Fareeha Anwar | Computer Science | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Fareeha Anwar
Imam Mohammad Ibn Saud Islamic University, Saudi Arabia

Fareeha Anwar
Affiliation Imam Mohammad Ibn Saud Islamic University
Country Saudi Arabia
Scopus ID 36197820200
Documents 5
Citations 145
h-index 5
Subject Area Computer Science
Event Environmental Scientists
ORCID 0000-0002-6993-7761

Fareeha Anwar is a researcher affiliated with Imam Mohammad Ibn Saud Islamic University whose scholarly work contributes to the field of Computer Science. Her publication record, citation profile, and international research visibility demonstrate continuing academic engagement and provide a foundation for professional recognition within multidisciplinary scientific communities. reflecting a commitment to solving complex real-world challenges through innovative technologies. Her publication record, citation profile, and international research visibility demonstrate sustained academic engagement and an active contribution to multidisciplinary scientific research.[1]

Abstract

This article summarizes the academic profile of Fareeha Anwar and highlights her research achievements, scholarly publications, and citation performance. The profile reflects measurable scientific activity in Computer Science and recognizes contributions through internationally indexed research outputs.[2]

Keywords

Computer Science, Artificial Intelligence, Academic Research, Scopus, Scholarly Publications, Citation Analysis, Research Excellence, Best Researcher Award.

Introduction

Academic excellence is evaluated through research quality, publication impact, and continued scholarly engagement. International indexing services provide transparent indicators that support the recognition of researchers across scientific disciplines.[1]

Research Profile

Fareeha Anwar has established a research profile supported by indexed publications and growing citation metrics. Her academic activities demonstrate sustained participation in Computer Science research while contributing to collaborative scientific advancement.[3]

Research Contributions

Her publications address contemporary computational topics and reflect an emphasis on knowledge development within Computer Science. These contributions support the dissemination of research findings through peer-reviewed scholarly communication.[4]

Publications

The available publication record includes five Scopus-indexed documents with accumulated citations that demonstrate academic visibility. Persistent citation activity indicates continued relevance and accessibility within the international research community.[1]

Research Impact

With 145 citations and an h-index of 5, the available bibliometric indicators suggest measurable research influence. These metrics provide objective evidence of scholarly recognition and support broader academic evaluation processes.[2]

Award Suitability

Based on her documented publication history, citation performance, and institutional affiliation, Fareeha Anwar demonstrates characteristics commonly considered during research excellence evaluations. These achievements align with the objectives of the Environmental Scientists Best Researcher Award program.[5]

Conclusion

Fareeha Anwar’s academic profile reflects continued scholarly productivity and measurable research impact within Computer Science. Her documented achievements represent a solid foundation for academic recognition and future research development.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Fareeha Anwar, Author ID 36197820200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=36197820200
  2. ORCID. (n.d.). Fareeha Anwar ORCID Record.
    https://orcid.org/0000-0002-6993-7761
  3. Google Scholar. (n.d.). Fareeha Anwar Citation Profile.
    https://scholar.google.com/citations?hl=en&user=7zlArcAAAAAJ
  4. Abdullah, S., Anwar, F., & Fatima, M. (2026). Spiking neural networks for real-time mapping of EBV-infected B cells in neuroinflammatory lesions. Annals of Medicine & Surgery. Advance online publication.
    https://doi.org/10.1097/MS9.0000000000005088
  5. Mohammad, U. G., Imtiaz, S., Shakya, M., Almadhor, A., & Anwar, F. (2022). An optimized feature selection method using ensemble classifiers in software defect prediction for healthcare systems. Wireless Communications and Mobile Computing, 2022, Article 1028175
    https://onlinelibrary.wiley.com/doi/10.1155/2022/1028175

Zulqurnain Sabir | Artificial Neural Networks | Best Researcher Award

Published on by Environmental Scientists

Best Researcher Award

Zulqurnain Sabir
Lebanese American University, Lebanon

Zulqurnain Sabir
Affiliation Lebanese American University
Country Lebanon
Scopus ID 56184182600
Documents 301
Citations 8494
h-index 54
Subject Area Artificial Neural Networks
Event Environmental Scientists
ORCID 0000-0001-7466-6233

Zulqurnain Sabir is a researcher affiliated with Lebanese American University whose scholarly work has contributed to the advancement of artificial neural networks and computational mathematics. His publication record, citation impact, and sustained research productivity demonstrate continuous academic engagement and international visibility within interdisciplinary scientific research.[1]

Abstract

This article summarizes the academic profile of Zulqurnain Sabir, highlighting his sustained research activity, publication performance, and scholarly influence in artificial neural networks. His scientific contributions reflect consistent engagement with computational methodologies and interdisciplinary applications.[1]

Keywords

Artificial neural networks, computational mathematics, numerical analysis, scientific computing, differential equations, optimization, machine learning, research excellence.

Introduction

Modern computational research increasingly integrates artificial intelligence with mathematical modelling to address complex scientific problems. Zulqurnain Sabir has contributed to this evolving field through peer-reviewed publications and collaborative academic research.[2]

Research Profile

The research profile demonstrates extensive publication activity supported by strong citation metrics and a high h-index. These indicators suggest sustained scholarly productivity and recognition within the international research community.[1]

Research Contributions

His work emphasizes artificial neural networks, numerical techniques, and mathematical optimization for solving engineering and scientific challenges. These studies support methodological improvements across computational science and applied mathematics.[3]

Publications

With more than 300 indexed publications, the research portfolio reflects consistent authorship in reputable international journals. The publication record illustrates long-term commitment to scientific dissemination and collaborative investigation.[1]

Research Impact

Citation performance exceeding eight thousand references demonstrates broad academic visibility and continued influence among researchers. Such impact indicates that the published work has contributed to ongoing developments in computational research.[4]

Award Suitability

The combination of research productivity, measurable citation impact, and international publication activity supports recognition for research excellence. These achievements align with the objectives of the Environmental Scientists recognition program in acknowledging sustained academic contributions.[5]

Conclusion

Zulqurnain Sabir has established a well-documented scholarly profile through continuous publication, citation growth, and interdisciplinary research. His academic achievements reflect meaningful contributions to computational science and artificial neural network research.[1]

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Zulqurnain Sabir, Author ID 56184182600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=56184182600
  2. ORCID. (n.d.). Zulqurnain Sabir ORCID record.
    https://orcid.org/0000-0001-7466-6233
  3. Sabir, Z., Muhammad, N., Zhang, S., & Khan, I. (2026). Hybrid radial basis and log-sigmoid neural network using Rprop for dengue–COVID-19 co-infection dynamics. SSRN Electronic Journal (Preprint).
    https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6910764
  4. Sabir, Z., Kobba, T., & Fadel, H. (2026). A computational design-based scale conjugate neural network to solve the nonlinear Rabinovich–Fabrikant model. Journal of Circuits, Systems and Computers. Advance online publication
    https://www.worldscientific.com/doi/10.1142/S0218126626501203
  5. Sabir, Z., Bichbich, I., Umar, M., Salahshour, S., & Bayram, M. (2026). An artificial neural network based on radial basis methodology using delay effects in the Parkinson’s disease model. Computational Biology and Chemistry, 115, Article 109033.

Khalil Abdelnaby | Computer Science | Research Excellence Award

Published on by Environmental Scientists

Mr. Vivek Dwivedi | Computer Science | Research Excellence Award

Al-Ahliyya Amman university | Jordan

Dr. Khalil Mohamed Khalil AbdElnaby is a researcher in Systems and Computers Engineering with expertise in Artificial Intelligence, cloud robotics, cybersecurity, embedded systems, IoT, and intelligent communication technologies. His research contributions focus on deep learning, network intrusion detection, hardware trojan detection, cloud computing, FPGA systems, and optimization techniques for intelligent engineering applications. He has authored and co-authored more than 10 scientific publications in reputable international journals and conferences. His research profile has achieved over 100 citations with an h-index of 5, reflecting the growing academic impact and relevance of his contributions to advanced engineering and AI-driven technologies.

Professional Profiles 

Education Background

Vivek Dwivedi | Computer Science | Research Excellence Award

Published on by Environmental Scientists

Mr. Vivek Dwivedi | Computer Science | Research Excellence Award

Research Scholar | The University of Slovak University of Technology | Slovakia

Mr. Vivek Dwivedi is an emerging researcher in the field of Computer Science, specializing in machine learning, robotics, and intelligent computational systems. His research emphasizes the development of real-time applications using computer vision, natural language processing, and advanced programming frameworks. He has worked on innovative solutions such as adaptive multi-camera systems for virtual environments and intelligent robotic mechanisms, showcasing strong technical expertise and research potential. With 12 published documents, 25 citations, and an h-index of 3, his contributions reflect steady academic growth and relevance. His work aims to bridge the gap between theoretical research and practical implementation, contributing to advancements in automation, smart technologies, and next-generation digital systems that address real-world challenges.

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