Dr. Nafiseh Soleymani | Multi-View Data | Best Researcher Award
Nafiseh Soleymani | Azad University of Mashhad | Iran
Dr. Nafiseh Soleymani is a computer scientist and Ph.D. candidate at Azad University of Mashhad, Iran, specializing in data science, machine learning, multi-view data analysis, and unsupervised learning. Her research focuses on developing efficient methods for analyzing high-dimensional and multi-view datasets, emphasizing non-negative matrix factorization (NMF), clustering algorithms, and feature selection techniques. With a strong technical foundation in programming (C++, C#, Matlab) and database systems, she combines theoretical research with applied computational methods. Dr. Soleymani’s studies contribute to improving data representation, dimensionality reduction, and classification performance in complex data environments, making her work valuable to the fields of artificial intelligence, bioinformatics, and big data analytics.
Profile: Google Scholar
Featured Publications
Soleymani, N., Moattar, M. H., & Sheibani, R. (2025). Dealing with high dimensional multi-view data: A comprehensive review of non-negative matrix factorization approaches in data mining and machine learning. Computer Science Review, 58, 100788. Citation count: —.
Soleymani, N., Moattar, M. H., & Sheibani, R. (2025). Corrigendum to “Dealing with high dimensional multi-view data: A comprehensive review of non-negative matrix factorization approaches in data mining and machine learning.” Computer Science Review, 100841. Citation count: —.
Soleymani, N., & Moattar, M. H. (2018, February). An approach based on resampling and feature selection to improve the classification of microarray data. 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), 61–64. IEEE. Citation count: 2.
Moattar, M. H., & Soleymani, N. (2019). Twin support vector machine-based clustering for feature selection in microarray data classification problem. Journal of Information Technology in Engineering Design, 12(1), 30–39. Citation count: —.