Samaneh Eftekhari Mahabadi | Data Science and Analytics | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Samaneh Eftekhari Mahabadi
University of Tehran, Iran

Samaneh Eftekhari Mahabadi
Affiliation University of Tehran
Country Iran
Google Scholar ID nKYIHE8AAAAJ&hl
Documents 47
Citations 238
h-index 8
Subject Area Data Science and Analytics
Event Computer Scientists Awards
ORCID 0000-0001-8938-5864

Samaneh Eftekhari Mahabadi is a researcher affiliated with the University of Tehran whose reported scholarly record includes work in statistical modelling, missing-data methodology, machine learning, longitudinal inference, and applications involving clinical and financial data. The documented publication portfolio provides a basis for academic recognition in data science and analytics.

Abstract

The research record associated with Samaneh Eftekhari Mahabadi spans statistical computation, machine learning, missing-data imputation, competing-risks analysis, and Bayesian sensitivity analysis. Her publications address methodological questions that arise when incomplete, masked, or non-ignorable data affect statistical inference. These topics are relevant to modern data science because reliable analysis increasingly depends on methods capable of accounting for uncertainty and incomplete observations. [1]

Keywords

Data science; statistical modelling; machine learning; missing data; multiple imputation; Bayesian inference; longitudinal analysis; competing risks; sensitivity analysis; clinical trial data.

Introduction

Contemporary statistical research frequently combines computational techniques with rigorous inferential frameworks. Mahabadi’s documented publications reflect this intersection, particularly through supervised learning approaches to missing-data imputation and machine-learning methods for masked competing-risks data. [2] Such work contributes to methodological discussions concerning the validity and robustness of conclusions derived from incomplete datasets.

Research Profile

The supplied scholarly record reports 47 documents, 238 citations, and an h-index of 8. The stated subject area is Data Science and Analytics. Her research profile is characterized by methodological statistics and applications involving complex datasets, with publications appearing in journals and scholarly book collections. [1]

Research Contributions

  • Development and evaluation of approaches for missing-data imputation using supervised learning methods. [4]
  • Application of machine-learning algorithms to masked competing-risks data. [2]
  • Bayesian second-order sensitivity analysis addressing non-ignorability in longitudinal inference. [1]
  • Analysis of monetary policy and Islamic-bank stability under differing governance models. [3]

Publications

  1. Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data. The International Journal of Biostatistics, 2024.  [1]
  2. Multiple imputation of masked competing risks data using machine learning algorithms. Journal of Statistical Computation and Simulation, 2022. [2]
  3. The Effects of Monetary Policy on the Stability of Islamic Banks with Different Governance Models: Case of Islamic Republic of Iran. 2021. [3]
  4. Missing data imputation using supervised learning methods. Journal of Statistical Modelling: Theory and Applications, 2021.  [4]

Research Impact

The reported citation count and h-index indicate that the publication record has received scholarly citations. More specifically, the documented research addresses recurring challenges in statistical analysis where missingness, masking, competing risks, or non-ignorability can influence inference. The supplied profile reports 238 citations across 47 documents, providing quantitative context for the recognition of this research record.

Award Suitability

For the Innovative Research Award, the documented combination of statistical methodology, machine learning, and applied research provides relevant evidence for consideration. The publication portfolio demonstrates engagement with methodological problems and their applications rather than relying on a single research theme. Final award decisions remain subject to the criteria and review procedures of the Computer Scientists Awards.

Conclusion

Samaneh Eftekhari Mahabadi’s documented research profile reflects sustained scholarly activity in data science and analytics, with particular emphasis on statistical inference, machine learning, and incomplete-data methodology. The cited publications provide a structured basis for evaluating her research contributions in the context of an academic recognition program.

References

  1. Eftekhari Mahabadi, S. et al. (2024). Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data. The International Journal of Biostatistics.
    https://doi.org/10.1515/ijb-2022-0014
  2. Eftekhari Mahabadi, S. et al. (2022). Multiple imputation of masked competing risks data using machine learning algorithms. Journal of Statistical Computation and Simulation.
    https://doi.org/10.1080/00949655.2022.2063864
  3. Eftekhari Mahabadi, S. et al. (2021). The Effects of Monetary Policy on the Stability of Islamic Banks with Different Governance Models: Case of Islamic Republic of Iran. Monetary Policy, Islamic Finance, and Islamic Corporate Governance.
    https://doi.org/10.1108/978-1-80043-786-920211009
  4. Eftekhari Mahabadi, S. (2021). Missing data imputation using supervised learning methods. Journal of Statistical Modelling: Theory and Applications.
    http://jsm.yazd.ac.ir/article_2049.html
  5. ORCID. (n.d.). Samaneh Eftekhari Mahabadi, ORCID record.
    https://orcid.org/0000-0001-8938-5864
  6. Computer Scientists Awards. (n.d.). Computer Scientists Awards official website.
    https://computerscientists.net/

Amr A. Mohy | Data Science and Analytics | Best Researcher Award

Published on by Computer Scientists Awards

Best Researcher Award

Amr A. Mohy
Affiliation Arab Academy for Science, Technology & Maritime Transport
Country Egypt
Scopus ID 57924030800
Documents 6
Citations 21
h-index 3
Subject Area Data Science and Analytics
Event Computer Scientists Awards
ORCID 0009-0004-6017-611X

Amr A. Mohy

Arab Academy for Science, Technology & Maritime Transport, Egypt

Amr A. Mohy is an emerging researcher whose scholarly activities focus on data science, artificial intelligence, construction engineering analytics, and computational decision-support systems. His research portfolio reflects interdisciplinary applications of machine learning, graph neural networks, computer vision, and predictive analytics to improve safety, cost estimation, procurement, and operational efficiency within construction engineering and management. With publications indexed in international scholarly databases and a growing citation record, his work contributes to the integration of intelligent analytical methods into engineering practice.[1]

Abstract

This article presents a concise academic overview of Amr A. Mohy’s research achievements and evaluates their relevance to the Best Researcher Award. His publications demonstrate growing expertise in intelligent construction systems, machine learning, deep learning, graph attention networks, predictive analytics, and uncertainty quantification. Recent studies investigate construction safety, procurement optimization, and cost prediction while combining engineering knowledge with modern data-driven methodologies. These contributions illustrate an interdisciplinary research direction aligned with contemporary developments in data science and digital engineering.[2]

Keywords

Data Science, Construction Analytics, Machine Learning, Computer Vision, Graph Attention Networks, Safety Management, Predictive Modeling, Research Evaluation.

Introduction

Modern engineering increasingly depends on artificial intelligence and analytical computing for solving practical challenges involving safety, scheduling, procurement, and project management. Amr A. Mohy’s research reflects this transition by applying advanced computational methods to complex construction environments. His work emphasizes evidence-based decision making, interpretable predictive models, and scalable analytical frameworks capable of supporting infrastructure management while encouraging interdisciplinary collaboration between engineering and computer science.[3]

Research Profile

According to available bibliometric indicators, the researcher has produced six indexed publications, accumulated twenty-one citations, and achieved an h-index of three. His scholarly activity centers on data-driven engineering applications, particularly machine learning, construction informatics, safety analytics, and optimization. These metrics indicate an active and developing research trajectory supported by internationally accessible publications.[1]

Research Contributions

  • Developed graph attention network models for spatiotemporal hazard prediction in construction safety.
  • Investigated deep learning and computer vision techniques for intelligent safety management.
  • Proposed machine learning frameworks for construction cost prediction and uncertainty estimation.
  • Contributed hybrid reinforcement learning approaches for strategic procurement optimization.

Publications

  • Meta-Analytical and Scientometric Review of Literature in Construction Engineering and Management.
  • Modeling Spatiotemporal Hazard Dynamics for Construction Safety Using Graph Attention Networks.
  • Improving Construction Cost Prediction and Uncertainty Quantification with a Machine Learning Imputation Framework.
  • Innovations in Safety Management for Construction Sites: The Role of Deep Learning and Computer Vision Techniques.

Research Impact

Although still at an early stage of scholarly development, the research portfolio demonstrates measurable scientific visibility through citations, interdisciplinary publications, and practical engineering applications. The combination of computational intelligence, predictive modeling, and construction analytics supports broader digital transformation initiatives within infrastructure engineering and promotes reproducible analytical methodologies.[4]

Award Suitability

The Best Researcher Award recognizes sustained scholarly excellence, research quality, innovation, and disciplinary impact. Amr A. Mohy’s interdisciplinary publications demonstrate meaningful contributions to intelligent engineering systems and applied data science. His emphasis on solving real-world engineering challenges through artificial intelligence aligns with the objectives of the Computer Scientists Awards and highlights continued potential for future academic advancement.[5]

Conclusion

Amr A. Mohy’s publication record illustrates an expanding research program integrating machine learning with engineering management and construction analytics. His scholarly output contributes to safer, smarter, and more efficient engineering practices while demonstrating an evidence-based approach to scientific inquiry. Continued publication activity and collaboration are expected to strengthen both academic visibility and practical impact across data-driven engineering disciplines.

References

  1. Elsevier. (n.d.). Scopus author details: Amr A. Mohy, Author ID 57924030800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57924030800
  2. Mohy, A. A. (2026). Meta-Analytical and Scientometric Review of Literature in Construction Engineering and Management.
    https://doi.org/10.31224/7370
  3. Mohy, A. A. (2026). Modeling Spatiotemporal Hazard Dynamics for Construction Safety Using Graph Attention Networks.
    https://doi.org/10.1108/jedt-12-2025-0720
  4. Mohy, A. A. (2026). Improving Construction Cost Prediction and Uncertainty Quantification with a Machine Learning Imputation Framework.
    https://doi.org/10.31224/7294
  5. Mohy, A. A. (2026). Innovations in Safety Management for Construction Sites: The Role of Deep Learning and Computer Vision Techniques.
    https://doi.org/10.1108/CI-04-2023-0062

Rania Sefti | Data Science | Best Researcher Award

Published on by Computer Scientists Awards

Ms. Rania Sefti | Data Science | Best Researcher Award

Phd student, Université Mohammed Premier Oujda, Morocco

Sefti Rania is a passionate researcher specializing in numerical analysis, optimization, and image processing. With a robust academic background and extensive teaching experience, she is currently pursuing a Ph.D. in a joint program between Morocco and France. Her research focuses on developing advanced methods for medical image segmentation using deep learning techniques.

Profile

Scopus

 

Education 🎓

Ph.D. in Mathematics and Computer Science (Specialization: Numerical Analysis and Optimization, Image Processing, Deep Learning), Mohammed First University, Oujda, Morocco, University of Orleans, France (Since 2020). Master in Numerical Analysis and Optimization (Honors: Good), Mohammed First University, Oujda, Morocco (2019). Bachelor’s Degree in Mathematical Sciences and Applications (Honors: Fairly Good), Mohammed First University, Oujda, Morocco (2017). High School Diploma in Experimental Sciences (Honors: Good), Ibn El Haytam High School, Nador, Morocco (2012)

Experience 💼

Adjunct Lecturer at Mohammed First University, Oujda, Morocco (2020 – Present). Higher School of Technology (Specialty: MCT and LPMI). Faculty of Sciences (Specialty: SVT and SMPC). Modules taught include Mathematics and Analysis with a total of over 200 hours of instruction. Reviewer for numerous articles in Mathematics and Computer Science since 2022

Research Interests 🔬

Numerical Analysis and Optimization, Image Processing, Deep Learning, Medical Image Segmentation.

Awards 🏆

Numerous Publications in renowned journals and conferences in the field of numerical analysis and optimization. Presentation Awards for contributions at international conferences such as MACMAS, NT2A, and SMAI-SIGMA

Publications

A CNN-based spline active surface method with an after-balancing step for 3D medical image segmentation, Mathematics and Computers in Simulation. Link – Cited by:

C2 composite spline methods for fitting data on the sphere, Springer special volume of the SEMA-SIMAI Springer Series. (Accepted in June 2023) – Cited by:

PID-Snake: Progressive Iterative Deformation of a Snake model for segmentation of a variety of images, Journal of Computational and Applied Mathematics. (Submitted in June 2024) – Cited by:

Fine-tuned cubic generalized composite spline interpolation with optimal parameter, Mathematics in Computer Science. (Submitted in June 2024) – Cited by:

A deep network-based spline active contour method for medical image segmentation, Springer special volume of the SEMA-SIMAI Springer Series. (Submitted in 2024) – Cited by: