Egypt

Rafea, A., and K. Shaalan, "Using expert systems as a training tool in the agriculture sector in Egypt", Expert Systems with Applications, vol. 11, issue 3, no. 3: Elsevier Science Ltd, pp. 343–349, 1996. Abstractusingesasatrainingtoolintheagriculture.pdfWebsite

{This paper describes the Egyptian experience in using Expert Systems (ES) as a training tool in the agriculture sector. The work described here is part of an ongoing research to study the use of ES in human resources development. In particular, we present the use of such a tool as an instructional device for increasing the efficiency of extension workers through improving their general decision-making skills in their jobs. To clarify this process, we conducted an experiment and analyzed its results.}

Shaalan, K., M. Riad, A. Amer, and H. Baraka, "Speculative Work in Neural Network Forecasting: An application to Egyptian Cotton Production", The Egyptian Computer Journal, vol. 27, no. 1: Institute of Statistical Studies and Research, pp. 58–79, 1999. Abstractcotton_nn.pdf

This paper describes a neural network approach for time series data forecasting. This study looks at the application of neural computing to an agricultural problem. In the selected application, the first step was to construct the multilayer feedforward neural network and the backpropagation training was employed. Elements of preprocessing, preparatory work, and network design are discussed. In order to improve efficiency and achieve stability, experiments were done while varying the learning rate and the number of hidden units. As a result, successful generalization has been observed on the test sets. Also a statistical approach (regression analysis) was applied to the same application. A comparative study between the applied methods was described. The comparative study can also be beneficial for implementing other forecasting applications. Our study suggests that the neural network is very useful for forecasting problems and in terms of comparative accuracy, the network was able to achieve more accurate results than the multipe regression model.

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