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Ahmad Shahi

Jeremiah Deng, Brendon Woodford
An efficient uncertain data stream clustering algorithm for arbitrary shaped clusters

Ahmad Shahi’s study aims to recognize sensor-based human activity recognition in an online mode in a smart home environment.

He focuses on Machine Learning, Data Mining, and advanced analytics approaches with in-depth experience and knowledge in designing, modelling, and developing techniques in big data and prediction analysis in both academic and business environments.

His main research domain is smart home environments which can save energy, improve safety and security, and automate gadgets, communication, etc. However, this is not limited and he is working on portfolio modeling and analytics.

Prior to embarking on this research, he was working on weather forecasting system using data mining and soft computing techniques such as fuzzy clustering, fuzzy logic, Neural Network, etc.


Shahi, A., Woodford, B. J., & Lin, H. (2017). Dynamic real-time segmentation and recognition of activities using a multi-feature windowing approach. In U. Kang, E.-P. Lim, J. X. Yu & Y.-S. Moon (Eds.), Trends and applications in knowledge discovery and data mining: Lecture notes in artificial intelligence (Vol. 10526). (pp. 26-38). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-67274-8_3

Shahi, A., Deng, J. D., & Woodford, B. J. (2017). A streaming ensemble classifier with multi-class imbalance learning for activity recognition. Proceedings of the International Joint Conference on Neural Networks (IJCNN). (pp. 3983-3990). IEEE. doi: 10.1109/IJCNN.2017.7966358

Shahi, A., Deng, J. D., & Woodford, B. J. (2017). Online hidden conditional random fields to recognize activity-driven behavior using adaptive resilient gradient learning. In D. Liu, S. Xie, Y. Li, D. Zhao & E.-L. M. El-Alfy (Eds.), Neural Information Processing: Lecture notes in computer science (Vol. 10634). (pp. 515-525). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-70087-8

Lin, H., Deng, J. D., Woodford, B. J., & Shahi, A. (2016). Online weighted clustering for real-time abnormal event detection in video surveillance. Proceedings of the Association for Computing Machinery (ACM) on Multimedia Conference. (pp. 536-540). New York, NY: ACM. doi: 10.1145/2964284.2967279

Shahi, A., Woodford, B. J., & Deng, J. D. (2015). Event classification using adaptive cluster-based ensemble learning of streaming sensor data. In B. Pfahringer & J. Renz (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 9457). (pp. 505-516). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-26350-2_45

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