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.

Deqian Fu | Data Science and Analytics | Best Researcher Award

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Prof. Dr. Deqian Fu | Data Science and Analytics | Best Researcher Award

Professor | Linyi University | China

Prof. Dr. Deqian Fu is a prominent researcher at Linyi University, China, with a strong focus on logistics, data exchange, and trust management in supply chain and intermodal transport systems. His research explores innovative methods for secure and efficient data sharing in the logistics industry, integrating advanced technologies such as blockchain, edge computing, and ontology-based frameworks. Fu has made notable contributions in developing trusted data access control mechanisms and non-intrusive data exchange models that enhance collaboration and operational efficiency across complex logistics networks. He has authored 39 publications, which have collectively garnered 127 citations, reflecting the growing impact of his work in the fields of applied sciences and industrial informatics. His research outputs demonstrate a commitment to advancing the intersection of information technology and logistics, emphasizing both theoretical development and practical applications. With an h-index of 7, Fu’s scholarly contributions have been recognized for their relevance and innovation, particularly in promoting secure and intelligent data-sharing frameworks within the logistics sector. Selected works include “Trusted Data Access Control Based on Logistics Business Collaboration Semantics” in Applied Sciences (2024), alongside conference papers such as “Data Exchange and Sharing Framework for Intermodal Transport Based on Blockchain and Edge Computing” and “Trusted Non-intrusive Data Exchange based on Ontology in Logistics Industry,” underscoring his focus on reliable, technology-driven logistics solutions.

Profiles : ORCID | Scopus 

Featured Publications

1. Wang, W., Li, Q., Jiang, Z., Fu, D., & Camacho, D. (2025). An efficient framework for general long-horizon time series forecasting with Mamba and diffusion probabilistic models. Engineering Applications of Artificial Intelligence.

2.Liu, Z., Shi, Z., Wang, W., Kong, R., Fu, D., & Qiu, J. (2025). Research on data ownership and controllable sharing schemes in the process of logistics data flow.

3.Wang, L., Zhang, X., Xu, L., Fu, D., & Qiu, J. (2024). Data exchange and sharing framework for intermodal transport based on blockchain and edge computing. In Communications in Computer and Information Science. Springer.

4.Zhang, X., Jing, C., Chen, Y.-C., Wang, L., Xu, L., & Fu, D. (2024). Trusted data access control based on logistics business collaboration semantics.

5.Zhang, X., Wang, L., Xu, L., & Fu, D. (2023). A distributed logistics data security sharing model based on semantics and CP-ABE. In Proceedings of the ACM International Conference (pp. 1–8).

Jingsheng Feng | Decision Sciences | Best Paper Award

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Mr. Jingsheng Feng | Decision Sciences | Best Paper Award

Research Assistant | Hefei University of Technology | China

Dr. Jingsheng Feng, a distinguished researcher at Hefei University of Technology, China, focuses on advanced logistics network optimization, supply chain resilience, and intelligent decision-making systems. His research integrates mathematical modeling, fuzzy logic, and multi-objective optimization to tackle complex challenges in customized logistics and industrial engineering. Notably, his work published in Computers & Industrial Engineering presents a reliable logistics network design model incorporating demand sensitivity to correlated disruptions, enhancing system robustness under uncertainty. In the International Journal of Systems Science: Operations & Logistics (, he co-developed disruption response strategy models for supplier selection and order allocation to support firms in maintaining operational stability during disruptions. His study in Expert Systems with Applications proposed fuzzy multi-objective team decision models for customer order decoupling point (CODP) and supplier selection, facilitating collaborative and data-driven decision-making in customized logistics supply chains. Additionally, his  work in Computers & Industrial Engineering explored battery swapping service network deployment, addressing behavioral factors such as driver range anxiety and impatience. Through his interdisciplinary contributions, Dr. Feng bridges theory and practice in logistics and supply chain engineering, promoting demand responsiveness, risk mitigation, and intelligent system design to advance sustainable, adaptive, and human-centered logistics strategies for modern industrial ecosystems. Her research impact is evident from 15 citations across 4 documents with an h-index of 2.

Profiles : ORCID | Scopus 

 

Featured Publications


1 .Feng, J., Hu, X., Xu, L., Luo, S., & Chen, J. (2025). Reliable logistics network design joint optimization problem applying demand sensitivity to correlated disruptions. Computers & Industrial Engineering.

2. Xu, L., Hu, X., Wu, Z., Luo, S., Feng, J., & Zhang, X. (2025). Disruption response strategy models for supplier selection and order allocation in customised logistics service supply chain. International Journal of Systems Science: Operations & Logistics.

3. Xu, L., Hu, X., Zhang, Y., Feng, J., & Luo, S. (2024). A fuzzy multiobjective team decision model for CODP and supplier selection in customized logistics service supply chain. Expert Systems with Applications, 213, 121387.

4. Hu, X., Zhang, X., Xu, L., Feng, J., & Luo, S. (2024). The battery swapping service network deployment problem: Impact of driver range anxiety and impatience. Computers & Industrial Engineering, 172, 110189.

Manammel Thankappan Ramesan | Optoelectronics | Best Researcher Award

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Dr. Manammel Thankappan Ramesan | Optoelectronics | Best Researcher Award

Professor | University of Calicut | India

Dr. Ramesan Manammel Thankappan, affiliated with the University of Calicut, India, is a leading researcher in polymer nanocomposites and multifunctional materials, with 209 publications, 5,148 citations, and an h-index of 48. His research focuses on the design, synthesis, and functionalization of polymer-based nanocomposites, targeting enhanced structural, optical, electrical, thermal, and antibacterial properties.He has made significant contributions to optoelectronics, energy storage, environmental sensing, and photocatalysis, often integrating eco-friendly and sustainable approaches. Key areas include metal-oxide-reinforced polymers (CuO, Mn₂O₃, lithium silver oxide), biopolymer-functionalized composites (chitosan, nanocurcumin), and hybrid systems such as boehmite- or titanium dioxide-infused matrices. His studies have demonstrated improved mechanical, dielectric, optical, and gas-sensing properties, bridging fundamental materials science with practical applications. Dr. Thankappan emphasizes green processing and high-performance material design, aligning sustainability with technological innovation. Notable works include chitosan-functionalized poly(thiophene-co-pyrrole) nanocomposites, CuO-reinforced polythiophene and polyindole systems, and poly(diphenylamine) composites for energy applications.Through a prolific publication record, interdisciplinary collaborations, and high citation impact, Dr. Thankappan has established a strong reputation in the development of versatile, multifunctional polymer nanocomposites, providing solutions for advanced energy devices, biomedical applications, optoelectronic systems, and environmental technologies.

Profiles : ORCID | Scopus 

Featured Publications

1. Ramesan, M. T., & Co-authors. (n.d.). Manganese (III) oxide-infused poly(thiophene-co-pyrrole) nanocomposites for optical, dielectric, and photocatalytic applications. Cited By : 2

2.Ramesan, M. T., & Co-authors. (n.d.). Tailoring poly(diphenylamine) with lithium silver oxide nanoparticles: Impact on structural, optical, and electrical properties for energy storage. Cited By : 1

3.Ramesan, M. T., & Co-authors. (n.d.). Synthesis of nanocurcumin conjugated titanium dioxide bio-nanocomposites for enhanced optical, electrical, and antibacterial applications. Cited By : 1

4.Ramesan, M. T., & Co-authors. (n.d.). Multifunctional chitosan/chloro-aspirin composites for energy storage and biomedical applications.

5.Ramesan, M. T., & Co-authors. (n.d.). Facile green fabrication of boehmite-infused PVA nanocomposites with superior mechanical, thermal, and electrical performance. Cited By : 1

Hadi Gokcen | Engineering | Best Researcher Award

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Prof. Hadi Gokcen | Engineering | Best Researcher Award

Professor | Gazi University Industrial Engineering Department | Turkey

Dr. Hadi Gökçen, affiliated with Gazi University, Ankara, Turkey, is a distinguished researcher recognized for his influential contributions to industrial engineering, operations research, and computational intelligence. With 51 published documents, an h-index of 23, and more than 1,920 citations from 1,367 citing documents, his scholarly impact spans data-driven decision systems, intelligent manufacturing, and applied artificial intelligence. His recent works reflect a strong integration of machine learning, optimization, and sustainability in solving real-world industrial and economic problems. In Computational Economics , he introduced a hybrid machine learning model that combines clustering and stacking ensemble approaches for improved real estate price prediction. His research published in Applied Sciences Switzerland, proposed a dynamic scheduling method for identical parallel-machine environments through a multi-purpose intelligent utility framework. In Flexible Services and Manufacturing Journal, he presented innovative balancing and sequencing strategies for mixed-model parallel robotic assembly lines, emphasizing energy-efficient production. Further, his Survey Review paper applied hybrid unsupervised learning to identify sub-real estate markets, enhancing prediction accuracy and market segmentation. His contribution to developing a Digital Transformation Perception Scale underscores his focus on organizational innovation and industrial adaptation within the Industry paradigm. Dr. Gökçen’s interdisciplinary research bridges artificial intelligence, optimization, and digital transformation, advancing the understanding and implementation of intelligent, sustainable, and adaptive systems in engineering and economic domains.

Profiles : ORCID | Scopus | Google Scholar 

Featured Publications

1. Demirel, N. Ö., & Gökçen, H. (2008). A mixed integer programming model for remanufacturing in reverse logistics environment. The International Journal of Advanced Manufacturing Technology, 39(11), 1197–1206.
Cited By : 258

2. Demirel, E., Demirel, N., & Gökçen, H. (2016). A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey. Journal of Cleaner Production, 112, 2101–2113.
Cited By : 247

3. Gökçen, H., Ağpak, K., & Benzer, R. (2006). Balancing of parallel assembly lines. International Journal of Production Economics, 103(2), 600–609.
Cited By : 226

4. Gökçen, H. (2007). Yönetim bilgi sistemleri. Ankara: Palme Yayıncılık.
Cited By : 217

5. Erel, E., & Gökçen, H. (1999). Shortest-route formulation of mixed-model assembly line balancing problem. European Journal of Operational Research, 116(1), 194–204.
Cited By : 189

Mohammad Taghilou | Engineering | Best Researcher Award

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Assoc. Prof. Dr. Mohammad Taghilou | Engineering | Best Researcher Award

Associate professor | University of Zanjan | Iran

Dr. Mohammad Taghilou is an Associate Professor in the Department of Mechanical Engineering at the University of Zanjan, Iran. His research expertise lies in heat transfer, phase change problems, energy storage, and porous media, with an emphasis on the lattice Boltzmann method (LBM) and its applications in thermal systems. Over his academic career, he has authored impactful studies published in leading journals such as Applied Thermal Engineering, Computers & Mathematics with Applications, and the International Journal of Thermal Sciences. His most cited works explore PCM solidification, nanofluid-based heat exchangers, and the thermal behavior of energy storage systems. Dr. Taghilou’s studies significantly contribute to advancing thermal management technologies, including applications in lithium-ion batteries, heat sinks, and double-pipe exchangers, with an aim to enhance energy efficiency and system reliability. His collaborations with international scholars from institutions such as the University of Tehran, Aalto University (Finland), and the University of Technology Sydney have expanded the interdisciplinary reach of his research. With 541 total citations, an h-index of 15, and an i10-index of 16, his work demonstrates both academic impact and global relevance. Through innovative numerical modeling and experimental approaches, Dr. Taghilou continues to advance understanding in phase-change thermal systems and nanomaterial-enhanced heat transfer, fostering sustainable energy applications and modern engineering solutions.

Profiles: ORCID | Scopus | Google Scholar | ResearchGate | LinkedIn

Featured Publications

1.Sajedi, R., Osanloo, B., Talati, F., & Taghilou, M. (2016). Splitter plate application on the circular and square pin fin heat sinks.  Cited by : 70

2.Taghilou, M., & Rahimian, M. H. (2014). Investigation of two-phase flow in porous media using lattice Boltzmann method. Cited By : 49

3.Talati, F., & Taghilou, M. (2015). Lattice Boltzmann application on the PCM solidification within a rectangular finned container. Cited By : 44

4.Taheri, A. A., Abdali, A., Taghilou, M., Alhelou, H. H., & Mazlumi, K. (2021). Investigation of mineral oil-based nanofluids effect on oil temperature reduction and loading capacity increment of distribution transformers.
Cited By : 40

5.Taghilou, M., & Khavasi, E. (2020). Thermal behavior of a PCM filled heat sink: The contrast between ambient heat convection and heat thermal storage. Cited By : 36

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

Amanpreet Kaur | Blockchain and Deep Learning | Best Researcher Award

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Dr. Amanpreet Kaur | Blockchain and Deep Learning | Best Researcher Award

Professor | Chitkara University | India

Dr. Amanpreet Kaur is an accomplished researcher in computer science with expertise in artificial intelligence, blockchain, IoT, big data analytics, and machine learning. She earned her PhD in computer science from Jaipur National University after completing advanced degrees including M.Tech, M.Sc. in computer science, B.C.A., and a diploma in computer engineering. Her professional journey includes teaching, mentoring, and supervising M.Tech and Ph.D. scholars in cutting-edge research projects such as deep learning for healthcare, blockchain-based security frameworks, and network optimization. She has authored 64 publications in reputed IEEE and Scopus indexed journals and conferences, showcasing her consistent contributions to the academic community. Her research interests lie in deep learning, data security, smart networks, and sustainable computing technologies. She has been an active member of several professional organizations including IRED, IAENG, SDIWC, and CSTA, and holds multiple certifications such as MCP, Java, Python, and Software Testing, which strengthen her applied research profile. Her work reflects a strong blend of academic scholarship, technical expertise, and leadership, enabling her to play a key role in advancing research and guiding future professionals. She is recognized for her dedication to fostering innovation and contributing to the growth of the computer science domain. Her research impact is demonstrated by 722 citations across 57 documents with an h-index of 9.

Profiles : ORCID | Scopus | Google Scholar

Featured Publications

1.Amanpreet Kaur, S. S., Singh, G., Kukreja, V., Yoon, B., & Sharma, S. (n.d.). Adaptation of IoT with blockchain in food supply chain management: An analysis-based review in development, benefits and potential applications. Sensors, 22(21).

2.Kukreja, V. S., Sharma, R., Kaur, A., & Sachdeva, R. K. (n.d.). Deep neural network for multi-classification of parsley leaf spot disease detection. In Proceedings of the 2nd International Conference on Advance Computing and Innovative Technologies.

3.Kaur, A., Dhaka, V. S., & Singh, G. (n.d.). ACO agent-based routing in AOMDV environment. MATEC Web of Conferences, 57, 02005.

4.Bathla, N., Kaur, A., & Singh, G. (n.d.). Relative inspection of TCP variants Reno, New Reno, SACK, Vegas i8285n AODV. International Journal of Research in Engineering and Applied Sciences, 4.

5.Bathla, N., Kaur, A., & Singh, G. (n.d.). Congestion control techniques in TCP: A critique. In Proceedings of the 3rd National Conference of Advances and Research in Engineering.

Dayu Jia | Big Data Management | Best Researcher Award

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Mr. Dayu Jia | Big Data Management | Best Researcher Award

Associate Professor | Liaoning University | China

Dr. Jia Dayu is an Associate Professor and Master’s Supervisor at the School of Information Science, Liaoning University. He completed his Ph.D. in Computer Science at Northeastern University under the guidance of Prof. Wang Guoren and further gained international experience as a joint Ph.D. student at the National University of Singapore under Prof. Ooi Beng Chin. He also worked as a postdoctoral fellow at the School of Information Science and Engineering, Northeastern University. Dr. Jia has been actively involved in national, provincial, and ministerial research projects and has collaborated on international research initiatives. His research focuses on big data management, blockchain data analysis, and artificial intelligence, with expertise in scalable storage, secure data retrieval, and privacy-preserving techniques. He has published 21 high-quality papers in reputed journals and conferences, including Q1 journals such as Advanced Materials and Light: Science & Applications, and has served as the first or corresponding author on 12 publications in prestigious venues like JCST, WWW, and Software Journal. Dr. Jia has also been granted 13 national invention patents, demonstrating his innovative contributions, and has hosted or participated in six funded research projects. His skills include blockchain architecture design, data analytics, AI-driven optimization, and secure distributed systems. His work has earned recognition with 176 citations by 14 documents and an h-index of 5, reflecting the impact and relevance of his research in the academic community.

Profile : Scopus 

Featured Publication

1. Jia, D., Hu, Y., Huang, M., Zhang, J., He, G., Xu, S., Liu, S., & Wang, X. (2025). Security risks and solutions of concurrent PBFT. Expert Systems with Applications, 294, 128737.

Sakshi Dua | Engineering | Best Researcher Award

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Assoc. Prof. Dr. Sakshi Dua | Engineering | Best Researcher Award

Associate Professor | Lovely Professional University | India

Dr. Sakshi Dua is an accomplished academic and researcher currently serving as Associate Professor at the School of Computer Applications, Lovely Professional University, Jalandhar-Phagwara, Punjab, India. She holds a Ph.D. in Computer Science and has over 14 years of professional experience as Assistant Professor before her current role. Her research interests span artificial intelligence, Internet of Things, Arduino, machine learning, fuzzy systems, network operating systems, and database management systems. She has contributed as Guest Editor for reputed ABDC and Scopus-indexed journals, authored book chapters with CRC Press, Taylor & Francis, and IGI Global, and is actively involved in book editorial projects with CRC Press and Emerald. She has published widely in SCIE, Scopus, ABDC, and UGC-indexed journals, as well as in IEEE and Springer conferences, and has presented her research internationally. Her contributions extend to applied innovation with patents and copyrights in diverse areas such as smart healthcare, ICT, and IoT-based solutions. She has chaired sessions at IEEE conferences, delivered workshops and FDPs, and guided students through impactful academic and research projects. Her skills include advanced data analysis, algorithm design, applied AI and IoT development, research writing, and academic leadership. Dr. Sakshi Dua has earned recognition through her impactful scholarly work, editorial leadership, and strong community engagement. She has received 71 citations by 9 documents with an h-index of 1.

Profile :  Scopus

Featured Publication

1. Dua, S. (2025). Blockchain-based node authentication algorithm for securing electronic health record data transmission.