Search

Your search keyword '"generative adversarial networks"' showing total 5 results
Start Over Descriptor "generative adversarial networks" Region egypt
5 results on '"generative adversarial networks"'

1. Findings in the Area of Cancer Detection Reported from University of Alexandria (An Enhanced Lung Cancer Detection Approach Using Dual-model Deep Learning Technique).

Subjects
CONVOLUTIONAL neural networks, LUNG cancer, GENERATIVE adversarial networks, LUNG tumors, INFORMATION technology
Abstract

A recent report from the University of Alexandria in Egypt highlights the importance of early detection in improving lung cancer treatment outcomes. The study introduces a dual-model deep learning technique combining Convolutional Neural Networks (CNN) and You Only Look Once (YOLOv8) architecture to enhance lung cancer detection accuracy and localization. The research achieved promising results, with the CNN model reaching a 97.67% accuracy in classifying lung tissues and the YOLOv8 model achieving an Intersection over Union (IoU) score of 0.85 for tumor localization. The incorporation of synthetic images generated by Deep Convolutional Generative Adversarial Networks (DCGAN) further improved classification accuracy and detection performance by 10%. [Extracted from the article]

Published
2025

2. A Generative Adversarial Network for Data Augmentation: The Case of Arabic Regional Dialects.

Author

Carrasco, Xavier A., Elnagar, Ashraf, and Lataifeh, Mohammed

Subjects
GENERATIVE adversarial networks, ARABIC dialects, DATA augmentation, SENTIMENT analysis
Abstract

Text Generation using Generative Adversarial Networks (GANs) has been successful in domains such as sentiment analysis using Sentimental GAN (SentiGAN) model. We adopt a similar model to generate sentences for five regional Arabic dialects (Egypt, Gulf, Maghreb, Levant, and Iraq). The objective is to overcome the scarcity of richly annotated Dialectal Arabic (DA) datasets by automatic generation of such corpora. The DA generation process for a specific dialect, relies on a generator to create new text, and a discriminator to evaluate that text, with a dynamic update that will allow the process to run automatically without supervision. Novelty and diversity are the two metrics used to verify the consistency and quality of the generated DA text before enriching the sought datasets. Experimental results confirm the reliability and value of the generated datasets when tested by different classifiers. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

3. Researchers from University of Science and Technology Report Recent Findings in CDC and FDA (Proposed Methodology for Gait Recognition Using Generative Adversarial Network With Different Feature Selectors).

Subjects
GENERATIVE adversarial networks, SCIENCE journalism, CONVOLUTIONAL neural networks, RESEARCH personnel, COVID-19 pandemic
Abstract

Researchers from the University of Science and Technology in Egypt have proposed a deep structure procedure for gait recognition using a generative adversarial network (GAN) with different feature selectors. Gait recognition is being investigated as a biometric technology, particularly in light of the COVID-19 pandemic. The researchers used a GAN to generate gait images from pedestrian datasets and extracted gait features using pretrained convolutional neural network (CNN) models. They then balanced the datasets and selected the best features using various feature selectors. The proposed deep neural network achieved high accuracy in identifying different datasets. The research has been peer-reviewed and published in Neural Computing and Applications. [Extracted from the article]

Published
2023

4. Study Findings from Menoufia University Provide New Insights into Melanoma (Deep Learning-based Hair Removal for Improved Diagnostics of Skin Diseases).

Subjects
HAIR removal, SKIN diseases, SKIN cancer, MELANOMA, GENERATIVE adversarial networks, CONNECTIVE tissue diseases
Abstract

Keywords: Menoufia; Egypt; Africa; Cancer; Cosmetic Techniques; Diagnostics and Screening; Hair Removal; Health and Medicine; Melanoma; Oncology; Skin Diseases and Conditions; Skin and Connective Tissue Diseases and Conditions EN Menoufia Egypt Africa Cancer Cosmetic Techniques Diagnostics and Screening Hair Removal Health and Medicine Melanoma Oncology Skin Diseases and Conditions Skin and Connective Tissue Diseases and Conditions 7021 7021 1 10/09/23 20231013 NES 231013 2023 OCT 13 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Current study results on Oncology - Melanoma have been published. The news reporters obtained a quote from the research from Menoufia University, "Dermoscopy, a reliable medical technique, utilizes specialized devices to examine the skin and detect melanoma. [Extracted from the article]

Published
2023

5. Studies from Menoufia University Have Provided New Data on Skin Diseases and Conditions (Deep-learning-based Super-resolution and Classification Framework for Skin Disease Detection Applications).

Subjects
SKIN diseases, GENERATIVE adversarial networks, QUANTUM electronics
Abstract

Keywords: Menoufia; Egypt; Africa; Health and Medicine; Skin Diseases and Conditions; Skin and Connective Tissue Diseases and Conditions EN Menoufia Egypt Africa Health and Medicine Skin Diseases and Conditions Skin and Connective Tissue Diseases and Conditions 950 950 1 05/02/23 20230502 NES 230502 2023 MAY 2 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Current study results on Skin Diseases and Conditions have been published. Menoufia, Egypt, Africa, Health and Medicine, Skin Diseases and Conditions, Skin and Connective Tissue Diseases and Conditions. [Extracted from the article]

Published
2023

Catalog

Books, media, physical & digital resources
See catalog results