Dr. Xing Fang
Associate Professor of Computer Science
School Information Technology
Office
OU Old Union 108
Office Phone
Email
- About
- Education
- Awards & Honors
- Research
Current Courses
340.001Introduction To Artificial Intelligence
440.001Introduction to Artificial Intelligence
179.004Introduction To Data Structures
Teaching Interests & Areas
Python, C/C++, Java
Research Interests & Areas
deep learning, natural language processing, machine learning
Ph D Computer Science
North Carolina A&T State University
Greensboro NC USA
MS Computer Science
North Carolina A&T State University
Greensboro NC USA
MS Information Assurance
Dakota State University
Madison, SD, USA
BS Electrical Engineering
Northwestern Polytechnical University
Xi'an, Shaanxi, China
Impact Award
University College
2020
Impact Award
University College
2017
Conference Proceeding
Tao, J., Fang, X., & Zhou, L. Unsupervised Deep Learning for Fake Content Detection in Social Media. HICSS (2021)
X. Fang and J. Tao, "A Transfer Learning based Approach for Aspect Based Sentiment Analysis," 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), Granada, Spain, 2019, pp. 478-483.
Nadeem, Mutahir, Ochaun Marshall, Sarbjit Singh, Xing Fang, and Xiaohong Yuan. "Semi-supervised deep neural network for network intrusion detection." (2016).
Journal Article
Chen, Y., Zhu, J., Wan, L., Fang, X., Tong, F., & Xu, X. Routing failure prediction and repairing for AUV-assisted underwater acoustic sensor networks in uncertain ocean environments. Applied Acoustics/ Elsevier 186 (2022)
Chen, Y., Tang, Y., Fang, X., Wan, L., Tao, Y., & Xu, X. PB-ACR: Node Payload Balanced Ant Colony Optimal Cooperative Routing for Multi-Hop Underwater Acoustic Sensor Networks. IEEE Access (2021)
Chen, Y., Zheng, K., Fang, X., Wan, L., & Xu, X. QMCR: A Q-Learning-Based Multi-Hop Cooperative Routing Protocol for Underwater Acoustic Sensor Networks. IEEE China Communications (2021)
Fang, X. Making recommendations using transfer learning. Neural Comput & Applic (2021)
Yang, F., Pluth, T., Xing, X., Francq, K., Jurjovec, M., & Tang, Y. Advanced machine learning application for odor and corrosion control at a water resource recovery facility. Water Environment Research, Wiley Journal (2021)
Presentations
Deep learning and AI. Physics Informal Seminar Series. (2019)
Develop Advanced Models to Predict and Optimize Chemical Dosage for Odor and Corrosion Control. the WEFTEC 2019 Intelligent Water Systems Challenge. (2019)
Introduction to Deep Learning. MAT 443. Illinois State Unviersity. (2016)
Grants & Contracts
New Faculty Start-up Program. Illinois State University. (2016)