Behzad Nemati Saray | Mathematics | Best Researcher Award

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Assoc. Prof. Dr. Behzad Nemati Saray | Mathematics | Best Researcher Award

Institute for Advanced Studies in Basic Sciences (IASBS) | Iran

Assoc. Prof. Dr. Behzad Nemati Saray is a distinguished mathematician whose research spans numerical analysis, applied mathematics, and computational methods. With an impressive record of 840 citations, h-index of 17, and i10-index of 22, his work demonstrates significant academic influence, particularly in developing efficient algorithms and multiwavelet-based methods for solving differential and integro-differential equations. He has published over 30 refereed journal papers and numerous conference articles that address complex mathematical problems, including fractional equations, Burgers equations, and Sturm–Liouville systems. As a reviewer for MathSciNet (AMS) and an active contributor to international mathematical conferences, he has earned recognition for his leadership roles, such as Director-in-Charge of the Seasonal School in Mathematics at IASBS and Executive Board member in academic seminars. His scholarly excellence was acknowledged when he was named Best Researcher of the Year (2023) at the Institute for Advanced Studies in Basic Sciences (IASBS). Dr. Saray’s contributions to the fields of computational mathematics and applied analysis continue to advance numerical modeling, multiscale methods, and spectral techniques, making his research a cornerstone for future developments in applied and computational mathematics. His sustained impact reflects a deep commitment to innovation and precision in mathematical sciences.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Liu, T., Ding, B., Nemati Saray, B., Juraev, D. A., & Elsayed, E. E. (2025). On the pseudospectral method for solving the fractional Klein–Gordon equation using Legendre cardinal functions. Fractal and Fractional, 9(3), 177. Multidisciplinary Digital Publishing Institute.

Shi, L., Nemati Saray, B., & Soleymani, F. (2024). Sparse wavelet Galerkin method: Application for fractional Pantograph problem. Journal of Computational and Applied Mathematics, 116081.

Liu, T., Xue, R., Ding, B., Juraev, D. A., Nemati Saray, B., & Soleymani, F. (2024). A novel and effective scheme for solving the fractional telegraph problem via the spectral element method. Fractal and Fractional, 8(12), 711. Multidisciplinary Digital Publishing Institute.

Ranjbari, S., Baghmisheh, M., Jahangiri Rad, M., & Nemati Saray, B. (2024). On the wavelet Galerkin method for solving the fractional Fredholm integro-differential equations. Computational Methods for Differential Equations.

Tarbiyati, H., & Nemati Saray, B. (2023). Weight initialization algorithm for physics-informed neural networks using finite differences. Engineering with Computers.

Afarideh, A., Dastmalchi Saei, F., & Nemati Saray, B. (2023). Eigenvalue problem with fractional differential operator: Chebyshev cardinal spectral method. Journal of Mathematical Modeling.

Shahriari, M., Nemati Saray, B., Mohammadalipour, B., & Saeidian, S. (2023). Pseudospectral method for solving the fractional one-dimensional Dirac operator using Chebyshev cardinal functions. Physica Scripta.

Pourfattah, E., Jahangiri Rad, M., & Nemati Saray, B. (2023). An efficient algorithm based on the pseudospectral method for solving Abel's integral equation using Hermite cubic spline scaling bases. Applied Numerical Mathematics.

Nemati Saray, B. (2022). On a multiwavelet spectral element method for integral equation of a generalized Cauchy problem. BIT Numerical Mathematics.

Nemati Saray, B. (2021). Pseudospectral method for solving fractional Sturm-Liouville problem using Chebyshev cardinal functions. Physica Scripta.

Nemati Saray, B. (2021). Abel’s integral operator: Sparse representation based on multiwavelets. BIT Numerical Mathematics.

Nemati Saray, B. (2021). On the sparse multiscale representation of 2-D Burgers equations by an efficient algorithm based on multiwavelets. Numerical Methods for Partial Differential Equations.

Nemati Saray, B. (2020). On the sparse multi-scale solution of the delay differential equations by an efficient algorithm. Applied Mathematics and Computation.

Nemati Saray, B. (2020). Reconstruction of the Sturm-Liouville differential operators with discontinuity conditions and a constant delay. Indian Journal of Pure and Applied Mathematics.

Nemati Saray, B. (2020). Heat and mass transfer investigation of MHD Eyring–Powell flow in a stretching channel with chemical reactions. Physica A: Statistical Mechanics and its Applications.

Nemati Saray, B. (2020). Sparse multiscale representation of Galerkin method for solving linear-mixed Volterra-Fredholm integral equations. Mathematical Methods in the Applied Sciences.

Vasil Angelov | Mathematics | Best Researcher Award

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Prof. Dr. Vasil Angelov | Mathematics | Best Researcher Award

University of Mining and Geology St. Ivan Rilski | Bulgaria

Prof. Dr. Vasil Angelov is a distinguished mathematician and researcher with an h-index of 11, 151 articles with 562 citations, reflecting his significant contributions to mathematics and applied sciences. He earned his PhD in Mathematics, followed by an Associate Professorship, Doctor of Sciences, and Full Professorship. He served as Chair of the Department of Mathematics at the University of Mining & Geology “St. I. Rilski,” Bulgaria, and as Deputy Rector before retiring. Prof. Angelov’s research focuses on delay differential equations, fixed point theory, classical electrodynamics, transmission lines, and nonlinear circuits, with over 150 research papers published. He authored notable monographs including Fixed Points in Uniform Spaces and Applications and A Method for Analysis of Transmission Lines Terminated by Nonlinear Loads. His work on periodic solutions of the 4-body electromagnetic problem and its application to the Li atom demonstrates his expertise in mathematical modeling of complex physical systems. Prof. Angelov has been widely recognized for his contributions, receiving awards such as Who’s Who in the World, Gold Medal of the American Biographical Institute, International Peace Prize, Scientist of the Year honors, and recognition from international research centers, highlighting his global impact in mathematics, physics, and applied sciences.

Profiles : Scopus | Google Scholar

Featured Publications

Angelov, V. G. (2025). Periodic solutions of the 4-body electromagnetic problem and application to Li atom. AppliedMath, 5(3), 112.

Angelov, V. (2025). Energy estimation of the moving particles in 2-body and 3-body problem of classical electrodynamics. Science Set Journal of Physics.

Angelov, V. G. (2024, December 25). Spin 3-body problem of classical electrodynamics in the 3D-Kepler form.

Angelov, V. G. (2024, August 21). Energy of the moving particles in 3-body problem of classical electrodynamics. Preprints.

Muhammad Tahir | Mathematics | Young Scientist Award

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Dr. Muhammad Tahir | Mathematics | Young Scientist Award

Gomal University | Pakistan

Dr. Muhammad Tahir is an emerging researcher and academic in the field of applied mathematics, with expertise spanning numerical analysis, fuzzy logic, artificial intelligence, and decision-making systems. He has served as a Lecturer in Mathematics at Beacon Light College, Pakistan, and as a Teaching Assistant at the Islamia University of Bahawalpur, where he contributed to curriculum development and quality enhancement initiatives. Dr. Tahir holds a Master of Science in Mathematics from the Islamia University of Bahawalpur, where his thesis focused on the “Numerical Study of Deformation of Bubbles Using Phase-Field Models with Decoupling Technique.” His research portfolio includes several high-impact publications in reputed journals such as Applied Soft Computing, European Journal of Pure and Applied Mathematics, and Engineering Applications of Artificial Intelligence. His studies cover topics like fuzzy set theory, cryptographic security, cyber risk mitigation, environmental sustainability, and multi-criteria decision-making. Dr. Tahir’s recent works emphasize hybrid intelligence frameworks, machine learning-integrated decision systems, and quantum-resistant cryptography. He has presented his research at international conferences in Turkey, demonstrating his commitment to interdisciplinary collaboration and computational innovation. His scholarly contributions continue to advance soft computing, mathematical modeling, and intelligent decision-making for industrial and environmental applications.

Profiles : Scopus | Orcid

Featured Publications

Kamran, M., Zhang, Q., Pamucar, D., Tahir, M., & Simic, V. (2025). Enhancing confidence level in decision-making frameworks using Fermatean fuzzy rough sets: Application in Industry 4.0. Applied Soft Computing.

Tahir, M., Kfueit, K., Rasheed, M., Hanan, A., & Shahid, M. I. (2025, October 4). Pythagorean soft sets and hypersoft sets: A comprehensive framework for advanced uncertainty modeling in decision making. Spectrum of Decision Making and Applications.

Juan Chen | Mathematics | Best Researcher Award

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Assoc. Prof. Dr. Juan Chen | Mathematics | Best Researcher Award

Wuhan University of Science and Technology | China

Assoc. Prof. Dr. Juan Chen is a distinguished scholar in computational mathematics and complex network dynamics, currently serving at the College of Science, Wuhan University of Science and Technology. With a Ph.D. from Wuhan University, Dr. Chen has made significant contributions to nonlinear systems, synchronization, and complex network theory. Her prolific academic record includes 33 research documents, 432 citations, and an h-index of 13, reflecting her impact in the field. Dr. Chen’s works have appeared in leading journals such as IEEE Transactions on Network Science and Engineering, Chaos, Solitons and Fractals, and Expert Systems with Applications. She has authored influential books, including Synchronization of Complex Dynamical Networks, and contributed chapters to Springer handbooks. As a principal investigator and key collaborator on multiple National Natural Science Foundation of China projects, her research bridges mathematical theory and applied network science. Dr. Chen’s focus on synchronization, control, and robustness of complex networks highlights her pivotal role in advancing interdisciplinary computational research. Her dedication to exploring network behavior, information processing, and system dynamics positions her as an influential figure in contemporary applied mathematics and systems engineering.

Profiles : Scopus | Orcid

Featured Publications

Yin, L., Chen, J., Gao, F., & Wu, X. (2025). Coevolution of opinion and consumption behavior under a two-layer network framework. Physica A: Statistical Mechanics and its Applications, 680, 131020.

Yu, X., Tu, L., Chai, L., Wang, X., & Chen, J. (2024). Construction of implicit social network and recommendation between users and items via the ISR-RRM algorithm. Expert Systems with Applications, 235, 121229.

Chai, L., Tu, L., Yu, X., Wang, X., & Chen, J. (2023). Link prediction and its optimization based on low-rank representation of network structures. Expert Systems with Applications, 219, 119680.

Chai, L., Tu, L., Wang, X., & Chen, J. (2022). Network-energy-based predictability and link-corrected prediction in complex networks. Expert Systems with Applications, 207, 118005.

Shen, H., Tu, L., Guo, Y., & Chen, J. (2022). The influence of cross-platform and spread sources on emotional information spreading in the 2E-SIR two-layer network. Chaos, Solitons and Fractals, 165, 112801.

Chen, J., Li, X., Wu, X., & Shen, G. (2022). Prescribed-time synchronization of complex dynamical networks with and without time-varying delays. IEEE Transactions on Network Science and Engineering, 9, 4017–4027.

Zhang, P., Chen, J., Tu, L., & Yin, L. (2022). Layout evaluation of new energy vehicle charging stations: A perspective using the complex network robustness theory. World Electric Vehicle Journal, 13, 127.

Zhou, J., Chen, J., & Lu, J. (2017). On applicability of auxiliary system approach to detect generalized synchronization in complex networks. IEEE Transactions on Automatic Control, 62(7), 3468–3473.

Chen, J., Lu, J., & Zhou, J. (2013). Topology identification of complex networks from noisy time series using ROC curve analysis. Nonlinear Dynamics, 74, 1–8.

Zhang, Q., Chen, J., & Wan, L. (2013). Impulsive generalized function synchronization of complex dynamical networks. Physics Letters A, 377, 2754–2760.

Chen, J., Lu, J., Lu, X., Wu, X., & Chen, G. (2013). Spectral coarse graining of complex clustered networks. Communications in Nonlinear Science and Numerical Simulation, 18, 3036–3045.

Hassan Ramchoun | Mathematics | Best Researcher Award

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Dr. Hassan Ramchoun | Mathematics | Best Researcher Award

Moulay Ismail University | Morocco

Author Profile

Scopus

Early Academic Pursuits

Dr. Hassan Ramchoun’s academic journey reflects a strong foundation in mathematics and computer science. Beginning with his bachelor’s studies in applied mathematics, he progressively advanced through a master’s degree in operations research and statistics, where he explored Bayesian approaches in artificial neural networks for medical imaging. His doctoral research in applied mathematics and computer science further solidified his expertise, focusing on parameter and hyper-parameter estimation in Bayesian neural networks with applications in classification and regression. These early experiences shaped his specialization in applied mathematics, artificial intelligence, and machine learning.

Professional Endeavors

Dr. Ramchoun has built a diverse academic career with teaching, research, and leadership responsibilities. Since joining ENCG Meknès, Moulay Ismail University, as a lecturer, he has also taught at multiple institutions including ENSAM Meknès and the Euro-Mediterranean University of Fès. His teaching portfolio covers a wide spectrum, from probability and statistics to optimization, data analysis, and artificial intelligence. His active engagement in curriculum design, supervision of students, and participation in doctoral juries reflects his deep commitment to higher education and student mentorship.

Contributions and Research Focus

Dr. Ramchoun’s research focuses on applied mathematics, artificial intelligence, and data science, with particular emphasis on Bayesian neural networks, deep learning, optimization, and statistical learning methods. He has contributed to developing new methods for training and optimizing neural networks, incorporating regularization and Bayesian inference. His work extends to multi-objective optimization for neural architectures, convolutional neural networks, and applications in big data analytics. His scholarly contributions are reflected in numerous publications in international journals and conferences, alongside editorial contributions to Springer volumes on artificial intelligence and industrial applications.

Accolades and Recognition

Dr. Ramchoun’s academic excellence has been acknowledged through distinctions such as the CNRST excellence research scholarship and his recognition for doctoral work with high honors. His research output is regularly published in reputed international journals including Neurocomputing, Knowledge-Based Systems, Soft Computing, and Evolving Systems. His active involvement as a reviewer for leading journals and as a member of scientific committees of conferences demonstrates recognition of his expertise by the global scientific community.

Impact and Influence

Through his work, Dr. Ramchoun has significantly influenced both the academic and applied dimensions of artificial intelligence. His innovations in neural network optimization and Bayesian learning methods have advanced understanding in machine learning research, while his teaching and supervision of master’s and doctoral students have contributed to developing future generations of researchers. By bridging theoretical modeling with practical applications, his contributions have relevance not only in academia but also in applied domains such as data science, robotics, and image processing.

Legacy and Future Contributions

The legacy of Dr. Ramchoun lies in his efforts to advance the integration of applied mathematics with artificial intelligence, particularly in deep learning and Bayesian modeling. His involvement in research projects, scientific committees, and international conferences ensures that his contributions extend beyond publications to shaping the field itself. Looking forward, his continued work in optimization techniques, advanced statistical learning, and interdisciplinary applications promises to strengthen the role of artificial intelligence in addressing scientific and industrial challenges.

Publications

  • Article: Convergence of batch gradient descent with learnable smooth masks for pruning feedforward neural networks
    Authors: Quasdane, M., Ramchoun, H., Masrour, T.
    Journal: Neurocomputing
    Year: 2025

  • Article: Enhancing CNN structure and learning through NSGA-II-based multi-objective optimization
    Authors: Elghazi, Khalid, Ramchoun, Hassan, Masrour, Tawfik
    Journal: Evolving Systems
    Year: 2024

  • Article: Implicitly adaptive optimal proposal in variational inference for Bayesian learning
    Authors: Bakhouya, Mostafa, Ramchoun, Hassan, Hadda, Mohammed, et al.
    Journal: International Journal of Data Science and Analytics
    Year: 2024

  • Article: Gaussian Mixture Models for Training Bayesian Convolutional Neural Networks
    Authors: Bakhouya, M., Ramchoun, H., Hadda, M., Masrour, T.
    Journal: Evolutionary Intelligence (Springer)
    Year: 2024

  • Article: Sparse smooth group L0L1/2 regularization method for convolutional neural networks
    Authors: Quasdane, M., Ramchoun, H., Masrour, T.
    Journal: Knowledge-Based Systems
    Year: 2023

Conclusion

Dr. Hassan Ramchoun exemplifies the qualities of a dedicated academic, innovative researcher, and committed educator. His career reflects a balance of teaching excellence, impactful research, and institutional service. With strong foundations in mathematics and expertise in artificial intelligence, his contributions are shaping the evolving landscape of machine learning and data science. His recognition, publications, and leadership in academic communities confirm his standing as an influential figure whose work will continue to resonate in both research and applied domains.