Sukumar Letchmunan | Computer Science | Best Researcher Award

Published on by Database Scientist

Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

University Sains Malaysia | Malaysia

Dr. Sukumar Letchmunan is an accomplished researcher and academic at Universiti Sains Malaysia, specializing in Software Engineering, Machine Learning, Software Metrics, and Service-Oriented Software Engineering. His research primarily focuses on developing intelligent computational models and data-driven frameworks that address complex real-world problems in domains such as crime prediction, healthcare analytics, uncertainty modeling, and sustainable digital systems. Over the years, Dr. Sukumar Letchmunan has made significant contributions to artificial intelligence applications through the integration of fuzzy logic, evidential theory, and deep learning, as reflected in his high-impact publications in leading journals including IEEE Access, ACM Transactions on Knowledge Discovery from Data, Knowledge-Based Systems, and Applied Soft Computing. His collaborations have produced novel methods for multi-view evidential clustering, belief function-based uncertainty representation, and medical decision-making systems, demonstrating his expertise in handling imprecision in data-centric environments. Notably, his works on AI-driven crime forecasting, machine learning-based diabetes prediction, and deep neural network analysis for human activity recognition have been widely cited, influencing interdisciplinary research across computer science and healthcare informatics. With a strong record of international collaboration, particularly with researchers from China, Turkey, and Saudi Arabia, Dr. Sukumar Letchmunan continues to advance the frontier of trustworthy and interpretable AI systems. His recent works in fuzzy similarity measures, transformer-based spatiotemporal modeling, and green decision-making frameworks exemplify his commitment to enhancing computational intelligence for sustainable and socially relevant applications. With 1,298 citations, an h-index of 19, and an i10-index of 22, Dr. Sukumar Letchmunan research impact underscores his influential role in advancing next-generation AI methodologies and their meaningful application to global scientific and societal challenges.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

1. Butt, U. M., Letchmunan, S., Ali, M., Hassan, F. H., Baqir, A., & Sherazi, H. H. R. (2021). Machine learning based diabetes classification and prediction for healthcare applications. Journal of Healthcare Engineering, 2021(1), 9930985.

2. Khaw, T. Y., Teoh, A. P., Abdul Khalid, S. N., & Letchmunan, S. (2022). The impact of digital leadership on sustainable performance: A systematic literature review. Journal of Management Development, 41(9–10), 514–534.

3. Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-temporal crime hotspot detection and prediction: A systematic literature review. IEEE Access, 8, 166553–166574.

4. Liu, Z., & Letchmunan, S. (2024). Enhanced fuzzy clustering for incomplete instance with evidence combination. ACM Transactions on Knowledge Discovery from Data, 18(3), 1–20.

5. Liu, Z., Huang, H., Letchmunan, S., & Deveci, M. (2024). Adaptive weighted multi-view evidential clustering with feature preference. Knowledge-Based Systems, 294, 111770.

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.