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Sivasankar Elango
Sivasankar Elango
Associate Professor ,Department of Computer Science & Engineering ,National Institute of Technology
Verified email at nitt.edu
Title
Cited by
Cited by
Year
Efficient feature selection techniques for sentiment analysis
A Madasu, S Elango
Multimedia Tools and Applications 79 (9), 6313-6335, 2020
692020
CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT
B Palani, S Elango, V Viswanathan K
Multimedia Tools and Applications 81 (4), 5587-5620, 2022
622022
A study of feature extraction techniques for sentiment analysis
M Avinash, E Sivasankar
Emerging Technologies in Data Mining and Information Security: Proceedings …, 2019
582019
An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing
J Vijaya, E Sivasankar
Cluster Computing 22, 10757-10768, 2019
512019
Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network
E Sivasankar, J Vijaya
Neural Computing and Applications 31 (11), 7181-7200, 2019
362019
Computing efficient features using rough set theory combined with ensemble classification techniques to improve the customer churn prediction in telecommunication sector
J Vijaya, E Sivasankar
Computing 100, 839-860, 2018
362018
A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach
C Selvi, E Sivasankar
Soft Computing 23, 1901-1916, 2019
342019
Anomaly detection on shuttle data using unsupervised learning techniques
S Shriram, E Sivasankar
2019 International Conference on Computational Intelligence and Knowledge …, 2019
282019
A novel Adaptive Genetic Neural Network (AGNN) model for recommender systems using modified k-means clustering approach
C Selvi, E Sivasankar
Multimedia Tools and Applications, 1-28, 2018
282018
Rough set-based feature selection for credit risk prediction using weight-adjusted boosting ensemble method
E Sivasankar, C Selvi, S Mahalakshmi
Soft Computing 24 (6), 3975-3988, 2020
242020
Hyperparameter tuning in convolutional neural networks for domain adaptation in sentiment classification (HTCNN-DASC)
K Krishnakumari, E Sivasankar, S Radhakrishnan
Soft Computing 24 (5), 3511-3527, 2020
242020
Knowledge discovery in medical datasets using a fuzzy logic rule based classifier
E Sivasankar, RS Rajesh
2010 2nd International Conference on Electronic Computer Technology, 208-213, 2010
232010
A study of feature selection techniques for predicting customer retention in telecommunication sector
E Sivasankar, J Vijaya
International Journal of Business Information Systems 31 (1), 1-26, 2019
192019
Modern framework for distributed healthcare data analytics based on Hadoop
PV Raja, E Sivasankar
Information and Communication Technology: Second IFIP TC5/8 International …, 2014
192014
Cross domain sentiment analysis using different machine learning techniques
S Mahalakshmi, E Sivasankar
Proceedings of the Fifth International Conference on Fuzzy and Neuro …, 2015
182015
Improved churn prediction based on supervised and unsupervised hybrid data mining system
J Vijaya, E Sivasankar
Information and Communication Technology for Sustainable Development …, 2018
172018
Multimodal tweet classification in disaster response systems using transformer-based bidirectional attention model
R Koshy, S Elango
Neural Computing and Applications 35 (2), 1607-1627, 2023
152023
A comparative study of feature selection and machine learning methods for sentiment classification on movie data set
C Selvi, C Ahuja, E Sivasankar
Intelligent Computing and Applications: Proceedings of the International …, 2015
142015
Scalable aspect-based summarization in the hadoop environment
K Krishnakumari, E Sivasankar
Big data analytics: Proceedings of CSI 2015, 439-449, 2018
132018
Diagnosing appendicitis using backpropagation neural network and bayesian based classifier
E Sivasankar, RS Rajesh, SR Venkateswaran
International Journal of Computer Theory and Engineering 1 (4), 358, 2009
132009
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