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One‐Class Autoencoders for Porcelain Art Attribution: The Case of William Billingsley

Archaeometry, EarlyView.
ABSTRACT This comprehensive study explores the application of advanced machine learning techniques, specifically one‐class autoencoders, for the authentication and attribution of English porcelain artworks. Focusing primarily on the works of William Billingsley (1758–1828), one of England's most celebrated porcelain decorators, we demonstrate how ...
Hassan Ugail, Morgan Denyer, Rachel Denyer, Howell Edwards   +3 more
wiley   +1 more source

Detection and analysis of prompt injection in indian multilingual large language models. [PDF]

Sci Rep
Srinivasan J, Regi SA, Anbarasan AK, A S, T V, Venu S.   +5 more
europepmc   +1 more source

A solid waste classification using reinforcement learning boosted by data augmentation and hyperparameter optimization. [PDF]

Sci Rep
Maleknya K, Alizadehsani R, Pedrammehr S, Shirini K.   +3 more
europepmc   +1 more source

A Diffusion-Based Time-Frequency Dual-Stream Contrastive Learning Model for Multivariate Time Series Anomaly Detection. [PDF]

Entropy (Basel)
Wu K, Xu C, Zhang R, Lu W, Ma Y, Zhang E, Tan K.   +6 more
europepmc   +1 more source

From Concrete to Code: A Survey of AI-Driven Transportation Infrastructure, Security, and Human Interaction. [PDF]

Sensors (Basel)
Kose NA, Kose K, Liang F.
europepmc   +1 more source

Symmetry-Driven Multimodal Adversarial Attacks: An Information-Theoretic Perspective on Cross-Modal Invariance and Robustness. [PDF]

Entropy (Basel)
Wei J, Wang X, Xu L, Li Y.
europepmc   +1 more source

Detection of disturbances and cyber-attacks in smart grids using explainable machine learning. [PDF]

Sci Rep
Farsi M, Alwateer M, Alsaedi SA, AlSahafi AI, Balaha HM, Aboelnaga MM, Badawy M, Elhosseini MA.   +7 more
europepmc   +1 more source

Adversarial machine learning in Network Intrusion Detection Systems

Expert Systems With Applications, 2021
Adversarial examples are inputs to a machine learning system intentionally crafted by an attacker to fool the model into producing an incorrect output.
Elie Alhajjar, Paul Maxwell, Nathaniel D Bastian   +2 more
exaly   +2 more sources

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Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

ACM Computing Surveys, 2022
Ishai Rosenberg, Asaf Shabtai, Yuval Elovici   +2 more
exaly