| FuseAD: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models M Munir, SA Siddiqui, MA Chattha, A Dengel, S Ahmed Sensors 19 (11), 2451, 2019 | 112 | 2019 |
| A comparative analysis of traditional and deep learning-based anomaly detection methods for streaming data M Munir, MA Chattha, A Dengel, S Ahmed 2019 18th IEEE international conference on machine learning and applications …, 2019 | 68 | 2019 |
| Kinn: Incorporating expert knowledge in neural networks MA Chattha, SA Siddiqui, MI Malik, L van Elst, A Dengel, S Ahmed arXiv preprint arXiv:1902.05653, 2019 | 14 | 2019 |
| Pilot: A precise imu based localization technique for smart phone users MA Chattha, IH Naqvi 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), 1-5, 2016 | 12 | 2016 |
| Deepex: Bridging the gap between knowledge and data driven techniques for time series forecasting MA Chattha, SA Siddiqui, M Munir, MI Malik, L van Elst, A Dengel, ... Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning …, 2019 | 5 | 2019 |
| DeepLSF: Fusing Knowledge and Data for Time Series Forecasting MA Chattha Authorea Preprints, 2021 | 3 | 2021 |
| A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models P Vadiraja, MA Chattha arXiv preprint arXiv:2008.05972, 2020 | 2 | 2020 |
| Method and system for predicting trajectories for maneuver planning based on a neural network S Zwicklbauer, MA Chattha, S Ahmed, VAN Ludger US Patent App. 18/249,214, 2023 | 1 | 2023 |
| KENN: enhancing deep neural networks by leveraging knowledge for time series forecasting MA Chattha, L van Elst, MI Malik, A Dengel, S Ahmed arXiv preprint arXiv:2202.03903, 2022 | 1 | 2022 |
| KRNN: A hybrid data and knowledge oriented time series forecasting approach for health care applications MA Chattha, MI Malik, A Dengel, S Ahmed Expert Systems with Applications, 126148, 2024 | | 2024 |
