2 citations · 5 across the 14 of their papers we have counts for
6 papers · 1 filter
Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era
Ferhat Ozgur Catak, Jungwon Seo, Umit Cali
Quantum machine learning (QML) is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanic…
Comparative Analysis of Attention Mechanisms for Automatic Modulation Classification in Radio Frequency Signals
Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali
Automatic Modulation Classification (AMC) is a critical component in cognitive radio systems and spectrum management applications. This study presents a comprehensive comparative a…
Building Trust in Healthcare with Privacy Techniques: Blockchain in the Cloud
Ferhat Ozgur Catak, Chunming Rong, Øyvind Meinich-Bache +2
This study introduces a cutting-edge architecture developed for the NewbornTime project, which uses advanced AI to analyze video data at birth and during newborn resuscitation, wit…
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong +2
Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but is challenged by client drift in highly heterogeneous data settings. Many existing dri…
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong
As privacy concerns and data regulations grow, federated learning (FL) has emerged as a promising approach for training machine learning models across decentralized data sources wi…
BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction
Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali
Massive MIMO (Multiple-Input Multiple-Output) is an advanced wireless communication technology, using a large number of antennas to improve the overall performance of the communica…