Yuanzhi Zhang | Machine learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Yuanzhi Zhang | Machine learning | Research Excellence Award

Nanjing University of Info. Sci. Techn. & Nantong Institute of Technology | China

Dr. Zhang Yuanzhi is a distinguished researcher at the University of Chinese Academy of Sciences, Beijing, China, specializing in advanced materials science and nanotechnology. He holds advanced degrees in materials science and engineering with a strong academic foundation in nanomaterials and electronic materials. Throughout his professional career, he has contributed extensively to academic research, collaborative scientific projects, and scholarly leadership within the materials science community. His research primarily focuses on nanomaterials, energy materials, and functional electronic materials, with significant contributions reflected in a large body of peer-reviewed publications and high citation impact. Dr. Zhang has authored numerous scientific articles and maintains a strong global research presence through collaborations with international scholars. His scholarly excellence is reflected in his high h-index, extensive citation record, and active participation in the scientific community through editorial contributions, peer-review activities, and professional memberships, demonstrating sustained commitment to advancing materials science research.

Citation Metrics (Scopus)

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View Scopus Profile View Google Scholar

Featured Publications

Two-Decadal Estimation of Sixteen Phytoplankton Pigments from Satellite Observations in Coastal Waters
International Journal of Applied Earth Observation and Geoinformation – Journal Article

Different Mechanisms for the Seasonal Variations of the Mesoscale Eddy Energy in the South China Sea
Deep Sea Research Part I: Oceanographic Research Papers – Journal Article

Effect of Melt Ponds Fraction on Sea Ice Anomalies in the Arctic Ocean
International Journal of Applied Earth Observation and Geoinformation – Journal Article

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)

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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)

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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

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)

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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.

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.