Results 81 to 90 of about 160,235 (299)

Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects

IEEE Access
Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify complex ...
Sabrine Ennaji, Fabio de Gaspari, Dorjan Hitaj, Alicia Kbidi, Luigi Vincenzo Mancini   +4 more
doaj   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Learning atomic forces from uncertainty-calibrated adversarial attacks

npj Computational Materials
Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials (MLIPs).
Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson, Morten Ledum, Simen Reine, Sigbjørn Løland Bore   +5 more
doaj   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Adversarial Halftone QR Code

IEEE Access
Recent studies have shown that machine-learning models are vulnerable to adversarial attacks. Adversarial attacks are deliberate attempts to modify the input data of a machine learning model in a way that causes it to produce incorrect predictions.
Palakorn Kamnounsing, Karin Sumongkayothin, Prarinya Siritanawan, Kazunori Kotani   +3 more
doaj   +1 more source

Attack-agnostic Adversarial Detection on Medical Data Using Explainable\n Machine Learning [PDF]

, 2021
Matthew Watson, Noura Al Moubayed
openalex   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches

, 2022
Maura Pintor, Daniele Angioni, Angelo Sotgiu, Luca Demetrio, Ambra Demontis, Battista Biggio, Fabio Roli   +6 more
openalex   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source