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Our Research in Action
Explore our cutting-edge work in AI, Machine Learning, and Image Processing
Machine Learning & Robotics
Advanced ML algorithms powering intelligent robotic systems
Research Focus Areas
Pioneering research in intelligent computing technology and artificial intelligence at the University of Jinan
Machine Learning
Advanced ML algorithms and deep learning architectures for intelligent systems
Artificial Intelligence
Cutting-edge AI research in computer vision, NLP, and autonomous systems
Image Processing
Novel techniques for image analysis, enhancement, and computer vision applications
Neural Networks
Deep neural network architectures and optimization techniques
Intelligent Computing
High-performance computing solutions for complex problem solving
Data Science
Big data analytics, data mining, and pattern recognition research
Research Impact
Making meaningful contributions to the scientific community
Featured Publications
Our latest research contributions to the field
This paper proposes a novel CNN compression method using sparse self-representation and particle swarm optimization to reduce model complexity while maintaining accuracy.
This paper proposes a novel CNN compression method using sparse self-representation and particle swarm optimization to reduce model complexity while maintaining accuracy.
This paper proposes a novel CNN compression method using sparse self-representation and particle swarm optimization to reduce model complexity while maintaining accuracy.
A novel deep ensemble neural-like P systems approach for accurate segmentation of central serous chorioretinopathy lesions in medical images.
A novel deep ensemble neural-like P systems approach for accurate segmentation of central serous chorioretinopathy lesions in medical images.
A novel deep ensemble neural-like P systems approach for accurate segmentation of central serous chorioretinopathy lesions in medical images.
Featured Projects
Cutting-edge research projects pushing the boundaries of AI
Research on compressing convolutional neural networks using sparse self-representation and particle swarm optimization techniques. Achieved significant model size reduction while maintaining accuracy for real-world deployment.
Development of advanced segmentation algorithms for retinal layer analysis and disease detection in OCT images. Focus on central serous chorioretinopathy and other retinal pathologies using deep learning and P systems.
Research on sparse subspace clustering and high-order methods for hyperspectral image classification and interpretation in remote sensing applications.