The use of spatial data mining methods for modeling HR challenges of generation Z in greater Poland region. (English) Zbl 07700327
Summary: Challenges connected with neuroscience and the use of machine learning to support analytical processes encompass more and more areas, thus supporting practitioners and managerial decisions. These changes can also be seen in the area of human resource management and support for decisions on key future spending on the remuneration of future employees. The article presents an original spatial data enrichment and spatial data mining methodology used for the analysis of primary data based on a sample of 1149 young candidates from generation Z to measure the effectiveness of data mining learning methods. The studies used data collected directly from surveys that were “enriched” with spatial geolocation. The fact that the spatial context was taken into account in the studies made it possible to develop a model explaining the spatio-temporal differentiation of professional expectations of respondents from generation Z who were studying professions connected with broadly understood IT. The analyzes used modeling with linear polynomial regression, the neural network of a multi-layer perceptron type and the multivariate adaptive regression splines method in the variant with and without spatial data filtration. The use of different spatial data mining methods made it possible to compare the reliability of models of knowledge extraction from the data and to explain the significance of individual factors which affected the respondents’ beliefs. The analysis shows that spatial filtering of the data generates twice lower mean squared error while effective application of machine learning methods requires the use of explanatory spatial data.
MSC:
| 90Bxx | Operations research and management science |
Keywords:
human resources; modeling; spatial data mining; multivariate adaptive regression splines; artificial neutral networks; geolocalizationReferences:
| [1] | Adi, E.; Anwar, A.; Baig, Z.; Zeadally, S., Machine learning and data analytics for the IoT, Neural Comput Appl, 32, 20, 16205-16233 (2020) · doi:10.1007/s00521-020-04874-y |
| [2] | Ashraf, MA, The mediating effect of service quality and organizational commitment on the effect of management process alignment on higher education performance in Makassar, Indonesia, J Organ Change Manag, 31, 2, 410-425 (2019) · doi:10.1108/JOCM-11-2016-0247 |
| [3] | Balachandar A, Kulkarni AD (2018) Recruitment Chatbot. Int Res J Eng Technol (IRJET) 5(8):1248-1250. https://www.irjet.net/archives/V5/i8/IRJET-V5I8212.pdf. Accessed 26 Feb 2021 |
| [4] | Batrinca, B.; Treleaven, PC, Social media analytics: a survey of techniques, tools and platforms, AI Soc, 30, 1, 89-116 (2014) · doi:10.1007/s00146-014-0549-4 |
| [5] | Beynon, MJ; Jones, P.; Pickernell, D., The role of entrepreneurship, innovation, and urbanity-diversity on growth, unemployment, and income: US state-level evidence and an fsQCA elucidation, J Bus Res, 101, 675-687 (2019) · doi:10.1016/j.jbusres.2019.01.074 |
| [6] | Black, JS; Esch, P., AI-enabled recruiting: what is it and how should a manager use it?, Bus Horiz, 63, 2, 215-226 (2020) · doi:10.1016/j.bushor.2019.12.001 |
| [7] | Boz, H.; Kose, U., Emotion extraction from facial expressions by using artificial intelligence techniques, BRAIN Broad Res Artif Intell Neurosci, 9, 1, 5-16 (2018) |
| [8] | Breschi, S.; Lissoni, F.; Temgoua, CN; Shearmur, R.; Carrincazeaux, C.; Doloreux, D., Migration and innovation: a survey of recent studies, Handbook on the geographies of innovation, 382-398 (2016), Cheltenham: Edward Elgar Publishing, Cheltenham · doi:10.4337/9781784710774.00041 |
| [9] | Burgess A (2018) AI in action. In: The executive guide to artificial intelligence. Palgrave Macmillan, Cham, pp 73-89. doi:10.1007/978-3-319-63820-1_5 |
| [10] | Calvard, TS; Jeske, D., Developing human resource data risk management in the age of big data, Int J Inf Manag, 43, 159-164 (2018) · doi:10.1016/j.ijinfomgt.2018.07.011 |
| [11] | Çevik, A.; Weber, GW; Eyüboğlu, BM; Oğuz, KK, Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI, Ann Oper Res, 258, 1, 31-57 (2017) · Zbl 1383.92039 · doi:10.1007/s10479-017-2405-7 |
| [12] | Chadi, A., How to distinguish voluntary from involuntary unemployment: on the relationship between the willingness to work and unemployment-induced unhappiness, Kyklos, 63, 3, 317-329 (2010) · doi:10.1111/j.1467-6435.2010.00476.x |
| [13] | Cheng, MM; Hackett, RD, A critical review of algorithms in HRM: definition, theory, and practice, Hum Resour Manag Rev, 31, 1, 100698 (2021) · doi:10.1016/j.hrmr.2019.100698 |
| [14] | Chien, CF; Chen, LF, Data mining to improve personnel selection and enhance human capital: a case study in high-technology industry, Expert Syst Appl, 34, 1, 280-290 (2008) · doi:10.1016/j.eswa.2006.09.003 |
| [15] | Chillakuri, B., Understanding Generation Z expectations for effective onboarding, J Organ Chang Manag, 33, 7, 1277-1296 (2020) · doi:10.1108/JOCM-02-2020-0058 |
| [16] | Chowdary MK, Nguyen TN, Hemanth DJ (2021) Deep learning-based facial emotion recognition for human-computer interaction applications. Neural Comput Appl 1-18. doi:10.1007/s00521-021-06012-8 |
| [17] | Corbane, C.; Syrris, V.; Sabo, F.; Politis, P.; Melchiorri, M.; Pesaresi, M.; Soille, P.; Kemper, T., Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery, Neural Comput Appl, 33, 12, 6697-6720 (2020) · doi:10.1007/s00521-020-05449-7 |
| [18] | Dai, W.; Shao, YE; Lu, CJ, Incorporating feature selection method into support vector regression for stock index forecasting, Neural Comput Appl, 23, 6, 1551-1561 (2013) · doi:10.1007/s00521-012-1104-1 |
| [19] | Daramola, JO; Oladipupo, OO; Musa, AG, A fuzzy expert system (FES) tool for online personnel recruitments, Int J Bus Inf Syst, 6, 4, 444-462 (2010) · doi:10.1504/IJBIS.2010.035741 |
| [20] | Esch, P.; Black, JS, Factors that influence new generation candidates to engage with and complete digital, AI-Enabled Recruiting Bus Horizons, 62, 6, 729-739 (2019) · doi:10.1016/j.bushor.2019.07.004 |
| [21] | Esch, P.; Black, JS; Ferolie, J., Marketing AI recruitment: the next phase in job application and selection, Comput Hum Behav, 90, 215-222 (2019) · doi:10.1016/j.chb.2018.09.009 |
| [22] | Fernandes, S.; Rinaldo, AAR, The effect of organization culture and technology on motivation, knowledge asset and knowledge management, Int J Law Manag, 60, 5, 1087-1096 (2018) · doi:10.1108/IJLMA-05-2017-0105 |
| [23] | Friedman, JH, Multivariate adaptive regression splines, Ann Stat, 19, 1, 1-67 (1991) · Zbl 0765.62064 |
| [24] | Garcia-Arroyo, J.; Osca, A., Big data contributions to human resource management: a systematic review, Int J Hum Resour Manag, 32, 20, 4337-4362 (2019) · doi:10.1080/09585192.2019.1674357 |
| [25] | Gong, M.; Zeng, H.; Xie, Y.; Li, H.; Tang, Z., Local distinguishability aggrandizing network for human anomaly detection, Neural Netw, 122, 364-373 (2020) · doi:10.1016/j.neunet.2019.11.002 |
| [26] | Graczyk-Kucharska M (2019) Knowledge management for human resources: profile of a Generation Z employee. In: Proceedings of the European conference on knowledge management, ECKM, vol 1, pp 434-442. doi:10.34190/KM.19.146 |
| [27] | Graczyk-Kucharska, M.; Spychała, M.; Goliński, M.; Szafrański, M.; Graczyk-Kucharska, M.; Spychała, M.; Goliński, M.; Szafrański, M., Good human resources management practices in enterprises—case study, Challenges of modern human resources management, 111-142 (2020), Radom: Spatium, Radom |
| [28] | Guha, R.; Khan, AH; Singh, PK; Sarkar, R.; Bhattacharjee, D., CGA: a new feature selection model for visual human action recognition, Neural Comput Appl, 33, 10, 5267-5286 (2020) · doi:10.1007/s00521-020-05297-5 |
| [29] | Gupta, P.; Fernandes, SF; Jain, M., Automation in recruitment: a new frontier, J Inf Technol Teach Cases, 8, 2, 118-125 (2018) · doi:10.1057/s41266-018-0042-x |
| [30] | Hao, Q.; Yang, W.; Shi, Y., Characterizing the relationship between conscientiousness and knowledge sharing behavior in virtual teams: an interactionist approach, Comput Hum Behav, 91, 59, 42-51 (2019) · doi:10.1016/j.chb.2018.09.035 |
| [31] | Hastie, T.; Tibshirani, R.; Friedman, J., The elements of statistical learning. Data mining, inference, and prediction. Springer series on statistics (2009), Berlin: Springer, Berlin · Zbl 1273.62005 |
| [32] | Hmoud, B.; Laszlo, V., Will artificial intelligence take over humanresources recruitment and selection?, Netw Intell Stud, 7, 13, 21-30 (2019) |
| [33] | Huang, LC; Wu, P., A neural network modeling on human resource talent selection, Int J Hum Resour Dev Manag, 1, 2-4, 206-219 (2020) · doi:10.1504/IJHRDM.2001.001006 |
| [34] | Huang LC, Huang KS, Huang HP, Jaw BS (2004) Applying fuzzy neural network in human resource selection system. In: Annual conference of the North American fuzzy information processing society—NAFIPS, vol 1. IEEE, pp 169-174. doi:10.1109/nafips.2004.1336271 |
| [35] | Huang, MJ; Tsou, YL; Lee, SC, Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge, Knowl-Based Syst, 19, 6, 396-403 (2006) · doi:10.1016/j.knosys.2006.04.003 |
| [36] | Ivanov M, Ivanov S, Terentieva N, Maltiz V, Kalyuzhnaya J (2020) Fuzzy modeling in human resource management. In: E3S web of conferences, vol 166. EDP Sciences, p 13010. doi:10.1051/e3sconf/202016613010 |
| [37] | Jantan, H.; Hamdan, AR; Othman, ZA; Devlin, G., Intelligent techniques for decision support system in human resource management, Decision support systems, 261-276 (2010), Rijeka: IntechOpen, Rijeka |
| [38] | Jia Q, Guo Y, Li R, Li Y, Chen Y (2018) A conceptual artificial intelligence application framework in human resource management. In: Proceedings of the international conference on electronic business, pp 106-114 |
| [39] | Kodithuwakku, M.; Jusoh, M.; Chinna, K., Impact of human resource practices on intention to leave among Generation Z—the future workforce, Int J Hum Resour Stud, 8, 4, 267 (2018) · doi:10.5296/ijhrs.v8i4.13845 |
| [40] | Kropat, E.; Tikidji-Hamburyan, RA; Weber, GW, Operations research in neuroscience, Ann Oper Res, 258, 1, 1-185 (2017) · Zbl 1381.00027 · doi:10.1007/s10479-017-2633-x |
| [41] | Kropat, E.; Türkay, M.; Weber, GW, Fuzzy analytics and stochastic methods in neurosciences, IEEE Trans Fuzzy Syst, 28, 1, 1-121 (2020) · doi:10.1109/TFUZZ.2019.2959462 |
| [42] | Kummitha, RKR, Why distance matters: the relatedness between technology development and its appropriation in smart cities, Technol Forecast Soc Change, 157, 120087 (2020) · doi:10.1016/j.techfore.2020.120087 |
| [43] | Kuter, S.; Weber, GW; Akyürek, Z.; Özmen, A., Inversion of top of atmospheric reflectance values by conic multivariate adaptive regression splines, Inverse Probl Sci Eng, 23, 4, 651-669 (2015) · doi:10.1080/17415977.2014.933828 |
| [44] | Law, KS; Wong, CS; Wang, KD, An empirical test of the model on managing the localization of human resources in the People’s Republic of China, Int J Hum Resour Manag, 15, 4-5, 635-648 (2004) · doi:10.1080/0958519042000192870 |
| [45] | Lüscher, P.; Weibel, R.; Burghardt, D., Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data, Comput Environ Urban Syst, 33, 5, 363-374 (2009) · doi:10.1016/j.compenvurbsys.2009.07.005 |
| [46] | Masum, AK; Beh, LS; Azad, AK; Hoque, K., Intelligent human resource information system (i-HRIS): a holistic decision support framework for HR excellence, Int Arab J Inf Technol, 15, 1, 121-130 (2018) |
| [47] | Matt, DT; Orzes, G.; Rauch, E.; Dallasega, P., Urban production—a socially sustainable factory concept to overcome shortcomings of qualified workers in smart SMEs, Comput Ind Eng, 139, 105384 (2020) · doi:10.1016/j.cie.2018.08.035 |
| [48] | Michailidis, MP, Hie challenges of AI and blockchain on HR recruiting practices, Cyprus Rev, 30, 2, 12 (2018) |
| [49] | Mitra, SK; Saphores, JDM, Why do they live so far from work? Determinants of long-distance commuting in California, J Transp Geogr, 80, 102489 (2019) · doi:10.1016/j.jtrangeo.2019.102489 |
| [50] | Mobasheri, A., A rule-based spatial reasoning approach for OpenStreetMap data quality enrichment; case study of routing and navigation, Sensors, 17, 11, 2498 (2017) · doi:10.3390/s17112498 |
| [51] | Monteiro, IP; Correia, MB, Transforming a company’s staffing process: implementing e-recruitment, J Spatial Organ Dyn, 7, 2, 144-157 (2019) |
| [52] | Mou, Y.; Xu, K., The media inequality: comparing the initial human-human and human-AI social interactions, Comput Hum Behav, 72, 432-440 (2017) · doi:10.1016/j.chb.2017.02.067 |
| [53] | Napoli C, Pappalardo G, Tramontana E, Nowicki RK, Starczewski JT, Woźniak M (2015) Toward work groups classification based on probabilistic neural network approach. In: International conference on artificial intelligence and soft computing. Springer, Cham, pp 79-89. doi:10.1007/978-3-319-19324-3_8 |
| [54] | Nawaz, N., How far have we come with the study of artificial intelligence for recuritment process, Int J Sci Technol Res, 8, 7, 488-493 (2019) |
| [55] | Nicolaescu, SS; Florea, A.; Kifor, CV; FioreU, CN; Receu, I.; Zanetti, P., Human capital evaluation in knowledge-based organizations based on big data analytics, Futur Gener Comput Syst, 111, 654-667 (2020) · doi:10.1016/j.future.2019.09.048 |
| [56] | Nishimura, Y.; Nakamura, Y.; Ishiguro, H., Human interaction behavior modeling using generative adversarial networks, Neural Netw, 132, 521-531 (2020) · doi:10.1016/j.neunet.2020.09.019 |
| [57] | Oluwatayo, AA; Adetoro, O., Influence of employee attributes, work context and human resource management practices on employee job engagement, Glob J Flex Syst Manag, 21, 4, 295-308 (2020) · doi:10.1007/s40171-020-00249-3 |
| [58] | Origo, F.; Pagani, L., Workplace flexibility and job satisfaction: some evidence from Europe, Int J Manpow, 29, 6, 539-566 (2008) · doi:10.1108/01437720810904211 |
| [59] | Ortega A, Fierrez J, Morales A, Wang Z, Ribeiro T (2021) Symbolic AI for XAI: evaluating LFIT inductive programming for fair and explainable automatic recruitment. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 78-87. https://ui.adsabs.harvard.edu/abs/2021arXiv210301592T/abstract |
| [60] | Ouirdi, ME; Ouirdi, AE; Segers, J.; Pais, I., Technology adoption in employee recruitment: the case of social media in Central and Eastern Europe, Comput Hum Behav, 57, 240-249 (2016) · doi:10.1016/j.chb.2015.12.043 |
| [61] | Özmen A, Weber GW (2012) Robust conic generalized partial linear models using RCMARS method-A robustification of CGPLM. In: AIP conference proceedings, 1499(1). American Institute of Physics, pp 337-343. doi:10.1063/1.4769011 |
| [62] | Özmen, A.; Weber, GW, RMARS: robustification of multivariate adaptive regression spline under polyhedral uncertainty, J Comput Appl Math, 259, 914-924 (2014) · Zbl 1314.62120 · doi:10.1016/j.cam.2013.09.055 |
| [63] | Özmen, A.; Weber, GW; Çavuşoğlu, Z.; Defterli, Ö., The new robust conic GPLM method with an application to finance: prediction of credit default, J Global Optim, 56, 2, 233-249 (2013) · Zbl 1273.62251 · doi:10.1007/s10898-012-9902-7 |
| [64] | Özmen, A.; Batmaz, İ.; Weber, GW, Precipitation modeling by polyhedral RCMARS and comparison with MARS and CMARS, Environ Model Assess, 19, 5, 425-435 (2014) · doi:10.1007/s10666-014-9404-8 |
| [65] | Park H, Motahari Nezhad HR (2018) Learning procedures from text: codifying how-to procedures in deep neural networks. In: Companion proceedings of the the web conference, pp 351-358. doi:10.1145/3184558.3186347 |
| [66] | Paschen, J.; Kietzmann, J.; Kietzmann, TC, Artificial intelligence (AI) and its implications for market knowledge in B2B marketing, J Bus Ind Mark, 34, 7, 1410-1419 (2019) · doi:10.1108/JBIM-10-2018-0295 |
| [67] | Pérez-Campdesuñer, R.; De-Miguel-Guzmán, M.; Sánchez-Rodríguez, A.; García-Vidal, G.; Martínez-Vivar, R., Exploring neural networks in the analysis of variables that affect the employee turnover in the organization, Int J Eng Bus Manag, 10, 1-11 (2018) · doi:10.1177/1847979018772738 |
| [68] | Pinzone, M.; Albè, F.; Orlandelli, D.; Barletta, I.; Berlin, C.; Johansson, B.; Taisch, M., A framework for operative and social sustainability functionalities in human-centric cyber-physical production systems, Comput Ind Eng, 139, 105132 (2020) · doi:10.1016/j.cie.2018.03.028 |
| [69] | Romero, D.; Stahre, J.; Taisch, M., The Operator 4.0: towards socially sustainable factories of the future, Comput Ind Eng, 139, 106128 (2020) · doi:10.1016/j.cie.2019.106128 |
| [70] | Sekhri, A.; Cheema, DJ, The new era of HRM: AI reinventing HRM functions, Int J Sci Res Rev, 7, 3, 3073-3077 (2019) |
| [71] | Sharma, K.; Papamitsiou, Z.; Giannakos, M., Building pipelines for educational data using AI and multimodal analytics: a “grey-box” approach, Br J Edu Technol, 50, 6, 3004-3031 (2019) · doi:10.1111/bjet.12854 |
| [72] | Sivaram, N.; Ramar, K., Applicability of clustering and classification algorithms for recruitment data mining, Int J Comput Appl, 4, 5, 23-28 (2010) · doi:10.5120/823-1165fhuang |
| [73] | Sivathanu, B.; Pillai, R., Smart HR 4.0—how industry 4.0 is disrupting HR, Hum Resour Manag Int Digest, 26, 4, 7-11 (2018) · doi:10.1108/HRMID-04-2018-0059 |
| [74] | Statistical Office in Poznań. https://poznan.stat.gov.pl/. Accessed 16 Dec 2021 |
| [75] | Stettler, M.; Francis, G., Using a model of human visual perception to improve deep learning, Neural Netw, 104, 40-49 (2018) · doi:10.1016/j.neunet.2018.04.005 |
| [76] | Stoet, G.; Geary, DC, The gender-equality paradox in science, technology, engineering, and mathematics education, Psychol Sci, 29, 4, 581-593 (2018) · doi:10.1177/0956797617741719 |
| [77] | Strohmeier, S.; Piazza, F., Artificial intelligence techniques in human resource management—a conceptual exploration, Intell Syst Ref Library, 87, 149-172 (2015) · doi:10.1007/978-3-319-17906-3_7 |
| [78] | Suen, HY; Chen, MYC; Lu, SH, Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes?, Comput Hum Behav, 98, 43, 93-101 (2019) · doi:10.1016/j.chb.2019.04.012 |
| [79] | Tai WS, Hsu CC (2006) A realistic personnel selection tool based on fuzzy data mining method. In: 9th Joint international conference on information sciences. Atlantis Press, pp 190-193. doi:10.2991/jcis.2006.46 |
| [80] | Tambe, P.; Cappelli, P.; Yakubovich, V., Artificial intelligence in human resources management: challenges and a path forward, Calif Manage Rev, 61, 4, 15-42 (2019) · doi:10.1177/0008125619867910 |
| [81] | The main office of geodesy and cartography. http://www.gugik.gov.pl/pzgik/dane-udostepniane-bez-oplat. Accessed 16 Dec 2021 |
| [82] | Tikidji-Hamburyan, RA; Kropat, E.; Weber, GW, Operations research in neuroscience II, Ann Oper Res, 289, 1-152 (2020) · Zbl 07563079 · doi:10.1007/s10479-019-03437-2 |
| [83] | Vasant, P.; Zelinka, I.; Weber, GW, Intelligent computing and optimization (2019), New York: Springer, New York · Zbl 1429.68009 · doi:10.1007/978-3-030-68154-8 |
| [84] | Wang, T., Intelligent employment rate prediction model based on a neural computing framework and human-computer interaction platform, Neural Comput Appl, 32, 21, 16413-16426 (2020) · doi:10.1007/s00521-019-04019-w |
| [85] | Wang, Q.; Chen, K., Multi-label zero-shot human action recognition via joint latent ranking embedding, Neural Netw, 122, 1-23 (2020) · doi:10.1016/j.neunet.2019.09.029 |
| [86] | Wang X, Jiang Y (2010) The study of enterprise human resource evaluation research based on neural network. In: 2010 Third international symposium on information processing. IEEE, pp 19-21. doi:10.1109/ISIP.2010.13 |
| [87] | Weber, GW; Batmaz, İ.; Köksal, G.; Taylan, P.; Yerlikaya-Özkurt, F., CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization, Inverse Probl Sci Eng, 20, 3, 371-400 (2012) · Zbl 1254.65020 · doi:10.1080/17415977.2011.624770 |
| [88] | Wesche, JS; Sonderegger, A., When computers take the lead: the automation of leadership, Comput Hum Behav, 101, 197-209 (2019) · doi:10.1016/j.chb.2019.07.027 |
| [89] | Wiedmer, T., Generations do differ: best practices in leading traditionalists, boomers, and generations X, Y, and Z, Delta Kappa Gamma Bull, 82, 1, 51 (2015) |
| [90] | Williams, J.; Bhanugopan, R.; Fish, A., Localization of human resources in the State of Qatar: emerging issues and research agenda, Educ Bus Soc Contemp Middle Eastern Issues, 4, 3, 193-206 (2011) · doi:10.1108/17537981111159966 |
| [91] | Wong A, Lin ZQ, Chwyl B (2019) AttoNets: compact and efficient deep neural networks for the edge via human-machine collaborative design. https://openaccess.thecvf.com/content_CVPRW_2019/papers/CEFRL/Wong_AttoNets_Compact_and_Efficient_Deep_Neural_Networks_for_the_Edge_CVPRW_2019_paper.pdf. Accessed 26 Mar 2021 |
| [92] | Xu, Z.; Yen, NY, Special issue on emergence in human-like intelligence toward cyber-physical systems, Neural Comput Appl, 31, 4451-4453 (2019) · doi:10.1007/s00521-019-04033-y |
| [93] | Xue, X.; Feng, J.; Gao, Y.; Liu, M.; Zhang, W.; Sun, X.; Zhao, A.; Guo, S., Convolutional recurrent neural networks with a self-attention mechanism for personnel performance prediction, Entropy, 21, 12, 1227 (2019) · doi:10.3390/e21121227 |
| [94] | Yan R, Song Y, Wu H (2016) Learning to respond with deep neural networks for retrieval-based human-computer conversation system. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, pp 55-64. doi:10.1145/2911451.2911542 |
| [95] | Yang, M.; Jiang, Q.; Shen, Y.; Wu, Q.; Zhao, Z.; Zhou, W., Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning, Neural Netw, 117, 240-248 (2019) · doi:10.1016/j.neunet.2019.05.021 |
| [96] | Yang, KC; Varol, O.; Davis, CA; Ferrara, E.; Flammini, A.; Menczer, F., Arming the public with artificial intelligence to counter social bots, Hum Behav Emerg Technol, 1, 1, 48-61 (2019) · doi:10.1002/hbe2.115 |
| [97] | Zehir, C.; Karaboğa, T.; Başar, D.; Hacioglu, U., The transformation of human resource management and its impact on overall business performance: big data analytics and AI technologies in strategic HRM, Digital business strategies in blockchain ecosystems, 265-279 (2020), Cham: Springer, Cham · doi:10.1007/978-3-030-29739-8_12 |
| [98] | Zhang, Z., BP neural network trade volume prediction and enterprises HRM optimization model based on ES-LM training, J Intell Fuzzy Syst (2020) · doi:10.3233/JIFS-189063 |
| [99] | Zou, C.; Zhao, W.; Siau, K., COVID-19 calls for remote reskilling and retraining, Cutter Bus Technol J, 33, 7, 21-25 (2020) |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
