Shougi Suliman Abosuliman
Papers
1
Total Citations
30
H-Index
1
About
Shougi Suliman Abosuliman is a researcher whose work bridges the critical intersection of artificial intelligence and logistics management. His primary research areas include computer vision, deep learning, and human-computer interaction, with a focus on applying these technologies to optimize supply chain and logistics operations. His most-cited paper, "Computer vision assisted human computer interaction for logistics management using deep learning" (2021), has garnered 30 citations, reflecting its relevance in exploring how AI-driven visual recognition can enhance decision-making and efficiency in logistics systems. This work demonstrates his commitment to integrating advanced computational methods into practical, real-world applications. While his research has contributed to the growing dialogue on automation in logistics, it is important to note that this particular paper has been retracted, a development that underscores the rigorous standards of academic integrity in the field. Despite this, Abosuliman’s broader contributions highlight the potential of deep learning to transform human-machine collaboration, offering valuable insights for students and researchers interested in the convergence of AI, computer vision, and operational management.
Research Focus
Key Achievements
Top Papers
- 1