Rami Ahmad El-Nabulsi | Neurocpmputing , Quantum Neural Networks and AI | Network Innovator Excellence Award

Prof. Dr. Rami Ahmad El-Nabulsi | Neurocpmputing , Quantum Neural Networks and AI | Network Innovator Excellence Award

CESNET | Czech Republic

Prof. Dr. Rami Ahmad El-Nabulsi is a highly prolific researcher with significant contributions across theoretical physics, applied mathematics, quantitative finance, and complex systems. His work spans advanced topics such as fractional calculus, quantum mechanics, Hamiltonian dynamics, fractal systems, and financial modeling, often integrating interdisciplinary approaches to address complex scientific problems. He has published extensively in high-impact journals, contributing to areas including quantum information, cosmology, and nonlinear dynamics. With an impressive h-index of 36, 286 documents, and 5,047 citations, his research demonstrates strong global impact and scholarly influence. His recent studies explore generalized Schrödinger equations, fractal Laplacian models, chaotic orbital dynamics, and financial systems in fractal dimensions, reflecting his continued innovation and leadership in cutting-edge scientific research.

Citation Metrics (Scopus)

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Citations
5,047

h-index
36

Documents
286

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h-index

Documents


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Hanbing Hao | Graph Data Structures and Algorithms | Young Researcher Award

Mr. Hanbing Hao | Graph Data Structures and Algorithms | Young Researcher Award

Anhui University | China

Mr. Hanbing Hao is an emerging researcher contributing to advanced methodologies in graph-based learning and computational intelligence, with a focus on integrating deep learning and network representations for complex data analysis. His recent work, GICLMorph: Self-supervised 3D neuronal morphology representation via graph-image contrastive learning, published in Expert Systems with Applications, highlights innovative approaches that combine graph structures and image-based features to enhance representation learning in neuroscience applications. His research reflects strong engagement with interdisciplinary domains, particularly in machine learning, graph analytics, and biomedical data modeling.  His contributions demonstrate a commitment to advancing intelligent systems through novel, data-driven frameworks and scalable analytical techniques.

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