Dabiah Alboaneen | Computer Science and Artificial Intelligence | Innovative Research Award

Innovative Research Award

Dabiah Alboaneen
Imam Abdulrahman bin Faisal University

Dabiah Alboaneen
Affiliation Imam Abdulrahman bin Faisal University
Country Saudi Arabia
Scopus ID 56530765900
Documents 30
Citations 553
h-index 13
Subject Area Computer Science and Artificial Intelligence
Event Global Best Achievements Awards
ORCID 0000-0003-2215-9963

Dabiah Alboaneen is a researcher affiliated with Imam Abdulrahman bin Faisal University in Saudi Arabia whose academic profile is situated within Computer Science and Artificial Intelligence. Her scholarly record reflects research activity supported by internationally indexed publications, citation performance, and continued engagement with computational research. The documented profile includes 30 research documents, 553 citations, and an h-index of 13, providing measurable indicators of scholarly visibility and research productivity.[1]

Abstract

Dabiah Alboaneen is a computer science researcher affiliated with Imam Abdulrahman bin Faisal University in Saudi Arabia, with an academic profile focused on Computer Science and Artificial Intelligence. Her research profile demonstrates sustained scholarly activity supported by indexed research publications and measurable citation influence. The supplied academic record identifies 30 documents, 553 citations, and an h-index of 13 under Scopus Author ID 56530765900. Her ORCID identifier provides an additional persistent connection to her scholarly identity. The combination of research productivity, citation visibility, and engagement with artificial intelligence provides an academic foundation for consideration for the Innovative Research Award in recognition of scholarly contribution.

Keywords

Artificial Intelligence, Computer Science, Machine Learning, Deep Learning, Data Science, Intelligent Systems, Computational Research, Research Innovation, Predictive Analytics, Natural Language Processing, Scholarly Communication, Saudi Arabia.

Introduction

Computer Science and Artificial Intelligence represent rapidly developing research fields that integrate computational methods, data-driven analysis, intelligent algorithms, and emerging technologies. Research within these disciplines contributes to the development of systems capable of supporting complex analytical and decision-making tasks. Within this academic context, Dabiah Alboaneen’s profile reflects sustained engagement with computer science and artificial intelligence research, supported by an internationally identifiable scholarly record and measurable bibliometric indicators.[1]

Research Profile

The research profile of Dabiah Alboaneen demonstrates sustained academic productivity within Computer Science and Artificial Intelligence. Her institutional affiliation with Imam Abdulrahman bin Faisal University places her research within a major academic environment in Saudi Arabia. The supplied Scopus record identifies 30 documents, 553 citations, and an h-index of 13, while ORCID provides a persistent researcher identifier for scholarly attribution and discovery.[1] [2]

Research Contributions

Alboaneen’s academic contribution is positioned within the broader development of artificial intelligence and computational research. Her research profile provides evidence of continuing scholarly activity and publication output in a field characterized by rapid methodological development. Through research connected with computer science and artificial intelligence, her academic work contributes to the broader body of knowledge concerned with computational approaches, intelligent technologies, data-driven methods, and the development of emerging digital solutions.[1]

Publications

The supplied profile records 30 scholarly documents associated with Dabiah Alboaneen through the Scopus author record. This publication activity provides evidence of sustained research engagement and scholarly communication within Computer Science and Artificial Intelligence. The citation record further indicates that the published research has received attention within the academic literature. Specific publication titles and DOI records were not included in the supplied profile data and therefore are not listed here without independent verification.[1]

Research Impact

The reported citation count of 553 and h-index of 13 provide measurable indicators of the academic visibility associated with the research profile. Such bibliometric measures can assist in understanding the extent to which published research has been referenced within scholarly literature. Together with 30 documented research documents, these indicators demonstrate an established publication record and continuing academic engagement in Computer Science and Artificial Intelligence.[1]

Award Suitability

The Innovative Research Award is intended to recognize meaningful research activity, scholarly productivity, and contributions to an identified research discipline. Dabiah Alboaneen’s profile aligns with these considerations through documented research activity in Computer Science and Artificial Intelligence, 30 indexed documents, 553 citations, and an h-index of 13. These indicators provide a quantitative basis for academic evaluation, while the final award determination remains subject to the applicable assessment process of the Global Best Achievements Awards.[1] [4]

Conclusion

Dabiah Alboaneen has established an identifiable academic research profile in Computer Science and Artificial Intelligence through sustained publication activity and measurable scholarly visibility. The supplied record of 30 documents, 553 citations, and an h-index of 13 demonstrates continued engagement with the research community. Her institutional affiliation and persistent ORCID and Scopus identifiers further support the traceability of her scholarly record and provide a foundation for consideration for the Innovative Research Award.[1] [2]

References

  1. Elsevier. (n.d.). Scopus author details: Dabiah Alboaneen, Author ID 56530765900. Scopus.
    https://www.scopus.com/pages/authors/56530765900
  2. ORCID. (n.d.). Dabiah Alboaneen โ€” ORCID record. ORCID.
    https://orcid.org/0000-0003-2215-9963
  3. Google Scholar. (n.d.). Dabiah Alboaneen โ€” Google Scholar profile.
    https://scholar.google.co.uk/citations?user=4ZvRmmQAAAAJ
  4. Global Best Achievements Awards. (n.d.). Global Best Achievements Awards.
    https://bestachievements.com/

Farooq Aziz | Data Science and Analytics | Industry Impact Award

Industry Impact Award

Farooq Aziz
Affiliation Systems Limited
Country Pakistan
Google Scholar YAdsqIAAAAJ&hl
Documents 12
Citations 60
h-index 2
Subject Area Data Science and Analytics
Event Global Best Achievements Awards

Farooq Aziz

Systems Limited

Farooq Aziz is associated with Systems Limited and has contributed to research in data science, analytics, business intelligence, and applied data-driven decision making. His scholarly publications explore the application of analytical methodologies across accounting, healthcare, sustainability, and business domains. The interdisciplinary nature of his work reflects an emphasis on practical implementation of data analytics for organizational performance and digital transformation while contributing to contemporary academic discussions within emerging analytical disciplines.[1]

Abstract

Farooq Aziz has established a developing academic profile through research focused on data science, analytics, business intelligence, sustainability, accounting, and healthcare applications. His publications demonstrate an interdisciplinary perspective that connects analytical methodologies with organizational decision making and technological innovation. The available scholarly indicators reflect continuing research activity supported by peer-reviewed publications and measurable citation performance. Collectively, his work emphasizes practical implementation, digital transformation, and evidence-based management while contributing to contemporary discussions surrounding analytical frameworks, emerging technologies, and industry-oriented research with relevance for both academic communities and professional practice.[1]

Keywords

Data Science, Data Analytics, Business Intelligence, Business Analytics, Digital Transformation, Sustainability, Healthcare Analytics, Accounting Analytics, Decision Support, Industry Applications.

Introduction

The growing adoption of analytical technologies has increased the importance of research that bridges academic knowledge with industrial implementation. Farooq Aziz contributes within this evolving landscape by examining how analytical tools improve business intelligence, accounting systems, sustainability reporting, and healthcare management. His research reflects an applied orientation that aligns theoretical concepts with practical organizational challenges while supporting informed decision making across multiple sectors.[2]

Research Profile

The research profile of Farooq Aziz demonstrates interdisciplinary engagement in analytical sciences with publications addressing accounting analytics, healthcare data platforms, sustainability frameworks, and business intelligence. His scholarly output illustrates continued participation in contemporary research themes supported by measurable publication records, citation activity, and an academic focus on integrating data-driven methodologies into organizational environments and professional decision-making processes.[1]

Research Contributions

His contributions primarily emphasize the application of advanced analytics to solve practical organizational problems. Research themes include accounting innovation, sustainable reporting, healthcare analytics, and business intelligence supported by modern data science approaches. These studies encourage evidence-based decision making while illustrating how analytical technologies can improve operational efficiency, strategic planning, and organizational adaptability across diverse industrial sectors.[2]

Publications

Among his representative publications are studies examining data analytics in accounting, data-driven sustainability frameworks, and next-generation healthcare analytics. These publications demonstrate consistent interest in emerging analytical technologies and their practical implementation. Collectively, they contribute to expanding academic understanding of modern analytical ecosystems while supporting interdisciplinary collaboration between industry and research communities.[3]

Research Impact

Available scholarly indicators report twelve indexed publications, sixty citations, and an h-index of two, reflecting growing academic visibility. Although still developing, these metrics indicate measurable engagement from the scholarly community. The practical orientation of his research also supports industrial relevance by promoting analytical solutions applicable to business operations, sustainability initiatives, and digital transformation strategies.[1]

Award Suitability

Based on the available academic profile, Farooq Aziz demonstrates characteristics that align with consideration for an Industry Impact Award through his emphasis on practical data science applications and interdisciplinary research. His publications focus on translating analytical innovations into organizational value, particularly within accounting, sustainability, and healthcare domains. The combination of scholarly output, applied research direction, and measurable academic recognition provides a reasonable basis for consideration under an industry-oriented research award category while remaining subject to the official evaluation criteria established by the Global Best Achievements Awards.[1]

Conclusion

Farooq Aziz has developed an interdisciplinary research portfolio centered on the practical application of data science and analytics. His work contributes to understanding how analytical technologies support organizational performance, sustainability, healthcare, and business intelligence. Continued scholarly activity and future publications may further strengthen the academic and industrial significance of his research while expanding its influence across emerging fields of applied analytics.

References

  1. Google Scholar. (n.d.). Farooq Aziz โ€“ Scholar Profile.
    https://scholar.google.com/citations?user=-YAdsqIAAAAJ&hl=en
  2. Aziz, F. (2023). Data analytics impacts in the field of accounting. World Journal of Advanced Research and Reviews.
    https://doi.org/10.30574/wjarr.2023.18.2.0863
  3. Next-Generation Healthcare Analytics: The Open Lakehouse Framework.
    https://dx.doi.org/10.2139/ssrn.5065660

Shanggerile Jiang | Machine Learning | Best Researcher Award

Mr. Shanggerile Jiang |Machine Learning | Best Researcher Award

Mr. Shanggerile Jiang, University of Shanghai for Science and Technology, China.

Shanggerile Jiang ๐ŸŽ“ is a Research Assistant at the University of Shanghai for Science and Technology, specializing in Opto-electronic Information Science and Engineering. His work focuses on Affective Computing, Signal Processing, and Vocal Technique Assessment using Deep Learning ๐Ÿง . He has published in SCI-indexed journals ๐Ÿ“š and serves as a reviewer for reputed journals. A passionate IEEE student member โšก, he collaborates with leading professors to bridge technology and education through innovative AI applications ๐Ÿค–.

๐Ÿ‘จโ€๐ŸŽ“Profile

ORCID

๐ŸŽ“ Early Academic Pursuits

Shanggerile Jiang began his academic journey at the University of Shanghai for Science and Technology, earning a Bachelor’s degree from the School of Optical-Electrical and Computer Engineering in 2024. His foundational interest in engineering and technology set the stage for his focus on Opto-electronic Information Science and Engineering. His academic trajectory showcases a strong orientation toward computational and signal-based disciplines. ๐ŸŽ“๐Ÿ”ฌ

๐Ÿงช Professional Endeavors

Currently serving as a Research Assistant, Jiang is associated with the University of Shanghai for Science and Technology. His work centers on interdisciplinary research that combines optical communication, affective computing, and signal processing. He actively collaborates with esteemed professors and contributes to ongoing lab research and publications. ๐Ÿง‘โ€๐Ÿ”ฌ๐Ÿ‘จโ€๐Ÿ’ป

๐Ÿ”ฌ Contributions and Research Focus On Machine Learning

His primary research contributions include developing a Dense Dynamic Convolutional Network (DDNet) that surpasses traditional CNN and Transformer models in vocal technique assessment. His study explores EEG-based data augmentation using CWGAN and deep neural networks, reflecting his technical command over AI-based voice analysis and emotion recognition. ๐Ÿ—ฃ๏ธ๐Ÿ“Š๐Ÿง 

๐ŸŒ Impact and Influence

Jiangโ€™s work has made measurable progress in enhancing the accuracy and performance of Bel Canto vocal technique assessments, with potential applications in remote education and voice training. His top-1 accuracy of 90.11% and mAP of 41.89% establish his contribution as both reliable and practical. ๐ŸŽฏ๐Ÿ“ˆ

๐Ÿง  Research Skills

Jiang is proficient in Deep Learning, Machine Learning, and Artificial Neural Networks. He is also skilled in using computer-aided analytical tools for signal processing and affective computing tasks. His technical portfolio includes CWGAN implementation, dynamic CNN modeling, and EEG signal extraction. ๐Ÿค–๐Ÿงฎ

๐Ÿ… Awards and Honors

He has submitted his nomination for the Best Researcher Award. While major awards are in the future pipeline, his editorial reviewer roles for Education and Information Technologies and Biomedical Signal Processing and Control demonstrate early recognition and trust in his peer-review capabilities. ๐Ÿ…๐Ÿ“‘

๐Ÿ”ฎ Legacy and Future Contributions

Poised at the frontier of AI-based voice diagnostics and education, Jiang aims to further explore the intersection of neurotechnology and audio processing. His work holds long-term potential to redefine how affective computing can be used in educational and therapeutic environments. ๐ŸŒ๐Ÿš€

Publications Top Notes

๐Ÿ“˜ 1. Classic Vocal Performance Training Through C-VaC Method
Journal: Journal of Voice
Year: 2024
๐Ÿ“… Published on: October 14, 2024
๐ŸŽต Focus: Vocal performance, core muscle stability, computer-aided analysis

๐Ÿ“„ 2. Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano
Journal: ArXiv (Preprint)
Year: 2024
๐Ÿ“Š WOSUID: PPRN:118941218
๐Ÿ’ก Focus: Transfer learning, vocal data, mezzo-soprano classification