Search Results for author: Erchin Serpedin

Found 13 papers, 1 papers with code

CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip Deformity

1 code implementation12 Oct 2023 Abdullah Hayajneh, Erchin Serpedin, Mohammad Shaqfeh, Graeme Glass, Mitchell A. Stotland

We undertook a transfer learning protocol testing different versions of StyleGAN-ADA (a generative adversarial network image generator incorporating adaptive data augmentation (ADA)) as the base model.

Data Augmentation Ethics +2

Utility of Traffic Information in Dynamic Routing: Is Sharing Information Always Useful?

no code implementations11 Mar 2021 Mohammad Shaqfeh, Salah Hessien, Erchin Serpedin

Real-time traffic information can be utilized to enhance the efficiency of transportation networks by dynamically updating routing plans to mitigate traffic jams.

Multiagent Systems

Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids

no code implementations5 Apr 2021 Osman Boyaci, Amarachi Umunnakwe, Abhijeet Sahu, Mohammad Rasoul Narimani, Muhammad Ismail, Katherine Davis, Erchin Serpedin

False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids.

Hybrid RF/VLC Systems: A Comprehensive Survey on Network Topologies, Performance Analyses, Applications, and Future Directions

no code implementations5 Jul 2020 Hisham Abuella, Mohammed Elamassie, Murat Uysal, Zhengyuan Xu, Erchin Serpedin, Khalid A. Qaraqe, Sabit Ekin

In parallel, visible light communication (VLC) has been proposed as an alternative solution, where a light source is used for both illumination and data transmission.

Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks

no code implementations25 Dec 2021 Osman Boyaci, Mohammad Rasoul Narimani, Katherine Davis, Erchin Serpedin

As a highly complex and integrated cyber-physical system, modern power grids are exposed to cyberattacks.

Spatio-Temporal Failure Propagation in Cyber-Physical Power Systems

no code implementations5 Feb 2022 Osman Boyaci, M. Rasoul Narimani, Katherine Davis, Erchin Serpedin

The interconnection between different components in a power system causes failures to easily propagate across the system.

Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids

no code implementations25 Jun 2022 Osman Boyaci, M. Rasoul Narimani, Katherine Davis, Erchin Serpedin

This study employs Infinite Impulse Response (IIR) Graph Neural Networks (GNN) to efficiently model the inherent graph network structure of the smart grid data to address the cyberattack localization problem.

Multi-Task and Transfer Learning for Federated Learning Applications

no code implementations17 Jul 2022 Cihat Keçeci, Mohammad Shaqfeh, Hayat Mbayed, Erchin Serpedin

So, supporting federated learning with meta-learning tools such as multi-task learning and transfer learning will help enlarge the set of potential applications of federated learning by letting clients of different but related tasks share task-agnostic models that can be then further updated and tailored by each individual client for its particular task.

Federated Learning Meta-Learning +1

Unsupervised Anomaly Appraisal of Cleft Faces Using a StyleGAN2-based Model Adaptation Technique

no code implementations12 Nov 2022 Abdullah Hayajneh, Mohammad Shaqfeh, Erchin Serpedin, Mitchell A. Stotland

This paper presents a novel machine learning framework to consistently detect, localize and rate congenital cleft lip anomalies in human faces.

Generative Adversarial Network

Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids

no code implementations17 Jun 2023 Cihat Keçeci, Katherine R. Davis, Erchin Serpedin

By employing federated learning for the detection of FDIA attacks, it is possible to train a model for the detection and localization of the attacks while preserving the privacy of sensitive user data.

Federated Learning

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