Results 41 to 50 of about 41,355 (238)

A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data

Advanced Engineering Materials, EarlyView.
Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour, Prithwish Tarafder, Anton Wiberg, Huanyu Li, Johan Moverare, Patrick Lambrix   +5 more
wiley   +1 more source

Advancements in Semi-Supervised Deep Learning for Brain Tumor Segmentation in MRI: A Literature Review

AI
For automatic tumor segmentation in magnetic resonance imaging (MRI), deep learning offers very powerful technical support with significant results. However, the success of supervised learning is strongly dependent on the quantity and accuracy of labeled
Chengcheng Jin, Theam Foo Ng, Haidi Ibrahim   +2 more
doaj   +1 more source

Patterning the Void: Combining L‐Systems with Archimedean Tessellations as a Perspective for Tissue Engineering Scaffolds

Advanced Functional Materials, EarlyView.
This study introduces a novel multi‐scale scaffold design using L‐fractals arranged in Archimedean tessellations for tissue regeneration. Despite similar porosity, tiles display vastly different tensile responses (1–100 MPa) and deformation modes. In vitro experiments with hMSCs show geometry‐dependent growth and activity. Over 55 000 tile combinations
Maria Kalogeropoulou, Kenny A. van Kampen, Pierpaolo Fucile, Carlos Mota, Lorenzo Moroni   +4 more
wiley   +1 more source

SEMI-SUPERVISED MARGINAL FISHER ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification ...
H. Huang, J. Liu, Y. Pan
doaj   +1 more source

Geostatistical semi-supervised learning for spatial prediction

Artificial Intelligence in Geosciences, 2022
Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms.
Francky Fouedjio, Hassan Talebi
doaj   +1 more source

Lγ-PageRank for semi-supervised learning [PDF]

Applied Network Science, 2019
PageRank for Semi-Supervised Learning has shown to leverage data structures and limited tagged examples to yield meaningful classification. Despite successes, classification performance can still be improved, particularly in cases of fuzzy graphs or unbalanced labeled data.
Esteban Bautista, Patrice Abry, Paulo Gonçalves   +2 more
openaire   +2 more sources

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Artificial Intelligence as the Next Visionary in Liquid Crystal Research

Advanced Functional Materials, EarlyView.
The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam, Ouassim L'Karkouri, Danqing Liu   +2 more
wiley   +1 more source

Semi-supervised learning in cancer diagnostics

Frontiers in Oncology, 2022
In cancer diagnostics, a considerable amount of data is acquired during routine work-up. Recently, machine learning has been used to build classifiers that are tasked with cancer detection and aid in clinical decision-making.
Jan-Niklas Eckardt, Jan-Niklas Eckardt, Martin Bornhäuser, Martin Bornhäuser, Martin Bornhäuser, Karsten Wendt, Karsten Wendt, Jan Moritz Middeke, Jan Moritz Middeke   +8 more
doaj   +1 more source

Graph Laplacian for Semi-supervised Learning

, 2023
Semi-supervised learning is highly useful in common scenarios where labeled data is scarce but unlabeled data is abundant. The graph (or nonlocal) Laplacian is a fundamental smoothing operator for solving various learning tasks. For unsupervised clustering, a spectral embedding is often used, based on graph-Laplacian eigenvectors.
Streicher, Or, Gilboa, Guy
openaire   +2 more sources