Extractive text summarization using F-RBM

References

• Agarwal, Basant, et al. “A deep network model for paraphrase detection in short text messages.” Information Processing & Management 54.6 (2018): 922-937.
   Web of Science ®Google Scholar
• Akhtar, Nadeem, M. M. Sufyan Beg, and Md Muzakkir Hussain. “Sparse two level topic model for extraction of general summary words.” Journal of Interdisciplinary Mathematics 23.1 (2020): 303-310.
   Web of Science ®Google Scholar
• Bansal, Neha, Arun Sharma, and R. K. Singh. “Recurrent neural network for abstractive summarization of documents.” Journal of Discrete Mathematical Sciences and Cryptography 23.1 (2020): 65-72.
   Web of Science ®Google Scholar
• Bonanno, David, et al. “An approach to explainable deep learning using fuzzy inference.” Next-Generation Analyst V. Vol. 10207. International Society for Optics and Photonics, 2017.
   Google Scholar
• Chang, Shiyu, et al. “Heterogeneous network embedding via deep architectures.” Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2015.
   Google Scholar
• Chen, CL Philip, et al. “Fuzzy restricted Boltzmann machine for the enhancement of deep learning.” IEEE Transactions on Fuzzy Systems 23.6 (2015): 2163-2173.
   Web of Science ®Google Scholar
• Fischer, Asja, and Christian Igel. “An introduction to restricted Boltzmann machines.” Iberoamerican Congress on Pattern Recognition. Springer, Berlin, Heidelberg, 2012.
   Google Scholar
• Ghasemi, Fahimeh, et al. “Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks.” Drug discovery today 23.10 (2018): 1784
   PubMed Web of Science ®Google Scholar
• Gu, Yanhui, et al. “An enhanced short text categorization model with deep abundant representation.” World Wide Web 21.6 (2018): 1705-1719.
   Google Scholar
• Jaderberg, Max, et al. “Reading text in the wild with convolutional neural networks.” International Journal of Computer Vision 116.1 (2016): 1-20.
   Web of Science ®Google Scholar
• Jiang, Liangxiao, et al. “Deep feature weighting for naive Bayes and its application to text classification.” Engineering Applications of Artificial Intelligence 52 (2016): 26-39.
   Web of Science ®Google Scholar
• Jiang, Mingyang, et al. “Text classification based on deep belief network and softmax regression.” Neural Computing and Applications 29.1 (2018): 61-70.
   Web of Science ®Google Scholar
• K. Sharma, Avinash, S. Patil, Pradeep, D.P. Salapurkar. Automated Document Summarization and Classification Using Deep Learning,International Journal of Engineering Research in Computer Science and Engineering Vol 5,Issue 6, JUNE-2018.
   Google Scholar
• Kale, Mandar, Prachi Mankame, and Gouri Kulkarni. “Deep learning for digital text analytics: Sentiment analysis.” arXiv preprint arXiv:1804.03673 (2018).
   Google Scholar
• Litjens, Geert, et al. “A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88.
   PubMed Web of Science ®Google Scholar
• Liu, Bang, et al. “Matching long text documents via graph convolutional networks.” arXiv preprint arXiv:1802.07459(2018).
   Google Scholar
• Lv, Yisheng, et al. “Traffic flow prediction with big data: a deep learning approach.” IEEE Transactions on Intelligent Transportation Systems 16.2 (2014): 865-873.
   Web of Science ®Google Scholar
• Muñoz-González, Luis, et al. “Towards poisoning of deep learning algorithms with back-gradient optimization.” Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security. ACM, 2017.
   Google Scholar
• Murali, Pranav. “An Overview of Machine Learning, Deep Learning and Neural Networks.” (2018).
   Google Scholar
• Olugu, Ezutah Udoncy, and Kuan Yew Wong. “Fuzzy logic evaluation of reverse logistics performance in the automotive industry.” Scientific Research and Essays 6.7 (2011): 1639-1649.
   Google Scholar
• Patel, Hima, et al. “Neural network with deep learning architectures.” Journal of Information and Optimization Sciences 39.1 (2018): 31-38.
   Web of Science ®Google Scholar
• Peng, Hao, et al. “Large-scale hierarchical text classification with recursively regularized deep graph-cnn.” Proceedings of the 2018 World Wide Web Conference. International World Wide Web Conferences Steering Committee, 2018.
   Google Scholar
• Ren, Xiaohang, et al. “A new unsupervised convolutional neural network model for chinese scene text detection.” 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2015.
   Google Scholar
• Ruder, Sebastian. “An overview of gradient descent optimization algorithms.” arXiv preprint arXiv:1609.04747 (2016 ).
   Google Scholar
• Sharma, Bharti, et al. “Automatic Text Summarization Using Fuzzy Extraction.” International Conference on Innovative Computing and Communications. Springer, Singapore, 2020.
   Google Scholar
• Sultana, Jabeen, M. Usha Rani, and M. A. H. Farquad. “An extensive survey on some deep learning applications.” 2018 IADS International Conference on Computing, Communications & Data Engineering (CCODE). 2018.
   Google Scholar
• Wang, Haipeng, et al. “Application of deep-learning algorithms to MSTAR data.” 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2015.
   Google Scholar
• Wongsuphasawat, Kanit, et al. “Visualizing dataflow graphs of deep learning models in tensorflow.”IEEE transactions on visualization and computer graphics 24.1(2017):1-12
   PubMed Web of Science ®Google Scholar
• Yang, Boyi, and Adam Wright. “Development of deep learning algorithms to categorize free-text notes pertaining to diabetes: convolution neural networks achieve higher accuracy than support vector machines.” arXiv preprint arXiv:1809.05814 (2018 ).
   Google Scholar
• Yousefi-Azar, Mahmood, and Len Hamey. “Text summarization using unsupervised deep learning.” Expert Systems with Applications 68 (2017): 93-105.
   Web of Science ®Google Scholar
• Zheng, Tingyi, and Li Wang. “Unlabeled Text Classification optimization Algorithm Based on Active Self-Paced Learning.” 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2018.
   Google Scholar