Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers developed three adaptive ensemble voting methods that assign weights based on per-class F1-scores from validation instead of overall accuracy. They tested the approaches on Gaussian Mixture, Spiral, and Moon synthetic datasets and on the Breast Cancer Wisconsin and UCI Heart Disease datasets, comparing against majority, weighted, and soft voting.
Researchers collected paired samples from 94 right-handed participants who each wrote the same standardized text with dominant and non-dominant hands, then scored 13 general and 19 individual characteristics. They found statistically significant differences in 61.5% of general and 57.9% of individual characteristics and trained a supervised logistic regression model to distinguish hand use.
Researchers built an optical sensor based on an ITO/CuBi2O4/LaNiO3 heterojunction with a hydrophobic surface that turns the curvature changes of moving biocide droplets into photocurrent signals. Machine learning was used to extract and analyze feature peaks from those signals to classify liquids.
Background Assessment of pulmonary vascularity on chest radiographs (CXRs) in congenital heart disease (CHD) is limited by subjectivity, and existing criteria lack sufficient validation. Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy.
Published December 5, 2025, this peer-reviewed study investigated how AI and Generative AI affect music streaming. Using two focus groups with users and with artists/performers, it explored perceptions of AI-generated music for listening and for artists' position and opportunities within the streaming model.
Published October 28 2024, this peer-reviewed study examined 6 commercial products using AI in creative industries to assess labor market effects. It found AI products were more labor intensive than traditional media because they required both traditional production skills and new computational expertise, while also enabling broader exploration in the ideation phase.
By March 2026, this peer-reviewed overview described how AI-based automation in Industry 4.0 optimized production, logistics, and resource management to reduce waste and energy use, and how Industry 5.0 expanded that with human-machine collaboration, generative AI, digital twins, and decentralized smart grids and microgrids.
A scoping review published May 25, 2026 examined 270 peer-reviewed studies from 2002 to 2024 on machine learning in sport. It found applications across 12 subject areas, most frequently computer science, biomechanics, and sport psychology, with common uses in action recognition, injury prediction/prevention, and athlete selection/talent identification.
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