The artistic image processing for visual healing in smart city.
Artistic image
Deep learning
Smart city
Visual attention model
Visual healing
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
22 Jul 2024
22 Jul 2024
Historique:
received:
27
10
2023
accepted:
19
07
2024
medline:
23
7
2024
pubmed:
23
7
2024
entrez:
22
7
2024
Statut:
epublish
Résumé
This study investigates the processing methods of artistic images within the context of Smart city (SC) initiatives, focusing on the visual healing effects of artistic image processing to enhance urban residents' mental health and quality of life. Firstly, it examines the role of artistic image processing techniques in visual healing. Secondly, deep learning technology is introduced and improved, proposing the overlapping segmentation vision transformer (OSViT) for image blocks, and further integrating the bidirectional long short-term memory (BiLSTM) algorithm. An innovative artistic image processing and classification recognition model based on OSViT-BiLSTM is then constructed. Finally, the visual healing effect of the processed art images in different scenes is analyzed. The results demonstrate that the proposed model achieves a classification recognition accuracy of 92.9% for art images, which is at least 6.9% higher than that of other existing model algorithms. Additionally, over 90% of users report satisfaction with the visual healing effects of the artistic images. Therefore, it is found that the proposed model can accurately identify artistic images, enhance their beauty and artistry, and improve the visual healing effect. This study provides an experimental reference for incorporating visual healing into SC initiatives.
Identifiants
pubmed: 39039163
doi: 10.1038/s41598-024-68082-7
pii: 10.1038/s41598-024-68082-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16846Informations de copyright
© 2024. The Author(s).
Références
Ghazal, T. M. et al. IoT for smart cities: Machine learning approaches in smart healthcare—A review. Future Internet 13(8), 218 (2021).
doi: 10.3390/fi13080218
Zhu, H., Shen, L. & Ren, Y. How can smart city shape a happier life? The mechanism for developing a happiness driven smart city. Sustain. Cities Soc. 80, 103791 (2022).
doi: 10.1016/j.scs.2022.103791
Radu, L. D. Disruptive technologies in smart cities: A survey on current trends and challenges. Smart Cities 3(3), 1022–1038 (2020).
doi: 10.3390/smartcities3030051
Sik, D. From lay depression narratives to secular ritual healing: An online ethnography of mental health forums. Cult. Med. Psychiatry 45(4), 751–774 (2021).
pubmed: 33372246
doi: 10.1007/s11013-020-09702-5
Kohrt, B. A., Ottman, K., Panter-Brick, C., Konner, M. & Patel, V. Why we heal: The evolution of psychological healing and implications for global mental health. Clin. Psychol. Rev. 82, 101920 (2020).
pubmed: 33126037
doi: 10.1016/j.cpr.2020.101920
Adjapong, E. & Levy, I. Hip-hop can heal: Addressing mental health through hip-hop in the urban classroom. New Educ. 17(3), 242–263 (2021).
doi: 10.1080/1547688X.2020.1849884
Basch, C. H., Donelle, L., Fera, J. & Jaime, C. Deconstructing TikTok videos on mental health: Cross-sectional, descriptive content analysis. JMIR Format Res. 6(5), e38340 (2022).
doi: 10.2196/38340
Bensaoud, A. & Kalita, J. Deep multi-task learning for malware image classification. J. Inf. Secur Appl. 64, 103057 (2022).
Ma, W., Tu, X., Luo, B. & Wang, G. Semantic clustering based deduction learning for image recognition and classification. Pattern Recognit. 124, 108440 (2022).
doi: 10.1016/j.patcog.2021.108440
Wang, Y., Huang, R., Song, S., Huang, Z. & Huang, G. Not all images are worth 16 × 16 words: Dynamic transformers for efficient image recognition. Adv. Neural Inf. Process. Syst. 34, 11960–11973 (2021).
Freeman, R. C. Jr. et al. Promoting spiritual healing by stress reduction through meditation for employees at a veterans hospital: A CDC framework-based program evaluation. Workplace Health Safety 68(4), 161–170 (2020).
pubmed: 31540567
doi: 10.1177/2165079919874795
Hass-Cohen, N., Bokoch, R., Goodman, K. & Conover, K. J. Art therapy drawing protocols for chronic pain: Quantitative results from a mixed method pilot study. Arts Psychother. 73, 101749 (2021).
doi: 10.1016/j.aip.2020.101749
Bowen-Salter, H. et al. Towards a description of the elements of art therapy practice for trauma: A systematic review. Int. J. Art Ther. 27(1), 3–16 (2022).
doi: 10.1080/17454832.2021.1957959
Lee, H., Kim, E. & Yoon, J. Y. Effects of a multimodal approach to food art therapy on people with mild cognitive impairment and mild dementia. Psychogeriatrics 22(3), 360–372 (2022).
pubmed: 35229407
doi: 10.1111/psyg.12822
Juliantino, C., Nathania, M. P., Hendarti, R., Darmadi, H. & Suryawinata, B. A. The development of virtual healing environment in VR platform. Proc. Comput. Sci. 216, 310–318 (2023).
doi: 10.1016/j.procs.2022.12.141
Skorburg, J. A., O’Doherty, K. & Friesen, P. Persons or data points? Ethics, artificial intelligence, and the participatory turn in mental health research. Am. Psychol. 79(1), 137 (2024).
pubmed: 38236221
doi: 10.1037/amp0001168
Mitro, N. et al. AI-enabled smart wristband providing real-time vital signs and stress monitoring. Sensors 23(5), 2821 (2023).
pubmed: 36905025
pmcid: 10007366
doi: 10.3390/s23052821
Ayana, G., Park, J., Jeong, J. W. & Choe, S. W. A novel multistage transfer learning for ultrasound breast cancer image classification. Diagnostics 12(1), 135 (2022).
pubmed: 35054303
pmcid: 8775102
doi: 10.3390/diagnostics12010135
Maniat, M., Camp, C. V. & Kashani, A. R. Deep learning-based visual crack detection using google street view images. Neural Comput. Appl. 33(21), 14565–14582 (2021).
doi: 10.1007/s00521-021-06098-0
Wang, X., Chen, Z., Ren, J., Chen, S. & Xing, F. Object status identification of X-ray CT images of microcapsule-based self-healing mortar. Cement Concr. Compos. 125, 104294 (2022).
doi: 10.1016/j.cemconcomp.2021.104294
Batziou, E., Ioannidis, K., Patras, I., Vrochidis, S. & Kompatsiaris, I. Artistic neural style transfer using CycleGAN and FABEMD by adaptive information selection. Pattern Recognit. Letter 165, 55–62 (2023).
doi: 10.1016/j.patrec.2022.11.026
Tehsin, S., Kausar, S., Jameel, A., Humayun, M. & Almofarreh, D. K. Satellite image categorization using scalable deep learning. Appl. Sci. 13(8), 5108 (2023).
doi: 10.3390/app13085108
McCrory, A., Best, P. & Maddock, A. The relationship between highly visual social media and young people’s mental health: A scoping review. Child. Youth Serv Rev. 115, 105053 (2020).
doi: 10.1016/j.childyouth.2020.105053
Yazdavar, A. H. et al. Multimodal mental health analysis in social media. Plos one 15(4), e0226248 (2020).
pubmed: 32275658
pmcid: 7147779
doi: 10.1371/journal.pone.0226248
Dobersek, U. et al. Meat and mental health: A systematic review of meat abstention and depression, anxiety, and related phenomena. Crit. Rev. Food Sci. Nutrit. 61(4), 622–635 (2021).
doi: 10.1080/10408398.2020.1741505
Meeks, K., Peak, A. S. & Dreihaus, A. Depression, anxiety, and stress among students, faculty, and staff. J. Am. College Health 71(2), 348–354 (2023).
doi: 10.1080/07448481.2021.1891913
Malaeb, D. et al. Problematic social media use and mental health (depression, anxiety, and insomnia) among Lebanese adults: Any mediating effect of stress?. Perspect. Psychiatr Care 57(2), 539–549 (2021).
pubmed: 32633428
doi: 10.1111/ppc.12576
Su, C., Xu, Z., Pathak, J. & Wang, F. Deep learning in mental health outcome research: A scoping review. Transl Psychiatry 10(1), 116 (2020).
pubmed: 32532967
pmcid: 7293215
doi: 10.1038/s41398-020-0780-3
Ashtiani, F., Geers, A. J. & Aflatouni, F. An on-chip photonic deep neural network for image classification. Nature 606(7914), 501–506 (2022).
pubmed: 35650432
doi: 10.1038/s41586-022-04714-0
AprilPyone, M. & Kiya, H. Privacy-preserving image classification using an isotropic network. IEEE MultiMed. 29(2), 23–33 (2022).
doi: 10.1109/MMUL.2022.3168441
Liu, X., Wang, L. & Han, X. Transformer with peak suppression and knowledge guidance for fine-grained image recognition. Neurocomputing 492, 137–149 (2022).
doi: 10.1016/j.neucom.2022.04.037
Zhang, Q. & Yang, Y. B. Rest: An efficient transformer for visual recognition. Adv Neural Inf. Process. Syst. 34, 15475–15485 (2021).
Kamal, M. B. et al. An innovative approach utilizing binary-view transformer for speech recognition task. Comput. Mater. Contin. 72(3), 5547–5562 (2022).
Rao, Y. et al. Dynamicvit: Efficient vision transformers with dynamic token sparsification. Adv. Neural Inf. Process. Syst. 34, 13937–13949 (2021).
Rajan, K., Zielesny, A. & Steinbeck, C. DECIMER 1.0: Deep learning for chemical image recognition using transformers. J. Cheminf 13(1), 1–16 (2021).
doi: 10.1186/s13321-021-00538-8
Yuan, F., Zhang, Z. & Fang, Z. An effective CNN and transformer complementary network for medical image segmentation. Pattern Recognit. 136, 109228 (2023).
doi: 10.1016/j.patcog.2022.109228
Sun, L., Zhao, G., Zheng, Y. & Wu, Z. Spectral–spatial feature tokenization transformer for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2022).
doi: 10.1109/TGRS.2022.3231215
Tashu, T. M., Hajiyeva, S. & Horvath, T. Multimodal emotion recognition from art using sequential co-attention. J. Imaging 7(8), 157 (2021).
pubmed: 34460793
pmcid: 8404915
doi: 10.3390/jimaging7080157
Goel, P. Realtime object detection using tensorflow an application of ML. Int. J. Sustain. Dev. Comput. Sci. 3(3), 11–20 (2021).
Naseer, I. et al. Performance analysis of state-of-the-art CNN architectures for luna16. Sensors 22(12), 4426 (2022).
pubmed: 35746208
pmcid: 9227226
doi: 10.3390/s22124426
Vaartio-Rajalin, H., Santamäki-Fischer, R., Jokisalo, P. & Fagerström, L. Art making and expressive art therapy in adult health and nursing care: A scoping review. Int. J. Nurs. Sci. 8(1), 102–119 (2021).
pubmed: 33575451