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Wyszukujesz frazę "Karanam, S." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
A study on the role of non-hyperlink text on web navigation
Autorzy:
Karanam, S.
Oostendorp Van, H.
Indurkhya, B.
Powiązania:
https://bibliotekanauki.pl/articles/305399.pdf
Data publikacji:
2012
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
web-navigation
main-content
text
semantics
Opis:
Cognitive models of web navigation have been used for evaluating websites and predicting user navigation behavior. Currently they predict the correct hyperlink by using information from the hyperlink text alone and ignore all other textual information on a webpage. The validity of this assumption is examined by investigating the role of non-hyperlink text on user navigation behavior. In the first experiment, we created two versions of a website by removing the non-hyperlink text from it. We found that there was no significant effect of non-hyperlink text on the user navigation behavior. Participants were equally accurate, selected the same set of pages to visit and spent the same amount of time on that common set with or without non-hyperlink text. This result validates the assumptions of those models of user-navigation behavior that consider information from the hyperlink text only. However, in a followup experiment, we included high-relevance and low-relevance pictures on the website, and repeated the experiment with and without non-hyperlink text. We found that participants were more accurate in the presence of non-hyperlink text than without it. This result suggests that the presence of pictures might prime the users to pay attention to non-hyperlink text, which increases the task accuracy.
Źródło:
Computer Science; 2012, 13 (3); 5-22
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A supervised approach to musculoskeletal imaging fracture detection and classification using deep learning algorithms
Autorzy:
Karanam, Santoshachandra Rao
Srinivas, Y.
Chakravarty, S.
Powiązania:
https://bibliotekanauki.pl/articles/38702595.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
musculoskeletal image
image processing
image enhancement
fracture diagnosis
fracture classification
deep neural network
obraz układu mięśniowo-szkieletowego
przetwarzanie obrazu
wzmocnienie obrazu
diagnoza złamania
klasyfikacja złamań
głęboka sieć neuronowa
Opis:
Bone fractures break bone continuity. Impact or stress causes numerous bone fractures. Fracture misdiagnosis is the most frequent mistake in emergency rooms, resulting in treatment delays and permanent impairment. According to the Indian population studies, fractures are becoming more common. In the last three decades, there has been a growth of 480 000, and by 2022, it will surpass 600 000. Classifying X-rays may be challenging, particularly in an emergency room when one must act quickly. Deep learning techniques have recently become more popular for image categorization. Deep neural networks (DNNs) can classify images and solve challenging problems. This research aims to build and evaluate a deep learning system for fracture identification and bone fracture classification (BFC). This work proposes an image-processing system that can identify bone fractures using X-rays. Images from the dataset are pre-processed, enhanced, and extracted. Then, DNN classifiers ResNeXt101, InceptionResNetV2, Xception, and NASNetLarge separate the images into the ones with unfractured and fractured bones (normal, oblique, spiral, comminuted, impacted, transverse, and greenstick). The most accurate model is InceptionResNetV2, with an accuracy of 94.58%.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 3; 369-385
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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