Rate My Professor Wei Zhong

Zhong's research specializations include machine learning, bioinformatics, computational biology, protein structure prediction, clustering algorithms, and cybersecurity applications of deep learning. Key publications encompass 'Applying Big Data Based Deep Learning System to Intrusion Detection' in Big Data Mining and Analytics (2020), co-authored with Ning Yu and Chunyu Ai; 'Multi-level Clustering Support Vector Machine Trees for Improved Protein Local Structure Prediction' with Jieyue He, Xiujuan Chen, and Yi Pan; 'Clustering Support Vector Machines for Local Protein Structure Prediction' in Expert Systems with Applications (2007); 'Predicting Local Protein 3D Structures Using Clustering Deep Recurrent Neural Network' (2022); 'A Dual-Approach Framework for Enhancing Network Traffic Anomaly Detection' (2024); and 'An Improved Method for Completely Uncertain Biological Networks' (2015). His work supports student research initiatives, including presentations on generating new protein samples for structure prediction accuracy and new malware samples using tree-based methods at the South Carolina Upstate Research Symposium. Zhong's contributions extend to conference proceedings and collaborations on topics like partner-matching frameworks for social activity communities and hybrid resampling algorithms for particle filters.