Results 121 to 130 of about 45,910 (264)

Understanding Generative Adversarial Networks (GANs): A Review

Control Systems and Optimization Letters
Generative Adversarial Networks (GANs) is an important breakthrough in artificial intelligence that uses two neural networks, a generator and a discriminator, that work in an adversarial framework. The generator generates synthetic data, while the discriminator evaluates the authenticity of the data.
Purwono Purwono, Annastasya Nabila Elsa Wulandari, Alfian Ma'arif, Wael A. Salah   +3 more
openaire   +1 more source

A Review of Overcurrent Protection in Smart Grids Under Cyber‐Physical Threats With a Cyber‐Physical Evaluation Framework

Energy Science &Engineering, EarlyView.
By manipulating current and voltage measurements, an assailant can induce unwanted relay action while attempting to avoid detection. Detecting advanced cyber intrusions in power protection environments requires specialised data analysis and anomaly detection methods.
Feras Alasali, Anas AlMajali, Naser Naily, Hassen Loukil, Salah AbuGhalyon, William Holderbaum, Abdelaziz Salah Saidi   +6 more
wiley   +1 more source

Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives

Health Care Science, EarlyView.
Current research of artificial intelligence (AI) in ophthalmology. ABSTRACT Artificial intelligence (AI) is revolutionizing ophthalmology by providing innovative solutions for disease screening, diagnosis, personalized treatment, and the delivery of global healthcare services.
She Chongyang, Tao Yong
wiley   +1 more source

Identifying Venous Insufficiency in Head and Neck Reconstruction Flaps Using Machine Learning and Deep Learning Methods

Head &Neck, EarlyView.
ABSTRACT Background Venous insufficiency is a major cause of flap failure in head and neck reconstruction. AI provides a reliable, convenient solution for early detection. Methods Clinical data and postoperative flap photos of head and neck cancer patients (2018–2024) at our center were retrospectively collected, categorized into normal and venous ...
Yurong He, Jugao Fang, Lizhen Hou, Zufei Li, Shizhi He, Yanming Zhao, Qijia Li, Rongjia Li, Xiaoyu Shi, Jiayi Yang, Qi Zhong   +10 more
wiley   +1 more source

Progress of metabolomics‐centric multi‐omics research in medicine

iMetaOmics, EarlyView.
The graphical abstract illustrates a holistic roadmap for metabolomics‐centric multi‐omics integration in medical research. The upper panel depicts the technological transition from traditional bulk analysis to high‐resolution single‐cell and spatial methodologies, specifically addressing inherent challenges such as molecular complexity and dynamic ...
Ziyi Wang, Shiwen Deng, Xiaoxu Wang, Xiao Chang, Na Guo, Peng Chen, Hongjun Yang   +6 more
wiley   +1 more source

Exploring deep generative models for improved data generation in hypertrophic cardiomyopathy

Ingenius: Revista de Ciencia y Tecnología
Data generation strategies are essential for addressing the challenge of limited training data in deep learning-based medical image analysis, particularly for hypertrophic cardiomyopathy (HCM) using magnetic resonance imaging (MRI).
Swarajya Madhuri Rayavarapu, Gottapu Sasibhushana Rao   +1 more
doaj   +1 more source

Deep learning‐based prediction of cervical lymph node metastasis and genetic alterations from whole‐slide images of thyroid cancer frozen sections

Interdisciplinary Medicine, EarlyView.
Deep learning models accurately predict cervical lymph node metastasis and key genetic mutations (BRAF/TERT) directly from thyroid cancer frozen sections. This AI‐driven pipeline provides a rapid real‐time tool to guide intraoperative surgical decisions, helping to optimize surgical extent and prevent both over‐ and under‐treatment without the need for
Mingxing Qiu, Jinbang Li, Shujing Guo, Danyi Li, Chengyu Lu, Chenglong Zhao, Jiankun He, Hanxi Wang, Qiao Bai, Aihetaimu Aimaier, Zhijian Cen, Rui Mao, Zimo Ye, Jiaxue Zang, Zhining Zhuo, Lijuan Qu, Yueping Liu, Jing Cui, Xiaohong Yao, Xiuwu Bian, Li Liang   +20 more
wiley   +1 more source

Boosting Deep Feature Fusion-Based Detection Model for Fake Faces Generated by Generative Adversarial Networks for Consumer Space Environment

IEEE Access
In the consumer space, deep fakes refer to highly realistic, AI-generated images, audio, or videos that mimic real people generated by cutting-edge technologies such as Generative Adversarial Networks (GANs). In the digital age, recognizing and detecting
Fadwa Alrowais, Asma Abbas Hassan, Wafa Sulaiman Almukadi, Meshari H. Alanazi, Radwa Marzouk, Ahmed Mahmud   +5 more
doaj   +1 more source

Clinical Feasibility of Deep Learning Contrast Synthesis From MR Fingerprinting in Knee Osteoarthritis

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background Magnetic Resonance Fingerprinting (MRF) enables rapid quantitative parameter mapping from which synthetic clinical contrast images can be derived using deep learning (DL). Purpose This study evaluates the reliability and interchangeability of MRF‐derived synthetic knee MRI relative to conventional MRI in patients with osteoarthritis.
Mika T. Nevalainen, Olli Nykänen, Jyri Järvinen, Antti Kemppainen, Lasse Räsänen, Victor Casula, Martijn Cloos, Riccardo Lattanzi, Mikko J. Nissi, Miika T. Nieminen   +9 more
wiley   +1 more source

DSF-GAN: DownStream Feedback Generative Adversarial Network

Utility and privacy are two crucial measurements of the quality of synthetic tabular data. While significant advancements have been made in privacy measures, generating synthetic samples with high utility remains challenging. To enhance the utility of synthetic samples, we propose a novel architecture called the DownStream Feedback Generative ...
Perets, Oriel, Rappoport, Nadav
openaire   +2 more sources