Ivica Kopriva | Pattern Recognition | Best Researcher Award

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Dr. Ivica Kopriva | Pattern Recognition | Best Researcher Award

Rudjer Boskovich Institute | Croatia

Dr. Ivica Kopriva is a distinguished Senior Scientist at the Ruđer Bošković Institute, Croatia, internationally recognized for his pioneering contributions to signal processing, machine learning, and blind source separation. Dr. Ivica Kopriva’s interdisciplinary research integrates statistical learning, low-rank sparse modeling, and nonlinear decomposition methods to address complex challenges in biomedical imaging, remote sensing, and hyperspectral data analysis. His highly influential publications, including “Multi-view Low-Rank Sparse Subspace Clustering” (Pattern Recognition, 2018) and “l₀-Motivated Low-Rank Sparse Subspace Clustering” (IEEE Transactions on Cybernetics, 2018), have significantly advanced subspace clustering and unsupervised learning, with the former ranked among the top 1% of highly cited papers in Engineering. Dr. Ivica Kopriva’s research further encompasses image co-segmentation, tumor detection, and signal demixing, contributing to innovations in medical imaging and AI-based diagnostics. With over 2,546 citations, an h-index of 21, and an i10-index of 50, Dr. Ivica Kopriva’s scholarly impact extends across multiple disciplines of computational and biomedical sciences. His exceptional achievements have been recognized through numerous awards, including the State Award of the Republic of Croatia for Scientific Achievement (2009), multiple Director’s Awards for Scientific Excellence (2010–2021), and MICCAI Outstanding Reviewer Awards. As a Senior Member of IEEE and OSA and an affiliated faculty member at Virginia Commonwealth University, Dr. Ivica Kopriva continues to contribute extensively to international scientific communities, shaping the global research landscape in computational imaging, data-driven signal analysis, and intelligent biomedical systems.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

  • Brbić, M., & Kopriva, I. (2018). Multi-view low-rank sparse subspace clustering. Pattern Recognition, 73, 247–258.

  • Huang, T. M., Kecman, V., & Kopriva, I. (2006). Kernel based algorithms for mining huge data sets: Supervised, semi-supervised, and unsupervised learning. Springer Berlin Heidelberg.

  • Ju, W., Xiang, D., Zhang, B., Wang, L., Kopriva, I., & Chen, X. (2015). Random walk and graph cut for co-segmentation of lung tumor on PET-CT images. IEEE Transactions on Image Processing, 24(12), 5854–5867.

  • Brbić, M., & Kopriva, I. (2020). l₀-Motivated low-rank sparse subspace clustering. IEEE Transactions on Cybernetics, 50(4), 1711–1725.

  • Tolić, D., Antulov-Fantulin, N., & Kopriva, I. (2018). A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering. Pattern Recognition, 82, 40–55.

Jingcheng Tong | Deep Learning | Best Researcher Award

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Mr. Jingcheng Tong | Deep Learning | Best Researcher Award

Beijing Institute of Graphic Communication | China

Mr. Jingcheng Tong, a postgraduate student at the Beijing Institute of Graphic Communication, China, is an emerging researcher whose work focuses on advancing artificial intelligence applications in industrial manufacturing through deep learning and computer vision technologies. As a student member actively contributing to this growing field, he has developed innovative object detection algorithms tailored for steel material identification and quality assessment, bridging the gap between advanced AI methods and traditional manufacturing practices. His notable research, including the publication CBH-YOLO: A steel surface defect detection algorithm based on cross-stage mamba enhancement and hierarchical semantic graph fusion in the SCI-indexed journal Neurocomputing, highlights his ability to design effective solutions that significantly improve defect detection accuracy, enhance efficiency, and reduce manual inspection costs. Mr. Tong’s interdisciplinary approach not only advances industrial automation and smart manufacturing initiatives but also demonstrates how applied artificial intelligence can modernize conventional production systems and elevate product quality standards. In addition to his technical expertise, he exhibits strong academic commitment and a forward-looking vision, aiming to extend his research toward broader industrial applications of AI that can support sustainable, intelligent, and globally competitive manufacturing. Through his scholarly contributions, practical innovations, and dedication to excellence, Mr. Jingcheng Tong exemplifies the promise and potential of the next generation of researchers committed to shaping the future of intelligent manufacturing technologies.

Profile : Orcid

Featured Publication

Tong, J. (2025). CBH-YOLO: A steel surface defect detection algorithm based on cross-stage mamba enhancement and hierarchical semantic graph fusion. Neurocomputing. Advance online publication.

Bo Gao | Deep Learning | Best Researcher Award

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Dr. Bo Gao | Deep Learning | Best Researcher Award

Beijing Institute of Graphic Communication | China

Dr. Gao Bo, PhD, is a Professor and Master’s Supervisor at the School of Information Engineering, Beijing Institute of Graphic Communication, following a distinguished tenure at Inner Mongolia University of Finance and Economics. His research encompasses nonlinear dynamics, cryptography, game theory, and computational mathematics, where he has made notable interdisciplinary contributions linking theory and practice. According to Scopus, Dr. Gao has published 32 documents, cited 593 times across 488 works, with an h-index of 14, reflecting both productivity and international recognition. His studies have been published in leading journals such as Physical Review E, Applied Mathematics and Computation, Chaos, and IEEE Transactions on Circuits & Systems II: Express Briefs, highlighting his expertise in applied and computational mathematics. Beyond research papers, he has authored academic monographs, secured multiple invention and utility model patents, and registered software copyrights, showcasing his commitment to innovation. He has also led several funded projects supported by national and regional foundations, addressing challenges in lightweight cryptography, wireless sensor networks, and Internet of Things security. In academic service, he reviews for respected journals including Nonlinear Dynamics, Applied Mathematics and Computation, PLOS ONE, and China Communications. He also contributes professionally as a member of the Education and Popular Science Committee of the Chinese Society of Cryptologism. Through sustained research, project leadership, and mentoring, Dr. Gao has advanced knowledge in information security, fostered innovation in engineering sciences, and contributed to training the next generation of researchers.

Profile: Scopus

Featured Publications

Gao B, Tao K, Mu C, et al. Asymmetry of individual activity promotes cooperation in the spatial prisoner’s dilemma game. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023, 33(9).

Gao B, Hong J, Guo H, et al. Cooperative evolution and symmetry breaking in interdependent networks based on alliance mechanisms. Physica A: Statistical Mechanics and its Applications, 2023, 609.

Lan Z Z, Dong S, Gao B, Shen Y J. Bilinear form and soliton solutions for a higher order wave equation. Applied Mathematics Letters, 2022, 134: 108340.

Dong S, Lan Z Z, Gao B, et al. Bäcklund transformation and multi-soliton solutions for the discrete Korteweg–de Vries equation. Applied Mathematics Letters, 2022, 125: 107747.

Gao B, Liu X, Lan Z Z, et al. The evolution of cooperation with preferential selection in voluntary public goods game. Physica A: Statistical Mechanics and its Applications, 2021, 584: 126267.

Gao B, Li B, Dong S, et al. Payoff-dependence learning ability resolves social dilemmas. International Journal of Modern Physics B, 2021, 35(08): 2150125.

Gao B, Liu X, Wu X, et al. The Stability of Nonlinear Feedback Shift Registers with Periodic Input. Computers, Materials & Continua, 2020, 62(2): 833-847.

Wang Q, Ren X, Gao B, et al. Heterogeneity reproductive ability promotes cooperation in spatial prisoner’s dilemma game. Chaos, Solitons & Fractals, 2020, 134: 109715.