Results 111 to 120 of about 39,370 (305)
Improving intrusion detection system performance using generative adversarial networks architecture
, 2023 Detecting attacks based on their behaviour is challenging for security defence mechanisms, including Anomaly Intrusion Detection Systems (AIDSes) due to the attacks’ behaviours, numbers, and architecture used on AIDSes.
Mohammad Arafah (9400163)
core +1 more source
Advanced Science, EarlyView.This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley +1 more source
Generative Adversarial Networks and Its Applications in Biomedical Informatics
Frontiers in Public Health, 2020 The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn
Lan Lan +7 more
doaj +1 more source
Correction to “Generative Adversarial Networks for Crystal Structure Prediction”
, 2022 Correction to “Generative Adversarial Networks for Crystal Structure ...
Sungwon Kim (242473) +4 more
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Advanced Science, EarlyView.This perspective proposes a cohesive machine learning strategy to decode microplastic aging. It advocates for Federated Learning to dismantle global data silos and introduces the TRACE framework (TRansport, Aging, Corona, Ecotoxicity). By integrating physics‐informed modeling with causal discovery, this approach bridges the laboratory‐field gap to ...
Yaping Lyu +6 more
wiley +1 more source
Prescribed Generative Adversarial Networks
CoRR, 2019 Generative adversarial networks (GANs) are a powerful approach to unsupervised learning. They have achieved state-of-the-art performance in the image domain. However, GANs are limited in two ways. They often learn distributions with low support---a phenomenon known as mode collapse---and they do not guarantee the existence of a probability density ...
Adji B. Dieng +3 more
openaire +2 more sources
AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
Advanced Science, EarlyView.This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi +4 more
wiley +1 more source
Advanced Energy Materials, EarlyView.Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source
Cormputed tomography (CT) scanning is an effective medical imaging modality widely used in clinical medicine for diagnosing various conditions. CT can generate three-dimensional images, thus providing more information than traditional two-dimensional ...
Alike, Y +6 more
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Advanced Intelligent Discovery, EarlyView.Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz +2 more
wiley +1 more source