Application of Intelligent Lie Recognition Technology in Laws and Regulations Based on Occupational Mental Health Protection

References

• Qin LL, Yu NW, Zhao DH. Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video. TEH VJESN. 2018;25(2):528–535. doi:10.17559/TV-20171229024444
   Web of Science ®Google Scholar
• Coombs C, Hislop D, Taneva SK, et al. The strategic impacts of Intelligent Automation for knowledge and service work: an interdisciplinary review. J Strat Inform Syst. 2020;29(4):101600. doi:10.1016/j.jsis.2020.101600
   Web of Science ®Google Scholar
• Lu J, Ren L, Zhang C, et al. Modified Carroll’s pyramid of corporate social responsibility to enhance organizational performance of SMEs industry. J Clean Prod. 2020;271:122456. doi:10.1016/j.jclepro.2020.122456
   Web of Science ®Google Scholar
• Pech M, Vrchota J, Bednář J. Predictive maintenance and intelligent sensors in smart factory. Sensors. 2021;21(4):1470. doi:10.3390/s21041470
   PubMed Web of Science ®Google Scholar
• Zaman U, Mehmood F, Iqbal N, et al. Towards secure and intelligent internet of health things: a survey of enabling technologies and applications. Electronics. 2022;11(12):1893. doi:10.3390/electronics11121893
   Web of Science ®Google Scholar
• Sarc R, Curtis A, Kandlbauer L, et al. Digitalisation and intelligent robotics in value chain of circular economy oriented waste management–A review. Waste Manage. 2019;95:476–492. doi:10.1016/j.wasman.2019.06.035
   PubMed Web of Science ®Google Scholar
• Abdullahi SB, Bature ZA, Gabralla LA, et al. Lie recognition with multi-modal spatial–temporal state transition patterns based on hybrid convolutional neural network–bidirectional long short-term memory. Brain Sci. 2023;13(4):555. doi:10.3390/brainsci13040555
   PubMed Web of Science ®Google Scholar
• Abdullahi SB, Khunpanuk C, Bature ZA, et al. Biometric information recognition using artificial intelligence algorithms: a performance comparison. IEEE Access. 2022;10:49167–49183. doi:10.1109/ACCESS.2022.3171850
   Web of Science ®Google Scholar
• Wang D. Application of polygraph in concealed information test. Open Access Library J. 2020;07(09):1–8. doi:10.4236/oalib.1106804
   Google Scholar
• Peleg D, Ayal S, Ariely D, Hochman G. The Lie Deflator - the effect of polygraph test feedback on subsequent (dis)honesty. Judgm Decis Mak. 2019;14(6):728. doi:10.1017/S1930297500005441
   Web of Science ®Google Scholar
• Yu R, Wu SJ, Huang A, et al. Using polygraph to detect passengers carrying illegal items. Front Psychol. 2019;10:322. doi:10.3389/fpsyg.2019.00322
   PubMed Web of Science ®Google Scholar
• Verma M, Vipparthi SK, Singh G, et al. LEARNet: dynamic imaging network for micro expression recognition. IEEE Trans Image Process. 2019;29:1618–1627. doi:10.1109/TIP.2019.2912358
   Web of Science ®Google Scholar
• Wang SJ, Li BJ, Liu YJ, et al. Micro-expression recognition with small sample size by transferring long-term convolutional neural network. Neurocomputing. 2018;312:251–262. doi:10.1016/j.neucom.2018.05.107
   Web of Science ®Google Scholar
• Wang C, Peng M, Bi T, et al. Micro-attention for micro-expression recognition. Neurocomputing. 2020;410:354–362. doi:10.1016/j.neucom.2020.06.005
   Web of Science ®Google Scholar
• Chirico F, Heponiemi T, Pavlova M, et al. Psychosocial risk prevention in a global occupational health perspective. A descriptive analysis. Int J Environ Res Public Health. 2019;16(14):2470. doi:10.3390/ijerph16142470
   PubMed Web of Science ®Google Scholar
• Moyce SC, Schenker M. Migrant workers and their occupational health and safety. Annu Rev Public Health. 2018;39(1):351–365. doi:10.1146/annurev-publhealth-040617-013714
   PubMed Web of Science ®Google Scholar
• Alawaqleh QA. The effect of internal control on employee performance of small and medium-sized enterprises in Jordan: the role of accounting information system. J Asian Finance Econ Bus. 2021;8(3):855–863. doi:10.13106/jafeb.2021.vol8.no3.0855
   Web of Science ®Google Scholar
• Pakurár M, Haddad H, Nagy J, et al. The service quality dimensions that affect customer satisfaction in the Jordanian banking sector. Sustainability. 2019;11(4):1113. doi:10.3390/su11041113
   Web of Science ®Google Scholar
• Chanana N. Employee engagement practices during COVID-19 lockdown. J Public Affairs. 2021;21(4):e2508. doi:10.1002/pa.2508
   PubMed Web of Science ®Google Scholar
• Ho H, Kuvaas B. Human resource management systems, employee well-being, and firm performance from the mutual gains and critical perspectives: the well-being paradox. Hum Resour Manage. 2020;59(3):235–253. doi:10.1002/hrm.21990
   Web of Science ®Google Scholar
• Bulińska-Stangrecka H, Bagieńska A. The role of employee relations in shaping job satisfaction as an element promoting positive mental health at work in the era of COVID-19. Int J Environ Res Public Health. 2021;18(4):1903. doi:10.3390/ijerph18041903
   PubMed Web of Science ®Google Scholar
• Bala H, Venkatesh V, Ganster DC, et al. How does an enterprise system implementation change interpersonal relationships in organizations. Industr Manage Data Syst. 2021;121:1824–1847. doi:10.1108/IMDS-06-2020-0380
   Web of Science ®Google Scholar
• Giorgi G, Lecca LI, Alessio F, et al. COVID-19-related mental health effects in the workplace: a narrative review. Int J Environ Res Public Health. 2020;17(21):7857. doi:10.3390/ijerph17217857
   PubMed Web of Science ®Google Scholar
• Matthews LR, Gerald J, Jessup GM. Exploring men’s use of mental health support offered by an Australian Employee Assistance Program (EAP): perspectives from a focus-group study with males working in blue-and white-collar industries. Int J Ment Health Syst. 2021;15(1):1–17. doi:10.1186/s13033-021-00489-5
   PubMed Web of Science ®Google Scholar
• Hibat-Allah M, Ganahl M, Hayward LE, et al. Recurrent neural network wave functions. Phys Rev Res. 2020;2(2):023358. doi:10.1103/PhysRevResearch.2.023358
   Google Scholar
• Milot M, Borkenhagen E. Job stress in users of an Employee Assistance Program and association with presenting status. J Workplace Behav Health. 2018;33(3–4):153–167. doi:10.1080/15555240.2018.1502044
   Web of Science ®Google Scholar
• Secapramana LVH, Hariyanto V, Anggoro S. Employee assistance program as the supporting system of quality of work life to cope with occupational stress. J Educ Health Commun Psychol. 2020;9(2):97–115.
   Google Scholar
• Shehab Mohamed B, Harikumar P, Indrajit P, Prabhu P. A machine learning based framework for preprocessing and classification of medical images. Electrochem Soc Transact. 2022;107(1):7589.
   Google Scholar
• Attridge M. A global perspective on promoting workplace mental health and the role of employee assistance programs. Am J Health Promot. 2019;33(4):622–629. doi:10.1177/0890117119838101c
   PubMed Web of Science ®Google Scholar
• Joseph B, Walker A, Fuller-Tyszkiewicz M. Evaluating the effectiveness of employee assistance programmes: a systematic review. Eur J Work Organ Psychol. 2018;27(1):1–15. doi:10.1080/1359432X.2017.1374245
   Web of Science ®Google Scholar
• Sivan TV, Sharma S, Rana J, et al. Employee Assistance Program (EAP) to take care of Psychosocial health of employees of IndianOil during COIVD-19 pandemic. Saf Health Work. 2022;13:S188. doi:10.1016/j.shaw.2021.12.1340
   Web of Science ®Google Scholar
• Chung A, Tabatabai R, Paetow G, et al. Assessment of an opt-out employee assistance program assessment to mitigate burnout and reduce barriers to seeking mental health care in emergency medicine residents. West J Emerg Med. 2019;20:70.
   Google Scholar
• Khan W, Crockett K, O'Shea J, et al. Deception in the eyes of deceiver: a computer vision and machine learning based automated deception detection. Expert Syst Appl. 2021;169:114341.
   Google Scholar
• Patel V, Chesmore A, Legner CM, et al. Trends in workplace wearable technologies and connected-worker solutions for next-generation occupational safety, health, and productivity. Adv Intell Syst. 2022;4(1):2100099.
   Google Scholar