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- W4241855287 abstract "Domestic and industrial pollution affected the water quality to a greater extent. Recent research studies have achieved reasonable success in predicting the water quality using several machine learning based techniques. In the current work, a proposed cuckoo search (CS) has been applied to improve the support in the classification process during the water quality prediction. The proposed model (NN-CS) gradually minimises an objective function; namely the root mean square error (RMSE) in order to find the optimal weight vector for the artificial neural network (ANN). The proposed model was compared with three other well-established models, namely NN-GA (ANN trained with genetic algorithm) and NN-PSO (ANN trained with particle swarm optimisation) in terms of accuracy, precision, recall, f-measure, Matthews correlation coefficient (MCC) and Fowlkes-Mallows index (FM index). The simulation results established superior accuracy of NN-CS over the other models." @default.
- W4241855287 created "2022-05-12" @default.
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- W4241855287 date "2017-01-01" @default.
- W4241855287 modified "2023-09-26" @default.
- W4241855287 title "Application of cuckoo search in water quality prediction using artificial neural network" @default.
- W4241855287 doi "https://doi.org/10.1504/ijcistudies.2017.089054" @default.
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