Formation of highly specialized chatbotsfor advanced search

• Autorzy: Yarovyi Andrii , Kudriavtsev Dmytro

Identyfikatory

DOI

10.35784/iapgos.5628

Warianty tytułu

PL

Tworzenie wysoce wyspecjalizowanych chatbotówdo zaawansowanego wyszukiwania

• Języki publikacji: EN

Abstrakty

EN

In this research, the formation of highly specialized chatbots was presented. The influence of multi-threading subject areas search was noted. The use of related subject areas in chatbot text analysing was defined. The advantages of using multiple related subject areas are noted using the example of an intelligent chatbot.

PL

W tym badaniu przedstawiono tworzenie wysoce wyspecjalizowanych chatbotów. Zwrócono uwagę na wpływ wielowątkowego wyszukiwania obszarów tematycznych. Zdefiniowano wykorzystanie powiązanych obszarów tematycznych w analizie tekstu chatbota. Na przykładzie inteligentnego chatbota odnotowano zalety korzystania z wielu powiązanych obszarów tematycznych.

Słowa kluczowe

EN

text processing; intelligent data analysis; chatbot; advanced search

PL

przetwarzanie tekstu; inteligentna analiza danych; chatboty; zaawansowane wyszukiwanie

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2024
• Tom: T. 14, nr 1
• Strony: 67--70
• Opis fizyczny: Bibliogr. 18 poz., tab., wykr.

Twórcy

autor

Yarovyi Andrii

• Vinnytsia National Technical University, Department for Computer Science, Vinnytsia, Ukraine

• https://orcid.org/0000-0002-6668-2425

autor

Kudriavtsev Dmytro

• Vinnytsia National Technical University, Department for Computer Science, Vinnytsia, Ukraine

• https://orcid.org/0000-0001-7116-7869

Bibliografia

• [1] Agarwal S., Rahul P., Neetu: New Text Detection Technique Using Machine Learning Architecture. Dwivedi S., Singh S., Tiwari M., Shrivastava A. (eds): Flexible Electronics for Electric Vehicles. Lecture Notes in Electrical Engineering 863. Springer, Singapore, 2023 [https://doi.org/10.1007/978-981-19-0588-9_1].
• [2] Arkoudas K.: ChatGPT is no Stochastic Parrot. But it also Claims that 1 is Greater than 1. Philos. Technol. 36(54), 2023 [https://doi.org/10.1007/s13347-023-00619-6A].
• [3] Cao Y., Xu G., Gao Y., Song C.: Application of natural language processing technology based on TensorFlow framework in text mining and discovery algorithm. IET Communications 17, 2022 [https://doi.org/10.1049/cmu2.12534].
• [4] Chen W. et al.: Improved Recurrent Neural Networks for Text Classification and Dynamic Sylvester Equation Solving. Neural Process Lett 55, 2023, 8755– 8784 [https://doi.org/10.1007/s11063-023-11176-6Raj].
• [5] Greco C. M., Tagarelli A.: Bringing order into the realm of Transformer-based language models for artificial intelligence and law. Artif Intell Law 2023) [https://doi.org/10.1007/s10506-023-09374-7].
• [6] Henrickson L., Meroño-Peñuela A.: Prompting meaning: a hermeneutic approach to optimising prompt engineering with ChatGPT. AI & Soc 2023 [https://doi.org/10.1007/s00146-023-01752-8].
• [7] Joseph J. F. J., Nonsiri S., Monsakul A.: Keras and TensorFlow: A Hands-On Experience. Prakash K. B., Kannan R., Alexander S., Kanagachidambaresan G. R. (eds): Advanced Deep Learning for Engineers and Scientists. EAI/Springer Innovations in Communication and Computing. Springer, Cham. 2021 [https://doi.org/10.1007/978-3-030-66519-7_4].
• [8] Karchi R. P., Hatture S. M., Tushar T. S., Prathibha B. N.: AI-Enabled Sustainable Development: An Intelligent Interactive Quotes Chatbot System Utilizing IoT and ML. Whig P., Silva N., Elngar A. A., Aneja N., Sharma P. (eds): Sustainable Development through Machine Learning, AI and IoT. ICSD 2023. Communications in Computer and Information Science 1939. Springer, Cham. [https://doi.org/10.1007/978-3-031-47055-4_17].
• [9] Kvyetnyy R., Ivanchuk Y., Yarovyi A., Horobets Y.: Algorithm for Increasing the Stability Level of Cryptosystems. Selected Papers of the VIII International Scientific Conference “Information Technology and Implementation" – IT&I2021, 293–301.
• [10] Meyer J. G. et al.: ChatGPT and large language models in academia: opportunities and challenges. BioData Mining 16(20), 2023 [https://doi.org/10.1186/s13040-023-00339-9].
• [11] Mondal B.: Best 25 Datasets for NLP Projects. Kaggle [https://www.kaggle.com/discussions/general/150720] (avaible 13.05.2020).
• [12] Pallis George, Trihinas D., Tryfonos A., Dikaiakos M.: DevOps as a Service: Pushing the Boundaries of Microservice Adoption. IEEE Internet Computing 22, 2018, 65–71 [https://doi.org/10.1109/MIC.2018.032501519].
• [13] Raj A., Jasmine K.: Building Microservices with Docker Compose. The International Journal of Analytical and Experimental Modal Analysis XIII, 2021, 1215.
• [14] Siad S. M.: The Promise and Perils of Google's Bard for Scientific Research. AI. 2023 [https://doi.org/10.17613/yb4n-mc79].
• [15] Thapa S., Adhikari S.: ChatGPT, Bard, and Large Language Models for Biomedical Research: Opportunities and Pitfalls. Ann Biomed Eng 51, 2023, 2647–2651 [https://doi.org/10.1007/s10439-023-03284-0].
• [16] Yarovyi A. et al.: Information technology in creating intelligent chatbots. Proc. SPIE 11176, 2019, 1117627 [https://doi.org/10.1117/12.2537415].
• [17] Yarovyi A., Kudriavtsev D.: Dictionary data structure for a text analysis task using cross-references. IEEE 17th International Conference on Computer Sciences and Information Technologies – CSIT, 2022, 61–64 [https://doi.org/10.1109/CSIT56902.2022.10000460].
• [18] Yarovyi A., Kudriavtsev D.: Method of multi-purpose text analysis based on a combination of knowledge bases for intelligent chatbot. CEUR Workshop Proceedings 2870, 2021, 1238–1248