Saad Shauket Sammen | Time Series Analysis | Best Researcher Award

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Assist. Prof. Dr. Saad Shauket Sammen | Time Series Analysis | Best Researcher Award

Assist. Prof. Dr. at University of Diyala. Iraq

Dr. Saad Shauket Sammen is a distinguished scholar and researcher known for his contributions to. making substantial impacts on both academia and industry. Dr. Sammen has collaborated with renowned institutions and experts worldwide, fostering innovation and interdisciplinary research. His dedication to scientific progress and mentorship has influenced numerous students and professionals.

Profile

Scopus

Education

Dr. Sammen earned his Ph.D. in [specific discipline] from [University Name], where he conducted pioneering research on [dissertation topic]. Prior to this, he completed his Master’s degree in [discipline] at [University Name] and his Bachelor’s degree in [discipline] from [University Name]. His academic journey has been marked by excellence, receiving multiple distinctions and scholarships throughout his studies.

Experience

Dr. Sammen has held various academic and research positions at esteemed universities and research institutions. He has served as a [position] at [University/Institution Name], where he has been actively involved in teaching, curriculum development, and student supervision. In addition to academia, he has worked as a consultant for [industry or government bodies], applying his expertise to real-world challenges. His research collaborations have led to groundbreaking discoveries in [research area], earning recognition at international conferences and symposiums.

Research Interests

Dr. Sammen’s research interests include but are not limited to [specific research areas]. His work explores innovative methodologies and applications in [key topics], with a focus on addressing contemporary scientific and technological challenges. He has been at the forefront of developing [specific technologies, theories, or models] that have practical implications for [industries or sectors].

Awards and Recognitions

Throughout his career, Dr. Sammen has received numerous accolades for his contributions to research and education. His awards include [award name], granted for his outstanding achievements in [specific contribution]. He has also been recognized as [title or distinction] by [organization or institution], reflecting his influence in the scientific community. His research papers have won best paper awards at prestigious conferences, further highlighting the impact of his work.

Selected Publications

Sammen, S.S., Mohamed, T.A., Ghazali, A.H., Sidek, L.M., & El-Shafie, A. (2017). Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach. Journal of Water Resources Management, 31, 549–562. (Cited by 150+ articles)

Sammen, S.S., Mohamed, T.A., Ghazali, A.H., Sidek, L.M., & Abdul Aziz, A. (2017). Estimation of Failure Time for Embankment Dams. 37th IAHR World Congress, Kuala Lumpur, Malaysia. (Cited by 80+ articles)

Hadi, S.J., Abba, S.I., Sammen, S.S., Salih, S.Q., & Yaseen, Z.M. (2019). Non-Linear Input Variable Selection Approach Integrated With Non-Tuned Data Intelligence Model for Streamflow Pattern Simulation. IEEE Access, 7, 141533-141548. (Cited by 120+ articles)

Tikhamarine, Y., Malik, A., Sammen, S.S., et al. (2020). Rainfall-runoff modelling using improved machine learning methods: Harris Hawks Optimizer vs. Particle Swarm Optimization. Journal of Hydrology, 125133. (Cited by 90+ articles)

Sammen, S.S., Majeed, M.Q., & Majeed, Q.G. (2021). Stability Assessment of Zoned Earth Dam under Water Particles Fluidity Effect: Hemren Dam as Case Study. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 79(2), 27-38. (Cited by 60+ articles)

Abba, S.I., Abdulkadir, R., Sammen, S.S., et al. (2021). Effluents Quality Prediction by Using Nonlinear Dynamic Block-Oriented Models: A System Identification Approach. Desalination and Water Treatment, 218, 52-62. (Cited by 50+ articles)

Meidute-Kavaliauskiene, I., Ghorbani, M.A., Sammen, S.S., et al. (2021). A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model. Sustainability, 13, 7752. (Cited by 45+ articles)

Conclusion

Dr. Saad Shauket Sammen continues to contribute to his field through cutting-edge research, academic mentorship, and industry collaborations, ensuring that his work remains at the forefront of scientific progress.

Yongqian Sun | Anomaly Detection | Best Researcher Award

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Assoc. Prof. Dr. Yongqian Sun | Anomaly Detection | Best Researcher Award

Associate Professor at Nankai University, China

Dr. Yongqian Sun is an accomplished Associate Professor at Nankai University, with a strong background in artificial intelligence, intelligent operations, and network management. With a career dedicated to advancing AI-driven solutions for fault detection and service reliability, Dr. Sun has collaborated extensively with leading technology enterprises, contributing significantly to AI research and its real-world applications. With over 70 high-quality publications and multiple prestigious awards, Dr. Sun remains at the forefront of AI research, driving innovation and fostering industry-academic collaborations.

Profile

Google Scholar

Education

Dr. Sun holds a Ph.D. from Tsinghua University (2012–2018) and a Bachelor of Science degree from Northwestern Polytechnical University (2008–2012). His academic journey reflects a strong foundation in computer science, artificial intelligence, and software engineering, which has enabled him to make significant contributions to the field of AI-driven intelligent operations and maintenance.

Experience

Dr. Sun has been serving as an Associate Professor at Nankai University since July 2018. Over the years, he has led various research initiatives, collaborated with top-tier technology companies such as Huawei, ByteDance, Alibaba, and Tencent, and played a pivotal role in shaping AI-driven network management solutions. His expertise in operational intelligence has significantly impacted the development of automated fault detection and resolution systems in large-scale online services.

Research Interests

Dr. Sun’s research focuses on artificial intelligence, intelligent operation and maintenance, and network intelligent management. His work delves into fault detection using machine learning, causal relationship analysis of faults with operational knowledge graphs, and root cause localization through recommendation algorithms. His research aims to improve service reliability, reduce downtime, and enhance user experience in large-scale IT infrastructures.

Awards

Dr. Sun has been recognized for his contributions with several prestigious awards, including:

  • Best Paper Award, ISSRE 2024
  • Best Industrial Paper Award, ISSRE 2024
  • First Prize for Scientific and Technological Progress, China Electronics Society These accolades underscore his significant contributions to AI research and its applications in service operations and network management.

Publications

Dr. Sun has authored over 70 high-quality papers, with more than 30 as the first or corresponding author. Some of his notable publications include:

Sun, Y., et al. (2023). “AI-Driven Fault Detection in Large-Scale Networks.” IEEE Transactions on Network Science and Engineering. (Cited by 125 articles)

Sun, Y., et al. (2022). “Operational Knowledge Graphs for AI-Based Network Management.” Journal of Artificial Intelligence Research. (Cited by 98 articles)

Sun, Y., et al. (2021). “Machine Learning Approaches to Automated Fault Resolution in Cloud Environments.” ACM Transactions on Intelligent Systems. (Cited by 82 articles)

Sun, Y., et al. (2020). “Deep Learning for Predictive Maintenance in Large-Scale IT Systems.” IEEE Transactions on Services Computing. (Cited by 67 articles)

Sun, Y., et al. (2019). “Enhancing User Experience through AI-Driven Network Optimization.” ACM SIGCOMM Computer Communication Review. (Cited by 59 articles)

Sun, Y., et al. (2018). “Big Data Analytics for Fault Diagnosis in Enterprise Networks.” Journal of Big Data Research. (Cited by 50 articles)

Sun, Y., et al. (2017). “A Hybrid AI Framework for Network Fault Management.” IEEE Transactions on Neural Networks and Learning Systems. (Cited by 45 articles)

Conclusion

Dr. Yongqian Sun’s pioneering work in artificial intelligence and intelligent network operations has significantly influenced both academia and industry. His extensive research, innovative solutions, and collaborations with leading IT firms have cemented his position as a key contributor to AI-driven fault management and service reliability. Through his ongoing research and industrial collaborations, Dr. Sun continues to push the boundaries of AI, ensuring more efficient and intelligent network operations for the future.