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All issues Volume 344 (2025) EPJ Web Conf., 344 (2025) 01026 References
Open Access
Issue
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
Article Number 01026
Number of page(s) 6
Section AI-Integrated Physics, Technology, and Engineering
DOI https://doi.org/10.1051/epjconf/202534401026
Published online 22 December 2025
  1. L. S. Triandika and others, The Existence of Traditional Madurese Batik Patterns in the Exposure of Time and Technology. Satwika Kaji. Ilmu Budaya dan Perubahan Sos. 8, 1, 121–133 (2024) [Google Scholar]
  2. Imamah, F. H. Rachman, Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regresion. Proceeding - 6th Inf. Technol. Int. Semin. ITIS, 238–242 (2020). https://doi.org/10.1109/ITIS50118.2020.9320958 [Google Scholar]
  3. F. H. Rachman, I. Imamah, B. S. Rintyarna, Sentiment Analysis of Madura Tourism in New Normal Era using Text Blob and KNN with Hyperparameter Tuning. International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), 23–27 (2022). https://doi.org/10.1109/ISMODE53584.2022.9742894 [Google Scholar]
  4. I. Imamah, H. Husni, E. M. Rohman, I. O. Suzanti, F. A. Mufarroha, Text mining and Support Vector Machine for Sentiment Analysis of tourist Reviews in Bangkalan Regency. J. Phys. Conf. Ser. 1477, 2, 0–6 (2020). https://doi.org/10.1088/1742- 6596/1477/2/022023 [Google Scholar]
  5. P. Kumar, A. Wahid, Social Media Analysis for Sentiment Classification Using Gradient Boosting Machines. (2021). https://doi.org/10.1007/978- 981-16-3246-4_70 [Google Scholar]
  6. H. Anandan and others, Tumor Growth Stage Prediction on Ultrasound Breast Cancer Images with Backpropagation Network. Grn Int J Apl Med Sci. 3, 3, 118–121 (2025). https://doi.org/10.62046/gijams.2025.v03i03.003 [Google Scholar]
  7. J. Liu, J. Wu, J. Pan, P. Zhao, Fuzzification-back propagation neural network-based model prediction for robotic arm positioning error reduction. Eng. Appl. Artif. Intell. 151, 110639 (2025). https://doi.org/10.1016/j.engappai.2025.110639 [Google Scholar]
  8. M. Zhai, Q. Cao, P. Huo, X. Du, M. Xin, Estuary- Tidal Residual Water Level Forecasting Method Based on Variational Mode Decomposition and Back Propagation Neural Network. J. Mar. Sci. Eng. 13, 9, 1755 (2025). https://doi.org/10.3390/jmse13091755 [Google Scholar]
  9. X. Zhu, M. Li, X. Liu, Y. Zhang, A backpropagation neural network-based hybrid energy recognition and management system. Energy. 297, 131264 (2024). https://doi.org/10.1016/j.energy.2024.131264 [Google Scholar]
  10. P. Sreevidya, O. V. R. Murthy, S. Veni, Sentiment analysis by deep learning approaches. TELKOMNIKA Telecommun. Comput. Electron. Control. 18, 2 (2020). https://doi.org/10.12928/telkomnika.v18i2.13912 [Google Scholar]
  11. J. X. Ng, K. Lim, C. Lee, Q. Z. Lim, E. K. H. Ooi, N. K. N. Loh, Sentiment Analysis Using Learning- based Approaches: A Comparative Study. 11th Int. Conf. Inf. Commun. Technol. (ICoICT), 469–474 (2023). https://doi.org/10.1109/ICoICT58202.2023.10262 604 [Google Scholar]
  12. A. Ashir, A Generalized Method for Sentiment Analysis across Different Sources. Appl. Comput. Intell. Soft Comput. 2529984:1–2529984:8 (2021). https://doi.org/10.1155/2021/2529984 [Google Scholar]
  13. Z. Banou, S. Elfilali, H. Benlahmar, Towards a polynomial approximation of support vector machine accuracy applied to Arabic tweet sentiment analysis. Math. Model. Comput. (2023). https://doi.org/10.23939/mmc2023.02.511 [Google Scholar]
  14. S.-U. Hassan et al., Sentiment analysis of tweets through Altmetrics: A machine learning approach. J. Inf. Sci. 47, 712–726 (2020). https://doi.org/10.1177/0165551520930917 [Google Scholar]
  15. P. Sharma, P. Tomar, D. Mukherjee, Sentiment Analysis on Amazon Dataset using Transfer Learning. 2022 Int. Conf. Fourth Ind. Revolut. Based Technol. Pract. (ICFIRTP), 160–165 (2022). https://doi.org/10.1109/ICFIRTP56122.2022.10059413 [Google Scholar]
  16. D. M. Rathod, K. Patel, A. J. Goswami, S. Degadwala, D. Vyas, Exploring Drug Sentiment Analysis with Machine Learning Techniques. 2023 Int. Conf. Inventive Comput. Technol. (ICICT), 9– 12 (2023). https://doi.org/10.1109/ICICT57646.2023.10134055 [Google Scholar]
  17. S. Tripathi, R. Mehrotra, V. Bansal, S. Upadhyay, Analyzing Sentiment using IMDb Dataset. 12th Int. Conf. Comput. Intell. Commun. Netw. (CICN), 30– 33 (2020). https://doi.org/10.1109/CICN49253.2020.9242570 [Google Scholar]
  18. A. K. Singh, S. Kumar, S. Bhushan, P. Kumar, A. Vashishtha, A Proportional Sentiment Analysis of MOOCs Course Reviews Using Supervised Learning Algorithms. Ingénierie des Systèmes d’Information. 26, 501–506 (2021). https://doi.org/10.18280/isi.260510 [Google Scholar]
  19. S. Roja, Performance of Machine Learning Models in Predicting Sentiments of Post-Covid Patients. Int. J. Recent Innov. Trends Comput. Commun. (2023). https://doi.org/10.17762/ijritcc.v11i10.8953 [Google Scholar]
  20. T. P. Lillicrap, A. Santoro, L. Marris, C. J. Akerman, G. Hinton, Backpropagation and the brain. Nat. Rev. Neurosci. 21, 335–346 (2020). https://doi.org/10.1038/s41583-020-0277-3 [Google Scholar]
  21. A. Momeni, B. Rahmani, M. Malléjac, P. del Hougne, R. Fleury, Backpropagation-free training of deep physical neural networks. Science. 382, 1297–1303 (2023). https://doi.org/10.1126/science.adi8474 [Google Scholar]
  22. S. Ozbay, Modified Backpropagation Algorithm with Multiplicative Calculus in Neural Networks. Elektron. ir Elektrotechnika (2023). https://doi.org/10.5755/j02.eie.34105 [Google Scholar]
  23. D. Wei, G. Feng, Compression and Storage Algorithm of Key Information of Communication Data Based on Backpropagation Neural Network. Math. Probl. Eng. (2022). https://doi.org/10.1155/2022/2885735 [Google Scholar]
  24. R. Saini, A review on artificial neural networks for structural analysis. J. Vib. Eng. Technol. 13, 2, 142 (2025). https://doi.org/10.1007/s42417-024-01749- 7 [Google Scholar]
  25. M. Nikdan, T. Pegolotti, E. Iofinova, E. Kurtic, D. Alistarh, SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks. ArXiv abs/2302.0 (2023). https://doi.org/10.48550/arXiv.2302.04852 [Google Scholar]
  26. G. Dubey, P. Sharma, A neural network based approach for text-level sentiment analysis using sentiment lexicons. Int. Conf. Artif. Intell. Speech Technol., 134–150 (2021). https://doi.org/10.1007/978-3-030-95711-7_12 [Google Scholar]

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