Optimizing the modified fuzzy ant-miner for efficient medical diagnosis

The advantage of efficient searches belonging to ant-miner over several other approaches leads to prominent achievements on rules mining. Fuzzy ant-miner, an extension of the ant-miner provides a fuzzy mining framework for the automatic extraction of fuzzy rules from labeled numerical data. However, it is easily trapped in local optimal, especially when it applies to medical cases, where real world accuracy is elusive; and the interpretation and integration of medical knowledge is necessary. In order to relieve such a local optimal difficulty, this paper proposes OMFAM which applies simulated annealing to optimize fuzzy set parameters associated with a modified fuzzy ant-miner (MFAM). MFAM employs attributes and training case weighting. The proposed method, OMFAM was experimented with six critical medical cases for developing efficient medical diagnosis systems. The performance measurement relates to accuracy as well as interpretability of the mined rules. The performance of the OMFAM is compared with such references as MFAM, fuzzy ant-miner (FAM), and other classification methods. At last, it indicates the superiority of the OMFAM algorithm over the others.     

An Integration of Stationary Wavelet Transform and Nonlinear Autoregressive Neural Network with Exogenous Input for Baseline and Future Forecasting of Reservoir Inflow

For effective water resources management and planning, an accurate reservoir inflow forecast is essential not only in training and testing phases but also in particular future periods. The objective of this study is to develop a reservoir inflow integrated forecasting model, relying on nonlinear autoregressive neural network with exogenous input (NARX) and stationary wavelet transform (SWT), namely SWT-NARX. Due to the elimination of down-sampling operation, SWT provides influential reinforcement of efficiently extracting the hidden significant, temporal features contained in the nonstationary inflow time series without information loss. The decomposed SWT sub-time series are determined as input-output for NARX forecaster; where a multi-model ensemble global mean (MMEGM) of downscaled precipitation based on nine global climate models (GCMs) represents as a climate-change exogenous input. Two major reservoirs in Thailand, Bhumibol and Sirikit ones are focused. Pearson’s correlation coefficient (r) and root mean square error (RMSE) are employed for performance evaluation. The achieved results indicate that the SWT-NARX explicitly outperforms the comparable forecasting approaches regarding a historical baseline period (1980–1999). Therefore, such SWT-NARX is further employed for future projection of the reservoir inflow over near (2010–2039) -, mid (2040–2069) - and far (2070–2099) - future periods against the inflow of the baseline one.