Results 61 to 70 of about 338,614 (301)

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

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning

Sensors
The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image ...
Shaolong Chen, Zhiyong Zhang
doaj   +1 more source

CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW

Iraqi Journal for Computers and Informatics, 2021
Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data.
Aska Ezadeen Mehyadin, Adnan Mohsin Abdulazeez   +1 more
doaj   +1 more source

Semi-supervised Learning for Photometric Supernova Classification

, 2011
We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest
Breiman, Breiman, Breiman, Breiman, Chad M. Schafer, Chapelle, Coifman, Darren Homrighausen, Dovi Poznanski, Dubath, Falck, Freeman, Gong, Grimmett, Hastie, Homeier, Johnson, Joseph W. Richards, Kessler, Kessler, Kunz, Kuznetsova, Lafon, Newling, Nugent, Peter E. Freeman, Poznanski, Poznanski, Poznanski, Richards, Richards, Richards, Rodney, Ruppert, Sullivan, Wasserman   +35 more
core   +1 more source

Prediction of Dry-Low Emission Gas Turbine Operating Range from Emission Concentration Using Semi-Supervised Learning [PDF]

, 2023
Mochammad Faqih, Madiah Omar, Rosdiazli Ibrahim   +2 more
openalex   +1 more source

In Vivo Skin 3‐D Surface Reconstruction and Wrinkle Depth Estimation Using Handheld High Resolution Tactile Sensing

Advanced Healthcare Materials, EarlyView.
A compact handheld GelSight probe reconstructs in vivo 3‐D skin topography with micron‐level precision using a custom elastic gel and a learning‐based surface normal to height map pipeline. The device quantifies wrinkle depth across various body locations and detects changes in wrinkle depth following moisturizer application.
Akhil Padmanabha, Arpit Agarwal, Catherine Li, Austin Williams, Dinesh K. Patel, Sankalp Chopkar, Achu Wilson, Ahmet Ozkan, Wenzhen Yuan, Sonal Choudhary, Arash Mostaghimi, Zackory Erickson, Carmel Majidi   +12 more
wiley   +1 more source

Active learning for deep object detection by fully exploiting unlabeled data

Connection Science, 2023
Object detection is a challenging task that requires a large amount of labeled data to train high-performance models. However, labeling huge amounts of data is expensive, making it difficult to train a good detector with limited labeled data.
Feixiang Tan, Guansheng Zheng
doaj   +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

Disentangled Variational Auto-Encoder for Semi-supervised Learning

, 2018
Semi-supervised learning is attracting increasing attention due to the fact that datasets of many domains lack enough labeled data. Variational Auto-Encoder (VAE), in particular, has demonstrated the benefits of semi-supervised learning.
Cambria, Erik, Li, Yang, Pan, Quan, Peng, Haiyun, Wang, Suhang, Yang, Tao   +5 more
core   +1 more source

siRNA Delivery via Cross‐Linked Gelatin Microparticles Enables Targeted Modulation of Osteogenic‐Vascular Cross‐Talk: An Advanced Human 3D in Vitro Test System for Therapeutic siRNA

Advanced Healthcare Materials, EarlyView.
Osteogenic‐angiogenic cross‐talk is a vital prerequisite for vascularized bone regeneration. In this study, we investigated the effects of siRNA‐mediated silencing of two inhibitory proteins, Chordin and WWP‐1, via CaP‐NP‐loaded gelatin microparticles in osteogenically differentiated microtissues.
Franziska Mitrach, Jonas Kubat, Stefan Simm, Alexandra H. Springwald, Burak Demir, Anton Liebezeit, Michael C. Hacker, Michaela Schulz‐Siegmund   +7 more
wiley   +1 more source