AMIBO: intelligent social conversational agent using artificial intelligence

References

• Qi J, Jiang G, Li G, et al. Intelligent human-computer interaction based on surface EMG gesture recognition. IEEE Access. 2019;7:61378–61387.
   Web of Science ®Google Scholar
• Xu W. Toward human-centered AI: a perspective from human-computer interaction. Interactions. 2019;26(4):42–46.
   Google Scholar
• Chaves AP, Gerosa MA. How should my chatbot interact? A survey on human-chatbot interaction design. arXiv preprint arXiv:1904.02743; 2019.
   Google Scholar
• Skjuve M, Følstad A, Fostervold KI, et al. My chatbot companion-a study of human-Chatbot relationships. Int J Hum Comput Stud. 2021;149:102601.
   Web of Science ®Google Scholar
• Fryer LK, Nakao K, Thompson A. Chatbot learning partners: connecting learning experiences,: interest and competence. Comput Human Behav. 2019;93:279–289.
   Web of Science ®Google Scholar
• Zhou L, Gao J, Li D, et al. The design and implementation of xiaoice, an empathetic social chatbot. Comput Linguist. 2020;46(1):53–93.
   Web of Science ®Google Scholar
• Chaves AP, Gerosa MA. How should My Chatbot interact? A survey on social characteristics in human–Chatbot interaction design. Int J Human–Computer Interact. 2020;37(8):729–758.
   Web of Science ®Google Scholar
• Albayrak N, Özdemir A, Zeydan E. An overview of artificial intelligence based chatbots and an example chatbot application. In 2018 26th Signal processing and communications applications conference (SIU) (pp. 1–4). IEEE; 2018, May.
   Google Scholar
• Pérez-Soler S, Guerra E, de Lara J. Collaborative modeling and group decision making using chatbots in social networks. IEEE Softw. 2018;35(6):48–54.
   Web of Science ®Google Scholar
• Lee S, Lee N, Sah YJ. Perceiving a mind in a Chatbot: effect of mind perception and social cues on co-presence,: closeness, and intention to use. Int J Human–Computer Interact. 2020;36(10):930–940.
   Web of Science ®Google Scholar
• Luo X, Tong S, Fang Z, et al. Frontiers: machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Sci. 2019;38(6):937–947.
   Web of Science ®Google Scholar
• Villegas-Ch W, Arias-Navarrete A, Palacios-Pacheco X. Proposal of an architecture for the integration of a Chatbot with artificial intelligence in a smart campus for the improvement of learning. Sustainability. 2020;12(4):1500.
   Web of Science ®Google Scholar
• Puntoni S, Reczek RW, Giesler M, et al. Consumers and artificial intelligence: an experiential perspective. J Mark. 2021;85(1):131–151.
   Web of Science ®Google Scholar
• Battineni G, Chintalapudi N, Amenta F. AI chatbot design during an epidemic like the novel coronavirus.Healthcare 2020 June; 8(2):154. Multidisciplinary Digital Publishing Institute.
   Web of Science ®Google Scholar
• Virmani D, Singh G, Aggarwal L, et al. Ensemble model using hybridization of angles and distances for emotion recognition (HADER). J Inf Optim Sci. 2020;41(6):1453–1461.
   Web of Science ®Google Scholar
• Khanna P, Mukundan S. Rule based system for recognizing emotions using multimodal approach. Int J Adv Comput Sci Appl. 2013;4:32–39. doi:10.14569/IJACSA.2013.040705.
   Google Scholar
• Liliana DY, Basaruddin T, Widyanto R, et al. Fuzzy emotion: a natural approach to automatic facial expression recognition from psychological perspective using fuzzy system. Cogn Process. 2019. doi: 10.1007/s10339-019-00923-0.
   PubMed Web of Science ®Google Scholar
• Qi C, Li M, Wang Q, et al. Facial expressions recognition based on cognition and mapped binary patterns. IEEE Access. 2018;6:18795–18803. doi:10.1109/access.2018.2816044.
   Web of Science ®Google Scholar
• Sharma G, Singh L, Gautam S. Automatic facial expression recognition using combined geometric features. 3D Res. 2019. doi:10.10.1007/s13319-019-0224-0.
   Google Scholar
• Esau N, Wetzel E, Kleinjohann L, et al. Real-time facial expression recognition using a fuzzy emotion model. 2007: 1–6. doi:10.1109/FUZZY.2007.4295451.
   Google Scholar
• Lim Y, Liao Z, Petridis S, et al. Transfer learning for action unit recognition; 2018.
   Google Scholar
• Lucey P, Cohn JF, Kanade T, et al. The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression. Proceedings of the Third; 2010.
   Google Scholar
• Ding Y, Zhao Q, Li B, et al. Facial expression recognition from image sequence based on LBP and Taylor expansion. IEEE Access. 2017;5:19409–19419. doi:10.1109/access.2017.2737821.
   Web of Science ®Google Scholar
• Valtolina S, Barricelli BR, Di Gaetano S. Communicability of traditional interfaces VS chatbots in healthcare and smart home domains. Behav Inf Technol. 2020;39(1):108–132.
   Web of Science ®Google Scholar
• Čupková D, Hendrichovský F, Papcun P, et al. Cloud-enabled assisted living: The role of robot receptionist in the home environment. In 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 129–133). IEEE; 2019, January.
   Google Scholar
• Egorov EE, Lebedeva TE, Prokhorova MP, et al. Opportunities and Prospects of Using Chatbots in HR. In Institute of Scientific Communications Conference (pp. 782–791). Cham: Springer; 2019, December.
   Google Scholar
• Park S, Choi J, Lee S, et al. Designing a chatbot for a brief motivational interview on stress management: qualitative case study. J Med Internet Res. 2019;21(4):e12231.
   PubMed Web of Science ®Google Scholar
• Mckie IAS, Narayan B. Enhancing the academic library experience with chatbots: an exploration of research and implications for practice. J Aust Library Inf Assoc. 2019;68(3):268–277.
   Google Scholar
• Ahmed U, Lin JCW, Srivastava G. Emotional intelligence attention unsupervised learning using lexicon analysis for irony based advertising. ACM Trans Asian Low-Resource Lang Inf Process. 2022.
   Google Scholar
• Lin JCW, Shao Y, Djenouri Y, et al. ASRNN: A recurrent neural network with an attention model for sequence labeling. Knowl Based Syst. 2021;212:106548.
   Web of Science ®Google Scholar
• Lin JCW, Shao Y, Zhang J, et al. Enhanced sequence labeling based on latent variable conditional random fields. Neurocomputing. 2020;403:431–440.
   Web of Science ®Google Scholar
• Lin JCW, Shao Y, Zhou Y, et al. A Bi-LSTM mention hypergraph model with encoding schema for mention extraction. Eng Appl Artif Intell. 2019;85:175–181.
   Web of Science ®Google Scholar
• Shao Y, Lin JCW, Srivastava G, et al. Self-attention-based conditional random fields latent variables model for sequence labeling. Pattern Recognit Lett. 2021;145:157–164.
   Web of Science ®Google Scholar
• Lin JCW, Shao Y, Fournier-Viger P, et al. BILU-NEMH: A BILU neural-encoded mention hypergraph for mention extraction. Inf Sci (Ny). 2019;496:53–64.
   Web of Science ®Google Scholar
• Bhagwan Parshuram Institute of Technology, http://www.bpitindia.com/
   Google Scholar
• Wu Y, Ji Q. Facial landmark detection: a literature survey. Int J Comput Vision. 2019;127(2):115–142.
   Web of Science ®Google Scholar
• Masi I, Wu Y, Hassner T, et al. Deep face recognition: a survey. In 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI) (pp. 471–478). IEEE; 2018, October.
   Google Scholar
• Dhaoui M. [cited 2022 Nov 11]. https://mohameddhaoui.github.io/deeplearning/Landmarks_recognition/.
   Google Scholar
• Kalita J, Das K. Recognition of facial expression using eigenvector based distributed features and euclidean distance based decision making technique. arXiv preprint arXiv:1303.0635; 2013.
   Google Scholar
• Kherchaoui S, Houacine A. Facial expression identification system with Euclidean distance of facial edges. In 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR) (pp. 6–10). IEEE; 2014, August.
   Google Scholar
• Juhong A, Pintavirooj C. Face recognition based on facial landmark detection. In 2017 10th Biomedical Engineering International Conference (BMEiCON) (pp. 1–4). IEEE; 2017, August.
   Google Scholar
• Ang L, Yim MH, Do JH, et al. A novel method in predicting hypertension using facial images. Appl Sci. 2021;11(5):2414.
   Google Scholar
• Er MB. A novel approach for classification of speech emotions based on deep and acoustic features. IEEE Access. 2020;8:221640–221653.
   Web of Science ®Google Scholar
• Mikhaylevskiy S, Chernyavskiy V, Pavlishen V, et al. Fast emotion recognition neural network for IoT devices. In 2021 International Seminar on Electron Devices Design and Production (SED) (pp. 1–6). IEEE; 2021, April.
   Google Scholar
• Yokoo K, Atsumi M, Tanaka K, et al. Deep Learning based Emotion Recognition IoT System. In 2020 International Conference on Advanced Mechatronic Systems (ICAMechS) (pp. 203–207). IEEE; 2020, December.
   Google Scholar
• Ranavare SS, Kamath RS. Artificial intelligence based chatbot for placement activity at college using dialogflow. Our Heritage. 2020;68(30):4806–4814.
   Google Scholar
• Mugalu BW, Wamala RC, Serugunda J, et al. Face Recognition as a Method of Authentication in a Web-Based System. arXiv preprint arXiv:2103.15144; 2021.
   Google Scholar
• Kim JH, Poulose A, Han DS. The extensive usage of the facial image threshing machine for facial emotion recognition performance. Sensors. 2021;21(6):2026.
   PubMed Web of Science ®Google Scholar
• Poulose A, Reddy CS, Kim JH, et al. Foreground Extraction Based Facial Emotion Recognition Using Deep Learning Xception Model. In 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 356–360). IEEE; 2021, August.
   Google Scholar
• Poulose A, Kim JH, Han DS. Feature Vector Extraction Technique for Facial Emotion Recognition Using Facial Landmarks. In 2021 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 1072–1076). IEEE; 2021, October.
   Google Scholar
• Yang KP, Jee A, Leblanc D, et al. Experimenting with Hotword detection: the Pao-Pal. Eur J Electr Eng Comput Sci. 2020;4(5).
   Google Scholar
• Siegert I, Sinha Y, Jokisch O, et al. Recognition Performance of Selected Speech Recognition APIs–A Longitudinal Study. In International Conference on Speech and Computer (pp. 520-529). Springer, Cham; 2020, October.
   Google Scholar
• Pimentel R. [cited 2021 Mar 18]. https://github.com/rodrigopivi/Chatito.
   Google Scholar
• Hołowiński M, Pańczyk B. Comparative analysis of the technology used to create multi-platform applications on the example of NW. js and electron. J Comput Sci Inst. 2020;17:396–400.
   Google Scholar