Maedeh Azadi Moghadam | Artificial Intelligence | Best Researcher Award

Published on by Scientific World Research Awards

Dr. Maedeh Azadi Moghadam | Artificial Intelligence | Best Researcher Award

Biomedical Engineer | Semnan University | Iran

Dr. Maedeh Azadi Moghadam is an emerging researcher whose work advances the fields of biomedical engineering, neurotechnology, and human–machine interaction, with a particular focus on developing more reliable and human-centered brain–computer interface (BCI) systems. Her research interests span neural signal processing, SSVEP-based BCI optimization, cognitive fatigue detection, biomarker-based performance measurement, and the integration of physiological signals into more adaptive computational models. She is especially interested in understanding how fatigue and cognitive variability influence BCI accuracy, and her work aims to design intelligent systems capable of adjusting in real time to user states, ultimately improving usability for rehabilitation, assistive technologies, and next-generation neuroengineering applications. Dr. Moghadam’s research skills include biosignal analysis, EEG processing, feature extraction, algorithmic modeling, quantitative measurement techniques, and scientific writing, demonstrating her multidisciplinary strengths across engineering and neuroscience. According to Scopus, she has 3 indexed documents, 2 citations, and an h-index of 1, reflecting growing visibility and early academic impact in her domain. Although no formal awards or honors are listed for her in the available Scopus record, her contributions to innovative metrics—such as a continuous fatigue index for SSVEP-based BCI performance—highlight her potential for future recognition in neurotechnology and biomedical measurement science. Her publications demonstrate a commitment to improving the efficiency, accuracy, and adaptability of neuroengineering systems, particularly those intended for people with motor impairments or communication limitations. In conclusion, Dr. Maedeh Azadi Moghadam represents a promising researcher whose interdisciplinary work is helping shape the future of intelligent BCIs, cognitive state monitoring, and biomedical signal-driven technologies. Her expanding scientific contributions, combined with her advancing research skill set, position her for continued impact in the global scientific community and future leadership in neurotechnology innovation.

Profiles: Scopus | Google Scholar | LinkedIn

Featured Publications

Azadi Moghadam, M., & Maleki, A. (2023). Fatigue factors and fatigue indices in SSVEP-based brain–computer interfaces: A systematic review and meta-analysis. Frontiers in Human Neuroscience, 17, 1248474. Citations: 33

Maleki, A., & Azadimoghadam, M. (2022). Fatigue assessment using frequency features in SSVEP-based brain–computer interfaces. Iranian Journal of Biomedical Engineering, 16(3), 229–240.
Citations: 4

Moghadam, M. A., & Maleki, A. (2023). Fatigue detection in SSVEP-based BCIs using biomarkers: A comparative study. 2023 31st International Conference on Electrical Engineering (ICEE), 496–500. Citations: 2

Azadi Moghadam, M., & Maleki, A. (2024). Comparative study of frequency recognition techniques for steady-state visual evoked potentials according to the frequency harmonics and stimulus number. Journal of Biomedical Physics and Engineering. Citations: 1

Moghadam, M. A., & Maleki, A. (2025). A continuous fatigue index based on biomarkers for SSVEP-based brain–computer interfaces. Measurement, 118598.

The Dr. Maedeh Azadi moghadam’s research advances global innovation in neurotechnology by improving the accuracy, stability, and human-centered design of brain–computer interface systems through biomarker-driven fatigue detection and advanced signal analysis. By enhancing the reliability of assistive technologies and cognitive monitoring tools, the nominee’s work contributes meaningful benefits to science, healthcare, and industry, ultimately supporting more accessible, intelligent, and high-performing human–machine interaction solutions for society.

 

Maliki Moustapha | Computer Science | Best Researcher Award

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Dr. Maliki Moustapha | Computer Science | Best Researcher Award

PhD | Erciyes University | Turkey

Dr. Maliki Moustapha, an accomplished researcher from Erciyes University, is recognized for his expertise in Artificial Intelligence (AI), Deep Transfer Learning, and Data Engineering, with a strong focus on the integration of intelligent algorithms and data-driven models to address real-world computational challenges. His academic background is rooted in computer science and engineering, where he developed advanced skills in machine learning, neural networks, data mining, and smart systems design. Professionally, Dr. Moustapha has been actively engaged in both research and academic mentorship, contributing to the development of innovative solutions in AI-powered automation, pattern recognition, and intelligent monitoring systems. His major research interests encompass computer vision, deep learning model optimization, spatiotemporal data analysis, and Internet of Things (IoT)-based smart healthcare systems. Among his most cited contributions is the publication titled “A Novel YOLOv5 Deep Learning Model for Handwriting Detection and Recognition” in the International Journal on Artificial Intelligence Tools (2023), which demonstrates superior accuracy and efficiency in image recognition. He has also published influential works on spatial and spatiotemporal clustering algorithms and IoT-based patient monitoring, bridging the gap between data intelligence and applied computing. His research skills span across Python programming, neural network modeling, big data analytics, data preprocessing, and model training for intelligent systems. Though early in his academic journey, Dr. Moustapha has earned recognition for his impactful work, showing promising potential in advancing AI technologies. According to Scopus and Google Scholar, he has achieved 9 citations, an h-index of 1, and several published documents reflecting growing international recognition. Dr. Moustapha’s research continues to contribute meaningfully to the fields of artificial intelligence and computational intelligence. In conclusion, his innovative approach, interdisciplinary mindset, and technological vision position him as a forward-thinking researcher committed to shaping the next generation of intelligent data systems and AI-driven innovations.

Profiles: ORCID | Google Scholar

Featured Publications

1. Moustapha, M., Taşyürek, M., & Öztürk, C. (2023). A novel YOLOv5 deep learning model for handwriting detection and recognition. International Journal on Artificial Intelligence Tools, 32(04), 2350016.

2. Moustapha, M. (2024). Spatial and spatiotemporal clustering algorithms in data mining. In Proceedings of the 3rd International Conference on Data and Electronics and Computing (ICDEC).

3. Moustapha, M. (2019). Alternative approach of patient monitoring system based on Internet of Things. In Proceedings of the II. International Science and Academic Congress (INSAC).

Jiatao Ding | Robotics and Automation | Best Researcher Award

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Dr. Jiatao Ding | Robotics and Automation | Best Researcher Award

Postdoctoral Researcher | University of Trento | Italy

Dr. Jiatao Ding is an accomplished robotics researcher whose work focuses on optimal control, robot learning, and legged robotics, with a strong record of international collaborations and impactful scientific contributions. He obtained his Bachelor’s degree in Mechanical Engineering from Wuhan University in 2014 (Cum Laude), followed by a Doctorate in Mechatronics Engineering from Wuhan University in 2020, during which he also served as a Ph.D. Fellow at the Italian Institute of Technology (2018–2020), gaining valuable international exposure. Professionally, Dr. Ding has held prestigious research appointments including Research Assistant Scientist at the Chinese University of Hong Kong (2020–2022), Postdoctoral Researcher at Delft University of Technology (2022–2025), and currently, Postdoctoral Researcher at the University of Trento, Italy (2025–present). His research interests lie in humanoid and quadruped locomotion, reinforcement learning, and bio-inspired robotic control, where he has actively contributed to major EU H2020 projects such as Inverse, Nature Intelligence, and CogIMon, along with NSFC-funded projects in China. Dr. Ding’s research skills span advanced reinforcement learning, trajectory optimization, hierarchical and model predictive control, and adaptive locomotion strategies, which have enabled breakthroughs in versatile bipedal and quadrupedal robotic systems. His scholarly output is extensive, with publications in flagship robotics venues such as IEEE ICRA, IROS, IEEE Transactions on Robotics, IEEE/ASME Transactions on Mechatronics, and Advanced Robotics, reflecting both quality and global reach. He has served the academic community as a reviewer for leading journals and conferences, session chair at AIM 2025, associate editor at UR 2025, and guest editor for special issues in reputed journals, demonstrating leadership and commitment to advancing robotics research. His awards and honors include invited talks, editorial board appointments, and recognition through collaborative project leadership across Europe and Asia. According to Scopus, Dr. Ding has achieved 262 citations across 241 documents with an h-index of 11, underscoring both productivity and research impact. In conclusion, Dr. Jiatao Ding exemplifies an emerging global leader in robotics whose academic excellence, technical expertise, and dedication to collaborative research position him strongly for future innovations in intelligent robotic systems, making him a deserving candidate for international recognition.

Profile: Google Scholar

Featured Publications

Atanassov, V., Ding, J., Kober, J., Havoutis, I., & Della Santina, C. (2024). Curriculum-based reinforcement learning for quadrupedal jumping: A reference-free design. IEEE Robotics & Automation Magazine, 32(2), 35–48. Citations: 24

Ding, J., Han, L., Ge, L., Liu, Y., & Pang, J. (2022). Robust locomotion exploiting multiple balance strategies: An observer-based cascaded model predictive control approach. IEEE/ASME Transactions on Mechatronics, 27(4), 2089–2097. Citations: 24

Ding, J., Wang, Y., Yang, M., & Xiao, X. (2018). Walking stabilization control for humanoid robots on unknown slope based on walking sequences adjustment. Journal of Intelligent & Robotic Systems, 90(3), 323–338. Citations: 16

Ding, J., Zhou, C., Xin, S., Xiao, X., & Tsagarakis, N. G. (2021). Nonlinear model predictive control for robust bipedal locomotion: Exploring angular momentum and CoM height changes. Advanced Robotics, 35(18), 1079–1097. Citations: 26*

Ding, J., Atanassov, V., Panichi, E., Kober, J., & Della Santina, C. (2024). Robust quadrupedal jumping with impact-aware landing: Exploiting parallel elasticity. IEEE Transactions on Robotics, 40(1), 3212–3231. Citations: 13