Journal ArticleDOI
Application of cuckoo search in water quality prediction using artificial neural network
Sankhadeep Chatterjee,Sarbartha Sarkar,Nilanjan Dey,Amira S. Ashour,Soumya Sen,Aboul Ella Hassanien +5 more
TLDR
The proposed cuckoo search (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).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.read more
Citations
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Journal ArticleDOI
Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis
TL;DR: It was observed that Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) are the most utilised artificial intelligence models for water quality monitoring and assessment in the last decade.
Proceedings ArticleDOI
Image based skin disease detection using hybrid neural network coupled bag-of-features
Shouvik Chakraborty,Kalyani Mali,Sankhadeep Chatterjee,Sumit Anand,Aavery Basu,Soumen Banerjee,Mitali Das,Abhishek Bhattacharya +7 more
TL;DR: Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model in terms of testing phase confusion matrix based performance measuring metrics.
Journal ArticleDOI
Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms
Journal ArticleDOI
Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications
TL;DR: A modified Flower Pollination Algorithm has been employed to train Artificial Neural Network to predict soil moisture quantity and the proposed method is compared with well known PSO supported ANN and Cuckoo Search supported ANN along with MLP-FFN classifier.
Journal ArticleDOI
Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value
TL;DR: A linear regression algorithm and stochastic gradient descent in a machine‐learning environment were used as novel methods to predict the HHV of biomass and the LRA model was observed to be more accurate than SGD.
References
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