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首页> 外文期刊>International Journal of Applied Engineering Research >A Comparative Study of the Efficient Data Mining Algorithm for Forecasting Least Prices in Oman Fish Markets
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A Comparative Study of the Efficient Data Mining Algorithm for Forecasting Least Prices in Oman Fish Markets

机译：阿曼鱼市场预测最低价格的高效数据挖掘算法的比较研究

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摘要

The economic growth is influenced by human reactions in various sectors. Fisheries sector is one of the sources that people can depend on it since a long-time. The customers and suppliers nowadays need a good application to assist them to overcome the issue of rising of fish prices. This study aims to help to forecast the future of least prices in Oman fish markets using data mining algorithms, by means of studying the history of data that will assist to make a proper decision. The study considered the fish markets in Sultanate of Oman where it selected 29 markets and 15 fish species in each market In addition, the data mining algorithms, namely Linear Regression, SMOReg, Multilayer Perceptron, MLP Regressor, and Random Forest has been applied to forecast the prices weekly and monthly. The suitable algorithm, which provides good performance, has been chosen for developing an application. This research study will add to the literature in the area of technology development that will handle the fluctuations in the prices and will support the suppliers in a useful manner. This application model will help customers and suppliers to forecast the current least prices in Oman fish markets.
机译：经济增长受到各个部门的人类反应的影响。渔业部门是人们自长时间以来的来源之一。现在，客户和供应商需要一个良好的申请，以帮助他们克服鱼类价格上升的问题。本研究旨在通过研究将有助于做出正确决定的数据历史，帮助预测使用数据挖掘算法的阿曼鱼市场中最少价格的未来。该研究审议了阿曼苏丹国的鱼市场，其中在每个市场中选择了29个市场和15条鱼类，而数据挖掘算法，即线性回归，Smoreg，Multidayer Perceptron，MLP回归和随机森林已被应用于预测每周和每月价格。选择了提供良好性能的合适算法用于开发应用程序。该研究将在技术开发领域的文献中加入文献，将处理价格的波动，并将以有用的方式支持供应商。本申请模式将帮助客户和供应商预测阿曼鱼市场的最不上价格。

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