Enhancing coffee bean classification: a comparative analysis of pre-trained deep learning models
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Enhancing coffee bean classification: a comparative analysis of pre-trained deep learning models ; volume:36 ; number:16 ; day:1 ; month:4 ; year:2024 ; pages:9023-9052 ; date:6.2024
Neural computing & applications ; 36, Heft 16 (1.4.2024), 9023-9052, 6.2024
- Creator
-
Hassan, Esraa
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s00521-024-09623-z
- URN
-
urn:nbn:de:101:1-2407221021498.717071402510
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:58 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Hassan, Esraa
- SpringerLink (Online service)
Other Objects (12)
Offline Pre-trained Multi-agent Decision Transformer
Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models
Generalization of vision pre-trained models for histopathology
Leveraging pre-trained language models for code generation
Biomedical event extraction using pre-trained SciBERT
Pre-trained transformer-based language models for Sundanese
Analyzing pre-trained and fine-tuned language models
How to train your pre-trained GAN models
Enhancing recognition and interpretation of functional phenotypic sequences through fine-tuning pre-trained genomic models
Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey
Leveraging pre-trained language models for mining microbiome-disease relationships
Surgicberta: a pre-trained language model for procedural surgical language
Offline Pre-trained Multi-agent Decision Transformer
Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models
Generalization of vision pre-trained models for histopathology
Leveraging pre-trained language models for code generation
Biomedical event extraction using pre-trained SciBERT
Pre-trained transformer-based language models for Sundanese
Analyzing pre-trained and fine-tuned language models
How to train your pre-trained GAN models
Enhancing recognition and interpretation of functional phenotypic sequences through fine-tuning pre-trained genomic models
Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey
Leveraging pre-trained language models for mining microbiome-disease relationships
Surgicberta: a pre-trained language model for procedural surgical language
Offline Pre-trained Multi-agent Decision Transformer
Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models
Generalization of vision pre-trained models for histopathology
Leveraging pre-trained language models for code generation
Biomedical event extraction using pre-trained SciBERT
Pre-trained transformer-based language models for Sundanese
Analyzing pre-trained and fine-tuned language models
How to train your pre-trained GAN models
Enhancing recognition and interpretation of functional phenotypic sequences through fine-tuning pre-trained genomic models
Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey
Leveraging pre-trained language models for mining microbiome-disease relationships