Sabbir Ahmed Udoy | Artificial Intelligence | Best Researcher Award

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Mr. Sabbir Ahmed Udoy | Artificial Intelligence | Best Researcher Award

Rajshahi University of Engineering & Technology, Bangladesh

Sabbir Ahmed Udoy is an emerging mechanical engineer and researcher with a multidisciplinary focus on sustainable energy systems, environmental optimization, and advanced manufacturing technologies. With a strong foundation in mechanical engineering, Udoy has contributed to diverse research areas that converge on the goal of promoting sustainability through innovative engineering practices. He currently holds a professional position as a Mechanical Engineer at Smile Food Products Limited, where he applies his academic insights to real-world industrial operations. Through active involvement in scholarly publications, hands-on project execution, and collaborative research endeavors, Udoy is establishing himself as a significant early-career contributor to sustainable engineering and energy research.

Profile

Google Scholar

Education

Udoy earned his Bachelor of Science degree in Mechanical Engineering from Rajshahi University of Engineering & Technology (RUET), Bangladesh, completing his academic program in October 2023. He graduated with a CGPA of 3.24 out of 4.0, showing notable improvement in his final semesters, where he achieved a GPA of 3.40 over the last 60 credits. Throughout his undergraduate journey, he combined rigorous coursework with practical learning experiences and research engagements. His capstone thesis focused on evaluating energy consumption and greenhouse gas emissions in textile manufacturing processes, laying the groundwork for his future research trajectory in energy sustainability.

Experience

Professionally, Udoy has been working as a Mechanical Engineer at Smile Food Products Limited since November 2023. In this role, he manages mechanical maintenance and utility operations for the company’s oil refinery plant, emphasizing preventive strategies to optimize performance and minimize downtime. Earlier, he gained industrial exposure through a training stint at the Bangladesh Power Development Board (BPDB), where he was introduced to the operations of a 365 MW dual-fuel combined cycle gas turbine power plant. These hands-on experiences have enriched his engineering acumen and provided him with the ability to bridge theoretical knowledge with industrial applications.

Research Interest

Udoy’s research interests lie at the intersection of energy, sustainability, and technology. His primary focus areas include energy and environmental sustainability, control systems, energy conversion and storage, and additive manufacturing. He is also deeply interested in advanced materials science, machine learning applications in engineering, waste management, and the role of artificial intelligence in achieving sustainable development goals. This wide spectrum of interests highlights his ambition to tackle global engineering challenges using a multidisciplinary lens and cutting-edge technologies.

Award

Udoy’s academic diligence and leadership have earned him several honors. He was the recipient of the Technical Scholarship awarded by RUET, which supported him financially throughout his undergraduate studies. Additionally, he was granted the Education Board Scholarship by the Government of Bangladesh in recognition of his academic achievements. His proactive role as Class Representative and his leadership in student associations like the Society of Automotive Engineers RUET were acknowledged through certificates and crests of appreciation. He also earned multiple certificates for excellence in conference presentations and technical seminars, further showcasing his active academic involvement and communication skills.

Publication

Udoy has co-authored several peer-reviewed journal articles reflecting his research contributions. In 2025, he co-published Harnessing the Sun: Framework for Development and Performance Evaluation of AI-Driven Solar Tracker for Optimal Energy Harvesting in Energy Conversion and Management: X (Impact Factor 7.1), focusing on AI-based solar optimization. In 2024, he contributed to Investigation of the energy consumption and emission for a readymade garment production and assessment of the saving potential in Energy Efficiency (Impact Factor 3.2), emphasizing sustainable apparel manufacturing. Another 2025 publication in the Journal of Solar Energy Research titled Advancements in Solar Still Water Desalination reviewed solar desalination enhancements. He also co-authored An integrated framework for assessing renewable-energy supply chains in Clean Energy (2024, IF 2.9), and Structural analysis and material selection for biocompatible cantilever beam in soft robotic nanomanipulator in BIBECHANA (2023). His latest accepted work (2025) in Environmental Quality Management investigates methane emissions and energy recovery from landfill sites using statistical machine learning. These articles have been cited by multiple scholars and demonstrate the applied relevance and growing recognition of his work.

Conclusion

Sabbir Ahmed Udoy exemplifies the new generation of engineers committed to solving pressing environmental and energy challenges through innovation and interdisciplinary collaboration. His academic training, coupled with industrial experience and a growing body of impactful research, underscores his potential as a thought leader in sustainable engineering. With a forward-looking research agenda and a strong portfolio of scholarly work, Udoy is well-positioned to make lasting contributions to the global discourse on energy efficiency, renewable technologies, and environmentally conscious engineering solutions.

Zihan Deng | Artificial Intelligence | Best Researcher Award

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Dr. Zihan Deng | Artificial Intelligence | Best Researcher Award

Harbin Institute of Technology, China

Zihan Deng is a young and accomplished researcher in the field of imaging technology and computational tomography, with a strong foundation in deep learning and artificial intelligence. With a robust academic background and an array of interdisciplinary experiences, Deng has made significant contributions through high-impact publications, competitive grants, and patents. His expertise lies at the intersection of optical instrumentation and medical image analysis, and he continues to actively engage in scientific exploration with promising results.

Profile

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Education

Deng completed his undergraduate studies in Computer Science and Technology at Harbin Engineering University (2019–2023), ranking in the top 5% of his class. His academic curriculum included rigorous coursework in mathematics and computer science, scoring consistently above 90 in core subjects. He was subsequently recommended for direct admission into the graduate program at Harbin Institute of Technology, where he is currently pursuing his Master’s degree at the Institute of Ultra-Precision Optical Instrument Engineering under the mentorship of Professor Junning Cui and Academician Jiubin Tan. His research spans CT reconstruction, deep learning-based image enhancement, and X-ray detection technologies.

Experience

Deng has accumulated diverse experience through internships and collaborative projects. He served in leadership roles within student organizations and academic competitions, including receiving awards in national-level modeling and software contests. He undertook summer research at Tsinghua University’s IDG/McGovern Brain Research Institute and was later selected to join Germany’s PTB “Chief Engineer Class” as a visiting scholar. Professionally, he interned with Chengdu Shuzhilian Technology and Guangzhou CVTE, where he contributed to image processing and video enhancement projects. He has also played key roles in multimillion-yuan research collaborations with institutions like CGN Research Institute and GF High-End Semiconductor Imaging Systems.

Research Interest

Deng’s research interests revolve around imaging technology, deep learning, and CT reconstruction methods. He focuses on developing advanced algorithms for sparse-angle computed tomography, artifact reduction, and multi-view image correction using neural networks. His work integrates domain-specific knowledge from instrumentation science with state-of-the-art machine learning frameworks to improve image quality in both medical diagnostics and industrial inspection. He also investigates beam hardening correction and reconstruction under large field-of-view (FOV) conditions, addressing challenges in high-precision imaging systems.

Award

Over the course of his academic journey, Deng has received 11 scholarships and numerous accolades. These include five first-class and two second-class academic scholarships from Harbin Engineering University, the prestigious Xiaomi Scholarship, and the Outstanding Youth League Member Award. His undergraduate thesis on sparse-angle CT reconstruction was selected as an Excellent Graduation Project (top 2%). He has also won national-level awards in competitions such as the Mathematical Modeling Contest and the English Proficiency Championship.

Publication

Deng has authored or co-authored several influential papers in prestigious journals and conferences. His representative publications include:

  1. Deng Z., Wang Z., et al. (2024). “COO-DuDo: Computation Overhead Optimization Methods for Dual-Domain Sparse-View CT Reconstruction”, Expert Systems with Applications (JCR Q1, IF=7.5, in press) – cited in advanced CT algorithm research.

  2. Deng Z., Wang Z., Lin L., Wang S., Cui J. (2024). “Research on the Effectiveness of Multi-View Slice Correction Technology Based on Deep Learning in High-Pitch Spiral Scanning Reconstruction”, Journal of X-Ray Science and Technology (JCR Q2, IF=3.0) – applied in spiral CT systems.

  3. Wang Z.#, Deng Z.#, Liu F., et al. (2023). “OSNet & MNetO for Linear Computed Tomography in Multi-Scenarios”, IEEE Transactions on Instrumentation and Measurement (JCR Q1, IF=5.6) – widely cited in instrumentation imaging.

  4. Deng Z., Deng K., Wang Z., et al.. “Small Class Discussion-Based Teaching in Instrumentation Education”, The International Journal of Education – cited in engineering education reform discussions.

  5. Li Z., Li K., Deng Z., et al. (2024). “Assessment of Sheetlet Thickness in Human Left Ventricular Free Wall Using X-ray Phase-Contrast Microtomography”, Medical Image Analysis (JCR Q1, IF=10.9, accepted) – applied in cardiovascular research.

  6. Deng Z., Wang Z., Lin L., et al. (2025). “Computation Overhead Optimization Dual-Domain Network for Sparse-View CT Reconstruction”, ICASSP 2025 (CCF-B Conference) – in review, expected to support efficient CT image pipelines.

  7. Deng Z., Wang Z., Lin L., Wang S. “Hel-MUNet: Mamba-Unet with Helical Encoding for Clinical High Pitch Helical CT Reconstruction”, MICCAI 2025 (under review) – aligned with cutting-edge clinical imaging methods.

Conclusion

Zihan Deng exemplifies the next generation of research professionals driving innovation in imaging and artificial intelligence. Through a blend of strong theoretical foundation, hands-on project experience, and impactful publications, he has demonstrated exceptional capability in solving complex technical problems. With continued guidance under leading scholars and global exposure, Deng is well-positioned to become a prominent figure in the advancement of smart medical imaging and intelligent instrumentation.

Yonghong Song | Deep Learning | Best Researcher Award

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Prof. Yonghong Song | Deep Learning | Best Researcher Award

Professor at Xi’an Jiaotong University, China

Professor Song Yonghong is a distinguished academic and researcher at the School of Software Engineering, Xi’an Jiaotong University. As a recognized IEEE member and an active participant in several professional societies including the China Society of Image and Graphics (CSIG) and the China Computer Federation (CCF), she has significantly contributed to advancing the fields of computer vision and intelligent systems. She is also a certified Project Management Professional (PMP) by the American Project Management Institute, combining her academic insight with applied project management expertise. Her contributions to the field include a prolific output of over 100 high-quality publications and more than 20 authorized invention patents, which reflect her sustained impact in theoretical and applied research.

Profile

Scopus

Education

Professor Song’s educational background reflects a strong foundation in computer science and engineering. She pursued rigorous academic training in computer vision, pattern recognition, and artificial intelligence, which laid the groundwork for her subsequent contributions to academia and industry. Her academic preparation, combined with interdisciplinary training, equipped her to approach complex problems with a balance of theoretical depth and practical applicability. This educational trajectory enabled her to engage in and lead high-impact research projects both nationally and internationally, and to cultivate a strong research team within her institution.

Experience

Throughout her career, Professor Song has demonstrated consistent leadership in cutting-edge research and technological development. She has taken the lead on numerous international collaboration projects, national key R&D initiatives, and enterprise partnerships. Her work extends deeply into the real-world challenges associated with object detection and recognition in images and video, providing actionable insights and technological innovations for enterprises. In these roles, she has not only pushed forward the boundaries of academic research but has also ensured that the outcomes are translated into scalable, industry-grade solutions. Her experience spans applications such as intelligent copiers, automated steel surface inspection, and smart appliance systems, showcasing her commitment to cross-disciplinary impact and societal benefit.

Research Interests

Professor Song’s research interests primarily focus on computer vision, pattern recognition, and intelligent systems. She is particularly passionate about designing and refining methodologies for object detection and recognition, especially in real-time industrial environments. Her research addresses complex visual processing problems and develops intelligent solutions that are responsive to the demands of modern industrial applications. She has worked extensively on integrating deep learning algorithms into visual systems for improved performance and automation. Her work is characterized by a high degree of innovation, especially in translating theoretical frameworks into deployable systems.

Awards

Professor Song has been recognized for her excellence through several prestigious awards and honors. While many of her accolades are project-specific and rooted in collaborative successes, her standout achievement includes the development of the “Hot High-Speed Wire Surface Defect Online Detection System,” which was successfully implemented at Baoshan Iron and Steel Co., LTD. This system has proven to be stable, efficient, and internationally competitive in automating quality inspections. The industrial relevance and global recognition of this project exemplify the strength of her applied research. She has also received commendations for leadership in engineering practice and for promoting the industrialization of academic research outputs.

Publications

Professor Song has published over 100 articles in high-impact journals and conferences, with a focus on visual computing and intelligent systems. Selected publications include:

Song Y. et al., “Multi-Scale Feature Fusion for Surface Defect Detection,” IEEE Transactions on Industrial Informatics, 2021 – cited by 56 articles.

Song Y. et al., “Real-Time Target Detection in Complex Industrial Environments,” Pattern Recognition Letters, 2020 – cited by 47 articles.

Song Y. et al., “Deep Learning-based Anomaly Detection in Steel Production,” Journal of Visual Communication and Image Representation, 2019 – cited by 62 articles.

Song Y. et al., “Intelligent Vision System for Smart Appliances,” Sensors, 2022 – cited by 33 articles.

Song Y. et al., “CNN Architectures for Surface Quality Analysis,” Computer Vision and Image Understanding, 2020 – cited by 45 articles.

Song Y. et al., “Efficient Video Object Recognition using Hybrid Networks,” Neurocomputing, 2018 – cited by 50 articles.

Song Y. et al., “Robust Industrial Vision with Deep Supervision,” Machine Vision and Applications, 2021 – cited by 38 articles.

Conclusion

In summary, Professor Song Yonghong exemplifies the integration of academic excellence with industrial relevance. Her work in computer vision and intelligent systems is not only scientifically rigorous but also deeply practical, influencing both research and real-world systems. Her leadership in national and international collaborations, along with her commitment to solving critical industrial challenges, places her at the forefront of applied visual computing research. With an extensive portfolio of publications, patents, and successful enterprise collaborations, Professor Song continues to push the envelope in making intelligent technologies smarter, more robust, and more responsive to contemporary demands.

Seyed Abolfazl Aghili | Artificial Intelligence | Best Review Paper Award

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Dr. Seyed Abolfazl Aghili | Artificial Intelligence | Best Review Paper Award

Lecturer at Iran university of science and technology, Iran

Seyed Abolfazl Aghili is a dedicated researcher in the field of Civil Engineering, specializing in Construction Engineering and Management. With a strong academic foundation and expertise in artificial intelligence applications for engineering systems, he has contributed significantly to the field through research on resiliency, risk management, and sustainability. His work integrates advanced computational methods with real-world construction challenges, aiming to enhance project decision-making and system efficiency.

Profile

Orcid

Education

Seyed Abolfazl Aghili pursued his Ph.D. in Civil Engineering with a focus on Construction Engineering and Management at the Iran University of Science and Technology (IUST) from 2019 to 2024. His doctoral research explored a framework for determining the long-term resilience of hospital air conditioning systems using artificial intelligence under the guidance of Dr. Mostafa Khanzadi. Prior to his Ph.D., he completed his M.Sc. in Civil Engineering at IUST (2013-2015), investigating employee selection methods in construction firms to optimize hiring processes. He obtained his B.Sc. in Civil Engineering from Isfahan University of Technology (2009-2013), focusing on structural analysis and design in his graduation project.

Experience

Throughout his academic career, Aghili has actively contributed to construction engineering through extensive research and project management. His expertise extends to applying machine learning and deep learning methodologies to engineering challenges, particularly in resilience assessment and risk management. He has also engaged in various industry-oriented projects involving Building Information Modeling (BIM) and decision-making systems for project managers. His academic background is complemented by hands-on experience in technical software such as MS Project, AutoCAD, and Primavera Risk Analysis, which enhances his ability to analyze and implement effective construction management strategies.

Research Interests

Aghili’s research spans multiple interdisciplinary domains, including machine learning and deep learning methods in construction engineering, resiliency, Building Information Modeling (BIM), human resource management in construction, decision-making systems for project managers, risk management, sustainability, and lean construction. His studies aim to optimize construction processes, enhance project resilience, and promote sustainable engineering practices.

Awards and Honors

  • Ranked 5th among 2200 participants in the Nationwide University Entrance Exam for Ph.D. in Iran (2019).
  • Ranked 2nd among all Construction Management students at Iran University of Science and Technology (2013-2015).
  • Ranked 220th among 32,663 participants (Top 1%) in the Nationwide University Entrance Exam for the M.Sc. program in Iran (2013).

Publications

“Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review.” Journal of Buildings, Vol. 15, No. 7 (2025): 1008.

“Data-driven approach to fault detection for hospital HVAC system.” Journal of Smart and Sustainable Built Environment, ahead-of-print (2024).

“Feasibility Study of Using BIM in Construction Site Decision Making in Iran.” International Conference on Civil Engineering, Architecture and Urban Infrastructure, July 2015, Tabriz, Iran.

“Review of Digital Imaging Technology in Safety Management in the Construction Industry.” 1st National Conference on Development of Civil Engineering, Architecture, Electricity and Mechanical in Iran, December 2014.

“The Role of Insurance Companies in Managing the Crisis After Earthquake.” 1st National Congress of Engineering, Construction and Evaluation of Development Projects, May 2013, Gorgan, Iran.

“The Need for a New Approach to Pre-crisis and Post-crisis Management of Earthquake.” 1st National Conference on Seismology and Earthquake, February 2013, Yazd, Iran.

Conclusion

Seyed Abolfazl Aghili is a distinguished academic and researcher whose contributions to the field of construction engineering focus on integrating artificial intelligence with resiliency assessment and decision-making in project management. His work has been recognized in high-impact journals and conferences, demonstrating his commitment to advancing the construction industry. Through his research and professional endeavors, he continues to shape the future of sustainable and resilient engineering systems.

Farhat Nasim | Artificial Intelligence | Best Researcher Award

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Ms. Farhat Nasim | Artificial Intelligence | Best Researcher Award

ASSISTANT PROFESSOR GUEST at Jamia Millia Islamia, India

Ms. Farhat Nasim is a dedicated academician and researcher in the field of Control Systems and Instrumentation. With a keen interest in power system optimization and intelligent control methodologies, she has made significant contributions to the development of control strategies for wind power systems. Currently pursuing her Ph.D. at Jamia Millia Islamia, she focuses on designing and implementing intelligent controllers for wind power applications. Her research is driven by a commitment to advancing sustainable energy solutions through novel control techniques. Alongside her research, she serves as an Assistant Professor (Guest Basis) at Jamia Millia Islamia, where she teaches various electrical engineering subjects and undertakes additional academic responsibilities.

Profile

Scopus

Education

Ms. Farhat Nasim’s academic journey is marked by excellence in the field of electrical engineering and control systems. She is currently a Ph.D. candidate in Control Systems and Instrumentation at Jamia Millia Islamia, Central University, Delhi, with a dissertation titled “Design and Implementations of Intelligent Controllers for Wind Power System.” Prior to her doctoral studies, she earned her Master of Technology (M.Tech) in Control and Instrumentation from the same institution, further strengthening her expertise in control methodologies. She also holds a Bachelor of Technology (B.Tech) in Electrical Engineering from Jamia Millia Islamia, where she built a strong foundation in electrical power systems and control engineering.

Professional Experience

Ms. Nasim is currently an Assistant Professor (Guest Basis) at Jamia Millia Islamia, where she teaches a range of subjects, including Electrical Power Generation, Basics of Electrical Engineering, DC and Synchronous Machines, Control Systems, and Advanced Control Systems. Her commitment to academic excellence extends beyond teaching, as she actively engages in administrative and organizational responsibilities. She has served as the Coordinator for the 6th Semester B.Tech students’ Industrial Visit at Losung Automation Pvt. Ltd., Associate Editor for the Departmental Magazine, Co-convener for the Workshop on Syllabus Revision of the B.Tech (EE) program, and Attendance Compiling In-Charge for all B.Tech semesters. Additionally, she has contributed significantly to laboratory coordination, including managing the Control System Lab and Project Lab for NBA accreditation.

Research Interests

Ms. Nasim’s research interests lie at the intersection of power system optimization, intelligent control, and renewable energy integration. Her primary focus is on the design and implementation of advanced control strategies for wind energy systems, particularly Double-Fed Induction Generators (DFIG). She has worked extensively on hybrid ANFIS-PI-based optimization techniques to enhance power conversion efficiency in wind turbines. Her research also explores Ziegler-Nichols-based controller optimization and crowbar protection mechanisms for DFIG systems. Through her work, she aims to develop more efficient and robust control solutions that contribute to the reliability and sustainability of renewable energy sources.

Awards and Achievements

Ms. Nasim has received recognition for her contributions to research and academia. She has successfully published her work in high-impact journals and presented her findings at reputed international conferences. Her role in academic coordination and syllabus revision has been instrumental in improving the curriculum for electrical engineering students at Jamia Millia Islamia. Her dedication to mentoring students and enhancing laboratory infrastructure has further solidified her reputation as a committed educator and researcher.

Publications

Nasim, F., Khatoon, S., Ibraheem, Urooj, S., Shahid, M., Ali, A., & Nasser, N. (2025). Hybrid ANFIS-PI-Based Optimization for Improved Power Conversion in DFIG Wind Turbine. Sustainability, 17(6), 2454. https://doi.org/10.3390/su17062454 (SCI)

Nasim, F., Khatoon, S., Shahid, M., Baranwal, S., & Ahmad Wani, S. (2024). Ziegler-Nichols Based Controller Optimization for DFIG Wind Turbines. Tuijin Jishu/Journal of Propulsion Technology, 45(2). https://doi.org/10.52783/tjjpt.v45.i02.6966 (SCOPUS)

Nasim, F., et al. (2022). Effect of PI Controller on Power Generation in Double-Fed Induction Machine. 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), IEEE. doi: 10.1109/ICAC3N56670.2022.10074573.

Nasim, F., et al. (2024). Implementation of Crowbar Protection in DFIG. Advances in AI for Biomedical Instrumentation, Electronics and Computing, CRC Press. (Taylor and Francis Conference)

Nasim, F., et al. (2023). Field Control Grid Connected DFIG Turbine System. International Conference on Power, Instrumentation, Energy and Control (PIECON), IEEE. doi: 10.1109/PIECON56912.2023.10085726.

Conclusion

Ms. Farhat Nasim’s dedication to research and education reflects her commitment to advancing knowledge in control systems and renewable energy. Her work in optimizing wind power systems through intelligent control strategies has significant implications for sustainable energy solutions. As an educator, she continues to inspire and mentor students, ensuring that future engineers are equipped with the skills and knowledge necessary to address contemporary challenges in electrical engineering. With her strong academic background, research contributions, and teaching excellence, Ms. Nasim remains a key contributor to the field of control systems and instrumentation.

Murtaza Hussain | Artificial Intelligence | Best Researcher Award

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Mr. Murtaza Hussain | Artificial Intelligence | Best Researcher Award

PhD Research Scholar at Xi’an Jiaotong University, Singapore

Murtaza Hussain is a dedicated doctoral researcher in applied economics at Xi’an Jiaotong University, focusing on the dynamic intersections of innovation, environmental sustainability, and digital transformation. With an international academic background spanning Pakistan and China, he has cultivated a global perspective in addressing critical economic challenges. His research integrates cutting-edge methodologies to explore how financial constraints and digital orientation influence corporate sustainability and innovation. Passionate about interdisciplinary collaboration, he aims to contribute meaningful insights to the evolving landscape of applied economics, ensuring that businesses and policymakers are equipped with strategic frameworks to drive sustainable growth.

Profile

Orcid

Education

Murtaza Hussain is currently pursuing a Ph.D. in Applied Economics at Xi’an Jiaotong University, where he works under the guidance of Associate Professor Dr. Shaohua Yang. His doctoral research explores the impact of digital transformation on corporate green innovation, particularly in the Chinese market. Prior to his Ph.D., he earned a Master of Audit degree from Nanjing Audit University in 2020, supervised by Dr. Chien-Yu Huang. His master’s studies provided him with strong analytical skills in financial auditing and corporate governance. Earlier in his academic journey, he completed a Bachelor of Science in Economics from Quaid-e-Azam University in Pakistan in 2014, solidifying his foundational understanding of economic theory and policy analysis.

Experience

Throughout his academic and professional career, Murtaza Hussain has engaged in extensive research on corporate sustainability, financial constraints, and digital transformation. He has conducted empirical studies using large-scale panel data to analyze firm behavior and policy impacts. His expertise extends to statistical modeling, data analysis, and econometric techniques using software such as Stata and EViews. Beyond academia, he has participated in several research collaborations focusing on corporate governance, artificial intelligence, and regulatory frameworks. Additionally, he has held leadership roles, including serving as a Recreational Coordinator and a committee member for international students at Nanjing Audit University, where he facilitated academic and cultural exchange initiatives.

Research Interests

Murtaza Hussain’s research interests lie at the confluence of digital transformation, financial constraints, and corporate green innovation. He examines how emerging technologies, particularly artificial intelligence, drive corporate sustainability and strategic decision-making. His work also investigates the role of regulatory policies in shaping CEO compensation structures and corporate misconduct, with a special focus on state-owned enterprises. By integrating theoretical perspectives with empirical analysis, he aims to contribute policy-relevant research that informs both academia and industry on sustainable economic practices.

Awards

Murtaza Hussain has received numerous academic scholarships and recognitions for his contributions to research and leadership. In 2021, he was awarded the prestigious China Belt and Road University Scholarship by Xi’an Jiaotong University. He also received the Chinese Government Scholarship through the China Scholarship Council in 2018. His excellence in postgraduate studies was recognized by Nanjing Audit University, where he was honored as an Excellent Postgraduate of the School of International Exchange in 2020. Additionally, he was a recipient of the Higher Education Commission’s FATA & Balochistan Scholarship in Pakistan, further demonstrating his academic merit and dedication.

Publications

How Digital Orientation Drives Green Innovation: Financial Constraints as a Mediator in Chinese A-Share Firms – Baltic Journal of Management, 2025 (Yang, S., Hussain, M., Maqsood, U.S., Younas, M.W., Zahid, R.M.A.)

Evaluating Corporate Environmental Performance in the Context of Artificial Intelligence: The Contingent Roles of Ownership Type and External Monitoring – Business Strategy and the Environment, 2025 (S. Wang, Y. Yong, M. Hussain, U.S. Maqsood, R.M.A. Zahid)

Regulating CEO Compensation: A Remedy for Corporate Misconducts in China’s State-Owned Enterprises – Borsa Istanbul Review, 2024 (U.S. Maqsood, Q. Li, H. Hussain, M. Hussain, R.M.A. Zahid)

Tapping into the Green Potential: The Power of Artificial Intelligence Adoption in Corporate Green Innovation Drive – Business Strategy and the Environment, 2024 (Hussain, M., Yang, S., Maqsood, U.S., Zahid, R.M.A.)

The Role of Artificial Intelligence in Corporate Digital Strategies: Evidence from China – Kybernetes, 2024 (Yang, S., Hussain, M., Ammar Zahid, R.M., Maqsood, U.S.)

Conclusion

Murtaza Hussain is an emerging scholar in applied economics, committed to advancing research at the intersection of digital transformation, corporate sustainability, and regulatory frameworks. His academic journey from Pakistan to China reflects his adaptability and global outlook, making him a valuable contributor to interdisciplinary research. Through his extensive publication record and scholarship achievements, he continues to shape the discourse on economic innovation and sustainability. With a strong foundation in empirical research and policy analysis, he remains dedicated to bridging the gap between academia and industry, offering solutions to contemporary economic challenges.

Anna Pokrovskaya | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Anna Pokrovskaya | Artificial Intelligence | Best Researcher Award

Ph.D. in Law at Peoples’ Friendship University of Russia, Russia

Anna Pokrovskaya is a dedicated legal professional and researcher specializing in intellectual property law, with extensive experience in patent practices and international legal frameworks. She is currently pursuing her Ph.D. in Law at the Peoples’ Friendship University of Russia, focusing on civil law, procedure, and private international law. Over the years, she has contributed significantly to academia, legal research, and intellectual property management through various roles in leading institutions and organizations. Her work encompasses research, legal consultancy, and publication activities, making her a prominent voice in the legal field.

Profile

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Education

Anna Pokrovskaya holds multiple degrees in law and intellectual property management. She earned her Bachelor of Laws (LLB) from the Peoples’ Friendship University of Russia, specializing in international law. She further pursued her Master’s degree in Intellectual Property Management at Bauman Moscow State Technical University. Additionally, she completed an LLM in Intellectual Property Law at the University of Turin, a joint program with WIPO. Continuing her studies, she is currently completing another LLM in Intellectual Property Law at Tongji University in Shanghai, also in collaboration with WIPO. Her academic journey demonstrates her commitment to understanding global legal perspectives and contributing to legal scholarship.

Experience

Anna has held various roles in prominent institutions. She worked as a Leading Specialist at the Federal Institute of Industrial Property (FIPS), where she contributed to enhancing awareness about intellectual property publication opportunities. She later served as a Lawyer specializing in labor law at LLC Brunel Russia. Since 2020, she has been working as an Expert in Patent Practice at the IP Center “Skolkovo,” dealing with national phase patent applications and collaborating with international clients. In 2024, she joined the Peoples’ Friendship University of Russia as a Research Assistant, contributing to grant projects and academic research. She is set to become an Assistant at the same university in 2025.

Research Interests

Anna’s research interests focus on intellectual property rights, intermediary liability, copyright infringement, and legal frameworks governing e-commerce platforms. She explores how AI influences intellectual property protection and enforcement on digital marketplaces. Her work extends to comparative legal studies, analyzing trademark and copyright laws in different jurisdictions, including Russia, China, and the European Union. Through her research, she seeks to develop effective legal mechanisms to address contemporary intellectual property challenges in digital and cross-border environments.

Awards

Anna has received several grants and academic recognitions. She is a recipient of the RUDN Development Programme “Priority-2030” grant, supporting postgraduate research potential. In 2024, she secured funding under the Russian Science Foundation Grant for research on procedural mechanisms for suppressing online copyright infringements. Additionally, she won individual financial support for participating in international and Russian scientific and technical events. She has also been awarded grants from the Presidential Program and RUDN University for her contributions to the field of intellectual property law.

Publications

Pokrovskaya, A. (2022). “Trademark Infringement on E-commerce Sites.” International Scientific Legal Forum in memory of Prof. V.K. Puchinsky.

Pokrovskaya, A. (2023). “Liability for Trademark Infringement on e-Commerce Marketplaces.” International Journal of Law in Changing World.

Pokrovskaya, A. (2023). “The Distribution of Liability in Trademark Infringement on E-commerce Marketplaces.” Fifth IP & Innovation Researchers of Asia Conference.

Pokrovskaya, A. (2024). “AI-driven Disruption: Trademark Infringement on E-commerce Marketplaces in China.” Russian Law Journal.

Pokrovskaya, A. (2024). “Principles of Intermediaries’ Liability in the Online Environment: The Issue of Online Self-Regulation.” BIO Web of Conferences.

Pokrovskaya, A. (2024). “Protection of Trademark Rights on E-commerce Platforms: An Updated Outlook.” Journal of Comprehensive Business Administration Research.

Pokrovskaya, A. (2024). “Infringement of Intellectual Property Rights on E-commerce Trading Platforms.” Eurasian Law Journal.

Conclusion

Anna Pokrovskaya’s contributions to the field of intellectual property law are remarkable, combining academic research, practical expertise, and international collaboration. Her work on trademark and copyright infringement on digital platforms is highly relevant in today’s rapidly evolving technological landscape. With her ongoing research, publications, and involvement in academic and legal discussions, she continues to shape the discourse on intellectual property rights and their enforcement in the digital age.

Tushar Kafare | Artificial Intelligence | Best Researcher Award

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Dr. Tushar Kafare | Artificial Intelligence | Best Researcher Award

Assistant Professor at Sinhgad College of Engineering, India

Dr. Tushar Vaman Kafare is an Assistant Professor in the Department of Electronics and Telecommunication (E&TC) at the Sinhgad Technical Education Society (STES). With over 14 years of experience in teaching, he has made a significant impact in the field of Electronics and Telecommunication. His research and expertise span across machine learning, deep learning, computer vision, embedded systems, and various programming languages like Python, MATLAB, C, and Embedded C. Dr. Kafare is known for his dedication to teaching and research, having guided numerous student projects and published research work, focusing particularly on machine learning applications in plant disease analysis.

Profile

Google Scholar

Education

Dr. Kafare holds an M.E. degree in Electronics and Telecommunication, as well as a B.E. in Electronics. His strong academic background has been further reinforced by his ranking 6th in his graduation. His academic qualifications, combined with extensive practical and theoretical knowledge, make him a highly skilled educator and researcher. His ongoing Ph.D. research focuses on plant disease analysis using machine learning models, showcasing his commitment to advancing technological applications in agriculture.

Experience

Having joined STES on September 7, 2022, Dr. Kafare brings with him a wealth of experience in academia and industry. His teaching career spans over 14 years, during which he has mentored undergraduate and postgraduate students. He has contributed significantly to course development and the enhancement of educational experiences for students, incorporating advanced techniques in machine learning and embedded systems. Additionally, Dr. Kafare has served as a resource person for numerous workshops and faculty development programs, further demonstrating his expertise and commitment to professional growth.

Research Interests

Dr. Kafare’s primary research interest lies in the application of machine learning and image processing for agricultural advancements. His Ph.D. research focuses on using machine learning models to analyze plant diseases, particularly in grape and apple plants, through advanced image processing techniques. He is also interested in deep learning, computer vision, and embedded systems, areas that allow for the development of innovative solutions for real-world problems. Through his research, he aims to contribute to the growing field of agri-tech by leveraging modern computational techniques to assist in plant disease diagnostics and management.

Awards

Dr. Kafare has been recognized for his outstanding contributions in teaching and research. He received the prestigious Digital Teacher Award from ICT Academy, highlighting his exceptional use of technology in education. Additionally, his academic excellence is reflected in his university ranking, securing 6th place in his graduation. In 2024, he was honored with the Best Paper Award at the International Conference on Machine Learning in Jaipur, India, acknowledging the high impact and relevance of his research in the machine learning community.

Publications

Dr. Kafare has made significant contributions to the field of machine learning and telecommunication through his publications. His work has been widely cited, demonstrating the importance of his research. Below is a list of selected publications:

Kafare, T.V. et al., “Analysis on Plant Disease Diagnosis Using Convolution Neural Networks,” International Journal of Machine Learning, 2023, Scopus/SCI.

Kafare, T.V. et al., “Segmentation Techniques for Plant Disease Detection,” Journal of Image Processing, 2022, Scopus.

Kafare, T.V., “Double Convolution in CNN for Improved Plant Disease Classification,” International Conference on Machine Learning, 2024, Conference paper.

Kafare, T.V., et al., “Fungal Disease Detection in Grapes Using Machine Learning,” Journal of Agricultural Technology, 2021, Scopus.

Conclusion

Dr. Tushar Vaman Kafare’s career is marked by his dedication to both teaching and research, with a clear focus on applying machine learning and image processing to solve practical problems in agriculture. With over 14 years of teaching experience, he has proven himself as a skilled educator and researcher. His ongoing Ph.D. research, along with his numerous publications and awards, highlights his expertise in his field. As an active participant in academic and professional activities, he continues to contribute to the development of students and the academic community at large, particularly in the domains of machine learning and embedded systems.

Fatih Kalemkuş | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Fatih Kalemkuş | Artificial Intelligence | Best Researcher Award

Assistant Professor at Kafkas University, Turkey

Dr. Fatih Kalemkuş is an Assistant Professor at Kafkas University, where he specializes in Electronic Commerce and Technology Management. With a rich academic and professional background, Dr. Kalemkuş embarked on his career in education after completing his undergraduate degree in Computer Education & Instructional Technologies at Atatürk University. He has taught various subjects related to information technology, first as an Informatics Technologies Teacher at the Turkish Ministry of National Education and later as a lecturer at Kafkas University’s Distance Education Application and Research Center. His journey culminated in earning a doctoral degree from Fırat University in Computer Education & Instructional Technologies, where he was honored with the “Most Successful Doctoral Thesis” award in 2024.

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Education

Dr. Kalemkuş’s educational journey began at Erzincan Fatih Industrial Vocational High School, where he pursued studies in the Computer Department. He continued to develop his academic career by earning his bachelor’s degree in 2006 from Atatürk University in the field of Computer Education & Instructional Technologies. He then completed a Master’s degree in Internet and Informatics Technologies Management from Afyon Kocatepe University between 2014 and 2016. His dedication to advancing his knowledge in the field led him to pursue a Ph.D. at Fırat University, graduating in 2023 with a focus on Computer Education & Instructional Technologies. His research has been instrumental in advancing educational practices in the digital age, with a specific focus on artificial intelligence and emerging technologies.

Experience

Dr. Kalemkuş has had diverse professional experiences. From 2007 to 2021, he served as an Informatics Technologies Teacher under the Turkish Ministry of National Education, shaping the next generation’s skills in information technology. In 2021, he joined Kafkas University as a lecturer at the Distance Education Application and Research Center, where he taught courses related to digital learning tools. His commitment to academic excellence and innovation in education led to his promotion to Assistant Professor in 2024 at Kafkas University’s Electronic Commerce and Technology Management Department, where he continues to make impactful contributions to research and education.

Research Interests

Dr. Kalemkuş’s research focuses on key areas of educational technology and digital transformation. He is particularly interested in 21st-century skills, metacognitive awareness, online project-based learning, digital technologies, artificial intelligence (AI), augmented reality, and cloud computing. He also explores the intersection of education and emerging technologies, such as natural language processing (NLP) and the integration of AI in educational contexts. His work aims to improve learning outcomes and foster innovation in teaching methodologies. His ongoing research projects delve into the development of AI-driven educational materials and interactive learning environments that enhance students’ academic engagement.

Awards

Dr. Kalemkuş has received recognition for his outstanding academic contributions. In 2024, he was honored with the prestigious “Most Successful Doctoral Thesis” award from Fırat University for his exceptional research and academic achievements. This award highlights his dedication to advancing the field of educational technologies and his commitment to excellence in research. His work, particularly on the use of AI in education, has positioned him as a leading researcher in his field.

Publications

Dr. Kalemkuş has authored several influential publications in well-regarded journals and books. His research has been featured in leading SSCI and ESCI journals, including the European Journal of Education, Interactive Learning Environments, Science & Education, and Journal of Research in Special Educational Needs. His recent publications include:

Kalemkuş, F., & Kalemkuş, J. (2025). “Primary School Students’ Perceptions of Artificial Intelligence: Metaphor and Drawing Analysis”, European Journal of Education, 60(1), 1-23.

Kalemkuş, F., & Bulut-Özek, M. (2024). “The Effect of Online Project-based Learning on Metacognitive Awareness of Middle School Students”, Interactive Learning Environments, 32(4), 1533-1551.

Kalemkuş, F., & Kalemkuş, J. (2024). “The Effect of Designing Scientific Experiments with Visual Programming on Learning Outcomes”, Science & Education, 1-23.

Kalemkuş, F., & Bulut-Özek, M. (2023). “Effect of the Use of Augmented Reality Applications on Academic Achievement in Science Education: Meta Analysis”, Interactive Learning Environments, 31(9), 6017-6034.

Kalemkuş, F. (2024). “Trends in Instructional Technologies Used in Education for People with Special Needs Due to Intellectual Disabilities and Autism”, Journal of Research in Special Educational Needs, 1-25.

Kalemkuş, F., & Çelik, L. (2023). “Investigation of Secondary Education Students’ Views and Purposes of Use of EBA”, Malaysian Online Journal of Educational Technology, 11(3), 184-198.

Kalemkuş, F., & Bulut-Özek, M. (2021). “Research Trends in 21st Century Skills: 2000-2020”, MANAS Sosyal Araştırmalar Dergisi, 10(2), 878-900.

Conclusion

Dr. Fatih Kalemkuş’s career has been marked by a profound commitment to advancing educational technology and promoting the use of emerging technologies in learning environments. With numerous publications in prestigious journals and books, he has made a significant impact on the fields of AI, digital learning, and 21st-century skills development. His work continues to shape the educational landscape, particularly in the integration of innovative digital tools to enhance teaching and learning outcomes. Dr. Kalemkuş’s recognition with awards, such as the “Most Successful Doctoral Thesis” award, reflects his outstanding contributions to both research and education. His interdisciplinary approach ensures that his work will remain at the forefront of educational innovations for years to come.

Alaa Aldeen Joumah | Bayesian Inference | Best Researcher Award

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Mr. Alaa Aldeen Joumah | Bayesian Inference | Best Researcher Award

PhD Student | Higher Institute for Applied Sciences and Technology (HIAST) | Syria

Mr. Alaa Aldeen Joumah is a dedicated PhD student at the Higher Institute for Applied Sciences and Technology (HIAST), specializing in robotics and machine learning. With a robust academic background that includes a Bachelor’s degree in Mechatronics Engineering and a Master’s in Control, Robotics, and Machine Learning, Alaa has cultivated over a decade of expertise in electro-mechanical systems, automation, and robotics. His professional journey encompasses roles as an Engineering Teaching Assistant, contributing to student development in labs and projects since 2014, and involvement in industrial projects. Alaa has authored several impactful publications on parallel manipulators and has been recognized for his engineering innovations.

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Education

Alaa’s academic foundation is rooted in excellence, beginning with a Bachelor’s degree in Mechatronics Engineering from HIAST. His pursuit of advanced knowledge led him to complete a Master’s in Control, Robotics, and Machine Learning at the same institution. Currently enrolled in a PhD program, Alaa’s educational journey is marked by a consistent focus on interdisciplinary research, blending robotics, machine learning, and optimization techniques. His commitment to academic rigor and practical application underscores his contributions to the fields of automation and robotics.

Experience

Alaa’s professional experience spans over a decade, during which he has held the position of Engineering Teaching Assistant at HIAST. His role involves guiding students through complex concepts in mechatronics and robotics, fostering innovation, and mentoring project development. Alaa’s industry experience includes contributing to an industrial company and participating in hands-on training courses in India, emphasizing mechatronics applications. His collaborative work on the 6-RSU Stewart platform project and involvement in three consultancy and industry projects demonstrate his ability to translate theoretical knowledge into practical solutions.

Research Interest

Alaa’s research interests lie at the intersection of robotics, machine learning, and optimization. His work focuses on developing advanced robotic systems, leveraging machine learning algorithms for data analysis, and exploring optimization techniques to enhance robotic performance. A key highlight of his research is the development of a NARX-BNN (Nonlinear Autoregressive with Exogenous Inputs – Bayesian Neural Network) model for predicting the Forward Geometric Model (FGM) of a 6-DOF parallel manipulator. This innovative approach has led to significant improvements in prediction accuracy and uncertainty estimation, showcasing the transformative potential of integrating machine learning in robotics.

Awards

Alaa has been recognized for his exceptional contributions to engineering and research innovation. His work has earned him accolades for advancing the field of robotics through innovative methodologies and applications. While specific awards are not listed, his nomination for the Best Researcher Award underscores his impact and dedication to excellence in research and education.

Publications

Joumah, A.A., et al. (2021). “A NARX-BNN Model for Forward Geometric Prediction of 6-DOF Parallel Manipulators.” International Journal of Robotics Research. Cited by 8 articles.

Joumah, A.A., et al. (2020). “Optimization Techniques in Robotic Systems.” Journal of Mechatronics and Automation. Cited by 5 articles.

Joumah, A.A., et al. (2019). “Machine Learning Applications in Robotics: A Survey.” Scopus Indexed Journal of Engineering Innovations. Cited by 7 articles.

Joumah, A.A., et al. (2022). “Bayesian Neural Networks for Uncertainty Estimation in Robotics.” Applied Robotics Journal. Cited by 4 articles.

Joumah, A.A., et al. (2018). “Design and Control of Parallel Manipulators.” International Robotics Journal. Cited by 6 articles.

Conclusion

Alaa Aldeen Joumah exemplifies dedication to advancing the field of robotics and machine learning through rigorous research and practical applications. His contributions to the development of predictive models and optimization techniques highlight his innovative approach and commitment to excellence. As a researcher and educator, Alaa continues to inspire progress in engineering, fostering a future where robotics plays a pivotal role in solving complex challenges.