15 декабр 2025
7KVANT BELGILAR ASOSIDA MATNLARNI TASNIFLASH ALGORITMI
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MAQOLA ANNOTATSIYASI
MUALIFLAR
N.Niyozmatova
Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti
N.Turgunova
Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti
N.Almuradova
Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti
N.Abduraxmanova
Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti
Teglar
Maqolani baholang
0
Maqola idintifikatorlari
DOI:
Mavjud emas
Foydalanilgan adabiyotlar
Al Ani, M. M., Omar, N., & Nafea, A. A. (2021). A hybrid method of long short term memory and auto encoder architectures for sarcasm detection. Journal of Computer Science, 17(11), 1093–1098. https://doi.org/10.3844/jcssp.2021.1093.1098
Alahakoon, D., & Tan, K. C. (2021). Data preprocessing in machine learning: A review. Information Fusion, 73, 1–13. https://doi.org/10.1016/j.inffus.2021.02.003
Alexander, A., & Widdows, D. (2023). Quantum text encoding for classification tasks. IEEE/ACM SEC.
Bamman, D., & Smith, N. (2021). Contextualized sarcasm detection on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 9(1), 574–577. https://doi.org/10.1609/ icwsm.v9i1.14655
Babanejad, M., Vechtomova, O., & Mohammad, S. (2020). Affective and contextual embeddings for sarcasm detection. Proceedings of COLING, 754–765. https://doi.org/10.18653/v1/2020.coling- main.20
Cerezo, M., Verdon, G., Huang, H.-Y., Cincio, L., & Coles, P. J. (2023). Challenges and opportunities in quantum machine learning. arXiv preprint.
Chatterjee, N., Das, A., & Bandyopadhyay, S. (2023). A BiLSTM variational autoencoder framework for sarcasm detection under class imbalance. Soft Computing, 27, 6543–6558. https://doi. org/10.1007/s00500-023-07956-w
Ghosh, A., & Veale, T. (2016). Fracking sarcasm using neural network. WASSA Proceedings, 161– 169. https://doi.org/10.18653/v1/W16-0425
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Mamatov, N. S., Turg‘unova, N. M., Turg‘unov, B. X., & Samijonov, B. N. (2024). Algorithm for analysis of texts in Uzbek language. In Artficial Intelligence and Information Technologies (pp. 511– 515). CRC Press.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv:1301.3781. https://doi.org/10.48550/arXiv.1301.3781
Nguyen, T., Sipola, T., & Hautamäki, J. (2024). Machine learning applications of quantum computing: A review. arXiv preprint. https://doi.org/10.34190/eccws.23.1.2258
Niyozmatova, N. A., & Turg‘unova, N. M. (2025). Matnlarni vektorlashni tf_idf kvant algoritmi [TF-IDF quantum algorithm for text vectorization]. Digital Transformation and Artificial Intelligence, 3(4), 1–11.
Poslavskaya, Ye., & Korolev, A. (2023). Encoding categorial data: Is there anything ‘hotter’ than one-hot encoding? Huawei Novosibirsk Research Center. https://doi.org/10.48550/arXiv.2312.16930
Rezvan, M. R., Sorkhi, A. G., Pirgazi, J., & Kallehbasti, M. M. P. (2024). AdvanceSplice: Integrating N-gram one-hot encoding and ensemble modeling for enhanced accuracy. Biomedical Signal Processing and Control, 92, 106017. https://doi.org/10.1016/j.bspc.2024.106017
Widdows, D., Aboumrad, W., Kim, D., Ray, S., & Mei, J. (2024). Quantum natural language processing. KI – Künstliche Intelligenz [AI – Artificial Intelligence], 38, 293–310.
Wu, S., Li, J., Zhang, P., & Zhang, Y. (2021). Natural language processing meets quantum physics: A survey and categorization. EMNLP.