@inproceedings{scholars11331,
           title = {Predictive analytics for machine failure using optimized recurrent neural network-gated recurrent unit (GRU)},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {Proceedings - 2019 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019},
           pages = {88--93},
            note = {cited By 1; Conference of 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019 ; Conference Date: 19 September 2019; Conference Code:157266},
             doi = {10.1109/AiDAS47888.2019.8970725},
            year = {2019},
        abstract = {This paper proposed a technique named Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU) to predict the condition of machines by using time series data generated by oil and gas company. The problem raised due to limited research of RNN-GRU in improving the accuracy through hyperparameter tuning. Hence, this paper will provide an optimization method that can improve the accuracy of RNN-GRU in forecasting time series data. The preliminary findings of the experiment conducted shows that RNN-GRU can utilize time series data to predict machine failure with improved high accuracy. {\^A}{\copyright} 2019 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079346213&doi=10.1109\%2fAiDAS47888.2019.8970725&partnerID=40&md5=ac076132c400990b24e1c17a4bc8214c},
        keywords = {Forecasting; Gas industry; Genetic algorithms; Predictive analytics; Public utilities; Time series, Forecasting time series; High-accuracy; Hyper-parameter; Machine failure; Oil and gas companies; Optimization method; Time-series data, Recurrent neural networks},
            isbn = {9781728130415},
          author = {Zainuddin, Z. and P Akhir, E. A. and Aziz, N.}
}