MACHINE LEARNING METHODS IN THERMOMETERS’ DATA EXTRACTION AND PROCESSING

1
Національний університет “Львівська політехніка”
2
Lviv Polytechnic National University

Research focuses on developing an all-encompassing algorithm for efficiently extracting, processing, and analyz- ing data about thermometers. The examination involves the application of a branch of artificial intelligence, in particular machine learning (ML) methods, as a means of automating processes. Such methods facilitate the identification and aggregation of pertinent data, the detection of gaps, and the conversion of unstructured text into an easily analyzable structured format. The paper details the employment of reinforcement learning for the automatic extraction of information from diverse resources, natural language pro- cessing for analysis of textual values, and the decision tree method for discerning patterns within the data.

  1. Nguyen, V. H., Sinnappan, S., and Huynh, M. (2021). Analyzing Australian SME Instagram Engagement via Web Scraping. Pacific Asia Journal of the Association for Information Systems, 13(2):11-43. Available: https://aisel.aisnet.org/pajais/vol13/iss2/2/
  2. Seliverstov, Y., Seliverstov, S., Malygin, I., and Korolev, O. (2020). Traffic safety evaluation in Northwestern Federal District using sentiment anal- ysis of Internet users’ reviews. Transportation Re- search Procedia, 50:626-635. Available: https://doi.org/10.1016/j.trpro.2020.10.074
  3. E. Suganya, S. Vijayarani, "Firefly Optimization Al- gorithm Based Web Scraping for Web Citation Ex- traction," Wireless Personal Communications, vol. 118, no. 2, May 2021. DOI:10.1007/s11277-021-08093-z.
  4. Rahmatulloh, A., and Gunawan, R. (2020). Web Scraping with HTML DOM Method for Data Collec- tion of Scientific Articles from Google Scholar. In- donesian Journal of Information Systems, 2(2):95-104. DOI:10.24002/ijis.v2i2.3029
  5. S. Kolli, P. Rama Krishna, P. Balakesava Reddy, "A Novel NLP and Machine Learning Based Text Ex- traction Approach from Online News Feed," May 2021.              [Online]. Available:             https://www.re- searchgate.net/publica-tion/351902660_A_NOVEL_NLP_AND_MACHI NE_LEARNING_BASED_TEXT_EXTRACTION_APPROACH_FROM_ONLINE_NEWS_FEED.
  6. Li, R. Y. M. (2020). Building updated research agenda by investigating papers indexed on Google Scholar: A natural language processing approach. In International Conference on Applied Human Factors and  Ergonomics.  Springer,  Cham:298-305. DOI:10.1007/978-3-030-51328-3_42
  7. Nicolas, C., Kim, J., and Chi, S. (2021). Natural lan- guage processing-based characterization of top-down communication in smart cities for enhancing citizen alignment. Sustainable Cities and Society, 66:102674.Available: https://doi.org/10.1016/j.scs.2020.102674
  8. Zhou, N. Duan, S. Liu, H.-Y. Shum, "Progress in Neural NLP: Modeling, Learning, and Reasoning," Engineering,[Online].                                         Available: https://doi.org/10.1016/j.eng.2019.12.014
  9. O. Lopatko, I. Mykytin, "Neural networks as a means of predicting the temperature value during the transient process," Measuring Equipment and Metrology: Inter- departmental Scientific and Technical Collection, vol. 77, pp. 65-70, 2016. Available: http://www.irbis- nbuv.gov.ua/cgi-bin/irbis_nbuv/cgiirbis_64.exe? I21DBN=LINK&P21DBN=UJRN&Z21ID=&S21RE F=10&S21CNR=20&S21STN=1&S21FMT=ASP_m eta&C21COM=S&2_S21P03=FILA=&2_S21STR=metrolog_2016_77_11
  10. O. Lopatko, I. Mykytyn, "Predicting the temperature of water and air flows using a neural network," Measuring Equipment and Metrology: Interdepart- mental Scientific and Technical Collection, vol. 79, no. 3, pp. 37-41, 2018. Available: https://journals. indexcopernicus.com/search/article?articleId= 2064465
  11. O. Lopatko, I. Mykytyn, "Predicting the temperature value using neural networks," All-Ukrainian Scien- tific and Practical Conference "Industrial Automa- tion in Ukraine. Education and Training," Lviv, 2016, pp. 57-58. Available: https://lpnu.ua/sites/de- fault/files/2020/dissertation/1498/areflopatkooo.pdf
  12. Z. Liu, X. Pan, "Comparison and analysis of applica- tions of ID3, CART decision tree models and neural network model in medical diagnosis and prognosis evaluation," Journal of Clinical Images and Medical Case Reports, vol. 2, 2021. DOI:10.52768/2766- 7820/1101
  13. K. Maharana, S. Mondal, B. Nemade, "A review: Data pre-processing and data augmentation techniques," [Online]. Available: https://doi.org/10.1016/j.gltp. 2022.04.020.
  14. I. A. Zamfirache, R.-E. Precup, R.-C. Roman, E. M. Petriu, "Reinforcement Learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system"DOI:10.1016/j.ins.2021.10.070
  15. C. Dann, Y. Mansour, M. Mohri, A. Sekhari, K. Sri- dharan, "Guarantees for Epsilon-Greedy Reinforce- ment Learning with Function Approximation," in Proceedings of the 39th International Conference on Machine Learning, PMLR, vol. 162, pp. 4666-4689, 2022.   Available: https://doi.org/10.48550/ arXiv.2206.09421
  16.         G. Singer, I. Cohen, "An Objective-Based En- tropy Approach for Interpretable Decision Tree Mod- els in Support of Human Resource Management: The Case of Absenteeism at Work," Faculty of Engineer- ing, Bar-Ilan University, Ramat-Gan 52900, Israel. DOI: 10.3390/e22080821