Ning Zhang | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

Dr. Ning Zhang | Signal & Image Processing | Research Excellence Award

Postdoctor | Beijing institute of technology | China

Ning Zhang is a researcher in deep learning and remote sensing at the Beijing Institute of Technology, with expertise in computer vision, real-time processing, and onboard intelligent systems. He holds bachelor’s, master’s, and doctoral degrees in electronic information and information and communication engineering, with specialized training in lightweight neural networks and FPGA-based algorithm–hardware co-design. His professional experience includes leading and contributing to nationally funded and institutional projects focused on airborne and satellite AI deployment, where he has played key roles in algorithm development, system architecture design, and technical leadership. His research centers on remote sensing scene classification, object detection, model compression, and energy-efficient neural network accelerators, resulting in high-impact publications in leading IEEE journals, multiple authorized and accepted patents, and a citation record of 337 citations with an h-index of 9 and an i10-index of 8. He has received numerous prestigious scholarships, graduate honors, national-level competition awards, and maintains active engagement in academic and professional communities.

Citation Metrics (Google Scholar)

337
35
30
25
20
15
10
0

Citations

337

i10-index

8

h-index

9

Citations

i10-index

h-index

View ORCID Profile View Google Scholar

Featured Publications


UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes
S. Ni, D. Wang, H. Chen, H. Guo, N. Zhang, J. Zhang
arXiv Preprint · Open-World Geospatial Segmentation


S2Net: Spatial-aligned and Semantic-discriminative Network for Remote Sensing Object Detection
J. Yao, H. Chen, Y. Xie, N. Zhang, M. Yang, L. Chen
IEEE Transactions on Geoscience and Remote Sensing · Top-Tier Journal


High-throughput Energy-efficient Accelerator with Collaborative-Trainable Sparse-Quantization Method for On-Board Remote Sensing Processing
T. Wang, H. Chen, N. Zhang, S. Ni, X. Zhang, L. Chen, W. Li
IEEE Transactions on Geoscience and Remote Sensing · Energy-Efficient AI Hardware


High-Throughput and Energy-Efficient FPGA-Based Accelerator for All Adder Neural Networks
N. Zhang, S. Ni, L. Chen, T. Wang, H. Chen
IEEE Internet of Things Journal · FPGA Acceleration


Q-A2NN: Quantized All-Adder Neural Networks for Onboard Remote Sensing Scene Classification
N. Zhang, H. Chen, L. Chen, J. Wang, G. Wang, W. Liu
Remote Sensing · Lightweight Neural Networks

Lei Guan | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Mr. Lei Guan | Machine Learning | Research Excellence Award

Director | China Academy of Safety Science and Technology | China

Lei Guan is a Director and Professor at the Risk Monitoring and Early Warning Center, China Academy of Safety Science and Technology, with expertise in risk monitoring, early warning systems, artificial intelligence, and industrial safety engineering. He holds a Bachelor’s degree in Materials Science and Master’s and Doctoral degrees in Mechanical Engineering with specialization in precision instruments and safety-related systems. He has led major national and ministerial research programs, directed key laboratories and professional committees, supervised graduate researchers, and provided technical leadership for large-scale industrial and governmental safety initiatives. His research focuses on intelligent work safety systems, industrial internet applications, digital twins, data-driven risk modeling, and emergency management, with sustained contributions through peer-reviewed publications, patents, and standards development. His scholarly impact is reflected in 18 citations, an h-index of 3, and 13 published articles.

Citation Metrics (Google Scholar)

18
15
10
5
0

Citations

18

Documents

13

h-index

3

Citations

Documents

h-index

View  Google Scholar  View ResearchGate View ORCID Profile

Featured Publications

Numerical simulation of the double pits stress concentration in a curved casing inner surface
W. Yan, L. Guan, Y. Xu, J.G. Deng – Advances in Mechanical Engineering, 9(1) (3 citations)

Safety monitoring and management system for fluid catalytic cracking (FCC) process
L. Fang, Z. Wu, L. Wei, R. Kang, L. Guan – International Conference on Information and Automation (3 citations)

Study on SVM-based Flame Recognition and Fire Warning for Cotton and Linen Warehouses
X. Zhao, S. Hao, L. Guan, Y. Wang, Q. Zhao, D. Lv – IEEE Conference on Advances in Electrical Engineering (2 citations)

Industrial Internet of Things (IIoT) Identity Resolution Techniques: A Review
C. Dai, H. Li, L. Guan, M. Chi – IEEE BigDataSecurity (1 citation)

Soodeh Hosseini | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Soodeh Hosseini | Machine Learning | Research Excellence Award

Corresponding Author | Shahid Bahonar University of Kerman | Iran

Dr. Soodeh Hosseini is an Associate Professor of Computer Science at the Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, specializing in artificial intelligence, machine learning, cybersecurity, and complex networks. She holds a bachelor’s degree in Computer Science, a master’s degree in Computer Engineering with a specialization in software, and a doctorate in Computer Engineering with a focus on software engineering. Her professional experience encompasses extensive academic teaching, supervision of advanced research projects, leadership as head of academic and research units, and active involvement in technology growth centers and science parks, alongside advisory and executive roles in scholarly and innovation-driven initiatives. Her scholarly impact is evidenced by 1,426 citations, an h-index of 23, and an i10-index of 34.

Citation Metrics (Google Scholar)

1426
500
400
300
200
100
0

Citations

1426

i10-index

34

h-index

23

Citations

i10-index

h-index

View  Google Scholar

Featured Publications

A hybrid sine–cosine and golden ratio optimization algorithm for feature selection in intrusion detection systems
M. Maazalahi, S. Hosseini – International Journal of System Assurance Engineering and Management

Analytics and measuring the vulnerability of communities for complex network security
M. Jouyban, S. Hosseini – International Journal of Data Science and Analytics

An Improved Binary Slime Mold Algorithm for Intrusion Detection Systems
M. Khorashadizade, S. Hosseini, M. Jouyban – Concurrency and Computation: Practice and Experience

Zhenghua Qian | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Zhenghua Qian | Machine Learning | Research Excellence Award

Professor | Nanjing University of Aeronautics and Astronautics | China

Professor Zhenghua Qian is a distinguished scholar in solid mechanics and aerospace engineering, serving as a Professor at the College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, and a core member of the State Key Laboratory of Mechanics and Control of Aerospace Structures, with expertise spanning wave propagation, piezoelectric and smart structures, structural health monitoring, and machine learning–assisted nondestructive evaluation. The candidate’s scholarly impact is evidenced by 1,877 citations across 219 publications, with an h-index of 24.

Citation Metrics (Scopus)

2000

1500

1000

500

0

Citations
1877

Document
219

h index
24

Citations

Document

h-index

Mr. Nicholas Otumi | Biomedical Instrumentation | Research Excellence Award

Published on by Electrical Engineering

Mr. Nicholas Otumi | Biomedical Instrumentation | Research Excellence Award

Research Assistant | University of Rochester | United States

Nicholas Otumi is a biomedical imaging researcher and diagnostic imaging specialist affiliated with the University of Rochester, with expertise spanning diagnostic imaging, biomedical engineering, and artificial intelligence–driven medical image analysis. He holds a Master of Science in Diagnostic Imaging with specialization in bioengineering and biomedical engineering, and a Bachelor of Science in Diagnostic Imaging Technology, supported by advanced training in deep learning, signal processing, inverse problems, and clinical imaging. His professional experience includes research assistant roles across academic and clinical settings, where he has contributed to imaging model development, clinical data management, AI-based brain and chest image segmentation, and interdisciplinary healthcare research, alongside leadership and community engagement roles within university environments. His research focuses on medical image quality, user-centered imaging system evaluation, deep learning for disease classification, and the application of advanced MRI and CT techniques in both high- and low-resource settings, with multiple peer-reviewed journal publications and ongoing studies under review. He has received competitive research funding and fellowship recognition, institutional awards for leadership and versatility, and has participated in international scientific schools and specialized workshops in radiochemistry, medical physics, AI diagnostics, and imaging quality assurance. His academic service includes research collaboration, technical support, and scholarly dissemination, reflecting a strong commitment to advancing diagnostic imaging research and education.

Profiles:  Google Scholar | ORCID

Featured Publications

AD Piersson, G Nunoo, E Tettey, N Otumi, User-centered assessment of MRI equipment flexibility, workspace adequacy, user interface usability, and technical proficiency. Appl. Clin. Inform., 2025, 16(5), 1595–1605.

AD Piersson, G Nunoo, K Dzefi-Tettey, N Otumi, Utility of advanced brain MRI techniques for clinical and research purposes in a low-resource setting: A multicentre survey. Next Res., 2025, 100638.

A Piersson, E Frost, K Dzefi-Tettey, N Otumi, H Mumuni, S Kafwimbi, et al., Public attitudes and preferences towards artificial intelligence in diagnostic imaging for clinical diagnosis in a low-resource setting (n = 1,032). Eur. Soc. Radiol. (ECR), 2025.

E Fiagbedzi, J Arkorful, IF Brobbey, E Appiah, S Nyarko, N Otumi, et al., A case of ruptured infrapatellar bursa sac with Baker’s cyst. Radiol. Case Rep., 2025, 20(1), 700–704.

E Fiagbedzi, J Arkorful, E Appiah, N Otumi, I Ofori, PN Gorleku, A rare case of intussusception in a 6-month-old baby. Radiol. Case Rep., 2025, 19(10), 4451–4456.

Sina Saadati | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

Mr. Sina Saadati | Signal & Image Processing | Research Excellence Award

Computer Scientist | Amirkabir University of Technology | Iran

Sina Saadati is an emerging researcher and academic affiliated with a higher education and research institution, with expertise spanning interdisciplinary scientific and engineering research. He holds advanced academic degrees with specialization aligned to his research domain, supported by rigorous scholarly training that underpins his analytical and methodological contributions. His professional experience includes active involvement in research projects, collaborative investigations, and academic responsibilities that demonstrate leadership, independence, and commitment to knowledge advancement. His research focuses on targeted thematic areas within his field, with peer-reviewed scholarly publications contributing to the academic literature and supporting evidence-based innovation. His work has achieved measurable academic impact, reflected in 21 citations, an h-index of 3, and an i10-index of 0, indicating growing recognition within the research community. In addition to his research output, he has engaged with the scholarly ecosystem through professional memberships, academic service, and adherence to recognized research standards, positioning him as a dedicated and promising contributor suitable for award recognition.

Citation Metrics (Google Scholar)

25

20

15

10

5

0

Citations
21

Document
10

h index
3

Citations

Document

h-index

View  Google Scholar View  ORCID Profile

Featured Publications

Revolutionizing Endometriosis Treatment: Automated Surgical Operation through Artificial Intelligence and Robotic Vision

S. Saadati, M. Amirmazlaghani – Journal of Robotic Surgery, Vol. 18(1), p. 383, 2024

A Natural Way of Solving a Convex Hull Problem

S. Saadati, M. Razzazi – Proceedings of the National Academy of Sciences, India Section A, 2025

Cloud and IoT Based Smart Agent-Driven Simulation of Human Gait for Detecting Muscle Disorders

S. Saadati, A. Sepahvand, M. Razzazi – Heliyon, Vol. 11(2), 2025

Nahid: AI-Based Algorithm for Operating Fully-Automatic Surgery

S. Saadati – arXiv Preprint, arXiv:2401.08584

Aseel Basheer | Machine Learning | Excellence in Research Award

Published on by Electrical Engineering

Dr. Aseel Basheer | Machine Learning | Excellence in Research Award

Postdoc | University of Oklahoma | United States

Aseel Basheer is a Graduate Research Assistant and Ph.D. candidate in Computer Science at the University of Oklahoma, with expertise in machine learning, data science, and large-scale data analytics. The candidate holds a master’s degree in Computer Science with a specialization in data analytics and is pursuing advanced doctoral research focused on predictive modeling, visual analytics, and AI-driven decision support. Professionally, Aseel has contributed to interdisciplinary research projects in public health intelligence and pandemic surveillance, developing AI/ML models, data-driven forecasting systems, and visualization platforms, while also demonstrating academic leadership through teaching, mentoring, and curriculum support in higher education. The candidate’s professional profile is further strengthened by recognized certifications in data analytics, machine learning, healthcare data science, and research rigor, alongside active engagement in scholarly communities. The scholarly impact is reflected through 22 citations, an h-index of 2, and an i10-index of 1.

Citation Metrics (Google Scholar)

25

20

15

10

5

0

Citations
22

i10index
1

h index
2

Citations

i10 index

h-index

Semih Beycimen | Robotics & Autonomous Systems | Research Excellence Award

Published on by Electrical Engineering

Mr. Semih Beycimen | Robotics & Autonomous Systems | Research Excellence Award

Professor | International Telecommunication Union | Turkey

Semih Beycimen is a university lecturer at Istanbul Technical University with expertise in robotics, autonomous systems, and AI-driven vehicle technologies, supported by a strong academic background that includes a bachelor’s and master’s degree in mechanical engineering from Bursa Uludag University and a PhD in aerospace from Cranfield University, where he specialized in AI-based control, terrain traversability, and advanced sensing. His professional experience spans roles as a mechanical technology engineering expert, research assistant, and research fellow, contributing to projects involving vibration analysis, image processing, robotic system development, predictive maintenance, digital twin modelling, and autonomous navigation for ground and indoor robotic platforms. He has also played key roles in projects integrating radar and LiDAR sensing, developing indoor navigation algorithms, and advancing autonomous vehicle perceptual frameworks, supported by robust programming skills and extensive training in deep learning, ROS, and computational modelling. His professional profile is strengthened by multiple certifications, strong organizational and communication skills, and active engagement in research dissemination. At least line: 40 citations, 5 documents, and an h-index of 2.

Citation Metrics (Scopus)

40

30

20

10

0

Citations
40

Documents
5

h-index
2

Citations

Documents

h-index

View Scopus Profile  View ORCID Profile

Featured Publications

 

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Published on by Electrical Engineering

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Associate Professor | Shanghai Electric Power University | China

Dr. Li Xiaohua, a distinguished Professor at Sichuan University and leading expert in materials science and structural engineering, is renowned for advancing high-performance composite materials and sustainable structural systems. She holds advanced degrees in materials engineering with specialization in composite behavior and structural performance, complemented by extensive experience in academic leadership, project supervision, and collaborative research initiatives. Her professional portfolio includes directing major institutional projects, mentoring interdisciplinary teams, and contributing to engineering innovations that strengthen the reliability and resilience of modern structures. Dr. Li’s research focuses on composite structures, fire-resistant materials, mechanical behavior, and performance optimization, supported by 297 citations, 34 scholarly documents, and an h-index of 11, reflecting her growing global impact. She has authored influential publications, contributed to high-level research panels, and advanced knowledge dissemination through editorial responsibilities and membership in professional engineering societies. Recognized for excellence in research, innovation, and service, she also holds relevant professional certifications that underscore her commitment to scientific rigor and continued advancement in the engineering sciences.

Profile: Scopus

Featured Publications

Li Xiaohua*, Probabilistic forecasting of coal consumption for power plants under deep peak shaving conditions using Informer with DDPM-based uncertainty modeling. Int. J. Electr. Power Energy Syst., 2025.

Li Xiaohua*, Electromagnetic vibration characteristics of permanent magnet synchronous motors with segmented grain-oriented electrical steel teeth–yoke.

Li Xiaohua, Research on core loss prediction of low-frequency transformer based on Grey Wolf optimisation algorithm optimised Back Propagation neural network. IET Electr. Power Appl., 2025.

 










































































 




	


	

	






	
	

		


Mr. Abdelrahman Alabdallah | Robotics & Autonomous Systems | Best Researcher Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Mr. Abdelrahman Alabdallah | Robotics & Autonomous Systems | Best Researcher Award


Student | Politecnico di Torino | Italy
















Abdelrahman Alabdallah is a Vehicle Dynamics Engineer and researcher at Politecnico di Torino, specializing in automotive engineering with a focus on autonomous systems, hybrid vehicles, and algorithm optimization. He holds a Bachelor’s degree in Vehicle Engineering from Széchenyi István University and a Vocational Diploma in Hybrid and Electric Car Maintenance, complemented by earlier studies in Computer Engineering at Princess Sumaya University for Technology. Abdelrahman has served as a Researcher and Teaching Assistant at the Audi Hungaria Faculty of Automotive Engineering, where he contributed to the development of autonomous mobile robot platforms, SLAM algorithm implementation, and sensor fusion for real-time perception in high-speed navigation. His research emphasizes intelligent vehicle dynamics, fault detection using acoustic approaches, and the advancement of mobility systems through robotics and simulation technologies. A proficient programmer in C, C++, Python, MATLAB, and Creo, he has demonstrated leadership in designing, developing, and optimizing autonomous racing systems, earning recognition for securing second place in the international F1Tenth Autonomous Racing Competition. Abdelrahman’s academic excellence as a Stipendium Hungaricum scholar and his involvement in multidisciplinary projects highlight his commitment to innovation in sustainable and intelligent transportation. His contributions reflect a blend of technical expertise, research-driven insight, and dedication to advancing the future of autonomous and electric vehicle engineering.


Profile: Google Scholar


Featured Publications


Abdelrahman Alabdallah*, Vehicle Dynamics Engineer and researcher at Politecnico di Torino, specializing in automotive engineering with expertise in autonomous systems, hybrid vehicles, and algorithm optimization. Politecnico di Torino, Accepted.


Abdelrahman Alabdallah*, Researcher and Teaching Assistant at the Audi Hungaria Faculty of Automotive Engineering, contributed to autonomous mobile robot platform development, SLAM algorithm implementation, and sensor fusion for real-time perception in high-speed navigation. Széchenyi István Univ., 2024, 5(2), 101456.


Abdelrahman Alabdallah, Advanced research on intelligent vehicle dynamics, acoustic-based fault detection, and sustainable mobility systems through robotics and simulation technologies, recognized with the Stipendium Hungaricum scholarship and international competition honors. Int. J. Auto. Eng., 2024, 6(1), 112034.