Emine Baş | Optimization Algorithms | Best Researcher Award

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Assoc. Prof. Dr. Emine Baş | Optimization Algorithms | Best Researcher Award

Author at Konya Technical University, Turkey📖

Dr. Emine Baş is a dedicated researcher and academic specializing in optimization algorithms, artificial intelligence, data mining, and machine learning. With a strong foundation in computer engineering and extensive experience in higher education, she has significantly contributed to both academia and applied research.

Profile

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Education Background🎓

  • Bachelor’s Degree (2006): Computer Engineering, Selçuk University
  • Master’s Degree (2013): Computer Engineering, Selçuk University (Thesis: RFID System Implementation and Application)
  • Doctorate (2020): Computer Engineering, Konya Technical University (Thesis: Performance Improvements in Continuous and Discrete Optimization Problems Using the Social Spider Algorithm)

Professional Experience🌱

Dr. Baş has been an instructor at Selçuk University since 2007. Initially appointed to Huğlu Vocational School, she transitioned to Kulu Vocational School in 2015, where she continues to educate and mentor students. She also holds administrative roles, such as Deputy Head of the Computer Technologies Department and ECTS Coordinator.

Research Interests🔬

Dr. Baş’s research focuses on swarm intelligence, heuristic algorithms, continuous and discrete optimization problems, artificial intelligence, database systems, machine learning, and big data analytics. She leverages these technologies to address complex optimization challenges and enhance data-driven decision-making.

Author Metrics

Dr. Baş has published extensively in high-impact journals such as Soft Computing and Expert Systems with Applications. Her work has received numerous citations, demonstrating her influence in fields like optimization and algorithm development. Her notable publications include advancements in binary social spider algorithms and their applications in feature selection and optimization tasks.

Publications Top Notes 📄

1. An Efficient Binary Social Spider Algorithm for Feature Selection Problem

  • Authors: Emine Baş, E. Ülker
  • Journal: Expert Systems with Applications, Vol. 146, Article 113185
  • Publication Year: 2020
  • Citations: 63
  • Summary: This paper introduces a binary social spider algorithm (SSA) tailored for feature selection problems. It demonstrates improved efficiency in selecting relevant features for machine learning tasks while maintaining solution quality.

2. A Binary Social Spider Algorithm for Uncapacitated Facility Location Problem

  • Authors: Emine Baş, E. Ülker
  • Journal: Expert Systems with Applications, Vol. 161, Article 113618
  • Publication Year: 2020
  • Citations: 51
  • Summary: This study applies the binary SSA to the uncapacitated facility location problem, achieving better performance in terms of cost and computational efficiency compared to traditional optimization methods.

3. Binary Aquila Optimizer for 0–1 Knapsack Problems

  • Author: Emine Baş
  • Journal: Engineering Applications of Artificial Intelligence, Vol. 118, Article 105592
  • Publication Year: 2023
  • Citations: 28
  • Summary: This paper presents a novel binary variant of the Aquila optimizer, addressing the 0–1 knapsack problem with improved accuracy and computational efficiency.

4. A Binary Social Spider Algorithm for Continuous Optimization Task

  • Authors: Emine Baş, E. Ülker
  • Journal: Soft Computing, Vol. 24(17), pp. 12953–12979
  • Publication Year: 2020
  • Citations: 26
  • Summary: The research adapts the SSA for continuous optimization tasks, showcasing its potential to solve complex mathematical problems with higher precision.

5. Improved Social Spider Algorithm for Large-Scale Optimization

  • Authors: Emine Baş, E. Ülker
  • Journal: Artificial Intelligence Review, Vol. 54(5), pp. 3539–3574
  • Publication Year: 2021
  • Citations: 22
  • Summary: This paper enhances the SSA for large-scale optimization problems, improving scalability and convergence rates, particularly for applications with high-dimensional datasets.

Conclusion

Dr. Emine Baş exemplifies excellence in research, academic mentorship, and innovation. Her impactful contributions to optimization algorithms, artificial intelligence, and machine learning position her as a deserving candidate for the Best Researcher Award.

With a strong academic foundation, proven research capabilities, and a focus on solving complex real-world problems, she has laid a robust groundwork for continued contributions to the field. Addressing areas such as broader collaborations and industrial engagement would further elevate her profile as a global leader in optimization and AI.

Qinglai Wei | Self-Learning Systems | Best Researcher Award

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Prof. Dr. Qinglai Wei | Self-Learning Systems | Best Researcher Award 

Associate Director, at Institute of Automation, Chinese Academy of Sciences, China.

Professor Qinglai Wei is a distinguished researcher and educator specializing in control systems, computational intelligence, and learning-based optimization. Serving as the Associate Director at The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, he has made significant contributions to adaptive dynamic programming, nonlinear control, and reinforcement learning. With an illustrious academic journey from Northeastern University and rich professional experience, Prof. Wei has authored numerous influential papers, books, and book chapters. His awards include multiple IEEE honors and recognition as a Clarivate Highly Cited Researcher. He is a prominent figure in advancing intelligent control systems and their applications in complex scenarios.

Professional Profile

Scopus

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Education 🎓

  • Ph.D. in Control Theory and Control Engineering (2009): Northeastern University, China. Advised by Prof. Huaguang Zhang, his research focused on intelligent control systems.
  • M.S. in Control Theory and Control Engineering (2005): Northeastern University, China, under Prof. Xianwen Gao’s mentorship.
  • B.S. in Automation (2002): Northeastern University, China, advised by Baodong Xu.
    These academic milestones laid the foundation for his expertise in adaptive dynamic programming and intelligent systems.

Professional Experience 💼

  • Associate Director (2018–Present): The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences.
  • Professor (2016–Present): The State Key Laboratory and the School of Artificial Intelligence, University of Chinese Academy of Sciences.
  • Visiting Scholar roles at University of Rhode Island (2018) and University of Texas at Arlington (2014) reflect his international collaboration and academic outreach.
    Earlier roles include Associate and Assistant Professor positions at The State Key Laboratory, showcasing steady growth in his academic career.

Research Interests 🔬

Prof. Wei’s research spans:

  • Computational Intelligence & Intelligent Control
  • Learning Control & Reinforcement Learning
  • Optimal & Nonlinear Control
  • Adaptive Dynamic Programming
    Applications include process control, smart grids, and multi-agent systems. His innovative methods continue to drive advancements in control theory and intelligent systems.

Awards 🏆

Prof. Wei’s excellence is marked by accolades like:

  • Best Paper Awards (2023 & 2022): International CSIS-IAC and China Automation Congress.
  • IEEE Outstanding Paper Awards (2018): Recognition for impactful contributions to the IEEE journals.
  • Highly Cited Researcher (2018 & 2019): By Clarivate Analytics for his influential publications.
    Other honors include National Natural Science Foundation Awards and Young Researcher Awards, emphasizing his leadership in the field.

Top Noted Publications 📚

  • “Learning and Controlling Multiscale Dynamics in Spiking Neural Networks” (2024, IEEE Transactions on Cybernetics): This study employs Recursive Least Square (RLS) modifications to manage multiscale dynamics in spiking neural networks. It advances neural control methods for adaptive tasks in dynamic environments【8】.
  • “Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems” (2024, IEEE/CAA Journal of Automatica Sinica): This paper focuses on event-triggered mechanisms to ensure robust consensus in multiagent systems under parallel optimal control.
  • “Primal-Dual Adaptive Dynamic Programming for Nonlinear Systems” (2024, Automatica): A framework using primal-dual adaptive dynamic programming tackles the stabilization and optimization of nonlinear systems.
  • “Class-Incremental Learning with Balanced Embedding Discrimination” (2024, Neural Networks): This work enhances class-incremental learning by introducing techniques to balance embeddings and improve discrimination among new and existing classes.

Conclusion

Qinglai Wei is exceptionally suited for the Research for Best Researcher Award. His prolific contributions to control theory, computational intelligence, and reinforcement learning, combined with his global recognition and leadership, exemplify his stature as a world-class researcher. With a proven track record of innovative research, impactful publications, and numerous accolades, he stands out as a strong candidate for this prestigious honor. Continued expansion into interdisciplinary collaborations and mentorship initiatives will further solidify his legacy as a pioneering researcher.