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All issues Volume 344 (2025) EPJ Web Conf., 344 (2025) 01036 References
Open Access
Issue
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
Article Number 01036
Number of page(s) 8
Section AI-Integrated Physics, Technology, and Engineering
DOI https://doi.org/10.1051/epjconf/202534401036
Published online 22 December 2025
  1. E. Nasarian, R. Alizadehsani, U. R. Acharya, K. L. Tsui, Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework. Inf. Fusion. 108, 102412 (2024). https://doi.org/10.1016/j.inffus.2024.102412 [Google Scholar]
  2. R. Deberdt et al., Artificial intelligence and ESG in resources-intensive industries: Reviewing the use of AI in fisheries, mining, plastics, and forestry. Extr. Ind. Soc. 23, 101690 (2025),https://doi.org/10.1016/j.exis.2025.101690 [Google Scholar]
  3. A. Casheekar, A. Lahiri, K. Rath, K. S. Prabhakar, K. Srinivasan, A contemporary review on chatbots, AI-powered virtual conversational agents, ChatGPT: Applications, open challenges and future research directions. Comput. Sci. Rev. 52, 100632 (2024),https://doi.org/10.1016/j.cosrev.2024.100632 [Google Scholar]
  4. A. Farea, F. Emmert-Streib, Understanding question-answering systems: Evolution, applications, trends, and challenges. Eng. Appl. Artif. Intell. 156, 110997 (2025). https://doi.org/10.1016/j.engappai.2025.110997 [Google Scholar]
  5. X. Cheng, X. Zhang, B. Yang, Y. Fu, An investigation on trust in AI-enabled collaboration: Application of AI-Driven chatbot in accommodation-based sharing economy. Electron. Commer. Res. Appl. 54, 101164 (2022). https://doi.org/10.1016/j.elerap.2022.101164 [Google Scholar]
  6. L. Pascazio et al., “Question-answering system for combustion kinetics,” Proc. Combust. Inst., vol. 40, no.1–4, p. 105428, 2024, https://doi.org/10.1016/j.proci.2024.105428 [Google Scholar]
  7. K. Du, Y. Zhao, R. Mao, F. Xing, E. Cambria, Natural language processing in finance: A survey. Inf. Fusion. 115, 102755 (2025). https://doi.org/10.1016/j.inffus.2024.102755 [Google Scholar]
  8. P. Upadhyay, R. Agarwal, S. Dhiman, A. Sarkar, S. Chaturvedi, A comprehensive survey on answer generation methods using NLP. Nat. Lang. Process. J. 8, 100088 (2024). https://doi.org/10.1016/j.nlp.2024.100088 [Google Scholar]
  9. M. S. Salim, S. I. Hossain, T. Jalal, D. K. Bose, and M. J. I. Basher, LLM based QA chatbot builder: A generative AI-based chatbot builder for question answering. SoftwareX. 29, 102029 (2025). https://doi.org/10.1016/j.softx.2024.102029 [Google Scholar]
  10. F. Caccavale, C. L. Gargalo, J. Kager, S. Larsen, K. V. Gernaey, U. Krühne, ChatGMP: A case of AI chatbots in chemical engineering education towards the automation of repetitive tasks. Comput. Educ. Artif. Intell. 8(2025). https://doi.org/10.1016/j.caeai.2024.100354 [Google Scholar]
  11. A. Abdi, S. Hasan, M. Arshi, S. M. Shamsuddin, N. Idris, A question answering system in hadith using linguistic knowledge. Comput. Speech Lang. 60, 101023 (2020). https://doi.org/10.1016/j.csl.2019.101023 [Google Scholar]
  12. A. B. Kamran, B. Abro, A. Basharat, SemanticHadith: An ontology-driven knowledge graph for the hadith corpus. J. Web Semant. 78, 100797 (2023). https://doi.org/10.1016/j.websem.2023.100797 [CrossRef] [Google Scholar]
  13. S. S. Sazali, N. A. Rahman, Z. A. Bakar. Characteristics of Malay translated hadith corpus. J. King Saud Univ. - Comput. Inf. Sci. 34, 5, 2151–2160 (2022). https://doi.org/10.1016/j.jksuci.2020.07.011 [Google Scholar]
  14. J. J. Kim, H. U. Kim, J. Adamowski, S. Hatami, H. Jeong, Comparative study of term-weighting schemes for environmental big data using machine learning. Environ. Model. Softw. 157, 105536 (2022). https://doi.org/10.1016/j.envsoft.2022.105536 [Google Scholar]
  15. T. Dogan, A. K. Uysal, On Term Frequency Factor in Supervised Term Weighting Schemes for Text Classification. Arab. J. Sci. Eng. 44, 11, 9545-9560 (2019). https://doi.org/10.1007/s13369-019-03920- 9 [Google Scholar]
  16. M. Okkalioglu, TF-IGM revisited: Imbalance text classification with relative imbalance ratio. Expert Syst. Appl. 217, 119578 (2023). https://doi.org/10.1016/j.eswa.2023.119578 [Google Scholar]
  17. S. Deo, D. Banik, P. K. Pattnaik, Customized long short-term memory architecture for multi- document summarization with improved text feature set. Data Knowl. Eng. 159, 102440, (2025). https://doi.org/10.1016/j.datak.2025.102440 [Google Scholar]
  18. K. Peyton, S. Unnikrishnan, A comparison of chatbot platforms with the state-of-the-art sentence BERT for answering online student FAQs. Results Eng. 7, 100856 (2023). https://doi.org/10.1016/j.rineng.2022.100856 [Google Scholar]
  19. M. S. Islam, K. M. Alam, Sentiment analysis of Bangla language using a new comprehensive dataset BangDSA and the novel feature metric skipBangla-BERT. Nat. Lang. Process. J. 7, 100069 (2024). https://doi.org/10.1016/j.nlp.2024.100069 [Google Scholar]
  20. C. Aparna, K. Rajchandar, A robust solution for recognizing accurate handwritten text extraction using quantum convolutional neural network and transformer models. Comput. Electr. Eng. 120, 109794. (2024). https://doi.org/10.1016/j.compeleceng.2024.109794 [Google Scholar]
  21. J. Yang et al., BERT and hierarchical cross attention-based question answering over bridge inspection knowledge graph. Expert Syst. Appl. 233, 120896 (2023). https://doi.org/10.1016/j.eswa.2023.120896 [CrossRef] [Google Scholar]
  22. Z. Chen et al., Improving BERT with local context comprehension for multi-turn response selection in retrieval-based dialogue systems. Comput. Speech Lang. 82, 101525 (2023). https://doi.org/10.1016/j.csl.2023.101525 [Google Scholar]
  23. Z. Li, K. Xu, Y. Liang, Y. Wang, Z. Zou, DiffuStory : Improving text diffusion models for creative story generation with contrastive learning and decoder-decoder Transformers. Expert Syst. Appl. 130154,(2025). https://doi.org/10.1016/j.eswa.2025.130154 [Google Scholar]
  24. I. C. Rico, J. P. Espada, Expert system for extracting keywords in educational texts and textbooks based on transformers models. Expert Syst. Appl. 282, 127735 (2025). https://doi.org/10.1016/j.eswa.2025.127735. [Google Scholar]
  25. H. Pan, B. Teng, Z. Li, Y. Fu, L. Li, A hybrid TH- LSTM-Transformer Model for text generation from EEG signals during imagined character speech. Biomed. Signal Process. Control, 113, 10887 (2026),https://doi.org/10.1016/j.bspc.2025.108871 [Google Scholar]
  26. J. Chen, P. K. Kudjo, S. Mensah, S. A. Brown, and G. Akorfu, An automatic software vulnerability classification framework using term frequency- inverse gravity moment and feature selection. J. Syst. Softw. 167,110616, (2020). https://doi.org/10.1016/j.jss.2020.110616 [Google Scholar]
  27. J. M. Sanchez-Gomez, M. A. Vega-Rodríguez, C. J. Pérez, The impact of term-weighting schemes and similarity measures on extractive multi- document text summarization. Expert Syst. Appl. 169, 114510 (2021). https://doi.org/10.1016/j.eswa.2020.114510 [Google Scholar]
  28. Z. Tang, W. Li, Y. Li, An improved supervised term weighing scheme for text representation and classification. Expert Syst. Appl. 189, 24, 115985 (2022). https://doi.org/10.1016/j.eswa.2021.115985 [Google Scholar]
  29. J. Choi, S. W. Lee, Improving FastText with inverse document frequency of subwords. Pattern Recognit. Lett. 133, 165–172 (2020). https://doi.org/10.1016/j.patrec.2020.03.003 [Google Scholar]
  30. L. Chen, L. Jiang, C. Li, Using modified term frequency to improve term weighting for text classification. Eng. Appl. Artif. Intell. 101, 104215 (2021). https://doi.org/10.1016/j.engappai.2021.104215 [Google Scholar]
  31. A. Thakkar and K. Chaudhari, Predicting stock trend using an integrated term frequency–inverse document frequency-based feature weight matrix with neural networks. Appl. Soft Comput. J. 96, 106684 (2020). https://doi.org/10.1016/j.asoc.2020.106684 [Google Scholar]

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