Digital transformation through advances in artificial intelligence and machine learning

被引:16
|
作者
Malik, Hasmat [1 ]
Chaudhary, Gopal [2 ]
Srivastava, Smriti [3 ]
机构
[1] Natl Univ Singapore, BEARS, NUS Campus, Singapore, Singapore
[2] Bharati Vidyapeeths Coll Engn, Delhi, India
[3] NSUT Delhi, Dept ICE, Delhi, India
关键词
Digital transformation; advancement; artificial intelligence; machine learning; application; data analytics; cyber-security; condition monitoring; fault detection and diagnosis; prediction; forecasting; renewable energy; feature extraction; feature selection; healthcare; greater sustainability; resilient infrastructure; automation; BIOMETRIC SYSTEM; POWER-SYSTEM; OPTIMIZATION; ALGORITHM; MANAGEMENT; MPPT; IDENTIFICATION; STRATEGY; MODEL; EMD;
D O I
10.3233/JIFS-189787
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The digital transformation (DT) is the acquiring the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading the manual process into the automation. The DT enable the efficacy of the system via automation, innovation, creativities. The another concept of DT in the engineering domain is to replace the manual and/or conventional process by means of automation to handle the big-data problems in an efficient way and harness the static/dynamic system information without knowing the system parameters. The DT represents the both opportunities and challenges to the developer and/or user in an organization, such as development and adaptation of new tool and technique in the system and society with respect to the various applications (i.e., digital twin, cybersecurity, condition monitoring and fault detection & diagnosis (FDD), forecasting and prediction, intelligent data analytics, healthcare monitoring, feature extraction and selection, intelligent manufacturing and production, future city, advanced construction, resilient infrastructure, greater sustainability etc.). Additionally, due to high impact of advanced artificial intelligent, machine learning and data analytics techniques, the harness of the profit of the DT is increased globally. Therefore, the integration of DT into all areas deliver a value to the both users as well as developer. In this editorial fifty-two different applications of DT of distinct engineering domains are presented, which includes its detailed information, state-of-the-art, methodology, proposed approach development, experimental and/or emulation-based performance demonstration and finally conclusive summary of the developed tool/technique along with the future scope.
引用
收藏
页码:615 / 622
页数:8
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