Analysis of interaction experience of sculpture product design based on perceptual machine model

[1] Barvir, R., Vondrakova, A., Brus, J. (2021). Efficient Interactive Tactile Maps: A Semi-Automated Workflow Using the TouchIt3D Technology and OpenStreetMap Data. ISPRS International Journal of Geo-Information, (8). Search in Google Scholar

[2] Wang, K. (2021). Human-Computer Interaction Design of Intelligent Vehicle-Mounted Products Based on the Internet of Things. Mobile Information Systems. Search in Google Scholar

[3] Ma, C., Pan, Y. H., Zeng, C. Y. (2020). Intelligent interaction design research based on block chain communication technology and fuzzy system. Journal of Intelligent and Fuzzy Systems, (1), 1-7. Search in Google Scholar

[4] Pati, R., Garud, N. (2021). Social Interaction and Crowdfunding Project Success: Moderating Roles of Product Development Stage and Product Innovativeness. IEEE Transactions on Engineering Management, PP (99). Search in Google Scholar

[5] Fu, B., Noy, N. F., Storey, M. A., et al. (2017). Eye tracking the user experience – An evaluation of ontology visualization techniques. Semantic Web, 8(1), 23-41. Search in Google Scholar

[6] Lee, S. Y., Choi, J. (2017). Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human - Computer Studies, 103, 95-105. Search in Google Scholar

[7] Berkouk, D., Bouzir, T., Boucherit, S., et al. (2022). Exploring the Multisensory Interaction between Luminous, Thermal and Auditory Environments through the Spatial Promenade Experience: A Case Study of a University Campus in an Oasis Settlement. Sustainability, 14. Search in Google Scholar

[8] Xu, Q., Zhou, L., Chen, Q. (2020). Optimal Pricing Decision of Fashion Apparel considering Experiential Service and Design Enhancement. Complexity, (10), 1-11. Search in Google Scholar

[9] Ishak, S. A., Din, R., Hasran, U. A. (2020). Defining Digital Game-Based Learning for STEM: A New Perspective on Design and Developmental Research (Preprint). Journal of Medical Internet Research, 23(2). Search in Google Scholar

[10] Wu, N. Y., Lu, N. T., Yuan, N. Z., et al. (2017). FreeScup: A Novel Platform for Assisting Sculpture Pose Design. IEEE Transactions on Multimedia, 19(1), 183-195. Search in Google Scholar

[11] Pei-Hsuan., Chiu., Po-Hsuan., et al. (2018). Interactive Mobile Augmented Reality System for Image and Hand Motion Tracking. IEEE Transactions on Vehicular Technology. Search in Google Scholar

[12] Chi, X., Zhang, Y., Maio, D. D., & Lieven, N. A. J. (2020). Viability of image compression in vibrothermography. Experimental Techniques (6). Search in Google Scholar

[13] Giaveno, S., Osello, A., Garufi, D., et al. (2021). Embodied Carbon and Embodied Energy Scenarios in the Built Environment. Computational Design Meets EPDs. Sustainability, 13(21), 11974. Search in Google Scholar

[14] Moosavinasab, S., Patterson, J., Wheeler, K. K., et al. (2016). 352Re: fine NEISS: a real-time interaction search system for consumer product-related injury ed visits in United States. Injury Prevention, 22(Suppl 2): A129.1-A129. Search in Google Scholar

[15] Lin, F., Wei, W. (2020). A Combined Method of r-NPS and t-NPS Evaluations for Identification of Negative Triggers of Detractors’ Experience. Sustainability, 12. (4), 1652. Search in Google Scholar

[16] Ma, C., Pan, Y. H., Zeng, C. Y. (2020). Intelligent interaction design research based on block chain communication technology and fuzzy system. Journal of Intelligent and Fuzzy Systems, (1), 1-7. Search in Google Scholar

[17] Deng, Y., Han, S. Y., Li, J., et al. (2020). The design of tourism product CAD three-dimensional modeling system using VR technology. PLoS ONE, 15(12), e0244205. Search in Google Scholar

[18] Ahmadlou, M., Al-Fugara, A., Abdel., et al. (2020). Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks. Journal of Flood Risk Management, 13(4), 1-22. Search in Google Scholar

[19] K, R. (2018). Constructive lower bounds on model complexity of shallow perceptron networks. Neural Computing and Applications, 29(7), 305-315. Search in Google Scholar

[20] Oja, T. (2021). Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia. Sustainability, 14. (1), 160. Search in Google Scholar

[21] Michel, O. O., Ying, Y., Wenyi, F., et al. (2021). Examining Land Use/Land Cover Change and Its Prediction Based on a Multilayer Perceptron Markov Approach in the Luki Biosphere Reserve, Democratic Republic of Congo. Sustainability, 13. (12), 6898. Search in Google Scholar

[22] Jude, A., Deshendran, M., Gavin, R., et al. (2017). Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control. Sensors (Basel, Switzerland), 17(4). Search in Google Scholar

[23] Zhou, S., Yin, K., Fei, F., et al. (2019). Surface electromyography–based hand movement recognition using the Gaussian mixture model, multilayer perceptron, and AdaBoost method. International Journal of Distributed Sensor Networks, 15(4), 155014771984606. Search in Google Scholar

[24] Koo, K. C., Min, G. R., Kim, J., et al. (2019). A multilayer perceptron artificial neural network model for predicting survival of patients with prostate cancer according to initial treatment strategy: Development of a web-based clinical decision support system. European Urology Supplements, 18(1). Search in Google Scholar

[25] Mosavi, A., Samadianfard, S., Darbandi, S., et al. (2021). Predicting soil electrical conductivity using multi-layer perceptron integrated with Grey Wolf Optimizer. Journal of Geochemical Exploration, 106639. Search in Google Scholar