S
Sarbartha Sarkar
Researcher at Indian Institutes of Technology
Publications - 8
Citations - 433
Sarbartha Sarkar is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Artificial neural network & Particle swarm optimization. The author has an hindex of 7, co-authored 8 publications receiving 367 citations. Previous affiliations of Sarbartha Sarkar include Indian Institute of Technology Dhanbad & Hooghly Engineering and Technology College.
Papers
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Journal ArticleDOI
Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings
Sankhadeep Chatterjee,Sarbartha Sarkar,Sirshendu Hore,Nilanjan Dey,Amira S. Ashour,Valentina Emilia Balas +5 more
TL;DR: A particle swarm optimization-based approach to train the NN (NN-PSO), capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistory reinforced concrete building structure in the future.
Journal ArticleDOI
Neural-based prediction of structural failure of multistoried RC buildings
Sirshendu Hore,Sankhadeep Chatterjee,Sarbartha Sarkar,Nilanjan Dey,Amira S. Ashour,Dana Balas-Timar,Valentina Emilia Balas +6 more
TL;DR: In this paper, the authors employed the multilayer perceptron feed-forward network (MLP-FFN) classifier to tackle the problem of predicting structural failure of reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future.
Journal ArticleDOI
Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm
Sankhadeep Chatterjee,Sarbartha Sarkar,Sirshendu Hore,Nilanjan Dey,Amira S. Ashour,Fuqian Shi,Dac-Nhuong Le +6 more
TL;DR: In this article, the authors employed a multi-objective genetic algorithm (MOGA) to train the Neural Network (NN) based model to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) of the weight vector of the NN.
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
TL;DR: 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).
Proceedings ArticleDOI
Water quality prediction: Multi objective genetic algorithm coupled artificial neural network based approach
Sankhadeep Chatterjee,Sarbartha Sarkar,Nilanjan Dey,Soumya Sen,Takaaki Goto,Narayan C. Debnath +5 more
TL;DR: The proposed model gradually minimizes two different objective functions; namely the root mean square error (RMSE) and Maximum Error in order to find the optimal weight vector for the artificial neural network (ANN) to improve its performance over its traditional counterparts.