This work is licensed under the Creative Commons Attribution 4.0 International License.
Radev, D. R., Prager, J., & Samn, V. (2016). Better answers to real questions. Bju International, 117(1), 16-19.Search in Google Scholar
Gonzalez, A. J., Sherwell, B., Nguyen, J., Becker, B. C., Hung, Víctor, & Brezillon, P. (2015). A knowledge preservation and re-use tool based on context-driven reasoning. International Journal on Artificial Intelligence Tools, 24(05), 150331182042004.Search in Google Scholar
Araujo, & Theo. (2018). Living up to the chatbot hype: the influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior.Search in Google Scholar
Ma, H., Wang, J., Lin, H., & Yang, L. (2022). Global and local interaction matching model for knowledge-grounded response selection in retrieval-based chatbots. Neurocomputing(Aug.1), 497.Search in Google Scholar
Sweidan, S. Z., Laban, S. S. A., Alnaimat, N. A., & Darabkh, K. A. (2021). Siaaa‐c: a student interactive assistant android application with chatbot during covid‐19 pandemic. Computer Applications in Engineering Education.Search in Google Scholar
Hardalov, M., Koychev, I., & Nakov, P. (2019). Machine reading comprehension for answer re-ranking in customer support chatbots. Information (Switzerland), 10(3), 82.Search in Google Scholar
Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. (2018). In the shades of the uncanny valley: an experimental study of human–chatbot interaction. Future Generation Computer Systems, 92(MAR.), 539-548.Search in Google Scholar
Wu, S. (2022). Design of intelligent customer service questioning and answering a system for power business scenario based on ai technology. Mathematical Problems in Engineering, 2022.Search in Google Scholar
Ji, M., & Zhang, X. (2022). Research on semantic similarity calculation methods in chinese financial intelligent customer service. International Journal of Computer Applications in Technology(2), 68.Search in Google Scholar
A, Y. T., A, H. X., B, Y. W., A, Z. Z., B, Y. A., & C, Y. X., et al. (2021). Research on knowledge driven intelligent question answering system for electric power customer service. Procedia Computer Science, 187, 347-352.Search in Google Scholar
Xu, R., Long, D., Liu, J., Yu, W., & Xu, L. (2021). Intelligent assistant decision-making method for power enterprise customer service based on iot data acquisition. Mobile Information Systems.Search in Google Scholar
Qi, X., Zhang, Y., Cao, S., Yan, S., & Su, H. (2022). Intelligent retrieval method of power system service user satisfaction based on human-computer interaction. Journal of Interconnection Networks.Search in Google Scholar
Ward, N. G., Werner, S. D., Garcia, F., & Sanchis, E. (2015). A prosody-based vector-space model of dialog activity for information retrieval. Speech Communication, 68, 85-96.Search in Google Scholar
Bertinussen, N. C., Asbjrn, F., & Alexander, B. C. (2019). An initial model of trust in chatbots for customer service—findings from a questionnaire study. Interacting with Computers(3), 3.Search in Google Scholar
Haiyang, C., Shengkui, Z., & Jianbin, G. (2019). Reliability assessment of man-machine systems subject to mutually dependent machine degradation and human errors. Reliability Engineering & System Safety, 190(OCT.), 106504.1-106504.11.Search in Google Scholar
Wozniak, M., Poap, D., Damasevicius, R., & Wei, W. (2018). Design of computational intelligence-based language interface for human-machine secure interaction. Journal of Universal Computer Science, 24(4).Search in Google Scholar
Fitzgerald, E., Pióro, Micha, & Tomaszewski, A. (2019). Network lifetime maximization in wireless mesh networks for machine-to-machine communication. Ad hoc networks, 95(Dec.), 101987.1-101987.12.Search in Google Scholar
Thomason, J., Padmakumar, A., Sinapov, J., Walker, N., & Mooney, R. (2020). Jointly improving parsing and perception for natural language commands through human-robot dialog. Journal of Artificial Intelligence Research, 67, 327-374.Search in Google Scholar
Kojima, H., Takaeda, K., Nihei, M., Sadohara, K., & Inoue, T. (2016). Acquisition and evaluation of a human-robot elderly spoken dialog corpus for developing computerized cognitive assessment systems. Journal of the Acoustical Society of America, 140(4), 2963-2963.Search in Google Scholar
Hiroi, Y., & Ito, A. (2016). Influence of the height of a robot on comfortableness of verbal interaction. IAENG Internaitonal journal of computer science, 43(4), 447-455.Search in Google Scholar
Luo, Y., Xiao, H., Ou, J., & Chen, X. (2022). Siamsmdfff: siamese network tracker based on shallow-middle-deep three-level feature fusion and clustering-based adaptive rectangular window filtering. Neurocomputing, 483, 160-170.Search in Google Scholar
Arco, J. E., Ortiz Andrés, Gallego-Molina Nicolás J., Górriz Juan M., & Ramírez Javier. (2023). Enhancing multimodal patterns in neuroimaging by siamese neural networks with self-attention mechanism. International Journal of Neural Systems.Search in Google Scholar
Zhang, Y. (2021). Temperature prediction of pmsms using pseudo-siamese nested lstm. World Electric Vehicle Journal, 12.Search in Google Scholar
Thapar, D., Jaswal, G., Nigam, A., & Arora, C. (2019). Gait metric learning siamese network exploiting dual of spatio-temporal 3d-cnn intra and lstm based inter gait-cycle-segment features. Pattern Recognition Letters, 125(JUL.), 646-653.Search in Google Scholar
Tong, Y., & Liu, J. (2022). Novel power-exponent-type modified rnn for rmp scheme of redundant manipulators with noise and physical constraints. Neurocomputing, 467, -.Search in Google Scholar
Cheng, P., Dai, J., & Liu, J. (2022). Catvrnn: generating category texts via multi-task learning. Knowledge-based systems, (May 23), 244.Search in Google Scholar
Gao, L., Li, H., Liu, Z., Liu, Z., & Feng, W. (2021). Rnn-transducer based chinese sign language recognition. Neurocomputing, 434(4), 45-54.Search in Google Scholar
Chen, R. (2021). Migration learning-based bridge structure damage detection algorithm. Sci. Program., 2021, 1102521:1-1102521:10.Search in Google Scholar
A, A. R. K., B, T. C. R. I., & C, N. T. S. A. (2021). My way is the highway: the role of plasticity in learning complex migration routes - sciencedirect. Animal Behaviour, 174, 161-167.Search in Google Scholar
Zhang, W., Gao, J., Chen, Y., Li, Z., Jiang, X., & Zhu, J. (2022). Deep-learning for accelerating prestack correlative least-squares reverse time migration. Journal of Applied Geophysics, 200, 104645-.Search in Google Scholar
Lee, D., Alam, S. R., Jiang, J., Zhang, P., Nadeem, S., & Hu, Y. C. (2021). Deformation driven seq2seq longitudinal tumor and organs-at-risk prediction for radiotherapy. Medical Physics(9).Search in Google Scholar
Li, M., Miao, Z., & Xu, W. (2021). A crnn-based attention-seq2seq model with fusion feature for automatic labanotation generation. Neurocomputing, 454(23).Search in Google Scholar