Mohammed Elbes
Al-Zaytoonah University of Jordan, Western Michigan University
Papers
3
Total Citations
30
H-Index
3
About
Mohammed Elbes is a researcher at the forefront of integrating artificial intelligence with cybersecurity and precision localization technologies. His work primarily spans two critical domains: AI-driven cybersecurity vulnerability management and intelligent sensor fusion for outdoor positioning systems. In his highly cited 2023 study, Elbes explores how machine learning algorithms can revolutionize threat detection and vulnerability management, addressing the escalating complexity of modern cyber-attacks. His foundational 2013 paper on intelligent data fusion techniques introduces a novel particle filter approach that enhances outdoor localization accuracy, a breakthrough with direct applications in autonomous robotics and augmented reality. Elbes further refined these methods in his 2019 work, combining phase shift fingerprints with inertial measurements to achieve precise urban positioning. With a growing citation footprint, his research bridges theoretical innovation and practical deployment, offering scalable solutions for both cybersecurity resilience and location-based services. Elbes’ contributions stand out for their interdisciplinary impact, demonstrating how AI and sensor fusion can solve real-world challenges in security and navigation.
Research Focus
Key Achievements
Top Papers
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