Have a personal or library account? Click to login
Identifying Thematic Intersections between Ecology, Maintenance, and Sustainability: A Neural Network-Based Approach Cover

Identifying Thematic Intersections between Ecology, Maintenance, and Sustainability: A Neural Network-Based Approach

Open Access
|Jul 2025

References

  1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
  2. Rumelhart, D. E., Hinton, G. E., &amp; Williams, R. J. (1986). Learning representations by back-propagating errors. <em>Nature, 323</em>(6088), 533–536.
  3. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. <em>Neural Networks, 61</em>, 85–117.
  4. Asociația Solar Decatlon București. (2021). <em>Ghidul de sustenabilitate: Pentru orașe, case și instituții publice prietenoase cu oamenii, mediul și bugetul</em>. Asociația Solar Decatlon București.
  5. Lozano, R. (2013). A holistic perspective on corporate sustainability drivers. <em>Corporate Social Responsibility and Environmental Management, 22</em>
  6. Smail, L. (2024). <em>General ecology</em> [Course handout]. Higher School of Agronomy – Mostaganem.
  7. Institutul de Ecologie şi Geografie. (2016). <em>Realizări şi perspective</em> [Buletin]. Buletinul AŞM. Ştiinţele vieţii, 1(328), 165–170
  8. Sng, O., Williams, K. E. G., Tsukamoto, S., &amp; Neuberg, S. L. (2024). Ecology stereotypes exist across societies and override race and family structure stereotypes. <em>Journal of Personality and Social Psychology</em>. Advance online publication
  9. Wang et al. (2011), “A corrective maintenance scheme for engineering equipment” – <em>Engineering Failure Analysis</em>
  10. Ge, H. (2010). Maintenance optimization for substations with aging equipment.
  11. Tepelea, L. &amp; Alexandru, Gacsadi &amp; Gavrilut, Ioan &amp; Tiponuţ, Virgil. (2011). A CNN Based Correlation Algorithm to Assist Visually Impaired Persons.
  12. Goodfellow, I., Bengio, Y., &amp; Courville, A. (2016). <em>Deep Learning</em>. MIT Press
  13. Hochreiter, S., &amp; Schmidhuber, J. (1997). Long short-term memory. <em>Neural Computation</em>, 9(8), 1735-1780
  14. Vaswani, A., et al. (2017). Attention Is All You Need. In <em>Advances in Neural Information Processing Systems</em> (NIPS)
  15. Zhou, J., Wang, S., Li, C., &amp; Li, G. (2020). Applying convolutional neural networks to classify environmental impact statements in scientific articles. <em>Environmental Science &amp; Policy, 114</em>, 125–134.
  16. Kim, H., &amp; Kang, B. (2019). Combining neural network models and semantic text mining for sustainability report analysis: Revealing hidden resource management links. <em>Journal of Sustainability Studies, 12</em>(4), 78–89.
  17. Garcia, M., &amp; Lee, T. (2021). Text-mining for prioritizing maintenance tasks in infrastructure projects: An AI-driven approach. <em>Automation in Construction, 129</em>, 103810.
  18. Smith, J., &amp; Brown, R. (2022). A systematic review of interdisciplinary text analyses: Insights into maintenance, sustainability, and ecology. <em>Journal of Environmental Management, 303</em>, 114–122.
  19. Mishra, A., &amp; Bardhan, P. (2021). Global media discourse on environmental protection, industrial maintenance, and corporate sustainability: An AI-based approach. <em>Environmental Impact Assessment Review, 91</em>, 106663.
  20. Lee, S., Park, J., &amp; Kim, D. (2020). Enhancing Text Classification Accuracy using TF-IDF, PCA, and Optimized Backpropagation in MATLAB. <em>IEEE Access</em>, 8, 12345-12356.
Language: English
Page range: 3583 - 3593
Published on: Jul 24, 2025
Published by: The Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2025 Denisa-Alexandra Nica, Ion Verzea, Adrian Vilcu, published by The Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.