Mohammad Eshghi
Papers
2
Total Citations
19
H-Index
2
About
Mohammad Eshghi is a researcher at the intersection of artificial intelligence, cybersecurity, and environmental conservation. His work focuses on developing secure, decentralized AI systems and applying machine learning to pressing ecological challenges. Eshghi’s most influential contribution, “Privacy Preserved Decentralized Deep Learning: A Blockchain Based Solution for Secure AI-Driven Enterprise” (2019), has garnered 17 citations, pioneering a framework that combines blockchain technology with deep learning to protect data privacy in enterprise AI applications—a critical advancement as organizations increasingly adopt AI while facing stringent data regulations. In his more recent work, “Autonomous oil spill and pollution detection for large-scale conservation in marine eco-cyber-physical systems” (2021), Eshghi addresses environmental threats in the Persian Gulf, proposing an autonomous detection system that leverages cyber-physical systems to identify oil spills and pollution early, aiming to mitigate damage to marine ecosystems and human health. This research underscores his commitment to applying cutting-edge technology for societal benefit. Eshghi’s dual focus on secure AI and environmental monitoring highlights his versatility and impact, making him a notable figure in both the technical and applied dimensions of modern computing.
Research Focus
Key Achievements
Top Papers
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