Tallha Akram on Muck Rack

Tallha Akram

As seen in: Nature, Wiley Online Library, MDPI, PeerJ
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Tallha

Contact Tallha, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Computer Science > Cryptography and Security

|
By Rohan Farooq, Anwar Shah, Tallha Akram
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (678) Issue with article?

Particle swarm optimized deep learning for jamming detection and throughput enhancement in cognitive radio networks - Scientific Reports

|
By Muhammad Imran, Khalid Ibrahim, Liu Zhihua Nan, Ayman Qahmash, Ghada Atteia, Tallha Akram, Pan Zhiwen
| Nature Verified
Abstract Cognitive Radio Networks (CRNs) are essential for improving spectrum efficiency in 5G and beyond; however, their open and adaptive nature makes them highly vulnerable to jamming attacks. The purpose of this work is to develop an anti-jamming framework that jointly addresses jammer detection and frequency selection under adversarial conditions.
Open in Who Shared Issue with article?

A cost-optimized medical digital twin framework for secure and efficient patient data management in smart healthcare - Scientific Reports

|
By Faisal Mohammed Alotaibi, Sadiq Ahmad, Tallha Akram, Moteeb Almoteri, Abdullah M Alotaibi
| Nature Verified
Abstract The increasing demand for personalized, real-time healthcare necessitates efficient, secure patient data management. Digital Twins (DTs) enable AI-powered monitoring and decision support but also introduce challenges related to latency, computational cost, and security. This paper proposes a cost-optimized, AI-driven Medical Digital Twin (MDT) framework that manages task allocation across heterogeneous edge, fog, and cloud infrastructures.
Open in Who Shared Issue with article?
See all 10 articles
 •  Privacy  •  Terms  •  Security and Legal  •   •   •   • 

© 2026 Muck Rack

SOC 2® Certified