Dean of the School of Mathematics and Physics, Hebei University of Engineering, China
Dr. Jiqiang Chen is a prominent Professor and the Dean of the School of Mathematics and Physics at Hebei University of Engineering, Handan, China . He earned his M.Sc. from Hebei University in 2008 and completed his Ph.D. from the Harbin Institute of Technology in 2018 . With a strong foundation in mathematics and artificial intelligence 🤖, Dr. Chen serves on the Youth Editorial Board for Fuzzy Information and Engineering His research spans dynamic neural networks, AI, fuzzy optimization, and optimal transport 🚀. He is widely recognized for his contributions to computational intelligence and engineering applications
Profile
Scopus
🎓 Education
Dr. Jiqiang Chen 🎓 obtained his M.Sc. in Mathematics from Hebei University, Baoding, China, in 2008, and later earned his Ph.D. in Applied Mathematics from the Harbin Institute of Technology, Harbin, China, in 2018 🎓. He currently serves as a Professor 👨🏫 and the Dean of the School of Mathematics and Physics at Hebei University of Engineering. In addition to his academic leadership, Dr. Chen holds an editorial position as a Member of the Youth Editorial Board for the Fuzzy Information and Engineering (SCI) journal, actively contributing to the advancement of research in computational intelligence and fuzzy systems.
📚 Professional Development
Dr. Chen has continually advanced his academic and leadership journey 📈through years of rigorous research, interdisciplinary collaboration , and academic stewardship. His role as Dean involves curriculum innovation, fostering international cooperation , and mentoring young researchers 👨🔬. As a Youth Editorial Board Member for Fuzzy Information and Engineering 📖, he contributes to the scientific community by reviewing and promoting cutting-edge research. Dr. Chen actively participates in conferences, workshops, and scientific forums , where he shares his insights on neural networks and fuzzy systems. His commitment to academic excellence and scientific innovation makes him a distinguished figure in his field .
🔬 Research Focus
Dr. Chen’s research focuses on the theoretical and practical aspects of dynamic neural networks , artificial intelligence 🤖, fuzzy optimization ⚙️, and optimal transport 🚛. He explores how dynamic systems interact under uncertainty using fuzzy logic 🌫️, and applies optimal transport theory to solve real-world problems efficiently . His work aims to enhance machine learning frameworks and intelligent systems, with applications ranging from data science to engineering . He also contributes to developing novel algorithms for complex systems where deterministic approaches fall short. His multidisciplinary approach strengthens the bridge between mathematical theory and AI-based applications .
Publication Top Notes
The Prediction Model of Water Level in Front of the Check Gate of the LSTM Neural Network Based on AIW-CLPSO
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Authors: Linqing Gao, Dengzhe Ha, Litao Ma, Jiqiang Chen
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Journal: Journal of Combinatorial Optimization, Volume 47, Issue 2, 2024
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Citations: 0 (as per latest available data)
Summary:
This study addresses the challenge of accurately predicting water levels in front of check gates, which is crucial for water resource management. The authors propose a hybrid model combining Long Short-Term Memory (LSTM) neural networks with an Adaptive Inertia Weight Comprehensive Learning Particle Swarm Optimization (AIW-CLPSO) algorithm. This integration aims to enhance the global optimization capability and convergence speed of the prediction model. The model was applied to the Chaohu Lake check gate, demonstrating superior performance with a Nash–Sutcliffe efficiency coefficient of 0.9851 and a root mean square error of 0.0273 meters. The results indicate that the proposed AIW-CLPSO-LSTM model effectively captures the nonlinear and stochastic characteristics of water level fluctuations, offering a valuable tool for intelligent gate control and water resource scheduling in long-distance water transfer projects.
Discrete Optimal Transport for Class-Imbalanced Classifications
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Authors: Jiqiang Chen, Jie Wan, Litao Ma
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Journal: Mathematics, Volume 12, Issue 4, Article 524, 2024
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Citations: 1 (as per latest available data)
Summary:
This paper introduces a novel approach to address the challenge of class imbalance in classification tasks using Regularized Discrete Optimal Transport (RDOT). The authors develop a framework that incorporates regularization into the discrete optimal transport problem, aiming to improve the performance of classifiers on imbalanced datasets. By formulating the classification problem as a transport problem between empirical distributions of different classes, the method seeks to find an optimal mapping that minimizes the cost while considering regularization terms to prevent overfitting. Experimental results on various benchmark datasets demonstrate that the proposed RDOT approach outperforms traditional methods in terms of accuracy and robustness, particularly in scenarios with significant class imbalance. This work contributes to the field by providing a mathematically grounded and effective solution for imbalanced classification problems.
Conclusion
Dr. Jiqiang Chen stands out as a dedicated scholar, innovative researcher, and visionary academic leader . His contributions to dynamic neural networks, artificial intelligence, and fuzzy optimization continue to drive progress in applied mathematics and intelligent systems Through his leadership as Dean 🏫and involvement in editorial activities 🧑🔬, he fosters academic excellence and collaborative research. Dr. Chen’s work not only enhances theoretical understanding but also supports real-world applications, solidifying his reputation as a key figure in modern computational science .