Results 91 to 100 of about 79,918 (254)
Assessing Capability Complexity Using Enterprise Architecture Framework
Systems Engineering, EarlyView.ABSTRACT This study proposes a structured and quantitative methodology to evaluate the holistic complexity of system‐of‐systems (SoSs), employing the Zachman Architecture Framework (ZAF) as its foundational analytical tool. A five‐phase analytical procedure is developed and empirically validated, encompassing: (1) refinement of complexity measures, (2)
Javad Bakhshi, Mahmoud Efatmaneshnik
wiley +1 more source
IEEE AccessSignificant advances have been made in recent years in improving the robustness of deep neural networks, particularly under adversarial machine learning scenarios where the data has been contaminated to fool networks into making undesirable predictions ...
Hossein Aboutalebi +3 more
doaj +1 more source
Defense-guided Transferable Adversarial Attacks
, 2020 Though deep neural networks perform challenging tasks excellently, they are susceptible to adversarial examples, which mislead classifiers by applying human-imperceptible perturbations on clean inputs. Under the query-free black-box scenario, adversarial examples are hard to transfer to unknown models, and several methods have been proposed with the ...
Zhang, Zifei +3 more
openaire +2 more sources
Factors influencing the nature of client complaint behaviour in the aftermath of adverse events
Veterinary Record, Volume 196, Issue 6, 15/22 March 2025.Abstract Background Negative veterinary client complaint behaviour poses wellbeing and reputational risks. Adverse events are one source of complaint. Identifying factors that influence adverse event‐related complaint behaviour is key to mitigating detrimental consequences and harnessing information that can be used to improve service quality, patient ...
Julie Gibson +3 more
wiley +1 more source
World Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.ABSTRACT Objective To provide an overview of potential biases resulting from the utilization of artificial intelligence (AI) in otolaryngology and techniques to mitigate them. Data Sources Literature review and expert opinion. Conclusions AI promises to fundamentally transform medicine.
Matthew T. Ryan, David A. Gudis
wiley +1 more source
Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification. [PDF]
Entropy (Basel), 2023 Da Q +6 more
europepmc +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL‐KDD benchmark dataset, this paper presents an improved anomaly detection system that combines an optimised sequential neural network ...
Seong‐O Shim +4 more
wiley +1 more source
A Probability‐Aware AI Framework for Reliable Anti‐Jamming Communication
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Adversarial jamming attacks have increased on communication systems, causing distortion and threatening transmissions. Typical attacks rely on traditional, well‐defined cryptographic protocols and frequency‐hopping techniques. Nevertheless, these techniques become vulnerable when facing intelligent jammers.
Tawfeeq Shawly, Ahmed A. Alsheikhy
wiley +1 more source
Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution. [PDF]
Entropy (Basel), 2023 Li S +5 more
europepmc +1 more source
AT‐AER: Adversarial Training With Adaptive Example Reuse
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Adversarial training (AT) is widely regarded as a crucial defense method for deep neural networks against adversarial attacks. Most of the existing AT methods suffer from the problems of insufficient coverage of perturbation space and robust overfitting.
Meng Hu +5 more
wiley +1 more source