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Name
John Mlyahilu

Academic Rank
Senior Lecturer

Department
Mathematics, Physics, and Informatics

Biography

Biography

John Mlyahilu is a researcher specializing in computational imaging, computer vision, and artificial intelligence applications for industrial quality control. His work focuses on developing advanced algorithms for defect detection, image and video classification and pattern analysis in manufacturing environments, particularly in 3D film production. With expertise in Fourier analysis, machine learning, and image processing, John's research bridges theoretical signal processing with practical industrial applications. His recent work includes innovative approaches combining Fast Fourier Transform, machine learning, neural networks, and deep learning for comprehensive defect characterization. John holds a background in mathematics,  statistics, and computer science contributes to advancing automated inspection systems that enhance manufacturing precision and efficiency.

Contacts

Email:

Email Address
john.mlyahilu@muce.ac.tz

Research Interest

Research Interest
My research focuses on developing intelligent computational systems through the integration of machine learning, image and video processing, and mathematical statistics for real-world applications. I work at the convergence of information technology and a

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Projects

Publications

Publications

  1. Mlyahilu, J. N. and Kim, J. N. (2026). Center point detection on shear reinforcement components in mobile robotic welding and dispatch applications using iterative algorithm. Results in Control and Optimization. 29(100794), 1-17. https://doi.org/10.1016/j.rico.2026.100794

  2. Kawonga, G. A., Fussy, D. S., and Mlyahilu J. N. (2026). Distribution of Students in University Academic Fields: Underlying Myths, Locality and Influences. International Journal of Adolescence and Youth. 31(1). 1-22. https://doi.org/10.1080/02673843.2026.2703093.

  3. Mlyahilu, J. N. and Kawonga, G. A. (2026). Modeling the Dynamics of Tanzanians’ Traditional Names: Insights from Statistical Analysis and Graph Theory. Studies in Ethnicity and Nationalism, 6(2) 1 – 26. https://doi.org/10.1111/sena.70054

  4. Mlyahilu, J. N. and Kim, J. N. (2026). Segmentation and Classification of Welding Beads on Shear Reinforcement with Dual Anchorage Using Deep Learning Models. Engineering Reports, 8(3), 1 – 16. https://doi.org/10.1002/eng2.70700.

  5. Mlyahilu, J. N. (2026).  Estimation of  from Statistical Features of Acoustic Time Series Using Optimized Monte-Carlo with Grid Search Algorithm. IEEE Access, 14(99), 18952 – 18963. https://doi.org/10.1109/ACCESS.2026.3660048

  6. Mussa A. Stephano, John N. Mlyahilu, Il Hyo Jung. (2026),.Modelling lymphatic filariasis dynamics using Levenberg Marquardt algorithm-artificial neural networks, Results in Control and Optimization, 22(100659), ISSN 2666-7207,
    https://doi.org/10.1016/j.rico.2026.100659 

  7. Kawonga, G. and Mlyahilu, J. (2025). Mathematical Approach for Sample Projection in Linguistic Landscape Studies. Digital Scholarship in the Humanities. 40(4), 1179 – 1187. https://doi.org/10.1093/llc/fqaf077

  8. Magidanga, F. S., Mwakifuna, J. N., Mhewa, M. M., Maliva, W. M., and Kawonga G. G., and Mlyahilu, J. N. (2025). A Systematic Review Comparing Teacher-Centred and Learner-Centred Teaching Approaches in Tanzanian Secondary Schools. Journal of Teaching and Learning, 1(1), 106 – 122. DOI:10.37759/ejtl.2025.1.1.6

  9. Mlyahilu, J., and Kim, J. (2022). Similarity Measurement Between Titles and Abstracts Using Bijection Mapping and Phi-Correlation Coefficient, The Journal of Korea Institute of Convergence and Signal Processing, 23(3), 143 – 149. DOI: 10.23087/jkicsp.2022.23.3.005.

  10. Mlyahilu, J., Mlyahilu, J., Lee, J., Kim, Y., and Kim, J. (2022). Morphological Geodesic Active Contour Algorithm for Segmentation of the Histogram-Equalized Weld Bead Image Edges. IET Image Processing, 16(10), 2680 – 2696. https://doi.org/10.1049/ipr2.12517.

  11. Mlyahilu, J. and Kim Jongnam. (2022). A Fast Fourier Transform with Brute-Force Algorithm for Detection and Localization of White Points on 3D Film Pattern Images. Journal of Imaging Science and Technology, 66(3), 1 – 13. DOI: https://doi.org/10.2352/J.ImagingSci.Technol.2022.66.3.030506.

  12. Mlyahilu, J., and Kim, J. (2020). Generative Probabilistic Model with Dirichlet Prior Distribution for Similarity Analysisof Research Topic. Journal of Korea Multimedia Society. 23(4), 595-602. https://doi.org/10.9717/kmms.2020.23.4.595.

  13. Mlyahilu, J. Kim, Y. and Kim, J. (2019). Classification of 3D Film Patterns with Deep Learning. Journal of Computerand Communications, 7, 158-165. DOI: 10.4236/jcc.2019.712015.