Mohammad Sadegh Taskhiri | Data Science | Most Cited Researcher Award

Most Cited Researcher Award

Mohammad Sadegh Taskhiri
Affiliation La Trobe University
Country Australia
Scopus ID 36683248800
Documents 34
Citations 776
h-index 15
Subject Area Data Science
Event The Scientist Global Awards
ORCID 0000-0002-9871-361X

Mohammad Sadegh Taskhiri
La Trobe University

The Most Cited Researcher Award recognizes researchers whose scholarly publications have demonstrated sustained academic influence through citation performance, research quality, interdisciplinary collaboration, and contributions to advancing scientific knowledge. Mohammad Sadegh Taskhiri of La Trobe University has established an active research profile in the field of Data Science, contributing to computational methodologies, intelligent data analysis, and applied research with measurable scholarly impact. His publication record, citation metrics, and international visibility collectively reflect continued engagement with globally relevant scientific challenges.[1]

Abstract

Citation performance represents one of the internationally recognized indicators of scholarly influence and research dissemination. The Most Cited Researcher Award highlights sustained scientific contributions that have achieved broad visibility within the academic community. Mohammad Sadegh Taskhiri’s research portfolio demonstrates engagement in Data Science through computational intelligence, machine learning applications, data analytics, and interdisciplinary research collaborations. His publication metrics, including 34 indexed documents, 776 citations, and an h-index of 15, indicate consistent academic recognition across the scientific literature.[1]

Keywords

Data Science, Machine Learning, Artificial Intelligence,Computational Intelligence,Data Analytics, Scientific Impact, Research Metrics, Citation Analysis

Introduction

Modern Data Science combines statistics, artificial intelligence, optimization, computational modeling, and domain expertise to address complex scientific and industrial problems. Researchers working in this field contribute to evidence-based decision making, predictive analytics, intelligent automation, and digital transformation. Recognition through citation-based awards acknowledges research that has achieved measurable academic visibility while supporting continued scientific advancement.[2]

Research Profile

Mohammad Sadegh Taskhiri is affiliated with La Trobe University, Australia. His scholarly activities encompass Data Science, intelligent computational systems, analytical modeling, and interdisciplinary applications of machine learning. Through peer-reviewed publications and collaborative research, his work contributes to expanding scientific understanding while supporting practical applications across multiple sectors. Bibliometric indicators available through Scopus demonstrate an established international research presence.[1]

Research Contributions

Development of computational approaches for data-driven scientific investigations. Application of machine learning methodologies to complex analytical problems. Promotion of interdisciplinary collaboration integrating data analytics with applied sciences. Contribution to peer-reviewed literature supporting reproducible and evidence-based research. Advancement of intelligent data processing techniques for practical research applications.

Publications

The researcher’s scholarly portfolio includes 34 Scopus-indexed publications covering topics within Data Science and related computational disciplines. The published work has accumulated significant citation activity, reflecting continued utilization by researchers across multiple scientific domains. Representative publications are indexed through major academic databases and include articles with DOI registration supporting long-term scholarly accessibility.[3]

Research Impact

Research impact is evaluated through publication quality, citation performance, scholarly visibility, and influence on subsequent investigations. Citation metrics associated with Mohammad Sadegh Taskhiri indicate sustained engagement by the international research community. These quantitative indicators complement qualitative contributions including interdisciplinary collaboration, scientific dissemination, and advancement of analytical methodologies within Data Science.[1]

Award Suitability

The Most Cited Researcher Award recognizes measurable scholarly influence supported by objective bibliometric evidence. Based on the documented publication record, citation count, h-index, and active contribution to Data Science research, Mohammad Sadegh Taskhiri demonstrates characteristics aligned with citation-based academic recognition. Evaluation for this award may additionally consider research quality, scientific integrity, international collaboration, and continuing impact on the broader research community.[1]

Conclusion

The scholarly profile presented here summarizes the documented academic achievements of Mohammad Sadegh Taskhiri within the field of Data Science. Bibliometric indicators, peer-reviewed publications, and research visibility collectively illustrate continued participation in international scientific research. Recognition through the Most Cited Researcher Award reflects objective academic performance while encouraging ongoing excellence in research, innovation, and knowledge dissemination.[2]

References

  1. Elsevier (2026). Scopus author details: Mohammad Sadegh Taskhiri, Author ID 36683248800. Scopus.
    https://www.scopus.com/pages/authors/36683248800
  2. The role of agricultural biomass in supply chain decarbonization.
    https://doi.org/10.1007/s10479-024-05979-6
  3. Crossref (2026). Economic impact of recycled and bioplastic packaging production in Australia – A Monte Carlo simulation model.
    https://doi.org/10.1016/j.procs.2021.01.001

Ahmed Hamza Osman Ahmed | Data Science | Best Researcher Award

Prof. Dr. Ahmed Hamza Osman Ahmed | Data Science | Best Researcher Award

Professor of Computer Science | King Abdulaziz University | Saudi Arabia

Prof. Dr. Ahmed Hamza Osman Ahmed is a distinguished computer scientist and cybersecurity expert whose research bridges artificial intelligence, information security, and data privacy. With over 70 peer-reviewed publications in prestigious journals such as IEEE, Elsevier, and Springer, his scholarly impact is evidenced by 709 citations across 658 documents and an h-index of 14, underscoring his significant contributions to the field. His research encompasses AI-driven cybersecurity systems, intrusion detection, digital forensics, and blockchain-based data integrity, with several funded projects advancing intelligent threat prediction and misinformation detection. Prof. Ahmed has played a pivotal role in developing ABET-aligned curricula, integrating machine learning into cybersecurity education, and supervising more than 25 postgraduate theses in cybersecurity and data science. Internationally recognized for academic excellence, he has received awards such as the Gold Medal at PECIPTA 2011 and Best Postgraduate Student at Universiti Teknologi Malaysia. His extensive collaborations across Saudi Arabia, Malaysia, and Sudan reflect his commitment to fostering global research partnerships and advancing secure, AI-empowered digital ecosystems. Through his leadership in teaching, research, and academic service, Prof. Ahmed continues to contribute to shaping the future of cybersecurity and artificial intelligence with profound educational and societal impact.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Elssied, N. O. F., Ibrahim, O., & Osman, A. H. (2014). A novel feature selection based on one-way ANOVA F-test for e-mail spam classification. Research Journal of Applied Sciences, Engineering and Technology, 7(3), 625–638. Citations: 223

  2. Elhadi, A. A. E., Maarof, M. A., & Osman, A. H. (2012). Malware detection based on hybrid signature behaviour application programming interface call graph. American Journal of Applied Sciences, 9(3), 283–293. Citations: 137

  3. Osman, A. H., Salim, N., Binwahlan, M. S., Alteeb, R., & Abuobieda, A. (2012). An improved plagiarism detection scheme based on semantic role labeling. Applied Soft Computing, 12(5), 1493–1502. Citations: 128

  4. Osman, A. H., & Aljahdali, H. M. (2020). An effective ensemble boosting learning method for breast cancer virtual screening using neural network model. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2976149 Citations: 93

  5. Abuobieda, A., Salim, N., Albaham, A. T., Osman, A. H., & Kumar, Y. J. (2012). Text summarization features selection method using pseudo genetic-based model. In Proceedings of the 2012 International Conference on Information Retrieval & Knowledge Management (pp. 84–89). Citations: 84