Zobrazeno 1 - 10
of 4 133
pro vyhledávání: '"Gurcan, A"'
Autor:
Enan, Abyad, Salek, M Sabbir, Chowdhury, Mashrur, Comert, Gurcan, Khan, Sakib M., Majumder, Reek
The Virtual Traffic Light (VTL) eliminates the need for physical traffic signal infrastructure at intersections, leveraging Connected Vehicles (CVs) to optimize traffic flow. VTL assigns right-of-way dynamically based on factors such as estimated tim
Externí odkaz:
http://arxiv.org/abs/2412.18776
As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure design principles: (1) using post-quantum cryptography, (2) employing qua
Externí odkaz:
http://arxiv.org/abs/2412.12373
Autor:
Majumder, Reek, Comert, Gurcan, Werth, David, Gale, Adrian, Chowdhury, Mashrur, Salek, M Sabbir
The network of services, including delivery, farming, and environmental monitoring, has experienced exponential expansion in the past decade with Unmanned Aerial Vehicles (UAVs). Yet, UAVs are not robust enough against cyberattacks, especially on the
Externí odkaz:
http://arxiv.org/abs/2412.02539
Autor:
Mamun, Abdullah Al, Enan, Abyad, Indah, Debbie A., Mwakalonge, Judith, Comert, Gurcan, Chowdhury, Mashrur
This study investigates crash severity risk modeling strategies for work zones involving large vehicles (i.e., trucks, buses, and vans) when there are crash data imbalance between low-severity (LS) and high-severity (HS) crashes. We utilized crash da
Externí odkaz:
http://arxiv.org/abs/2412.02094
Autor:
Gyimah, Nana Kankam, Mwakalonge, Judith, Comert, Gurcan, Siuhi, Saidi, Akinie, Robert, Sulle, Methusela, Ruganuza, Denis, Izison, Benibo, Mukwaya, Arthur
In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML framework. Our methodology combines multiple machine learning algorithms,
Externí odkaz:
http://arxiv.org/abs/2411.15920
Autor:
Puspa, Sefatun-Noor, Enan, Abyad, Majumdar, Reek, Salek, M Sabbir, Comert, Gurcan, Chowdhury, Mashrur
Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). ICs are largely susceptible to various hardware attacks with malicious intents. One of the stealthiest threats is the insertion of a hardw
Externí odkaz:
http://arxiv.org/abs/2411.12721
This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). We performed a detailed comparison of both platforms, demons
Externí odkaz:
http://arxiv.org/abs/2410.23433
The safety and robustness of large language models (LLMs) based applications remain critical challenges in artificial intelligence. Among the key threats to these applications are prompt hacking attacks, which can significantly undermine the security
Externí odkaz:
http://arxiv.org/abs/2410.13901
Graph embeddings play a critical role in graph representation learning, allowing machine learning models to explore and interpret graph-structured data. However, existing methods often rely on opaque, high-dimensional embeddings, limiting interpretab
Externí odkaz:
http://arxiv.org/abs/2410.01778
Graph Neural Networks (GNNs) have revolutionized the domain of graph representation learning by utilizing neighborhood aggregation schemes in many popular architectures, such as message passing graph neural networks (MPGNNs). This scheme involves ite
Externí odkaz:
http://arxiv.org/abs/2410.02158