Youness Javid | Reliability | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Youness Javid — Kharazmi University, Iran

Youness Javid
Affiliation Kharazmi University
Country Iran
Scopus ID 49561332700
Documents 19
Citations 273
h-index 8
Subject Area Reliability
Event Global CSE Awards

Youness Javid is a researcher affiliated with Kharazmi University whose indexed scholarly record includes research activity in reliability and related analytical applications. The available profile information records 19 documents, 273 citations, and an h-index of 8. His recent collaborative publication applies optimized machine learning and multi-criteria decision analysis to high-risk pregnancy prediction, illustrating an interdisciplinary research direction involving computational methods and applied decision support. [1][2]

1. Abstract

Youness Javid is affiliated with Kharazmi University and has an indexed research record comprising 19 documents, 273 citations, and an h-index of 8. His research profile includes work relevant to reliability and computational analysis. A 2026 Scientific Reports article involving Javid applies Taguchi optimization, supervised machine learning, and TOPSIS-based model selection to high-risk pregnancy prediction using Iranian hospital data. [1]

2. Keywords

Youness Javid; Kharazmi University; reliability; machine learning; predictive modeling; Taguchi optimization; TOPSIS; multi-criteria decision making; high-risk pregnancy prediction; computational analysis; clinical decision support; research impact. [2]

3. Introduction

Research on complex prediction problems increasingly combines statistical optimization, machine learning, and structured decision analysis. Javid’s recent publication demonstrates this interdisciplinary approach by examining high-risk pregnancy prediction through optimized preprocessing, feature selection, classifier tuning, and multi-criteria ranking. The study used hospital-based Iranian data and evaluated several supervised learning models. [1]

4. Research Profile

The available Scopus information identifies Javid through author identifier 49561332700 and records 19 documents, 273 citations, and an h-index of 8. The supplied subject classification is Reliability. These indicators provide a quantitative view of indexed scholarly activity, while the recent publication illustrates engagement with applied machine learning and analytical research questions. [2]

5. Research Contributions

Javid’s recent collaborative research contributes an integrated framework for high-risk pregnancy prediction by combining supervised classifiers with Taguchi-based optimization and TOPSIS-based model ranking. The study compares demographic, pregnancy-related, and complete-case feature groups and evaluates KNN, Random Forest, Decision Tree, SVM, and MLP models using multiple performance criteria. [1]

6. Publications

A documented recent publication is “High-risk pregnancy prediction using Taguchi-optimized machine learning methods and TOPSIS-based model selection,” published in Scientific Reports in 2026. The article lists Maryam Mousavi Nogholi, Ashkan Mozdgir, and Youness Javid as authors and reports analysis of clinical data from 62 pregnant women, with model optimization and multi-criteria evaluation. [1]

7. Research Impact

The supplied Scopus record reports 273 citations across 19 indexed documents, indicating measurable scholarly visibility. The h-index of 8 further reflects a sustained citation record across multiple publications. The recent Scientific Reports study also demonstrates potential applied relevance by addressing early identification of high-risk pregnancies through computational prediction methods. [1][2]

8. Award Suitability

Based on the supplied evidence, Javid presents characteristics relevant to an innovative research recognition: an indexed publication record, measurable citation activity, and recent work integrating optimization, machine learning, and decision analysis. His documented research applies computational techniques to a clinically significant prediction problem, providing a reasonable scholarly basis for consideration under an innovation-focused award. [2]

9. Conclusion

Youness Javid’s supplied research profile combines established indexed scholarly activity with recent interdisciplinary computational research. The available evidence records 19 documents, 273 citations, and an h-index of 8, while his 2026 publication demonstrates the application of optimized machine learning and TOPSIS to a complex healthcare prediction problem. [1][2]

11. References

  1. Mousavi Nogholi, M., Mozdgir, A., & Javid, Y. (2026). High-risk pregnancy prediction using Taguchi-optimized machine learning methods and TOPSIS-based model selection. Scientific Reports, 16, 23966.
    https://doi.org/10.1038/s41598-026-55011-z
  2. Scopus. (2026). Youness Javid: Scopus Author Profile, Author ID 49561332700. Elsevier.
    https://www.scopus.com/pages/authors/49561332700

Ying Yi Tan | Smart Manufacturing | Best Researcher Award

Published on by Global CSE Awards

Dr. Ying Yi Tan | Smart Manufacturing | Best Researcher Award

Research Fellow | Singapore University of Technology and Design | Singapore

Dr. Tan Ying Yi is a Research Fellow at the Singapore University of Technology and Design (SUTD) whose research lies at the intersection of digital fabrication, smart textiles, and computational design. The focus of his work is the development of digital knitting technologies and multi-material additive manufacturing methods for creating functional, mechanically graded, and intelligent textile-based systems. His investigations explore how knitted fabrics can be engineered with integrated electrical and mechanical properties, transforming traditional textiles into high-performance materials applicable to both architectural and biomedical domains. Ying Yi has played a significant role in advancing customized technical textiles for applications such as structural membranes, façade systems, prosthetic interfaces, and wearable technologies. His leadership in projects involving smart garments for body joint monitoring has contributed to innovations in digital health and human–machine interaction, demonstrating the potential of computational design and materials research to improve quality of life. Collaborative projects with institutions like SingHealth Polyclinics, Tan Tock Seng General Hospital, and Hyundai Motor Group have led to impactful real-world solutions such as smart knee braces for gait assessment and smart shirts for motion tracking. His work is characterized by an interdisciplinary approach, blending engineering precision, material science, and architectural design principles to create responsive systems that interact dynamically with users and environments. Recognized with awards for excellence in architectural membranes and advanced manufacturing, Ying Yi continues to contribute to the integration of digital fabrication, computational modeling, and soft robotics in technical textile research. His studies have been featured by major media outlets for their innovation and societal relevance, showcasing how fabric-based systems can bridge the gap between engineering and design. Citations 19 Documents 5 h-index View.

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Weeger, O., Sakhaei, A. H., Tan, Y. Y., Quek, Y. H., Lee, T. L., Yeung, S. K., & Kaijima, S. (2018). Nonlinear multi-scale modelling, simulation and validation of 3D knitted textiles. Applied Composite Materials, 25(4), 797–810. Citations: 43

Sakhaei, A. H., Kaijima, S., Lee, T. L., Tan, Y. Y., & Dunn, M. L. (2018). Design and investigation of a multi-material compliant ratchet-like mechanism. Mechanism and Machine Theory, 121, 184–197. Citations: 31

Gupta, S. S., Tan, Y. Y., Chia, P. Z., Pambudi, C. P., Quek, Y. H., Yogiaman, C., & Tracy, K. J. (2020). Prototyping knit tensegrity shells: A design-to-fabrication workflow. SN Applied Sciences, 2(6), 1062. Citations: 25

Do, H., Tan, Y. Y., Ramos, N., Kiendl, J., & Weeger, O. (2020). Nonlinear isogeometric multiscale simulation for design and fabrication of functionally graded knitted textiles. Composites Part B: Engineering, 202, 108416. Citations: 20

Gupta, U., Lau, J. L., Chia, P. Z., Tan, Y. Y., Ahmed, A., Tan, N. C., Soh, G. S., & Low, H. Y. (2023). All knitted and integrated soft wearable of high stretchability and sensitivity for continuous monitoring of human joint motion. Advanced Healthcare Materials, 12(21), 2202987. Citations: 17

Pal, A., Chan, W. L., Tan, Y. Y., Chia, P. Z., & Tracy, K. J. (2020). Knit concrete formwork. Proceedings of the 25th CAADRIA Conference, 1, 213–222. Citations: 7