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All issues Volume 344 (2025) EPJ Web Conf., 344 (2025) 01053 References
Open Access
Issue
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
Article Number 01053
Number of page(s) 7
Section AI-Integrated Physics, Technology, and Engineering
DOI https://doi.org/10.1051/epjconf/202534401053
Published online 22 December 2025
  1. E. J. Ijatuyi, K. Yessoufou, H. O. Patrick, Sustainable tourism and green space: exploring how green spaces and natural attractions contribute to local tourism economies and revenue generation in Gauteng province. Discov. Sustain. 6, 1, 236 (2025). https://doi.org/10.1007/s43621-025-00958- 8 [Google Scholar]
  2. N. Bakalo, V. Makhovka, I. Krekoten, A. Glebova, S. Kulakova, Local tourism as financial and economic development driver of the community: Management aspect. World Dev. Perspect. 38, 100693 (2025). https://doi.org/10.1016/j.wdp.2025.100693 [Google Scholar]
  3. L. Judijanto, Inclusive and Sustainable Growth Through Culture-Based and Creative Economy Tourism: A Review. Multitech J. Sci. Technol. 2, 9, 667–688 (2025). https://doi.org/10.59890/mjst.v2i9.71 [Google Scholar]
  4. I. Nurhayati et al., Community-Based Halal Tourism and Information Digitalization: Sustainable Tourism Analysis. Tour. Hosp. 6, 3 (2025). https://doi.org/10.3390/tourhosp6030148 [Google Scholar]
  5. J. Pan, H. Yang, Z. Wang, B. Peng, S. Li, Optimizing Sustainable Tourism: A Multi- Objective Framework for Juneau and Beyond. Sustainability 17, 16 (2025). https://doi.org/10.3390/su17167344 [Google Scholar]
  6. I. F. Pasaribu, T. D. Hapsari, G. Y. Prasetya, M. Sciences, Strategy For Developing Blue Economic Potential Through Sustainable Tourism In The Coastal Area Of Semarang City. J. Teknol. Perikan. dan Kelaut. 16, 2, 173–188 (2025). https://doi.org/10.24319/jtpk.16.173-188 [Google Scholar]
  7. S. Priatmoko, A. F. Rahmat, Digging up Rural Community-based tourism (CBT) in developing country, Indonesia’s framework finding. Geoj. Tour. Geosites 61, 3, 1420–1429 (2025). https://doi.org/10.30892/gtg.61302-1512 [Google Scholar]
  8. S. Shin, T. Kim, S. Hlee, C. Koo, Destination Advertising on YouTube: Effects of Native Advertising and Comment Management on Tourist Perception. J. Hosp. & Tour. Res. 49, 4, 798–815 (2025). https://doi.org/10.1177/10963480231194689 [Google Scholar]
  9. L. Fang, Y. Liu, G. Li, From Residents to Tourists: Influence of Online Review Negativity Dominance on Tourism Public Opinion. In HCI in Business, Government and Organizations. (2025),3–17 [Google Scholar]
  10. X. Huang, S. Chelliah, Attributes Influencing Tourist Satisfaction: Sentiment Analysis and Topic Modeling of Online Reviews. J. China Tour. Res. 21, 3, 819–838 (2025). https://doi.org/10.1080/19388160.2024.2440323 [Google Scholar]
  11. N. A. Sharma, A. B. M. S. Ali, M. A. Kabir, A review of sentiment analysis: tasks, applications, and deep learning techniques. Int. J. Data Sci. Anal. 19, 3, 351–388 (2025). https://doi.org/10.1007/s41060-024-00594-x [Google Scholar]
  12. E. W. T. Ngai, A. K. H. Lui, B. C. W. Kei, Natural language processing in government applications: a literature review and a case analysis. Ind. Manag. Data Syst. 125, 6, 2067–2104 (2025). https://doi.org/10.1108/IMDS-07-2024-0711 [Google Scholar]
  13. S. Lu, News editorial decision optimization and intelligent assistance based on social media and natural language processing. Serv. Oriented Comput. Appl. (2025). https://doi.org/10.1007/s11761-025-00457-8 [Google Scholar]
  14. S. Arifin, M. Azinuddin, A. P. Mat Som, A. Ibrahim, M. H. Hanafiah, Collaborative communication for sustainable tourism in Asia: a case study from Madura Island. Worldw. Hosp. Tour. Themes 17, 3, 413–421 (2025). https://doi.org/10.1108/WHATT-01-2025-0042 [Google Scholar]
  15. S. Tunca, Exploring visitor sentiment trends at Alanya Cleopatra Beach using natural language processing techniques: insights from online reviews. Tour. Recreat. Res. 0, 0, 1–20 (2025). https://doi.org/10.1080/02508281.2025.2503992 [Google Scholar]
  16. J. Köckritz, B. İlgen, C. Cohrdes, G. Hattab, Current applications and future directions in natural language processing for news media and mental health. Sci. Rep. 15, 1, 32532 (2025). https://doi.org/10.1038/s41598-025-18413-z [Google Scholar]
  17. P. López-Úbeda, T. Martín-Noguerol, A. Luna, Natural language processing and LLMs in liver imaging: a practical review of clinical applications. Abdom. Radiol. (2025). https://doi.org/10.1007/s00261-025-05127-z [Google Scholar]
  18. N. S. Jonnala et al., Leveraging hybrid model for accurate sentiment analysis of Twitter data. Sci. Rep. 15, 1, 24438 (2025). https://doi.org/10.1038/s41598-025-09794-2 [Google Scholar]
  19. R. Alhejaili, Machine Learning Approaches for Sentiment Analysis on Social Media. In AI-Driven: Social Media Analytics and Cybersecurity (2025),21–43. https://doi.org/10.1007/978-3-031-80334- 5_2 [Google Scholar]
  20. M. A. Bouke, O. I. Alramli, A. Abdullah, XAIRF- WFP: a novel XAI-based random forest classifier for advanced email spam detection. Int. J. Inf. Secur. 24, 1, 5 (2024). https://doi.org/10.1007/s10207-024-00920-1 [Google Scholar]
  21. N. S. K. M. K. Tirumanadham et al., Optimizing Lung Cancer Prediction Models: A Hybrid Methodology Using GWO and Random Forest. In Enabling Person-Centric Healthcare (2025), 59–77. https://doi.org/10.1007/978-3-031-82516-3_3 [Google Scholar]
  22. E. Faiella et al., Promising Results About the Possibility to Identify Prostate Cancer Patients Employing a Random Forest Classifier. Diagnostics 15, 4 (2025). https://doi.org/10.3390/diagnostics15040421 [Google Scholar]
  23. A. Yaqoob et al., SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis. Sci. Rep. 15, 1, 10944 (2025). https://doi.org/10.1038/s41598-025-95786-1 [Google Scholar]
  24. S. Wali, Y. A. Farrukh, I. Khan, Explainable AI and Random Forest based reliable intrusion detection system. Comput. Secur. 157, 104542 (2025). https://doi.org/10.1016/j.cose.2025.104542 [Google Scholar]
  25. E. Caputi et al., Comparison of Tree Typologies Mapping Using Random Forest. Remote Sens. 17, 3 (2025). https://doi.org/10.3390/rs17030356 [Google Scholar]
  26. W. Hu et al., Intelligent irrigation strategy model for farmland using dung beetle optimization- random forest algorithms. Agric. Water Manag. 317, 109653 (2025). https://doi.org/10.1016/j.agwat.2025.109653 [Google Scholar]
  27. M. Kanwar, B. Pokharel, S. Lim, A new random forest method for landslide susceptibility mapping. Int. J. Environ. Sci. Technol. 22, 11, 10635–10650 (2025). https://doi.org/10.1007/s13762-024-06310- 3 [Google Scholar]
  28. Q. Zhou, F. Xue, Automatic information gain- guided convergence for refining building design parameters. Build. Environ. 275, 112788 (2025). https://doi.org/10.1016/j.buildenv.2025.112788 [Google Scholar]
  29. S. Dhanka et al., Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification. Arch. Comput. Methods Eng. (2025). https://doi.org/10.1007/s11831-025-10309- 5 [Google Scholar]
  30. K. Saneep et al., State of charge estimation of lithium-ion batteries using PSO optimized random forest algorithm. J. Energy Storage 114, 115879 (2025). https://doi.org/10.1016/j.est.2025.115879 [Google Scholar]
  31. O. M. Alyasiri et al., Text classification based on optimization feature selection methods: a review. Multimed. Tools Appl. 84, 15, 14187–14233 (2025). https://doi.org/10.1007/s11042-024-19769- 6 [Google Scholar]
  32. R. Nasfi, G. de Tré, A. Bronselaer, Improving Data Cleaning by Learning From Unstructured Textual Data. IEEE Access 13, 36470–36491 (2025). https://doi.org/10.1109/ACCESS.2025.3543953 [Google Scholar]
  33. B. Priya Kamath et al., Comprehensive Analysis of Word Embedding Models and Feature Vector Design. IEEE Access 13, 25239–25255 (2025). https://doi.org/10.1109/ACCESS.2025.3536631 [Google Scholar]
  34. E. J. Hadi, M. F. Ibrahim, A. I. Mohammed, Towards News Classification: A Machine Learning Approach. J. Inf. & Knowl. Manag. 24, 05, 2550045 (2025). https://doi.org/10.1142/S0219649225500455 [Google Scholar]
  35. M. Basirati, A. Laachach, Clustering sustainable tourism destinations through Instagram photo analysis. Tour. Rev. (2025). https://doi.org/10.1108/TR-11-2024-0987 [Google Scholar]
  36. S. Khalighi et al., Evaluating the impact of data pre- processing methods on classification of ATR-FTIR spectra. Fuel 376, 132701 (2024). https://doi.org/10.1016/j.fuel.2024.132701 [Google Scholar]
  37. B. Dayananda et al., Pre-processing Applied to Instrumental Data in Analytical Chemistry. Crit. Rev. Anal. Chem. 54, 8, 2745–2753 (2024). https://doi.org/10.1080/10408347.2023.2199864 [Google Scholar]
  38. Z. Labd et al., Text classification supervised algorithms with TF-IDF and GloVe: a comparative study. Int. J. Electr. Comput. Eng. 14, 1, 589–599 (2024). https://doi.org/10.11591/ijece.v14i1.pp589-599 [Google Scholar]
  39. I. Emmanuel, Y. Sun, Z. Wang, A machine learning-based credit risk prediction engine system. J. Big Data 11, 1, 23 (2024). https://doi.org/10.1186/s40537-024-00882-0 [Google Scholar]
  40. G. Obaido et al., Detecting Thyroid Disease Using Filter-Based Selection and Stacking Ensemble. IEEE Access 12, 89098–89112 (2024). https://doi.org/10.1109/ACCESS.2024.3418974 [Google Scholar]
  41. M. Burukanli, N. Yumuşak, StackGridCov: stacking ensemble model with GridSearchCV for mutation prediction. Neural Comput. Appl. 36, 35, 22379–22401 (2024). https://doi.org/10.1007/s00521-024-10428-3 [Google Scholar]
  42. A. Ahmed Bilal et al., Children’s Sentiment Analysis From Texts Using Random Forest. IEEE Access 12, 70089–70104 (2024). https://doi.org/10.1109/ACCESS.2024.3400992 [Google Scholar]
  43. O. Alsemaree et al., Sentiment analysis of Arabic social media texts. Heliyon 10, 9 (2024). https://doi.org/10.1016/j.heliyon.2024.e27863 [Google Scholar]
  44. Y. Rimal et al., Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest. Sci. Rep. 15, 1, 13444 (2025). https://doi.org/10.1038/s41598-025-93675- 1 [Google Scholar]
  45. M. M. Mijwil, M. M. Mijwil, Comparative Analysis of Machine Learning Algorithms for Diabetes Classification. Baghdad Sci. J. 21, 5, 24 (2024). https://doi.org/10.21123/bsj.2023.9010 [Google Scholar]

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