approach. 2020 International Conference on Computational Performance Evaluation (ComPE), 777-780.
Heydari, R. and Haj Seyed Javady, M.R. (2022). The application of hybrid data mining model (genetic algorithm-wavelet-deep neural network-Monte Carlo method) for the price forecasting of agricultural products (Case study: future price of saffron in agricultural commodity exchange). Iranian Journal of Agricultural economics and Development, in Publishing. (In Farsi)
Hirapara, J. and Vanjara, D.P. (2022). A comparative study of data mining techniques for agriculture crop price prediction. 7th International conference for Convergence in Technology (I2CT), Pune, India. Apr 2022, 1-6.
Hoseini Yekani, S. A. and Kashiri Kalaei, F. (2016). Investigating the effect of price fluctuations of agricultural products on the optimal pattern of agricultural products exploitation in Sari city. Iranian Journal of Agricultural Economy, 11)2): 75-94. (In Farsi)
Houshmand, R. and Moazzami, M. (2014). Iranian Journal of Electrical and Electronics Engineering, 11(1): 37-48. (In Farsi)
Iran Mercantile Exchange. (2022). <https://www.ime.co.ir>.
Li, C., Chen, Z., Liu, J., Li, D., Gao, X., Di, F., Li, L. and Ji, X. (2019). Power load forecasting based on the combined model of LSTM and XGBoost. Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence, Aug 2019.
Menhaj, M.H. and Kavoosi-Kalashami, M. (2022). Developing a hybrid forecasting system for agricultural commodity prices (case study: Thailand rice free on-board price). Journal of Agribusiness-Cienc Rural, 52(8): 1-11.
Mousavi. S. N. and Kavousi Kalashami, M. (2016). Evaluation of seasonal, ANN, and hybrid models in modeling urban water consumption a case Study of Rash city. Iranian Journal of Water and Wastewater, 4: 84-89. (In Farsi)
Mysen, S. J. and Thornton, E. M. (2021). Forecasting the price of aluminum using machine learning. Master Thesis [3906], Norwegian School of Economics, Bergen, 2021.
Nielsen, D. (2016). Tree Boosting With XGBoost: Why Does XGBoost Win "Every" Machine Learning Competition? Norwegian University of Science and Technology, Department of Mathematical Sciences, Trondheim.
Nosratabadi, S., Szell. K., Beszedes. B., Imre. F., Ardabili. S. and Mosavi. A. (2022). Hybrid Machine Learning Models for Crop Yield Prediction. Journal of Computer Science, Neural and Evolutionary Computing, 1-5.
Patel, J., Shah, S., Thakkar, P. and Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Journal of Expert System, 42: 259-268.
Paul, R. K, Yeasin, MD., Paul, K., Balasubramanian. M., Roy. H. S. and Gupta. A. (2022). Machine learning techniques for forecasting agricultural prices: A case of Brinjal in Odisha, India. Journal of Pone, e0270553, 1-17.
Phama, B. T, Bui, D. T., Indra Prakash, I. and Dholakia, M.B. (2017). Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Journal of Catena, 149: 52-63.
Poyanfar, A., Falahpour, S., Nowruzian Lequan, I. and Farhadi Shuli, A. H. (2015). Using the hybrid method of feature selection and the nearest neighbor algorithm to predict the daily movement direction of the index of the five most active companies of Tehran Stock Exchange. Iranian Journal of Financial Engineering and Securities Management, 25: 1-20. (In Farsi)
Qureshi, Sh., Chu, B. M. and Demers, F. S. (2020). Forecasting Canadian GDP growth using XGBoost. Carleton University, Department of Economics, Carleton University, Canada.
Rajaei, T. and Ziniwand, A. (2013). Groundwater level modeling using hybrid wavelet-artificial neural network model (Case study: Sharifabad Plain). Iranian Journal of Civil and Environmental Engineering, University of Tabriz, 44(4): 51-63. (In Farsi)
Ribeiro, M.H.D. and Coelho, L.D.S. (2020). Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series. Journal Applied Soft Computing, 86: 1-17.
Roshan, R., Akbari, A. and Rostami, K. (2016). Comparison of quantitative and qualitative methods in wheat price forecasting (case study in Iran). Iranian Journal of Agricultural Economic Research, 9(3): 123-144. (In Farsi)
Sadeghi, H. and Dehghani Firouzabadi, Z. (2017). Denoising financial time series using wavelet analysis. Iranian Journal of Financial Engineering and Securities Management, 33: 299-315. (In Farsi)
Saif Al-Hosseini, F., Mohammadi Nejad, A. and Moghaddasi, R. (2014). Comparing forecasting ability of artificial neural networks and ARIMA methods in forecasting of Iran’s leather and skin exports. Iranian Journal of Agricultural Economic Research, 7(2): 125-142. (In Farsi)
Samek, W., Wiegand, T. and Muller, K.R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.
Shabri, A. and Samsudin, R. (2014). Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model. Hindawi Publishing Corporation Mathematical Problems Engineering, Volume 2014, Article ID 201402, 10 pages.
Shirdeli, A. and Tavassoli, A. (2015). Predicting yield and water use efficiency in saffron using models of artificial neural network based on climate factors and water. Iranian Journal of the Technology and Agriculture and of Saffron, 3(2): 121-131. (In Farsi)
Srinivas T, A. S., Somula R., K. G., Saxena, A., and Reddy A. P. (2019). Estimating rainfall using machine learning strategies based on weather radar data. International Journal of Commun System, 1-11.
Steurer, M., Hill, R. J. and Pfeifer, N. (2021). Metrics for evaluating the performance of machine learning based automated valuation. Journal of Property Research, 38(2): 99-129.
Vafaei Ghaeini, V., Kimiagari, A. M. and Jafarzadeh Atrabi, M. (2018). Forecasting Stock Price using Hybrid Model based on Wavelet Transform in Tehran and New York Stock Market. International Journal of Finance and Managerial Accounting, 3(11).
Vignesh, K. and Askarunisa, A. (2020). A Survey on Machine Learning and Deep Learning Techniques used for Agricultural Crop Prediction, Soil Classification and Rainfall Prediction. International Research Journal of Engineering and Technology (IRJET), 7(6):1896-1903.
Weston, J., Elisseeff, A. and Scholkopf, B. (2003). Use of zero-norm with linear models and kernel methods. Journal of Machine Learning Research, 3(5):1439-1461.
Wihartiko, F.D., Nurdiati, S., Buono, A. and Santosa, E. (2021). Agricultural Price Prediction Models: A Systematic Literature Review. International Conference on Industrial Engineering and Operations Management Singapore, March 7-11: 2927-2934.
Yang, Y., Chen, Y., Wang, Y., Li, C. and Li, L. (2016). Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting. Journal of Applied Soft Computing, 49: 663-675.
Yasrebi, S. E., Zabbah, I., Behzadiyan, B., Maroosi, A. and Rezaie, R. (2019). Classification of saffron based on its apparent characteristics using artificial neural networks, Iranian Journal of the Technology and Agriculture and of Saffron, 7(4): 521-535. (In Farsi)
Yeh, C., Chi, D.J. and Lin, Y.R. (2014). Going-concern prediction using hybrid random forests and rough set approach. Journal of Information Sciences, 254, 98-110.
Zareei, M. R. and Iranmanesh, M. (2022). Ultimate strength assessment of cracked stiffened plates using optimized XGBoost method. . Iranian Journal of Marine Engineering, 18(36): 25-32. (In Farsi)
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