This work is licensed under the Creative Commons Attribution 4.0 International License.
Wagenknecht, & Susann. (2017). The evocative object-introspection and emotional reflection through computer use. Interacting with computers.Search in Google Scholar
Cheng-Hung, Wang, Hao-Chiang, Koong, & Lin. (2018). Emotional design tutoring system based on multimodal affective computing techniques. International journal of distance education technologies: An official publication of the Information Resources Management Association: IJDET, 16(1), 103-117.Search in Google Scholar
Chen, Y. (2023). Design and simulation of ai remote terminal user identity recognition system based on reinforcement learning. International Journal of Modeling, Simulation, and Scientific Computing, 14(01).Search in Google Scholar
Zhou, K., Sisman, B., Rana, R., Schuller, B. W., & Li, H. (2023). Emotion intensity and its control for emotional voice conversion. IEEE transactions on affective computing.Search in Google Scholar
Sun, X., Ye, J., & Ren, F. (2016). Detecting influenza states based on hybrid model with personal emotional factors from social networks. Neurocomputing, 210(OCT.19), 257-268.Search in Google Scholar
Zhou, Q., Ji, D., Ren, Y., & Tang, H. (2021). Dual-copying mechanism and dynamic emotion dictionary for generating emotional responses. Neurocomputing, 454(3–4).Search in Google Scholar
Naoki, Masuyama, Chu, Kiong, Loo, & Manjeevan, et al. (2018). Personality affected robotic emotional model with associative memory for human-robot interaction. Neurocomputing.Search in Google Scholar
Provost, E. M., Shangguan, Y., & Busso, C. (2017). Umeme: university of michigan emotional mcgurk effect data set. IEEE Transactions on Affective Computing, 6(4), 395-409.Search in Google Scholar
Baghbani, F., Akbarzadeh-T, M. R., & Sistani, M. B. N. (2021). Cooperative adaptive emotional neuro-control for a class of higher-ordered heterogeneous uncertain nonlinear multi-agent systems. Neurocomputing.Search in Google Scholar
Wu, C. H., & Liang, W. B. (2015). Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels (extended abstract). IEEE transactions on affective computing.Search in Google Scholar
Akt, E., Karwowski, W., & Servi, L. (2020). Application of soft computing techniques for estimating emotional states expressed in twitter (r) time series data. Neural computing & applications(8), 32.Search in Google Scholar
Hsieh, Y. Z., Lin, S. S., Luo, Y. C., Jeng, Y. L., Tan, S. W., & Chen, C. R., et al. (2020). Arcs-assisted teaching robots based on anticipatory computing and emotional big data for improving sustainable learning efficiency and motivation. Sustainability, 12.Search in Google Scholar
Jesús B. Alonso, Josué Cabrera, Medina, M., & Travieso, C. M. (2015). New approach in quantification of emotional intensity from the speech signal: emotional temperature. Expert Systems with Applications, 42( 24), 9554-9564.Search in Google Scholar
Liu, M., Bao, X., Liu, J., Zhao, P., & Shen, Y. (2021). Generating emotional response by conditional variational auto-encoder in open-domain dialogue system. Neurocomputing, 460(2).Search in Google Scholar
Guerrero Razuri, J. F. (2015). Decisional-emotional support system for a synthetic agent : influence of emotions in decision-making toward the participation of automata in society. j radiol electrol arch electr medicale, 189(3), 915-929.Search in Google Scholar
Ling, W., Gongliang, H., & Tiehua, Z. (2018). Semantic analysis of learners’ emotional tendencies on online mooc education. Sustainability, 10(6), 1921-.Search in Google Scholar
Chen, C. (2021). An analysis of mandarin emotional tendency recognition based on expression spatiotemporal feature recognition. International Journal of Biometrics(2/3), 13.Search in Google Scholar
Wang, L., Bai, S., & Wang, D. (2019). Emotional tendency recognition of self-media contents based on word relevance multidimensional time series. Basic & clinical pharmacology & toxicology.(S9), 125.Search in Google Scholar