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