References
- Maior CBS, Santana JMM, Nascimento LM, et al. Personal protective equipment detection in industrial facilities using camera video streaming. Saf Reliab – Safe Soc a Chang World [Internet]. CRC Press; 2018. p. 2863–2868. Available from: https://www.taylorfrancis.com/books/9781351174657/chapters/10.12019781351174664-359
- Hämäläinen P, Takala J, Kiat TB. Global estimates of occupational accidents and work-related illnesses 2017. Work Saf Heal Inst. 2017: 3–4.
- Silva JF, Jacinto C. Finding occupational accident patterns in the extractive industry using a systematic data mining approach. Reliab Eng Syst Saf [Internet]. 2012;108:108–122. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0951832012001354
- Abdat F, Leclercq S, Cuny X, et al. Extracting recurrent scenarios from narrative texts using a Bayesian network: application to serious occupational accidents with movement disturbance. Accid Anal Prev [Internet]. 2014;70:155–166. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0001457514001092
- Bavaresco R, Arruda H, Rocha E, et al. Internet of things and occupational well-being in industry 4.0: a systematic mapping study and taxonomy. Comput Ind Eng [Internet]. 2021;161:107670. Available from: https://linkinghub.elsevier.com/retrieve/pii/S036083522100574X
- Young IJB, Luz S, Lone N. A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis. Int J Med Inform. 2019;132:103971.
- Sadeghi S, Sadeghi L, Tricot N, et al. Design and application of a tool for structuring, capitalizing and making more accessible information and lessons learned from accidents involving machinery. Int J Occup Saf Ergon [Internet]. 2017;23:457–471. Available from: https://www.tandfonline.com/doi/full/10.108010803548.2016.1231785
- Bertke SJ, Meyers AR, Wurzelbacher SJ, et al. Development and evaluation of a naïve Bayesian model for coding causation of workers’ compensation claims. J Safety Res [Internet]. 2012;43:327–332. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0022437512001016
- Single JI, Schmidt J, Denecke J. Knowledge acquisition from chemical accident databases using an ontology-based method and natural language processing. Saf Sci. 2020;129:104747. doi:10.1016/j.ssci.2020.104747
- Ballesteros MF, Sumner SA, Law R, et al. Advancing injury and violence prevention through data science. J Safety Res [Internet]. 2020;73:189–193. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0022437520300256
- Wong ZSY, So HY, Kwok BSC, et al. Medication-rights detection using incident reports: a natural language processing and deep neural network approach. Health Informatics J. 2020;26:1777–1794. doi:10.1177/1460458219889798
- Agarwalla K, Nandan S, Nair VA, et al. Fake news detection using machine learning and natural language processing. Int J Recent Technol Eng. 2019;7:844–847.
- Boselli R, Cesarini M, Mercorio F, et al. Classifying online job advertisements through machine learning. Futur Gener Comput Syst. 2018;86:319–328. doi:10.1016/j.future.2018.03.035
- Hassan S-U, Aljohani NR, Idrees N, et al. Predicting literature’s early impact with sentiment analysis in Twitter. Knowledge-Based Syst [Internet]. 2020;192:105383. Available from: https://linkinghub.elsevier.com/retrieve/pii/S095070511930629X
- Bodendorf F, Merkl P, Franke J. Intelligent cost estimation by machine learning in supply management: a structured literature review. Comput Ind Eng [Internet]. 2021;160:107601. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0360835221005052
- Manochandar S, Punniyamoorthy M. Scaling feature selection method for enhancing the classification performance of support vector machines in text mining. Comput Ind Eng [Internet]. 2018;124:139–156. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0360835218303255
- Macêdo JB, das Chagas Moura M, Aichele D, et al. Identification of risk features using text mining and BERT-based models: application to an oil refinery. Process Saf Environ Prot. 2022;158:382–399. doi:10.1016/j.psep.2021.12.025
- Pan X, Wang H, You W, et al. Assessing the reliability of electronic products using customer knowledge discovery. Reliab Eng Syst Saf [Internet]. 2020;199:106925. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0951832019307732
- Zhang X, Mahadevan S, Deng X. Reliability analysis with linguistic data: an evidential network approach. Reliab Eng Syst Saf [Internet]. 2017;162:111–121. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0951832017300595
- Nanda G, Grattan KM, Chu MT, et al. Bayesian decision support for coding occupational injury data. J Safety Res [Internet]. 2016;57:71–82. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0022437516000128
- Andrzejczak C, Karwowski W, Thompson W. The identification of factors contributing to self-reported anomalies in civil aviation. Int J Occup Saf Ergon [Internet]. 2014;20:3–18. Available from: http://www.tandfonline.com/doi/full/10.108010803548.2014.11077029
- Baker H, Hallowell MR, Tixier AJP. Automatically learning construction injury precursors from text. Autom Constr. 2020;118:103145. doi:10.1016/j.autcon.2020.103145
- Muguro JK, Sasaki M, Matsushita K, et al. Trend analysis and fatality causes in Kenyan roads: a review of road traffic accident data between 2015 and 2020. Cogent Eng. 2020;7(1):179798110.1080/23311916.2020.1797981.
- Hughes P, Shipp D, Figueres-Esteban M, et al. From free-text to structured safety management: introduction of a semi-automated classification method of railway hazard reports to elements on a bow-tie diagram. Saf Sci. 2018;110:11–19. doi:10.1016/j.ssci.2018.03.011
- Ahmadpour-geshlagi R, Gillani N, Azami-Aghdash S, et al. Investigating the status of accident precursor management in East Azarbaijan Province Gas Company. Int J Occup Saf Ergon [Internet]. 2020;28(1):428–439. Available from: https://www.tandfonline.com/doi/full/10 .108010803548.2020.1770451.
- Ansaldi SM, Simeoni C, Francesco AD, et al. Extracting knowledge from near miss reports using machine-learning techniques.Venice, Italy. 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15. 2020.
- Gulijk CV, Holmes V. Verification of safety rules using NLP.2020.Venice, Italy. 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15. 2020.
- Guarav N, Kirsten V, Lehto M. Intelligent human–machine approaches for assigning groups of injury codes to accident narratives. Saf Sci. 2020;125:104585. doi:10.1016/j.ssci.2019.104585
- Maior CBS, Santana JMM, Moura MC, et al. Automated classification of injury leave based on accident description and natural language processing. 2020. Venice, Italy. 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15. 2020.
- Sarkar S, Vinay S, Raj R, et al. Application of optimized machine learning techniques for prediction of occupational accidents. Comput Oper Res [Internet]. 2019;106:210–224. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0305054818300601
- Madeira T, Melício R, Valério D, et al. Machine learning and natural language processing for prediction of human factors in aviation incident reports. Aerospace. 2021;8:47. doi:10.3390/aerospace8020047
- Zhang F, Fleyeh H, Wang X, et al. Construction site accident analysis using text mining and natural language processing techniques. Autom Constr. 2019;99:238–248. doi:10.1016/j.autcon.2018.12.016
- Kurian D, Sattari F, Lefsrud L, et al. Using machine learning and keyword analysis to analyze incidents and reduce risk in oil sands operations. Saf Sci. 2020;130:104873. doi:10.1016/j.ssci.2020.104873
- Suh Y. Sectoral patterns of accident process for occupational safety using narrative texts of OSHA database. Saf Sci. 2021;142:105363. doi:10.1016/j.ssci.2021.105363
- NBR 14280. 2000. NBR 14280:2000.Cadastro de acidente do trabalho – Procedimento e classificação.NBR 2001:94
- Guimarães MS, Araújo HHG, Lucas TC, et al. An NLP and text mining-based approach to categorize occupational accidents. Venice, Italy. 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15. 2020.
- Bird S, Klein E, Loper E. Natural language processing with python. O’Reilly Media Inc; 2009.
- Rehurek R, Sojka P. Software framework for topic modelling with large corpora.Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks. 2010.
- Li T, Mei T, Kweon IS, et al. Contextual bag-of-words for visual categorization. IEEE Trans Circuits Syst Video Technol. 2011;21:381–392. doi:10.1109/TCSVT.2010.2041828
- Havrlant L, Kreinovich V. A simple probabilistic explanation of term frequency-inverse document frequency (TF-IDF) heuristic (and variations motivated by this explanation). Int J Gen Syst. 2017;46:27–36. doi:10.1080/03081079.2017.1291635
- Lau JH, Baldwin T. An empirical evaluation of Doc2Vec with practical insights into document embedding generation. arXiv preprint . 2016: 78-86. arXiv:1607.05368.
- Yun J, Geum Y. Automated classification of patents: a topic modeling approach. Comput Ind Eng [Internet]. 2020;147:106636. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0360835220303703
- Xu W, Guo L, Liang L. Mapping the academic landscape of the renewable energy field in electrical and electronic disciplines. Appl Sci. 2020;10(8):2879.
- Dhalmahapatra K, Shingade R, Mahajan H, et al. Decision support system for safety improvement: an approach using multiple correspondence analysis, t-SNE algorithm and k-means clustering. Comput Ind Eng [Internet]. 2019;128:277–289. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0360835218306478
- El Akrouchi M, Benbrahim H, Kassou I. End-to-end LDA-based automatic weak signal detection in web news. Knowledge-Based Syst [Internet]. 2021;212:106650. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0950705120307796
- Min KB, Song SH, Min JY. Topic modeling of social networking service data on occupational accidents in Korea: latent dirichlet allocation analysis. J Med Internet Res. 2020;22:1–12.
- Pimm C, Raynal C, Tulechki N, et al. Natural Language Processing (NLP) tools for the analysis of incident and accident reports. Conference on Human-Computer Interaction in Aerospace (HCI-Aero). 2012.
- Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–2830.
- Maior CBS, Moura MC, Lins ID. Particle swarm-optimized support vector machines and pre-processing techniques for remaining useful life estimation of bearings. Eksploat i Niezawodn – Maint Reliab [Internet]. 2019;21:610–619. Available from: http://www.ein.org.pl/sites/default/files/2019-04-10.pdf
- Vapnik V, Izmailov R. Rethinking statistical learning theory: learning using statistical invariants. Mach Learn. 2019;108:381–423. doi:10.1007/s10994-018-5742-0
- Ramos PMS, Maior CBS, Moura MC, et al. Automatic drowsiness detection for safety-critical operations using ensemble models and EEG signals. Process Saf Environ Prot [Internet]. 2022;164:566–581. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0957582022005717
- Wang S-W, Yu D-L. Adaptive air-fuel ratio control with MLP network. Int J Autom Comput. 2005;2:125–133. doi:10.1007/s11633-005-0125-y
- Breiman L. Random forests. Mach Learn. 2001;45:5–32. doi:10.1023/A:1010933404324
- Moura MDC, Azevedo RV, Droguett EL, et al. Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model. Reliab Eng Syst Saf. 2016;150:136–146.
- Wei J, Zou K. EDA: easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint . 2019; arXiv:1901.11196.
- Ma E. NLP Augmenation.2019.Available from: https://github.com/makcedward/nlpaug
- Lee H, Yoon Y. Engineering Doc2Vec for automatic classification of product descriptions on O2O applications. Electron Commer Res. 2018;18:433–456. doi:10.1007/s10660-017-9268-5
- Battiato S, Farinella GM, Gallo G, et al. On-board monitoring system for road traffic safety analysis. Comput Ind [Internet]. 2018;98:208–217. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0166361517305353
- Maior CBS, Moura MJC, Santana JMM, et al. Real-time classification for autonomous drowsiness detection using eye aspect ratio. Expert Syst Appl [Internet]. 2020;158:113505. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0957417420303298
- Macedo J, Moura C, Ramos M, et al. Machine learning-based models to prioritize scenarios in a quantitative risk analysis: an application to an actual atmospheric distillation unit. J Loss Prev Process Ind. 2022;77:104797. doi:10.1016/j.jlp.2022.104797
- Tixier AJP, Hallowell MR, Rajagopalan B, et al. Application of machine learning to construction injury prediction. Autom Constr. 2016;69:102–114. doi:10.1016/j.autcon.2016.05.016
- Lombardi M, Fargnoli M, Parise G. Risk profiling from the European statistics on accidents at work (ESAW) accidents’ databases: a case study in construction sites. Int J Environ Res Public Health. 2019;16(23):474810.3390/ijerph16234748.
- Cheng C-W, Leu S-S, Cheng Y-M, et al. Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan’s construction industry. Accid Anal Prev [Internet]. 2012;48:214–222. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0001457511000959
- Devlin J, Chang M, Kenton L, et al. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint. 2018; arXiv:1810.04805.