ARCHIVES
Review Article
Deep Learning Approaches to Multimodal Sustainable Report Analysis
Sarah Chepkogei Sawe1
Anthony Wanjoya2
Andrew Kipkebut3
1 Department of Computer Science and Information Technology, Co-operative University of Kenya, Kenya. 2 3 Department of Mathematical Sciences, Co-operative University of Kenya, Nairobi, Kenya.
Published Online: September-December 2025
Pages: 72-78
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403014
References
1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... Zheng, X. (2016).TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI '16).
2. Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423– 443.
3. Brown, T. B., Mann, B., Ryder, N., Subbiah, D., Kaplan, J., Dhariwal, P., ... Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165.
4. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
5. Durkin, J. (1994). Expert systems: A view of the field. IEEE Expert, 9(4), 56–63.
6. Firmansyah, B. (2021). The effectiveness of multimodal approaches in learning. EDUTEC Journal of Education and Technology, 469-479.
7. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778).
8. Johnson, L., Rinaldi, L., De Micco, P., Vitale, G., Cupertino, S., & Maraghini, M. P. (2020). Challenges in Sustainability Reporting. Journal of Environmental Accounting and Management, 8(3), 200–215.
9. Kaur Hora, T., & Shelke, S. (2024). Multimodal machine Learning. PICT's International Journal of Engineering and Technology (PIJET), 66–73.
10. ufel, J., Bargieł-Łączek, K., Kocot, S., Koźlik, M., Bartnikowska, W., Janik, M., ... Gruszczyńska, K. (2023). What Is Machine Learning, Artificial Neural Networks and Deep Learning? Examples of Practical Applications in Medicine. National Library of Medicine, 13(15), 2582. https://doi.org/10.3390/diagnostics13152582
11. Ozili, P. K. (2022). Sustainability and sustainable development research around the world. Managing Global Transitions.
12. Razavi, A. (2021). Introduction to Artificial Neural Networks. CRC Press.
13. Smith, J., & Green, L. (2021). The Evolution of Corporate Sustainability Reporting. Environmental Science & Policy Journal, 14(2), 150–165.
14. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
15. Tang, G. (2024, June 26). Application and Development of Artificial Intelligence in the ESG Investment and Financing Field. HKAIFT. Retrieved from https://hkaift.org/en/article/show.php?itemid=227.
16. Wang, Y., & Ma, Y. (2024). Application of Deep Learning Models to Predict Panel Flutter in Aerospace Structures. Aerospace, 11(5), 677.
17. Werbos, P. (1988). Backpropagation: Past and future. In IEEE Xplore.
18. Zhang, L., & Wang, Q. (2018). Artificial Intelligence in Data Analysis. International Journal of Machine Learning and Cybernetics, 9(5),
789–800.
2. Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423– 443.
3. Brown, T. B., Mann, B., Ryder, N., Subbiah, D., Kaplan, J., Dhariwal, P., ... Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165.
4. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
5. Durkin, J. (1994). Expert systems: A view of the field. IEEE Expert, 9(4), 56–63.
6. Firmansyah, B. (2021). The effectiveness of multimodal approaches in learning. EDUTEC Journal of Education and Technology, 469-479.
7. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778).
8. Johnson, L., Rinaldi, L., De Micco, P., Vitale, G., Cupertino, S., & Maraghini, M. P. (2020). Challenges in Sustainability Reporting. Journal of Environmental Accounting and Management, 8(3), 200–215.
9. Kaur Hora, T., & Shelke, S. (2024). Multimodal machine Learning. PICT's International Journal of Engineering and Technology (PIJET), 66–73.
10. ufel, J., Bargieł-Łączek, K., Kocot, S., Koźlik, M., Bartnikowska, W., Janik, M., ... Gruszczyńska, K. (2023). What Is Machine Learning, Artificial Neural Networks and Deep Learning? Examples of Practical Applications in Medicine. National Library of Medicine, 13(15), 2582. https://doi.org/10.3390/diagnostics13152582
11. Ozili, P. K. (2022). Sustainability and sustainable development research around the world. Managing Global Transitions.
12. Razavi, A. (2021). Introduction to Artificial Neural Networks. CRC Press.
13. Smith, J., & Green, L. (2021). The Evolution of Corporate Sustainability Reporting. Environmental Science & Policy Journal, 14(2), 150–165.
14. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
15. Tang, G. (2024, June 26). Application and Development of Artificial Intelligence in the ESG Investment and Financing Field. HKAIFT. Retrieved from https://hkaift.org/en/article/show.php?itemid=227.
16. Wang, Y., & Ma, Y. (2024). Application of Deep Learning Models to Predict Panel Flutter in Aerospace Structures. Aerospace, 11(5), 677.
17. Werbos, P. (1988). Backpropagation: Past and future. In IEEE Xplore.
18. Zhang, L., & Wang, Q. (2018). Artificial Intelligence in Data Analysis. International Journal of Machine Learning and Cybernetics, 9(5),
789–800.
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