no code implementations • 20 Oct 2022 • Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda, XiaoFeng Wang, Song Wang
To effectively address partial-label classification, this paper proposes an end-to-end Generic Game-theoretic Network (G2NetPL) for partial-label learning, which can be applied to most partial-label settings, including a very challenging, but annotation-efficient case where only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled.
Multi-Label Classification Multi-Label Image Classification +2
no code implementations • 2 Dec 2020 • Mahmoud M. Badr, Mohamed I. Ibrahem, Mohamed Mahmoud, Mostafa M. Fouda, Waleed Alasmary
Based on the data analysis, we propose a general multi-data-source deep hybrid learning-based detector to identify the false-reading attacks.
no code implementations • 8 Nov 2019 • Attayeb Mohsen, Muftah Al-Mahdawi, Mostafa M. Fouda, Mikihiko Oogane, Yasuo Ando, Zubair Md Fadlullah
As we are about to embark upon the highly hyped "Society 5. 0", powered by the Internet of Things (IoT), traditional ways to monitor human heart signals for tracking cardio-vascular conditions are challenging, particularly in remote healthcare settings.
no code implementations • 2 May 2019 • Ahmed Shafee, Mohamed Baza, Douglas A. Talbert, Mostafa M. Fouda, Mahmoud Nabil, Mohamed Mahmoud
In this paper, we propose the use of mimic learning to enable the transfer of intrusion detection knowledge through a teacher model trained on private data to a student model.