Journal of Engineering and Applied Sciences

Year: 2020
Volume: 15
Issue: 1
Page No. 81 - 87

References

Bellman, R.E. and L.A. Zadeh, 1970. Decision making in a fuzzy environment. Manage. Sci., 17: 141-164.
Direct Link  |  

Benkouider, F., L. Hamami and A. Abdellaoui, 2014. Optimized neural networks using principal component analysis for automatic road extraction from remote sensing. J. Eng. Appl. Sci., 9: 427-433.
Direct Link  |  

Bousqaoui, H., I. Slimani and S. Achchab, 2018. Information sharing as a coordination tool in supply chain using multi-agent system and neural networks. Proceedings of the 6th World Conference on Information Systems and Technologies (WorldCIST’18) Vol. 745, March 27-29, 2018, Springer, Naples, Italy, ISBN:978-3-319-77702-3,-pp: 626.

Chawla, A., A. Singh, A. Lamba, N. Gangwani and U. Soni, 2019. Demand Forecasting Using Artificial Neural Networks-A Case Study of American Retail Corporation. In: Applications of Artificial Intelligence Techniques in Engineering: Advances in Intelligent Systems and Computing, Malik, H., S. Srivastava, Y.R. Sood and A. Ahmad (Eds.). Springer, Singapore, ISBN:978-981-13-1821-4, pp: 79-89.

Chen, C.F., M.C. Lai and C.C. Yeh, 2012. Forecasting tourism demand based on empirical mode decomposition and neural network. Knowl. Based Syst., 26: 281-287.
CrossRef  |  Direct Link  |  

Chen, T., H. Yin, H. Chen, L. Wu and H. Wang et al., 2018. TADA: Trend alignment with dual-attention multi-task recurrent neural networks for sales prediction. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), November 17-20, 2018, IEEE, Singapore, ISBN:978-1-5386-9160-1, pp: 49-58.

Claveria, O. and S. Torra, 2014. Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Econ. Model., 36: 220-228.
CrossRef  |  Direct Link  |  

Hribar, R., P. Potocnik, J. Silc and G. Papa, 2019. A comparison of models for forecasting the residential natural gas demand of an urban area. Energy, 167: 511-522.
CrossRef  |  Direct Link  |  

Kim, S. and H. Kim, 2016. A new metric of absolute percentage error for intermittent demand forecasts. Intl. J. Forecasting, 32: 669-679.
CrossRef  |  Direct Link  |  

Kochak, A. and S. Sharma, 2015. Demand forecasting using neural network for supply chain management. Intl. J. Mech. Eng. Rob. Res., 4: 96-104.
Direct Link  |  

Leyva, L.L.L., M.A.Y. Huaca, Y.M. Santos, R.V.S. Piarpuezan and I.D.H. Granda et al., 2018. Applying lean manufacturing in the production process of rolling doors: A case study. J. Eng. Appl. Sci., 13: 1774-1781.
Direct Link  |  

Lorente-Leyva, L.L., I.D. Herrera-Granda, P.D. Rosero-Montalvo, K.L. Ponce-Guevara and A.E. Castro-Ospina et al., 2018. Developments on solutions of the normalized-cut-clustering problem without eigenvectors. Proceedings of the 15th International Symposium on Neural Networks (ISNN 2018), June 25-28, 2018, Springer, Cham, Switzerland, ISBN:978-3-319-92536-3, pp: 318-328.

Marroquin, M.G.V., M.C.A. Cervantes, J.L.M. Flores and M. Cabrera-Rios, 2009. Time series: Empirical characterization and artificial neural network-based selection of forecasting techniques. Intl. Data Anal., 13: 969-982.
CrossRef  |  Direct Link  |  

Rubio, J.D.J., I. Elias, D.R. Cruz, J. Pacheco and G.J. Gutierrez et al., 2017. A fuzzy algorithm for the prediction of future data. IEEE. Latin Am. Trans., 15: 1361-1367.
CrossRef  |  Direct Link  |  

Sarmiento, AT. and O.C. Soto, 2014. New product forecasting demand by using neural networks and similar product analysis. Dyna, 81: 311-317.
CrossRef  |  Direct Link  |  

Slimani, I., I.E. Farissi and S. Achchab, 2015. Artificial neural networks for demand forecasting: Application using Moroccan supermarket data. Proceedings of the 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), December 14-16, 2015, IEEE, Marrakech, Morocco, ISBN:978-1-4673-8709-5, pp: 266-271.

Slimani, I., I.E. Farissi and S. Achchab, 2017. Configuration and implementation of a daily artificial neural network-based forecasting system using real supermarket data. Intl. J. Logist. Syst. Manage., 28: 144-163.
CrossRef  |  Direct Link  |  

Sultan, J.A. and R.M. Jasim, 2016. Demand forecasting using artificial neural networks optimized by artificial bee colony. Intl. J. Manage. Inf. Technol. Eng., 4: 77-88.
Direct Link  |  

Tiwari, M.K. and J.F. Adamowski, 2013. Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resour. Res., 49: 6486-6507.
CrossRef  |  Direct Link  |  

Verma, A., A. Karan, A. Mathur and S. Chethan, 2017. Analysis of time-series method for demand forecasting. J. Eng. Appl. Sci., 12: 3102-3107.
CrossRef  |  Direct Link  |  

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