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Analysis of Gene Regulatory Networks from Gene Expression Using Graph Neural Networks

Autor: Otal, Hakan T., Subasi, Abdulhamit, Kurt, Furkan, Canbaz, M. Abdullah, Uzun, Yasin

Unraveling the complexities of Gene Regulatory Networks (GRNs) is crucial for understanding cellular processes and disease mechanisms. Traditional computational methods often struggle with the dynamic nature of these networks. This study explores the

Externí odkaz: http://arxiv.org/abs/2409.13664

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A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights

Autor: Otal, Hakan T., Faraone, Stephen V., Canbaz, M. Abdullah

Attention-Deficit/Hyperactivity Disorder (ADHD) is a challenging disorder to study due to its complex symptomatology and diverse contributing factors. To explore how we can gain deeper insights on this topic, we performed a network analysis on a comp

Externí odkaz: http://arxiv.org/abs/2409.12853

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Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots

Autor: AlMakinah, Rawan, Norcini-Pala, Andrea, Disney, Lindsey, Canbaz, M. Abdullah

Access to mental health support remains limited, particularly in marginalized communities where structural and cultural barriers hinder timely care. This paper explores the potential of AI-enabled chatbots as a scalable solution, focusing on advanced

Externí odkaz: http://arxiv.org/abs/2410.02783

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Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity

Autor: Huang, Hao Jian, Iskandarov, Bekzod, Rahman, Mizanur, Otal, Hakan T., Canbaz, M. Abdullah

This paper presents the design and implementation of a Federated Learning (FL) testbed, focusing on its application in cybersecurity and evaluating its resilience against poisoning attacks. Federated Learning allows multiple clients to collaborativel

Externí odkaz: http://arxiv.org/abs/2409.09794

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LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems

Autor: Otal, Hakan T., Canbaz, M. Abdullah

The rapid evolution of cyber threats necessitates innovative solutions for detecting and analyzing malicious activity. Honeypots, which are decoy systems designed to lure and interact with attackers, have emerged as a critical component in cybersecur

Externí odkaz: http://arxiv.org/abs/2409.08234

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Harnessing Deep Learning and Satellite Imagery for Post-Buyout Land Cover Mapping

Autor: Otal, Hakan T., Zavar, Elyse, Binder, Sherri B., Greer, Alex, Canbaz, M. Abdullah

Environmental disasters such as floods, hurricanes, and wildfires have increasingly threatened communities worldwide, prompting various mitigation strategies. Among these, property buyouts have emerged as a prominent approach to reducing vulnerabilit

Externí odkaz: http://arxiv.org/abs/2401.07500

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LLM-Assisted Crisis Management: Building Advanced LLM Platforms for Effective Emergency Response and Public Collaboration

Autor: Otal, Hakan T., Canbaz, M. Abdullah

Emergencies and critical incidents often unfold rapidly, necessitating a swift and effective response. In this research, we introduce a novel approach to identify and classify emergency situations from social media posts and direct emergency messages

Externí odkaz: http://arxiv.org/abs/2402.10908

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