Open Access
Int. J. Simul. Multidisci. Des. Optim.
Volume 11, 2020
Article Number 25
Number of page(s) 17
Published online 04 December 2020
  1. S. Natale, If the software is narrative: Joseph Weizenbaum, artificial intelligence and the biographies of ELIZA, New Media Soc. 21 , 712–728 (2019) [CrossRef] [Google Scholar]
  2. E. Adamopoulou, L. Moussiades, An overview of chatbot technology, in: IFIP International Conference on Artificial Intelligence Applications and Innovations , Springer, Cham, 2020, pp. 373–383. [CrossRef] [Google Scholar]
  3. P. Goyal, S. Pandey, K. Jain, Developing a chatbot, in: Deep Learning for Natural Language Processing, Apress, Berkeley , 2018, pp. 169–229 [CrossRef] [Google Scholar]
  4. H. Salehinejad, S. Sankar, J. Barfett, E. Colak, S. Valaee, Recent advances in recurrent neural networks, 2017. arXiv preprint arXiv:1801.01078 [Google Scholar]
  5. A. Prieto, J. Cabestany, F. Sandoval, Computational intelligence and bioinspired systems, Neurocomputing 70, 16–18, 2701 (2007) [CrossRef] [Google Scholar]
  6. S. Lawrence, C. Giles, Overfitting and neural networks: conjugate gradient and backpropagation, presented at Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium , 2000 [Google Scholar]
  7. Z.C. Lipton, J. Berkowitz, C. Elkan, A critical review of recurrent neural networks for sequence learning, 2015. arXiv preprint arXiv:1506.00019 [Google Scholar]
  8. J.F. Kolen, S.C. Kremer, Gradient flow in recurrent nets: The difficulty of learning longterm dependencies, 237–243 (2001) [Google Scholar]
  9. C. Olah, Understanding LSTM networks, 2015. [Google Scholar]
  10. S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 , 1735–1780 (1997) [CrossRef] [PubMed] [Google Scholar]
  11. F. Gers, Learning to forget: continual prediction with LSTM, presented at 9th International Conference on Artificial Neural Networks: ICANN 99 , 1999 [Google Scholar]
  12. J. Chung, C. Gulcehre, K. Cho, Y. Bengio, J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, 2014. arXiv:1412.3555 [Google Scholar]
  13. M. Collier, J. Beel, M. Collier, J. Beel, Implementing neural turing machines, 2018. arXiv:1807.08518 [Google Scholar]
  14. D. Opitz, R. Maclin, Popular ensemble methods: an Empirical Study, J. Artif. Intell. Res. 11 , 169–198 (1999) [CrossRef] [Google Scholar]
  15. L. Hansen, P. Salamon, Neural network ensembles, IEEE Trans. Pattern Anal. Mach. Intell. 12 , 993–1001 (1990) [CrossRef] [Google Scholar]
  16. C. Chen, C. Wu, C. Lo, F. Hwang, An augmented reality question answering system based on ensemble neural networks, IEEE Access 5 , 17425–17435 (2017) [CrossRef] [Google Scholar]
  17. S. Ayanouz, B.A. Abdelhakim, M. Benhmed, A smart chatbot architecture based NLP and machine learning for health care assistance, Proceedings of the 3rd International Conference on Networking, Information Systems & Security , Association for Computing Machinery, New York, 2020, pp. 1–6 [Google Scholar]
  18. S. Dey, D. Shukla, Analytical study on use of AI techniques in tourism sector for smarter customer experience management, 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) , 2020, IEEE, pp. 1–5 [Google Scholar]
  19. A.S. Sreelakshmi, S.B. Abhinaya, A. Nair, S.J. Nirmala, A question answering and quiz generation chatbot for education, 2019 Grace Hopper Celebration India (GHCI) , 2019, IEEE, pp. 1–6 [Google Scholar]
  20. F. Patel, R. Thakore, I. Nandwani, S.K. Bharti, Combating depression in students using an intelligent chatBot: a cognitive behavioral therapy, 2019 IEEE 16th India Council International Conference (INDICON) , 2019, IEEE, pp. 1–4 [Google Scholar]
  21. M.C. Lee, S.Y. Chiang, S.C. Yeh, T.F. Wen, Study on emotion recognition and companion Chatbot using deep neural network, Multimed. Tools. Appl. 79, 19629–19657 (2020) [CrossRef] [Google Scholar]
  22. G. Aalipour, P. Kumar, S. Aditham, T. Nguyen, A. Sood, Applications of sequence to sequence models for technical support automation, 2018 IEEE International Conference on Big Data (Big Data) , 2018, IEEE, pp. 4861–4869 [CrossRef] [Google Scholar]
  23. H. Cuayáhuitl, D. Lee, S. Ryu, Y. Cho, S. Choi, S. Indurthi, S. Yu, H. Choi, I. Hwang, J. Kim, Ensemble-based deep reinforcement learning for chatbots. Neurocomputing 366 , 118–130 (2019) [CrossRef] [Google Scholar]
  24. M. Bali, S. Mohanty, S. Chatterjee, M. Sarma, R. Puravankara, Diabot: a predictive medical chatbot using ensemble learning, Int. J. of Recent Technol. and Eng. 8, 2277–3878 (2019) [Google Scholar]
  25. R. Chakraborty, K. Vats, K. Baradia, T. Khan, S. Sarkar, S. Roychowdhury, Recommendence and fashionsence: online fashion advisor for offline experience, Proceedings of the ACM India Joint International Conference on Data Science and Management of Data , Association for Computing Machinery, New York, 2019, pp. 256–259 [Google Scholar]
  26. Y.M. Çetinkaya, İ.H. Toroslu, H. Davulcu, Developing a Twitter bot that can join a discussion using state-of-the-art architectures, Soc. Netw. Anal. Min. 10 , 1–21 (2020) [CrossRef] [Google Scholar]
  27. B. Arora, D.S. Chaudhary, M. Satsangi, M. Yadav, L. Singh, P.S. Sudhish, Agribot: a natural language generative neural networks engine for agricultural applications, 2020 International Conference on Contemporary Computing and Applications (IC3A) , 2020, IEEE, pp. 28–33 [CrossRef] [Google Scholar]
  28. Y.T. Wan, C.C. Chiu, K.W. Liang, P.C. Chang, Midoriko Chatbot: LSTM-Based Emotional 3D Avatar, 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE) , 2019, IEEE, pp. 937–940 [CrossRef] [Google Scholar]
  29. G. Sperlí, A deep learning based chatbot for cultural heritage, Proceedings of the 35th Annual ACM Symposium on Applied Computing , Association for Computing Machinery, New York, 2020, pp. 935–937 [CrossRef] [Google Scholar]
  30. G. Dzakwan, A. Purwarianti, Comparative study of topology and feature variants for non-task-oriented chatbot using sequence to sequence learning, 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA) , 2018, IEEE, pp. 135–140 [CrossRef] [Google Scholar]
  31. P. Rivas, C. Chelsi, N. Nishit, L. Ravula, Application-agnostic chatbot deployment considerations: a case study, 2019 International Conference on Computational Science and Computational Intelligence (CSCI) , 2019, IEEE, pp. 361–365 [CrossRef] [Google Scholar]
  32. S. Al Humoud, A. Al Wazrah, W. Aldamegh, Arabic chatbots: a survey, Int. J. Adv. Comp. Sci. Appl. 9 , 535–541 (2018) [Google Scholar]
  33. M. Nuruzzaman, O.K. Hussain, A survey on chatbot implementation in customer service industry through deep neural networks, 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE) , 2018, IEEE, pp. 54–61 [CrossRef] [Google Scholar]
  34. K. Pathak, A. Arya, A metaphorical study of variants of recurrent neural network models for a context learning chatbot, 2019 4th International Conference on Information Systems and Computer Networks (ISCON), 2019, IEEE, pp. 768–772 [CrossRef] [Google Scholar]
  35. G. Dzakwan, A. Purwarianti, Comparative study of topology and feature variants for non-task-oriented chatbot using sequence to sequence learning, 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), Krabi , 2018, pp. 135–140 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.