Seyyedmorteza Ghamari | Engineering | Best Researcher Award

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Best Researcher Award

Seyyedmorteza Ghamari
Edith Cowan University, Australia

Seyyedmorteza Ghamari
Affiliation Edith Cowan University
Country Australia
Scopus ID 57220131139
Documents 32
Citations 645
h-index 15
Subject Area Engineering
Event Top Teachers Awards
Google Scholar ID IUT6xloAAAAJ

Seyyedmorteza Ghamari is an engineering researcher affiliated with Edith Cowan University, Australia, whose scholarly work focuses on advanced control systems, power electronics, intelligent optimization algorithms, and electric vehicle energy technologies. Through a portfolio of peer-reviewed publications and engineering innovations, he has contributed to the development of adaptive control methodologies that integrate transfer learning, reinforcement learning, fractional-order control, and metaheuristic optimization techniques. His research activity has generated measurable academic influence, reflected by a substantial citation record and an established h-index, demonstrating sustained engagement within the international engineering research community.[1]

Abstract

This article presents an overview of the academic achievements and engineering contributions of Seyyedmorteza Ghamari. His research emphasizes intelligent control strategies for power electronic converters, electric drives, and energy-efficient systems. By combining deep learning, transfer learning, reinforcement learning, and advanced optimization methods, he has developed innovative control frameworks that enhance system stability, efficiency, and robustness under varying operating conditions. His scholarly output contributes to emerging developments in smart energy systems and next-generation electrical engineering technologies.[2]

Keywords

Power Electronics, Transfer Learning, Reinforcement Learning, Brushless DC Motors, Fractional-Order Control, Electric Vehicles, Intelligent Optimization, Engineering Research.

Introduction

The increasing demand for efficient energy conversion and intelligent automation has encouraged the integration of artificial intelligence into control engineering. Seyyedmorteza Ghamari has contributed to this interdisciplinary field through investigations into adaptive controllers, machine learning-assisted optimization, and robust power electronic systems. His work addresses practical engineering challenges while maintaining a strong theoretical foundation, thereby supporting both industrial applications and academic advancement.[3]

Research Profile

Seyyedmorteza Ghamari’s research profile is characterized by expertise in control systems, electric drives, renewable energy technologies, and computational intelligence. His publications demonstrate a consistent focus on improving system performance through advanced learning algorithms and adaptive control methodologies. The combination of engineering theory and practical validation techniques, including hardware-in-the-loop experimentation, highlights the applied significance of his research activities.[1]

Research Contributions

  • Development of hybrid deep transfer learning controllers for DC–DC boost converters.
  • Research on adaptive fractional-order super-twisting sliding mode control for motor speed regulation.
  • Integration of reinforcement learning and optimization algorithms into intelligent control architectures.
  • Design and validation of power factor correction systems for electric vehicle applications.
  • Advancement of hardware-in-the-loop validation methodologies for engineering systems.

Publications

  • A Universal Hybrid Model-Free Deep Quantum–Transfer Learning Controller Enhanced by Grey Wolf Optimization for DC–DC Boost Converters With Hardware-in-Loop Validation (2026).
  • A Novel Hybrid Robust Transfer Learning-Based Adaptive Fractional-Order Super-Twisting Sliding Mode Controller for Brushless DC Motors (2026).
  • Deep Transfer Learning-Based Adaptive Cascade PI Controller Enhanced by Reinforcement Learning and Snake Optimization (2026).
  • Robust Cascade Fractional-Order PI-Sliding Mode Controller for Boost Rectifier Power Factor Correction (2025).
  • Adaptive Cascade Fractional-Order PID Controller Enhanced by Reinforcement Learning for Speed Regulation Applications (2025).

Research Impact

With 32 indexed publications, 645 citations, and an h-index of 15, Seyyedmorteza Ghamari has established a notable academic footprint within engineering research. His publications contribute to ongoing discussions concerning intelligent energy systems, advanced motor control, and optimization-driven automation. The citation performance of his work indicates recognition by researchers working in related fields of power electronics and control engineering.[1]

Award Suitability

The Best Researcher Award recognizes individuals who demonstrate scholarly productivity, research quality, innovation, and measurable academic impact. Seyyedmorteza Ghamari’s publication record, interdisciplinary research scope, and contributions to intelligent control technologies align with these criteria. His work reflects sustained efforts toward advancing engineering knowledge and practical technological development through rigorous scientific investigation.[4]

Conclusion

Seyyedmorteza Ghamari has contributed to contemporary engineering research through studies that integrate artificial intelligence, optimization methods, and advanced control theory. His work supports the development of efficient and reliable energy systems while addressing emerging technological challenges. The combination of scholarly productivity, citation impact, and practical engineering relevance supports his recognition within the framework of the Best Researcher Award.

References

  1. Elsevier. (n.d.). Scopus author details: Seyyedmorteza Ghamari, Author ID 57220131139. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57220131139
  2. Ghamari, S.M., Aziz, A. (2026). A Universal Hybrid Model-Free Deep Quantum–Transfer Learning Controller Enhanced by Grey Wolf Optimization for DC–DC Boost Converters.
    https://doi.org/10.1002/2051-3305.70263
  3. Ghamari, S.M., Aziz, A., Habibi, D. (2026). Adaptive Fractional-Order Super-Twisting Sliding Mode Controller Research.
    https://doi.org/10.1002/cta.70129
  4. Top Teachers Awards. (n.d.). Best Researcher Award Evaluation Framework.
    https://topteachers.net/
  5. Ghamari, S.M., Ghahramani, M., Habibi, D., Aziz, A. (2025). Adaptive Cascade Fractional-Order PID Controller Enhanced by Reinforcement Learning.
    https://doi.org/10.3390/en18195056

Amy Cerato | Engineering | Best Researcher Award

Published on by Top Teachers

Best Researcher Award

Amy Cerato
University of Oklahoma

Amy Cerato
Affiliation University of Oklahoma
Country United States
Scopus ID 6508388588
Documents 74
Citations 2438
h-index 26
Subject Area Engineering
Event Top Teachers Awards
ORCID 0000-0002-5377-7767

Amy Cerato is an engineering researcher affiliated with the University of Oklahoma whose scholarly work has contributed significantly to the fields of geotechnical engineering, soil stabilization, expansive soil behavior, and infrastructure materials characterization. Through a substantial publication record, strong citation impact, and sustained research productivity, Amy Cerato has established a recognized profile in engineering research. Her investigations integrate laboratory experimentation, field applications, microstructural analysis, and advanced characterization techniques to improve understanding of soil performance and infrastructure resilience. The academic contributions of Amy Cerato demonstrate a commitment to advancing practical engineering solutions while expanding scientific knowledge in transportation and geotechnical engineering disciplines.[1]

Abstract

Amy Cerato has developed a research portfolio focused on geotechnical materials, expansive soils, stabilization technologies, and engineering applications for transportation infrastructure. The research integrates laboratory-based investigations with field-oriented methodologies, enabling the development of practical solutions for soil improvement and performance assessment. Recent studies have explored soil microstructure evolution, portable X-ray fluorescence applications, and characterization techniques for chemically treated soils, contributing to both theoretical understanding and engineering practice.[2]

Keywords

Geotechnical Engineering, Expansive Soils, Soil Stabilization, Infrastructure Engineering, X-ray Fluorescence, Environmental Scanning Electron Microscopy, Transportation Geotechnics, Materials Characterization.

Introduction

Engineering infrastructure depends heavily on the behavior and long-term performance of soils. Amy Cerato has contributed to this field through investigations that address challenges associated with expansive soils, stabilization treatments, and material characterization. By combining advanced laboratory techniques with engineering analysis, Amy Cerato has helped improve understanding of soil mechanics and infrastructure sustainability. The resulting body of work supports improved engineering decision-making and contributes to safer and more resilient civil engineering systems.[3]

Research Profile

According to available scholarly metrics, Amy Cerato has authored more than seventy indexed publications and accumulated over two thousand citations, reflecting substantial visibility within the engineering research community. With an h-index of 26, the research profile demonstrates sustained influence across multiple areas of geotechnical engineering. The work spans soil stabilization, environmental geotechnics, transportation infrastructure, and advanced analytical methods for material characterization.[1]

Research Contributions

Amy Cerato has contributed to the understanding of expansive soil behavior under varying environmental conditions and has advanced the use of modern analytical tools for soil assessment. Research examining suction hysteresis through Environmental Scanning Electron Microscopy has provided insights into microstructural evolution in expansive soils. Additional studies have focused on rapid field detection of calcium-based stabilizers using portable X-ray fluorescence technologies and quantification methods for gypsum content in soils. These investigations support more efficient and accurate approaches to geotechnical evaluation and infrastructure management.[2][4]

Publications

  • Microstructural Evolution of Expansive Soils Under Suction Hysteresis Using Environmental Scanning Electron Microscopy (ESEM), Geotechnics (2026).
  • Rapid Field Detection of Calcium-Based Stabilizers in Soils via Portable X-ray Fluorescence Spectrometry, Transportation Geotechnics (2024).
  • Comparison of Whole Rock XRF and Portable XRF for Quantifying Calcium-Based Stabilizers in Chemically Treated Soil, Transportation Infrastructure Geotechnology (2024).
  • Using Fractal Geometry Theory to Quantify Pore Structure Evolution and Particle Morphology of Stabilized Kaolinite, Journal of Materials in Civil Engineering (2024).

Research Impact

The research impact of Amy Cerato is reflected through extensive citation activity and the continued relevance of published studies within geotechnical engineering. The adoption of analytical methodologies involving portable XRF technologies and microstructural characterization techniques has enhanced engineering assessment capabilities. These contributions support infrastructure planning, construction quality assurance, and sustainable management of soil resources across diverse engineering applications.[5]

Award Suitability

Amy Cerato demonstrates several characteristics associated with recognition through the Best Researcher Award. These include a strong publication record, measurable citation impact, interdisciplinary engineering contributions, and continued advancement of practical research applications. The combination of scientific rigor and engineering relevance illustrates a sustained commitment to research excellence and knowledge dissemination within the global academic community.[1]

Conclusion

Amy Cerato has established a distinguished academic profile through contributions to geotechnical engineering, soil stabilization research, and infrastructure-related investigations. The combination of influential publications, substantial citation performance, and innovative methodologies highlights the significance of the research portfolio. Through continued scholarly activity and practical engineering applications, Amy Cerato remains an important contributor to the advancement of engineering science and professional practice.[6]

References

  1. Elsevier. (n.d.). Scopus author details: Amy Cerato, Author ID 6508388588. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=6508388588
  2. Cerato, A. et al. (2026). Microstructural Evolution of Expansive Soils Under Suction Hysteresis Using Environmental Scanning Electron Microscopy (ESEM). Geotechnics.
    DOI: https://doi.org/10.3390/geotechnics6020056
  3. Cerato, A. et al. (2024). Rapid Field Detection of Calcium-Based Stabilizers in Soils via Portable X-ray Fluorescence Spectrometry. Transportation Geotechnics.
    DOI: https://doi.org/10.1016/j.trgeo.2024.101446
  4. Cerato, A. et al. (2024). Comparison of Whole Rock XRF and Portable XRF for Quantifying Calcium-Based Stabilizers in Chemically Treated Soil. Transportation Infrastructure Geotechnology.
    DOI: https://doi.org/10.1007/s40515-024-00409-3
  5. Cerato, A. et al. (2024). Using Fractal Geometry Theory to Quantify Pore Structure Evolution and Particle Morphology of Stabilized Kaolinite. Journal of Materials in Civil Engineering.
    DOI: https://doi.org/10.1061/JMCEE7.MTENG-17391
  6. Cerato, A. et al. (2024). Quantification of Gypsum in Soils via Portable X-ray Fluorescence Spectrometry. Geotechnical Testing Journal.
    DOI: https://doi.org/10.1520/GTJ20230480

Jisheng Dang | Computer Science | Research Excellence Award

Published on by Top Teachers

Research Excellence Award

Jisheng Dang
Professor, Lanzhou University, China
Jisheng Dang
Affiliation Lanzhou University
Country China
Scopus ID 57216844335
Documents 36
Citations 207
h-index 10
Subject Area Computer Science
Event Top Teachers Awards
IEEE Xplore 37088932779

Jisheng Dang is a Chinese computer scientist and academic researcher affiliated with the School of Information Science and Engineering at Lanzhou University. His research primarily focuses on multimodal learning, video understanding, computer vision, video object segmentation, and embodied intelligence. He has contributed to several peer-reviewed publications in internationally recognized journals and conferences, including IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, IJCAI, AAAI, and ICME.[1] His scholarly activities additionally include professional reviewing services for major international conferences and journals such as IEEE TPAMI, CVPR, ICML, NeurIPS, ACM MM, and ICLR.[2]

Abstract

Jisheng Dang is a faculty member at Lanzhou University specializing in computer vision, multimodal large language models, and video understanding. His research focuses on video object segmentation, adaptive memory networks, and intelligent visual reasoning systems. He has published scholarly work in internationally recognized journals and conferences, contributing to advancements in multimodal artificial intelligence and efficient video analysis technologies.[3] The article further evaluates his academic contributions and professional suitability for recognition within the framework of the Top Teachers Awards initiative.

Keywords

Computer Vision, Multimodal Learning, Video Understanding, Video Object Segmentation, Artificial Intelligence, Long-Video Understanding, Adaptive Memory Networks, Large Language Models, Intelligent Transportation Systems, Video Data Processing, IEEE Transactions on Image Processing, Neural Networks.

Introduction

The rapid advancement of artificial intelligence and multimodal machine learning has increased the demand for efficient video understanding systems. In this field, Jisheng Dang has contributed to research on long-video processing, adaptive memory modeling, and unified video segmentation frameworks, supporting developments in computer vision and intelligent multimedia analysis.[4]

Jisheng Dang completed his doctoral research at Sun Yat-sen University under Professor Jianhuang Lai and Professor Huicheng Zheng, and later served as a Research Fellow at the NExT++ Laboratory, National University of Singapore, under Professor Tat-Seng Chua. These experiences strengthened his international collaborations in video analysis and multimodal intelligence research.[2]

Research Profile

Jisheng Dang is a tenured Associate Professor at the School of Information Science and Engineering, Lanzhou University, China. His research focuses on multimodal learning, video understanding, video object segmentation, embodied intelligence, and multimodal large language models, with additional contributions in adaptive memory networks and spatiotemporal information processing.[1]

Jisheng Dang actively serves as a reviewer for leading journals and conferences, including IEEE TPAMI, CVPR, ICML, NeurIPS, AAAI, and IJCAI. He has also collaborated with prominent institutions such as the National University of Singapore, Tsinghua University, and Peking University, along with industry partners including Tencent and Huawei.[2]

  • Research Areas: Computer Vision, Multimodal Large Language Models, Video Object Segmentation
  • Professional Reviewing Experience Across International AI Conferences and Journals

Research Contributions

Jisheng Dang has contributed to efficient and scalable frameworks for video segmentation and multimodal reasoning. His research on adaptive memory systems and spatio-temporal propagation methods improves computational efficiency in long-video processing and video analysis.[3]

His scholarly contributions include the proposal of innovative frameworks such as TW-GRPO, DeSa2VA, and MUPA, designed to improve segmentation accuracy and contextual reasoning in multimodal systems. These approaches attempt to bridge theoretical machine learning research with practical industrial applications involving intelligent transportation systems and advanced multimedia analysis.[5]

  • Research on unified video segmentation frameworks for accurate and efficient video object tracking.
  • Development of quality-guided dynamic memory approaches for long-video understanding systems.
  • Investigation of hallucination mitigation techniques in large video-language models.
  • Contributions to multimodal reasoning and adaptive contextual memory architectures.
  • Participation in interdisciplinary collaborations linking AI theory with industrial applications.

Publications

Selected publications associated with Jisheng Dang include journal articles and conference papers published in IEEE Transactions on Image Processing, IEEE ICME proceedings, and Neural Networks. Several publications focus on efficient video segmentation, dynamic memory networks, and multimodal understanding systems.[3]

  1. Dang, J., Zheng, H., Guo, Y., Lai, J., Hu, B., & Chua, T.-S. (2026). Video Decoupling Networks for Accurate, Efficient, Generalizable, and Robust Video Object Segmentation. IEEE Transactions on Image Processing, Volume 35.
  2. Dang, J., Zheng, H., Chen, Z., Li, Z., Guo, Y., & Chua, T.-S. (2026). Fast Track Anything With Sparse Spatio-Temporal Propagation for Unified Video Segmentation. IEEE Transactions on Image Processing, Volume 35.
  3. Wang, B., Wen, F., Dang, J., He, H., Wang, X., Zhu, N., & Weng, J. (2025). Mitigating Hallucination in Large Video-Language Models with Injected Semantics. Proceedings of the 2025 IEEE International Conference on Multimedia and Expo (ICME).
  4. Wang, B., Jiao, J., Dang, J., Jiang, Q., Lin, J., Chen, Z., Wang, T., & Yang, J. (2025). Quality-Guided Dynamic Memory for LLMs-based Long-Term Video Understanding. Proceedings of the 2025 IEEE International Conference on Multimedia and Expo (ICME).
  5. Zhang, L., Dang, J., Zhang, S., Gan, W., Wang, J., Hu, B., Feng, G., & Peng, H. (2026). Graph-enhanced dual low-rank correlation embedding for spatio-temporal EEG fusion in depression recognition. Neural Networks, Volume 198, Article 108609.

Research Impact

The research impact associated with Jisheng Dang is reflected through peer-reviewed publications, scholarly citations, interdisciplinary collaborations, and professional service activities. His work has contributed to research discussions surrounding multimodal large language models, long-video understanding, and scalable segmentation systems.[1]

His publications in IEEE Transactions on Image Processing and conference proceedings have contributed to ongoing advancements in efficient visual processing architectures and multimodal reasoning systems. The application relevance of his research additionally extends to intelligent transportation systems, automated visual understanding, and multimedia analytics.[4]

  • Peer-reviewed publications in internationally indexed journals and conferences.
  • International collaborations with academic and industrial institutions.
  • Reviewer contributions to high-impact AI and computer vision venues.
  • Research contributions in video understanding and multimodal AI systems.
  • Academic recognition through thesis awards and institutional honors.

Award Suitability

Jisheng Dang has made sustained contributions to computer vision and multimodal machine learning through scholarly publications, collaborative research, and academic service. His publication record and participation in leading international conferences and journals reflect active engagement in the global artificial intelligence research community.[2]

His research in video segmentation, adaptive memory networks, and multimodal understanding systems reflects strong contributions to research excellence and academic innovation. His scholarly achievements and professional collaborations support his recognition within the Top Teachers Awards program.[5]

Conclusion

Jisheng Dang has established a research profile centered on multimodal learning, computer vision, and video understanding technologies. Through scholarly publication, international collaboration, and professional academic service, he has contributed to advancements in video segmentation frameworks and adaptive memory systems for artificial intelligence applications. His academic activities, publication record, and interdisciplinary research collaborations collectively reflect a sustained engagement with contemporary developments in artificial intelligence and multimedia computing research.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Jisheng Dang, Author ID 57216844335. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57216844335
  2. IEEE. (n.d.). IEEE Xplore Author Profile: Jisheng Dang. IEEE Xplore Digital Library. https://ieeexplore.ieee.org/author/37088932779
  3. Dang, J., Zheng, H., Guo, Y., Lai, J., Hu, B., & Chua, T.-S. (2026). Video Decoupling Networks for Accurate, Efficient, Generalizable, and Robust Video Object Segmentation. IEEE Transactions on Image Processing, Volume 35. https://doi.org/10.1109/TIP.2025.3649360
  4. Dang, J., Zheng, H., Chen, Z., Li, Z., Guo, Y., & Chua, T.-S. (2026). Fast Track Anything With Sparse Spatio-Temporal Propagation for Unified Video Segmentation. IEEE Transactions on Image Processing, Volume 35. https://doi.org/10.1109/TIP.2025.3649365
  5. Zhang, L., Dang, J., Zhang, S., Gan, W., Wang, J., Hu, B., Feng, G., & Peng, H. (2026). Graph-enhanced dual low-rank correlation embedding for spatio-temporal EEG fusion in depression recognition. Neural Networks, Volume 198, Article 108609. https://doi.org/10.1016/j.neunet.2026.108609

Sema Servi | Computer Science | Best Research Article Award

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Assist. Prof. Dr. Sema Servi | Computer Science | Best Research Article Award

Selçuk University | Turkey

Asst. Prof. Dr. Sema Servi is a researcher in computer engineering with a strong foundation in applied mathematics, specializing in machine learning, artificial intelligence, and numerical methods for complex problem solving. Her work focuses on data-driven approaches, including clustering algorithms, optimization techniques, and computer vision applications in healthcare and engineering. She has contributed to interdisciplinary research spanning digital competence analysis, bioinformatics, and intelligent systems. Asst. Prof. Dr. Sema Servi actively supervises postgraduate research and advises innovative, technology-driven projects supported by national programs. She has a solid research impact with 62 Scopus citations, 15 indexed documents, and an h-index of 5.

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