AIMC Topic: Machine Learning

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Management of sustainable urban green spaces through machine learning-supported MCDM and GIS integration.

Environmental science and pollution research international
This study evaluates green space suitability in İzmir's Konak district using the analytic hierarchy process, machine learning, weighted linear combination, and the technique for order preference by similarity to ideal solution methods, integrated wit...

Kolmogorov-Arnold networks for predicting drug-gene associations of HDAC1 inhibitors in periodontitis.

Computational biology and chemistry
Periodontal disease, or periodontitis, is a chronic inflammatory condition affecting the tissues supporting teeth, with epigenetic mechanisms such as DNA methylation, histone modifications, and RNA molecules playing a crucial role in its progression....

Shear wave elastography values in endometrioma: Clinical findings and machine learning-based prediction models.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: To evaluate the diagnostic value of shear wave elastography (SWE) in assessing endometriomas and its correlation with clinical symptoms. Furthermore, the study investigates the use of machine learning (ML) models to predict clinical outcom...

Applications of Machine Learning in Image Analysis to Identify Craniosynostosis: A Systematic Review and Meta-Analysis.

Orthodontics & craniofacial research
Craniosynostosis is a condition characterised by the premature fusion of cranial sutures, which can lead to significant neurodevelopmental and aesthetic issues if not diagnosed and treated early. This study aimed to systematically review and conduct ...

High-order diversity feature learning for pedestrian attribute recognition.

Neural networks : the official journal of the International Neural Network Society
Pedestrian attribute recognition (PAR) involves accurately identifying multiple attributes present in pedestrian images. There are two main approaches for PAR: part-based method and attention-based method. The former relies on existing segmentation o...

Predicting rheological properties of HAMA/GelMA hybrid hydrogels via machine learning.

Journal of the mechanical behavior of biomedical materials
- Rheological properties are pivotal in determining the printability of biomaterials, directly impacting the success of 3D bioprinted constructs. Understanding the intricate relationship between biomaterial formulations, rheological behavior and prin...

The efficacy and toxicity equilibrium of emodin for liver injury: A bidirectional meta-analysis and machine learning.

Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND: Emodin, a hepatoprotective agent derived from various herbs, exhibits dual effects on liver injury, necessitating further investigation into its therapeutic and toxic properties. Traditional meta-analyses lack predictive capability for do...

International Symposium on Ruminant Physiology: Leveraging computer vision, large language models, and multimodal machine learning for optimal decision making in dairy farming.

Journal of dairy science
This article explores various applications of artificial intelligence (AI) technologies in dairy farming, including the use of computer vision systems (CVS) for animal identification, BCS and body shape analysis, and potential uses of large language ...