Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Abstract

Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes.

Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.

Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including ?cognitive Industrial Internet of Things?, ?cognitive automation?, ?cognitive manufacturing systems?, ?cognitively-enhanced machine?, ?cognitive technology-driven automation?, ?cognitive computing technologies,? and ?cognitive technologies.? The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools.

Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.

Keywords

cognitive manufacturing, Artificial Intelligence of Things, cyber-physical system, big data-driven deep learning, real-time scheduling algorithm, smart device, sustainable product lifecycle management

References

1. Altaf, A., Abbas, H., Iqbal, F., Khan, F. A., Rubab, S., & Derhab, A. (2021). Context-oriented trust computation model for Industrial Internet of Things. Computers & Electrical Engineering, 92, 107123. doi: 10.1016/j.compeleceng .2021.107123. DOI: https://doi.org/10.1016/j.compeleceng.2021.107123
   View in Google Scholar
2. Andronie, M., Lăzăroiu, G., Iatagan, M., U?ă, C., ?tefănescu, R., & Coco?atu, M. (2021a). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics, 10(20), 2497. doi: 10.3390/electronics10202497. DOI: https://doi.org/10.3390/electronics10202497
   View in Google Scholar
3. Andronie, M., Lăzăroiu, G., ?tefănescu, R., U?ă, C., & Dijmărescu, I. (2021b). Sustainable, smart, and sensing technologies for cyber-physical manufacturing systems: a systematic literature Review. Sustainability, 13(10), 5495. doi: 10.3390/su13105495. DOI: https://doi.org/10.3390/su13105495
   View in Google Scholar
4. Andronie, M., Lăzăroiu, G., Iatagan, M., Hurloiu, I., & Dijmărescu, I. (2021c). Sustainable cyber-physical production systems in big data-driven smart urban economy: a systematic literature review. Sustainability, 13(2), 751. doi: 10.339 0/su13020751. DOI: https://doi.org/10.3390/su13020751
   View in Google Scholar
5. Androniceanu, A., Nica, E., Georgescu, I., & Sabie, O. M. (2021a). The influence of the ICT on the control of corruption in public administrations of the EU member states: a comparative analysis based on panel data. Administratie si Management Public, 37, 41?59. doi: 10.24818/amp/2021.37-03. DOI: https://doi.org/10.24818/amp/2021.37-03
   View in Google Scholar
6. Androniceanu, A.-M., Căplescu, R. D., Tvaronavičien?, M, & Dobrin, C. (2021b). The interdependencies between economic growth, energy consumption and pollution in Europe. Energies, 14(9), 2577. doi: 10.3390/en14092577. DOI: https://doi.org/10.3390/en14092577
   View in Google Scholar
7. Androniceanu, A. (2021). Transparency in public administration as a challenge for a good democratic governance. Administratie si Management Public, 36, 149?164. doi: 10.24818/amp/2021.36-09. DOI: https://doi.org/10.24818/amp/2021.36-09
   View in Google Scholar
8. Bailey, L. (2021). The digital fabric of reproductive technologies: fertility, pregnancy, and menstrual cycle tracking apps. Journal of Research in Gender Studies, 11(2), 126?138. doi: 10.22381/JRGS11220219. DOI: https://doi.org/10.22381/JRGS11220219
   View in Google Scholar
9. Balica, R.-?. (2022). Machine and deep learning technologies, wireless sensor networks, and virtual simulation algorithms in digital twin cities. Geopolitics, History, and International Relations, 14(1), 59?74. doi: 10.22381/GHIR14120 224. DOI: https://doi.org/10.22381/GHIR14120224
   View in Google Scholar
10. Barbu, C. M., Florea, D. L., Dabija, D. C., & Barbu, M. C. R. (2021). Customer experience in Fintech. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1415?1433. doi: 10.3390/jtaer16050080. DOI: https://doi.org/10.3390/jtaer16050080
    View in Google Scholar
11. Beckett, S. (2022). Machine and deep learning technologies, location tracking and obstacle avoidance algorithms, and cognitive wireless sensor networks in in-telligent transportation planning and engineering. Contemporary Readings in Law and Social Justice, 14(1), 41?56. doi: 10.22381/CRLSJ14120223. DOI: https://doi.org/10.22381/CRLSJ14120223
    View in Google Scholar
12. Blake, R. (2022). Metaverse technologies in the virtual economy: deep learning computer vision algorithms, blockchain-based digital assets, and immersive shared worlds. Smart Governance, 1(1), 35?48. doi: 10.22381/sg1120223.
    View in Google Scholar
13. Bratu, S., & Sabău, R. I. (2022). Digital commerce in the immersive metaverse environment: cognitive analytics management, real-time purchasing data, and seamless connected shopping experiences. Linguistic and Philosophical Investigations, 21, 170?186. doi: 10.22381/lpi21202211. DOI: https://doi.org/10.22381/lpi21202211
    View in Google Scholar
14. Casadei, R., Fortino, G., Pianini, D., Russo, W., Savaglio, C., & Viroli, M. (2019). A development approach for collective opportunistic edge-of-things services. Information Sciences, 498, 154?169. doi: 10.1016/j.ins.2019.05.058. DOI: https://doi.org/10.1016/j.ins.2019.05.058
    View in Google Scholar
15. Cavallo, F., Semeraro, F., Mancioppi, G., Betti, S., & Fiorini, L. (2021). Mood classification through physiological parameters. Journal of Ambient Intelligence and Humanized Computing, 12, 4471?4484. doi: 10.1007/s12652-019-01595-6 DOI: https://doi.org/10.1007/s12652-019-01595-6
    View in Google Scholar
16. Chang, Z., Liu, S., Xiong, X., Cai, Z., & Tu, G. (2021). A survey of recent advanc-es in edge-computing-powered artificial Intelligence of Things. IEEE Internet of Things Journal, 8(18), 13849?13875. doi: 10.1109/JIOT.2021.3088875. DOI: https://doi.org/10.1109/JIOT.2021.3088875
    View in Google Scholar
17. Chung, K., Yoo, H., Choe, D., & Jung, H. (2019). Blockchain network based topic mining process for cognitive manufacturing. Wireless Personal Communications, 105, 583?597. doi: 10.1007/s11277-018-5979-8. DOI: https://doi.org/10.1007/s11277-018-5979-8
    View in Google Scholar
18. Chung, K., & Yoo, H. (2020). Edge computing health model using P2P-based deep neural networks. Peer-to-Peer Networking and Applications, 13, 694?703. doi: 10.1007/s12083-019-00738-y DOI: https://doi.org/10.1007/s12083-019-00738-y
    View in Google Scholar
19. Cug, J., Suler, P., & Taylor, E. (2022). Digital twin-based cyber-physical produc-tion systems in immersive 3D environments: virtual modeling and simulation tools, spatial data visualization techniques, and remote sensing technologies. Economics, Management, and Financial Markets, 17(2), 82?96. doi: 10.22381 /emfm17220225. DOI: https://doi.org/10.22381/emfm17220225
    View in Google Scholar
20. Dawson, A. (2022). Data-driven consumer engagement, virtual immersive shop-ping experiences, and blockchain-based digital assets in the retail metaverse. Journal of Self-Governance and Management Economics, 10(2), 52?66. doi: 10.22381/jsme10220224. DOI: https://doi.org/10.22381/jsme10220224
    View in Google Scholar
21. Din, I. U., Guizani, M., Rodrigues, J. J. P. C., Hassan, S., & Korotaev, V. V. (2019). Machine learning in the Internet of Things: designed techniques for smart cities. Future Generation Computer Systems, 100, 826?843. doi: 10.1016/j.future.2019.04.017. DOI: https://doi.org/10.1016/j.future.2019.04.017
    View in Google Scholar
22. Ding, K., Zhang, Y., Chan, F. T. S., Zhang, C., Lv, J., Liu, Q., Leng, J., & Fue, H. (2021). A cyber-physical production monitoring service system for energy-aware collaborative production monitoring in a smart shop floor. Journal of Cleaner Production, 297, 126599. doi: 10.1016/j.jclepro.2021.126599. DOI: https://doi.org/10.1016/j.jclepro.2021.126599
    View in Google Scholar
23. Dumitrache, I., Caramihai, S. I., Moisescu, M. A., & Sacala, I. S. (2019). Neuro-inspired framework for cognitive manufacturing control. IFAC-PapersOnLine, 52, 910?915. doi: 10.1016/j.ifacol.2019.11.311. DOI: https://doi.org/10.1016/j.ifacol.2019.11.311
    View in Google Scholar
24. Durana, P., Krulicky, T., & Taylor, E. (2022). Working in the metaverse: virtual recruitment, cognitive analytics management, and immersive visualization systems. Psychosociological Issues in Human Resource Management, 10(1), 135?148. doi: 10.22381/pihrm101202210. DOI: https://doi.org/10.22381/pihrm101202210
    View in Google Scholar
25. Elia, G., & Margherita, A. (2021). A conceptual framework for the cognitive enterprise: pillars, maturity, value drivers. Technology Analysis & Strategic Management, 34(4), 377?389. doi: 10.1080/09537325.2021.1901874. DOI: https://doi.org/10.1080/09537325.2021.1901874
    View in Google Scholar
26. ElMaraghy, H., & ElMaraghy, W. (2022). Adaptive cognitive manufacturing system (ACMS) ? a new paradigm. International Journal of Production Research, 60(24), 7436?7449. doi: 10.1080/00207543.2022.2078248. DOI: https://doi.org/10.1080/00207543.2022.2078248
    View in Google Scholar
27. Emmer, C., Hofmann, T. M., Schmied, T., Stjepandić, J., & Strietzel, M. (2018). A neutral approach for interoperability in the field of 3D measurement data management. Journal of Industrial Information Integration, 12, 47?56. doi: 10.1016/j.jii.2018.01.006. DOI: https://doi.org/10.1016/j.jii.2018.01.006
    View in Google Scholar
28. Ferreras-Higuero, E., Leal-Mu?oz, E., García de Jalón, J., Chacón, E., & Vizán, A. (2020). Robot-process precision modelling for the improvement of productivity in flexible manufacturing cells. Robotics and Computer-Integrated Manufacturing, 65, 101966. doi: 10.1016/j.rcim.2020.101966. DOI: https://doi.org/10.1016/j.rcim.2020.101966
    View in Google Scholar
29. Gain, U. (2021). Applying frameworks for cognitive services in IIoT. Journal of Systems Science and Systems Engineering, 30, 59?84. doi: 10.1007/s11518-021-5480-x. DOI: https://doi.org/10.1007/s11518-021-5480-x
    View in Google Scholar
30. Gordon, S. (2022). Computer vision algorithms, vehicle navigation and remote sensing technologies, and smart traffic planning and analytics in urban trans-portation systems. Contemporary Readings in Law and Social Justice, 14(1), 9?24. doi: 10.22381/CRLSJ14120221. DOI: https://doi.org/10.22381/CRLSJ14120221
    View in Google Scholar
31. Grondys, K., & Ślusarczyk, O. (2022). Passenger potential and the operating re-sult of the public transport organization. Administratie si Management Public, 38, 104?119. doi: 10.24818/amp/2022.38-06.
    View in Google Scholar
32. Hawkins, M. (2022a). Virtual employee training and skill development, workplace technologies, and deep learning computer vision algorithms in the immersive metaverse environment. Psychosociological Issues in Human Resource Management, 10(1), 106?120. doi: 10.22381/pihrm10120228. DOI: https://doi.org/10.22381/pihrm10120228
    View in Google Scholar
33. Hawkins, M. (2022b). Metaverse live shopping analytics: retail data measurement tools, computer vision and deep learning algorithms, and decision intelligence and modeling. Journal of Self-Governance and Management Economics, 10(2), 22?36. doi: 10.22381/jsme10220222. DOI: https://doi.org/10.22381/jsme10220222
    View in Google Scholar
34. Hu, P., Han, Z., Fu, H., & Han, D. (2016). Architecture and implementation of closed-loop machining system based on open STEP-NC controller. International Journal of Advanced Manufacturing Technology, 83, 1361?1375. doi: 10.1007/s00170-015-7631-z. DOI: https://doi.org/10.1007/s00170-015-7631-z
    View in Google Scholar
35. Hu, L., Miao, Y., Wu, G., Hassan, M. M., & Humar, I. (2019). iRobot-Factory: an intelligent robot factory based on cognitive manufacturing and edge computing. Future Generation Computer Systems, 90, 569?577. doi: 10.1016/j.future.201 8.08.006. DOI: https://doi.org/10.1016/j.future.2018.08.006
    View in Google Scholar
36. Hudson, J. (2022). Internet of Medical Things-driven remote monitoring systems, big healthcare data analytics, and wireless body area networks in COVID-19 detection and diagnosis. American Journal of Medical Research, 9(1), 81?96. doi: 10.22381/ajmr9120226. DOI: https://doi.org/10.22381/ajmr9120226
    View in Google Scholar
37. Ionescu, L. (2020). Digital data aggregation, analysis, and infrastructures in FinTech operations. Review of Contemporary Philosophy, 19, 92?98. doi: 10.22381/RCP19202010. DOI: https://doi.org/10.22381/RCP19202010
    View in Google Scholar
38. Kliestik, T., Poliak, M., & Popescu, G. H. (2022a). Digital twin simulation and modeling tools, computer vision algorithms, and urban sensing technologies in immersive 3D environments. Geopolitics, History, and International Rela-tions, 14(1), 9?25. doi: 10.22381/GHIR14120221. DOI: https://doi.org/10.22381/GHIR14120221
    View in Google Scholar
39. Kliestik, T., Novak, A., & Lăzăroiu, G. (2022b). Live shopping in the metaverse: Visual and spatial analytics, cognitive artificial intelligence techniques and algorithms, and immersive digital simulations. Linguistic and Philosophical Investigations, 21, 187?202. doi: 10.22381/lpi21202212. DOI: https://doi.org/10.22381/lpi21202212
    View in Google Scholar
40. Kovacova, M., Machova, V., & Bennett, D. (2022a). Immersive extended reality technologies, data visualization tools, and customer behavior analytics in the metaverse commerce. Journal of Self-Governance and Management Economics, 10(2), 7?21. doi: 10.22381/jsme10220221. DOI: https://doi.org/10.22381/jsme10220221
    View in Google Scholar
41. Kovacova, M., Novak, A., Machova, V., & Carey, B. (2022b). 3D virtual simula-tion technology, digital twin modeling, and geospatial data mining in smart sustainable city governance and management. Geopolitics, History, and International Relations, 14(1), 43?58. doi: 10.22381/GHIR14120223. DOI: https://doi.org/10.22381/GHIR14120223
    View in Google Scholar
42. Krüger, J., Zhao, H., Reis de Ascencao, G., Jacobi, P., Surdilovic, D., Schöll, S., & Polley, W. (2016). Concept of an offline correction method based on historical data for milling operations using industrial robots. Production Engineering, 10, 409?420. doi: 10.1007/s11740-016-0686-3. DOI: https://doi.org/10.1007/s11740-016-0686-3
    View in Google Scholar
43. Ksentini, A., Jebalia, M., Tabbane, S. (2021). IoT/Cloud?enabled smart services: a review on QoS requirements in fog environment and a proposed approach based on priority classification technique. International Journal of Communication Systems, 34, e4269. doi: 10.1002/dac.4269. DOI: https://doi.org/10.1002/dac.4269
    View in Google Scholar
44. Kumar, A., & Jaiswal, A. (2021). A deep swarm-optimized model for leveraging industrial data analytics in cognitive manufacturing. IEEE Transactions on Industrial Informatics, 17, 2938?2946. doi: 10.1109/TII.2020.3005532. DOI: https://doi.org/10.1109/TII.2020.3005532
    View in Google Scholar
45. Lăzăroiu, G., Pera, A., ?tefănescu-Mihăilă, R. O., Mircică, N., & Neguri?ă, O. (2017). Can neuroscience assist us in constructing better patterns of economic decision-making? Frontiers in Behavioral Neuroscience, 11, 188. doi: 10.3389/ fnbeh.2017.00188. DOI: https://doi.org/10.3389/fnbeh.2017.00188
    View in Google Scholar
46. Lăzăroiu, G., Andronie, M., Iatagan, M., Geamănu, M., ?tefănescu, R., & Dijmărescu, I. (2022). Deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms in the Internet of Manufacturing Things. ISPRS International Journal of Geo-Information, 11(5), 277. doi: 10.3390/ijgi11050277. DOI: https://doi.org/10.3390/ijgi11050277
    View in Google Scholar
47. Li, Y., Liu, C., Gao, J. X., & Shen, W. (2015). An integrated feature-based dynamic control system for on-line machining, inspection and monitoring. Integrated Computer-Aided Engineering, 22, 187?200. doi: 10.3233/ICA-150483. DOI: https://doi.org/10.3233/ICA-150483
    View in Google Scholar
48. Li, S., Wang, R., Zheng, P., & Wang, L. (2021a). Towards proactive human?robot collaboration: a foreseeable cognitive manufacturing paradigm. Journal of Manufacturing Systems, 60, 547?552. doi: 10.1016/j.jmsy.2021.07.017. DOI: https://doi.org/10.1016/j.jmsy.2021.07.017
    View in Google Scholar
49. Li, X., Zheng, P., Bao, J., Gao, L., & Xu, X. (2021b). Achieving cognitive mass personalization via the self-X cognitive manufacturing network: an industrial-knowledge-graph- and graph-embedding-enabled pathway. Engineering. Advance online publication. doi: 10.1016/j.eng.2021.08.018. DOI: https://doi.org/10.1016/j.eng.2021.08.018
    View in Google Scholar
50. Liu, M., Li, X., Li, J., Liu, Y., Zhou, B., & Bao, J. (2022). A knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing. Advanced Engineering Informatics, 51, 101515. doi: 10.1016/j.aei.2021.1015 15. DOI: https://doi.org/10.1016/j.aei.2021.101515
    View in Google Scholar
51. Lyons, N. (2022a). Deep learning-based computer vision algorithms, immersive analytics and simulation software, and virtual reality modeling tools in digital twin-driven smart manufacturing. Economics, Management, and Financial Markets, 17(2), 67?81. doi: 10.22381/emfm17220224. DOI: https://doi.org/10.22381/emfm17220224
    View in Google Scholar
52. Lyons, N. (2022b). Talent acquisition and management, immersive work environments, and machine vision algorithms in the virtual economy of the metaverse. Psychosociological Issues in Human Resource Management, 10(1), 121?134. doi: 10.22381/pihrm10120229. DOI: https://doi.org/10.22381/pihrm10120229
    View in Google Scholar
53. Maier, P., Sachenbacher, M., Rühr, T., & Kuhn, L. (2010). Automated plan assessment in cognitive manufacturing. Advanced Engineering Informatics, 24, 308?319. doi: 10.1016/j.aei.2010.05.015 DOI: https://doi.org/10.1016/j.aei.2010.05.015
    View in Google Scholar
54. Michalkova, L., Machova, V., & Carter, D. (2022). Digital twin-based product development and manufacturing processes in virtual space: data visualization tools and techniques, cloud computing technologies, and cyber-physical production systems. Economics, Management, and Financial Markets, 17, 37?51. doi: 10.22381/emfm17220222. DOI: https://doi.org/10.22381/emfm17220222
    View in Google Scholar
55. Mihăilă, R., & Brani?te, L. (2021). Digital semantics of beauty apps and filters: Big data-driven facial retouching, aesthetic self-monitoring devices, and augmented reality-based body-enhancing technologies. Journal of Research in Gender Studies, 11(2), 100?112. doi: 10.22381/JRGS11220217. DOI: https://doi.org/10.22381/JRGS11220217
    View in Google Scholar
56. Mircică, N. (2020). Restoring public trust in digital platform operations: machine learning algorithmic structuring of social media content. Review of Contemporary Philosophy, 19, 85?91. doi: 10.22381/RCP1920209. DOI: https://doi.org/10.22381/RCP1920209
    View in Google Scholar
57. Mladineo, M., Crnjac Zizic, M., Aljinovic, A., & Gjeldum, N. (2022). Towards a knowledge-based cognitive system for industrial application: case of personalized products. Journal of Industrial Information Integration, 27, 100284. doi: 10.1016/j.jii.2021.100284. DOI: https://doi.org/10.1016/j.jii.2021.100284
    View in Google Scholar
58. Nagy, M., & Lăzăroiu, G. (2022). Computer vision algorithms, remote sensing data fusion techniques, and mapping and navigation tools in the Industry 4.0-based Slovak automotive sector. Mathematics, 10(19), 3543. doi: 10.3390/math1019 3543. DOI: https://doi.org/10.3390/math10193543
    View in Google Scholar
59. Nica, E., Kliestik, T., Valaskova, K., & Sabie, O.-M. (2022). The economics of the metaverse: immersive virtual technologies, consumer digital engagement, and augmented reality shopping experience. Smart Governance, 1(1), 21?34. doi: 10.22381/sg1120222.
    View in Google Scholar
60. Palombarini, J., & Martínez, E. (2012). SmartGantt ? an intelligent system for real time rescheduling based on relational reinforcement learning. Expert Systems with Applications, 39, 10251?10268. doi: 10.1016/j.eswa.2012.02.176. DOI: https://doi.org/10.1016/j.eswa.2012.02.176
    View in Google Scholar
61. Pelau, C., Dabija, D.-C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthro-pomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122, 106855. doi: 10.1016/j.ch b.2021.106855. DOI: https://doi.org/10.1016/j.chb.2021.106855
    View in Google Scholar
62. Penumuru, D. P., Muthuswamy, S., & Karumbu, P. (2020). Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. Journal of Intelligent Manufacturing, 31, 1229?1241. doi: 10.1007/s10845-019-01508-6. DOI: https://doi.org/10.1007/s10845-019-01508-6
    View in Google Scholar
63. Perzylo, A., Grothoff, J., Lucio, L., Weser, M., Malakuti, S., Venet, P., Aravantinos, V., & Deppe, T. (2019). Capability-based semantic interoperability of manufacturing resources: A BaSys 4.0 perspective. IFAC-PapersOnLine, 52, 1590?1596. doi: 10.1016/j.ifacol.2019.11.427. DOI: https://doi.org/10.1016/j.ifacol.2019.11.427
    View in Google Scholar
64. Peters, E. (2022a). Big geospatial data analytics, connected vehicle technologies, and visual perception and sensor fusion algorithms in smart transportation networks. Contemporary Readings in Law and Social Justice, 14(1), 73?88. doi: 10.22381/CRLSJ14120225. DOI: https://doi.org/10.22381/CRLSJ14120225
    View in Google Scholar
65. Peters, M. A. (2022b). A post-Marxist reading of the knowledge economy: open knowledge production, cognitive capitalism, and knowledge socialism. Analysis and Metaphysics, 21, 7?23. doi: 10.22381/am2120221. DOI: https://doi.org/10.22381/am2120221
    View in Google Scholar
66. Poliak, M., Jurecki, R., & Buckner, K. (2022). Autonomous vehicle routing and navigation, mobility simulation and traffic flow prediction tools, and deep learning object detection technology in smart sustainable urban transport sys-tems. Contemporary Readings in Law and Social Justice, 14(1), 25?40. doi: 10.22381/CRLSJ14120222. DOI: https://doi.org/10.22381/CRLSJ14120222
    View in Google Scholar
67. Qin, Z., & Lu, Y. (2021). Self-organizing manufacturing network: a paradigm towards smart manufacturing in mass personalization. Journal of Manufacturing Systems, 60, 35?47. doi: 10.1016/j.jmsy.2021.04.016 DOI: https://doi.org/10.1016/j.jmsy.2021.04.016
    View in Google Scholar
68. Rice, L. (2022). Digital twins of smart cities: spatial data visualization tools, monitoring and sensing technologies, and virtual simulation modeling. Geo-politics, History, and International Relations, 14(1), 26?42. doi: 10.22381/GHIR1412 0222. DOI: https://doi.org/10.22381/GHIR14120222
    View in Google Scholar
69. Robinson, R. (2022). Digital twin modeling in virtual enterprises and autonomous manufacturing systems: deep learning and neural network algorithms, immer-sive visualization tools, and cognitive data fusion techniques. Economics, Management, and Financial Markets, 17(2), 52?66. doi: 10.22381/emfm1722 0223. DOI: https://doi.org/10.22381/emfm17220223
    View in Google Scholar
70. Rogers, S., & Zvarikova, K. (2021). Big data-driven algorithmic governance in sustainable smart manufacturing: robotic process and cognitive automation technologies. Analysis and Metaphysics, 20, 130?144. doi: 10.22381/am2020 219. DOI: https://doi.org/10.22381/AM2020219
    View in Google Scholar
71. Sharma, A., Zhang, Z., & Rai, R. (2021). The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing. International Journal of Production Research, 59, 4960?4994. doi: 10.1080/00207543.2021.1930234. DOI: https://doi.org/10.1080/00207543.2021.1930234
    View in Google Scholar
72. Shpak, N., Kulyniak, I., Gvozd, M., Pyrog, O., & Sroka, W. (2021). Shadow econ-omy and its impact on the public administration: aspects of financial and eco-nomic security of the country?s industry. Administratie si Management Public, 36, 81?101. doi: 10.24818/amp/2021.36-05. DOI: https://doi.org/10.24818/amp/2021.36-05
    View in Google Scholar
73. Siafara, L. C., Kholerdi, H., Bratukhin, A., Taherinejad, N., & Jantsch, A. (2018). SAMBA ? an architecture for adaptive cognitive control of distributed cyber-physical production systems based on its self-awareness. E & i Elektrotechnik und Informationstechnik, 135, 270?277. doi: 10.1007/s00502-018-0614-7. DOI: https://doi.org/10.1007/s00502-018-0614-7
    View in Google Scholar
74. Stone, D., Michalkova, L., & Machova, V. (2022). Machine and deep learning techniques, body sensor networks, and Internet of Things-based smart healthcare systems in COVID-19 remote patient monitoring. American Journal of Medical Research, 9(1), 97?112. doi: 10.22381/ajmr9120227. DOI: https://doi.org/10.22381/ajmr9120227
    View in Google Scholar
75. Valaskova, K., Androniceanu, A-M., Zvarikova, K., & Olah, J. (2021). Bonds between earnings management and corporate financial stability in the context of the competitive ability of enterprises. Journal of Competitiveness, 13(4), 167?184. doi: 10.7441/joc.2021.04.10. DOI: https://doi.org/10.7441/joc.2021.04.10
    View in Google Scholar
76. Valaskova, K., Nagy, M., Zabojnik, S., & Lăzăroiu, G. (2022). Industry 4.0 wire-less networks and cyber-physical smart manufacturing systems as accelerators of value-added growth in Slovak exports. Mathematics, 10(14), 2452. doi: 10.3390/math10142452. DOI: https://doi.org/10.3390/math10142452
    View in Google Scholar
77. Vătămănescu, E.-M., Alexandru, V.-A., Mitan, A., & Dabija, D.-C. (2020). From the deliberate managerial strategy towards international business performance: a psychic distance vs. global mindset approach. Systems Research and Behavioral Science, 37(2), 374?387. doi: 10.1002/sres.2658. DOI: https://doi.org/10.1002/sres.2658
    View in Google Scholar
78. Vătămănescu, E.-M., Brătianu, C., Dabija, D.-C., & Popa, S. (2022). Capitalizing online knowledge networks: from individual knowledge acquisition towards organizational achievements. Journal of Knowledge Management. Advance online publication. doi: 10.1108/JKM-04-2022-0273. DOI: https://doi.org/10.1108/JKM-04-2022-0273
    View in Google Scholar
79. Watson, R. (2022). Tradeable digital assets, immersive extended reality technologies, and blockchain-based virtual worlds in the metaverse economy. Smart Governance, 1(1), 7?20. doi: 10.22381/sg1120221.
    View in Google Scholar
80. Welch, H. (2021). Algorithmically monitoring menstruation, ovulation, and pregnancy by use of period and fertility tracking apps. Journal of Research in Gender Studies, 11(2), 113?125. doi: 10.22381/JRGS11220218. DOI: https://doi.org/10.22381/JRGS11220218
    View in Google Scholar
81. Woo, W-S., Kim, E-J., Jeong, H-I., & Lee, C.-M. (2020). Laser-assisted machining of Ti-6Al-4V fabricated by DED additive manufacturing. International Journal of Precision Engineering and Manufacturing-Green Technology, 7, 559?572. doi: 10.1007/s40684-020-00221-7. DOI: https://doi.org/10.1007/s40684-020-00221-7
    View in Google Scholar
82. Zeba, G., Dabić, M., Čičak, M., Daim, T., & Yalcin, H. (2021). Technology min-ing: artificial intelligence in manufacturing. Technological Forecasting and Social Change, 171, 120971. doi: 10.1016/j.techfore.2021.120971. DOI: https://doi.org/10.1016/j.techfore.2021.120971
    View in Google Scholar
83. Zhao, Y. F., & Xu, X. (2010). Enabling cognitive manufacturing through automated on-machine measurement planning and feedback. Advanced Engineering Informatics, 24, 269?284. doi: 10.1016/j.aei.2010.05.009. DOI: https://doi.org/10.1016/j.aei.2010.05.009
    View in Google Scholar
84. Zheng, P., Xia, L., Li, C., Li, X., & Liu, B. (2021). Towards Self-X cognitive manufacturing network: an industrial knowledge graph-based multi-agent re-inforcement learning approach. Journal of Manufacturing Systems, 61, 16?26. doi: 10.1016/j.jmsy.2021.08.002. DOI: https://doi.org/10.1016/j.jmsy.2021.08.002
    View in Google Scholar
85. Zvarikova, K., Rowland, M., & Krulicky, T. (2021). Sustainable Industry 4.0 wireless networks, smart factory performance, and cognitive automation in cyber-physical system-based manufacturing. Journal of Self-Governance and Management Economics, 9(4), 9?21. doi: 10.22381/jsme9420211. DOI: https://doi.org/10.22381/jsme9420211
    View in Google Scholar
86. Zvarikova, K., Frajtova Michalikova, K., & Rowland, M. (2022). Retail data measurement tools, cognitive artificial intelligence algorithms, and metaverse live shopping analytics in immersive hyper-connected virtual spaces. Linguistic and Philosophical Investigations, 21, 9?24. doi: 10.22381/lpi2120221. DOI: https://doi.org/10.22381/lpi2120221
    View in Google Scholar