Classification of sentiment reviews using n-gram machine learning approach A Tripathy, A Agrawal, SK Rath Expert Systems with Applications 57, 117-126, 2016 | 744 | 2016 |
Classification of sentimental reviews using machine learning techniques A Tripathy, A Agrawal, SK Rath Procedia Computer Science 57, 821-829, 2015 | 311 | 2015 |
Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data RK Behera, M Jena, SK Rath, S Misra Information Processing & Management 58 (1), 102435, 2021 | 254 | 2021 |
A survey on one-hop clustering algorithms in mobile ad hoc networks S Chinara, SK Rath Journal of Network and Systems Management 17 (1), 183-207, 2009 | 194 | 2009 |
Document-level sentiment classification using hybrid machine learning approach A Tripathy, A Anand, SK Rath Knowledge and Information Systems 53, 805-831, 2017 | 132 | 2017 |
Real-time sentiment analysis of twitter streaming data for stock prediction S Das, RK Behera, SK Rath Procedia computer science 132, 956-964, 2018 | 116 | 2018 |
Effective fault prediction model developed using least square support vector machine (LSSVM) L Kumar, SK Sripada, A Sureka, SK Rath Journal of Systems and Software 137, 686-712, 2018 | 110 | 2018 |
Feature selection and classification of microarray data using MapReduce based ANOVA and K-nearest neighbor M Kumar, NK Rath, A Swain, SK Rath Procedia Computer Science 54, 301-310, 2015 | 101 | 2015 |
Performance comparison of soap and rest based web services for enterprise application integration S Kumari, SK Rath 2015 International Conference on Advances in Computing, Communications and …, 2015 | 97 | 2015 |
Investigating the structural linkage between IT capability and organizational agility: A study on Indian financial enterprises S Panda, SK Rath Journal of Enterprise Information Management 29 (5), 751-773, 2016 | 96 | 2016 |
Early stage software effort estimation using random forest technique based on use case points SM Satapathy, BP Acharya, SK Rath IET Software 10 (1), 10-17, 2016 | 93 | 2016 |
Empirical validation of neural network models for agile software effort estimation based on story points A Panda, SM Satapathy, SK Rath Procedia Computer Science 57, 772-781, 2015 | 93 | 2015 |
An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes L Kumar, S Misra, SK Rath Computer standards & interfaces 53, 1-32, 2017 | 73 | 2017 |
Information technology capability, knowledge management capability, and organizational agility: The role of environmental factors S Panda, SK Rath Journal of Management & Organization 27 (1), 148-174, 2021 | 71 | 2021 |
The effect of human IT capability on organizational agility: an empirical analysis S Panda, SK Rath Management Research Review 40 (7), 800-820, 2017 | 70 | 2017 |
Class point approach for software effort estimation using various support vector regression kernel methods SM Satapathy, SK Rath Proceedings of the 7th India Software Engineering Conference, 1-10, 2014 | 67 | 2014 |
Strategic IT-business alignment and organizational agility: from a developing country perspective S Panda, SK Rath Journal of Asia Business Studies 12 (4), 422-440, 2018 | 62 | 2018 |
Classification of microarray using MapReduce based proximal support vector machine classifier M Kumar, SK Rath Knowledge-Based Systems 89, 584-602, 2015 | 60 | 2015 |
Empirical assessment of machine learning models for agile software development effort estimation using story points SM Satapathy, SK Rath Innovations in Systems and Software Engineering 13 (2), 191-200, 2017 | 59 | 2017 |
Effectiveness of software metrics for object-oriented system Y Suresh, J Pati, SK Rath Procedia technology 6, 420-427, 2012 | 56 | 2012 |