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Information Journal Paper

Title

DEMAND FORECASTING MEDICAL EQUIPMENT BASED ON ARTIFICIAL NEURAL NETWORKS AND ARIMA METHODS

Pages

  171-198

Abstract

 Health sector and its infrastructure needs in both software and hardware sector has always been emphasized. Among importance of the MEDICAL EQUIPMENT and items in the health system of the country is not covered on any one. Organizations and companies active in this sector should be able to take correct decisions with regard to information in the volatile business environment today on time. Thus, estimating demand in future periods seems vital. There are various methods and tools for FORECASTING DEMAND that each have advantages and disadvantage its own special. In this paper, using a multilayer neural network with two hidden layers that has been learned with GENETIC ALGORITHM as the learning algorithm, the comparative system with Common method used in the prediction (Box-Jenkins Method) with model ARIMA (2, 1, 1) has been presented for the FORECASTING DEMAND CT-Scan set, that According to the measure of the accuracy of models, the mean squared error (MSE), the neural network model shown of the more effectiveness and efficiency as compared to ARIMA method according to the data and information in FORECASTING DEMAND CT-Scan set.

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    APA: Copy

    JAFARNEJAD, AHMAD, & SOLEYMANI, MOHSEN. (2011). DEMAND FORECASTING MEDICAL EQUIPMENT BASED ON ARTIFICIAL NEURAL NETWORKS AND ARIMA METHODS. JOURNAL OF ECONOMIC RESEARCH AND POLICIES, 19(57), 171-198. SID. https://sid.ir/paper/89489/en

    Vancouver: Copy

    JAFARNEJAD AHMAD, SOLEYMANI MOHSEN. DEMAND FORECASTING MEDICAL EQUIPMENT BASED ON ARTIFICIAL NEURAL NETWORKS AND ARIMA METHODS. JOURNAL OF ECONOMIC RESEARCH AND POLICIES[Internet]. 2011;19(57):171-198. Available from: https://sid.ir/paper/89489/en

    IEEE: Copy

    AHMAD JAFARNEJAD, and MOHSEN SOLEYMANI, “DEMAND FORECASTING MEDICAL EQUIPMENT BASED ON ARTIFICIAL NEURAL NETWORKS AND ARIMA METHODS,” JOURNAL OF ECONOMIC RESEARCH AND POLICIES, vol. 19, no. 57, pp. 171–198, 2011, [Online]. Available: https://sid.ir/paper/89489/en

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