Results 181 to 190 of about 1,171,833 (351)

Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation

Advanced Intelligent Systems, EarlyView.
Feature from recent image foundation models (DINOv2) are useful for vision tasks (segmentation, object localization) with little or no human input. Once upsampled, they can be used for weakly supervised micrograph segmentation, achieving strong results when compared to classical features (blurs, edge detection) across a range of material systems.
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper   +2 more
wiley   +1 more source

Identification of sensorineural hearing loss subtypes using unsupervised machine learning and assessment of their replicability. [PDF]

Sci Rep
Dimitrov L, Lilaonitkul W, Mehta N.
europepmc   +1 more source

Terrestrial Cyborg Insects for Real‐Life Applications

Advanced Intelligent Systems, EarlyView.
This article reviews the development of terrestrial cyborg insects from their emergence in 1997 to mid‐2025, examining three key aspects: locomotion control methods, associated challenges with proposed solutions, and practical applications. Framing these biohybrid systems as insect‐scale mobile robots, the review provides foundational insights for new ...
Hai Nhan Le, Lachlan Fitzgerald, Phuoc Thien Phan, Robbie Wilson, Christofer Clemente, Huu Duoc Nguyen, Hirotaka Sato, Hoang‐Phuong Phan, Peter Ross McAree, Thanh Nho Do, Tat Thang Vo‐Doan   +10 more
wiley   +1 more source

Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study. [PDF]

JMIR Med Inform
Huang PY, Hong WL, Hee HZ, Chang WK, Lee CH, Ting CK.   +5 more
europepmc   +1 more source

An unsupervised machine learning approach to segmentation of clinician-entered free text.

, 2007
Jesse O. Wrenn, Peter D. Stetson, Stephen B. Johnson   +2 more
openalex   +1 more source

Automatic Electromagnetic Radiation Source Imaging and Localization Using Active and Unsupervised Machine Learning [PDF]

Jinghai Guo, Ling Zhang
openalex   +1 more source

Deep Reinforcement Learning Approaches for Sensor Data Collection by a Swarm of UAVs

Advanced Intelligent Systems, EarlyView.
This article presents four decentralized reinforcement learning algorithms for autonomous data harvesting and investigates how collaboration improves collection efficiency. It also presents strategies to minimize training times by improving model flexibility, enabling algorithms to operate with varying number of agents and sensors.
Thiago de Souza Lamenza, Bruno José Olivieri de Souza, Markus Endler   +2 more
wiley   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Utilising unsupervised machine learning to predict outbreaks of respiratory tract infections in acute Irish hospitals (2016-2021). [PDF]

Public Health Pract (Oxf)
Amin D, Vellinga A.
europepmc   +1 more source

Heart rate variability in soccer players and the application of unsupervised machine learning [PDF]

Wollner Materko, Silvia Alayón Miranda, Thiago Henrique Lobato Bezerra, C. Figueira   +3 more
openalex   +1 more source