A REVIEW OF INDUSTRY AND AI IMPACT ON HUMAN RESOURCE MANAGEMENT
DOI:
https://doi.org/10.36755/jac.v2i1.60Keywords:
Machine learning, Algorithm, Artificial Intelligent, Resource Management, IndustryAbstract
It is crucial to build an institution on strong foundations if you want it to be durable and institutional. It is obvious how vital competition is when looking at the market conditions of today. To boost their ability to compete and remain in the market, organizations must allocate the proper resources to the appropriate investments. The human resource management (HRM) division has also started the digitalization process in this regard. Artificial intelligence (AI) has helped the human resources (HR) department's digitization phase advance significantly, especially in the recruitment process. The process of calling candidates and placing the qualified candidates results in a loss of value for the organization because it requires choosing the best candidate from among hundreds or even thousands of applications. Because evaluations using AI technology may be done without losing money or time, they can be used to available jobs inside the organization. As a result, the AI technique ensures that interviews are handled swiftly and affordably during the hiring process. Additionally, AI makes it possible for the HRM unit to carry out a number of tasks efficiently, including training, orientation, and career planning. The current study uses a literature review to attempt to explain how Industry 4.0 and AI are affecting HRM procedures.
References
K. A. V. Kumar and D. Arivudainambi, “Performance analysis of security framework for software defined network architectures,”International Journal of Advances in Applied Sciences, vol. 8, no. 3, pp. 232–242, Sep. 2019, doi: 10.11591/ijaas.v8.i3.pp232-242.
S. Mukkamala, G. Janoski, and A. Sung, “Intrusion detection using neural networks and support vector machines,” inProceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), pp. 1702–1707.doi: 10.1109/IJCNN.2002.1007774.
M. K. Moridani, A. K. Moridani, and M. Gholipour, “Powerful processing to three-dimensional facial recognition using tripleinformation,” International Journal of Advances in Applied Sciences, vol. 9, no. 4, pp. 326–332, Dec. 2020, doi:10.11591/ijaas.v9.i4.pp326-332.
O. M. Olaniyi, E. Daniya, J. G. Kolo, J. A. Bala, and A. E. Olanrewaju, “A computer vision-based weed control system for low-land rice precision farming,” International Journal of Advances in Applied Sciences, vol. 9, no. 1, pp. 51–61, Mar. 2020, doi:10.11591/ijaas.v9.i1.pp51-61.
K. P. Rani, L. Lakshmi, C. Sabitha, B. D. Lakshmi, and S. Sreeja, “Top-K search scheme on encrypted data in cloud,”International Journal of Advances in Applied Sciences, vol. 9, no. 1, pp. 67–69, Mar. 2020, doi: 10.11591/ijaas.v9.i1.pp67-69.
C.-P. Lee and J. P. Shim, “An exploratory study of radio frequency identification (RFID) adoption in the healthcare industry,”European Journal of Information Systems, vol. 16, no. 6, pp. 712–724, Dec. 2007, doi: 10.1057/palgrave.ejis.3000716
L. Fang, C. Yin, L. Zhou, Y. Li, C. Su, and J. Xia, “A physiological and behavioral feature authentication scheme for medicalcloud based on fuzzy-rough core vector machine,” Information Sciences, vol. 507, pp. 143–160, Jan. 2020, doi:10.1016/j.ins.2019.08.020
.S. Nagavalli and G. Ramachandran, “A secure data transmission scheme using asymmetric semi-homomorphic encryptionscheme,” International Journal of Advances in Applied Sciences, vol. 7, no. 4, pp. 369–376, Dec. 2018, doi:10.11591/ijaas.v7.i4.pp369-376.
Ullah and N. M. Nawi, “An improved in tasks allocation system for virtual machines in cloud computing using HBACalgorithm,” Journal of Ambient Intelligence and Humanized Computing, pp. 1–14, Oct. 2021, doi: 10.1007/s12652-021-03496-z.
Ullah, N. M. Nawi, and M. H. Khan, “BAT algorithm used for load balancing purpose in cloud computing: An overview,”International Journal of High Performance Computing and Networking, vol. 16, no. 1, pp. 43–54, 2020, doi:10.1504/IJHPCN.2020.110258.
H. Aznaoui, A. Ullah, S. Raghay, L. Aziz, and M. H. Khan, “New efficient GAF routing protocol using an optimized weightedsum model in WSN,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 1, pp. 396–406, Apr.2021, doi: 10.11591/ijeecs.v22.i1.pp396-406.
G. A. Akpakwu, B. J. Silva, G. P. Hancke, and A. M. Abu-Mahfouz, “A survey on 5G networks for the internet of things:Communication technologies and challenges,” IEEE Access, vol. 6, pp. 3619–3647, 2018, doi: 10.1109/ACCESS.2017.2779844.
H. Ghazzai, E. Yaacoub, M.-S. Alouini, Z. Dawy, and A. Abu-Dayya, “Optimized LTE cell planning with varying spatial andtemporal user densities,” IEEE Transactions on Vehicular Technology, vol. 65, no. 3, pp. 1575–1589, Mar. 2016, doi:
ISSN: 2252-8814Int J Adv Appl Sci, Vol. 11, No. 3, September 2022: 187-19319210.1109/TVT.2015.2411579.[16] G. K. Kurt et al., “A vision and framework for the high altitude platform station (HAPS) networks of the future,” IEEECommunications Surveys & Tutorials, vol. 23, no. 2, pp. 729–779, 2021, doi: 10.1109/COMST.2021.3066905.[17] F. Fang and X. Wu, “A win–win mode: The complementary and coexistence of 5G networks and edge computing,” IEEE Internetof Things Journal, vol. 8, no. 6, pp. 3983–4003, Mar. 2021, doi: 10.1109/JIOT.2020.3009821.
K. Al-Shouiliy, “The impact of real big data on our future and risk identification,” Ph.D. dissertation, Dept. Elect. Eng. andComput. Sci., Univ. of Cincinnati, Cincinnati, United States 2020.
F. Guo, F. R. Yu, H. Zhang, X. Li, H. Ji, and V. C. M. Leung, “Enabling massive IoT toward 6G: A comprehensive survey,”IEEE Internet of Things Journal, vol. 8, no. 15, pp. 11891–11915, Aug. 2021, doi: 10.1109/JIOT.2021.3063686.
S. Verma, Y. Kawamoto, Z. M. Fadlullah, H. Nishiyama, and N. Kato, “A survey on network methodologies for real-timeanalytics of massive IoT data and open research issues,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1457–1477, 2017, doi: 10.1109/COMST.2017.2694469.[21] R. Cong, Z. Zhao, G. Min, C. Feng, and Y. Jiang, “EdgeGO: A mobile resource-sharing framework for 6G Edge computing inmassive IoT systems,” IEEE Internet of Things Journal, pp. 1–1, 2021, doi: 10.1109/JIOT.2021.3065357.
P. Porambage, J. Okwuibe, M. Liyanage, M. Ylianttila, and T. Taleb, “Survey on multi-access edge computing for internet ofthings realization,” IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2961–2991, 2018, doi:10.1109/COMST.2018.2849509.
Mistry, S. Tanwar, S. Tyagi, and N. Kumar, “Blockchain for 5G-enabled IoT for industrial automation: A systematic review,solutions, and challenges,” Mechanical Systems and Signal Processing, vol. 135, p. 106382, Jan. 2020, doi:10.1016/j.ymssp.2019.106382.[24] K. Zhang, Y. Zhu, S. Maharjan, and Y. Zhang, “Edge intelligence and blockchain empowered 5G beyond for the industrialinternet of things,” IEEE Network, vol. 33, no. 5, pp. 12–19, Sep. 2019, doi: 10.1109/MNET.001.1800526.
L. Chettri and R. Bera, “A comprehensive survey on internet of things (IoT) toward 5G wireless systems,” IEEE Internet ofThings Journal, vol. 7, no. 1, pp. 16–32, Jan. 2020, doi: 10.1109/JIOT.2019.2948888.
Gupta and R. K. Jha, “A survey of 5G network: Architecture and emerging technologies,” IEEE Access, vol. 3, pp. 1206–1232,2015, doi: 10.1109/ACCESS.2015.2461602.
S. P. Singh, A. Nayyar, R. Kumar, and A. Sharma, “Fog computing: From architecture to edge computing and big dataprocessing,” The Journal of Supercomputing, vol. 75, no. 4, pp. 2070–2105, Apr. 2019, doi: 10.1007/s11227-018-2701-2.
S. S. Gill et al., “Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: Evolution, vision,trends and open challenges,” Internet of Things, vol. 8, pp. 1–30, Dec. 2019, doi: 10.1016/j.iot.2019.100118.
Y. Ai, M. Peng, and K. Zhang, “Edge computing technologies for internet of things: A primer,” Digital Communications andNetworks, vol. 4, no. 2, pp. 77–86, Apr. 2018, doi: 10.1016/j.dcan.2017.07.001.
N. T. Le, M. A. Hossain, A. Islam, D. Kim, Y.-J. Choi, and Y. M. Jang, “Survey of promising technologies for 5G networks,”Mobile Information Systems, pp. 1–25, 2016, doi: 10.1155/2016/2676589
G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, no. 1, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.
Haghiabi, A. H., Azamathulla, H. M., & Parsaie, A. (2017). Prediction of head loss on cascade weir using ANN and SVM. ISH Journal of Hydraulic Engineering, 23(1), 102-110.
Rai, S., Kuan, W. L., & Mustafa, R. (2023). An Enhanced Compression Method for Medical Images Using SPIHT Encoder for Fog Computing. International Journal of Image and Graphics, 2550025.
Sadie, M., & Aznaoui, H. (2023). Using a User-Centered Framework, We Can Assess the Usability of E-Agriculture Applications. Journal of Advancement in Computing, 1(1), 14-20.
Mistry, I., Tanwar, S., Tyagi, S., & Kumar, N. (2020). Blockchain for 5G-enabled IoT for industrial automation: A systematic review, solutions, and challenges. Mechanical systems and signal processing, 135, 106382.
Alam, T., Gupta, R., Qamar, S. (2022). Recent applications of Artificial Intelligence for Sustainable Development in smart cities. In Recent Innovations in Artificial Intelligence and Smart Applications (pp. 135-154). Cham: Springer International Publishing.
Ostrom, E. (2019). Institutional rational choice: An assessment of the institutional analysis and development framework. In Theories of the policy process, second edition (pp. 21-64). Routledge.
Ostrom, E. (2019). Institutional rational choice: An assessment of the institutional analysis and development framework. In Theories of the policy process, second edition (pp. 21-64). Routledge.
Jennings, P. D., & Zandbergen, P. A. (1995). Ecologically sustainable organizations: An institutional approach. Academy of management review, 20(4), 1015-1052.
Bruton, G. D., Ahlstrom, D., & Li, H. L. (2010). Institutional theory and entrepreneurship: where are we now and where do we need to move in the future?. Entrepreneurship theory and practice, 34(3), 421-440.
Maki, P. L. (2023). Assessing for learning: Building a sustainable commitment across the institution. Routledge.
Battilana, J., & Dorado, S. (2010). Building sustainable hybrid organizations: The case of commercial microfinance organizations. Academy of management Journal, 53(6), 1419-1440.
Agrawal, A. (2001). Common property institutions and sustainable governance of resources. World development, 29(10), 1649-1672.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Sundas Naqeeb Khan , Samra Urooj Khan
This work is licensed under a Creative Commons Attribution 4.0 International License.
