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

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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 — ORCID record. ORCID.
    https://orcid.org/0000-0003-2215-9963
  3. Google Scholar. (n.d.). Dabiah Alboaneen — 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/

Wenqiang Hua | Deep Learning | Research Excellence Award

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Dr. Wenqiang Hua | Deep Learning | Research Excellence Award

Xi’an University of Posts and Telecommunications | China

Wenqiang Hua is a Lecturer in the School of Computer Science at Xi’an University of Posts and Telecommunications and a member of the Key Laboratory of Big Data and Intelligent Computing. He holds a Ph.D. in Electronic Circuits and Systems with a strong research focus on deep learning, image classification, and remote sensing image analysis, particularly Polarimetric SAR image classification. His work emphasizes semi-supervised learning, contrastive learning, domain adaptation, feature fusion, and multi-modal neural networks for complex remote sensing scenarios. Dr. Hua has published extensively in leading international journals, including IEEE Geoscience and Remote Sensing Letters, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Remote Sensing, Knowledge-Based Systems, and the International Journal of Applied Earth Observation and Geoinformation. He has also led nationally and provincially funded research projects related to small-sample PolSAR terrain classification. Known for his extroverted, optimistic, and enthusiastic character, he actively engages in interdisciplinary research and academic collaboration.

Citation Metrics (Scopus)

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Citations
419

Documents
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h-index
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h-index


View Scopus Profile       View Orcid Profile

Featured Publications


Knowledge and Data Co-Driven Deep Learning Model for PolSAR Image Classification

– Results in Engineering

Class-Discrepancy Dynamic Weighting for Cross-Domain Few-Shot Hyperspectral Image Classification

– Remote Sensing

Semi-Supervised Hybrid Contrastive Learning for PolSAR Image Classification

– Knowledge-Based Systems

Global–Local Multigranularity Transformer for Hyperspectral Image Classification

– IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Yabo Wu | Computer Science | Best Researcher Award

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Mr. Yabo Wu | Computer Science | Best Researcher Award

Mr. Yabo Wu, Guizhou University, China.

🎓 Mr. YaBo Wu is a Ph.D. scholar in Software Engineering at Guizhou University, focusing on computer vision, especially image enhancement and depth estimation using deep learning. 🧠 He has published in SCI Q1 and Q3 (CCF-C) journals, contributing innovative AI methods for image dehazing. 📸 His research bridges theory and application, driving AI-powered solutions for real-world systems. 🤖 A fast learner and team player, he thrives in dynamic R&D environments. 💡

🎓 Early Academic Pursuits

Mr. YaBo Wu embarked on his academic journey at Guizhou University, earning his Bachelor’s degree in Computer Science and Technology. Demonstrating early promise in technology and innovation, he continued at the same institution to pursue a Ph.D. in Software Engineering. His foundational academic background laid the groundwork for his future contributions to cutting-edge research in computer vision and artificial intelligence.

💼 Professional Endeavors

Currently immersed in doctoral research, Mr. Wu exhibits a strong commitment to bridging theoretical knowledge with real-world solutions. He excels in collaborative R&D settings, where his adaptability and technical acumen stand out. His professional demeanor is complemented by his ability to swiftly acquire new skills and integrate into multidisciplinary teams.

🔬 Contributions and Research Focus On Computer Science 

Mr. Wu’s primary research lies in computer vision, with a focus on image enhancement and depth estimation, utilizing deep learning models. He has contributed to the field through his work on single-image dehazing, which is vital for multimedia clarity and autonomous systems. His models emphasize frequency and spatial domain decoupling, enhancing feature recognition and semantic restoration.

🌍 Impact and Influence

Through his innovative contributions such as DAF-Net and DDLNet, Mr. Wu has enhanced the robustness of AI-driven solutions. His research advances not only academic knowledge but also real-world applications, especially in autonomous systems, multimedia processing, and environmental perception technologies.

🧠 Research Skills

YaBo Wu exhibits exceptional expertise in:

  • Deep learning algorithm design

  • Computer vision model optimization

  • Image dehazing and depth estimation techniques

  • Frequency and spatial domain feature analysis
    He combines technical rigor with creative problem-solving, enabling him to produce high-impact research.

🏅 Awards and Honors

Mr. Wu’s research achievements and published works in top-tier SCI journals underscore his recognition in the academic community. His ability to publish in Q1 and Q3 journals speaks to the quality and relevance of his work.

🏛️ Legacy and Future Contributions

With a passion for pushing the boundaries of AI, Mr. Wu is poised to make lasting contributions to both academic research and technological innovation. His focus on developing robust, real-time solutions for vision-based systems ensures that his work will continue influencing autonomous navigation, smart surveillance, and multimedia enhancement for years to come.

Publications Top Notes

🧪 1.  Distribution-Decouple Learning Network: An Innovative Approach for Single-Image Dehazing with Spatial and Frequency Decoupling
📘 Journal: The Visual Computer
📅 Year: March 2025
📌 Key Focus: Proposes DDLNet, decoupling haze and object features across spatial and frequency domains for superior dehazing.

🧠 2 . A Frequency-Domain Dynamic Amplitude Filtering Method for Single-Image Dehazing with Harmony Enhancement
📘 Journal: Expert Systems with Applications
📅 Year: 2025
📌 Key Focus: Introduces DAF-Net for dehazing, using amplitude components and global-local feature balancing for improved semantic recovery.

Ali Raza | Deep Learning | Best Researcher Award

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Dr. Ali Raza | Deep Learning | Best Researcher Award

Dr. Ali Raza, Harbin Engineering University, China.

Dr. Ali Raza 🎓 is a Ph.D. Research Scholar at Harbin Engineering University, China, specializing in AI, deep learning, and acoustic signal processing. He has developed innovative models like MSDFA and a Multi-Branch Residual Fusion Network, contributing to marine bioacoustics and underwater communication 🌊🤖.

Yayang Duan | Deep learning | Best Researcher Award

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Dr. Yayang Duan | Deep learning | Best Researcher Award

Dr. Yayang Duan, Affiliated First Hospital of Anhui University, China.

Dr. Yayang Duan , an accomplished physician and medical researcher, specializes in ultrasound diagnostics and AI applications in liver disease imaging. With a Doctorate in Imaging and Nuclear Medicine from Anhui Medical University, he has published 8+ SCI papers 📄 and reviewed for top journals like European Radiology 🔍. A recipient of the 2024 Wiley China High Contribution Author Award 🏆, he currently serves at the First Affiliated Hospital of Anhui Medical University, combining clinical excellence with impactful research.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

🎓 Early Academic Pursuits

Dr. Yayang Duan embarked on an impressive academic journey rooted in medical imaging. She completed her Bachelor’s degree in Medical Imaging from Bengbu Medical College (2012–2017), followed by a Master’s in Imaging and Nuclear Medicine at Dalian Medical University (2017–2020). Her academic trajectory culminated in a Professional Doctorate in Imaging and Nuclear Medicine from Anhui Medical University (2020–2023), reflecting her unwavering commitment to advanced medical education. 🎓📚

💼 Professional Endeavors

Since July 2023, Dr. Duan has served as a Physician in the Department of Ultrasound Medicine at the First Affiliated Hospital of Anhui Medical University. Her clinical practice centers on medical ultrasound diagnosis across various body systems. Her solid educational background seamlessly integrates with her hands-on clinical skills, enabling her to contribute significantly to patient care and medical research. 🏥🩺

🔬 Contributions and Research Focus On Deep learning

Dr. Duan’s research primarily focuses on liver diseases and the integration of artificial intelligence in clinical diagnostics. She has authored eight SCI-indexed papers as the first or corresponding author, along with one paper in a Chinese core journal, and contributed to ten additional SCI publications. Her expertise bridges the gap between diagnostic imaging and cutting-edge AI applications, driving forward the capabilities of non-invasive diagnostics. 🧬📊

🌍 Impact and Influence

With more than 15 expert peer reviews for prestigious journals such as European Radiology, European Journal of Nuclear Medicine and Molecular Imaging, and iScience, Dr. Duan plays an integral role in shaping the scientific discourse in medical imaging. Her influence extends beyond her own publications, reflecting a trusted voice within the global academic community. 🌐📝

🧠 Research Skills

Dr. Duan is highly proficient in medical ultrasound diagnostics, particularly in abdominal and soft tissue applications. She combines this clinical acumen with technical research expertise in AI-driven imaging analysis, liver pathology, and nuclear medicine. Her skills span both laboratory-based research and real-time patient diagnostics. 🖥️🔍

🏅 Awards and Honors

In recognition of her scholarly impact, Dr. Duan was awarded the 2024 Q1 Wiley China High Contribution Author Award 🏆—a testament to her dedication, high-quality publications, and thought leadership in medical research.

🏛️ Legacy and Future Contributions

With a promising career ahead, Dr. Duan is poised to make lasting contributions to ultrasound medicine, AI-integrated diagnostics, and clinical education. As a rising star in medical imaging, she embodies a unique blend of academic excellence, clinical dedication, and innovation that will shape the future of diagnostic medicine. 🚀📌

Publications Top Notes

  • 📄 Title: Performance of a generative adversarial network using ultrasound images to stage liver fibrosis and predict cirrhosis based on a deep-learning radiomics nomogram
    📘 Journal: Clinical Radiology
    📊 Citations: 16
    📅 Year: 2022

  • 📄 Title: Clinical value of hemodynamic changes in diagnosis of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt
    📘 Journal: Scandinavian Journal of Gastroenterology
    📊 Citations: 14
    📅 Year: 2022

  • 📄 Title: Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma
    📘 Journal: Journal of Translational Medicine
    📊 Citations: 11
    📅 Year: 2024

  • 📄 Title: Radiomics analysis of breast lesions in combination with coronal plane of ABVS and strain elastography
    📘 Journal: Breast Cancer: Targets and Therapy
    📊 Citations: 7
    📅 Year: 2023

  • 📄 Title: Performance of two-dimensional shear wave elastography for detecting advanced liver fibrosis and cirrhosis in patients with biliary atresia: a systematic review and meta-analysis
    📘 Journal: Pediatric Radiology
    📊 Citations: 6
    📅 Year: 2023

  • 📄 Title: A sonogram radiomics model for differentiating granulomatous lobular mastitis from invasive breast cancer: A multicenter study
    📘 Journal: La Radiologia Medica
    📊 Citations: 6
    📅 Year: 2023

  • 📄 Title: An overview of ultrasound-derived radiomics and deep learning in liver
    📘 Journal: Medical Ultrasonography
    📊 Citations: 5
    📅 Year: 2023

  • 📄 Title: Multimodal radiomics and nomogram‐based prediction of axillary lymph node metastasis in breast cancer: An analysis considering optimal peritumoral region
    📘 Journal: Journal of Clinical Ultrasound
    📊 Citations: 5
    📅 Year: 2023

  • 📄 Title: Diagnostic accuracy of contrast-enhanced ultrasound for detecting clinically significant portal hypertension and severe portal hypertension in chronic liver disease: a meta-analysis
    📘 Journal: Expert Review of Gastroenterology & Hepatology
    📊 Citations: 3
    📅 Year: 2023

  • 📄 Title: Ultrasound-based deep learning radiomics nomogram for the assessment of lymphovascular invasion in invasive breast cancer: a multicenter study
    📘 Journal: Academic Radiology
    📊 Citations: 2
    📅 Year: 2024

  • 📄 Title: Enhancing malignancy prediction in thyroid nodules: A multimodal ultrasound radiomics approach in TI‐RADS category 4 lesions
    📘 Journal: Journal of Clinical Ultrasound
    📊 Citations: 2
    📅 Year: 2024

 

 

Shanggerile Jiang | Machine Learning | Best Researcher Award

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Mr. Shanggerile Jiang |Machine Learning | Best Researcher Award

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

Shanggerile Jiang 🎓 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 🧠. He has published in SCI-indexed journals 📚 and serves as a reviewer for reputed journals. A passionate IEEE student member ⚡, he collaborates with leading professors to bridge technology and education through innovative AI applications 🤖.

👨‍🎓Profile

ORCID

🎓 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. 🎓🔬

🧪 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. 🧑‍🔬👨‍💻

🔬 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. 🗣️📊🧠

🌍 Impact and Influence

Jiang’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. 🎯📈

🧠 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. 🤖🧮

🏅 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. 🏅📑

🔮 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. 🌍🚀

Publications Top Notes

📘 1. Classic Vocal Performance Training Through C-VaC Method
Journal: Journal of Voice
Year: 2024
📅 Published on: October 14, 2024
🎵 Focus: Vocal performance, core muscle stability, computer-aided analysis

📄 2. Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano
Journal: ArXiv (Preprint)
Year: 2024
📊 WOSUID: PPRN:118941218
💡 Focus: Transfer learning, vocal data, mezzo-soprano classification