Appropriate Combination of Artificial Intelligence and Algorithms for Increasing Predictive Accuracy Management

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ID: 68771
2010
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Abstract
In this paper a simple and effective expert system to predict random data fluctuation in short-term period is established. Evaluation process includes introducing Fourier series, Markov chain model prediction and comparison (Gray) combined with the model prediction Gray- Fourier- Markov that the mixed results, to create an expert system predicted with artificial intelligence, made this model to predict the effectiveness of random fluctuation in most data management programs to increase. The outcome of this study introduced artificial intelligence algorithms that help detect that the computer environment to create a system that experts predict the short-term and unstable situation happens correctly and accurately predict. To test the effectiveness of the algorithm presented studies (Chen Tzay len,2008), and predicted data of tourism demand for Iran model is used. Results for the two countries show output model has high accuracy.
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nia2010appropriatejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nia, Shahram Gilani;
Journal journal of information technology management
Year 2010
DOI
DOI not found
URL
https://jitm.ut.ac.ir/article_20915_7324619f7daa2530d45556e2fb0a3c64.pdf
Keywords
chemistry Biology (General) Engineering (General). Civil engineering (General) Technology physics environmental sciences neurology. diseases of the nervous system business economics as a science electronic computers. computer science hospitality industry. hotels, clubs, restaurants, etc. food service information resources (general) artificial intelligence hybrid model the heuristic algorithminformation resources (general)

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