| Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 16, 2025
Multi-modal Information Learning and Analytics on Cross-Media Data Integration
|
|
|---|---|---|
| Article Number | 15 | |
| Number of page(s) | 16 | |
| DOI | https://doi.org/10.1051/smdo/2025017 | |
| Published online | 03 October 2025 | |
- X. Zhao, S. Ye, Space reconstruction of audiovisual media based on artificial intelligence and virtual reality, J. Intell. Fuzzy Syst. 40, 7285–7296 (2021) [Google Scholar]
- B. Caramiaux, S. Fdili Alaoui, “Explorers of unknown planets” practices and politics of artificial intelligence in visual arts, Proc. ACM Human-Computer Interaction 6, 1–24 (2022) [Google Scholar]
- R. Wingström, J. Hautala, R. Lundman, Redefining creativity in the era of AI? Perspectives of computer scientists and new media artists, Creat. Res. J. 36, 177–193 (2024) [Google Scholar]
- Å. Stige, E.D. Zamani, P. Mikalef et al., Artificial intelligence (AI) for user experience (UX) design: a systematic literature review and future research agenda, Inform. Technol. People 37, 2324–2352 (2024) [Google Scholar]
- O. Tapalova, N. Zhiyenbayeva, Artificial intelligence in education: AIEd for personalised learning pathways, Electr. J. e-Learning 20, 639–653 (2022) [Google Scholar]
- M. Santana, M. Díaz-Fernández, Competencies for the artificial intelligence age: visualisation of the state of the art and future perspectives, Rev. Manag. Sci. 17, 1971–2004 (2023) [Google Scholar]
- H. Vartiainen, M. Tedre, Using artificial intelligence in craft education: crafting with text-to-image generative models, Digital Creat. 34, 1–21 (2023) [Google Scholar]
- B. Rathore, Digital transformation 4.0: integration of artificial intelligence and metaverse in marketing, Eduzone 12, 42–48 (2023) [Google Scholar]
- H. Benbya, F. Strich, T. Tamm, Navigating generative artificial intelligence promises and perils for knowledge and creative work, J. Assoc. Inform. Syst. 25, 23–36 (2024) [Google Scholar]
- S.D. Kilari, Use artificial intelligence into facility design and layout planning work in manufacturing facility, Eur. J. Artific. Intell. Mach. Learn. 4, 27–30 (2025) [Google Scholar]
- I. Taj, N. Zaman, Towards industrial revolution 5.0 and explainable artificial intelligence: challenges and opportunities, Int. J. Comput. Digital Syst. 12, 295–320 (2022) [Google Scholar]
- D. Kem, Personalised and adaptive learning: Emerging learning platforms in the era of digital and smart learning, Int. J. Social Sci. Human Res. 5, 385–391 (2022) [Google Scholar]
- K. Zhang, J. Cao, Y. Zhang, Adaptive digital twin and multiagent deep reinforcement learning for vehicular edge computing and networks, IEEE Trans. Ind. Inform. 18, 1405–1413 (2021) [Google Scholar]
- D. Baidoo-Anu, L.O. Ansah, Education in the era of generative artificial intelligence (AI): understanding the potential benefits of ChatGPT in promoting teaching and learning, Journal A.I. of 7, 52–62 (2023) [Google Scholar]
- P. Radanliev, D. De Roure, R. Nicolescu et al., Digital twins: artificial intelligence and the IoT cyber-physical systems in Industry 4.0, Int. J. Intell. Robot. Appl. 6, 171–185 (2022) [Google Scholar]
- Y. Wang, L. Wang, K.L. Siau, Human-centered interaction in virtual worlds: a new era of generative artificial intelligence and metaverse, Int. J. Human–Computer Interact. 41, 1459–1501 (2025) [Google Scholar]
- C. He, B. Sun, Application of artificial intelligence technology in computer aided art teaching, Computer-Aided Des. Appl. 18, 118–129 (2021) [Google Scholar]
- J. Mao, B. Chen, J.C. Liu, Generative artificial intelligence in education and its implications for assessment, TechTrends 68, 58–66 (2024) [Google Scholar]
- M. Virvou, Artificial intelligence and user experience in reciprocity: contributions and state of the art, Intell. Decis. Technolog. 17, 73–125 (2023) [Google Scholar]
- Y.M. Lin, Y. Gao, M.G. Gong et al., Federated learning on multimodal data: a comprehensive survey, Mach. Intell. Res. 20, 539–553 (2023) [Google Scholar]
- N. Gahlan, D. Sethia, Federated learning inspired privacy sensitive emotion recognition based on multi-modal physiological sensors, Cluster Comput. 27, 3179–3201 (2024) [Google Scholar]
- X. Zhou, Q. Yang, X. Zheng et al., Personalized federated learning with model-contrastive learning for multi-modal user modeling in human-centric metaverse, IEEE J. Selected Areas Commun. 42, 817–831 (2024) [Google Scholar]
- E. Blasch, T. Pham, C.Y. Chong et al., Machine learning/artificial intelligence for sensor data fusion–opportunities and challenges, IEEE Aerospace Electr. Syst. Mag. 36, 80–93 (2021) [Google Scholar]
- J.W. Peltier, A.J. Dahl, J.A. Schibrowsky, Artificial intelligence in interactive marketing: a conceptual framework and research agenda, J. Res. Interactive Market. 18, 54–90 (2024) [Google Scholar]
- R. Haripriya, N. Khare, M. Pandey et al., Navigating the fusion of federated learning and big data: a systematic review for the AI landscape, Cluster Comput. 28, 1–27 (2025) [Google Scholar]
- Y. Shi, T. Gao, X. Jiao et al., Understanding design collaboration between designers and artificial intelligence: a systematic literature review, Proc. ACM Human-Computer Interact. 7, 1–35 (2023) [Google Scholar]
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