NSTL国家科技图书文献中心

1. Rare Fungi Image Classification Based on Few-Shot Learning and Data Augmentation NSTL国家科技图书文献中心

Jiayi Hao | Yulin Feng ... - 《Pattern Recognition,Part XVI》 - International Conference on Pattern Recognition - 2025, - 50～62 - 共13页

摘要：Fungi image classification is highly | classification of rare fungi* is made more difficult by the* | Danish Fungi* 2020 dataset, utilizing the LibFewShot* | -shot learning to classify uncommon fungi* and* | challenging due to the high degree of similarity in the

关键词： Fungi image classification | Few-Shot learning | Data augmentation | Self-Supervised learning tasks

2. MMAT: Multi-scale Multi-attention Transformer for Fine-Grained Wild Fungi Visual Classification NSTL国家科技图书文献中心

Qinyan Dai | Yuxiang Lu ... - 《PRICAI 2024,Part III》 - Pacific Rim International Conference on Artificial Intelligence - 2025, - 41～53 - 共13页

摘要： wild fungi* dataset. The results of the experiments* | Fine-Grained Visual Classification (FGVC) is a | computer vision task that involves classifying subtle | differences in images. While the Vision Transformer (ViT) is | excellent at capturing long-range dependencies in

关键词： FGVC | Vision transformer | Part-CNN | Multi-Attention

3. The comparative study of microbial classification using machine learning and neural network models based on PCA for feature generation NSTL国家科技图书文献中心

Jiangshuai Cheng - 《Fourth International Conference on Computer Vision,Application,and Algorithm (CVAA 2024)》 - International Conference on Computer Vision,Application,and Algorithm - 2025, - 134862M.1～134862M.7 - 共7页

摘要：. Microorganisms include bacteria, viruses, fungi, protozoa | The classification of microorganisms is an | important branch of microbiology and plays a crucial role | in understanding microbial diversity | , algae, etc, and they exhibit significant differences

关键词： Machine learning | Neural networks | Microorganisms

4. Green synthesis and characterization of Zirconium Oxide with antimicrobial activities NSTL国家科技图书文献中心

B N Veerabhadraswamy | H K Pradeep ... - 《International Conference on Physics of Materials and Nanotechnology (ICPN 2023)》 - International Conference on Physics of Materials and Nanotechnology - 2025, - 227～232 - 共6页

摘要： bacteria and fungi. The results demonstrated potent | Green synthesis methods have garnered | considerable attention due to their eco-friendly and | sustainable nature. In this study, we report a green | synthesis approach to fabricate Zirconium Oxide

关键词： Green synthesis | Zirconium oxide nanoparticles | Cinnamon | Citric acid | Antibacterial activity | Antifungal activity | Nanomaterials | Eco-friendly synthesis.

5. Pneumonia Classification in Chest X-Ray Images Using Explainable Slot-Attention Mechanism NSTL国家科技图书文献中心

Shipra Madan | Santanu Chaudhury ... - 《Pattern Recognition,Part V》 - International Conference on Pattern Recognition - 2025, - 271～286 - 共16页

摘要： lungs, typically due to a bacterial, viral, or fungal | Pneumonia is an inflammation of one or both | infection. Pneumonia diagnosis involves highly skilled | professionals to examine a chest radiograph and is prone to | subjective variability. Also, computer-aided classification

关键词： Explainable few-shot learning | Chest x-ray | Explainable slot-attention | Medical image analysis | Computer-aided pneumonia classifier

6. Characterization of Fungi at Daycare Centers: A Systematic Review NSTL国家科技图书文献中心

S.K Yusof | A. Norhidayah ... - 《4th Symposium on Industrial Science and Technology (SISTEC2022) : Pahang, Malaysia, 23-24 November 2022》 - Symposium on Industrial Science and Technology - 2024, - 030007-1～030007-11 - 共11页

摘要：Exposure to indoor airborne fungi* may cause* | effects of airborne fungi* than adults due to their age* | the characterization of fungi* and the parameter* | affecting the characteristic of fungi* at daycare centers* | characterization of fungi* at daycare centers published from 2011*

关键词： PRISMA | Fungi | characterization

7. Fungi Classification: Enhancing Diagnosis Using Deep Learning NSTL国家科技图书文献中心

Jasmitha Bhimavarapu | Anuradha Chinta ... - 《2024 2nd World Conference on Communication & Computing》 - World Conference on Communication & Computing - 2024, - 1～6 - 共6页

摘要：. This curated repository of microscopic fungi* images* | dermatophyte fungi, serves as the foundation of our | , accurately identifying fungal* infections on time is* | struggle to differentiate between fungal* species due to* | classification of fungal* species, utilizing the DeFungi dataset*

关键词： Fungi | Deep learning | Visualization | Microscopy | Precision medicine | Image processing | Classification algorithms | Delays | Medical diagnosis | Medical diagnostic imaging

8. Isolation of Chitinolitic Fungi From Exoskeleton Waste of Three Shrimp Spesies NSTL国家科技图书文献中心

Meri Yusrida | Samingan ... - 《The 12th Annual International Conference (AIC) 2022 : the 12th Annual International Conference on Sciences and Engineering (AIC-SE) 2022 : Banda Aceh, Indonesia, 12-13 October 2022》 - Annual International Conference - 2024, - 040049-1～040049-7 - 共7页

摘要：Chitinolytic fungi* produce chitinase. Three* | media for chitinolytic fungi. This study was aimed to | determine the species of chitinolytic fungi* from the shell* | fungi species. There were six species of chitinolitic | fungi found in the waste which belong to three genera

关键词： PDA | Chitinolitic Fungi | Three Shrimp Spesies

9. Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification NSTL国家科技图书文献中心

Muchamad Galih Angga... | Ahmad Hindasyah ... - 《2024 International Conference on Computer,Control,Informatics and its Applications》 - International Conference on Computer,Control,Informatics and Its Applications - 2024, - 60～65 - 共6页

摘要： pathogenic fungi* is a challenge in the field of mycology* | classes of fungi* present in the dataset. This* | of fungal* classes. The weights on these layers are* | Accurate image classification of microscopic | . This study proposes a Swin Transformer-based approach

关键词： Fungi | Training | Digital control | Adaptation models | Accuracy | Microscopy | Transformers | Informatics | Tuning | Image classification

10. Fungi Image Segmentation using Efficient U-Net Architecture with ImageNet Pre-trained Model NSTL国家科技图书文献中心

G. M. Putra | U. Chasanah ... - 《2024 4th International Conference of Science and Information Technology in Smart Administration》 - International Conference of Science and Information Technology in Smart Administration - 2024, - 183～188 - 共6页

摘要： ImageNet and U-Net architecture for fungi* microscopic* | In recent years, deep learning techniques have | shown remarkable performance in image segmentation | tasks. Deep learning with a pre-trained model has been | widely used to address the limited training data

关键词： Fungi | Deep learning | Image segmentation | Image analysis | Microscopy | Noise | Training data | Robustness | Data models | Information technology

摘要：Fungi image classification is highly | classification of rare fungi is made more difficult by the | Danish Fungi 2020 dataset, utilizing the LibFewShot | -shot learning to classify uncommon fungi and | challenging due to the high degree of similarity in the

关键词： Fungi image classification | Few-Shot learning | Data augmentation | Self-Supervised learning tasks

摘要： wild fungi dataset. The results of the experiments | Fine-Grained Visual Classification (FGVC) is a | computer vision task that involves classifying subtle | differences in images. While the Vision Transformer (ViT) is | excellent at capturing long-range dependencies in

关键词： FGVC | Vision transformer | Part-CNN | Multi-Attention

摘要：. Microorganisms include bacteria, viruses, fungi, protozoa | The classification of microorganisms is an | important branch of microbiology and plays a crucial role | in understanding microbial diversity | , algae, etc, and they exhibit significant differences

关键词： Machine learning | Neural networks | Microorganisms

摘要： bacteria and fungi. The results demonstrated potent | Green synthesis methods have garnered | considerable attention due to their eco-friendly and | sustainable nature. In this study, we report a green | synthesis approach to fabricate Zirconium Oxide

关键词： Green synthesis | Zirconium oxide nanoparticles | Cinnamon | Citric acid | Antibacterial activity | Antifungal activity | Nanomaterials | Eco-friendly synthesis.

摘要： lungs, typically due to a bacterial, viral, or fungal | Pneumonia is an inflammation of one or both | infection. Pneumonia diagnosis involves highly skilled | professionals to examine a chest radiograph and is prone to | subjective variability. Also, computer-aided classification

关键词： Explainable few-shot learning | Chest x-ray | Explainable slot-attention | Medical image analysis | Computer-aided pneumonia classifier

摘要：Exposure to indoor airborne fungi may cause | effects of airborne fungi than adults due to their age | the characterization of fungi and the parameter | affecting the characteristic of fungi at daycare centers | characterization of fungi at daycare centers published from 2011

关键词： PRISMA | Fungi | characterization

摘要：. This curated repository of microscopic fungi images | dermatophyte fungi, serves as the foundation of our | , accurately identifying fungal infections on time is | struggle to differentiate between fungal species due to | classification of fungal species, utilizing the DeFungi dataset

关键词： Fungi | Deep learning | Visualization | Microscopy | Precision medicine | Image processing | Classification algorithms | Delays | Medical diagnosis | Medical diagnostic imaging

摘要：Chitinolytic fungi produce chitinase. Three | media for chitinolytic fungi. This study was aimed to | determine the species of chitinolytic fungi from the shell | fungi species. There were six species of chitinolitic | fungi found in the waste which belong to three genera

关键词： PDA | Chitinolitic Fungi | Three Shrimp Spesies

摘要： pathogenic fungi is a challenge in the field of mycology | classes of fungi present in the dataset. This | of fungal classes. The weights on these layers are | Accurate image classification of microscopic | . This study proposes a Swin Transformer-based approach

关键词： Fungi | Training | Digital control | Adaptation models | Accuracy | Microscopy | Transformers | Informatics | Tuning | Image classification

摘要： ImageNet and U-Net architecture for fungi microscopic | In recent years, deep learning techniques have | shown remarkable performance in image segmentation | tasks. Deep learning with a pre-trained model has been | widely used to address the limited training data

关键词： Fungi | Deep learning | Image segmentation | Image analysis | Microscopy | Noise | Training data | Robustness | Data models | Information technology

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国家科技图书文献中心

摘要：Fungi image classification is highly | classification of rare fungi* is made more difficult by the* | Danish Fungi* 2020 dataset, utilizing the LibFewShot* | -shot learning to classify uncommon fungi* and* | challenging due to the high degree of similarity in the

摘要： wild fungi* dataset. The results of the experiments* | Fine-Grained Visual Classification (FGVC) is a | computer vision task that involves classifying subtle | differences in images. While the Vision Transformer (ViT) is | excellent at capturing long-range dependencies in

摘要：Exposure to indoor airborne fungi* may cause* | effects of airborne fungi* than adults due to their age* | the characterization of fungi* and the parameter* | affecting the characteristic of fungi* at daycare centers* | characterization of fungi* at daycare centers published from 2011*

摘要：Chitinolytic fungi* produce chitinase. Three* | media for chitinolytic fungi. This study was aimed to | determine the species of chitinolytic fungi* from the shell* | fungi species. There were six species of chitinolitic | fungi found in the waste which belong to three genera

摘要： pathogenic fungi* is a challenge in the field of mycology* | classes of fungi* present in the dataset. This* | of fungal* classes. The weights on these layers are* | Accurate image classification of microscopic | . This study proposes a Swin Transformer-based approach

摘要： ImageNet and U-Net architecture for fungi* microscopic* | In recent years, deep learning techniques have | shown remarkable performance in image segmentation | tasks. Deep learning with a pre-trained model has been | widely used to address the limited training data