Výsledky vyhledávání - "Generative Adversarial Networks (GANs)"

Akademický článek

Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies

Autor: Muhammed Halil Akpinar, Abdulkadir Sengur, Massimo Salvi, Silvia Seoni, Oliver Faust, Hasan Mir, Filippo Molinari, U. Rajendra Acharya

Publikováno v: IEEE Open Journal of Engineering in Medicine and Biology, Vol 6, Pp 183-192 (2025)

Generative Adversarial Networks (GANs) have emerged as a powerful tool in artificial intelligence, particularly for unsupervised learning. This systematic review analyzes GAN applications in healthcare, focusing on image and signal-based studies acro

Externí odkaz: https://doaj.org/article/08ddb280e7c04d0da76ecae3552648f9

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Akademický článek

MCGAN—a cutting edge approach to real time investigate of multimedia deepfake multi collaboration of deep generative adversarial networks with transfer learning

Autor: Shahid Karim, Xin Liu, Abdullah Ayub Khan, Asif Ali Laghari, Akeel Qadir, Irfana Bibi

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)

Abstract The proliferation of multimedia-based deepfake content in recent years has posed significant challenges to information security and authenticity, necessitating the use of methods beyond dependable dynamic detection. In this paper, we utilize

Externí odkaz: https://doaj.org/article/bd2a37181574435eb6a9b3f5b2f21d2a

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Akademický článek

Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation

Autor: Gunjan Shandilya, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)

Abstract Cervical cancer is one of the biggest challenges in global health, thus it forms a critical need for early detection technologies that could improve patient prognosis and inform treatment decisions. This development in the form of an early d

Externí odkaz: https://doaj.org/article/c18e4551514c43418a36d1d8151e6c27

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Akademický článek

A systematic review of deep learning chemical language models in recent era

Autor: Hector Flores-Hernandez, Emmanuel Martinez-Ledesma

Publikováno v: Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-25 (2024)

Abstract Discovering new chemical compounds with specific properties can provide advantages for fields that rely on materials for their development, although this task comes at a high cost in terms of complexity and resources. Since the beginning of

Externí odkaz: https://doaj.org/article/46c4a59873a9454683ba3f8faff4113e

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Akademický článek

Automated Detection of Cybersecurity Threats Using Generative Adversarial Networks (GANs)

Autor: Anastasiia Khlaponina, Salam Omar Alo, Abeer Salim Jamil, Mohammed Jabbar Hussein, Mohammed K.H. Al-Dulaimi, Sarmad Waleed Taha

Publikováno v: Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 36, Iss 1, Pp 566-577 (2024)

Introduction: Traditional network intrusion detection systems (NIDS) face significant challenges in detecting ever-evolving cyber-threats. With the evolution of cyber-attacks, comes a mounting requirement for predictive methods which are competent in

Externí odkaz: https://doaj.org/article/54846ed4c38e42348fb398a2119d510b

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Akademický článek

SMART-PET: a Self-SiMilARiTy-aware generative adversarial framework for reconstructing low-count [18F]-FDG-PET brain imaging

Autor: Confidence Raymond, Dong Zhang, Jorge Cabello, Linshan Liu, Paulien Moyaert, Jorge G. Burneo, Michael O. Dada, Justin W. Hicks, Elizabeth Finger, Andrea Soddu, Andrea Andrade, Michael T. Jurkiewicz, Udunna C. Anazodo

Publikováno v: Frontiers in Nuclear Medicine, Vol 4 (2024)

IntroductionIn Positron Emission Tomography (PET) imaging, the use of tracers increases radioactive exposure for longitudinal evaluations and in radiosensitive populations such as pediatrics. However, reducing injected PET activity potentially leads

Externí odkaz: https://doaj.org/article/8d21803a170c4e2981c0fe4b52c0a764

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Akademický článek

PSA-HWT: handwritten font generation based on pyramid squeeze attention

Autor: Hong Zhao, Jinhai Huang, Wengai Li, Zhaobin Chang, Weijie Wang

Publikováno v: PeerJ Computer Science, Vol 10, p e2261 (2024)

The generator, which combines convolutional neural network (CNN) and Transformer as its core modules, serves as the primary model for the handwriting font generation network and demonstrates effective performance. However, there are still problems wi

Externí odkaz: https://doaj.org/article/2aeb12d232a44ab18e515755da443d8e

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Akademický článek

BYG-drop: a tool for enhanced droplet detection in liquid–liquid systems through machine learning and synthetic imaging

Autor: Grégory Bana, Fabrice Lamadie, Sophie Charton, Tojonirina Randriamanantena, Didier Lucor, Nida Sheibat-Othman

Publikováno v: Frontiers in Chemical Engineering, Vol 6 (2024)

A new image processing machine learning algorithm for droplet detection in liquid–liquid systems is here introduced. The method combines three key numerical tools—YOLOv5 for object detection, Blender for synthetic image generation, and CycleGAN f

Externí odkaz: https://doaj.org/article/f083b6a802d74239a4bb874ab0f20388

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