Machine learning methods in thermometers’ data extraction and processing
| dc.citation.epage | 45 | |
| dc.citation.issue | 2 | |
| dc.citation.journalTitle | Вимірювальна техніка та метрологія | |
| dc.citation.spage | 40 | |
| dc.contributor.affiliation | Lviv Polytechnic National University | |
| dc.contributor.author | Skoropad, Pylyp | |
| dc.contributor.author | Yuras, Andrii | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.date.accessioned | 2025-03-13T08:43:21Z | |
| dc.date.created | 2024-02-27 | |
| dc.date.issued | 2024-02-27 | |
| dc.description.abstract | Research focuses on developing an all-encompassing algorithm for efficiently extracting, processing, and analyzing 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 processing for analysis of textual values, and the decision tree method for discerning patterns within the data. | |
| dc.format.extent | 40-45 | |
| dc.format.pages | 6 | |
| dc.identifier.citation | Skoropad P. Machine learning methods in thermometers’ data extraction and processing / Skoropad Pylyp, Yuras Andrii // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 85. — No 2. — P. 40–45. | |
| dc.identifier.citationen | Skoropad P. Machine learning methods in thermometers’ data extraction and processing / Skoropad Pylyp, Yuras Andrii // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 85. — No 2. — P. 40–45. | |
| dc.identifier.doi | doi.org/10.23939/istcmtm2024.02.040 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/64147 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Вимірювальна техніка та метрологія, 2 (85), 2024 | |
| dc.relation.ispartof | Measuring Equipment and Metrology, 2 (85), 2024 | |
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| dc.relation.references | [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 | |
| dc.relation.references | [15] C. Dann, Y. Mansour, M. Mohri, A. Sekhari, K. Sridharan, “Guarantees for Epsilon-Greedy Reinforcement 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 | |
| dc.relation.references | [16] G. Singer, I. Cohen, “An Objective-Based Entropy Approach for Interpretable Decision Tree Models in Support of Human Resource Management: The Case of Absenteeism at Work”, Faculty of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel. DOI: 10.3390/e22080821 | |
| dc.relation.referencesen | [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/ | |
| dc.relation.referencesen | [2] Seliverstov, Y., Seliverstov, S., Malygin, I., and Korolev, O. (2020). Traffic safety evaluation in Northwestern Federal District using sentiment analysis of Internet users’reviews. Transportation Research Procedia, 50:626–635. Available: https://doi.org/10.1016/j.trpro.2020.10.074 | |
| dc.relation.referencesen | [3] E. Suganya, S. Vijayarani, "Firefly Optimization Algorithm Based Web Scraping for Web Citation Extraction", Wireless Personal Communications, vol. 118, no. 2, May 2021. DOI: 10.1007/s11277-021-08093-z. | |
| dc.relation.referencesen | [4] Rahmatulloh, A., and Gunawan, R. (2020). Web Scraping with HTML DOM Method for Data Collection of Scien- tific Articles from Google Scholar. Indonesian Journal of Information Systems, 2(2):95–104. DOI: 10.24002/ijis.v2i2.3029 | |
| dc.relation.referencesen | [5] S. Kolli, P. Rama Krishna, P. Balakesava Reddy, "A Novel NLP and Machine Learning Based Text Extraction Approach from Online News Feed", May 2021 [Online]. Available: https://www.researchgate.net/publication/351902660_A_NOVEL_NLP_AND_MACHINE_LEARNING_BASED_TEXT_EXTRACTION_APPROACH_FROM_ONLINE_NEWS_FEED. | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [7] Nicolas, C., Kim, J., and Chi, S. (2021). Natural language 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 | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [9] O. Lopatko, I. Mykytin, "Neural networks as a means of predicting the temperature value during the transient process", Measuring Equipment and Metrology: Interdepartmental 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=&S21REF=10&S21CNR=20&S21STN=1&S21FMT=ASP_meta&P.21COM=S&2_S21P03=FILA=&2_S21STR=metrolog_2016_77_11 | |
| dc.relation.referencesen | [10] O. Lopatko, I. Mykytyn, "Predicting the temperature of water and air flows using a neural network", Measuring Equipment and Metrology: Interdepartmental Scientific and Technical Collection, vol. 79, no. 3, pp. 37–41, 2018. Available: https://journals.indexcopernicus.com/search/article?articleId=2064465 | |
| dc.relation.referencesen | [11] O. Lopatko, I. Mykytyn, "Predicting the temperature value using neural networks", All-Ukrainian Scientific and Practical Conference "Industrial Automation in Ukraine. Education and Training", Lviv, 2016, pp. 57–58. Available: https://lpnu.ua/sites/default/files/2020/dissertation/1498/areflopatkooo.pdf | |
| dc.relation.referencesen | [12] Z. Liu, X. Pan, "Comparison and analysis of applications 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 | |
| dc.relation.referencesen | [13] K. Maharana, S. Mondal, B. Nemade, "A review: Data preprocessing and data augmentation techniques" [Online]. Available: https://doi.org/10.1016/j.gltp.2022.04.020. | |
| dc.relation.referencesen | [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 | |
| dc.relation.referencesen | [15] C. Dann, Y. Mansour, M. Mohri, A. Sekhari, K. Sridharan, "Guarantees for Epsilon-Greedy Reinforcement 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 | |
| dc.relation.referencesen | [16] G. Singer, I. Cohen, "An Objective-Based Entropy Approach for Interpretable Decision Tree Models in Support of Human Resource Management: The Case of Absenteeism at Work", Faculty of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel. DOI: 10.3390/e22080821 | |
| dc.relation.uri | https://aisel.aisnet.org/pajais/vol13/iss2/2/ | |
| dc.relation.uri | https://doi.org/10.1016/j.trpro.2020.10.074 | |
| dc.relation.uri | https://www.researchgate.net/publication/351902660_A_NOVEL_NLP_AND_MACHINE_LEARNING_BASED_TEXT_EXTRACTION_APPROACH_FROM_ONLINE_NEWS_FEED | |
| dc.relation.uri | https://doi.org/10.1016/j.scs.2020.102674 | |
| dc.relation.uri | https://doi.org/10.1016/j.eng.2019.12.014 | |
| dc.relation.uri | http://www.irbis-nbuv.gov.ua/cgi-bin/irbis_nbuv/cgiirbis_64.exe?I21DBN=LINK&P21DBN=UJRN&Z21ID=&S21REF=10&S21CNR=20&S21STN=1&S21FMT=ASP_meta&C21COM=S&2_S21P03=FILA=&2_S21STR=metrolog_2016_77_11 | |
| dc.relation.uri | https://journals.indexcopernicus.com/search/article?articleId=2064465 | |
| dc.relation.uri | https://lpnu.ua/sites/default/files/2020/dissertation/1498/areflopatkooo.pdf | |
| dc.relation.uri | https://doi.org/10.1016/j.gltp.2022.04.020 | |
| dc.relation.uri | https://doi.org/10.48550/arXiv.2206.09421 | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2024 | |
| dc.subject | Artificial intelligence | |
| dc.subject | Machine learning | |
| dc.subject | Prediction | |
| dc.subject | Temperature measurement | |
| dc.subject | Thermometer | |
| dc.title | Machine learning methods in thermometers’ data extraction and processing | |
| dc.type | Article |
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