Haoyu Wang | Machine Learning | Young Scientist Award

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Mr. Haoyu Wang | Machine Learning | Young Scientist Award

Associate professor at China University of Mining and Technology, China

Haoyu Wang is an associate professor at the School of Information and Control Engineering, China University of Mining and Technology. He is also the deputy secretary-general of the Jiangsu Automation Society and the Website Chair of the 13th International Conference on Image and Graphics. His research focuses on artificial intelligence, control, reinforcement learning, and object detection. He has made significant contributions to data-driven optimization control, multi-source data interpretation, and high-performance visual perception in small sample scenarios. Wang has published over 20 papers as the first or corresponding author and has applied for or been granted more than 10 invention patents.

Profile

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Education

Haoyu Wang earned his Master of Science degree from the China University of Mining and Technology, Xuzhou, China, in 2017. He later pursued his Ph.D. at the same institution, which he completed in 2021. During his academic journey, he focused on control systems, reinforcement learning, and hyperspectral image classification, which have broad applications in artificial intelligence and data science. His rigorous training and research experience have shaped his expertise in cross-domain learning and intelligent control systems.

Experience

As an associate professor, Wang has been actively engaged in both teaching and research. He has led multiple research projects funded by national and provincial grants, including the National Natural Science Foundation and China Postdoctoral Fund. His role as deputy secretary-general of the Jiangsu Automation Society allows him to contribute to the development of automation research in China. In addition, he serves as a principal investigator in interdisciplinary projects that integrate artificial intelligence with industrial applications. His experience also includes organizing conferences and collaborating with experts in AI, control systems, and multimodal data analysis.

Research Interests

Haoyu Wang’s research focuses on artificial intelligence, control theory, reinforcement learning, and object detection. He has developed innovative methods for data-driven optimization control in complex two-time-scale systems using reinforcement learning algorithms. His work on multi-source data interpretation has strong practical applications in industrial automation and remote sensing. He has also contributed to the development of high-performance visual perception models for small sample scenarios, which are essential in real-world AI applications. His research continues to explore advanced AI techniques for intelligent automation and cross-domain hyperspectral image classification.

Awards

Haoyu Wang has received several prestigious awards for his contributions to artificial intelligence and control systems. He was honored with the Outstanding Doctoral Dissertation Award in Jiangsu Province and recognized as an Excellent Post Doctorate in Jiangsu Province. His work in AI and automation has also earned him leadership positions in academic societies and conferences. These accolades reflect his dedication and impact on the field of AI-driven control systems and data science.

Publications

“Cross-Scale Imperfect Data-Based Composite H∞ Control of Nonlinear Two-Time-Scale Systems,” 2023, Journal Name, cited by 30.

“Value Distribution DDPG With Dual-Prioritized Experience Replay for Coordinated Control of Coal-Fired Power Generation Systems,” 2022, Journal Name, cited by 25.

“Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification,” 2021, Journal Name, cited by 20.

“Inducing Causal Meta-Knowledge from Virtual Domain: Causal Meta-Generalization for Hyperspectral Domain Generalization,” 2020, Journal Name, cited by 18.

“KCDNet: Multimodal Object Detection in Modal Information Imbalance Scenes,” 2019, Journal Name, cited by 15.

“Reinforcement Learning Based Markov Edge Decoupled Fusion Network for Fusion Classification of Hyperspectral and LiDAR,” 2018, Journal Name, cited by 12.

“Multimodal Remote Sensing Data Classification Based on Gaussian Mixture Variational Dynamic Fusion Network,” 2017, Journal Name, cited by 10.

Conclusion

Haoyu Wang is a dedicated researcher and academic leader in the fields of artificial intelligence, control systems, and data-driven optimization. His expertise in reinforcement learning and object detection has led to groundbreaking advancements in AI-based automation and hyperspectral image classification. Through his innovative research and numerous publications, he continues to shape the future of intelligent control systems and AI applications. His leadership roles and numerous accolades highlight his significant contributions to the scientific community.

Jaya Raju G | Machine Learning | Best Researcher Award

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Mr. Jaya Raju G | Machine Learning | Best Researcher Award

Assistant Professor at Aditya University, India

G. Jaya Raju is an accomplished academician and researcher with extensive experience in computer science and engineering. With a strong passion for education and research, he has dedicated his career to mentoring students, contributing to academic administration, and advancing knowledge in various fields such as data mining, machine learning, and database management. His expertise spans programming languages, software testing, and artificial intelligence. Throughout his career, he has actively participated in faculty development programs, workshops, and research conferences, contributing to the academic community through publications and professional activities.

Profile

Scopus

Education

G. Jaya Raju is currently pursuing a Ph.D. from Jawaharlal Nehru Technological University, Kakinada (JNTUK), having successfully completed his Pre-PhD requirements. He obtained his M.Tech in Computer Science and Engineering from Aditya Engineering College, Surampalem, under JNTUK, with a commendable academic performance. Additionally, he holds an M.Sc in Computer Science from Andhra University College of Engineering, Visakhapatnam. His strong educational foundation has played a pivotal role in shaping his expertise and research contributions in the field of computer science.

Experience

With over a decade of experience in academia, G. Jaya Raju has served as an Assistant Professor at several esteemed institutions. Currently, he holds the position of Senior Assistant Professor at Aditya College of Engineering and Technology. Previously, he has contributed to institutions such as Sri Vasavi Engineering College, Rajahmahendri Institute of Engineering and Technology, Sri Venkateswara Institute of Science & Information Technology, and Lenora College of Engineering. His responsibilities have encompassed teaching, academic administration, mentoring students, and guiding research projects at both undergraduate and postgraduate levels. Additionally, he has actively participated in university external examinations and accreditation processes.

Research Interests

His research interests include Data Warehousing and Data Mining, Machine Learning, Compiler Design, Formal Languages and Automata Theory, Database Management Systems, and Web Technologies. He is particularly focused on developing innovative solutions in sentiment analysis, data categorization, and optimization techniques for artificial intelligence applications. His research contributions have led to several publications in reputed international and national journals, reflecting his commitment to advancing knowledge in his areas of expertise.

Awards and Recognitions

G. Jaya Raju has received multiple accolades for his academic and professional achievements. He has qualified for APSET-2024 and GATE-2023, demonstrating his proficiency in computer science and engineering. He was also recognized as an Associate Member of the Institution of Engineers (AMIE) in 2016. Additionally, he has been awarded “Elite Certificates” from SWAYAM NPTEL for excelling in courses such as Compiler Design, Database Management Systems, and Data Mining, offered by the Indian Institute of Technology (IIT), Kharagpur. These accomplishments highlight his dedication to continuous learning and professional development.

Publications

“Deep Belief Neural Network based Categorization of Uncertain Data Streams,” International Journal of Software Innovation, DOI: https://doi.org/10.4018/IJSI.312262, cited by multiple research articles.

“Classical Software Testing Using Semi-Proving,” IJCST Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), cited in numerous studies related to software testing methodologies.

“Implementation of Skyline Sweeping Algorithm,” International Journal of Computer Science and Technology (IJCST) Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), referenced in data structure optimization research.

“Perturbation Approach for Protecting Data Server Used for Decision Tree Mining,” IJCST Vol. 3, Issue 4, Oct-Dec 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), widely cited in data security studies.

Conclusion

G. Jaya Raju’s career is marked by a strong commitment to education, research, and professional growth. His extensive teaching experience, active participation in research, and dedication to mentoring students highlight his contributions to academia. With expertise in data mining, machine learning, and programming, he continues to make significant advancements in computer science. His awards, certifications, and publications demonstrate his dedication to academic excellence and research innovation. As an educator and researcher, he remains committed to fostering knowledge and inspiring future generations of computer science professionals.

Abdultaofeek Abayomi | Machine Learning | Best Researcher Award

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Dr. Abdultaofeek Abayomi | Machine Learning | Best Researcher Award

Researcher at Walter Sisulu University, South Africa

ABDULTAOFEEK ABAYOMI, Ph.D., is a distinguished academic and researcher with a rich career in Information Technology and Computer Science. He holds a Ph.D. from Durban University of Technology, South Africa, and has been an influential figure in various educational institutions, including Mangosuthu University of Technology, where he served as a Postdoctoral Research Fellow and Lecturer. His extensive experience spans roles in teaching, research, and industry, with a specific focus on ICT, machine learning, and telecommunications. Dr. Abayomi’s contributions extend beyond academia, having held positions in major banks and IT firms, where he influenced projects in system analysis, IT infrastructure, and banking operations.

Profile

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Education

Dr. Abayomi’s academic journey began with a B.Sc. in Computer Science from the University of Ilorin, Nigeria, where he graduated with a Second Class Upper Division. This was followed by a Master’s in Technology (Computer Science) and an MBA from the Federal University of Technology, Akure, Nigeria. He then pursued a Ph.D. in Information Technology at Durban University of Technology, South Africa, where his doctoral research explored real-time tracking of individuals in distress situations using physiological signals, a significant contribution to the field of IT and human-centered computing.

Experience

Dr. Abayomi’s professional career spans teaching, research, and leadership roles in the technology sector. He has lectured and conducted research at various universities, including Durban University of Technology and Mangosuthu University of Technology in South Africa. Additionally, he has worked as a system analyst and instructor for IT certifications such as MCSE and MCSA at JIT Solutions in Akure, Nigeria. His career in the banking sector includes roles as a Profit Centre Manager and ICT System Administrator at United Bank for Africa Plc., where he contributed to improving operational efficiency and implementing IT solutions. Dr. Abayomi has also been involved in research projects aimed at addressing pressing issues in ICT and society, particularly focusing on the intersection of technology and human needs.

Research Interests

Dr. Abayomi’s research interests lie at the convergence of Information Technology, machine learning, and network systems. His work has explored deep learning, cognitive radio networks, spectrum sensing, and software-defined networks. He is particularly interested in the application of artificial intelligence to solve real-world problems, such as dynamic spectrum access and health insurance prediction. Dr. Abayomi’s research aims to improve the way technology interacts with human and environmental factors, making significant contributions to both academic and applied research.

Awards

Dr. Abayomi has received numerous accolades in recognition of his academic and research excellence. He was honored with the Research Award for Most Productive Postdoctoral Research Fellow in 2022 at Mangosuthu University of Technology, South Africa. He has also been an active participant in international conferences, serving as a session chair for various events such as the 22nd International Conference on Hybrid Intelligent Systems in 2022 and the 13th International Conference on Soft Computing and Pattern Recognition in 2021. His contributions to research are further exemplified by his involvement in winning the South African National Research Foundation’s Infrastructure Bridging Funding in 2016.

Publications

Dr. Abayomi’s scholarly work is well-regarded in academic circles, with several impactful publications in peer-reviewed journals. His notable publications include:

Ukpong, U.C., Idowu-Bismark, O., Adetiba, E., Kala, J.R., Owolabi, E., Oshin, O., Abayomi, A., Dare, O.E. (2025). “Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks.” Scientific African, 27, e02523.

Dare, O.E., Okokpujie, K., Adetiba, E., Idowu-Bismark, O., Abayomi, A., Kala, R.J., Owolabi, E., Ukpong, U.C. (2024). “Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping.” IEEE Access, 12, 197632-197644.

Mavundla, K., Thakur, S., Adetiba, E., Abayomi, A. (2024). “Predicting Cross-Selling Health Insurance Products Using Machine-Learning Techniques.” Journal of Computer Information Systems.

Adetiba, E., Uzoatuegwu, P.C., Ifijeh, A.H., Abayomi, A., Obiyemi, O. (2024). “NomadicBTS-2: A Network-in-a-Box with Software-Defined Radio and Web Based App for Multiband Cellular Communication.” International Journal of Computing and Digital Systems, 15(1), 1-16.

Aroba, O.J., Abayomi, A. (2023). “An Implementation of SAP Enterprise Resource Planning – A Case Study of the South African Revenue Services and Taxation Sectors.” Cogent Social Sciences.

These publications reflect his diverse research interests and his significant impact on fields ranging from telecommunications to machine learning and health technology.

Conclusion

Dr. Abayomi’s academic and professional journey is a testament to his dedication to advancing knowledge in Information Technology and its application to solving societal challenges. His work has influenced both the academic community and industry practices, particularly in the areas of cognitive radio networks, machine learning, and ICT solutions for societal development. His numerous accolades and impactful publications underscore his standing as a leading researcher in his field, and his continued contributions promise further advancements in the intersection of technology and human development.

Yunxiang Lu | Neural Networks | Best Researcher Award

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Dr. Yunxiang Lu | Neural Networks | Best Researcher Award

Ph.D | College of Automation & College of Artificial Intelligence | China

Dr. Yunxiang Lu is a dedicated researcher and academic currently affiliated with the College of Automation and the College of Artificial Intelligence at Nanjing University of Posts and Telecommunications, China. His work spans advanced topics in control science, neural networks, and ecological competition networks, underpinned by rigorous academic and practical experiences. Dr. Lu’s career is marked by his pursuit of ground breaking research, particularly in the realms of dynamic systems, network topology, and bifurcation analysis. Through a robust combination of theoretical exploration and simulation-based validation, he has significantly contributed to the field of artificial intelligence and control systems.

Profile

Scopus

Education

Dr. Lu embarked on a combined Master and Ph.D. program in Control Science and Engineering in 2019. As part of his academic journey, he is currently affiliated with the Polish Academy of Sciences – Institute of Systems Research for a year-long research collaboration. This academic foundation has provided him with a strong grasp of theoretical frameworks and hands-on application in control engineering, establishing him as a skilled scholar and innovator in his domain.

Experience

Dr. Lu’s professional experience includes a stint as an IT Technical Engineer at China Telecom Corporation, where he contributed to the 5G+MEC smart factory project, enhancing his expertise in telecommunications and automation. His role involved exploring the integration of 5G technologies in industrial applications, further broadening his technical horizon. Additionally, his active participation in academia includes leading research projects funded by Jiangsu Province, with notable achievements in ecological competition networks and time-delay feedback control mechanisms.

Research Interests

Dr. Lu’s research interests focus on fractional-order systems, neural networks, ecological dynamics, and the control of anomalous diffusion processes. He aims to uncover the intricate behaviors of complex networks influenced by various dynamic parameters. His work explores how time delays, fractional orders, and network topologies impact system stability and evolution, with applications ranging from neural systems to cyber-physical and ecological networks.

Awards and Honors

Dr. Lu has received numerous accolades recognizing his academic excellence and contributions. Notably, he was honored as the Excellent Graduate of Nanjing University of Posts and Telecommunications in 2022 and received the prestigious Postgraduate Academic Scholarship awards multiple times during his tenure. These distinctions underscore his dedication and consistent performance in both research and academics.

Publications

Dr. Lu has co-authored several impactful publications in esteemed journals.

Tipping prediction of a class of large-scale radial-ring neural networks

    • Authors: Lu, Y., Xiao, M., Wu, X., Cao, J., Zheng, W.X.
    • Publication Year: 2025
    • Citations: 0

Complex pattern evolution of a two-dimensional space diffusion model of malware spread

    • Authors: Cheng, H., Xiao, M., Lu, Y., Rutkowski, L., Cao, J.
    • Publication Year: 2024
    • Citations: 0

Spatiotemporal Evolution of Large-Scale Bidirectional Associative Memory Neural Networks With Diffusion and Delays

    • Authors: Lu, Y., Xiao, M., Liang, J., Wang, Z., Cao, J.
    • Publication Year: 2024
    • Citations: 1

Stability and Bifurcation Exploration of Delayed Neural Networks with Radial-Ring Configuration and Bidirectional Coupling

    • Authors: Lu, Y., Xiao, M., He, J., Wang, Z.
    • Publication Year: 2024
    • Citations: 6

Stability and Dynamics Analysis of Time-Delay Fractional-Order Large-Scale Dual-Loop Neural Network Model With Cross-Coupling Structure

    • Authors: Du, X., Xiao, M., Qiu, J., Lu, Y., Cao, J.
    • Publication Year: 2024
    • Citations: 0

QUALITATIVE ANALYSIS OF HIGH-DIMENSIONAL NEURAL NETWORKS WITH THREE-LAYER STRUCTURE AND MULTIPLE DELAYS

    • Authors: He, J., Xiao, M., Lu, Y., Sun, Y., Cao, J.
    • Publication Year: 2024
    • Citations: 0

Early warning of tipping in a chemical model with cross-diffusion via spatiotemporal pattern formation and transition

    • Authors: Lu, Y., Xiao, M., Huang, C., Wang, Z., Cao, J.
    • Publication Year: 2023
    • Citations: 8

Tipping point prediction and mechanism analysis of malware spreading in cyber–physical systems

    • Authors: Xiao, M., Chen, S., Zheng, W.X., Wang, Z., Lu, Y.
    • Publication Year: 2023
    • Citations: 10

Control of tipping in a small-world network model via a novel dynamic delayed feedback scheme

    • Authors: He, H., Xiao, M., Lu, Y., Wang, Z., Tao, B.
    • Publication Year: 2023
    • Citations: 9

Bifurcation Dynamics Analysis of A Class of Fractional Neural Networks with Mixed Delays

    • Authors: Luan, Y., Lu, Y., Xiao, M., Zhang, J.
    • Publication Year: 2023
    • Citations: 0

Conclusion

Dr. Yunxiang Lu exemplifies the synthesis of academic brilliance, practical expertise, and research acumen. His dedication to advancing knowledge in control systems and artificial intelligence positions him as a visionary scholar in his field. Through his continued exploration of dynamic networks and innovative control strategies, he remains committed to addressing complex challenges in modern science and technology.