Results 71 to 80 of about 33,606 (255)

Machine learning security and privacy:a survey

网络与信息安全学报, 2018
As an important method to implement artificial intelligence,machine learning technology is widely used in data mining,computer vision,natural language processing and other fields.With the development of machine learning,it brings amount of security and ...
Lei SONG,Chunguang MA,Guanghan DUAN
doaj   +3 more sources

How Advanced Artificial Intelligence Technologies Shape Drug–Drug and Drug–Target Interaction Modeling

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

Exploiting Machine Learning to Subvert Your Spam Filter

, 2008
Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. This paper shows how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—
Nelson, Blaine, Tygar, J. D., Xia, Kai, Rubinstein, Benjamin I. P., Sutton, Charles, Barreno, Marco, Chi, Fuching Jack, Joseph, Anthony D., Saini, Udam   +8 more
core  

Statistical Feature-Based Detection of Adversarial Noise and Patch Attacks in Image and Deepfake Analysis

195208Adversarial attacks pose a significant threat to the reliability and trustworthiness of machine learning systems, particularly in image classification tasks like deepfake detection.
Bunzel, Niklas, Göller, Nicolas, Steinebach, Martin, Graner, Lukas, Frick, Raphael   +4 more
core   +1 more source

Machine learning enhanced network security

, 2020
Cotton, ChaseThe coupling of machine learning with subject matter expertise is increasingly essential to enable the rapid detection of sophisticated attacks occurring at machine speed.
De Lucia, Michael J.
core   +1 more source

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, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

ORBIT‐AMD: Ordinal Risk, Bilateral Imaging, and Trajectory Learning for Age‐Related Macular Degeneration in Multi‐Cohorts

Advanced Science, EarlyView.
Eligibility flow and real‐world AMD burden in the UKB retinal imaging cohort and TMUEH external‐validation cohort. Overview of the ORBIT‐AMD architecture, integrating retinal representation pretraining, bilateral eye‐graph modeling and concept bottleneck learning to support ordered risk, bilateral context, interpretable lesion concepts, longitudinal ...
Xuehao Cui, Dejia Wen, Patrick Yu‐Wai‐Man, Xiaorong Li   +3 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

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, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Active machine learning approach to adversarial training improves trade-off between natural accuracy and adversarial robustness

Scientific Reports
Understanding our world which is open and diverse requires foundation models that generalize well while trustworthy. Adversarial training has been considered to be one of the most effective strategies to achieve robust learning systems, yet adversarial ...
Seyed Mohammad Hadi Mirsadeghi
doaj   +1 more source

Bilevel Models for Adversarial Learning and a Case Study

Mathematics
Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated structure of most machine learning models, the mechanism of adversarial ...
Yutong Zheng, Qingna Li
doaj   +1 more source