Paper Infomation
Research Needs and Applications of Machine Learning — Predicting Logistics Stress by Machine Learning
Full Text(PDF, 165KB)
Author: Bin Yan
Abstract: Machine learning is the use of computers to learn the intrinsic laws and information contained in data through algorithms to gain new experience and knowledge, in order to improve the intelligence of computers, so that they can make decisions similar to those made by humans when faced with problems. With the development of various industries, the amount of data has increased and the efficiency of data processing and analysis has become more demanding, a series of machine learning algorithms have emerged. Machine learning algorithms are essentially steps and processes that apply a large number of statistical principles to solve optimisation problems. Appropriate machine learning algorithms can be used to solve practical problems more efficiently for a wide range of model requirements. This paper presents the interim state of a dynamic disruption management software solution for logistics, using machine learning methods to study the extent to which stress is predicted based on physiological and subjective parameters, to prevent physical and mental stress on workers in the logistics industry, to maintain their health, to make them more optimistic and better able to adapt to their work, and to facilitate more accurate deployment of human resources by companies according to the real-time requirements of the logistics industry.
Keywords: Machine Learning, Pressure, Logistics, Rest Regulation, Sensor Technology
References:
[1] Hinkle Jr L E. The concept of “stress” in the biological and social sciences[J]. The International Journal of Psychiatry in Medicine, 1974, 5(4): 335-357.
[2] Batish A, Singh T P. MHAC—An assessment tool for analysing manual material handling tasks[J]. International Journal of Occupational Safety and Ergonomics, 2008, 14(2): 223-235.
[3] Lloyd C, King R, Chenoweth L. Social work, stress and burnout: A review[J]. Journal of mental health, 2002, 11(3): 255-265.
[4] Cho S S, Ju Y S, Paek D, et al. The combined effect of long working hours and low job control on self-rated health: an interaction analysis[J]. Journal of occupational and environmental medicine, 2018, 60(5): 475.
[5] Dieen J H V. Evaluation of work-rest schedules with respect to the effects of postural workload in standing work[J]. Ergonomics, 1998, 41(12): 1832-1844.
[6] Toorajipour R, Sohrabpour V, Nazarpour A, et al. Artificial intelligence in supply chain management: A systematic literature review[J]. Journal of Business Research, 2021, 122: 502-517.
[7] O'keeffe S T, Kazeem H, Philpott R M, et al. Gait disturbance in Alzheimer's disease: a clinical study[J]. Age and Ageing, 1996, 25(4): 313-316.
[8] Vuorinen T, Noponen K, Vehkaoja A, et al. Validation of Printed, Skin‐Mounted Multilead Electrode for ECG Measurements[J]. Advanced Materials Technologies, 2019, 4(9): 1900246.
[9] Bobade P, Vani M. Stress detection with machine learning and deep learning using multimodal physiological data[C]//2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2020: 51-57.
[10] Li L, Li Y, Yang L, et al. Continuous and accurate blood pressure monitoring based on wearable optical fiber wristband[J]. IEEE Sensors Journal, 2020, 21(3): 3049-3057.
[11] Achat H, Kawachi I, Levine S, et al. Social networks, stress and health-related quality of life[J]. Quality of life research, 1998, 7(8): 735-750.
[12] Talagala T S, Hyndman R J, Athanasopoulos G. Meta-learning how to forecast time series[J]. Monash Econometrics and Business Statistics Working Papers, 2018, 6(18): 16.
[13] Gomes H M, Bifet A, Read J, et al. Adaptive random forests for evolving data stream classification[J]. Machine Learning, 2017, 106(9): 1469-1495.
[14] Gal Y, Islam R, Ghahramani Z. Deep bayesian active learning with image data[C]//International Conference on Machine Learning. PMLR, 2017: 1183-1192.
[15] Lecuona J R, Reitzig M. Knowledge worth having in ‘excess’: The value of tacit and firm‐specific human resource slack[J]. Strategic management journal, 2014, 35(7): 954-973.
[16] Warr P. The measurement of well‐being and other aspects of mental health[J]. Journal of occupational Psychology, 1990, 63(3): 193-210.
[17] Ruiz-Jiménez J M, Fuentes-Fuentes M M. Management capabilities, innovation, and gender diversity in the top management team: An empirical analysis in technology-based SMEs[J]. BRQ Business Research Quarterly, 2016, 19(2): 107-121.