Prevention of Occupational Accidents and Occupational Diseases with Supervised Machine Learning Algorithms: Different Sector Applications

Öz

Abstract − The Occupational health and safety is a discipline that prevents work accidents and occupational diseases with a proactive method. For employee health, countries have legal responsibilities within the scope of international conventions, and employers have national responsibilities. It is obligatory for employers to carry out risk assessments, provide occupational safety trainings, carry out inspections, employ occupational safety specialists and workplace physicians, and record all work regard work safety. In countries, inspections are carried out with labor inspectors and private companies provide occupational safety services. However, it is difficult for the authorities to monitor occupational safety in large industrial facilities such as petrochemicals and refineries, where the flow of workers, materials and work equipment is too much and very fast. As workplace capacity, number of employees and material flow increase, the type and number of work accidents and occupational diseases also increase. Artificial intelligence technologies facilitate these follow-ups. The purpose of this article is to investigate the proactive prevention of the factors that cause work accidents and occupational diseases with supervised machine learning algorithms in different sectors. A literature search was conducted on sciencedirect, scopus, googlescholar databases. It has been examined what kind of algorithms are used in which sectors. According to the studies in the literature and applications in different sectors, the data collected with sensors and stored with cloud computing are fed to the relevant supervised machine learning algorithms that have been trained and tested before, and the factors that cause work accidents and occupational diseases are determined in advance. In addition to sound, image, health, location and environment data, physical parameters such as distance, level and pressure are monitored instantly with sensors. Managers are warned when a dangerous situation or behavior is detected in and threshold values are exceeded. In addition to employee and vehicle location tracking, predictive maintenance is provided by monitoring the performance of work and production vehicles. With the decrease in occupational accidents and diseases, occupational safety performance increases and costs decrease.

Anahtar Kelimeler

• Algorithm
• Work Accident
• Occupational Disease
• Algoritma
• İş Kazası
• Meslek Hastalığı

Denetimli Makine Öğrenme Algoritmalarıyla İş Kazası ve Meslek Hastalıklarının Önlenmesi: Farklı Sektör Uygulamaları

Öz

Özet- İş sağlığı ve güvenliği disiplini proaktif bir yöntemle iş kazalarını ve meslek hastalıklarını önleyen bir disiplinidir. Çalışan sağlığı için ülkelerin uluslararası sözleşmeler ve işverenlerin ulusal mevzuatta sorumlulukları bulunmaktadır. İşverenlerce risk değerlendirmesinin yapılması, iş güvenliği eğitimlerinin verilmesi, denetimlerin gerçekleştirilmesi, iş güvenliği uzmanı ve işyeri hekimi çalıştırılması ve ile ilgili tüm çalışmaların kayıt altına alınması zorunludur. Ülkelerde iş müfettişleri ile denetimler yapılmakta ve özel şirketler iş güvenliği hizmeti vermektedir. Ancak, işçi, malzeme, iş ekipmanı akışının çok hızlı ve fazla olduğu petrokimya, rafineri gibi büyük sanayi tesislerinde yetkililerin iş güvenliğini izlemesi zorlaşmaktadır. İşyeri kapasitesi, çalışan sayısı ve malzeme akışı arttıkça iş kazaları ve meslek hastalıklarının türü ve sayısı da artmaktadır. Yapay zekâ teknolojileri, bu takipleri kolaylaştırmaktadır. Bu makalenin amacı, iş kazaları ve meslek hastalıklarına neden olan etkenlerin proaktif şekilde denetimli makine öğrenme algoritmalarıyla önlenmesinin farklı sektörlerde araştırılmasıdır. Sciencedirect, scopus, googlescholar veri tabanları üzerinde liteartür taraması yapılmış, sektörlerde kullanılan algoritmalar incelenmiştir. Literatürdeki çalışmalar ve farklı sektörlerdeki uygulamalara göre, sensörlerle toplanıp bulut bilişimle saklanan veriler, daha önceden eğitilip test edilmiş ilgili denetimli makine öğrenmesi algoritmalarına beslenerek iş kazaları ve meslek hastalıklarına neden olan faktörler önceden belirlenebilmekte ve tahminler yapılabilmektedir. Ses, metin ve görüntü verilerinin yanı sıra sağlık, konum, ortam, mesafe, seviye ve basınç gibi fiziksel parametreler de sensörlerle anlık takip edilebilmektedir. Aşılan eşik değerlerde, tehlikeli bir durum veya davranış tespitinde yöneticiler uyarılmaktadır. Çalışan ve araç konum takibinin yanı sıra iş ve üretim araçlarının performansı da izlenerek öngörücü bakım sağlanabilmektedir. Azalan iş kazası ve meslek hastalıklarıyla iş güvenliği performansı artmakta, maliyetler de azalmaktadır.

Anahtar Kelimeler

• Algoritma
• İş Kazası
• Meslek Hastalığı

Kaynakça

1. Aki, Koray & Dirik, A. E. Derin Öğrenme Tabanlı Ve Pıd Kontrol Tabanlı Sürücüsüz Araç Sistemleri. Mühendislik Bilimleri Ve Tasarım Dergisi, 8(5), 306-316, 2020. Erişim adresi: https://dergipark.org.tr/en/download/article-file/1409300
2. Akşehir, Z. D., Pekel, E., Akleylek, S., Kılıç, E., & Yalçın, Oruç, İş Sağlığı Ve Güvenliği Sektöründe Bayes Ağları Uygulaması. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 12(1), 47-59. Erişim adresi: https://dergipark.org.tr/en/download/article-file/697396
3. Altunkaya, C. (2022). Sürücü davranışlarını tespit eden ve tanımlayan yeni bir algoritma ile akıllı takograf geliştirilmesi= Development of smart tachograph with a novel algorithm detecting and recognition of driver behaviour. Erişim adresi: https://acikerisim.sakarya.edu.tr/handle/20.500.12619/98431
4. Alwan, M.; Rajendran, P.J.; Kell, S.; Mack, D.; Dalal, S.; Wolfe, M.; Felder, R. A smart and passive floor-vibration based fall detector for elderly. In Proceedings of the 2006 2nd International Conference on Information & Communication Technologies, Damascus, Syria, 24–28 April 2006; pp. 1003–1007. Erişim adresi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=16845 11 Advancedsciencenews, Artificial neural networks that mimic the flexibility and computing power of the brain. Erişim adresi: https://www.advancedsciencenews.com/artificial-neural-networks-that-mimic-the-flexibility-andcomputing- power-of-the-brain/
5. Bilgin, M. (2017). Gerçek veri setlerinde klasik makine öğrenmesi yöntemlerinin performans analizi. Breast, 2(9), 683. Erişim adresi: https://ab.org.tr/ab17/bildiri/101.pdf
6. Bhavsar, H., & Ganatra, A. (2012). A comparative study of training algorithms for supervised machine learning. International Journal of Soft Computing and Engineering (IJSCE), 2(4), 2231-2307. Erişim adresi: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=18ca69ec35a0ab52922cb8a81d5041ac99005f 3a
7. Brynjolfsson, Erik, Tom Mitchell, and Daniel Rock. 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?" AEA Papers and Proceedings, 108: 43-47. Erişim adresi: https://www.aeaweb.org/articles/pdf/doi/10.1257/pandp.20181019
8. Bagnell, J. A. (2005, July). Robust supervised learning. In AAAI (pp. 714-719). Erişim adresi: https://cdn.aaai.org/AAAI/2005/AAAI05-112.pdf Botao Zhong, Xing Pan, Peter E.D. Love, Lieyun Ding, Weili Fang, Deep learning and network analysis: Classifying and visualizing accident narratives in construction, Automation in Construction, Volume 113, 2020, 103089,ISSN 0926-5805. doi: https://doi.org/10.1016/j.autcon.2020.103089

9. Barani, R.; Lakshmi, V.J. Oil well monitoring and control based on wireless sensor networks using Atmega 2560 controller. Int. J. Comput. Sci. Commun. Netw. 2013, 3, 341. Erişim adresi: https://www.semanticscholar.org/paper/Oil-Well-Monitoring-and-Control-Based-on-Wireless-Baranilakshmi/ 6dab898aecc3a91908202c08faa12b7f7866bc82
10. Bekiaris, E.; Amditis, A.; Wevers, K. Advanced driver monitoring-the awake project. In Proceedings of the 8th World Congress on ITS, Sydney, Australia, 30 September–4 October 2001. Erişim adresi: https://trid.trb.org/View/742734
11. Britton, J.W.; Frey, L.C.; Hopp, J.L.; Korb, P.; Koubeissi, M.Z.; Lievens, W.E.; Pestana-Knight, E.M.; St. Louis, E.K. Electroencephalography (EEG): An Introductory Text. and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants; American Epilepsy Society: Chicago, IL, USA, 2016. Erişim adresi: https://europepmc.org/article/nbk/nbk390354
12. Bretzner, L.; Krantz, M. Towards low-cost systems for measuring visual cues of driver fatigue and inattention in automotive applications. In Proceedings of the IEEE International Conference on Vehicular Electronics and Safety, Xi’an, Shaan’xi, China, 14–16 October 2005; pp. 161–164. Erişim adresi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=15636
13. Çelik, N. (2019). Sanayinin geleceği Endüstri 4.0 ve iş sağlığı ve güvenliği. Doktora tezi. İstanbul Medeniyet Üniversitesi, Lisansüstü Eğitim Enstitüsü, İş Sağlığı Ve Güvenliği Anabilim Dalı. İstanbul, Türkiye. Erişim adresi: https://acikbilim.yok.gov.tr/bitstream/handle/20.500.12812/116492/yokAcikBilim_10269958.pdf?sequence=- 1&isAllowed=y Chao, W. L. , 2011. Machine Learning Tutorial. Erişim adresi: https://cdn.gecacademy.cn/oa/upload/2021-09- 28%2011-54-57-Machine%20Learning%20Tutorial.pdf
14. Choudhary, R., & Gianey, H. K. (2017, December). Comprehensive review on supervised machine learning algorithms. In 2017 International Conference on Machine Learning and Data Science (MLDS) (pp. 37-43). IEEE. Erişim adresi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=83202 56 Ciortuz, L. Support Vector Machines for MicroRNA Identification, 2008. Erişim adresi: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=01652570befe1ef844cc60ec50a64ebd32dd62 d1
15. Calvo, A.; Romano, E.; Preti, C.; Schillaci, G.; Deboli, R. Upper limb disorders and hand-arm vibration risks with hand-held olive beaters. Int. J. Ind. Ergon. 2018, 65, 36–45. doi: https://doi.org/10.1016/j.ergon.2018.01.018
16. Cheng, B.; Zhang, W.; Lin, Y.; Feng, R.; Zhang, X. Driver drowsiness detection based on multisource information. Hum. Factors Ergon. Manuf. Serv. Ind. 2012, 22, 450–467. doi: https://doi.org/10.1002/hfm.20395
17. Doğan, F., & Türkoğlu, İ. (2018). Derin öğrenme algoritmalarının yaprak sınıflandırma başarımlarının Karşılaştırılması. Sakarya University Journal Of Computer And Information Sciences, 1(1), 10-21. Erişim adresi: http://saucis.sakarya.edu.tr/en/download/article-file/479189
18. De Luca, C.J. Myoelectrical manifestations of localized muscular fatigue in humans. Crit. Rev. Biomed. Eng. 1984, 11, 251–279. Erişim adresi: https://europepmc.org/article/med/6391814
19. Dwivedi, K.; Biswaranjan, K.; Sethi, A. Drowsy driver detection using representation learning. In Proceedings of the 2014 IEEE International Advance Computing Conference (IACC), Gurgaon, New Delhi, India, 21–22 February 2014; pp. 995–999. Erişim tarihi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=67794 59 Dorman P. Estimating the economic costs of occupational injuries and diseases in developing countries: essential information for decision makers. Geneva: International Labor Organization; 2012. Erişim adresi: https://www.ilo.org/wcmsp5/groups/public/---ed_protect/---protrav/--- safework/documents/publication/wcms_207690.pdf
20. EU OSHA (European Occupational Health and Safety Agency). An international comparison of the costs of occupational accidents and sickness. 2017. Erişim adresi: https://osha.europa.eu/sites/default/files/2021- 11/international_comparison-of_costs_work_related_accidents.pdf EU-OSHA, Smart Dıgıtal Monıtorıng Systems For Occupatıonal Safety And Health: Optımısıng The Uptake. Erişim adresi: https://osha.europa.eu/sites/default/files/Smart-digital-monitoring-systems-Optimising-theuptake_ en.pdf
21. Freitas, G.; Zhang, J.; Hamner, B.; Bergerman, M.; Kantor, G. A low-cost, practical localization system for agricultural vehicles. In Proceedings of the International Conference on Intelligent Robotics and Applications, Montreal, QC, Canada, 3–5 October 2012; pp. 365–375. Erişim tarihi: https://link.springer.com/chapter/10.1007/978-3-642-33503-7_36
22. Furugori, S.; Yoshizawa, N.; Iname, C.; Miura, Y. Estimation of driver fatigue by pressure distribution on seat in long term driving. Rev. Automot. Eng. 2005, 26, 53–58. Erişim adresi: https://www.researchgate.net/publication/294656833_Estimation_of_driver_fatigue_by_pressure_distribution_o n_seat_in_long_term_driving
23. Freitas, G.; Zhang, J.; Hamner, B.; Bergerman, M.; Kantor, G. A low-cost, practical localization system for agricultural vehicles. In Proceedings of the International Conference on Intelligent Robotics and Applications, Montreal, QC, Canada, 3 5 October 2012; pp. 365–375. Erişim adresi: https://link.springer.com/chapter/10.1007/978-3-642-33503-7_36
24. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press. Ghasemi, E.; Ataei, M.; Shahriar, K.; Sereshki, F.; Jalali, S.E.; Ramazanzadeh, A. Assessment of roof fall risk during retreat mining in room and pillar coal mines. Int. J. Rock Mech. Min. Sci. 2012, 54, 80–89. doi: https://doi.org/10.1016/j.ijrmms.2012.05.025
25. Gengler, A. Are you on Track? Money 2007. p. 114. Available online. Erişim adresi: https://money.cnn.com/magazines/moneymag/ moneymag_archive/2007/01/01/8397408/index.htm
26. Gibson, R.M.; Amira, A.; Ramzan, N.; Casaseca-de-la-Higuera, P.; Pervez, Z. Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic. Appl. Soft Comput. 2016, 39, 94–103. doi: https://doi.org/10.1016/j.asoc.2015.10.062
27. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554. doi: https://doi.org/10.1162/neco.2006.18.7.1527 Hernán M.A., J. Hsu, B. HealyA second chance to get causal inference right: A classification of data science tasks
28. Chance, 32 (1) (2019), pp. 42-49. doi: https://doi.org/10.1080/09332480.2019.1579578 ILO, World Statistic. Erişim adresi: https://www.ilo.org/moscow/areas-of-work/occupational-safety-andhealth/ WCMS_249278/lang--en/index.htm
29. Ingram, R. (2014). DoC Professor disputes whether computer ‘Eugene Goostman’ passed Turing Test. Imperial College London. Issa, S.F.; Patrick, K.; Thomson, S.; Rein, B. Estimating the Number of Agricultural Fatal Injuries Prevented by Agricultural Engineering Developments in the United States. Safety 2019, 5, 63. Erişim adresi: https://www.mdpi.com/2313-576X/5/4/63
30. J. Wu, J. He ve Y. Todo, " Dendritic neuron model is a universal predictor ", 2019 6. Uluslararası Sistemler ve Bilişim Konferansı (ICSAI) , 2019, s. 589-594. Erişim adresi: https://www.researchgate.net/publication/339556596_The_dendritic_neuron_model_is_a_universal_approximat or J. Alzubi, A. Nayyar, A. Kumar, Journal of Physics: Conference Series , Volume 1142 , Second National
31. Conference on Computational Intelligence (NCCI 2018), IOP Publishing Ltd. Erişim adresi: https://iopscience.iop.org/article/10.1088/1742-6596/1142/1/012012/pdf Javapoint , Artificial Neural Network Tutorial. Erişim adresi: https://www.javatpoint.com/artificial-neuralnetwork Jiang T., Jaimie L. Gradus, Anthony J. Rosellini, Supervised Machine Learning: A Brief Primer, Behavior Therapy, Volume 51, Issue 5, 2020, Pages 675-687, ISSN 0005-7894. Erişim adresi: https://pubmed.ncbi.nlm.nih.gov/32800297/
32. Jung, J.; Song, B. The possibility of wireless sensor networks for industrial pipe rack safety monitoring. In Proceedings of the 2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, USA, 6–9 January 2014; pp. 5129–5134.
33. Johnson, L. GPS in mining. Mining Magazine, 7 August 1998; 387–389.
34. Jones, K.W. Environmental Sensors. In Sensors: Micro- and Nanosensor Technology-Trends in Sensor Markets; Meixner, H., Jones, R., Eds.; Wiley-VCH: Weinheim, Germany, 1995; pp. 451–489.
35. Jian, H.; Chen, H. A portable fall detection and alerting system based on k-NN algorithm and remote medicine. China Commun. 2015, 12, 23–31. Erişim adresi: https://www.researchgate.net/publication/277562142_A_Portable_Fall_Detection_and_Alerting_System_Based _on_k-NN_Algorithm_and_Remote_Medicine Körber, M.; Cingel, A.; Zimmermann, M.; Bengler, K. Vigilance decrement and passive fatigue caused by monotony in automated driving. Procedia Manuf. 2015, 3, 2403–2409. doi: https://doi.org/10.1016/j.promfg.2015.07.499
36. Kaida, K.; Takahashi, M.; Åkerstedt, T.; Nakata, A.; Otsuka, Y.; Haratani, T.; Fukasawa, K. Validation of the Karolinska sleepiness scale against performance and EEG variables. Clin. Neurophysiol. 2006, 117, 1574–1581. doi: https://doi.org/10.1016/j.clinph.2006.03.011 Khan, S.S.; Hoey, J. Review of fall detection techniques: A data availability perspective. Med. Eng. Phys. 2017, 39, 12–22. doi: https://doi.org/10.1016/j.medengphy.2016.10.014
37. Kaynar, O., Görmez, Y., Yıldız, M., & Albayrak, A. (2016, September). Makine öğrenmesi yöntemleri ile Duygu Analizi. In International Artificial Intelligence and Data Processing Symposium (IDAP'16) (Vol. 17, No. 18, pp. 17-18). Erişim adresi: https://www.researchgate.net/profile/Yasin- Goermez/publication/311136507_Makine_Ogrenmesi_Yontemleri_ile_Duygu_Analizi_- _Sentiment_Analysis_with_Machine_Learning_Techniques/links/583eaaac08ae8e63e617b96e/Makine- Oegrenmesi-Yoentemleri-ile-Duygu-Analizi-Sentiment-Analysis-with-Machine-Learning-Techniques.pdf
38. Leigh, J., Macaskill, P., Kuosma, E., & Mandryk, J. (1999). Global burden of disease and injury due to occupational factors. Epidemiology, 626-631. Erişim adresi: https://journals.lww.com/epidem/abstract/1999/09000/global_burden_of_disease_and_injury_due_to.32.aspx
39. Li, G.; Chung, W.-Y. Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier. Sensors 2013, 13, 16494–16511. doi: https://doi.org/10.3390/s131216494
40. Lee, Y.-C.; Shariatfar, M.; Rashidi, A.; Lee, H.W. Evidence-driven sound detection for prenotification and identification of construction safety hazards and accidents. Autom. Constr. 2020, 113, 103127. doi: https://doi.org/10.1016/j.autcon.2020.103127 Lyons PG, Arora VM, Farnan JM. Adverse events and near-misses relating to intensive care unit–ward transfer: a qualitative analysis of resident perceptions. Ann Am Thorac Soc. 2016;13:570–572. doi: https://doi.org/10.1513/AnnalsATS.201512-789LE
41. Lilley R, Feyer AM, Kirk P, et al. A survey of forest workers in New Zealand: do hours of work, rest, and recovery play a role in accidents and injury? J Safety Res. 2002;33:53–71. doi: https://doi.org/10.1016/S0022- 4375(02)00003-8 Lundqvist P, Gustafsson B. Accidents and accident prevention in agriculture a review of selected studies. Int J Ind Ergonom. 1992;10:311–319. doi: https://doi.org/10.1016/0169-8141(92)90098-K
42. Muhammad, L. J., Algehyne, E. A., Usman, S. S., Ahmad, A., Chakraborty, C., & Mohammed, I. A. (2021). Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN computer science, 2(1), 1-13. Erişim adresi: https://link.springer.com/article/10.1007/s42979-020-00394- 7%23auth-L__J_-Muhammad
43. Muhammad, S., Petridis, A., Cornelius, J. F., & Hänggi, D. (2020). Letter to editor: Severe brain haemorrhage and concomitant COVID-19 Infection: A neurovascular complication of COVID-19. Brain, behavior, and immunity, 87, 150. Erişim adresi: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7199686/ Moor, James. AI MAGAZINE; WIN 2006; 27; 4; p87-p91.
44. Mardonova, M.; Choi, Y. Review of Wearable Device Technology and Its Applications to the Mining Industry. Energies 2018, 11, 547 doi: https://doi.org/10.3390/en11030547
45. Mubashir, M.; Shao, L.; Seed, L. A survey on fall detection: Principles and approaches. Neurocomputing 2013, 100, 144–152. doi: https://doi.org/10.1016/j.neucom.2011.09.037
46. Marsh P, Kendrick D. Near miss and minor injury information - can it be used to plan and evaluate injury prevention programmes? Accident Anal Prev. 2000;32:345–354. doi: https://doi.org/10.1016/S0001- 4575(99)00054-8
47. Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. b, 4, 51-62. Erişim adresi: https://www.researchgate.net/profile/Vladimir- Nasteski/publication/328146111_An_overview_of_the_supervised_machine_learning_methods/links/5c1025194 585157ac1bba147/An-overview-of-the-supervised-machine-learning-methods.pdf Number and Rate of Occupational Mining Fatalities by Year, 1983–2019. Erişim adresi: https://wwwn.cdc.gov/NIOSHMining/MMWC/Fatality/NumberAndRate
48. Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), 128-138. Erişim adresi: https://www.researchgate.net/profile/J-E-TAkinsola/ publication/318338750_Supervised_Machine_Learning_Algorithms_Classification_and_Comparison/l inks/596481dd0f7e9b819497e265/Supervised-Machine-Learning-Algorithms-Classification-and- Comparison.pdf
49. Ozigis, M.S.; Kaduk, J.D.; Jarvis, C.H. Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: A case site within the Niger Delta region of Nigeria. Environ. Sci. Pollut. Res. Int. 2019, 26, 3621–3635. Erişim adresi: https://link.springer.com/article/10.1007/s11356-018-3824-y
50. Ozcan, K.; Velipasalar, S. Wearable camera-and accelerometer-based fall detection on portable devices. IEEE Embed. Syst. Lett. 2015, 8, 6–9. Erişim adresi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=72893 90
51. Özgür, A. , 2004. Supervised and unsupervised machine learning techniques for text document categorization, Doktora Tezi-Bogaziçi Üniversitesi.
52. Özden, C., & Çiğdem, A. C. I. (2018). Makine öğrenmesi yöntemleri ile yaralanmalı trafik kazalarının analizi: Adana örneği. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(2), 266-275. Erişim adresi: https://dergipark.org.tr/en/download/article-file/465768
53. Peres R.S., X. Jia, J. Lee, K. Sun, AW Colombo ve J. Barata, "Industrial artificial intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook", IEEE Access , cilt. 8, s. 220121-220139, 2020, Erişim adresi: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9285283
54. Pishgar, M., Issa, S. F., Sietsema, M., Pratap, P., & Darabi, H. (2021). REDECA: a novel framework to review artificial intelligence and its applications in occupational safety and health. International journal of environmental research and public health, 18(13), 6705. Erişim adresi: https://www.mdpi.com/1660-4601/18/13/6705 Priyadarshy, S. IoT revolution in oil and gas industry. In Internet of Things and Data Analytics Handbook; Wiley Telecom: New York, NY, USA, 2017; pp. 513–520. doi: https://doi.org/10.1002/9781119173601.ch31
55. Parate, A.; Ganesan, D. Detecting Eating and Smoking Behaviors Using Smartwatches. In Mobile Health; Springer: Berlin/Heidelberg, Germany, 2017; pp. 175–201. Erişim adresi: https://people.cs.umass.edu/~dganesan/papers/mHealthBook-Parate17.pdf
56. Phimister JR, Oktem U, Kleindorfer PR, et al. Near-miss system analysis: phase I. Philadelphia (PA): Wharton School, Center for Risk Management and Decision Processes; 2000. doi: https://doi.org/10.1111/1539- 6924.00326 Rimminen, H.; Lindström, J.; Linnavuo, M.; Sepponen, R. Detection of falls among the elderly by a floor sensor using the electric near field. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 1475–1476. Erişim adresi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=54771 80
57. Reason JT. Managing the risks of organizational accidents. Aldershot: Ashgate; 1997.
58. Reason, J. The Contribution of Latent Human Failures to the Breakdown of Complex Systems. Philos. Trans. R. Soc. Lond. Ser. B 1990, 327, 475–484. Erişim adresi: https://royalsocietypublishing.org/doi/epdf/10.1098/rstb.1990.0090
59. Raviv, G., Fishbain, B., & Shapira, A. (2017). Analyzing risk factors in crane-related near-miss and accident reports. Safety science, 91, 192-205. doi: https://doi.org/10.1016/j.ssci.2016.08.022 Singh A., N. Thakur and A. Sharma, "A review of supervised machine learning algorithms," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 1310-1315. Erişim adresi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=77273 82
60. Suthaharan, S. (2016). Supervised learning algorithms. In Machine learning models and algorithms for big data classification (pp. 183-206). Springer, Boston, MA.
61. Shetty, S. H., Shetty, S., Singh, C., & Rao, A. (2022). Supervised Machine Learning: Algorithms and Applications. Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications, 1-16. doi: https://doi.org/10.1002/9781119821908.ch1
62. Sakhakarmi, S.; Park, J.; Cho, C. Enhanced machine learning classification accuracy for scaffolding safety using increased features. J. Constr. Eng. Manag. 2019, 145, 04018133. doi: https://doi.org/10.1061/(ASCE)CO.1943- 7862.0001601
63. Takala J., Päivi Hämäläinen, Kaija Leena Saarela, Loke Yoke Yun, Kathiresan Manickam, Tan Wee Jin, Peggy Heng, Caleb Tjong, Lim Guan Kheng, Samuel Lim & Gan Siok Lin (2014) Global Estimates of the Burden of Injury and Illness at Work in 2012, Journal of Occupational and Environmental Hygiene, 11:5, 326-337. doi: https://doi.org/10.1080/15459624.2013.863131
64. Tkach, I.; Bechar, A.; Edan, Y. Switching between collaboration levels in a human–robot target recognition system. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 2011, 41, 955–967. Erişim adresi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=57403
65. Tompa, E., Mofidi, A., van den Heuvel, S. ve ark. İş yaralanmaları ve hastalıklarının ekonomik yükü: beş Avrupa Birliği ülkesinde bir çerçeve ve uygulama. BMC Halk Sağlığı 21, 49 (2021). Erişim adresi: https://745e9234ede24d509e2ae15e4d48ef6be2b3b85c.vetisonline.com/article/10.1186/s12889-020-10050-7 Turhost, Makine Öğrenmesi. Erişim adresi: https://www.turhost.com/blog/makine-ogrenmesi-machine-learningnedir/ Towardsdatascience, Understanding Confusion Matrix. Erişim adresi: https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62
66. Uth HJ, Wiese N. Central collecting and evaluating of major accidents and near-miss-events in the Federal Republic of Germany - results, experiences, perspectives. J Hazard Mater. 2004;111:139–145. Erişim adresi: https://doi.org/10.1016/j.jhazmat.2004.02.022 V7labs, Görüntü Tanıma Tanımı, Algoritmaları ve Kullanımları. Erişim adresi: https://www.v7labs.com/blog/image-recognition-guide
67. Wang, J. Electrochemical biosensors: Towards point-of-care cancer diagnostics. Biosens. Bioelectron. 2006, 21, 1887–1892. doi: https://doi.org/10.1016/j.bios.2005.10.027 Wright L, Schaaf T. Accident versus near miss causation: a critical review of the literature, an empirical test in the UK railway domain, and their implications for other sectors. J Hazard Mater. 2004;111:105–110. doi: https://doi.org/10.1016/j.jhazmat.2004.02.049
68. Wu W, Gibb AG, Li Q. Accident precursors and near misses on construction sites: an investigative tool to derive information from accident databases. Safety Sci. 2010;48:845–858. doi: https://doi.org/10.1016/j.ssci.2010.04.009
69. Wu, W., Yang, H., Chew, D. A., Yang, S. H., Gibb, A. G., & Li, Q. (2010). Towards an autonomous real-time tracking system of near-miss accidents on construction sites. Automation in Construction, 19(2), 134-141. doi: https://doi.org/10.1016/j.autcon.2009.11.017 Webtekno. Erişim adresi: https://www.webtekno.com/turing-testi-gelisen-robotik-bilimi-nedeniyleguncelleniyor- h73504.html
70. Xin Zhang, Wang Dahu, Application of artificial intelligence algorithms in image processing, Journal of Visual Communication and Image Representation, Volume 61, 2019, Pages 42-49, ISSN 1047-3203. doi: https://doi.org/10.1016/j.jvcir.2019.03.004
71. Yu, H.; Guo, M. An efficient oil and gas pipeline monitoring systems based on wireless sensor networks. In Proceedings of the 2012 International Conference on Information Security and Intelligent Control, Yunlin, Taiwan, 14–16 August 2012; pp. 178–181. Erişim adresi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=64497 35
72. Yu, X. Approaches and principles of fall detection for elderly and patient. In Proceedings of the HealthCom 2008- 10th International Conference on E-health Networking, Applications and Services, Singapore, 7–9 July 2008; pp. 42–47. Erişim adresi: https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/stamp/stamp.jsp?tp=&arnumber=46001 07
73. Yang, K.; Ahn, C.R.; Kim, H. Validating ambulatory gait assessment technique for hazard sensing in construction environments. Autom. Constr. 2019, 98, 302–309. doi: https://doi.org/10.1016/j.autcon.2018.09.017
74. Yokoyama, K., Iijima, S., Ito, H., & Kan, M. (2013). The socio-economic impact of occupational diseases and injuries. Industrial health, 51(5), 459–461. Erişim adresi: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4202730/
75. Zhang, S.; Teizer, J.; Lee, J.-K.; Eastman, C.M.; Venugopal, M. Building Information Modeling (BIM) and Safety: Automatic Safety Checking of Construction Models and Schedules. Autom. Constr. 2013, 29, 183–195. doi: https://doi.org/10.1016/j.autcon.2012.05.006
76. Zhang, M.; Cao, T.; Zhao, X. Using Smartphones to Detect and Identify Construction Workers’ Near-Miss Falls Based on ANN. J. Constr. Eng. Manag. 2019, 145, 04018120. doi: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001582