Mr. Rakhmon Saparbaev | Deep Learning | Research Excellence Award

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Mr. Rakhmon Saparbaev | Deep Learning | Research Excellence Award

Urgench State University | Uzbekistan

Mr. Raxmon Saparbayev Komiljonovich is a telecommunications engineering researcher specializing in information transmission systems, network modeling, and signal processing. His work focuses on modeling virus propagation in telecommunication networks, LTE channel resource optimization, and FIR-based signal analysis using MATLAB. He has contributed to peer-reviewed journals and international conference proceedings, including IEEE and AIP publications, reflecting interdisciplinary expertise in IoT, electromagnetic systems, and network traffic analysis. His research integrates machine learning and simulation approaches to improve network reliability and performance. According to Scopus metrics, he has 3 indexed documents, 2 citations, and an h-index of 1, demonstrating emerging scholarly impact.

Citation Metrics (Scopus)

5

4

3

2

1

0

Citations
2

Documents
3

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1

          Citations    Documents    h-index


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Featured Publications

Multi-use Models of Channel Resources of LTE Technology
– Conference Paper

Method for the Correction of Spectral Distortions in X-Ray Photon-Counting Detectors
– Research Work

Modeling of Virus Spread Processes in Telecommunication Networks
– Research Contribution

Dr. Xianchen Liu | Machine Learning | Best Researcher Award

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Dr. Xianchen Liu | Machine Learning | Best Researcher Award

Researcher | Florida International University | United States

Dr. Xianchen Liu is a computer scientist specializing in machine learning, natural language processing, recommender systems, predictive analytics, and data-driven optimization. His research integrates deep learning architectures such as BERT, LSTM, attention mechanisms, and swarm intelligence to address challenges in sentiment analysis, financial risk prediction, dynamic pricing, and energy systems modeling. He has contributed to peer-reviewed journals including Systems and the Journal of Software Engineering and Applications, and presented work at international conferences. According to Scopus, he has 2 indexed documents with 3 citations and an h-index of 1; Google Scholar reports 17 citations with an h-index of 2.

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Featured Publications

Mr. Zeeshan Rasheed | Machine Learning | Research Excellence Award

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Mr. Zeeshan Rasheed | Machine Learning | Research Excellence Award

Lecturer Computer Science | Mir Chakar Khan Rind University Sibi Balochistan | Pakistan

Mr. Zeeshan Rasheed is a computer science researcher whose work spans machine learning, data intelligence, wireless networks, and AI-driven decision systems. His research focuses on optimizing network cooperation, developing neural models for sustainable wireless resource management, improving early disease prediction, and analyzing AI’s role in media and social systems. He has contributed to studies on sentiment analysis, intelligent network strategies, pandemic modelling, and crowdsourced data reliability. His scholarly output reflects a continuous commitment to advancing practical and socially relevant AI applications, supported by publications across multidisciplinary journals. His work also demonstrates growing academic impact with ongoing contributions to emerging technological challenges.

Citation Metrics (Google Scholar)

20

15

10

5

0

Citations
17

Documents
17

h-index
3

Citations
Documents
h-index


View Google Scholar Profile

Featured Publications

Prof. Zhiguo Zhao | Machine Learning | Best Researcher Award

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Prof. Zhiguo Zhao | Machine Learning | Best Researcher Award

Professor | Huaiyin Institute of Technology | China

Prof. Zhiguo Zhao is a distinguished academic and researcher in automotive engineering, currently serving as Dean at the School of Traffic Engineering, Huaiyin Institute of Technology. His research primarily focuses on automotive system dynamics and control, intelligent connected vehicles, new energy vehicle technology, and energy equipment fault diagnosis. He has made significant contributions to battery State of Health (SOH) estimation, vehicle safety, and energy management systems, developing advanced models integrating artificial intelligence and optimization algorithms. Professor Zhao has authored over 20 high-impact publications in leading SCI and EI journals, alongside securing 10 invention patents. His research outputs have received provincial and national recognition, particularly for their practical applications in intelligent transportation and energy-efficient vehicle systems. He has successfully led multiple national and provincial research projects and has cultivated innovative industry-university collaboration models for talent development. According to Scopus, his academic record includes 36 indexed documents with 147 citations and an h-index of 7, while Google Scholar reports higher citation metrics, reflecting his growing international academic influence. His interdisciplinary expertise bridges theoretical modeling and industrial applications, fostering advancements in intelligent mobility, new energy systems, and vehicular safety technology.

Profile

Scopus

Featured Publications

Zhao, Z. (2025). Estimation of lithium battery state of health using hybrid deep learning with multi-step feature engineering and optimization algorithm integration. Energies, 18(21), 5849.

Zhao, Z. (2019). Construction and verification of equivalent mechanical model for liquid sloshing in hazardous material tankers. Journal of Huaiyin Institute of Technology, 5, 1–10.

Zhao, Z. (2023). Integrated energy management strategy for hybrid electric vehicles based on adaptive control and machine learning. Journal of Energy Storage, 59, 106781.

Zhao, Z. (2022). Fault diagnosis of power equipment using hybrid neural network and sensor fusion techniques. IEEE Transactions on Industrial Electronics, 69(8), 8123–8134.

Zhao, Z. (2021). Dynamic modeling and control optimization for intelligent connected vehicles in complex traffic environments. Vehicle System Dynamics, 59(4), 613–631.

Dr. Yonglin Ren | Computer Science | Innovative Research Award

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Dr. Yonglin Ren | Computer Science | Innovative Research Award

Senior Project Engineer & Researcher | Concordia University | Canada

Dr. Yonglin Ren is a distinguished Senior Project Engineer and Researcher at Concordia University, recognized for his interdisciplinary expertise in mathematical modeling, logistics optimization, and sustainable engineering systems. His research bridges theoretical optimization frameworks and industrial applications, focusing on metaheuristic algorithms, CAD/CAE-based modeling, and supply chain design for humanitarian and sustainable logistics. Dr. Ren’s contributions have advanced methodologies for capacitated location allocation problems, high-speed rail freight transport, and dynamic mechanical system modeling. His work integrates computational intelligence with real-world challenges in water resource management, transportation networks, and crisis logistics, making a significant impact in both academia and industry. His publications are widely cited, reflecting his influence in the fields of operational research and applied optimization, with a Scopus record of 3 indexed documents, 6 citations, and an h-index of 1, alongside a Google Scholar citation count of 26. Dr. Ren has collaborated on multiple international engineering and research projects, driving innovations that contribute to sustainable development and global resource optimization.

Profile

Scopus

Featured Publications 

Ren, Y., & Awasthi, A. (2014). Investigating metaheuristics applications for capacitated location allocation problem on logistics networks. Chaos Modeling and Control Systems Design, 213–238.

Ren, Y., & Awasthi, A. (2012). Location allocation planning of logistics depots using genetic algorithm. Research in Logistics & Production, 2, 247–257.

Ren, Y. (2011). Metaheuristics for multiobjective capacitated location allocation on logistics networks. Concordia University.

Ren, Y., Hajiebrahimi, S., Azad, M., Awasthi, A., & Salah, S. (2020). Humanitarian aid for Wuhan with crisis logistics management approach. Proceedings of the International Conference on Industrial Engineering and Operations Management.

Ren, Y., & Awasthi, A. (2025). Logistics hub location for high-speed rail freight transport—Case Ottawa–Quebec City corridor. Logistics, 9(4), 158.

Assist. Prof. Dr. Manolis Adamakis | Technologies | Best Researcher Award

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Assist. Prof. Dr. Manolis Adamakis | Technologies | Best Researcher Award

Assist. Prof. Dr. Manolis Adamakis | National and Kapodistrian University of Athens | Greece

Dr. Manolis Adamakis is an accomplished Assistant Professor and Researcher specializing in Physical Education, Physical Activity, Health, and Wellbeing. His scholarly work bridges theoretical and experimental perspectives, with strong expertise in new technologies applied to physical activity and in-depth data analysis using both quantitative and qualitative approaches. His research explores the intersections of physical activity, education, mental health, and digital innovation, contributing significantly to European physical education and public health. Dr. Adamakis is recognized for his leadership in designing, validating, and implementing innovative instruments and methodologies that enhance educational practice and research quality. A highly cited researcher, he has authored 32 documents indexed in Scopus, accumulating 440 citations from 412 sources, and holds an h-index of 10. His Google Scholar record reflects 1,025 citations, an h-index of 16, and an i10-index of 22, highlighting his global academic impact. His collaborative work with international teams has advanced knowledge in teacher education, child motor development, and mental well-being through physical activity. Dr. Adamakis’s commitment to interdisciplinary and evidence-based research underlines his contribution to shaping the future of physical education and health promotion.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

O’Brien, W., Adamakis, M., O’Brien, N., Onofre, M., Martins, J., & Dania, A. (2020). Implications for European physical education teacher education during the COVID-19 pandemic: A cross-institutional SWOT analysis. European Journal of Teacher Education, 43(4), 503–522.

Lopes, L., Santos, R., Coelho-e-Silva, M., Draper, C., Mota, J., Jidovtseff, B., & Adamakis, M. (2021). A narrative review of motor competence in children and adolescents: What we know and what we need to find out. International Journal of Environmental Research and Public Health, 18(1), 18.

Adamakis, M., & Zounhia, K. (2016). The impact of occupational socialization on physical education pre-service teachers’ beliefs about four important curricular outcomes: A cross-sectional study. European Physical Education Review, 22(3), 279–297.

Rocliffe, P., Adamakis, M., O’Keeffe, B. T., Walsh, L., & Bannon, A. (2024). The impact of school physical activity provision on adolescent mental health and well-being: A systematic literature review. Adolescent Research Review, 9(2), 339–364.

Wälti, M., Sallen, J., Adamakis, M., Ennigkeit, F., & Gerlach, E. (2022). Basic motor competencies of 6-to-8-year-old primary school children in 10 European countries: A cross-sectional study. Frontiers in Psychology, 13, 804753.*

Mr. Zhenduo Meng | Machine Learning | Best Researcher Award

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Zhenduo Meng | Machine Learning | Best Researcher Award

Inner Mongolia University, China

Zhenduo Meng is a graduate student pursuing his M.Sc. in Electronic Information Engineering at the School of Electronic Information Engineering, Inner Mongolia University, with a strong academic foundation built during his B.Eng. studies in Automation at Guangxi University. His research primarily focuses on multi-agent reinforcement learning (MARL), deep reinforcement learning, cooperative control of multi-agent systems, and the broader applications of artificial intelligence in intelligent decision-making. He has actively participated in several research projects, where he contributed to the development of algorithms integrating attention mechanisms and value decomposition methods to improve collaboration efficiency in MARL environments. Recently, his research work, “DDWCN: A Dual-Stream Dynamic Strategy Modeling Network for Multi-Agent Elastic Collaboration,” was accepted for publication in Applied Sciences (2025), highlighting his innovative contributions in the field. Despite being at the early stage of his academic journey, his scholarly output includes 2 documents, and his current citation count stands at zero, reflecting the fresh and emerging nature of his research profile. His h-index is also recorded as zero, consistent with his recent entry into the publication landscape. Proficient in Python, MATLAB, PyTorch, and TensorFlow, along with strong command of both Chinese and English, Meng demonstrates promising potential for impactful contributions in intelligent systems research.

Profile: Scopus

Featured Publications

Meng, Z., Na, X., Wang, T., Liu, J., & Wang, W. (2025). DDWCN: A dual-stream dynamic strategy modeling network for multi-agent elastic collaboration.

Wang, T., Na, X., Nie, Y., Liu, J., Wang, W., & Meng, Z. (2025). Parallel task offloading and trajectory optimization for UAV-assisted mobile edge computing via hierarchical reinforcement learning. Drones, 9(2),

Md. Khabir Uddin Ahamed | Machine Learning | Best Researcher Award

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Mr. Md. Khabir Uddin Ahamed | Machine Learning | Best Researcher Award

Mr. Md. Khabir Uddin Ahamed – Lecturer, Jamalpur Science and Technology University, Bangladesh.

Md. Khabir Uddin Ahamed is a dynamic Bangladeshi academic and researcher in Computer Science & Engineering. Known for his contribution to data-driven technologies, he has authored several impactful publications in domains like machine learning, computer vision, and AI. With strong analytical and problem-solving skills, he’s actively engaged in academic instruction and cutting-edge research. He is currently a Lecturer at Jamalpur Science and Technology University. Khabir combines technical prowess with a passion for innovation, contributing to both academic and social sectors through technological projects and scientific publications.

Publication Profile

Scopus

ORCID

Google Scholar

Education Background

Md. Khabir Uddin Ahamed holds a B.Sc. and M.Sc. in Computer Science & Engineering from Jagannath University, where he secured the 2nd merit position in both undergraduate and postgraduate programs. His academic foundation is further solidified by earlier education from Govt. Science College and BCSIR High School under the Dhaka Board. His strong educational background has shaped his ability to undertake impactful research, particularly in artificial intelligence and data science, and contributed to his success as a university lecturer and researcher.

Professional Experience

Khabir began his teaching career as a Lecturer in the Department of Computer Science & Engineering at Bangladesh University (2022–2023). Since December 2023, he has been serving as a Lecturer at Jamalpur Science and Technology University. In his academic roles, he has taught core courses, guided student research, and contributed to institutional development. He has also participated in multiple training programs under the University Grants Commission of Bangladesh, focusing on modern teaching methods, digital compliance, and administrative tools for higher education.

Awards and Honors

While there are no direct individual award mentions, Khabir’s academic distinction—earning the 2nd merit rank in both B.Sc. and M.Sc.—reflects his scholastic excellence. Furthermore, his publications have earned significant citations, indicating international recognition of his research contributions. His training certifications from the University Grants Commission and Bangladesh Accreditation Council add further credibility to his professional qualifications, reflecting national-level validation and involvement in academic quality assurance systems.

Research Focus

Md. Khabir Uddin Ahamed’s research spans several high-impact areas within computer science, including machine learning, deep learning, data science, computer vision, and blockchain technology. His recent work has explored disease detection using deep learning, behavioral analysis on social media, and intelligent transportation systems. He is passionate about leveraging AI for societal benefit and continues to explore innovative applications of technology to solve real-world problems in agriculture, health, and cybersecurity through interdisciplinary collaboration.

Top Publications 

 

Farzaneh Zareian | Machine Learning | Best Researcher Award

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Ms. Farzaneh Zareian | Machine Learning | Best Researcher Award

Ms. Farzaneh Zareian – Graduate Student, Amirkabir University of Technology, Iran.

Farzaneh Zareian is a dynamic civil engineering researcher with a specialization in earthquake engineering and machine learning applications in structural analysis. Holding a master’s degree from the prestigious Amirkabir University of Technology and a bachelor’s from the University of Tehran, she has consistently demonstrated academic excellence and innovation. Farzaneh has contributed significantly through teaching, research, and scholarly publications in seismic assessment and structural resilience. With experience in AI-powered modeling, fragility curve generation, and passive control systems, she stands at the intersection of engineering and intelligent computation, contributing to safer, more resilient infrastructure in seismic-prone regions.

Publication Profile

Google Scholar

🎓 Education Background

Farzaneh Zareian earned her M.Sc. in Civil Engineering (Earthquake Engineering) from Amirkabir University of Technology, Tehran (2020–2023) with an excellent-rated thesis supervised by Dr. Mehdi Banazadeh. Her research focused on nonlinear dynamic response estimation using machine learning. Prior to that, she completed her B.Sc. in Civil Engineering at the University of Tehran (2016–2020), with coursework emphasizing earthquake engineering, bridge design, and hydraulic structures. Her academic journey highlights a deep commitment to blending structural theory with advanced computational methods, maintaining strong GPAs and securing top ranks in national entrance exams at both undergraduate and postgraduate levels.

💼 Professional Experience

Farzaneh Zareian has accumulated valuable academic experience through teaching and research roles. She worked as a sessional instructor for the “Soft Computing” course at Shahab Danesh University during 2023–2024 and currently serves as a Teaching Assistant in “Theory of Structural Analysis” at Amirkabir University of Technology. Her practical engagements also include academic projects involving seismic hazard analysis, vulnerability assessment, and AI-driven structural modeling. These roles reflect her dual strength as both an educator and practitioner in earthquake-resistant design and computational engineering, making her a well-rounded and impactful civil engineering professional.

🏅 Awards and Honors

Farzaneh’s academic excellence has been widely recognized through several honors. In 2024, she was selected as a distinguished Ph.D. candidate by Amirkabir University’s Committee of Exceptional Talents. She ranked 1st among her peers in the Earthquake Engineering master’s program in 2022 and was among the top 0.2% in both bachelor’s and master’s national entrance exams in 2016 and 2020, respectively. Additionally, she was the top high school student at NODET. These accolades reflect her exceptional dedication, intelligence, and potential as a future leader in structural and earthquake engineering research.

🔬 Research Focus

Farzaneh’s research focuses on AI-enabled structural design and optimization, particularly in seismic contexts. She specializes in applying machine learning and physics-informed models to estimate structural responses, assess risk and reliability, and enhance infrastructure resilience. Her projects include probabilistic seismic hazard analysis, fragility curve generation, and the use of deep learning for crack detection in masonry. She is deeply committed to integrating data-driven approaches with classical civil engineering practices to improve safety, sustainability, and performance of critical infrastructure under seismic hazards.

🧾 Conclusion

Farzaneh Zareian exemplifies the emerging generation of civil engineers who are leveraging artificial intelligence to redefine structural safety and resilience. Her academic accomplishments, hands-on project experiences, teaching engagements, and scholarly contributions highlight a well-rounded professional profile. As she progresses toward doctoral research, her innovative mindset and strong foundation in both theory and practice make her a prime candidate for research excellence in AI-integrated earthquake engineering. With her interdisciplinary approach, she is poised to make impactful contributions to the global civil and seismic engineering community.

📚 Publication Top Notes

 Prediction of nonlinear dynamic responses and generation of seismic fragility curves for steel moment frames using boosting machine learning techniques
📅 Year: 2024 (Nov.)
📘 Journal: Computers & Structures
🔢 Cited by: 1

 Machine learning-based seismic risk assessment of steel moment structures: a reliability analysis framework
📅 Year: In Preparation (Expected 2025)
📘 Journal: Engineering Structures
🔢 Cited by: –

Dr. Zeynep Ilkilic Aytac | Artificial Intelligence | Best Researcher Award

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Dr. Zeynep Ilkilic Aytac | Artificial Intelligence | Best Researcher Award

Dr Lecturer, Ondokuzmayıs University, Turkey

Dr. Zeynep Ilkilic Aytac is a dynamic and innovative academician serving as a Lecturer at Ondokuz Mayıs University, Yeşilyurt Demir Çelik Vocational School, Department of Mechatronics 🏫. With over eight years of teaching experience, she has contributed significantly to interdisciplinary research that merges mechatronics, artificial intelligence 🤖, and sustainable technologies 🌱. Her strong academic foundation and passion for practical innovation enable her to mentor engineering students while advancing the frontiers of medical diagnostics and control systems. She is widely recognized for her work in MEMS gyroscope control, CNN-based cancer detection, and emission modeling using AI.

Publication Profile

🎓 Education Background

Dr. Aytac earned her BSc, MSc, and PhD degrees in Mechatronics Engineering from Fırat University, Turkey . Her academic journey showcases a strong foundation in mechanical-electrical integration, AI-driven design, and intelligent control systems. Her doctoral research focused on developing robust control strategies for MEMS gyroscopes, laying the groundwork for her multifaceted research career.

💼 Professional Experience

Currently a Lecturer at Ondokuz Mayıs University, Dr. Aytac brings over eight years of higher education teaching and project supervision experience. She has led various academic initiatives and research projects that combine engineering principles with AI and sustainability 🌐. Her interdisciplinary projects have strengthened both academic and industry collaborations, reflecting her commitment to applied research and impactful innovation.

🏅 Awards and Honors

Dr. Aytac has gained recognition for her research through publication in reputable international journals and conference proceedings 🏆. Although specific awards are not listed, her extensive interdisciplinary contributions and active role in innovation-driven education suggest an academic career marked by peer respect and institutional acknowledgment.

🔬 Research Focus

Her research interests lie in the robust control of MEMS gyroscopes, artificial intelligence in medical imaging 🧠, and emission prediction from internal combustion systems using neural networks. She has also focused on CNN-based thyroid cancer detection, leveraging hybrid metaheuristic optimization algorithms like COOT, GWO, PSO, and CMA-ES. Her contributions uniquely combine mechatronics, control theory, deep learning, and sustainability for real-world applications across engineering and healthcare.

🧩 Conclusion

Dr. Zeynep Ilkilic Aytac exemplifies the spirit of modern engineering innovation—bridging theoretical knowledge with hands-on impact. Her work continues to shape the convergence of control systems, AI, and biomedical diagnostics, enriching both academic fields and practical industries 🔧🧬. Through dedicated teaching, collaborative research, and a commitment to sustainable technology, she inspires the next generation of engineers and scientists.

📚 Top Publications 

AI-Based Emission Prediction Using Artificial Neural Networks Optimized by CMA-ES Algorithm.
Journal: Energy Reports, Year: 2022
Cited by: 24 articles

Robust Control of MEMS Gyroscopes Using Adaptive Sliding Mode Techniques.
Journal: Microsystem Technologies, Year: 2021
Cited by: 17 articles

Deep CNN Optimization for Thyroid Cancer Detection Using GWO and PSO.
Journal: Sensors, Year: 2023
Cited by: 12 articles

Hybrid AI Approaches in Digital Pathology: A CNN-Based Study.
Journal: IEEE Access, Year: 2022
Cited by: 9 articles

 Metaheuristic Optimization in CNNs for Histopathological Image Classification.
Journal: Expert Systems with Applications, Year: 2023
Cited by: 7 articles