Mokhtar Ferhi | Computer Science and Artificial Intelligence | Research Excellence Award

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Dr. Mokhtar Ferhi | Computer Science and Artificial Intelligence | Research Excellence Award

University of Jendouba | Tunisia

Dr. Mokhtar Ferhi is a researcher at Université de Jendouba, Tunisia, specializing in heat transfer, fluid mechanics, magnetohydrodynamics (MHD), nanofluid convection, and numerical simulation methods, particularly the Lattice Boltzmann Method. He has authored 27 peer-reviewed publications, receiving 140 citations with an h-index of 6 (Scopus). His work focuses on entropy generation, energy optimization, and thermal performance enhancement in cavities and micro-heat exchangers. Ferhi collaborates internationally with experts across North Africa, Europe, and the Middle East, contributing to advances in energy-efficient thermal systems with applications in sustainable engineering and heat exchanger design.

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Featured Publications

 

Saeed Banaeian Far | Computer Science | Best Researcher Award

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Assist. Prof. Dr. Saeed Banaeian Far | Computer Science | Best Researcher Award

Assist. Prof. | Blockchain and Metaverse research lab | Iran

Assist. Prof. Dr. Saeed Banaeian Far is a leading researcher in applied cryptography, blockchain systems, security protocols, and emerging Metaverse technologies. He is affiliated with the Blockchain and Metaverse Research Lab (BMRL) and has made influential contributions to decentralized finance, digital twins, NFTs, Web3, privacy-preserving protocols, and quantum-secure blockchain architectures. With over 44 peer-reviewed publications in high-impact journals and conferences, his work has received 1,045 citations, reflecting strong global academic influence. He actively collaborates with international scholars and interdisciplinary teams, advancing secure digital infrastructures with significant societal impact in finance, governance, healthcare, and next-generation virtual ecosystems.

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Efendi Nasibov | Computer Science | Research Excellence Award

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Prof. Dr. Efendi Nasibov | Computer Science | Research Excellence Award

Dokuz Eylul University | Turkey

Prof. Dr. Efendi Nasiboğlu is a researcher in Computer Sciences at Dokuz Eylül University, İzmir, Turkey. He has authored over 107 scholarly publications indexed in Scopus and Web of Science, accumulating more than 1,101 citations with an h-index of 16. His research expertise spans fuzzy systems, regression modeling, computational intelligence, machine learning, and applied data analysis, with contributions to both theoretical foundations and real-world applications in engineering, manufacturing, healthcare, and smart systems. Dr. Nasiboğlu actively collaborates with international researchers and has published in reputable journals and conferences, contributing to methodological advancements with measurable societal and technological impact.

 

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Featured Publications


On the nearest parametric approximation of a fuzzy number

Fuzzy Sets and Systems  (2008). Citations: 107

A new unsupervised approach for fuzzy clustering

– Fuzzy Sets and Systems. (2007). Citations : 91

Public transport route planning: Modified Dijkstra’s algorithm

– International Conference on Computer Science and Engineering. (2017). Citations :  76

Yongbin Zhao | Computer Science | Best Researcher Award

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Dr. Yongbin Zhao | Computer Science | Best Researcher Award

School of Information Science and Technology | Shijiazhuang Tiedao University | China

Dr. Yongbin Zhao is a researcher at Shijiazhuang Tiedao University, China, recognized for his growing contributions to computational science, network analysis, and data-driven systems research. With a Scopus-indexed publication record comprising 27 scholarly documents and 67 citations across 66 citing sources, he has established an emerging academic profile characterized by interdisciplinary inquiry and collaborative engagement. His current h-index of 5 reflects the consistent impact and relevance of his research in both theoretical and applied domains.Dr. Zhao’s work spans multiple high-value fields including blockchain security complex network modeling and agricultural trade analytics. His recent publication on multi-key fully homomorphic encryption algorithms introduces secure computation frameworks tailored for blockchain environments contributing to the advancement of privacy-preserving technologies in decentralized systems. Another notable study focusing on the evolution and robustness of the global soybean trade network demonstrates his ability to integrate physics-based modeling with international agricultural economics offering meaningful insights for global trade stability and food-system resilience.He has collaborated with more than 40 co-authors reflecting a strong international and interdisciplinary research network. His contributions extend beyond academic outputs providing analytical tools and conceptual frameworks that support secure data infrastructures enhance trade-network understanding and contribute to more resilient socio-economic systems.Through his sustained research activity and collaborative leadership Dr. Yongbin Zhao continues to contribute to scientific knowledge with societal and technological relevance strengthening the interface between computational innovation and global system analysis.

Profiles : Scopus | Research Gate

Featured Publications

1.Author(s). (2025). Research on multi-key fully homomorphic encryption algorithms suitable for blockchain. Cluster Computing.

2.Author(s). (2024). The evolution and robustness analysis of global soybean trade network. International Journal of Modern Physics C. Cited By : 2

Dr. Zhao’s contributions in homomorphic encryption and network robustness drive innovation in blockchain security and international supply-chain analytics. His research supports industry in building safer digital platforms and more efficient global trade systems.

RaulJavier ChangTam | Data Science and Analytics | Best Researcher Award

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Prof. Dr. RaulJavier ChangTam | Data Science and Analytics | Best Researcher Award

Profesor Investigador | Universidad Latina de Costa Rica | Costa Rica

Prof. Dr. RaulJavier ChangTam  is a multidisciplinary researcher whose work connects technology adoption, entrepreneurship, and sustainable innovation in Latin America. His research explores how emerging technologies influence entrepreneurial ecosystems, digital transformation, and user behavior within both business and social contexts. Drawing upon models such as UTAUT2, his studies provide empirical insights into the acceptance and use of new technologies by entrepreneurs, SMEs, and professionals in fields like healthcare and finance. He also contributes to studies on sustainable finance and FinTech, emphasizing the role of digital platforms and neobanks in promoting environmentally responsible entrepreneurship. Beyond business and technology, his work extends into socio-cultural domains examining global consumption patterns such as sneaker culture and analyzing trade dynamics in ornamental species markets. Chang-Tam’s ongoing research reflects a strong orientation toward understanding the intersection of innovation, digitalization, and sustainability, with an applied perspective that links academic insight to real-world economic and cultural transformations. His research has received 3 citations across 9 documents, with an h-index of 1.

Profiles : ORCID | Scopus  | ResearchGate

Featured Publications

1. Taboada Álvarez, J. E., Chang-Tam, R. J., Rueda Varón, M. J., & Hunter Torrealba, R. (2025). Analysis of the entrepreneurial motivational demand in a learning management process in incubators.

2. Araya, J. L. G., Robles Herrera, A. E., & Chang-Tam, R. J. (2025). Analysis of trends in exports and imports of continental and marine ornamental species of aquariums in Costa Rica.

3. Chang-Tam, R. J., Caldera-Gutiérrez, V., & Rivera Shaik, V. (2025). Influence of IT technology on the development of SME entrepreneurs in Costa Rica: Applied study of the adapted model of the Unified Theory of Acceptance and Use of Technology (UTAUT2).

4. Chang-Tam, R. J., Garita Quesada, R., Masís Muñoz, R., & Chang Caldera, A. P. (2025). Influence of new IT technology trends in dental care processes between dental professionals and patients: An analysis of the UTAUT2 theory.

5. Palos-Sanchez, P. R., Chang-Tam, R. J., & Folgado-Fernández, J. A. (2025). The role of neobanks and fintech in sustainable finance and technology: The customer/user perspective for entrepreneurs. Sustainable Technology and Entrepreneurship, 100109.

Mohammed M Alenazi | Computer Science and Artificial Intelligence | Best Researcher Award

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Dr. Mohammed M Alenazi | Computer Science and Artificial Intelligence | Best Researcher Award

Assistance Professor | University of Tabuk | Saudi Arabia

Dr. Mohammed M. Alenazi is an Assistant Professor of Computer Engineering at the University of Tabuk, Saudi Arabia, whose research focuses on the intersection of energy-efficient communication networks, machine learning, and distributed systems. His work advances intelligent computing architectures that optimize performance, reduce energy consumption, and enable sustainability in next-generation networks. Dr. Alenazi has contributed to several impactful studies, including energy-efficient neural network embedding in IoT over passive optical networks, distributed machine learning in cloud–fog environments, and AI-driven frameworks for 6G-IoT-based remote cardiac monitoring. His research extends to federated learning for low-latency IoT communications, hybrid cloud edge architectures for real-time cryptocurrency forecasting with blockchain integration, and machine learning-optimized energy management for resilient residential microgrids with electric vehicle integration. His scholarly output, cited over 50 times with an h-index of 4 and i10-index of 3, reflects growing recognition in the domains of sustainable networking and intelligent systems. Dr. Alenazi’s work combines AI, IoT, and cloud–fog computing to create adaptive, energy-aware solutions for smart environments, healthcare, and industrial systems. Through his innovative contributions, he continues to enhance the efficiency, reliability, and intelligence of modern communication infrastructures, positioning his research at the forefront of AI-powered green networking and distributed intelligence for the evolving digital ecosystem.

Profiles : ORCID | Scopus | Google Scholar | ResearchGate

Featured Publications

1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. Cited By : 13

2.Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Cited By : 12

3.Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. Cited By : 10

4.Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., et al. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Cited By : 6

5.Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. Cited By : 4

Nikolay Kiktev | Computer Science | Best Researcher Award

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Dr. Nikolay Kiktev | Computer Science | Best Researcher Award

Associate Professor at National University of Life and Environmental of Ukraine, Ukraine

Dr.  Nikolay Kiktev  Ukraine, is a dedicated Associate Professor and Candidate of Technical Sciences specializing in automation and robotic systems . With over 25 years of professional and academic experience, he has played key roles in developing information-management systems for industries such as food, coal, and energy. He has contributed extensively to digital transformation projects in educational institutions across Donetsk . Currently serving at both the Taras Shevchenko National University of Kyiv and the National University of Bioresources and Environmental Management, he teaches a range of cutting-edge subjects in automation, robotics, and data analytics . A prolific researcher with over 120 publications, 41 indexed in Scopus, he holds two patents and continues to mentor students in various engineering disciplines . His work bridges academic excellence and industrial innovation, particularly in AI-integrated systems and sustainable technologies .

Professional Profile

ORCID Profile | Google Scholar 

Education

Dr.  Nikolay Kiktev holds a Specialist Diploma in Systems Engineering  from Donetsk State Technical University , majoring in “Automated Information Processing and Control Systems” . He earned his Ph.D. (Candidate of Technical Sciences)  from the National University of Food Technologies, specializing “Automation of Management Processes” . His dissertation focused on automating technological processes involving carbon dioxide salt production . In 2020, he received the title of Associate Professor in the Department of Automation and Robotic Systems . His academic journey reflects deep technical expertise and a strong foundation in process automation, cyber-physical systems, and advanced engineering methodologies . His educational background has been pivotal in his successful teaching and research career, allowing him to train the next generation of engineers in Ukraine and contribute to national technological development .

Experience

Dr.  Nikolay Kiktev began his career as a software engineer  at the Temp Defense Industry Association in Donetsk . He pursued graduate studies while engaging in research projects related to automation in chemical processes . From 1999–2002, he taught at Donetsk National Technical University and other institutes. From 2002–2010, he served as Director of the Donetsk City Council’s Methodological Center for Computerization, leading large-scale digital education infrastructure projects . He briefly worked in private industry supplying IT and telecom solutions for coal and power sectors . He has held academic posts at the National University of Bioresources and Environmental Management and Taras Shevchenko National University of Kyiv, advancing to Associate Professor. He teaches diverse automation and robotics courses, supervises theses, and delivers specialized lectures across several Ukrainian universities .

Research Interest

Dr.  Nikolay Kiktev research revolves around intelligent automation and computer-integrated technologies . His focus areas include automated information-management systems for agriculture , electrochemical industries , compound feed production, and mining systems . He works on developing distributed information systems and software-hardware complexes that enhance control, efficiency, and sustainability of industrial processes . His interests extend into industrial and agricultural robotics, applying smart technologies to real-world production systems. He integrates geoinformatics, AI, and CAD into engineering curricula and projects . His interdisciplinary approach merges technical innovation with practical needs in automation, data processing, and system modeling . His international research footprint includes publications in journals from Bulgaria, Poland, Russia, and Switzerland , reflecting a strong commitment to global knowledge exchange. With a focus on advancing Industry 4.0 applications in Ukraine, Kiktev’s work contributes significantly to both academic knowledge and applied engineering development .

Award and Honor

Dr.  Nikolay Kiktev has received several distinctions over his career that recognize both academic excellence and applied innovation . He holds the official title of Associate Professor (2020) and earned his Candidate of Technical Sciences degree in 2011 for his pioneering work on electrochemical process automation . He was awarded two patents and three copyright certificates, demonstrating inventive contributions to process automation and information systems . With over 50 peer reviews  journals (such as MDPI) , he is an internationally recognized evaluator of scientific excellence. His extensive publication record includes 41 Scopus-indexed articles, reflecting high academic impact . He has contributed to national technology projects such as Ukraine’s “Digital City” education initiative . His leadership in these projects has had a transformative effect on digital infrastructure and education across Donetsk. With an h-index of 14, his influence in engineering research is substantial and growing .

Research Skill

Dr.  Nikolay Kiktev possesses robust research skills spanning automation, control systems, data analytics, and software-hardware integration . He is highly skilled in designing and modeling technological processes using modern CAD tools, developing distributed control systems, and implementing intelligent automation solutions . His expertise in electrochemical process modeling, experimental design, and algorithm development makes him a multidisciplinary innovator . He actively engages in patent drafting, system simulation, and software architecture for process management systems . With strong programming and data handling skills, he supervises student projects in data analytics and smart technology integration . His research involves cross-platform system development, geoinformatics, and IoT-based autonomous systems . As a seasoned educator, he imparts practical research techniques and computational methods to graduate and doctoral students . His contributions have strengthened Ukraine’s capacity in smart infrastructure and AI-enhanced automation systems .

Publication Top Notes

Title: Cyber security risk modeling in distributed information systems
Authors: D. Palko, T. Babenko, A. Bigdan, N. Kiktev, T. Hutsol, M. Kuboń, H. Hnatiienko, …
Journal: Applied Sciences, 2023
Citations: 43

Title: Automated microclimate regulation in agricultural facilities using the air curtain system
Authors: N. Kiktev, T. Lendiel, V. Vasilenkov, O. Kapralуuk, T. Hutsol, S. Glowacki, …
Journal: Sensors, 2021
Citations: 30

Title: Robotic platform for horticulture: assessment methodology and increasing the level of autonomy
Authors: A. Kutyrev, N. Kiktev, M. Jewiarz, D. Khort, I. Smirnov, V. Zubina, T. Hutsol, …
Journal: Sensors, 2022
Citations: 27

Title: Computer vision system for recognizing the coordinates location and ripeness of strawberries
Authors: D. Khort, A. Kutyrev, I. Smirnov, V. Osypenko, N. Kiktev
Conference: Int’l Conf. on Data Stream Mining and Processing, 2020
Citations: 27

Title: Robotized platform for picking of strawberry berries
Authors: D. Khort, A. Kutyrev, R. Filippov, N. Kiktev, D. Komarchuk
Conference: IEEE Int’l Scientific-Practical Conf., 2019
Citations: 26

Title: Neural network for identifying apple fruits on the crown of a tree
Authors: I. Smirnov, A. Kutyrev, N. Kiktev
Journal: E3S Web of Conferences, 2021
Citations: 25

Title: Simulation of Multi-Agent Architectures for Fruit and Berry Picking Robot in Active-HDL
Authors: N. Kiktev, A. Didyk, M. Antonevych
Conference:IEEE PIC S&T, 2020
Citations: 22

Title: Automated mobile hot mist generator: A quest for effectiveness in fruit horticulture
Authors: D. Khort, A. Kutyrev, N. Kiktev, T. Hutsol, S. Glowacki, M. Kuboń, T. Nurek, …
Journal: Sensors, 2022
Citations: 21

Title: Prioritizing Cybersecurity Measures with Decision Support Methods Using Incomplete Data
Authors: H. Hnatiienko, N.A. Kiktev, T. Babenko, A. Desiatko, L. Myrutenko
Conference: ITS, 2021
Citations: 21

Title: The role of innovation in economic growth: information and analytical aspect
Authors: O. Kalivoshko, V. Kraevsky, K. Burdeha, I. Lyutyy, N. Kiktev
Conference: IEEE PIC S&T, 2021
Citations: 19

Title: Financial infrastructure of telecommunication space: Accounting information attributive of syntalytical submission
Authors: V. Kraevsky, O. Kostenko, O. Kalivoshko, N. Kiktev, I. Lyutyy
Conference: IEEE Int’l Conf., 2019
Citations: 19

Title: Input data clustering for the efficient operation of renewable energy sources in a distributed information system
Authors: N. Kiktev, V. Osypenko, N. Shkurpela, A. Balaniuk
Conference: IEEE CSIT, 2020
Citations: 16

Title: Application of the Internet of Things Technology in the Automation of the Production of Compound Feed and Premixes
Authors: N.A. Kiktev, T. Lendiel, V. Osypenko
Journal/Conference: IT&I, 2020
Citations: 14

Title: Information model of traction ability analysis of underground conveyors drives
Authors: N. Kiktev, H. Rozorinov, M. Masoud
Conference: IEEE PTMR, 2017
Citations: 14

Conclusion

Dr. Nikolay Kiktev is a highly suitable candidate for the Best Researcher Award due to his exceptional contributions to the fields of automation, intelligent robotics, and cybersecurity. His research portfolio demonstrates a rare blend of interdisciplinary strength, combining advanced machine learning techniques with practical applications in agricultural robotics, computer vision, and distributed systems security. Notably, his works such as “Cybersecurity Risk Modeling in Distributed Information Systems” and “Computer Vision for Recognizing Strawberry Ripeness” have been widely cited, reflecting both academic recognition and practical relevance. Dr. Kiktev has consistently focused on solving real-world problems developing automated systems for climate control in agriculture, intelligent subsystems for feed accounting, and IoT-driven architectures for horticultural monitoring.

Chaoli Zhang | Computer Science | Best Researcher Award

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Assist. Prof. Dr ChaoliZhang | Computer Science | Best Researcher Award

Lecturer at Zhejiang Normal University, China

Dr. Chaoli Zhang is a Lecturer at the College of Computer Science and Technology, Zhejiang Normal University . He received his Ph.D. in Computer Science and Technology from Shanghai Jiao Tong University  and has previously worked at Alibaba DAMO Academy as a Senior Engineer . With deep expertise in time series anomaly detection, intelligent systems, and wireless data center networks , he has authored several influential papers in top-tier conferences and journals like IEEE ToN, KDD, and CIKM . He holds multiple patents in AI-driven fault detection and data analysis . Known for blending academic excellence with industrial innovation , he actively contributes to national and provincial-level research projects. His work has earned him prestigious recognitions, including a championship in a global 5G fault localization challenge . Dr. Zhang continues to push the boundaries of AI applications in realworld intelligent systems .

🔹Professional Profile

GOOGLE SCHOLAR

🎓 Education & Experience

Dr. Zhang obtained his bachelor’s degree in Information Security and Law from Nankai University (2011–2015)  and earned his Ph.D. from Shanghai Jiao Tong University (2015–2020) in Computer Science and Technology . After completing his doctorate, he worked from 2020 to 2023 at the Machine Intelligence Lab of Alibaba DAMO Academy , where he led advanced AI projects related to anomaly detection and intelligent monitoring . Since January 2024, he has served as a Lecturer at Zhejiang Normal University, where he continues research in AI and teaches advanced computing topics . His education blends theoretical depth with multidisciplinary training, while his work experience bridges top-tier academia and cutting-edge industry R&D . This combination allows him to explore highly applied, intelligent systems with real-world impact .

📈 Professional Development

Dr. Zhang has demonstrated rapid professional growth through impactful roles in both academia and industry . At Alibaba DAMO Academy, he focused on intelligent systems for real-time anomaly detection in large-scale infrastructure . He has since transitioned into academia, taking a faculty role at Zhejiang Normal University where he now leads funded research projects on smart healthcare analytics and IoT anomaly diagnostics . His professional development is characterized by an emphasis on translational research—converting algorithms into deployable solutions for real-world systems . As a project leader, he has secured competitive funding from the Zhejiang Natural Science Foundation and municipal science programs . Dr. Zhang regularly presents at global conferences (e.g., KDD, CIKM), reflecting his active engagement with the international research community . With a strong portfolio of publications, patents, and leadership, his professional path exemplifies AI-driven innovation and academic-industrial synergy .

🧠 Research Focus

Dr. Chaoli Zhang’s research interests lie at the intersection of time series anomaly detection, intelligent computing, and wireless data center networks . He develops novel algorithms for fault root cause analysis, time-frequency decomposition, and multivariate data analysis . His work on models like TFAD and DCdetector introduces advanced methods combining attention mechanisms, contrastive learning, and decomposition techniques for real-time monitoring . His recent projects also explore heterogeneous IoT anomaly detection and healthcare time series analysis, contributing to the development of robust, interpretable, and scalable AI systems . These innovations support applications in smart cities, cloud platforms, and industrial diagnostics ⚙️. With a foundation in graph modeling and deep learning, Dr. Zhang’s research aims to enhance system resilience, operational intelligence, and automation reliability across complex environments .

🏅 Awards & Honors

Dr. Zhang has earned several notable awards that reflect the excellence and impact of his research work . He was the champion of the 2022 SP Grand Challenge on 5G network fault root cause localization, prevailing over 338 global teams . His practical AI deployment solutions earned him the AAAI/IAAI’23 Deployed Application Innovation Award, one of only 10 globally recognized projects that year . He holds multiple Chinese patents related to time series analysis and cloud-based diagnostic methods 🔬, underscoring his ability to translate theory into tangible technological advances. His papers have been featured in leading journals and conferences, where he served as first or co-first author (IEEE ToN, CIKM, KDD, TCS) . These accolades highlight his cross-domain innovation, commitment to real-world impact, and leadership in the intelligent systems community .

🔹Publication of Top Notes

1.Transformers in Time Series: A Survey

Authors: Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun
Year: 2023
Citations: 1328

2.DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Authors: Y. Yang, C. Zhang, T. Zhou, Q. Wen, L. Sun
Year: 2023
Citations: 225

3.Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Authors: K. Zhang, Q. Wen, C. Zhang, R. Cai, M. Jin, Y. Liu, J.Y. Zhang, Y. Liang, …
Year: 2024
Citations: 222

4.Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

Authors: M. Jin, Q. Wen, Y. Liang, C. Zhang, S. Xue, X. Wang, J. Zhang, Y. Wang, …
Year: 2023
Citations: 166

5. Large Language Models for Education: A Survey and Outlook

Authors: S. Wang, T. Xu, H. Li, C. Zhang, J. Liang, J. Tang, P.S. Yu, Q. Wen
Year: 2024
Citations: 146

6.TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

Authors: C. Zhang, T. Zhou, Q. Wen, L. Sun
Year: 2022
Citations: 106

7.A Survey on Diffusion Models for Time Series and Spatio-Temporal Data

Authors: Y. Yang, M. Jin, H. Wen, C. Zhang, Y. Liang, L. Ma, Y. Wang, C. Liu, B. Yang, …
Year: 2024
Citations: 76

8.LogiCoT: Logical Chain-of-Thought Instruction-Tuning

Authors: H. Liu, Z. Teng, L. Cui, C. Zhang, Q. Zhou, Y. Zhang
Year: 2023
Citations: 51

9. Transformers in Time Series: A Survey (arXiv version)

Authors: Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun
Year: 2022
Citations: 45

10. Bringing Generative AI to Adaptive Learning in Education

Authors: H. Li, T. Xu, C. Zhang, E. Chen, J. Liang, X. Fan, H. Li, J. Tang, Q. Wen
Year: 2024
Citations: 43

11.Pricing and Allocation Algorithm Designs in Dynamic Ridesharing System

Authors: C. Zhang, J. Xie, F. Wu, X. Gao, G. Chen
Year: 2020
Citations: 35

12.Transformers in Time Series: A Survey (repeat entry, possibly updated citation)

Authors: Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun
Year: 2023
Citations: 23

13.AHPA: Adaptive Horizontal Pod Autoscaling on Alibaba Cloud Kubernetes

Authors: Z. Zhou, C. Zhang, L. Ma, J. Gu, H. Qian, Q. Wen, L. Sun, P. Li, Z. Tang
Year: 2023
Citations: 22

14.Free Talk in the Air: A Hierarchical Topology for 60 GHz Wireless Data Center Networks

Authors: C. Zhang, F. Wu, X. Gao, G. Chen
Year: 2017
Citations: 19

15.Logical Reasoning in Large Language Models: A Survey

Authors: H. Liu, Z. Fu, M. Ding, R. Ning, C. Zhang, X. Liu, Y. Zhang
Year: 2025
Citations: 14

16.Online Auctions with Dynamic Costs for Ridesharing

Authors:C. Zhang, F. Wu, X. Gao, G. Chen
Year:2017
Citations:14

17.NetRCA: An Effective Network Fault Cause Localization Algorithm

Authors: C. Zhang, Z. Zhou, Y. Zhang, L. Yang, K. He, Q. Wen, L. Sun
Year: 2022
Citations: 13

📌 Conclusion 

Dr. Chaoli Zhang exemplifies the ideal recipient of the Best Researcher Award due to his proven research excellence, industry-validated innovations, and impactful contributions across multiple disciplines. His work seamlessly bridges the gap between theoretical advancements and real-world applications, particularly in artificial intelligence, anomaly detection, and time series analysis. With a strong publication record in top-tier journals and conferences, and recognized achievements such as the SP Grand Challenge 2022 and the AAAI/IAAI Innovation Award, Dr. Zhang has demonstrated both academic depth and practical relevance. His leadership in developing AI-driven solutions for complex, large-scale systems solidifies his standing as one of the top emerging voices in the field. These accomplishments collectively make him exceptionally worthy of recognition as a Best Researcher Award.

Seyed Hessameddin Zegordi | Mathematical Modeling | Best Researcher Award

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Prof. Dr.Seyed Hessameddin Zegordi | Mathematical Modeling | Best Researcher Award

Tarbiat Modares University, Iran

Professor Seyed Hessameddin Zegordi  is a distinguished academic in Industrial & Systems Engineering at Tarbiat Modares University, Tehran. With an impressive career spanning over three decades , he has made lasting contributions to academia, industry, and government. Holding a Ph.D. from Tokyo Institute of Technology , he has specialized in optimization, production systems, and supply chain disruption management. He served as department head for 9 years and contributed as a research and education deputy for 3 years . Beyond academia, Dr. Zegordi has consulted for top industrial firms and government ministries on business process reengineering and strategic systems design . Fluent in English and Japanese , he bridges global knowledge with local impact. With an h-index of 28 , he remains a thought leader in operational research and smart industrial strategies. His lifelong dedication to engineering education and innovation continues to shape the next generation of industrial experts .

Professional Profile

SCOPUS

ORCID

Education & Experience 

Professor Zegordi earned his BSc in Industrial Engineering from Isfahan University of Technology (1987), followed by an MSc from Sharif University of Technology (1990), and later, a Ph.D. in Industrial Engineering & Management from Tokyo Institute of Technology (1994) . Starting as an Assistant Professor in 1994, he rose to become a Full Professor by 2016 . He has held key academic roles including Head of Department for 9 years and Research Deputy at the Engineering Faculty for 3 years 🏢. His industrial journey began even earlier — as General Manager at Avand Plastic and Production Planning Director at Darou Pakhsh . Since 2000, he has served as Strategy Advisor at Iran Khodro, one of the country’s largest automakers . His dual-track experience in academia and industry reflects a rare blend of theoretical depth and practical leadership, making him a mentor and innovator in industrial systems development .

Professional Development 

Prof. Zegordi has steadily advanced in both academia and industry . Starting his academic journey in 1994 as Assistant Professor, he moved up to Full Professor by 2016, based on his research, mentorship, and teaching excellence . He has led multiple strategic roles including Department Head, Research Deputy, and Education Advisor, influencing curriculum design, faculty development, and cross-disciplinary collaboration . His expertise in optimization, layout design, and supply chain disruption has made him a key consultant for national ministries and major companies like Iran Khodro . He has supervised numerous PhD and Master’s theses and is regularly invited to review scientific publications and contribute to engineering textbooks . Fluent in English and Japanese , he frequently collaborates internationally and has translated significant academic work into Farsi. Prof. Zegordi embodies a commitment to bridging theory and application in the dynamic world of industrial engineering and systems innovation .

Research Focus 

Prof. Zegordi’s research lies at the intersection of operations research, production systems, and supply chain engineering . He specializes in mathematical modeling, intelligent optimization techniques like simulated annealing and genetic algorithms, and performance measurement systems . A pioneer in disruption management, he has developed robust strategies for supply chain continuity amid uncertainties . His work in Business Process Reengineering (BPR) has been instrumental for Iran’s Ministries of Foreign Affairs, Labor, and Cooperatives, guiding institutional transformation using data-driven methods . Other focal areas include facility layout, quality function deployment (QFD), and lean manufacturing systems . By combining computational models with strategic frameworks, he enhances both tactical decision-making and long-term resilience in operations . His multidisciplinary approach, integrating engineering, management, and technology, positions him as a key contributor to the evolution of smart and sustainable industrial ecosystems .

Awards & Honors 

While specific awards were not listed, Prof. Zegordi’s accolades are reflected in his long-standing leadership, international collaborations, and scholarly achievements . With an h-index of 28 on Google Scholar , he has significantly influenced the fields of industrial engineering, optimization, and supply chain management. His publications, translated books, and invited book chapters with Springer and IGI Global demonstrate global recognition . Serving as an academic advisor and curriculum developer for nearly 30 years, he has guided numerous students to successful academic and industry careers . His selection as a consultant for national ministries and major companies like Iran Khodro signifies his trustworthiness and strategic vision at a national level . Furthermore, his multilingual abilities and cross-cultural education in Japan enhance his role in international partnerships . These cumulative honors underscore Prof. Zegordi’s reputation as a respected, forward-thinking, and impactful researcher and educator in the global engineering community .

Publication of Top Notes

1.Proposing a Model Based on Deep Reinforcement Learning for Real‑Time Scheduling of Collaborative Customization Remanufacturing
  • Authors: Seyed Ali Yazdanparast; Seyed Hessameddin Zegordi; Toktam Khatibi
  • Year: 2025 (published February 18, early issue dated August 2025)
  • Journal: Robotics and Computer‑Integrated Manufacturing, Vol. 94, Article 102980 sciencedirect.com+9booksci.cn+9dblp.org+9
  • DOI: 10.1016/j.rcim.2025.102980
  • Summary: The study presents a multi-agent deep Q-network approach to schedule real-time tasks in remanufacturing customized products. Simulations in a smartphone assembly context (46,656 configurations) show the approach outperforms combined genetic algorithms, reducing factory cost by over 6%. This model addresses the challenge of disruption and real-time rescheduling in remanufacturing lines efficiently dblp.org+8booksci.cn+8ivysci.com+8.
2.The Type of Supplier Involvement in New Product Development in the Automotive Industry: Metaheuristic-based K-Means Clustering and Analytic Hierarchical Process Methods
  • Authors: Esmat Taghipour Anari; Seyed Hessameddin Zegordi; Amir Albadvi
  • Year: 2025 (published online January 14)
  • Journal: Journal of Advances in Management Research
  • DOI: 10.1108/JAMR‑03‑2024‑0095
  • Summary: This article investigates supplier roles in automotive product development. Combining metaheuristic clustering and AHP methods, it categorizes supplier involvement types and their strategic importance. Contributions include a novel classification framework guiding supplier integration and collaboration in innovation contexts.
3.An Integrated System Dynamics Model of Electricity Production, Consumption, and Export Policy in Iran Considering Carbon Emissions
  • Authors: Maryam Doroodi; Bakhtiar Ostadi; Ali Husseinzadeh Kashan; Seyed Hessameddin Zegordi
  • Year: 2024 (October)
  • Journal: Utilities Policy
  • DOI: 10.1016/j.jup.2024.101795
  • Summary: Using system dynamics, the study models Iran’s electricity sector, forecasting production, consumption, and export policies under carbon constraints. The model quantifies trade-offs between energy policy and emissions targets, offering strategic insights for policy adaptation.
4.A Sustainable Supply Chain Model for Time‑Varying Deteriorating Items Under Promotional Cost-Sharing Policy and Three‑Level Trade Credit Financing
  • Authors: Leyla Aliabadi; Seyed Hessameddin Zegordi; Ali Husseinzadeh Kashan; Mohammad Ali Rastegar
  • Year: 2024 (June)
  • Journal: Operational Research
  • DOI: 10.1007/s12351‑024‑00824‑x
  • Summary: This research develops a sustainable supply chain framework for perishable goods, incorporating promotional cost-sharing and multi-tier trade credit. The model optimizes inventory planning, financing, and pricing under varying deterioration rates, improving profit and sustainability.
5.A Tailored Meta-Heuristic for the Autonomous Electric Vehicle Routing Problem Considering the Mixed Fleet
  • Authors: Maryam Farahani; Seyed Hessameddin Zegordi; Ali Husseinzadeh Kashan
  • Year: 2023
  • Journal: IEEE Access
  • DOI: 10.1109/ACCESS.2023.3237481
  • Summary: The article proposes a customized metaheuristic algorithm to route autonomous electric vehicles in mixed fleets. The algorithm balances cost, distance, emissions, and charging constraints, offering improved routing efficiency and environmental impact.
6.A Multi‑Stage Stochastic Programming Approach for Supply Chain Risk Mitigation via Product Substitution
  • Authors: Seyed Mahdi Ghorashi Khalilabadi; Seyed Hessameddin Zegordi; Ehsan Nikbakhsh
  • Year: 2020 (November)
  • Journal: Computers & Industrial Engineering
  • DOI: 10.1016/j.cie.2020.106786
  • Summary: This study formulates a stochastic programming model that enables product substitution to buffer against supply chain disruptions. The multi-stage approach balances cost, service level, and risk, offering a quantitative tool for resilient operations planning.

Conclusion

 Prof. Zegordi’s contributions bridge research innovation and strategic industrial impact. His publications in IEEE Access, Robotics and Computer-Integrated Manufacturing, and Computers & Industrial Engineering emphasize both technical rigor and societal relevance. His leadership, academic service, and collaborative industry roles make him a strong and deserving candidate for the Best Researcher Award in engineering and systems sciences.