EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 16 (2025) Int. J. Simul. Multidisci. Des. Optim., 16 (2025) 10 References
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 16, 2025
Multi-modal Information Learning and Analytics on Cross-Media Data Integration
Article Number 10
Number of page(s) 17
DOI https://doi.org/10.1051/smdo/2025008
Published online 23 July 2025
  1. M.A. Goralski, T.K. Tan, Artificial intelligence and sustainable development, Int. J. Manag. Educ. 18, 100330 (2020) [Google Scholar]
  2. T. Huynh-The, Q.V. Pham, X.Q. Pham et al., Artificial intelligence for the metaverse: a survey, Eng. Appl. Artific. Intell. 117, 105581 (2023) [Google Scholar]
  3. Y. Gu, Q. Wang, W. Gu, The innovative application of visual communication design in modern art design, Electronics 12, 1150 (2023) [Google Scholar]
  4. S.H. Yudhanto, F. Risdianto, A.T. Artanto, Cultural and communication approaches in the design of visual communication design works, J. Linguist. Culture Commun. 1, 79–90 (2023) [Google Scholar]
  5. F. Feng, W. Na, J. Jin et al., Artificial neural networks for microwave computer-aided design: the state of the art, IEEE Trans. Microwave Theory Tech. 70, 4597–4619 (2022) [Google Scholar]
  6. J. Naranjo-Torres, M. Mora, R. Hernández-García et al., A review of convolutional neural network applied to fruit image processing, Appl. Sci. 10, 3443 (2020) [Google Scholar]
  7. Y. Rao, W. Zhao, Z. Zhu et al., Global filter networks for image classification, Adv. Neural Inf. Process. Syst. 34, 980–993 (2021) [Google Scholar]
  8. H. Dai, N. Pears, W. Smith et al., Statistical modeling of craniofacial shape and texture, Int. J. Comput. Vision 128, 547–571 (2020) [Google Scholar]
  9. W. Samek, G. Montavon, S. Lapuschkin et al., Explaining deep neural networks and beyond: a review of methods and applications, Proc. IEEE 109, 247–278 (2021) [Google Scholar]
  10. C. Liu, T. Arnon, C. Lazarus et al., Algorithms for verifying deep neural networks, Found. Trends Optim. 4, 244–404 (2021) [Google Scholar]
  11. W. Qayyum, R. Ehtisham, A. Bahrami et al., Assessment of convolutional neural network pre-trained models for detection and orientation of cracks, Materials 16, 826 (2023) [Google Scholar]
  12. M. Ketelsen, M. Janssen, U. Hamm, Consumers' response to environmentally-friendly food packaging − a systematic review, J. Cleaner Prod. 254, 120123 (2020) [Google Scholar]
  13. F.M. Alhamdi, Role of packaging in consumer buying behavior, Manag. Sci. Lett. 10, 1191–1196 (2020) [Google Scholar]
  14. M. Mahmoudi, I. Parviziomran, Reusable packaging in supply chains: A review of environmental and economic impacts, logistics system designs, and operations management, Int. J. Product. Econ. 228, 107730 (2020) [Google Scholar]
  15. M. Roosen, N. Mys, M. Kusenberg et al., Detailed analysis of the composition of selected plastic packaging waste products and its implications for mechanical and thermochemical recycling, Environ. Sci. Technol. 54, 13282–13293 (2020) [Google Scholar]
  16. S. Chen, S. Brahma, J. Mackay et al., The role of smart packaging system in food supply chain, J. Food Sci. 85, 517–525 (2020) [Google Scholar]
  17. J.L. Orquin, M.P. Bagger, E.S. Lahm et al., The visual ecology of product packaging and its effects on consumer attention, J. Business Res. 111, 187–195 (2020) [Google Scholar]
  18. I. Vermeir, G. Roose, Visual design cues impacting food choice: a review and future research agenda, Foods 9, 1495 (2020) [Google Scholar]
  19. M. Fan, Y. Li, The application of computer graphics processing in visual communication design, Journal of Intelligent & Fuzzy Systems, 2020, 39, 5183–5191. [Google Scholar]
  20. G. Branca, R. Resciniti, S.M.C. Loureiro, Virtual is so real! Consumers' evaluation of product packaging in virtual reality, Psychol. Market. 40, 596–609 (2023) [Google Scholar]
  21. S. Maleki, S.F. Amiri Aghdaie, A. Shahin et al., Investigating the relationship among the Kansei-based design of chocolate packaging, consumer perception, and willingness to buy, J. Market. Commun. 26, 836–855 (2020) [Google Scholar]
  22. I. Gil-Perez, R. Rebollar, I. Lidon, Without words: the effects of packaging imagery on consumer perception and response, Curr. Opin. Food Sci. 33, 69–77 (2020) [Google Scholar]
  23. J. Lydekaityte, T. Tambo, Smart packaging: definitions, models and packaging as an intermediator between digital and physical product management, Int. Rev. Retail Distrib. Consumer Res. 30, 377–410 (2020) [Google Scholar]
  24. Y. Wang, Y. Pan, M. Yan et al., A survey on ChatGPT: AI-generated contents, challenges, and solutions, IEEE Open J. Comput. Soc. 4, 280–302 (2023) [Google Scholar]
  25. R. Yuan, B. Wang, Y. Sun et al., Conditional style-based generative adversarial networks for renewable scenario generation, IEEE Trans. Power Syst. 38, 1281–1296 (2022) [Google Scholar]
  26. D. Fokina, E. Muravleva, G. Ovchinnikov et al., Microstructure synthesis using style-based generative adversarial networks, Phys. Rev. E 101, 043308 (2020) [Google Scholar]
  27. A. Aggarwal, M. Mittal, G. Battineni, Generative adversarial network: an overview of theory and applications, Int. J. Inform. Manag. Data Insights 1, 100004 (2021) [Google Scholar]
  28. J. Seo, P. Rao, B. Raeymaekers, Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks, Friction 12, 1283–1298 (2024) [Google Scholar]
  29. M. Garg, J.S. Ubhi, A.K. Aggarwal, Neural style transfer for image steganography and destylization with supervised image to image translation, Multimedia Tools Appl. 82, 6271–6288 (2023) [Google Scholar]
  30. M.A. Rajab, F.A. Abdullatif, T. Sutikno, Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets, TELKOMNIKA (Telecommunication Computing Electronics and Control) 22, 445–453 (2024) [Google Scholar]
  31. C. Gu, X. Lu, Y. He et al., Blur removal via blurred-noisy image pair, IEEE Trans. Image Process. 30, 345–359 (2020) [Google Scholar]
  32. S. Aouat, I. Ait-Hammi, I. Hamouchene, A new approach for texture segmentation based on the gray level co-occurrence matrix, Multimedia Tools Appl. 80, 24027–24052 (2021) [Google Scholar]
  33. I. Bakurov, M. Buzzelli, R. Schettini et al., Structural similarity index (SSIM) revisited: a data-driven approach, Expert Syst. Appl. 189, 116087 (2022) [Google Scholar]
  34. K. Park, J.S. Hong, W. Kim, A methodology combining cosine similarity with classifier for text classification, Appl. Artific. Intell. 34, 396–411 (2020) [Google Scholar]
  35. F.A. Croitoru, V. Hondru, R.T. Ionescu et al., Diffusion models in vision: a survey, IEEE Trans. Pattern Anal. Machine Intell. 45, 10850–10869 (2023) [Google Scholar]
  36. O. Dalmaz, M. Yurt, T. Çukur, ResViT: residual vision transformers for multimodal medical image synthesis, IEEE Trans. Med. Imag. 41, 2598–2614 (2022) [Google Scholar]
  37. S. Goel, H. Bansal, S. Bhatia et al., Cyclip: cyclic contrastive language-image pretraining, Adv. Neural Inform. Process. Syst. 35, 6704–6719 (2022) [Google Scholar]
  38. J.H. Moon, H. Lee, W. Shin et al., Multi-modal understanding and generation for medical images and text via vision-language pre-training, IEEE J. Biomed. Health Inform. 26, 6070–6080 (2022) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.