The application of Disruptive Technologies (DT), using Artificial Intelligence (AI) and Machine Learning (ML), is still a challenge for many industries in the modern age. Quick transformation of business’ models and enhancement of consumer expectation are fundamental elements of Intelligent Process Automation (IPA) to boosting delivery and production of goods and services. In this research contribution, we will evaluate IPAs and their influence in the management of industries. In that case, major elements of AI and ML will be discussed comprehensively. Areas of application and analysis will be discussed in relation to digital industries. The results in this contribution will be used as a recommend action plan for industries to enhance their management and optimization when it comes to AI and ML.
Keywords
Intelligent Process Automation (IPA), Artificial Intelligence (AI), Machine Learning (ML)
R. Oakey, "Are disruptive technologies disruptive [disruptive technologies]", Engineering Management, vol. 17, no. 2, pp. 10-13, 2007. Available: 10.1049/em:20070201.
S. Robbins, "AI and the path to envelopment: knowledge as a first step towards the responsible regulation and use of AI-powered machines", AI & SOCIETY, vol. 35, no. 2, pp. 391-400, 2019. Available: 10.1007/s00146-019-00891-1.
I. Parshina and E. Frolov, "Development of a digital twin of the production system on the basis of modern digital technologies", Russian Journal of Industrial Economics, vol. 13, no. 1, pp. 29-34, 2020. Available: 10.17073/2072-1633-2020-1-29-34.
A. Ogawa and T. Hori, "Error detection and accuracy estimation in automatic speech recognition using deep bidirectional recurrent neural networks", Speech Communication, vol. 89, pp. 70-83, 2017. Available: 10.1016/j.specom.2017.02.009.
H. Miyazaki, "Information Technology in Industrial Applications. III. AI, Petri Net, & Genetic Algorithm Application Technology in Industrial Systems. 1. AI Application Technology.", IEEJ Transactions on Industry Applications, vol. 113, no. 12, pp. 1355-1358, 1993. Available: 10.1541/ieejias.113.1355.
K. Tangrand and B. Bremdal, "Using Deep Learning Methods to Monitor Non-Observable States in a Building", Proceedings of the Northern Lights Deep Learning Workshop, vol. 1, p. 6, 2020. Available: 10.7557/18.5159.
O. Prokopenko and V. Omelyanenko, "Marketing aspect of the innovation communications development", Innovative Marketing, vol. 14, no. 2, pp. 41-49, 2018. Available: 10.21511/im.14(2).2018.05.
S. Kwak, "Strategies on Overseas Direct Sales in Cross-Border B2C e-Commerce", The e-Business Studies, vol. 18, no. 6, pp. 279-296, 2017. Available: 10.20462/tebs.2017.12.18.6.279.
I. Lee and Y. Shin, "Machine learning for enterprises: Applications, algorithm selection, and challenges", Business Horizons, vol. 63, no. 2, pp. 157-170, 2020. Available: 10.1016/j.bushor.2019.10.005.
H. Yang, "A special issue on: Bayesian statistics and machine learning in business", Applied Stochastic Models in Business and Industry, vol. 32, no. 3, pp. 309-310, 2016. Available: 10.1002/asmb.2172.
P. Addo, D. Guegan and B. Hassani, "Credit Risk Analysis Using Machine and Deep Learning Models", Risks, vol. 6, no. 2, p. 38, 2018. Available: 10.3390/risks6020038.
M. Leo, S. Sharma and K. Maddulety, "Machine Learning in Banking Risk Management: A Literature Review", Risks, vol. 7, no. 1, p. 29, 2019. Available: 10.3390/risks7010029.
G. Keilbar and W. Wang, "Modelling Systemic Risk Using Neural Network Quantile Regression", SSRN Electronic Journal, 2020. Available: 10.2139/ssrn.3685748.
S. PK, "Enhancing User Experience Using Machine Learning", International journal of simulation: systems, science & technology, 2019. Available: 10.5013/ijssst.a.20.05.01.
T. Wuest, D. Weimer, C. Irgens and K. Thoben, "Machine learning in manufacturing: advantages, challenges, and applications", Production & Manufacturing Research, vol. 4, no. 1, pp. 23-45, 2016. Available: 10.1080/21693277.2016.1192517.
S. Konopatov and N. Saliyenko, "Platform-based business models", Economics and Environmental Management, pp. 21-32, 2018. Available: 10.17586/2310-1172-2018-11-1-21-32.
P. Pereira, "Using ML Models to Detect Malicious Traffic: Testing ML Models", International Journal for Information Security Research, vol. 10, no. 1, pp. 906-909, 2020. Available: 10.20533/ijisr.2042.4639.2020.0104.
A. Rogers, O. Kovaleva, M. Downey and A. Rumshisky, "Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks", Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 05, pp. 8722-8731, 2020. Available: 10.1609/aaai.v34i05.6398.
V. Reshetnyk, "Information in the Natural Cyber Systems (To Definition of the Term Information)", Journal of Automation and Information Sciences, vol. 52, no. 8, pp. 59-67, 2020. Available: 10.1615/jautomatinfscien.v52.i8.50.
S. Dey and A. Das, "Robotic process automation: assessment of the technology for transformation of business processes", International Journal of Business Process Integration and Management, vol. 9, no. 3, p. 220, 2019. Available: 10.1504/ijbpim.2019.100927.
K. Gharehbaghi, "Process Automation in Intelligent Transportation System (ITS)", International Journal of Machine Learning and Computing, vol. 8, no. 3, pp. 294-297, 2018. Available: 10.18178/ijmlc.2018.8.3.702.
V. Kommera, "Robotic Process Automation", American Journal of Intelligent Systems, vol. 9, no. 2, pp. 49-53, 2019. Available: 10.5923/j.ajis.20190902.01.
Acknowledgements
Author(s) thanks to Dr.Jian'an Sun for this research completion and Data validation support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Filippo Fabrocini
Filippo Fabrocini
School of Software, Beijing Institute of Technology, Beijing, China, 100081.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Filippo Fabrocini and Jian'an Sun, “Intelligent Process Automation of Industries Using Artificial Intelligence and Machine Learning”, Journal of Computing and Natural Science, vol.1, no.2, pp. 045-056, April 2021. doi: 10.53759/181X/JCNS202101009.
