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Dr. Zakir Hussain | Neural Networks | Best Researcher Award

Dr. Zakir Hussain, University of Baltistan, Skardu, Pakistan

Dr. Zakir Hussain is the Head of the Department of Mathematics at the University of Baltistan, Skardu, Pakistan. He holds a PhD in Fluid Mechanics from Quaid-i-Azam University, Islamabad, and has extensive expertise in data science, artificial intelligence, and computational fluid dynamics. With over 140 research publications and a significant impact factor, Dr. Hussain is an established reviewer for international journals and an active contributor to advancements in data-driven solutions, numerical simulations, and AI applications in fluid mechanics. His research and teaching emphasize machine learning, mathematical modeling, and innovative computational techniques in applied mathematics.

Summary:

Dr. Zakir Hussain exemplifies the qualities of a Best Researcher Award recipient through his extensive contributions to fluid mechanics, AI, and applied mathematics. With a solid academic background, proven research impact, and substantial publication record, he has made significant advancements in computational fluid dynamics and data science applications. His leadership role in the Department of Mathematics also underscores his commitment to fostering research and innovation within his institution and beyond.

 

Professional Profiles:

Scopus

šŸŽ“Education:

Dr. Hussain completed a PhD in Fluid Mechanics from Quaid-i-Azam University, Islamabad, followed by multiple diplomas and certifications, most recently in Data Science and Artificial Intelligence at the National University of Sciences and Technology, Islamabad. His earlier qualifications include an MSc and MPhil in Applied Mathematics from Quaid-i-Azam University.

šŸ¢Experience:

With over a decade in academia, Dr. Hussain currently serves as Head of the Mathematics Department at the University of Baltistan, Skardu. His roles have included lecturer positions and research associate roles at multiple institutions, with a focus on applied mathematics and fluid mechanics. He has progressively taken on more administrative responsibilities, including serving on various university committees and leading examination processes for affiliated colleges.

šŸ› ļøSkills:

Dr. Zakir Hussain possesses extensive expertise in various technical and computational techniques, including Python, MATLAB, Mathematica, ANSYS, COMSOL, and advanced numerical methods like the Keller Box and Finite Volume Method. His skills span data science, machine learning, artificial intelligence, and computational fluid dynamics, along with specific proficiencies in data visualization, image processing, and numerical simulations.

Awards :

Dr. Hussain is a highly recognized academic, having reviewed over 20 leading international journals and authored more than 141 articles, with a collective impact factor exceeding 150. His accolades include merit scholarships during both his MPhil and PhD studies, in addition to his participation in varsity athletics, representing Quaid-i-Azam University in hockey and athletic championships.

Conclusion:

Dr. Zakir Hussainā€™s credentials make him a suitable candidate for the Best Researcher Award, with his numerous high-impact publications, academic service, and research contributions establishing him as a leader in his field. Enhancing international collaborations and research funding opportunities would further bolster his already strong candidacy for this honor.

 

Research Focus:

Dr. Hussainā€™s research interests are centered on data science, artificial intelligence, and fluid dynamics, with a specific focus on machine learning, deep learning, and neural networks. His expertise includes applied mathematics in computational fluid dynamics and non-Newtonian fluid flow, numerical simulations in blood flow and boundary layer analysis, as well as innovative applications of AI in mathematical modeling and data-driven solutions in fluid dynamics.

Publications :
  • Hussain, Z., Alghamdi, M., Aslam, M. N., & Muhammad, T. (2025). Morlet-wavelet neural networks and sensitivity analysis in magnetized peristaltic flow subject to Soretā€“Dufour effects: An unsupervised approach. International Communications in Heat and Mass Transfer, 160, 108259.

 

  • Hussain, Z., Fazia, & Anwar, M. S. (2024). Neural network approach to investigate heat transfer in SWCNTs nanofluid within trapezoidal cavity with varied corrugated rod amplitudes. Physica Scripta, 99(10), 105257.

 

  • Hussain, Z., Alghamdi, M., Muhammad, T., & Anwar, M. S. (2024). Lid-driven effect on convective heat transfer with heated rods in a modified equilateral triangular cavity. Case Studies in Thermal Engineering, 61, 104908.

 

  • Anwar, M. S., Alam, M. M., Khan, M. A., Hussain, Z., & Puneeth, V. (2024). Generalized viscoelastic flow with thermal radiations and chemical reactions. Geoenergy Science and Engineering, 232, 212442.

 

  • Hussain, Z., Alghamdi, M., Fozia, Ali, M. R., & Aslam, M. (2023). Significance of mixed convective heat transfer model in an equilateral triangular enclosure subjected to cylindrical heated objects inside. Case Studies in Thermal Engineering, 47, 103027.

 

  • Anwar, M. S., Hussain, M., Hussain, Z., Puneeth, V., & Irfan, M. (2023). Clay-based cementitious nanofluid flow subjected to Newtonian heating. International Journal of Modern Physics B, 37(14), 2350140.

 

  • Alghamdi, M., Fatima, B., Hussain, Z., Nisar, Z., & Alghamdi, H. A. (2023). Peristaltic pumping of hybrid nanofluids through an inclined asymmetric channel: A biomedical application. Materials Today Communications, 35, 105684.

 

  • Raza, U., Anwar, M. S., Ali, H., Irfan, M., & Hussain, Z. (2023). Fixed points in n-gonal graphical b-metric spaces under contractive conditions. International Journal of Modern Physics B, 37(4), 2350039.

 

  • Anwar, M. S., Puneeth, V., Hussain, M., Hussain, Z., & Irfan, M. (2023). Heat Convection in a Viscoelastic Nanofluid Flow: A Memory Descriptive Model. Journal of Applied Nonlinear Dynamics, 12(2), 363ā€“378.

 

  • Hussain, Z., Alshomrani, A. S., Muhammad, T., & Anwar, M. S. (2022). Entropy analysis in mixed convective flow of hybrid nanofluid subject to melting heat and chemical reactions. Case Studies in Thermal Engineering, 34, 101972.
Zakir Hussain | Neural Networks | Best Researcher Award

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