the source of a data element, lea...
Generation of ultrasonic and audible sound waves for the automatic classification of packaging waste in reverse vending machines.
Waste management (New York, N.Y.)
Jun 8, 2025
Reverse vending machines (RVMs) are essential for promoting waste sorting at the source by offering incentives for recycling. However, current RVMs, which primarily rely on expensive sensors such as barcode scanners and computer vision systems, face ...
Implications From the Analogous Relationship Between Evolutionary and Learning Processes.
BioEssays : news and reviews in molecular, cellular and developmental biology
Jun 8, 2025
Organismal evolution is a process of discovering better-fitting phenotypes through trial and error across generations. This iterative process resembles learning processes, an analogy recognized since the 1950s. Recognizing this parallel suggests that...
Using nursing data for machine learning-based prediction modeling in intensive care units: A scoping review.
International journal of nursing studies
Jun 7, 2025
BACKGROUND: Nursing data can help detect patient deterioration early and predict patient outcomes. Moreover, rapid advancements in machine learning have highlighted the need for clinical prognosis prediction models for intensive care unit patients. A...
Single-cell sequencing and machine learning reveal the role of dioxin-interacting genes in HCC prognosis and immune microenvironment.
Ecotoxicology and environmental safety
Jun 7, 2025
Dioxins are persistent environmental pollutants that bioaccumulate in the food chain, posing significant risks to human health. Despite their low environmental concentrations, dioxins accumulate in tissues, particularly in top predators and humans, r...
Machine learning classifiers to detect data pattern change of continuous emission monitoring system: A typical chemical industrial park as an example.
Environment international
Jun 7, 2025
Continuous Emission Monitoring Systems (CEMS) are critical for real-time pollutant measurement, widely deployed to supervise industrial emissions and ensure regulatory compliance. Despite their utility, CEMS data face challenges of data fabrications,...
Identification of macrophage-associated diagnostic biomarkers and molecular subtypes in gestational diabetes mellitus based on machine learning.
Artificial cells, nanomedicine, and biotechnology
Jun 6, 2025
Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages play a crucial role in its development. This study integrated scRNA-seq and RNA-seq data to explore m...
Assessing the readiness of dental electronic health records for machine learning prediction of procedure outcomes: Insights from the bigmouth repository on composite and amalgam restoration survival rates.
Journal of dentistry
Jun 6, 2025
OBJECTIVE: Dental electronic health records (EHRs) often lack comprehensive data for evaluating procedure outcomes. Machine learning (ML) enables predictive modeling but its applicability to dental EHR data remains unclear. This study assessed the re...
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