Ling Zhang | Artificial Intelligence | Best Researcher Award

Published on by Scientific World Research Awards

Best Researcher Award

Ling Zhang

Research Information
Affiliation Ocean University of China
Country China
Scopus ID 57851292900
Documents 56
Citations 537
h-index 13
Subject Area Artificial Intelligence
Event Scientific World Research Awards
ORCID 0000-0002-1679-7128

Ling Zhang is a researcher affiliated with Ocean University of China whose scholarly work integrates artificial intelligence, radar signal processing, maritime surveillance, and autonomous marine systems. Her publication portfolio demonstrates contributions to high-frequency surface wave radar technologies, target detection, and intelligent ocean engineering applications.[1]

Abstract

Ling Zhang has developed a research portfolio focused on artificial intelligence applications in maritime sensing, radar target detection, signal processing, and autonomous vessel technologies. Her work addresses challenges associated with shipborne high-frequency surface wave radar systems, clutter suppression, motion compensation, direction finding, and intelligent detection frameworks. Through publications in leading engineering and remote sensing journals, she has contributed methodologies that combine machine learning, deep feature fusion, and advanced radar analytics. These studies support improved situational awareness, marine monitoring, and autonomous ocean operations while advancing interdisciplinary collaboration between artificial intelligence and marine engineering research.[2]

Keywords

Artificial Intelligence, HFSWR, Radar Signal Processing, Target Detection, Marine Engineering, Autonomous Vessels.

Introduction

The integration of artificial intelligence into ocean observation and radar systems has become increasingly important for maritime safety and environmental monitoring. Ling Zhang’s research aligns with these developments through investigations into intelligent sensing technologies and data-driven detection methods.[3]

Research Profile

Her research profile encompasses radar engineering, machine learning, remote sensing, ocean engineering, and autonomous navigation systems. Published studies demonstrate continuous engagement with marine surveillance and intelligent maritime technologies.[2]

Research Contributions

Key contributions include deep feature fusion for radar target detection, direction-finding correction techniques, clutter suppression frameworks, and AI-enhanced path-planning algorithms for unmanned surface vessels. These studies strengthen the accuracy and operational effectiveness of maritime monitoring systems.[4]

Publications

Selected publications appear in IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, Ocean Engineering, IEEE Access, and Engineering Applications of Artificial Intelligence, reflecting interdisciplinary research activity and international visibility.[5]

Research Impact

With 56 indexed documents, 537 citations, and an h-index of 13, Ling Zhang’s work demonstrates measurable academic influence and engagement within radar technology, marine engineering, and artificial intelligence research communities.

Award Suitability

The combination of sustained publication activity, interdisciplinary innovation, and contributions to intelligent maritime technologies supports consideration for recognition through the Scientific World Research Awards program.

Conclusion

Ling Zhang’s research reflects ongoing efforts to advance artificial intelligence-enabled radar systems and marine technologies. Her scholarly output contributes to improved sensing, detection, and autonomous operational capabilities within maritime environments.

References

  1. Elsevier. (n.d.). Scopus author details: Ling Zhang, Author ID 57851292900. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57851292900
  2. ORCID. (2026). Ling Zhang ORCID Record.
    https://orcid.org/0000-0002-1679-7128
  3. Wang, C., Zhang, L., et al. (2023). Accurate Direction Finding for Shipborne HFSWR Through Platform Motion Compensation.
    https://doi.org/10.1109/TGRS.2023.3328264
  4. Wu, T., Zhang, L., et al. (2025). Two-Stage Target Detection for Compact HFSWR With Space-to-Depth YOLOv8 and Multiframe ViT.
    DOI:10.1109/JSTARS.2025.3556138
  5. Lu, Y., Li, G., Zhang, L., et al. (2026). Orthogonal Momentum Progressive Subnetwork Representation Learning with Feature Fusion for Surface Wave Radar Target Detection.
    https://doi.org/10.1016/j.engappai.2026.114821

Mengying Zhang | Information Technology | Women Researcher Award

Published on by Scientific World Research Awards

Women Researcher Award

Mengying Zhang

Mengying Zhang
Affiliation Anhui University
Country China
Scopus ID 57191031563
Documents 24
Citations 330
h-index 8
Subject Area Information Technology
Event Scientific World Research Awards
ORCID 0000-0002-2789-0459

Mengying Zhang is an academic researcher associated with operational research, supply chain management, pricing strategy, and information technology studies. Her scholarly work includes investigations into online platform systems, cap-and-trade allocation models, opaque selling mechanisms, and probabilistic supply chain structures.[1]

Abstract

Mengying Zhang has contributed to research in information technology and operational research with emphasis on supply chain systems, probabilistic selling, and online marketplace structures. Her publications examine pricing models, capacity allocation mechanisms, and competitive interactions in fashion and digital commerce environments. The Women Researcher Award recognizes her scholarly participation in analytical and technology-driven research addressing modern business operations and decision-making frameworks. Her work reflects interdisciplinary engagement in optimization, platform economics, and strategic operational planning within contemporary information and management systems.[2]

Keywords

Operational Research, Supply Chain Management, Information Technology, Pricing Strategy, Marketplace Systems.

Introduction

Operational research and information technology increasingly influence modern supply chain optimization and digital marketplace systems. Mengying Zhang has participated in research exploring pricing structures, market coordination, and decision-making strategies within technology-driven commercial environments.[3]

Research Profile

Her research profile includes studies on platform supply chains, probabilistic selling, online marketplace systems, and operational optimization models. These publications contribute to analytical approaches in digital commerce and economic decision systems.[1]

Research Contributions

Mengying Zhang has contributed to research concerning cap-and-trade regulations, pricing competition in fashion supply chains, reseller marketplace strategies, and allocation mechanisms within operational systems.[4]

Publications

  • Impact of power structure on probabilistic selling in supply chains
  • Pricing and Capacity Allocation in Opaque Selling
  • Marketplace or reseller? The effect of asymmetric selling cost and demand information

Research Impact

Her research activities contribute to understanding digital commerce structures and operational management systems. The studies provide analytical perspectives applicable to platform economics, resource allocation, and supply chain decision-making processes.[5]

Award Suitability

The Women Researcher Award acknowledges academic engagement, interdisciplinary research participation, and contributions to operational research and information technology studies through peer-reviewed scholarly publications.

Conclusion

Mengying Zhang’s research profile reflects continued scholarly participation in operational research, supply chain systems, and analytical modeling relevant to digital marketplace structures and information technology applications.

References

  1. ORCID. (2026). Mengying Zhang researcher profile and publication record.
    https://orcid.org/0000-0002-2789-0459
  2. Elsevier. (n.d.). Scopus author details: Mengying Zhang, Author ID 57191031563.
    https://www.scopus.com/authid/detail.uri?authorId=57191031563
  3. International Journal of Production Economics. (2026). Impact of power structure on probabilistic selling in supply chains.
    https://doi.org/10.1016/j.ijpe.2026.109940
  4. European Journal of Operational Research. (2024). Pricing and Capacity Allocation in Opaque Selling.
    https://doi.org/10.1016/j.ejor.2024.05.022
  5. International Transactions in Operational Research. (2024). Cap allocation rules for an online platform supply chain under cap-and-trade regulation.
    https://doi.org/10.1111/itor.13268

Amna Ikram | Artificial Intelligence | Best Researcher Award

Published on by Scientific World Research Awards

Dr. Amna Ikram | Artificial Intelligence | Best Researcher Award

Senior Lecturer | Government Sadiq College Women University | Pakistan

Dr. Amna Ikram is an accomplished researcher recognized for her pioneering contributions in machine learning, image processing, Internet of Things (IoT), obstacle detection, and smart agriculture. Her work emphasizes the integration of artificial intelligence and data-driven technologies to develop intelligent, efficient, and socially impactful systems. With a citation count exceeding 170, an h-index of 7, and an i10-index of 5, Dr. Ikram’s scholarly record highlights her commitment to addressing modern challenges in automation, healthcare, and sustainable agriculture.Her research focuses on creating AI-enabled frameworks and hybrid computational models that enhance decision-making and predictive accuracy in real-world applications. In agriculture, her widely cited paper, “Crop Yield Maximization Using an IoT-Based Smart Decision System” (Journal of Sensors, 2022), presents a robust model for optimizing crop productivity using sensor data, environmental parameters, and predictive algorithms. This work has significantly influenced the development of precision agriculture and IoT-driven farming systems.Expanding her expertise into healthcare and assistive technologies, Dr. Ikram has contributed to several innovative studies such as “Forensic Radiology: A Robust Approach to Biological Profile Estimation from Bone Image Analysis Using Deep Learning” and “Transformer-Based ECG Classification for Early Detection of Cardiac Arrhythmias.” These contributions showcase her ability to blend AI with biomedical imaging for diagnostic advancements and patient support.Her recent publications, including “A DETR-Based Approach for Enhancing Object Detection in Assistive Technology for the Visually Impaired” and “AI-Enabled Vision Transformer for Automated Weed Detection,” highlight her continuous drive to apply machine learning and computer vision to enhance accessibility and agricultural efficiency. Furthermore, her exploration of fuzzy-optimized hybrid neural networks and IoT sensor integration has resulted in innovative frameworks for yield prediction, crop disease detection, and obstacle recognition.Dr. Ikram’s interdisciplinary approach bridges technology and sustainability. By merging deep learning, IoT infrastructure, and intelligent vision systems, her work supports the creation of smarter, adaptive environments that empower both humans and industries. Her research continues to advance the frontiers of AI-driven automation, smart agriculture, and assistive IoT technologies, contributing profoundly to sustainable innovation and societal betterment.

Profiles: ORCID | Google Scholar

Featured Publications

  1. Ikram, A., Aslam, W., Aziz, R. H. H., Noor, F., Mallah, G. A., Ikram, S., & Ahmad, M. S. (2022). Crop yield maximization using an IoT-based smart decision system. Journal of Sensors, 2022(1), 2022923.
    Citations: 71

  2. Batool, S. N., Yang, J., Gilanie, G., Latif, A., & Ikram, A. (2025). Forensic radiology: A robust approach to biological profile estimation from bone image analysis using deep learning. Biomedical Signal Processing and Control, 105.
    Citations: 19

  3. Malik, M., Ikram, A., Batool, S. N., & Aslam, W. (2018). A performance assessment of rose plant classification using machine learning. In Proceedings of the International Conference on Intelligent Technologies and Applications (pp. 745–756).
    Citations: 15

  4. Hassan, J. U., Missen, M. M. S., Firdous, A., Maham, A., & Ikram, A. (2023). An adaptive M-learning usability model for facilitating M-learning for slow learners. International Journal of Interactive Mobile Technologies, 17(19).
    Citations: 14

  5. Naveed, S., Husnain, M., Alsubaie, N., Samad, A., Ikram, A., Afreen, H., & Gilanie, G. (2024). Drug efficacy recommendation system of glioblastoma (GBM) using deep learning. IEEE Access.
    Citations: 13

Dr. Amna Ikram’s research bridges artificial intelligence, IoT, and data-driven innovation to create intelligent solutions that enhance agriculture, healthcare, and assistive technologies. Her pioneering work advances sustainable development, automation, and societal well-being through smart, human-centered innovations that connect science with real-world impact.