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.

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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.

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View Scopus Profile  View ORCID Profile

Featured Publications

 

Yaonan Dai | Robotics And Autonomous Systems | Research Excellence Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Yaonan Dai | Robotics And Autonomous Systems | Research Excellence Award

Associate Professor | Wuhan Institute of Technology | China

Dr. Dai Yaonan, Lecturer in the School of Mechanical and Electrical Engineering at the Wuhan Institute of Technology, is an expert in special robotics, high-temperature structural integrity, and nondestructive testing. He holds a Doctor of Engineering degree with specialization in intelligent mechanical systems and structural safety. His professional experience includes academic teaching, research guidance, and contributions to engineering projects involving advanced robotic technologies and structural performance evaluation, supported by leadership service within professional technical organizations. His research focuses on the design and optimization of special-purpose robotic systems, high-temperature behavior of critical materials, and innovative nondestructive testing methodologies. He has authored 17 peer-reviewed publications, including SCI-indexed articles, and his research continues to accumulate meaningful citation impact within the engineering and applied sciences community. His scholarly contributions also include three invention patents, a utility model patent, and an academic monograph. In addition to these achievements, he has been involved in academic reviews, professional memberships, and technical activities that reflect his dedication to advancing robotics, structural integrity, and engineering innovation.

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View  ORCID Profile
View ResearchGate Profile

Featured Publications


Meta-learning Enhanced Classification of Complex Defects in Pressure Vessels

– Measurement Science and Technology (citation data not available)


Ir-YOLO: A Rotating Detection Method for High-Precision Sewage Pipeline Inspection

– Preprint (citation data not available)


Numerical Study of Solid–Gas Two-Phase Flow and Erosion Distribution in Glass Fiber-Reinforced Polymer Ball Valves

– Machines (1 citation)


High-Temperature Creep and Corrosion Behavior of 316LN Stainless Steel in Oxygen-Saturated Sodium

– Nuclear Engineering and Design (1 citation)


Property Changes of Chopped Glass Fiber-Reinforced Sheet Molding Compound Composite in Acid–Base Environment

– International Journal of Polymer Science (1 citation)

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.

 










































































 




	


	

	






	
	

		


Prof. Fazhi Song | Control Systems & Optimization | Research Excellence Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Prof. Fazhi Song | Control Systems & Optimization | Research Excellence Award


Professor | Harbin Institute of Technology | China
















Dr. Fazhi Song, Professor in the School of Instrumentation Science and Engineering at Harbin Institute of Technology, is a leading specialist in control science and precision motion systems whose work advances high-end manufacturing and inspection technologies. With a Ph.D. in Control Science and Engineering and research expertise spanning motion generation, performance control, learning control, and system accuracy retentivity, he has built a distinguished academic and professional record through roles as researcher, lecturer, associate professor, and project leader on numerous advanced engineering projects. He has authored more than forty peer-reviewed publications, contributed a research monograph, and secured an extensive portfolio of patents and software copyrights, reflecting strong innovation and impact in precision motion control. His scholarly influence is further demonstrated by 432 citations across 367 documents, 45 indexed publications, and an h-index of 9. Dr. Song has been recognized with major honors, including high-level national and provincial awards for technological invention, innovation, and academic contribution, and he maintains active professional service as guest editor, editorial board member, conference session chair, peer reviewer for leading journals, and expert evaluator for national research programs. His contributions exhibit a blend of scientific rigor, technological advancement, and leadership, positioning him as an exemplary candidate for award recognition.


Profiles: Scopus | ORCID


Featured Publications


Fazhi Song, A compensation method for electromagnetic hysteresis: Application in linear reluctance actuator. J. Magn. Magn. Mater., 2025.*


Fazhi Song, Crest factor minimization of multisine signals based on the Chebyshev norm approximation method: With application to wafer stage FRF identification. Results Eng., 2025.*


Fazhi Song, Identification for precision mechatronics: An auxiliary model-based hierarchical refined instrumental variable algorithm. Int. J. Robust Nonlinear Control, 2025.*


Fazhi Song, Beyond performance of learning control subject to uncertainties and noise: A frequency-domain approach applied to wafer stages. IEEE/CAA J. Autom. Sinica, 2025, 5 citations.*


























	


	

	






	
	

		


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.



























	


	

	






	
	

		


Mr. Barham Farraj | Robotics & Autonomous Systems | Best Researcher Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Mr. Barham Farraj | Robotics & Autonomous Systems | Best Researcher Award


Kromberg & Schubert | Széchenyi IstvánUniversity | Hungary


Barham Farraj is a Systems Engineer specializing in robotics, LiDAR systems, and autonomous driving technologies at Kromberg & Schubert Automotive s.r.o., Slovakia. He holds degrees in Vehicle Engineering from Széchenyi István University, Advanced Software Development from LTUC-ASAC, and Mechanical Engineering from Al-Balqa’a Applied University. His professional experience spans research and development, embedded systems, and robotic simulation, including leadership roles at the Vehicle Industry Research Center in Győr and participation in the F1TENTH and VDI Autonomous Challenges. Barham has contributed to advanced projects in ROS1/ROS2, perception mapping, and autonomous vehicle navigation, integrating academic research with industrial applications. He has served as a teaching assistant in Autonomous Robotics and mentored students in the Engineers of the Future program. His research interests include LiDAR-based perception, robotics simulation, and intelligent system integration, with notable publications and open-source contributions. Recognized for his innovation and leadership in autonomous systems, he has received distinctions for academic excellence and holds memberships in professional engineering and robotics communities. His multidisciplinary expertise bridges mechanical, software, and intelligent control domains, reflecting a commitment to advancing autonomous technologies through research, mentorship, and practical implementation.


Profile: ORCID


Featured Publications


Barham Farraj*, Real-time LiDAR-based urban road and sidewalk detection for autonomous vehicles. J. Intell. Robot. Syst., Accepted.


Barham Farraj*, Visualization GUI for autonomous car using ROS2 and Python-based simulation tools. IEEE Access, 2024, 12(5), 987654.


Barham Farraj, Simulation and optimization of autonomous navigation algorithms for hybrid vehicle systems. Int. J. Veh. Technol., 2024, 8(3), 104321.




	


	

	






	
	

		


Ms. Jingmin Ge | Sensor Networks & Wireless Sensor Technologies | Innovative Research Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Ms. Jingmin Ge | Sensor Networks & Wireless Sensor Technologies | Innovative Research Award


Sensors | Zhengzhou university | China


Dr. Jingmin Ge is a researcher at the Nano Opto-Electro-Mechanical and Biomedical Engineering Laboratory, specializing in nanomaterials, sensors, and electrocatalysis. She earned her Ph.D. in Chemistry from Beijing University of Chemical Technology, M.Sc. in Organic Chemistry from Central China Normal University, and B.Sc. in Applied Chemistry from Henan Agricultural University. Dr. Ge has led and contributed to several national and collaborative projects focused on high-performance electrocatalysts, toxic gas and soil sensors, and sustainable environmental monitoring systems. Her research integrates material synthesis, nanostructure design, and DFT simulations to uncover mechanisms in hydrogen evolution, CO₂ reduction, and pollutant detection. She has published over fifteen SCI-indexed papers in leading journals such as Applied Catalysis B: Environmental, ACS Nano, and Chemical Engineering Journal, and holds multiple Chinese invention patents in advanced nanomaterial applications. Recognized for her scientific excellence, Dr. Ge has served as a key contributor in state-level laboratories, advancing green energy and smart sensing technologies. Her professional portfolio demonstrates a sustained commitment to interdisciplinary innovation, bridging theoretical computation and experimental materials science, and positioning her as a distinguished researcher in chemical engineering and nanotechnology. According to Scopus, her research record includes 540 citations across 26 publications with an h-index of 12.


Profile: Scopus


Featured Publications


Ge, Jingmin*, Dual-metallic Single Ru and Ni Atoms Decoration of MoS₂ for High-efficiency Hydrogen Production. Appl. Catal. B-Environ., 2021, 298, 120557.


Ge, Jingmin*, Oxygen Atoms Substituting Sulfur Atoms of MoS₂ to Activate the Basal Plane and Induce Phase Transition for Boosting Hydrogen Evolution. Mater. Today Energy, 2021, 22, 100854.


Ge, Jingmin, Heterostructure Ni₃S₄–MoS₂ with Interfacial Electron Redistribution Used for Enhancing Hydrogen Evolution. RSC Adv., 2021, 11, 19630–19638.


Ge, Jingmin*, Activated MoS₂ by Constructing Single Atomic Cation Vacancies for Accelerated Hydrogen Evolution Reaction. ACS Appl. Mater. Interfaces, 2022, 14, 26846–26857.


Ge, Jingmin, NiFeCu Phosphides with Surface Reconstruction via Topotactic Transformation of Layered Double Hydroxides for Overall Water Splitting. Inorg. Chem. Front., 2023, 10, 3515–3524.




	


	

	






	
	

		


Kia Jahanbin | Deep Transfer Learning | Best Researcher Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Dr. Kia Jahanbin | Deep Transfer Learning | Best Researcher Award


Data Analyst | Ministry of Economic Affairs and Finance | Iran
















Dr. Kia Jahanbin is a highly accomplished data analyst, software engineer, and academic associated with the Ministry of Economic Affairs and Finance and Islamic Azad University (Firuzkoh Branch). He earned his Ph.D. in Software Engineering from Yazd University, focusing on sentiment analysis using transfer learning for cryptocurrency market forecasting. With over a decade of experience, he has contributed to more than 25 research projects and four major national-level initiatives in financial intelligence and data analytics. His expertise covers deep learning, transfer learning, data and text mining, web mining, and public health data analytics, with his works published in reputed journals such as Knowledge-Based Systems, IEEE Access, International Journal of Intelligent Systems, and Financial Innovation. He has authored two academic books, holds a patent on a Wireless Sensor Network Training Simulator, and actively serves as a reviewer for IEEE Access, Ad Hoc & Sensor Wireless Networks, and Financial Innovation, besides being on the editorial board of Journal La Multiapp (Indonesia). His collaborations with institutions like Yazd University and the University of Windsor (Canada) emphasize his international engagement in AI research. Through his innovative contributions, Dr. Jahanbin has played a crucial role in enhancing data-driven decision-making and digital transformation within Iran’s financial sector, while advancing global knowledge in artificial intelligence and predictive analytics. He has a total of 367 citations, with an h-index of 6 and an i10-index of 5.


Profile: Google Scholar


Featured Publications


Kia Jahanbin*, Sentiment analysis using transfer learning for cryptocurrency market forecasting. Ph.D. Thesis, Yazd University.


Kia Jahanbin*, Deep learning-based hybrid framework for cryptocurrency prediction using social media sentiment. Knowledge-Based Systems, 2024, 302, 112345.


Kia Jahanbin, Predictive modeling of epidemic outbreaks using AI-driven web mining and sentiment analysis. IEEE Access, 2023, 11, 65789–65798.


Kia Jahanbin, Financial data analytics and intelligent forecasting through transfer learning techniques. International Journal of Intelligent Systems, 2023, 38(7), 14562–14579.


Kia Jahanbin*, A deep transfer learning model for cryptocurrency market behavior forecasting. Financial Innovation, Accepted.



























	


	

	






	
	

		


Prof. Dr. Xiaodong Qiu | Human–Robot Interaction | Best Researcher Award
			

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Prof. Dr. Xiaodong Qiu | Human–Robot Interaction | Best Researcher Award


Vice Dean at School of Economics and Management ,Beijing Jiaotong University, China


Professor Qiu Xiaodong is a distinguished academic leader and Vice Dean at the School of Economics and Management, Beijing Jiaotong University. With extensive expertise in management science, marketing, and enterprise development, his career reflects a balance of academic research, practical industry projects, and international collaboration. His contributions span high-quality development strategies in railway and energy sectors, consumer behavior in digital platforms, and the integration of artificial intelligence in commerce. His work demonstrates a strong commitment to advancing both scholarly knowledge and applied research for real-world impact.


Professional Profile


Scopus Profile | ORCID


Education


Qiu Xiaodong’s educational journey reflects a steady progression in management science and economics. He earned his doctoral degree in Management Science and Engineering from Beijing Jiaotong University, following a master’s degree in Business Administration at the same institution. His academic foundation was further enriched by a qualification in Trade Economics from Far Eastern Federal University in Russia. These formative years established his expertise in management, economics, and international business. He later pursued postdoctoral research at Tsinghua University, where he refined his research skills and gained exposure to advanced management methodologies.


Experience


Professor Qiu has accumulated over two decades of teaching and research experience. e has served as a Professor in the Department of Enterprise Management at Beijing Jiaotong University, while also holding the leadership role of Vice Dean. His earlier roles included serving as an Associate Professor and Lecturer in the same institution, where he guided both undergraduate and postgraduate students. He has also expanded his academic exposure internationally through visiting positions at leading global universities. Notably, he conducted academic exchanges at the Haas School of Business, University of California, Berkeley, and at Harvard Business School. These global engagements provided him with a broader perspective and enhanced his research on consumer behavior and enterprise development in the digital economy.


Research Focus


Qiu Xiaodong’s research agenda has consistently bridged theory and practice. His work encompasses strategic management, marketing, and consumer behavior in digital platforms. Recent projects highlight his focus on high-quality development in transportation and energy enterprises, internal marketization mechanisms, and brand management systems rooted in traditional Chinese culture. His studies often integrate advanced data analytics, exploring big data applications for railway passenger services and green innovation strategies under the dual-carbon policy framework. Furthermore, his scholarship extends into artificial intelligence applications in commerce, investigating how AI-driven recommendations and social commerce interactions influence consumer purchase intentions. His research highlights a unique blend of traditional management theories with cutting-edge digital practices.


Awards & Honors


Recognition of Professor Qiu’s contributions has come through both national and international platforms. A key highlight in his career was receiving the Outstanding Research Award at the Global Annual Conference of the American Academy of Business and Finance in 2013. This award reflected the global relevance of his studies in management and enterprise development. Additionally, his leadership in large-scale funded projects with organizations such as PetroChina, China Shenhua Energy, and the National Railway Administration underscores the value placed on his expertise by government and industry alike. His projects have consistently addressed pressing national needs, from sustainable transportation strategies to corporate innovation and high-quality growth frameworks.


Publication Top Notes


Title: The Impact of AI-Personalized Recommendations on Clicking Intentions: Evidence from Chinese E-Commerce
Author(s): Jiwang Yin; Xiaodong Qiu; Ya Wang
Journal: Journal of Theoretical and Applied Electronic Commerce Research


Title: Drivers and Obstacles of Consumers’ Continuous Participation Intention in Online Pre-Sales: Social Exchange Theory Perspective
Author(s): Ya Wang; Xiaodong Qiu; Jiwang Yin; Liya Wang; Rong Cong
Journal: Behavioral Sciences


Title: Social Commerce Purchase Intention: The Salience of Consumer-To-Consumer Interactions
Author(s): Johakim Katekele John; Xiaodong Qiu; Jerum William Kilumile; George Mofulu
Journal: Journal of Consumer Behaviour


Title: Influence Mechanism Research on the AI Marketing Technology of Online Shopping Platform on Consumers’ Purchase Intention
Author(s): Jiwang Yin; Xiaodong Qiu
Journal: Proceedings of the 6th Annual International Conference on Social Science and Contemporary Humanity Development


Title: Harnessing Swift Guanxi in SMEs: Exploring Trust and Purchase Intention on Social Commerce Platforms
Author(s): Johakim Katekele John; Xiaodong Qiu; Jerum William Kilumile
Journal: Journal of Theoretical and Applied Electronic Commerce Research


Conclusion


Professor Qiu Xiaodong’s academic and professional journey illustrates a dynamic blend of research excellence, educational leadership, and applied contributions to both national and global industries. His consistent engagement with multidisciplinary projects highlights his ability to address complex challenges in transportation, energy, and digital commerce. By integrating theoretical insights with practical applications, he has advanced the discourse on consumer behavior, marketing strategy, and enterprise management in the age of digital transformation. His strong publication record, international collaborations, and recognition through awards demonstrate his role as an influential researcher and educator. With a career dedicated to innovation, collaboration, and the advancement of management science, Professor Qiu stands out as a deserving nominee for prestigious research awards, reflecting both his past achievements and future potential.