Md. Touhidul Islam | AI in Business | Best Researcher Award

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Assist. Prof. Dr. Md. Touhidul Islam | AI in Business | Best Researcher Award

Assistant Professor at NPI University of Bangladesh | Bangladesh

Assist. Prof. Dr. Md. Touhidul Islam is a dedicated academic and researcher in Business Administration whose work spans marketing thought, strategic service excellence, customer behavior, and the evolving dynamics of online commerce. His professional identity is shaped by a commitment to student-centered teaching, scholarly contribution, and institutional development, reflected in his extensive involvement in academic instruction, curriculum enhancement, quality assurance activities, and mentorship. He has built a strong research footprint across areas such as service marketing, customer satisfaction, sustainable online business practices, agile and neuro-marketing, corporate responsibility, product and service quality, and digital consumer experiences, contributing numerous studies to international journals and collaborating on diverse research initiatives in emerging marketing contexts. His publications explore themes including cashless economies, ethical leadership, blockchain opportunities in hospitality, green innovations, artificial intelligence in business, logistics strategies, and cross-industry consumer perceptions, illustrating both analytical breadth and practical relevance. Beyond journal work, he has authored an academic book on Human Resource Information Systems and contributed to a globally published book chapter on AI-driven transformations in customer relationship service, extending his influence into specialized business and technology domains. His teaching philosophy emphasizes education as a purposeful and student-driven service that nurtures critical thinking, real-world application, and intellectual curiosity. He champions blended and flexible teaching methods, the use of contemporary business examples, and learning environments built on receptiveness, engagement, and reflective practice. Alongside teaching and research, he contributes to the global academic community through editorial and reviewer roles for multiple international journals, helping uphold scholarly rigor and support emerging researchers. Continuously expanding his academic horizon through advanced research and interdisciplinary inquiry, he represents a blend of pedagogical commitment, research-driven insight, and forward-looking academic leadership.

Profile: Google Scholar

Featured Publications

Islam, M. T., Hasan, M. M., Redwanuzzaman, M., & Hossain, M. K. (2024). Practices of artificial intelligence to improve the business in Bangladesh.

Islam, M. T. (2023). Newly developed green technology innovations in business: Paving the way toward sustainability.

Islam, M. T. (2019). Future impact of 4G on business in Bangladesh.

Islam, M. T. (2019). Market failure: Reasons and its accomplishments.

Islam, M. T., & Hasan, M. T. (2016). Corporate social responsibility of commercial banks in Bangladesh: A comparative study on nationalized and private banks.

Xuewen Dong | Network Security | Best Researcher Award

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Prof. Xuewen Dong | Network Security | Best Researcher Award

Professor at Xidian University | China

Professor Xuewen Dong is a distinguished scholar recognized for his influential contributions to wireless network security, AI security, and service intelligence, playing a pivotal role in advancing secure and intelligent computing technologies. His research spans a wide spectrum of critical domains, including mobile edge computing, blockchain scalability, adversarial machine learning, differential privacy, federated learning, and large-scale distributed systems. Through his extensive publication record in leading international journals and premier global conferences, he has consistently delivered innovative solutions that address emerging challenges in data privacy, intelligent connectivity, and trustworthy AI. His work on autonomous aerial vehicle–assisted computing, backdoor attack modeling, privacy-attack frameworks, and high-performance blockchain mechanisms demonstrates a unique ability to merge theoretical rigor with practical applicability, contributing significantly to the evolution of next-generation digital ecosystems. Beyond his research achievements, he is widely respected for his leadership within the academic community, offering strategic guidance, fostering collaborative research environments, and supporting interdisciplinary advancements across intelligent security technologies. His roles in major research centers and professional committees highlight his dedication to shaping technological development, mentoring the next generation of innovators, and strengthening global standards in secure computing practices. Over the course of his accomplished career, he has earned multiple prestigious recognitions for technological innovation, excellence in computing research, contributions to regional software development, and impactful guidance in academic competitions. These honors reflect his enduring influence and the far-reaching impact of his work across the fields of computer science and intelligent systems. With a strong commitment to scientific progress, innovation, and the responsible advancement of digital technologies, Professor Dong continues to be a driving force in the global pursuit of secure, adaptive, and intelligent computational infrastructures.

Profile: Google Scholar

Featured Publications

Tong, W., Dong, X., & Zheng, J. (2019). Trust-PBFT: A peer-trust-based practical Byzantine consensus algorithm.

Tong, W., Dong, X., Shen, Y., & Jiang, X. (2019). A hierarchical sharding protocol for multi-domain IoT blockchains.

Dong, X., Wu, F., Faree, A., Guo, D., Shen, Y., & Ma, J. (2019). Selfholding: A combined attack model using selfish mining with block withholding attack.

Yang, L., Dong, X., Xing, S., Zheng, J., Gu, X., & Song, X. (2019). An abnormal transaction detection mechanism on Bitcoin.

Gao, S., Chen, X., Zhu, J., Dong, X., & Ma, J. (2022). TrustWorker: A trustworthy and privacy-preserving worker selection scheme for blockchain-based crowdsensing.

Mahendra Gaikwad | Machine Learning | Best Researcher Award

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Dr. Mahendra Gaikwad | Machine Learning | Best Researcher Award

Assistant Professor at Veermata Jijabai Technological Institute (VJTI) | Mumbai | India

Dr. Mahendra Uttam Gaikwad is a forward-thinking mechanical and manufacturing engineering professional whose work reflects a deep commitment to advancing modern machining, smart materials research, sustainable manufacturing, and AI-driven optimization in industrial systems. Renowned for his ability to bridge theoretical innovation with practical engineering applications, he has built a strong scholarly footprint through impactful publications in SCI and Scopus-indexed journals, contributions to influential book chapters, and editorial leadership in notable international volumes focused on advanced materials and digital-age manufacturing. His research explores critical themes such as electrical discharge machining, surface integrity analysis, optimization algorithms, additive manufacturing, fatigue modelling, and machine learning applications in production environments, consistently demonstrating an aptitude for tackling complex engineering challenges through empirical investigation and computational modelling. In addition to his academic contributions, he has shown commendable innovation through multiple national and international patents addressing smart systems, sustainable material utilization, and intelligent manufacturing solutions. He has also been an active collaborator with academic institutions, research groups, and industry partners, contributing to advancements in machining automation, performance benchmarking, and data-driven design methodologies. A dedicated mentor, he has guided numerous undergraduate and postgraduate research projects, fostering a research-oriented learning environment and supporting the next generation of engineers. His work as a reviewer, conference contributor, and knowledge disseminator further underscores his commitment to strengthening global engineering discourse. Known for his leadership qualities, professional integrity, and continuous pursuit of technological excellence, Dr. Gaikwad has earned recognition for his contributions to teaching and research, positioning himself as a noteworthy contributor to the evolving landscape of smart and sustainable manufacturing.

Profiles: ORCID | Google Scholar

Featured Publications

Gaikwad, M. U., Somatkar, A. A., Ghadge, M., Majumder, H., Shinde, A. M., & Lohakare, A. V. (2025). Effect of dry and wet machining environments on surface quality of Al6061 using particle swarm optimization (PSO).

Sargar, T., Gautam, N. K., Jadhav, A., & Gaikwad, M. U. (2025). A comparative investigation of kerf width during CO₂ and fiber laser machining of SS 316L material.

Khan, M. A. J., Pohekar, S. D., Bagade, P. M., Gaikwad, M. U., & Singh, M. (2025). CFD analysis of NACA 4415 marine propeller ducts for managing flow separation.

Nishandar, S. V., Pise, A. T., Bagade, P. M., Gaikwad, M. U., & Singh, A. (2025). Computational modelling and analysis of heat transfer enhancement in straight circular pipe with pulsating flow.

Gaikwad, M. U., Gaikwad, P. U., Ambhore, N., Sharma, A., & Bhosale, S. S. (2025). Powder bed additive manufacturing using machine learning algorithms for multidisciplinary applications: A review and outlook.

Ean Teng Khor | Learning Analytics | Best Researcher Award

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Dr. Ean Teng Khor | Learning Analytics | Best Researcher Award

Faculty at Nanyang Technological University | Singapore

Dr. Ean Teng Khor is a distinguished scholar in the fields of learning analytics, educational technology, and data-driven approaches to teaching and learning, recognized for advancing innovative methodologies that enhance how learners interact with digital environments. With a strong academic foundation in information technology, she has developed an interdisciplinary research portfolio centered on machine learning, data mining, social network analysis, and artificial intelligence applications in education. Her work consistently bridges theoretical insight with real-world educational challenges, exploring how data can meaningfully inform instructional decision-making, personalize learning pathways, and support predictive models that improve student performance. She has contributed extensively to international journals through influential publications that examine emerging trends such as multimodal learning analytics, generative AI feedback, intelligent educational chatbots, and networked learning in professional settings. Her scholarship also includes systematic reviews that synthesize global research on personalized learning, MOOCs, workplace learning behaviors, and K-12 teacher experiences with data literacy. Known for her ability to articulate complex analytical approaches in ways that are accessible to educators and policymakers, she plays a significant role in shaping conversations around the responsible and impactful use of AI in education. Her collaborations with multidisciplinary teams reflect her commitment to enhancing both technological design and pedagogical practice, ensuring that learning technologies are grounded in evidence-based strategies that support diverse learners. Through her leadership in research, teaching, and academic community engagement, she continues to influence the development of more intelligent, equitable, and data-empowered learning ecosystems, earning recognition as an important contributor to the future of technology-enhanced education.

Profiles: Scopus | ORCID

Featured Publications

Khor, E. T., Chee, A., & Lee, S. S. (2025). Beyond the numbers: K–12 teachers’ experiences, beliefs, and challenges in developing data literacy.

Khor, E. T., Chan, L., Koh, E., & Seow, P. (2025). Exploring students’ perceptions of generative AI-generated feedback in learning programming.

Khor, E. T., How, Z. J., Koh, E., Looi, C. K., & Lok, C. (2025). Learning analytics techniques: An overview and future research possibilities.

Ani, A., & Khor, E. T. (2024). Development and evaluation of predictive models for predicting students’ performance in MOOCs.

Khor, E. T., & Darshan, D. (2024). Prediction of students’ performance in online learning using supervised machine learning.

Nikolaos Gkrekas | Mathematics | Best Researcher Award

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Mr. Nikolaos Gkrekas | Mathematics | Best Researcher Award

Researcher at University of Kansas | United States

Mr. Nikolaos Gkrekas is a mathematician whose research and academic contributions bridge the domains of dynamical systems, mathematical modeling, and applied analysis. His scholarly work demonstrates an interdisciplinary approach, uniting mathematical theory with practical applications in science, engineering, and education. He has authored several peer-reviewed papers in internationally recognized journals, addressing complex phenomena such as chaos, quasi-geostrophic equations, and epidemiological models, while also exploring the evolving role of artificial intelligence in mathematics education and research. His research reveals a consistent focus on nonlinear dynamics, mathematical modeling, and the interplay between theoretical structures and real-world systems. In addition to his research, he has participated in numerous international conferences, seminars, and workshops hosted by institutions such as Harvard University, Kyoto University, and the University of Essex, reflecting his active engagement with the global mathematics community. His involvement as a peer reviewer for top-ranked journals, including Chaos, Solitons & Fractals and Nonlinear Engineering, alongside his editorial role in applied mathematics publications, underscores his academic credibility and contribution to maintaining high standards in scientific communication. Nikolaos is also affiliated with prominent mathematical societies and research groups, emphasizing his dedication to advancing mathematical sciences and fostering collaborative research. His intellectual versatility, combined with his passion for analytical problem-solving, positions him as an emerging figure in modern mathematical research, recognized for integrating rigorous mathematical theory with insightful applications to complex systems and education.

Profile: Google Scholar

Featured Publications

Rizos, I., & Gkrekas, N. (2023). Incorporating history of mathematics in open-ended problem solving: An empirical study.

Rizos, I., & Gkrekas, N. (2022). Teaching and learning sciences within the COVID-19 pandemic era in a Greek university department.

Rizos, I., & Gkrekas, N. (2023). Is there room for conjectures in mathematics? The role of dynamic geometry environments.

Gkrekas, N. (2024). Applying Laplace transformation on epidemiological models as Caputo derivatives.

Rizos, I., & Gkrekas, N. (2022). The historical background of a famous indeterminate problem and some teaching perspectives.

Abraham Marquez | Power Electronics | Best Researcher Award

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Dr. Abraham Marquez | Power Electronics | Best Researcher Award

Researcher at Universidad de Sevilla | Spain

Dr. Abraham Márquez Alcaide is a distinguished researcher in electronic engineering, renowned for his pioneering contributions to power electronics, advanced modulation strategies, and predictive control systems. Currently based at the University of Seville, he has played a vital role in the TIC-109 research group, advancing modular power converter technologies with a focus on improving system reliability, efficiency, and lifetime through smart thermal and predictive maintenance control. His prolific academic output includes over ninety peer-reviewed publications in leading high-impact journals and international conferences, several of which are recognized as highly cited by the Web of Science. Dr. Márquez has collaborated globally with eminent scholars and research institutions, including the Harbin Institute of Technology in China and Universidad Técnica Federico Santa María in Chile, fostering innovation in renewable energy integration, electric vehicle charging systems, and industrial automation. A multiple recipient of the IEEE Industrial Electronics Best Paper Award, he is widely respected for his ability to bridge theoretical advancements and industrial applications. Beyond research, he is actively engaged in academic leadership, mentoring numerous postgraduate students, organizing international conference sessions, and contributing to editorial and peer-review processes in reputed journals. His expertise spans modulation techniques, model predictive control, and active thermal management in high-reliability power electronic systems. With his visionary approach and international recognition, Dr. Márquez stands out as a leading figure shaping the future of smart, efficient, and sustainable power conversion technologies.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

Vazquez, S., Leon, J. I., Franquelo, L. G., Rodriguez, J., Young, H. A., & Marquez, A., et al. (2014). Model predictive control: A review of its applications in power electronics.

Liu, J., Vazquez, S., Wu, L., Marquez, A., Gao, H., & Franquelo, L. G. (2016). Extended state observer-based sliding-mode control for three-phase power converters.

Vazquez, S., Marquez, A., Aguilera, R., Quevedo, D., Leon, J. I., & Franquelo, L. G. (2014). Predictive optimal switching sequence direct power control for grid-connected power converters.

Liu, J., Shen, X., Alcaide, A. M., Yin, Y., Leon, J. I., Vazquez, S., Wu, L., et al. (2021). Sliding mode control of grid-connected neutral-point-clamped converters via high-gain observer.

Zhang, J., Tian, J., Alcaide, A. M., Leon, J. I., Vazquez, S., Franquelo, L. G., Luo, H., et al. (2023). Lifetime extension approach based on the Levenberg–Marquardt neural network and power routing of DC–DC converters.

Rahul Wankhede | A/B Testing | Rising Star in Data Science Award

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Mr. Rahul Wankhede | A/B Testing | Rising Star in Data Science Award

Director at Humana | United States

Mr. Rahul Wankhede is a distinguished data science leader and marketing analytics innovator with over a decade of proven excellence in transforming enterprise decision-making through data-driven intelligence. Renowned for architecting large-scale analytics frameworks and AI-driven marketing solutions across Fortune 50 companies, he has pioneered unified measurement systems integrating media mix modeling, causal inference, and machine learning for end-to-end marketing optimization. His leadership has driven measurable business outcomes, including multi-million-dollar ROI enhancements, customer engagement uplift, and enterprise adoption of AI-led strategies. As Director of Data Science at Humana, Rahul has built high-performing analytics teams that bridge business strategy with advanced data science, enabling unified performance measurement across marketing, customer, and healthcare domains. His earlier tenure at Walmart saw him revolutionize marketing science through the OMEGA measurement platform and drive cross-channel innovation in membership, loyalty, and advertising analytics. Recognized through prestigious honors such as the ANA Genius Award, Humana Excellence Award, and Invoca Rookie of the Year, Rahul exemplifies thought leadership and strategic foresight in the evolving intersection of AI, marketing, and customer intelligence. A member of the Forbes Technology Council and IEEE Senior Member, he actively contributes to shaping the global data science community through advisory roles and industry judging panels. His expertise spans marketing measurement, personalization, AI product development, and enterprise data strategy—anchored by a strong vision to empower organizations to make smarter, data-informed decisions that inspire growth and innovation.

Profile: ORCID

Featured Publications

Wankhede, R. (2026). Proven product design strategies cybersecurity teams should adopt. Forbes.

Wankhede, R. (2025). Post-quantum cryptography solutions for business leaders. Forbes.

Wankhede, R. (2025). On-prem data centers still matter — here’s why. Forbes.

Wankhede, R. (2025). How to build robust AI for regulated, high-impact sectors. Forbes.

Wankhede, R. (2025). Digital marketing ROI: From clicks to causality. Forbes.

Nanfu Zong | AI & Materials | Best Researcher Award

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Dr. Nanfu Zong | AI & Materials | Best Researcher Award

Leader of Digital Intelligence Research Institute at Ben Gang Group Corporation | China

Dr. Nanfu Zong is a visionary leader and pioneering researcher recognized for his transformative work at the intersection of artificial intelligence and the iron and steel industry. As the Director of the Digital Intelligence Research Institute at Ben Gang Group Corporation and a senior engineer of distinction, he has driven the intelligent, high-end, and green evolution of steel production through advanced AI integration. His expertise spans digital modeling, intelligent control, and sustainable process optimization, bridging theoretical innovation with industrial practice. With a robust research portfolio encompassing over forty SCI-indexed journal publications, sixteen major projects, and fifteen patents, Dr. Zong has significantly advanced digital manufacturing intelligence and industrial innovation. His leadership has inspired collaborations with global research powerhouses such as the University of Leicester, Tsinghua University, and major steel enterprises, reinforcing his role as a key figure in the digital transformation of metallurgical engineering. An active member of professional societies and editorial boards, he contributes to shaping the future of intelligent manufacturing through thought leadership and scientific rigor. His research excellence has earned him numerous accolades, including top provincial awards for scientific achievement, underscoring his impact on both academia and industry. Through his strategic vision and pioneering spirit, Dr. Zong continues to redefine how artificial intelligence can revolutionize traditional industries, promoting efficiency, sustainability, and innovation within the global steel sector.

Profile: Scopus

Featured Publications

Zong, N. F., Jing, T., & Gebelin, J. (2025). Intelligent empowerment for green steel manufacturing: Artificial intelligence‐driven process optimization.

Zong, N. F., Jing, T., & Gebelin, J. (2025). Machine learning for tandem cold rolling: Exploring innovations, challenges, and industrial applications.

Zong, N. F., Jing, T., & Gebelin, J. (2025). Machine learning techniques for the comprehensive analysis of the continuous casting processes: Slab defects.

Quanmin Zhu | Data Driven Decision Making | Best Researcher Award

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Prof. Quanmin Zhu | Data Driven Decision Making | Best Researcher Award

Distinguished Professor at University of the West of England | United Kingdom

Professor Quanmin Zhu is a distinguished academic and leading authority in control systems engineering, currently serving at the School of Engineering, University of the West of England, Bristol. With a career grounded in rigorous research and scholarly excellence, he has significantly advanced the fields of complex system modelling, identification, and control through both theoretical innovation and practical application. His prolific contributions include the authorship of over 300 peer-reviewed publications and the editorial oversight of major works with prestigious publishers such as Springer and Elsevier. A Chartered Engineer and Fellow of the Institution of Engineering and Technology (FIET) as well as the Higher Education Academy (FHEA), Professor Zhu is widely recognized for his commitment to bridging the gap between academic research and industrial practice. His expertise has been instrumental in shaping methodologies that enhance system performance, reliability, and adaptability across diverse engineering domains. As Editor of Elsevier’s Emerging Methodologies and Applications in Modelling, Identification and Control series, he continues to influence emerging directions in modern control theory and intelligent systems. Professor Zhu’s academic leadership, combined with his dedication to mentorship and collaboration, underscores his enduring impact on the global engineering community and his role in fostering innovation at the intersection of computation, automation, and control science.

Profile: Google Scholar

Featured Publications

Azar, A. T., & Zhu, Q. (2015). Advances and applications in sliding mode control systems.

Zhu, Q., & Azar, A. T. (2015). Complex system modelling and control through intelligent soft computations.

Li, S., Zhang, Y., & Zhu, Q. (2005). Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem.

Billings, S. A., & Zhu, Q. M. (1994). Nonlinear model validation using correlation tests.

Chen, J., Zhu, Q., & Liu, Y. (2020). Modified Kalman filtering based multi-step-length gradient iterative algorithm for ARX models with random missing outputs.

Sohong Dhar | Data Science | Analytics Excellence Award

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Dr. Sohong Dhar | Data Science | Analytics Excellence Award

Data Scientist at Jadavpur University | India

Dr. Sohong Dhar is a distinguished Information Scientist whose career bridges the fields of data science, digital marketing, and business analytics with remarkable proficiency. He is recognized for his ability to transform complex data into actionable insights that drive innovation, efficiency, and strategic growth across diverse industries. With expertise spanning machine learning, artificial intelligence, cloud computing, and advanced statistical analysis, he demonstrates an exceptional command of both theoretical and applied aspects of data-driven problem-solving. His multidisciplinary academic foundation, strengthened through advanced studies in data science and information science, has empowered him to approach challenges with analytical precision and creative foresight. Sohong has made impactful contributions to research, data modeling, and algorithmic development, delivering intelligent systems that enhance operational performance and decision-making processes. His fluency in multiple languages, combined with an understanding of literature and information systems, reflects a rare synthesis of technical acumen and intellectual versatility. He has collaborated effectively in cross-functional environments, employing platforms such as Microsoft Azure, SQL, and GCP to implement scalable and efficient data solutions. Beyond his technical mastery, Sohong’s work reflects a strong commitment to continuous learning, innovation, and excellence in the evolving domain of information and data science. His professional journey stands as a testament to the integration of analytical rigor, technological depth, and strategic thinking, establishing him as a forward-thinking expert dedicated to advancing the digital transformation landscape through intelligent, evidence-based insights and data-led decision frameworks.

Profile: Scopus

Featured Publications

Melba Kani, R., Karimli Maharram, V., Dhar, S., Samisha, B., Rajendran, P., & Ahmed, S. A. (2025). Automating grading to enhance student feedback and efficiency in higher education with a hybrid ensemble learning model.

Deepti, Nalluri, M., Mupparaju, C. B., Rongali, A. S., Dhar, S., & Ajitha, P. (2023). Retracted: Analyzing the impact of deep learning approaches on real-time data analysis in machine learning.