A Novel Expert System in Hospital Location Analysis with the Aid of Adaptive Artificial Bee Colony (AABC)

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It offers, indeed, immense challenges to assess clearly the exact variance of the optimal solutions with the recent remedies, when we set out to study the locations of modern hospitals in the context of the perplexing state of affairs and the intricate decisive factors prevalent in the current scenario. In order to do so, we first identify the main classes of criteria used in location planning. In the existing hospital location analysis technique a non linear mathematical model is developed to find the highly sensitive roads. But this method has such a drawback that the obtained optimal weights are not generally distributed. To overcome this, a new expert system developed for hospital location analysis with optimization method is presented. In our proposed technique, a new expert system is developed based on the Radial Basis Function (RBF). In the RBF the weights are optimized by using the well renowned optimization technique Adaptive Artificial Bee Colony (AABC) Algorithm. After that, the different length pattern values are mined and locations are computed by using the pattern mining and clustering techniques. Thus our proposed technique performance is analyzed by using the hospital location dataset and compared with the exiting hospital locations analysis techniques and optimizations methods PSO and Conventional ABC.
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Radial Basis Function (RBF); Adaptive Artificial Bee Colony (AABC); Pattern Mining; Location Analysis; Clustering Techniques

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