| dc.relation | Abma, F. I., Brouwer, S., de Vries, H. J., Arends, I., Robroek, S. J., Cuijpers, M. P., van der Wilt, G. J., Bültmann, U., & van der Klink, J. J. (2016). The capability set for work: Development and validation of a new questionnaire. Scandinavian Journal of Work, Environment & Health, 42(1), 34–42. https://doi.org/10.5271/sjweh.3532
Adamson, A. W., & Gast, A. P. (1967). Arthur W. Adamson, Alice P. Gast—Physical chemistry of surfaces. New York: Interscience Publishers, 150(180).
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49. https://doi.org/10.1016/j.tele.2019.01.007
Aliabadi, M. (2015). Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach. Int Arch Occup Environ Health, 88(6), 779–787.
Alpaydin, E. (2020). Introduction to Machine Learning (Fourth). The MIT Press.
Al-Shargie, F. (2019). Multilevel Assessment of Mental Stress using SVM with ECOC: An EEG Approach [Preprint]. engrXiv. https://doi.org/10.31224/osf.io/7v9ks
Anand, R. S., & Kumar, V. (2018). EEG-metric based mental stress detection. Network Biology, 8(1), 25–34.
Andayani, U., Nababan, E. B., Siregar, B., Muchtar, M. A., Nasution, T. H., & Siregar, I. (2017). Optimization backpropagation algorithm based on Nguyen-Widrom adaptive weight and adaptive learning rate. 2017 4th International Conference on Industrial Engineering and Applications (ICIEA), 363–367. https://doi.org/10.1109/IEA.2017.7939239
Ansoleaga, E., Portales, U. D., Toro, J. P., & Portales, U. D. (2014). Mental Health and Nature of Work: When the Emotional Demands Becomes Inevitable. Revista Psicologia: Organizações e Trabalh, 14(2), 180–189.
Baradaran, V., Ghadami, S., & Malihi, S. E. (2008). A Multi Objective approach for selecting solutions to improve job satisfaction an empirical case analysis. 2008 IEEE International Conference on Industrial Engineering and Engineering Management, 1945–1948. https://doi.org/10.1109/IEEM.2008.4738211
Bauer, G. F., & Hämmig, O. (2014). Bridging Occupational, Organizational and Public Health. Springer Netherlands. https://doi.org/10.1007/978-94-007-5640-3
Belkin, M., Hsu, D., Ma, S., & Mandal, S. (2019). Reconciling modern machine learning practice and the bias-variance trade-off. ArXiv:1812.11118 [Cs, Stat]. http://arxiv.org/abs/1812.11118
Benítez, R., Escudero, G., & Kanaan, S. (2014). Inteligencia artificial avanzada (2da ed.). UOC.
Berens, J., Schneider, K., Görtz, S., Oster, S., & Burghoff, J. (2019). Early Detection of Students at Risk—Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods. Journal of Educational Data Mining, 11(3), 1–41.
Berry, M. V. (1971). The molecular mechanism of surface tension. Physics Education, 6(2). https://doi.org/10.1088/0031-9120/6/2/001
Bitalino. (2017). BITalino [OpenSignals]. http://www.bitalino.com/
Botero Alvarez, C. C. (2013). Riesgo psicosocial intralaboral y “burnout” en docentes universitarios de algunos países latinoamericanos. Cuadernos de Administración, 28(48), 118–133. https://doi.org/10.25100/cdea.v28i48.460
British Standards Institution (BSI). (2011). PAS1010: Guidance on the management of psychosocial risks in the workplace. BSI.
Brown, R. C. (1947). The fundamental concepts concerning surface tension and capillarity. Proceedings of the Physical Society, 59(3), 429–448. https://doi.org/10.1088/0959-5309/59/3/310
Bruhn, A., & Frick, K. (2011). Why it was so difficult to develop new methods to inspect work organization and psychosocial risks in Sweden. Safety Science, 49(4), 575–581. https://doi.org/10.1016/j.ssci.2010.07.011
Burke, R. J., & Pignata, S. (2020). Handbook of Research on Stress and Well-Being in the Public Sector (First, Vol. 1). Edward Elgar.
Canadian Standards Association. (2013). Psychological health and safety in the workplace—Prevention, promotion, and guidance to staged implementation. Bureau de normalisation du Québec.
Cardenas Gonzalo, D. (2015). Influencia de los síntomas físicos sobre el estrés laboral y familiar. Dyna Management, 3(3), 248–262. https://doi.org/10.6036/MN7831
Chakraborty, U. (2020). Artificial Intelligence for All: Transforming Every Aspect of Our Life (1a ed.). Bpb publications.
Chen, L., Pan, X., Zhang, Y.-H., Liu, M., Huang, T., & Cai, Y.-D. (2019). Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network. Computational and Structural Biotechnology Journal, 17, 49–60. https://doi.org/10.1016/j.csbj.2018.12.002
Cheng, J., Wang, L., & Xiong, Y. (2019). Ensemble of cuckoo search variants. Computers & Industrial Engineering, 135, 299–313. https://doi.org/10.1016/j.cie.2019.06.015
Cox, T., Griffiths, A., Rial-González, E., & Agencia Europea para la Seguridad y la Salud en el Trabajo. (2006). Investigación sobre el estrés relacionado con el trabajo. Oficina de publicaciones officiales de las Comunidades europeas.
Cuixart, C. N. (2012). NTP:926 Factores psicosociales: Metodología de evaluación. Centro Nacional de Condiciones del Trabajo. https://www.insst.es/documents/94886/326879/926w.pdf/cdecbd91-70e8-4cac-b353-9ea39340e699
Czaja, S. J., & Nair, S. N. (2006). Human Factors Engineering and Systems Design. En G. Salvendy (Ed.), Handbook of Human Factors and Ergonomics (pp. 32–49). John Wiley & Sons, Inc. https://doi.org/10.1002/0470048204.ch2
Darvishi, E., Khotanlou, H., Khoubi, J., Giahi, O., & Mahdavi, N. (2017). Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers. Journal of Manipulative and Physiological Therapeutics, 40(7), 486–493. https://doi.org/10.1016/j.jmpt.2017.03.012
Dediu, V., Leka, S., & Jain, A. (2018). Job demands, job resources and innovative work behaviour: A European Union study. European Journal of Work and Organizational Psychology, 27(3), 310–323. https://doi.org/10.1080/1359432X.2018.1444604
Dimsdale, J. E. (2019). Trastorno de síntomas somáticos [Profesional]. Manual MSD. https://www.msdmanuals.com/es/professional/trastornos-psiqui%C3%A1tricos/trastornos-de-s%C3%ADntomas-som%C3%A1ticos-y-relacionados/trastorno-de-s%C3%ADntomas-som%C3%A1ticos
Dollard, M. F., & Neser, D. Y. (2013). Worker health is good for the economy: Union density and psychosocial safety climate as determinants of country differences in worker health and productivity in 31 European countries. Social Science & Medicine, 92, 114–123. https://doi.org/10.1016/j.socscimed.2013.04.028
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (Second). John Wiley & Sons.
El Yafrani, M., & Ahiod, B. (2018). Efficiently solving the Traveling Thief Problem using hill climbing and simulated annealing. Information Sciences, 432, 231–244. https://doi.org/10.1016/j.ins.2017.12.011
El-Batawi, M. A. (1988). Psychosocial health problems of workers in developing countries. En Psychosocial factors at work and their relationship with health (1a ed., Vol. 1, pp. 15–20). World Health Organization.
Elhoone, H., Zhang, T., Anwar, M., & Desai, S. (2020). Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0. International Journal of Production Research, 58(9), 2841–2861. https://doi.org/10.1080/00207543.2019.1671627
Esling, P., & Devis, N. (2020). Creativity in the era of artificial intelligence. ArXiv:2008.05959 [Cs]. http://arxiv.org/abs/2008.05959
Espinoza, L. M., & Villalobos, D. G. (2015). Prevalencia de riesgo psicosocial en un grupo de docentesy directivos del distrito capital [Tesis de Maestría]. Universidad del Rosario.
European agency for safety and health and work. (2011). Annual_report_2010_summary_es.pdf. EU-Osha 2010. https://osha.europa.eu/es/publications/annual_report/ar_summary_2010/view
European Agency for Safety and Health at Work. (2012). Drivers and barriers for psychosocial risk management: An analysis of the findings of the European Survey of Enterprises on New and Emerging Risks (ESENER). report. Publ. Office of the Europ. Union.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Fecode. (2015). Acta-de-acuerdos-fecode_MEN-Mayo-7-2015. https://fecode.edu.co/index.php/actas-de-acuerdo
Fernandes de Mello, R., & Antonelli Ponti, M. (2018). Machine Learning: A Practical Approach on the Statistical Learning Theory. Springer International Publishing. https://doi.org/10.1007/978-3-319-94989-5
Fowkes, F. M. (1961). Determination of interfacial tensions, contact angles, and dispersion forces in surfaces. 66, 1.
Frone, M. R., & Tidwell, M.-C. O. (2015). The meaning and measurement of work fatigue: Development and evaluation of the Three-Dimensional Work Fatigue Inventory (3D-WFI). Journal of Occupational Health Psychology, 20(3), 273–288. https://doi.org/10.1037/a0038700
Galatzer-Levy, I. R., Karstoft, K.-I., Statnikov, A., & Shalev, A. Y. (2014). Quantitative forecasting of PTSD from early trauma responses: A Machine Learning application. Journal of Psychiatric Research, 59, 68–76. https://doi.org/10.1016/j.jpsychires.2014.08.017
García Castro, C. M., & Muñoz Sánchez, A. I. (2013). Health and work of district schools faculty of the district one of Bogotá. AVANCES EN ENFERMERÍA, 13.
García-Herrero, S., Lopez-Garcia, J. R., Herrera, S., Fontaneda, I., Báscones, S. M., & Mariscal, M. A. (2017). The Influence of Recognition and Social Support on European Health Professionals’ Occupational Stress: A Demands-Control-Social Support-Recognition Bayesian Network Model. BioMed Research International, 2017, 1–14. https://doi.org/10.1155/2017/4673047
González Fuentes, A., Busto Serrano, N. M., Sánchez Lasheras, F., Fidalgo Valverde, G., & Suárez Sánchez, A. (2020). Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms. Energies, 13(10), 2475. https://doi.org/10.3390/en13102475
González Herranz, R. (2016). Sistemas de ayuda al diagnóstico y a la terapia funcional en enfermedades neurodegenerativas [Disertación doctoral]. Universidad Politécnica de Madrid.
Hadi, W., El-Khalili, N., AlNashashibi, M., Issa, G., & AlBanna, A. A. (2019). Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan. Computers in Biology and Medicine, 114, 103474. https://doi.org/10.1016/j.compbiomed.2019.103474
Hallner, D., & Hasenbring, M. (2004). Classification of psychosocial risk factors (yellow flags) for the development of chronic low back and leg pain using artificial neural network. Neuroscience Letters, 361(1–3), 151–154. https://doi.org/10.1016/j.neulet.2003.12.107
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3rd ed). Morgan Kaufmann Publishers.
Hegde, J., & Rokseth, B. (2020). Applications of machine learning methods for engineering risk assessment – A review. Safety Science, 122, 104492. https://doi.org/10.1016/j.ssci.2019.09.015
Hernández, M. G. G., Martínez, R. M. R., Maldonado-Macias, A. A., & Leal, J. S. (Eds.). (2020). Work Stress and Psychosocial Factors in the Manufacturing Industry: A Literature Review (First, Vol. 1). IGI Global. https://doi.org/10.4018/978-1-7998-1052-0
Hesser, D. F., & Markert, B. (2019). Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks. Manufacturing Letters, 19, 1–4. https://doi.org/10.1016/j.mfglet.2018.11.001
Holte, R. C. (1993). Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. Machine Learning, 11, 63–91.
Houtman, I., van Zwieten, M., Leka, S., Jain, A., & de Vroome, E. (2020). Social Dialogue and Psychosocial Risk Management: Added Value of Manager and Employee Representative Agreement in Risk Perception and Awareness. International Journal of Environmental Research and Public Health, 17(10), 3672. https://doi.org/10.3390/ijerph17103672
Iavicoli, S., Leka, S., Jain, A., Persechino, B., Rondinone, B. M., Ronchetti, M., & Valenti, A. (2014). Hard and soft law approaches to addressing psychosocial risks in Europe: Lessons learned in the development of the Italian approach. Journal of Risk Research, 17(7), 855–869. https://doi.org/10.1080/13669877.2013.822911
Jacobson, S. H., & Yücesan, E. (2004). Analyzing the Performance of Generalized Hill Climbing Algorithms. Journal of Heuristics, 10(4), 387–405. https://doi.org/10.1023/B:HEUR.0000034712.48917.a9
Jain, A., Dediu, V., Zwetsloot, G., & Leka, S. (2017). Workplace Innovation and Wellbeing at Work: A Review of Evidence and Future Research Agenda. En P. Oeij, D. Rus, & F. D. Pot (Eds.), Workplace Innovation (pp. 111–128). Springer International Publishing. https://doi.org/10.1007/978-3-319-56333-6_8
Jasper, J. J. (1972). The Surface Tension of Pure Liquid Compounds. 1(4), 841–1010. https://doi.org/10.1063/1.3253106
Jebelli, H., Khalili, M. M., & Lee, S. (2019). Mobile EEG-Based Workers’ Stress Recognition by Applying Deep Neural Network. En I. Mutis & T. Hartmann (Eds.), Advances in Informatics and Computing in Civil and Construction Engineering (pp. 173–180). Springer International Publishing. https://doi.org/10.1007/978-3-030-00220-6_21
Johnson, J. V., & Hall, E. M. (1988). Job strain, work place social support, and cardiovascular disease: A cross-sectional study of a random sample of the Swedish working population. American Journal of Public Health, 78(10), 1336–1342.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Jung, J. S., Park, S. J., Kim, E. Y., Na, K.-S., Kim, Y. J., & Kim, K. G. (2019). Prediction models for high risk of suicide in Korean adolescents using machine learning techniques. PLOS ONE, 14(6), e0217639. https://doi.org/10.1371/journal.pone.0217639
Karasek, R. A. (1979). Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign. Administrative Science Quarterly, 24(2), 285. https://doi.org/10.2307/2392498
Kataoka, H., Kano, H., Yoshida, H., Saijo, A., Yasuda, M., & Osumi, M. (1998). Development of a skin temperature measuring system for non-contact stress evaluation. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286), 2, 940–943. https://doi.org/10.1109/IEMBS.1998.745598
Kato, E. R. R., Aranha, G. D. de A., & Tsunaki, R. H. (2018). A new approach to solve the flexible job shop problem based on a hybrid particle swarm optimization and Random-Restart Hill Climbing. Computers & Industrial Engineering, 125, 178–189. https://doi.org/10.1016/j.cie.2018.08.022
Kohonen, T. (1989). Self-Organization and Associative Memory (Vol. 8). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-88163-3
Kortum, E., & Leka, S. (2014). Tackling psychosocial risks and work-related stress in developing countries: The need for a multilevel intervention framework. International Journal of Stress Management, 21(1), 7–26. https://doi.org/10.1037/a0035033
Kortum, E., Leka, S., & Cox, T. (2010). Psychosocial risks and work-related stress in developing countries: Health impact, priorities, barriers and solutions. International Journal of Occupational Medicine and Environmental Health, 23(3). https://doi.org/10.2478/v10001-010-0024-5
Kubat, M. (2017). An Introduction to Machine Learning. Springer International Publishing. https://doi.org/10.1007/978-3-319-63913-0
Lahmiri, S., & Bekiros, S. (2019). Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design. Quantitative Finance, 19(9), 1569–1577. https://doi.org/10.1080/14697688.2019.1588468
Lal, A., Dhinesh, R., Harish, C. S., Moorthy, D. M. J., & Madhumathi, R. (2018). Stress Detection using Machine Learning. International Journal for Trends in Engineering & Technology, 28(1), 37–39.
Langenhan, M. K., Leka, S., & Jain, A. (2013). Psychosocial Risks: Is Risk Management Strategic Enough in Business and Policy Making? Safety and Health at Work, 4(2), 87–94. https://doi.org/10.1016/j.shaw.2013.04.003
Larrabee, J. H., Janney, M. A., Ostrow, C. L., Withrow, M. L., Hobbs, G. R., & Burant, C. (2003). Predicting Registered Nurse Job Satisfaction and Intent to Leave: JONA: The Journal of Nursing Administration, 33(5), 271–283. https://doi.org/10.1097/00005110-200305000-00003
Law, K.-Y., & Zhao, H. (2016). Surface wetting: Characterization, contact angle, and fundamentals. (2a ed.). Springer International Publishing.
Lawrence, S. A., Jordan, P. J., & Callan, V. J. (2015). Initial validation of the support mobilization for work stressors inventory. Australian Journal of Management, 40(4), 587–612. https://doi.org/10.1177/0312896214528186
Leka, S, & Cox, T. (2011). Guide Prima-EF. Guidance on the European Framework for Psychosocial Risk Management. A Resource for Employers and Workers Representatives. WHO.
Leka, Stavroula, Jain, A., & Lerouge, L. (2017). Work-Related Psychosocial Risks: Key Definitions and an Overview of the Policy Context in Europe. En L. Lerouge (Ed.), Psychosocial Risks in Labour and Social Security Law (pp. 1–12). Springer International Publishing. https://doi.org/10.1007/978-3-319-63065-6_1
Leka, Stavroula, Van Wassenhove, W., & Jain, A. (2015). Is psychosocial risk prevention possible? Deconstructing common presumptions. Safety Science, 71, 61–67. https://doi.org/10.1016/j.ssci.2014.03.014
Li, B., Mendenhall, J., & Meiler, J. (2019). Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking. Computational and Structural Biotechnology Journal, 17, 699–711. https://doi.org/10.1016/j.csbj.2019.05.005
Li, L., Cen, Z.-Y., Tseng, M.-L., Shen, Q., & Ali, M. H. (2021). Improving short-term wind power prediction using hybrid improved cuckoo search arithmetic—Support vector regression machine. Journal of Cleaner Production, 279, 123739. https://doi.org/10.1016/j.jclepro.2020.123739
Li, Z., Sun, D., Zhu, R., & Lin, Z. (2017). Detecting event-related changes in organizational networks using optimized neural network models. PLOS ONE, 12(11), e0188733. https://doi.org/10.1371/journal.pone.0188733
Lippel, K., & Quinlan, M. (2011). Regulation of psychosocial risk factors at work: An international overview. Safety Science, 49(4), 543–546. https://doi.org/10.1016/j.ssci.2010.09.015
Lotfan, S., Shahyad, S., Khosrowabadi, R., Mohammadi, A., & Hatef, B. (2019). Support vector machine classification of brain states exposed to social stress test using EEG-based brain network measures. Biocybernetics and Biomedical Engineering, 39(1), 199–213. https://doi.org/10.1016/j.bbe.2018.10.008
Loy-Benitez, J., Heo, S., & Yoo, C. (2020). Soft sensor validation for monitoring and resilient control of sequential subway indoor air quality through memory-gated recurrent neural networks-based autoencoders. Control Engineering Practice, 97, 104330. https://doi.org/10.1016/j.conengprac.2020.104330
Macleod, D. B. (1923). On a relation between surface tension and density. Transactions of the Faraday Society, 19(July), 38. https://doi.org/10.1039/tf9231900038
Maqsoom, A., Mughees, A., Zahoor, H., Nawaz, A., & Mazher, K. M. (2020). Extrinsic psychosocial stressors and workers’ productivity: Impact of employee age and industry experience. Applied Economics, 52(26), 2807–2820. https://doi.org/10.1080/00036846.2019.1696936
Márquez Gómez, M. (2020). Prediction of work-related musculoskeletal discomfort in the meat processing industry using statistical models. International Journal of Industrial Ergonomics, 75, 102876. https://doi.org/10.1016/j.ergon.2019.102876
Maynard, M. (2020). Maynard, Morgan—Neural Networks_ Introduction to Artificial Neurons, Backpropagation and Multilayer Feedforward Neural Networks with Real-World Applications (Advance. Maynard.
McClelland, J. L., & Rumelhart, E. (eds. ). (1986). Parallel Distributed Processing (Vol. 2). MIT Press.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259
Mehanović, D., Mašetić, Z., & Kečo, D. (2020). Prediction of Heart Diseases Using Majority Voting Ensemble Method. En A. Badnjevic, R. Škrbić, & L. Gurbeta Pokvić (Eds.), CMBEBIH 2019 (Vol. 73, pp. 491–498). Springer International Publishing. https://doi.org/10.1007/978-3-030-17971-7_73
Memish, K., Martin, A., Bartlett, L., Dawkins, S., & Sanderson, K. (2017). Workplace mental health: An international review of guidelines. Preventive Medicine, 101, 213–222. https://doi.org/10.1016/j.ypmed.2017.03.017
Meneses, A. A. de M., da Silva, P. V., Nast, F. N., Araujo, L. M., & Schirru, R. (2020). Application of Cuckoo Search algorithm to Loading Pattern Optimization problems. Annals of Nuclear Energy, 139, 107214. https://doi.org/10.1016/j.anucene.2019.107214
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems, 3111–3119.
Ministerio de Educación. (2015). Decreto_1655_de_2015.pdf. Min_educación.
Ministerio de la Protección Social. (2008). Resolución número 002646 de 2008. http://www.saludcapital.gov.co/Documentos%20Salud%20Ocupacional/RESOL.%202646%20DE%202008%20RIESGO%20PSICOSOCIAL.pdf
Ministerio del Trabajo. (2014). Decreto_1477_del_5_de_agosto_de_2014. https://www.mintrabajo.gov.co/documents/20147/36482/decreto_1477_del_5_de_agosto_de_2014.pdf/b526be63-28ee-8a0d-9014-8b5d7b299500
Ministerio del trabajo. (2015). Guía técnica general para la Promoción, prevención e intervención de los factorespsicosociales y sus efectos en población trabajadora. Javegraf. http://fondoriesgoslaborales.gov.co/wp-content/uploads/2018/09/01-Guia-tecnica-general.pdf
Ministerio del Trabajo, & Organización Iberoamericana de Seguridad Social, Oiss. (2013). II_ENCUESTA_NACIONAL_CONDICIONES_SST_COLOMBIA_2013.pdf (Oiss).
Moncada, S., Utzet, M., Molinero, E., Llorens, C., Moreno, N., Galtés, A., & Navarro, A. (2014). The copenhagen psychosocial questionnaire II (COPSOQ II) in Spain-A tool for psychosocial risk assessment at the workplace: Copenhagen Psychosocial Questionnaire II in Spain. American Journal of Industrial Medicine, 57(1), 97–107. https://doi.org/10.1002/ajim.22238
Morrow, A. S., Campos Vega, A. D., Zhao, X., & Liriano, M. M. (2020). Leveraging Machine Learning to Identify Predictors of Receiving Psychosocial Treatment for Attention Deficit/Hyperactivity Disorder. Administration and Policy in Mental Health and Mental Health Services Research, 47(4). https://doi.org/10.1007/s10488-020-01045-y
Mosquera Navarro, R. (2019). Psychosocial risk level on teachers schools V1. IEEE Dataport, 1(1). http://dx.doi.org/10.21227/jdzw-7e82
Mosquera Navarro, R., Gómez, O. D. C., Osorio, L. P., & García, A. C. (2018). Classification system for the predicting of psychosocial risk level in public-school teachers based on Artificial Intelligence. XVIII Conferencia de La Asociación Española Para La Inteligencia Artificial (CAEPIA), 1367–1372.
Mosquera, R, Castrillón Gómez, O. D., & Parra-Osorio, L. (2019). Algorithm based on Physical Surface Tension for the Prediction of Psychosocial-risk Level in Public School Teachers. [Source Code]. Doi.org/10.24433/CO.4268666.v1.1. (1.1) [Matlab]. Code Ocean.
Mosquera, Rodolfo. (2018). Psychosocial Risk Level Colombian Teachers School Repository 2016-2017. Zenodo, 2. https://doi.org/10.5281/zenodo.1298610.
Mosquera, Rodolfo, Castrillón, O. D., & Parra, L. (2018a). Support Vector Machines, Naïve Bayes Classifier and Genetic Algorithms for the Prediction of Psychosocial Risks in Teachers of Colombian Public Schools. Información tecnológica, 29(6), 153–162. https://doi.org/10.4067/S0718-07642018000600153
Mosquera, Rodolfo, Castrillón, O. D., & Parra, L. (2018b). Predicción de Riesgos Psicosociales en Docentes de Colegios Públicos Colombianos utilizando Técnicas de Inteligencia Artificial. Información tecnológica, 29(4), 267–280. https://doi.org/10.4067/S0718-07642018000400267
Mosquera, Rodolfo, Castrillón, O. D., & Parra, L. (2018c). Máquinas de Soporte Vectorial, Clasificador Naïve Bayes y Algoritmos Genéticos para la Predicción de Riesgos Psicosociales en Docentes de Colegios Públicos Colombianos. Información tecnológica, 29(6), 153–162. https://doi.org/10.4067/S0718-07642018000600153
Mosquera, Rodolfo, Castrillón, O. D., & Parra-Osorio, L. (2019). Aplicación del modelo hibrido k-nearest neighbors- Support Vector Machine para la predicción del riesgo psicosocial en docentes de colegios públicos colombianos. Proceedings of the 17th Latin American and Caribbean Conference for Engineering and Technology, 1, 5.
Mosquera, Rodolfo, Gómez, O. D. C., Osorio, L. P., & García, A. C. (2018). Classification system for the predicting of psychosocial risk level in public-school teachers based on Artificial Intelligence. Proceedings of XVIII Conferencia de La Asociación Española Para La Inteligencia Artificial, 1, 1367–1372.
Mosquera, Rodolfo, Parra-Osorio, L., & Castrillón, O. D. (2016). Metodología para la Predicción del Grado de Riesgo Psicosocial en Docentes de Colegios Colombianos utilizando Técnicas de Minería de Datos. Información tecnológica, 27(6), 259–272. https://doi.org/10.4067/S0718-07642016000600026
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Interpretable machine learning: Definitions, methods, and applications. Proceedings of the National Academy of Sciences, 116(44), 22071–22080. https://doi.org/10.1073/pnas.1900654116
Navascués, G. (1979). Liquid surfaces: Theory of surface tension. Reports on Progress in Physics, 42(7), 59. https://doi.org/10.1088/0034-4885/42/7/002
Neal, B., Mittal, S., Baratin, A., Tantia, V., Scicluna, M., Lacoste-Julien, S., & Mitliagkas, I. (2019). A Modern Take on the Bias-Variance Tradeoff in Neural Networks. ArXiv:1810.08591 [Cs, Stat]. http://arxiv.org/abs/1810.08591
Niedhammer, I., & Chastang, J.-F. (2013). Psychosocial work factors and sickness absence in 31 countries in Europe. European Journal of Public Health, 23(4), 622–628.
Olaya Arévalo, C. (2015). Síndrome de burnout o síndrome de agotamiento profesional (sap) en el trabajo de los docentes distritales de la localidad de usme [Tesis de Maestría].
Pavelka, A., & Prochazka, A. (2004). Algorithms for initialization of neural network weights. Proceedings of the 12th Annual Conference, 453–459.
Posada Quintero, J. I., Molano Vergara, P. N., Parra Hernández, R. M., Brito Osorio, F. Y., & Rubio Orozco, E. A. (2019). Prevalencia del Síndrome de Burnout en docentes: Factores asociados al estatuto de vinculación laboral en Colombia. Revista Interamericana de Psicología Ocupacional, 37(2), 119–133. https://doi.org/10.21772/ripo.v37n2a04
Posada-Quintero, H. F., Molano-Vergara, P. N., Parra-Hernández, R. M., & Posada-Quintero, J. I. (2020). Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation. Social Sciences, 9(3), 30. https://doi.org/10.3390/socsci9030030
Priya, A., Garg, S., & Tigga, N. P. (2020). Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms. Procedia Computer Science, 167, 1258–1267. https://doi.org/10.1016/j.procs.2020.03.442
Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R. (2019). Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology, 6, 237428951987308. https://doi.org/10.1177/2374289519873088
Restrepo, G. C. (2011). Factores de riesgo psicosocial que afectan el ejercicio pedagógico de los docentes del núcleo educativo no 8 de la ciudadela cuba de pereira [Tesis de Maestría]. Católica de Pereira.
Robnik-Sˇ, M., & Kononenko, I. (2003). Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning, 53(1–2), 23–69. https://doi.org/10.1023/A:1025667309714
Robnik-Sˇ, M., & Kononenko, I. (2013). Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning, 53, 23–69.
Rodriguez, L. (2007). Probabilidad y estadística básica para ingenieros. Escuela Superior Politécnica del LitoralInstituto de Ciencias Matemáticas.
Rodríguez-Arce, J., Lara-Flores, L., Portillo-Rodríguez, O., & Martínez-Méndez, R. (2020). Towards an anxiety and stress recognition system for academic environments based on physiological features. Computer Methods and Programs in Biomedicine, 190, 105408. https://doi.org/10.1016/j.cmpb.2020.105408
Rosero, A. C. T., & Álvarez, C. C. B. (2012). Riesgos psicosociales intralaborales en docencia. Revista Iberoamericana de Psicologia: Ciencia y Tecnología., 5(2), 95–106.
Rosset, S., & Tibshirani, R. J. (2020). From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation. Journal of the American Statistical Association, 115(529), 138–151. https://doi.org/10.1080/01621459.2018.1424632
Rumelhart, D. E., & MacClelland, J. L. (Eds). (1986). Parallel Distributed Processing (Vol. 1). MIT Press.
Salal, Y. K., Abdullaev, S. M., & Kumar, M. (2019). Educational Data Mining: Student Performance Prediction in Academic. International Journal of Engineeringand Advanced Technology, 8(4), 54–59.
Santos, M. (2011). Un Enfoque Aplicado del Control Inteligente. Revista Iberoamericana de Automática e Informática Industrial RIAI, 8(4), 283–296. https://doi.org/10.1016/j.riai.2011.09.016
Sauter, S. L., & Murphy, L. R. (1984). Factores psicosociales y de organización. Enciclopedia de salud y seguridad en el trabajo, 34.2-34.75.
Schlkopf, B., Smola, A. J., & Bach, F. (2018). Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press.
Schweidtmann, A. M., & Mitsos, A. (2019). Deterministic Global Optimization with Artificial Neural Networks Embedded. Journal of Optimization Theory and Applications, 180(3), 925–948. https://doi.org/10.1007/s10957-018-1396-0
Siegrist, J. (1996). Adverse Health Effects of High-Effort/Low-Reward Conditions. Journal of Occupational Health Psychology, 1(1), 27–41.
Silva Gutiérrez, B. N., & Vicente Flores, R. (2015). Académicos universitarios y salud ocupacional. Importancia de los factores psicosociales y variables sociodemográficas, el caso de un centro universitario regional de la Universidad de Guadalajara, México. Población y Desarrollo - Argonautas y Caminantes, 10, 33–43. https://doi.org/10.5377/pdac.v10i0.1736
Sriramprakash, S., Prasanna, V. D., & Murthy, O. V. R. (2017). Stress Detection in Working People. Procedia Computer Science, 115, 359–366. https://doi.org/10.1016/j.procs.2017.09.090
Suárez Sánchez, A., Riesgo Fernández, P., Sánchez Lasheras, F., de Cos Juez, F. J., & García Nieto, P. J. (2011). Prediction of work-related accidents according to working conditions using support vector machines. Applied Mathematics and Computation, 218(7), 3539–3552. https://doi.org/10.1016/j.amc.2011.08.100
Subhani, A. R., Mumtaz, W., Saad, M. N. B. M., Kamel, N., & Malik, A. S. (2017). Machine Learning Framework for the Detection of Mental Stress at Multiple Levels. IEEE Access, 5, 13545–13556. https://doi.org/10.1109/ACCESS.2017.2723622
Sushant K, S. (2020). A Commentary on the Application of Artificial Intelligence in the Insurance Industry. Trends in Artificial Intelligence, 4(1). https://doi.org/10.36959/643/305
Tettamanzi, A., & Tomassini, M. (2001). Soft Computing. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-04335-6
Teuscher, C. (Ed.). (2004). Alan Turing: Life and Legacy of a Great Thinker. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-05642-4
Tiffin, P. A., & Paton, L. W. (2018). Rise of the machines? Machine learning approaches and mental health: opportunities and challenges. The British Journal of Psychiatry, 213(3), 509–510. https://doi.org/10.1192/bjp.2018.105
Tuttle, J. F., Vesel, R., Alagarsamy, S., Blackburn, L. D., & Powell, K. (2019). Sustainable NO x emission reduction at a coal-fired power station through the use of online neural network modeling and particle swarm optimization. Control Engineering Practice, 93, 104167. https://doi.org/10.1016/j.conengprac.2019.104167
Tyson, W. R., & Miller, W. A. (1977). Surface free energies of solid metals: Estimation from liquid surface tension measurements. Surface Science, 62(1), 267–276. https://doi.org/10.1016/0039-6028(77)90442-3
Tzeng, H.-M., Hsieh, J.-G., & Lin, Y.-L. (2004b). Predicting Nurses’ Intention to Quit With a Support Vector Machine. Computers, Informatics, Nursing, 22(4), 232–242.
Uronen, L., Moen, H., Teperi, S., Martimo, K.-P., Hartiala, J., & Salanterä, S. (2020). Towards automated detection of psychosocial risk factors with text mining. Occupational Medicine, 70(3), 203–206. https://doi.org/10.1093/occmed/kqaa022
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer New York. https://doi.org/10.1007/978-1-4757-2440-0
Vieco Gómez, G. F., & Abello Llanos, R. (2014). Psychosocial factors at work, stress and morbidity around the world. Psicología desde el Caribe, 31(2), 354–385. https://doi.org/10.14482/psdc.31.2.5544
Villalobos F, G. H. (2004). Vigilancia Epidemiológica de los FactoresPsicosociales. Aproximación Conceptual y Valorativa. Ciencia &Trabajo, 6(14), 194–201.
Villalobos, G. (2005). Diseño de un sistema de vigilancia epidemiológica de factores de riesgo psicosocial en el trabajo. Escuela Nacional de Salud Pública.
Villalobos, G., Vargas M, A., Escobar, J., Jiménez, M. L., & Rondón, M. A. (2010). Batería de instrumentos para la evaluación de factores de riesgo psicosocial (1a ed., Vol. 1). Ministerio de protección social. http://fondoriesgoslaborales.gov.co/documents/publicaciones/estudios/
Wang, S., Cao, Y., Huang, T., Chen, Y., Li, P., & Wen, S. (2020). Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm. Neural Networks, 121, 140–147. https://doi.org/10.1016/j.neunet.2019.09.001
Wang, X., Wang, Z., Song, Q., Shen, H., & Huang, X. (2020). A waiting-time-based event-triggered scheme for stabilization of complex-valued neural networks. Neural Networks, 121, 329–338. https://doi.org/10.1016/j.neunet.2019.09.032
Weissbrodt, R., & Giauque, D. (2017). Labour inspections and the prevention of psychosocial risks at work: A realist synthesis. Safety Science, 100, 110–124. https://doi.org/10.1016/j.ssci.2017.02.012
Wenhui Liao, Weihong Zhang, Zhiwei Zhu, & Qiang Ji. (2005). A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) - Workshops, 3, 70–70. https://doi.org/10.1109/CVPR.2005.394
Werboz, P. J. (1974). Beyond Regression: New Tools for Prediction and Analysisin Behavioral Sciences. Harvard.
Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits. Stanford Univ Ca Stanford Electronics Labs.
Wikimedia Commons. (2006, agosto 26). Waterstrider—Aquarius remigis. WaterstriderEnWiki.Jpg. https://commons.wikimedia.org/wiki/File:WaterstriderEnWiki.jpg
Yadav, S. K., & Hashmi, A. (2018). An Investigation of Occupational stress Classification by using Machine Learning Techniques. International Journal of Computer Sciences and Engineering, 6(6), 842–850. https://doi.org/10.26438/ijcse/v6i6.842850
Yang, X.-S. & Suash Deb. (2009). Cuckoo Search via Lévy flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210–214. https://doi.org/10.1109/NABIC.2009.5393690
Ye, L., Ferdinando, H., & Alasaarela, E. (2014). Techniques in Pattern Recognition for School Bullying Prevention: Review and Outlook. Journal of Pattern Recognition Research, 9(1), 50–63. https://doi.org/10.13176/11.586
Yigit, I. O., & Shourabizadeh, H. (2017). An approach for predicting employee churn by using data mining. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 1–4. https://doi.org/10.1109/IDAP.2017.8090324
Zhou, G., Moayedi, H., Bahiraei, M., & Lyu, Z. (2020). Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. Journal of Cleaner Production, 254, 120082. https://doi.org/10.1016/j.jclepro.2020.120082
Zhou, H., Wang, J., Wu, J., Zhang, L., Lei, P., & Chen, X. (2013). Application of the Hybrid SVM-KNN Model for Credit Scoring. 2013 Ninth International Conference on Computational Intelligence and Security, 174–177. https://doi.org/10.1109/CIS.2013.43
Zubair, M., & Yoon, C. (2020). Multilevel mental stress detection using ultra-short pulse rate variability series. Biomedical Signal Processing and Control, 57, 101736. https://doi.org/10.1016/j.bspc.2019.101736 | |
