HUMS2023 Data Challenge Result Submission

no code implementations • 8 Jul 2023 • Dhiraj Neupane, Lakpa Dorje Tamang, Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Sunil Aryal

We implemented a simple method for early detection in this research.

The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data

1 code implementation • 18 Jun 2023 • Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle, Robin Schon, Katja Ludwig, Rainer Lienhart, Simon Jegou, Guang Li, Cong Chen, Qi Wang, Derik Shi, Mayug Maniparambil, Dominik Muller, Silvan Mertes, Niklas Schroter, Fabio Hellmann, Miriam Elia, Ine Dirks, Matias Nicolas Bossa, Abel Diaz Berenguer, Tanmoy Mukherjee, Jef Vandemeulebroucke, Hichem Sahli, Nikos Deligiannis, Panagiotis Gonidakis, Ngoc Dung Huynh, Imran Razzak, Reda Bouadjenek, Mario Verdicchio, Pasquale Borrelli, Marco Aiello, James A. Meakin, Alexander Lemm, Christoph Russ, Razvan Ionasec, Nikos Paragios, Bram van Ginneken, Marie-Pierre Revel Dubois

STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.

Computed Tomography (CT)

Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey

no code implementations • 22 Feb 2022 • Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Imran Razzak, Kevin Lee, Chetan Arora, Ali Hassani, Arkady Zaslavsky

Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications.

Adversarial Attack BIG-bench Machine Learning +3