Aspect-level sentiment analysis within multimodal contexts, focusing on the precise identification and interpretation of sentiment attitudes linked to the target aspect across diverse data modalities, remains a focal research area that perpetuates the advancement of discourse and innovation in artificial intelligence.However, most existing methods
Universal adversarial perturbations are image-agnostic and model-independent noise that, when added to any image, can mislead the trained deep convolutional neural networks into the wrong prediction.Since these universal adversarial perturbations can seriously jeopardize the security and integrity lubes of practical deep learning applications, the
Alaa Elmazny, Sherif M Hamdy, Maged Abdel-Naseer, Nevin M Shalaby, Hatem S Shehata, Nirmeen A Kishk, Mona A Nada, Husam S Mourad, Mohamed I Hegazy, Ahmed Abdelalim, Sandra M Ahmed, Ghada Hatem, Amr M Fouad, Hadel Mahmoud, Amr Hassan Neurology Department, Faculty of Medicine, Cairo University, Cairo, EgyptCorrespondence: Hatem S ShehataNeurology Dep
The adversarial training technique has been shown to Countertop Oven improve the robustness of Machine Learning and Deep Learning models to adversarial attacks in the Computer Vision field.However, the effectiveness of this approach needs to be proven in the field of Anomaly Detection on industrial environments, where adversarial training has criti