The digital transformation revolution is targeting all aspects of our lives. Keywords: digital transformation, advanced manufacturing and engineering, bibliometric analysis, topic modeling, systematic review The study also contributes to adopting and demonstrating the ML-based topic modeling for intelligent and systematic bibliometric analysis, particularly for unveiling advanced engineering research trends through domain literature. Six dominant topics are identified, namely smart factory, sustainability and product-service systems, construction digital transformation, public infrastructure-centric digital transformation, techno-centric digital transformation, and business model-centric digital transformation. A systematic review process is developed based on the comprehensive DT in manufacturing systems and engineering literature (i.e., 99 articles). Our work addresses the challenge of understanding DT trends by presenting a machine learning (ML) approach for topic modeling to review and analyze advanced DT technology research and development. Studies of DT are growing rapidly and heterogeneously, covering the aspects of product design, engineering, production, and life-cycle management due to the fast and market-driven industrial development under Industry 4.0. DT intertwines with customer requirements, domain knowledge, and theoretical and empirical insights for value propagations. Sufficient experiments show that ELM-RBF-SAGA has excellent performance in multi-label classification.ĭigital transformation (DT) is the process of combining digital technologies with sound business models to generate great value for enterprises. One is used for adjusting the range of fitness value, the other is applied to update crossover and mutation probability. In addition, two optimization methods are employed collaboratively in SAGA. In ELM-RBF-SAGA, we present a synergistic adaptive genetic algorithm (SAGA) to optimize the performance of ELM-RBF. To this end, a modified ELM-RBF with a synergistic adaptive genetic algorithm (ELM-RBF-SAGA) is proposed in this paper. However, because of the lack of effective optimization methods, conventional extreme learning machines are always unstable and tend to fall into local optimum, which leads to low prediction accuracy in practical applications. Different from traditional machine learning methods which are time-consuming during the training phase, ELM-RBF (extreme learning machine-radial basis function) is more efficient and has become a research hotspot in multi-label classification. As a result, multi-label classification has aroused widespread concern. Profiting from the great progress of information technology, a huge number of multi-label samples are available in our daily life. The experimental results from applying the hybrid approach on synthetic program transformation problems show a significant improve in the optimized output on which the hybrid approach achieved an LoC decline rate of 50.51% over the application of basic genetic algorithm only where 17.34% LoC decline rate was reached. In this research we targeted the program size, to reach the lowest possible decline rate of the number of Lines of Code (LoC) of a targeted program. It succeeded in optimizing the search process for the optimal program transformation sequence that targets a specific optimization goal. In this paper, we introduce a hybrid approach for program optimization. Developing applications that run on top of mobile devices requires the software developer to consider the limited resources of these devices, which on one side give them their mobile advantages, however, on the other side, if an application is developed without the consideration of these limited resources then the mobile application will neither work properly nor allow the device to run smoothly. Software development as well as other branches of software engineering has been affected by this progress. ![]() The vast field of software engineering that has witnessed a significant progress in the past years is responsible for this form of digital transformation. One form of the digital transformation revolution appears in the transformation of our routine everyday tasks into computer executable programs in the form of web, desktop and mobile applications. The digital transformation revolution has been crawling toward almost all aspects of our lives.
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