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Wyświetlanie 1-3 z 3
Tytuł:
A DHCR_ SmartNet: A smart Devanagari handwritten character recognition using level-wised CNN architecture
Autorzy:
Deore, Shalaka Prasad
Powiązania:
https://bibliotekanauki.pl/articles/27312907.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
convolutional neural network
VGG16
fine-tuned
handwritten script
Devanagari characters
Opis:
Handwritten script recognition is a vital application of the machine-learning domain. Applications like automatic license plate detection, pin-code detection, and historical document management increases attention toward handwritten script recognition. English is the most widely spoken language in India; hence, there has been a lot of research into identifying a script using a machine. Devanagari is a popular script that is used by a large number of people on the Indian subcontinent. In this paper, a level-wised efficient transfer-learning approach is presented on the VGG16 model of a convolutional neural network (CNN) for identifying isolated Devanagari handwritten characters. In this work, a new dataset of Devanagari characters is presented and made accessible to the public. This newly created dataset is comprised of 5800 samples for 12 vowels, 36 consonants, and 10 digits. Initially, a simple CNN is implemented and trained on this new small dataset. During the next stage, a transfer-learning approach is implemented on the VGG16 model, and during the last stage, the efficient fine-tuned VGG16 model is implemented. The obtained accuracy of the fine-tuned model’s training and testing came to 98.16% and 96.47%, respectively.
Źródło:
Computer Science; 2022, 23 (3); 301--320
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Advancing Chipboard Milling Process Monitoring through Spectrogram-Based Time Series Analysis with Convolutional Neural Network using Pretrained Networks
Autorzy:
Kurek, Jarosław
Szymanowski, Karol
Chmielewski, Leszek
Orłowski, Arkadiusz
Powiązania:
https://bibliotekanauki.pl/articles/27323142.pdf
Data publikacji:
2023
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
Tematy:
convolutional neural networks
CNN
vgg16
vgg19
resnet34
tool state monitoring
chipboard milling
Opis:
This paper presents a novel approach to enhance chipboard milling process monitoring in the furniture manufacturing sector using Convolutional Neural Networks (CNNs) with pretrained architectures like VGG16, VGG19, and RESNET34. The study leverages spectrogram representations of time-series data obtained during the milling process, providing a unique perspective on tool condition monitoring. The efficiency of the CNN models in accurately classifying tool conditions into distinct states (‘Green’, ‘Yellow’, and ‘Red’) based on wear levels is thoroughly evaluated. Experimental results demonstrate that VGG16 and VGG19 achieve high accuracy, however with longer training times, while RESNET34 offers faster training at the cost of reduced precision. This research not only highlights the potential of pretrained CNNs in industrial applications but also opens new avenues for predictive maintenance and quality control in manufacturing, underscoring the broader applicability of AI in industrial automation and monitoring systems.
Źródło:
Machine Graphics & Vision; 2023, 32, 2; 89--108
1230-0535
2720-250X
Pojawia się w:
Machine Graphics & Vision
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification and segmentation of periodontal cystfor digital dental diagnosis using deep learning
Autorzy:
Lakshmi, T. K.
Dheeba, J.
Powiązania:
https://bibliotekanauki.pl/articles/38700996.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
CNN
dental radiograph
deep learning
health care
machine transfer learning
periodontal cyst
predictive analytics
segmentation
U-Net
VGG16
rentgenowskie zdjęcie zębów
uczenie głębokie
opieka zdrowotna
uczenie się z transferu maszynowego
torbiel przyzębia
analityka predykcyjna
segmentacja
Opis:
The digital revolution is changing every aspect of life by simulating the ways humansthink, learn and make decisions. Dentistry is one of the major fields where subsets ofartificial intelligence are extensively used for disease predictions. Periodontitis, the mostprevalent oral disease, is the main focus of this study. We propose methods for classifyingand segmenting periodontal cysts on dental radiographs using CNN, VGG16, and U-Net.Accuracy of 77.78% is obtained using CNN, and enhanced accuracy of 98.48% is obtainedthrough transfer learning with VGG16. The U-Net model also gives encouraging results.This study presents promising results, and in the future, the work can be extended withother pre-trained models and compared. Researchers working in this field can develop novelmethods and approaches to support dental practitioners and periodontists in decision-making and diagnosis and use artificial intelligence to bridge the gap between humansand machines.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 131-149
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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