Detection of invisible cracks in ceramic materials using by pre-trained deep convolutional neural network
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1433-3058
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Detection of invisible cracks in ceramic materials using by pre-trained deep convolutional neural network ; volume:34 ; number:2 ; day:8 ; month:11 ; year:2021 ; pages:1423-1432 ; date:1.2022
Neural computing & applications ; 34, Heft 2 (8.11.2021), 1423-1432, 1.2022
- Urheber
-
Nogay, Hidir Selcuk
Akinci, Tahir Cetin
Yilmaz, Musa
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s00521-021-06652-w
- URN
-
urn:nbn:de:101:1-2022050222063020752291
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:23 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Nogay, Hidir Selcuk
- Akinci, Tahir Cetin
- Yilmaz, Musa
- SpringerLink (Online service)
Ähnliche Objekte (12)
Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study
Offline Pre-trained Multi-agent Decision Transformer
Generalization of vision pre-trained models for histopathology
Leveraging pre-trained language models for code generation
Pre-trained transformer-based language models for Sundanese
Analyzing pre-trained and fine-tuned language models
How to train your pre-trained GAN models
Biomedical event extraction using pre-trained SciBERT
Adapting the pre-trained convolutional neural networks to improve the anomaly detection and classification in mammographic images
Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images
A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis
Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey
Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study
Offline Pre-trained Multi-agent Decision Transformer
Generalization of vision pre-trained models for histopathology
Leveraging pre-trained language models for code generation
Pre-trained transformer-based language models for Sundanese
Analyzing pre-trained and fine-tuned language models
How to train your pre-trained GAN models
Biomedical event extraction using pre-trained SciBERT
Adapting the pre-trained convolutional neural networks to improve the anomaly detection and classification in mammographic images
Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images
A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis
Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey
Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study
Offline Pre-trained Multi-agent Decision Transformer
Generalization of vision pre-trained models for histopathology
Leveraging pre-trained language models for code generation
Pre-trained transformer-based language models for Sundanese
Analyzing pre-trained and fine-tuned language models
How to train your pre-trained GAN models
Biomedical event extraction using pre-trained SciBERT
Adapting the pre-trained convolutional neural networks to improve the anomaly detection and classification in mammographic images
Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images
A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis