Results 141 to 150 of about 148,046 (305)

Manifold-driven decomposition for adversarial robustness

Frontiers in Computer Science
The adversarial risk of a machine learning model has been widely studied. Most previous studies assume that the data lie in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration.
Wenjia Zhang, Yikai Zhang, Xiaoling Hu, Yi Yao, Mayank Goswami, Chao Chen, Dimitris Metaxas   +6 more
doaj   +1 more source

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source

Optimal online prediction in adversarial environments

, 2010
In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes.
Bartlett, Peter L., Peter L. Bartlett
core   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models

IET Communications
Automatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully
Fanghao Xu, Chao Wang, Jiakai Liang, Chenyang Zuo, Keqiang Yue, Wenjun Li   +5 more
doaj   +1 more source

Universal adversarial defense in remote sensing based on pre-trained denoising diffusion models

International Journal of Applied Earth Observations and Geoinformation
Deep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO). However, their susceptibility to adversarial examples poses a critical challenge, compromising the reliability of AI4EO ...
Weikang Yu, Yonghao Xu, Pedram Ghamisi
doaj   +1 more source

A NEAT Approach to Evolving Neural‐Network‐Based Optimization of Chiral Photonic Metasurfaces: Application of a NeuroEvolution‐of‐Augmenting‐Topologies Pipeline

Advanced Intelligent Systems, EarlyView.
Neuro‐evolution can boost machine‐learning optimization of chiral metasurfaces. By integrating the NEAT algorithm into a deep‐learning framework, we enable the efficient design of visible‐spectrum chiroptical responses. NEAT autonomously evolves neural‐network architectures and weights, reducing manual tuning.
Davide Filippozzi, Arash Rahimi‐Iman
wiley   +1 more source

An Intelligent Microfluidics Enabled by Data‐Driven Deep Learning‐Based Microrobot‐Assisted Flow Manipulation

Advanced Intelligent Systems, EarlyView.
An AI‐enabled micromixing framework is developed by integrating cGAN with Bayesian optimization for predictive control of microrobot‐driven flow manipulation. Through this framework, the spatiotemporal evolution of micromixing is learned directly from experimental images, while rapid identification of optimized microrobot actuation strategies is ...
Dineshkumar Loganathan, Chia‐Yuan Chen
wiley   +1 more source

On Adversarial Robust Generalization of DNNs for Remote Sensing Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep neural networks (DNNs)-based deep learning is an important technical support in the task of remote sensing image classification. But DNNs are susceptible to adversarial attacks.
Wei Xue, Yonghao Wang, Shaoquan Zhang, Xiao Zheng, Ping Zhong   +4 more
doaj   +1 more source

Adversarial Attacks on Supervised Energy-Based Anomaly Detection in Clean Water Systems [PDF]

Critical National Infrastructure includes large networks such as telecommunications, transportation, health services, police, nuclear power plants, and utilities like clean water, gas, and electricity.
Maglaras, Leandros, Robles-Durazno, Andres, Leandros Maglaras, Moradpoor, Naghmeh, Naghmeh Moradpoor, Abah, Ezra, Ezra Abah, Andres Robles-Durazno   +7 more
core   +1 more source