Results 151 to 160 of about 6,131,436 (285)

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

Single-station analysis of Campi Flegrei (Italy) seismic signals using multiscale entropy and unsupervised learning. [PDF]

Sci Rep
Grimaldi A, Amoroso O, Scarpetta S, Convertito V, Napolitano F, Messuti G, Capuano P, Nardone L, Gaudiosi G, Galluzzo D.   +9 more
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

Leveraging productivity indicators for anomaly detection in swine breeding herds with unsupervised learning. [PDF]

Front Vet Sci
Pedro Mil-Homens M, Wang C, Trevisan G, Dórea F, Linhares DCL, Holtkamp D, Silva GS.   +6 more
europepmc   +1 more source

GraphNeuralCloth: A Graph‐Neural‐Network‐Based Framework for Non‐Skinning Cloth Simulation

Advanced Intelligent Systems, EarlyView.
This study presents a cloth motion capture system and a point‐cloud‐to‐mesh processing method to support the prediction of real‐world fabric deformation. GraphNeuralCloth, a graph neural‐network (GNN)‐based framework is also proposed to estimate the cloth morphology change in real time.
Yingqi Li, Xiaomei Wang, Xiaoqi Song, Chenchen Fan, Bohao Zhu, Jianan Peng, Qixin Liang, James Lam, Kam‐Yim Sze, Ka‐Wai Kwok   +9 more
wiley   +1 more source

PROTECT: Protein circadian time prediction using unsupervised learning. [PDF]

iScience
Ansary Ogholbake A, Cheng Q.
europepmc   +1 more source

Unsupervised Learning by Probabilistic Latent Semantic Analysis

Machine-mediated learning, 2004
Thomas Hofmann
semanticscholar   +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

An Unsupervised Learning Algorithm for the Automatic Classification of Coronary Artery Lesions. [PDF]

Cureus
Szopinska J, Regulski PA, Mazurek M, Grabowski M, Wendykier P, Jozwiak R.   +5 more
europepmc   +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