Results 151 to 160 of about 2,268,403 (339)

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker, Tamzid Ul Islam, Md. Robiul Islam   +2 more
wiley   +1 more source

Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences

Sensors
The threat posed by adversarial examples (AEs) to deep learning applications has garnered significant attention from the academic community. In response, various defense strategies have been proposed, including adversarial example detection.
Hua Mu, Chenggang Li, Anjie Peng, Yangyang Wang, Zhenyu Liang   +4 more
doaj   +1 more source

Improving Adversarial Robustness of Deep Neural Networks via Adaptive Margin Evolution. [PDF]

Neurocomputing (Amst), 2023
Ma L, Liang L.
europepmc   +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

Analyzing the Adversarial Robustness and Interpretability of Deep SAR Classification Models: A Comprehensive Examination of Their Reliability

Remote Sensing
Deep neural networks (DNNs) have shown strong performance in synthetic aperture radar (SAR) image classification. However, their “black-box” nature limits interpretability and poses challenges for robustness, which is critical for sensitive applications ...
Tianrui Chen, Limeng Zhang, Weiwei Guo, Zenghui Zhang, Mihai Datcu   +4 more
doaj   +1 more source

Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness. [PDF]

Apert Neuro, 2023
McClure P, Moraczewski D, Lam KC, Thomas A, Pereira F.   +4 more
europepmc   +1 more source

Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness [PDF]

, 2023
Bao Gia Doan, Shuiqiao Yang, Paul Montague, Olivier De Vel, Tamas Abraham, Seyit Camtepe, Salil S. Kanhere, Ehsan Abbasnejad, Damith C. Ranasinghe   +8 more
openalex   +1 more source

Robust Decision Trees Against Adversarial Examples

, 2019
Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited.
Chen, H, Zhang, H, Boning, D, Hsieh, CJ
openaire   +3 more sources

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

Adversarial robustness guarantees for quantum classifiers

npj Quantum Information
Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data.
Neil Dowling, Maxwell T. West, Angus Southwell, Azar C. Nakhl, Martin Sevior, Muhammad Usman, Kavan Modi   +6 more
doaj   +1 more source