1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org.
2. Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing;AL-Qutami;Expert. Syst. Appl.,2018
3. Anderson, R., Huchette, J., Ma, W., Tjandraatmadja, C., Vielma, J. P., 2018. Strong mixed-integer programming formulations for trained neural networks. arXiv:1811.01988.
4. A new decomposition algorithm for a liquefied natural gas inventory routing problem;Andersson;Int. J. Prod. Res.,2016
5. Integrating timetabling and crew scheduling at a freight railway operator;Bach;Transp. Sci.,2016