Abstract Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject’s intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies. 

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Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

How do digitalization capabilities enable open innovation in manufacturing enterprises? A multiple case study based on resource integration perspective

Applications of Blockchain in Industry 4.0: a Review

Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy

According to the Industry 4.0 vision, humans in a smart factory, should be equipped with formidable and seamless communication capabilities and integrated into a cyber-physical system (CPS) that can be utilized to monitor and recognize human activity via artificial intelligence (e.g., deep learning). Recent advances in the accuracy of deep learning have contributed significantly to solving the human activity recognition issues, but it remains necessary to develop high performance deep learning models that provide greater accuracy. In this paper, three models: long short-term memory (LSTM), convolutional neural network (CNN), and combined CNN-LSTM are proposed for classification of human activities. These models are applied to a dataset collected from 36 persons engaged in 6 classes of activities – downstairs, jogging, sitting, standing, upstairs, and walking. The proposed models are trained using TensorFlow framework with a hyper-parameter tuning method to achieve high accuracy. Experimentally, confusion matrices and receiver operating characteristic (ROC) curves are used to assess the performance of the proposed models. The results illustrate that the hybrid model CNN-LSTM provides a better performance than either LSTM or CNN in the classification of human activities. The CNN-LSTM model provides the best performance, with a testing accuracy of 97.76%, followed by the LSTM with a testing accuracy of 96.61%, while the CNN shows the least testing accuracy of 94.51%. The testing loss rates for the LSTM, CNN, and CNN-LSTM are 0.236, 0.232, and 0.167, respectively, while the precision, recall, $F1$ -Measure, and the area under the ROC curves (AUCS) for the CNN-LSTM are 97.75%, 97.77%, 97.76%, and 100%, respectively.

Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition

Triggered by the development of information and communications technologies, Industry 4.0 opens up a new era for the manufacturing industry.Currently, Industry 4.0 has attracted much attention from industry and academia. Research on Industry 4.0 is still evolving towards the development of frameworks linking Industry 4.0’s enabling technologies to specific goals and to their impact on the manufacturers’ businesses.Accordingly, this study presents a systematic review of the scope of Industry 4.0, its goals and implementations, as well as the barriers to the implementation of Industry 4.0. Solutions for overcoming the barriers and challenges are discussed.

Dazhi Chong

Papers

Industry 4.0 and its Implementation: a Review