Mohamed Khayri Rahmani | Data Science and Deep Learning | Best Researcher Award

Published on by Global Mechanics Awards

 

Best Researcher Award

Mohamed Khayri Rahmani
National School of Engineers in Sousse, Tunisia

Mohamed Khayri Rahmani
Affiliation National School of Engineers in Sousse
Country Tunisia
Subject Area Data Science and Deep Learning
Event Global Mechanics Awards
ORCID 0009-0007-8845-9703

Mohamed Khayri Rahmani is a researcher affiliated with the National School of Engineers in Sousse, Tunisia, whose identified subject area is Data Science and Deep Learning. This academic recognition profile presents the Best Researcher Award in the context of the Global Mechanics Awards and summarizes the research domain, potential scholarly contributions, and relevant academic resources associated with the researcher. The profile is intended to provide a structured overview of the recognition and its academic context.

Abstract

The Best Researcher Award profile recognizes Mohamed Khayri Rahmani of the National School of Engineers in Sousse, Tunisia, within the research area of Data Science and Deep Learning. These fields encompass computational approaches for extracting knowledge from data and developing machine-learning architectures capable of representation learning, prediction, classification, and related analytical tasks. Deep learning forms a major branch of contemporary machine learning and commonly employs multilayer neural-network architectures for learning complex patterns from structured and unstructured datasets.[1][2] This page organizes the available recognition information into an academic reference format and provides links to the researcher’s ORCID record and the Global Mechanics Awards website.

Keywords

  • Data Science
  • Deep Learning
  • Machine Learning
  • Artificial Intelligence
  • Neural Networks
  • Computational Research

Introduction

Data science combines statistical reasoning, computational methods, data management, and domain knowledge to derive useful information from datasets. Deep learning extends machine-learning methodologies through multilayer neural networks that can learn hierarchical representations from data.[1]

The rapid development of these approaches has created applications across scientific research, engineering, computer vision, natural-language processing, and other computational domains.[2]

Within this broader research landscape, the stated specialization of Mohamed Khayri Rahmani is Data Science and Deep Learning. The association with the National School of Engineers in Sousse places the researcher within an engineering-oriented academic environment in Tunisia. The present article therefore focuses on the relationship between the researcher’s identified field and the academic criteria commonly associated with research recognition.

Research Profile

Mohamed Khayri Rahmani is identified in the supplied academic information as a researcher in Data Science and Deep Learning. Data science research may include data preparation, statistical learning, predictive modeling, feature engineering, data-driven decision systems, and evaluation of computational models. Deep-learning research may involve neural-network architectures, representation learning, model optimization, and the application of learning systems to complex datasets.[1]

[2]

Researcher Mohamed Khayri Rahmani
Institution National School of Engineers in Sousse
Country Tunisia
Primary Subject Area Data Science and Deep Learning

Research Contributions

The stated research area provides a foundation for work involving computational analysis and learning from data. Relevant contributions in this field can include the development or evaluation of machine-learning methods, deep-neural-network architectures, data-processing pipelines, predictive models, and experimental frameworks. The academic assessment of such contributions generally considers methodological rigor, reproducibility, relevance to a defined research problem, and the dissemination of results through scholarly channels.[1]

[2]

  • Application of data-driven methods to scientific and engineering problems.
  • Development or evaluation of deep-learning models for complex data analysis.
  • Investigation of computational approaches for pattern recognition and predictive modeling.
  • Integration of machine-learning methodologies within interdisciplinary research contexts.

Publications

A verified publication list was not included in the supplied input data. Accordingly, specific publication titles, journal names, citation counts, and DOI identifiers are not attributed to Mohamed Khayri Rahmani in this section without independent bibliographic verification. The researcher’s ORCID record may be consulted as an authoritative identifier for locating associated scholarly works and distinguishing the researcher from other authors with similar names.

Where applicable, individual publications should be evaluated using bibliographic metadata such as author list, title, journal or conference, publication date, volume and issue, pages or article number, and DOI. DOI information should be reproduced only when it can be reliably matched to the corresponding scholarly work.

Research Impact

Research impact in Data Science and Deep Learning may be assessed through several complementary indicators, including scholarly publications, citation activity, methodological reuse, software or datasets where applicable, interdisciplinary adoption, and contributions to practical or scientific problems. Citation metrics alone do not fully characterize research quality and should be interpreted alongside the nature, rigor, and context of the underlying scholarly contributions.[1]

For Mohamed Khayri Rahmani, the supplied information establishes the researcher’s institutional affiliation and subject specialization but does not provide independently verified publication or citation metrics. Consequently, no quantitative impact claims are made here beyond the documented research-field information.

Award Suitability

The Best Researcher Award is presented in the supplied event information as part of the Global Mechanics Awards. Mohamed Khayri Rahmani’s identified specialization in Data Science and Deep Learning provides a clearly defined academic field for evaluating research activity. A formal award assessment should consider documented scholarly outputs, originality, methodological quality, research relevance, academic contributions, and independently verifiable evidence in accordance with the award’s published criteria.

The award designation should therefore be understood as an academic recognition associated with the stated event rather than as an independent measurement of research quality. Verification of eligibility and recognition details should be conducted using the official event information and the researcher’s persistent scholarly identifiers.

Conclusion

Mohamed Khayri Rahmani is identified as a researcher affiliated with the National School of Engineers in Sousse, Tunisia, with a subject specialization in Data Science and Deep Learning. The Best Researcher Award profile provides a structured academic overview of the research area and its relevance to the Global Mechanics Awards. Further assessment of research achievements should rely on verifiable scholarly records, publications, persistent identifiers, and the official award criteria.

References

  1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.
    https://doi.org/10.1038/nature14539
  2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
    https://www.deeplearningbook.org/
  3. Elsevier. (n.d.). Scopus author details: Mohamed Khayri Rahmani, Author ID not supplied. Scopus. A specific Scopus record is not included because no verified author ID was provided in the supplied information.
  4. ORCID. (n.d.). ORCID record for Mohamed Khayri Rahmani, ORCID iD 0009-0007-8845-9703.
    https://orcid.org/0009-0007-8845-9703
  5. Global Mechanics Awards. (n.d.). Official award website.
    https://globalmechanicsawards.com/

Yogesh Kumar | Data Science and Deep Learning | Best Researcher Award

Published on by Global Mechanics Awards

Best Researcher Award

Yogesh Kumar
Affiliation Pandit Deendayal Energy University, Gandhinagar
Country India
Scopus ID 57225085312
Documents 242
Citations 5,410 citations by 4,601 documents
h-index 40
Subject Area Data Science and Deep Learning
Event Global Mechanics Awards
Google Scholar 5OvNUVoAAAAJ
ORCID 0000-0002-2879-0441
Yogesh Kumar
Pandit Deendayal Energy University, Gandhinagar, India

Yogesh Kumar is an academic researcher affiliated with Pandit Deendayal Energy University, Gandhinagar, India. His scholarly work spans the interdisciplinary fields of Data Science and Deep Learning, with research contributions in intelligent computing, artificial intelligence, machine learning, healthcare analytics, and advanced computational methodologies. His publication record, citation impact, and sustained research productivity have established a recognized academic profile in data-driven technologies.[1] [2]

Abstract

This article summarizes the academic profile of Yogesh Kumar, emphasizing contributions to Data Science and Deep Learning through peer-reviewed publications, interdisciplinary research, and scientific collaboration. His work integrates computational intelligence with practical applications in engineering, healthcare, and data analytics while maintaining an active scholarly presence in international research communities.[1]

Keywords

Data Science, Deep Learning, Artificial Intelligence, Machine Learning, Neural Networks, Intelligent Systems, Healthcare Analytics, Pattern Recognition, Predictive Analytics, Scientific Computing.

Introduction

The rapid advancement of data-centric technologies has accelerated innovation across scientific and engineering disciplines. Researchers working in Data Science and Deep Learning contribute to predictive modeling, intelligent automation, computer vision, natural language processing, and decision-support systems. Yogesh Kumar has contributed to this evolving landscape through research that combines algorithmic development with practical implementation in multidisciplinary environments.[2]

Research Profile

According to publicly available academic databases, Yogesh Kumar has authored or co-authored 242 indexed publications and accumulated more than 5,410 citations, resulting in an h-index of 40. These indicators reflect sustained scholarly productivity and influence within the international research community. His research portfolio includes interdisciplinary collaborations involving artificial intelligence, deep learning, optimization techniques, medical image analysis, intelligent decision systems, and computational data analytics.[1] [3]

Research Contributions

Research contributions include the development of deep neural network architectures, machine learning models, intelligent healthcare systems, explainable artificial intelligence, image processing algorithms, and predictive analytics methodologies. These studies demonstrate practical applications across biomedical engineering, smart computing, and advanced information systems while contributing to the broader advancement of intelligent technologies.[4]

Publications

Representative publication themes include deep learning architectures, medical image analysis, explainable artificial intelligence, intelligent diagnosis, machine learning optimization, computer vision, biomedical data analytics, predictive healthcare systems, and advanced computational intelligence. Numerous articles have appeared in internationally indexed journals and conference proceedings with DOI registration.[4]

Research Impact

Bibliometric indicators demonstrate consistent scholarly influence through citations, collaborative publications, and interdisciplinary engagement. The combination of publication volume, citation performance, and recognized h-index indicates continued academic visibility and measurable research impact across Data Science and Deep Learning disciplines.[1]

Award Suitability

The academic profile of Yogesh Kumar demonstrates characteristics commonly associated with recognition through research excellence awards, including sustained publication output, significant citation impact, interdisciplinary scientific contributions, and active participation in advancing Data Science and Deep Learning. These attributes align with the objectives of the Global Mechanics Awards in recognizing impactful research achievements.[5]

Conclusion

Yogesh Kumar has established a notable academic presence through sustained contributions to Data Science and Deep Learning. His publication record, citation metrics, interdisciplinary collaborations, and ongoing research activities illustrate a strong commitment to scientific advancement and knowledge dissemination within the global research community.[1]

References

  1. Elsevier. (n.d.). Scopus Author Details: Yogesh Kumar, Author ID 57225085312.

    https://www.scopus.com/authid/detail.uri?authorId=57225085312
  2. Google Scholar. (n.d.). Scholar Profile of Yogesh Kumar.

    https://scholar.google.com/citations?user=5OvNUVoAAAAJ
  3. ORCID. (n.d.). ORCID Record: Yogesh Kumar.

    https://orcid.org/0000-0002-2879-0441
  4. Representative scholarly article on artificial intelligence and deep learning.

    https://doi.org/10.1016/j.future.2021.05.010
  5. Global Mechanics Awards. (n.d.). Official Award Information.

    Global Mechanics Awards


Hafeez Noor | Data Science and Deep Learning | Best Researcher Award

Published on by Global Mechanics Awards

Dr. Hafeez Noor | Data Science and Deep Learning | Best Researcher Award

Dryland Agriculture & Water Management at Institute of Functional Agriculture, Shanxi Agricultural University | China

Dr. Hafeez Noor is an accomplished agronomy scientist and international researcher specializing in crop physiology, nitrogen and water use efficiency, drought tolerance, and sustainable dryland agriculture, with extensive expertise in experimental design, field trials, greenhouse and laboratory management, advanced statistical analysis, and modern breeding approaches, actively contributing to high-impact peer-reviewed publications, interdisciplinary collaborations, graduate student mentorship, and innovative solutions for climate-resilient, resource-efficient cropping systems in semi-arid agroecosystems.


View ORCID Profile

Featured Publications

Tao Hu | Artificial Intelligence| Best Researcher Award

Published on by Global Mechanics Awards

Dr. Tao Hu | Artificial Intelligence | Best Researcher Award

The Affiliated Yuyao Yangming Hospital of Medical School of Ningbo University | China

Dr. Tao Hu is a highly accomplished medical professional and researcher from China, serving at The Affiliated Yuyao Yangming Hospital of the Medical School of Ningbo University, with specialization in thyroid surgery, breast surgery, and anorectal surgery. Having completed his doctoral education in health sciences, Dr. Hu has developed an expertise in combining surgical practice with advanced computational methods, particularly artificial intelligence and machine learning applications in clinical diagnostics and predictive modeling. His professional experience includes independently completing over surgical operations and contributing to multiple provincial-level scientific research projects, including support from the Zhejiang Health Information Association Research Program , which highlights his ability to bridge medical practice with innovative research applications. Dr. Hu’s research interests lie primarily in developing predictive tools that integrate clinical information data with artificial intelligence to forecast disease occurrence, progression, and postoperative risks, especially in thyroid carcinoma, where his recent work has introduced novel models for preoperative risk stratification and lymph node metastasis prediction. His research skills are demonstrated through proficiency in clinical data analysis, ultrasound imaging interpretation, radiomics, and the application of machine learning frameworks to enhance diagnostic accuracy and surgical decision-making. In recent years, Dr. Hu has published several impactful articles in high-quality, peer-reviewed journals such as Endocrine, Frontiers in Endocrinology, and the Journal of Clinical Ultrasound, marking him as a significant contributor to evidence-based surgical practices. While his awards and honors primarily reflect academic and clinical achievements, his recognition through this nomination underscores his growing international reputation as a leader in health sciences research. In conclusion, Dr. Hu’s blend of clinical excellence, innovative research in artificial intelligence applications, and dedication to improving surgical outcomes make him a highly deserving recipient of the Best Researcher Award, as his work holds great promise for advancing both scientific knowledge and patient care globally.

Profile:  Orcid

Featured Publications:

Hu, T., Cai, Y., Zhou, T., Zhang, Y., Huang, K., Huang, X., Qian, S., Wang, Q., & Luo, D. (2025). Machine learning‐based prediction of lymph node metastasis and volume using preoperative ultrasound features in papillary thyroid carcinoma. Journal of Clinical Ultrasound. Advance online publication.

Hu, T., Zhou, T., Zhang, Y., Zhou, L., Huang, X., Cai, Y., Qian, S., Huang, K., & Luo, D. (2024). The predictive value of the thyroid nodule benign and malignant based on the ultrasound nodule‐to‐muscle gray‐scale ratio. Journal of Clinical Ultrasound, 52(1).

Zhao, L., Hu, T., Cai, Y., Zhou, T., Zhang, W., Wu, F., Zhang, Y., & Luo, D. (2023). Preoperative risk stratification for patients with ≤ 1 cm papillary thyroid carcinomas based on preoperative blood inflammatory markers: Construction of a dynamic predictive model. Frontiers in Endocrinology, 14, 1254124.

Zhou, T., Xu, L., Shi, J., Zhang, Y., Lin, X., Wang, Y., Hu, T., Xu, R., Xie, L., & Sun, L., et al. (2023). US of thyroid nodules: Can AI-assisted diagnostic system compete with fine needle aspiration? European Radiology. Advance online publication.

Zhou, T., Hu, T., Ni, Z., Yao, C., Xie, Y., Jin, H., Luo, D., & Huang, H. (2023). Comparative analysis of machine learning-based ultrasound radiomics in predicting malignancy of partially cystic thyroid nodules. Endocrine. Advance online publication.

Afrah Yahya Al Rezami | Data Analysis | Best Scholar Award

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Assoc. Prof. Dr. Afrah Yahya Al Rezami | Data Analysis | Best Scholar Award

University professor at Prince Sattam bin Abdulaziz University, Saudi Arabia

Assoc. Prof. Dr. Afrah Yahya Mohammed Al Rezami is a Yemeni academic specializing in Applied Statistics, currently serving at the College of Science and Humanities in Al Aflaj, Prince Sattam Bin Abdulaziz University, Saudi Arabia. She earned her Ph.D. and M.A. in Statistics from Al-Mustansiriya University, Iraq, and holds a Bachelor’s degree in Statistics from Sana’a University, Yemen. With extensive experience in statistical analysis, research supervision, and academic leadership, Dr. Al Rezami has held various roles, including Head of the Measurement and Evaluation Department and Supervisor of the Scientific Research Unit. Her expertise includes performance indicators, educational evaluation, and statistical modeling, and she has taught a wide range of undergraduate and graduate-level courses. Dr. Al Rezami is also an active member of data and research committees and has participated in numerous training workshops related to data analysis and statistical software.

Professional Profile

Scopus

Orcid

Education

Assoc. Prof. Dr. Afrah Yahya Mohammed Al Rezami possesses a robust academic background in the field of statistics, spanning undergraduate to doctoral levels. She began her academic journey by earning a Bachelor’s degree in Statistics from Sana’a University, Yemen, in 1992, where she acquired foundational knowledge in statistical theory and quantitative analysis. Driven by her passion for the discipline, she pursued graduate studies at Al-Mustansiriya University in Iraq, obtaining her Master’s degree in Statistics in 2000. Her master’s work focused on enhancing her skills in data analysis, statistical modeling, and research methodologies. Building upon this, she continued her scholarly pursuits at the same university and was awarded a Ph.D. in Statistics in 2004. During her doctoral studies, she specialized in Applied Statistics, further strengthening her analytical capabilities and laying the groundwork for her future contributions in teaching, research, and institutional development.

Experience

Assoc. Prof. Dr. Afrah Yahya Mohammed Al Rezami has accumulated extensive academic and professional experience in the field of applied statistics, both in Yemen and Saudi Arabia. Her career began as an Instructor and later Assistant Professor in the Department of Statistics and Information at the College of Commerce and Economics, Sana’a University, Yemen. She transitioned to Saudi Arabia, where she joined Prince Sattam Bin Abdulaziz University (PSAU) in 2012, taking on multiple academic and administrative roles. At PSAU’s College of Science and Humanities in Al Aflaj, she has served as an Assistant Professor and currently holds the rank of Associate Professor in the Department of Mathematics. In addition to her teaching duties, Dr. Al Rezami has contributed significantly to academic development and quality assurance. She has served as the Head of the Measurement and Evaluation Department at the Applied College in Al Kharj, where she led efforts to assess academic programs and student performance. She is also the Supervisor of the Scientific Research Unit and an active member of the Data and Statistics Unit at the College of Humanities and Social Sciences. Her responsibilities have included overseeing statistical analysis for master’s and doctoral theses, evaluating institutional performance indicators, and participating in various workshops related to SPSS, Excel, Minitab, and Power BI. With a diverse teaching portfolio spanning statistical inference, linear programming, actuarial mathematics, and software-based analysis, Dr. Al Rezami continues to play a vital role in both instructional and institutional development at PSAU.

Research Interests

Assoc. Prof. Dr. Afrah Yahya Mohammed Al Rezami’s research interests are deeply rooted in the field of Applied Statistics, with a strong focus on data-driven approaches to support decision-making in education, institutional development, and social sciences. She is particularly engaged in educational measurement and evaluation, where she analyzes academic performance indicators and develops effective assessment strategies to enhance the quality of learning outcomes. Dr. Al Rezami is also skilled in statistical modeling and multivariate data analysis, supporting graduate students and faculty through the design and interpretation of complex datasets in master’s and doctoral research. Her interest in statistical software applications such as SPSS, Excel, Minitab, and Power BI reflects her dedication to practical analytics and modern data visualization techniques. In addition, she explores areas such as risk analysis, actuarial mathematics, and probability theory, applying these tools to real-world challenges in education and beyond. Her interdisciplinary approach allows her to contribute to both academic research and institutional improvement through informed statistical insight.

Top Noted Publications

Bayesian Estimation of the Pareto Model Based on Type-II Censoring Data by Employing Non-linear Programming

  • Authors: L.A. Al-Essa, F.S. Al-Duais, W. Aydi, A.Y. Al-Rezami
  • Journal: Alexandria Engineering Journal
  • Year: 2024
  • DOI: 10.1016/j.aej.2023.12.051
  • EID: 2-s2.0-85181767525
  • ISSN: 1110-0168
  • Publisher: Elsevier
  • Scope: Bayesian inference methods applied to censored Pareto distributions using non-linear optimization techniques.

Defining and Analyzing New Classes Associated with (λ,γ)-Symmetrical Functions and Quantum Calculus

  • Authors: H. Louati, A.Y. Al-Rezami, A.A. Darem, F. Alsarari
  • Journal: Mathematics (MDPI)
  • Year: 2024
  • DOI: 10.3390/math12162603
  • EID: 2-s2.0-85202574837
  • ISSN: 2227-7390
  • Publisher: MDPI
  • Scope: Introduces function classes based on symmetrical properties within the framework of quantum calculus.

Diagnostic Power of Some Graphical Methods in Geometric Regression Model Addressing Cervical Cancer Data

  • Authors: Z. Hussain, A. Akbar, M.M.A. Almazah, A.Y. Al-Rezami, F.S. Al-Duais
  • Journal: AIMS Mathematics
  • Year: 2024
  • DOI: 10.3934/math.2024198
  • EID: 2-s2.0-85182243851
  • ISSN: 2473-6988
  • Publisher: AIMS Press
  • Scope: Evaluates graphical techniques in diagnostic modeling for real-world biomedical data, particularly in cancer prediction.

Exploring Quasi-Probability Husimi-Distributions in Nonlinear Two Trapped-Ion Qubits: Intrinsic Decoherence Effects

  • Authors: L.A. Al-Essa, A.Y. AL-Rezami, F.M. Aldosari, A.-B.A. Mohamed, H. Eleuch
  • Journal: Optical and Quantum Electronics
  • Year: 2024
  • DOI: 10.1007/s11082-024-06284-z
  • EID: 2-s2.0-85183574852
  • ISSN: 0306-8919 (print), 1572-817X (electronic)
  • Publisher: Springer
  • Scope: Theoretical study on decoherence in quantum qubit systems using Husimi quasi-probability distributions.

Integration of Three Drought Indices Based on Triple Collocation and Multi-Scalar Weighted Amalgamated Drought Index

  • Authors: Z. Badar, M.M.A. Almazah, M.A. Raza, I. Hussain, F.S. Al-Duais, A.Y. Al-Rezami
  • Journal: Stochastic Environmental Research and Risk Assessment
  • Year: 2024
  • DOI: 10.1007/s00477-023-02623-w
  • EID: 2-s2.0-85179359120
  • ISSN: 1436-3240 (print), 1436-3259 (electronic)
  • Publisher: Springer
  • Scope: Combines drought indices using a novel statistical method for improved environmental risk modeling.

Conclusion

Given her sustained excellence in research, commitment to teaching, and contributions to statistical education and institutional evaluation, Dr. Afrah Yahya Mohammed AL Rezami is exceptionally well-suited for the Best Scholar Award. Her leadership, academic rigor, and impactful service to higher education mark her as a role model in the field of applied statistics.