mlsanityData to 7 Oct 2026

The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions

arXiv 2104.02425 ↗First posted 6 Apr 2021cs.AIcited by 3

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Abstract (arXiv)

The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers, has paved the way for Artificial Intelligence (AI) and Big Data in support of smart manufacturing. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, Big data, Blockchain, 5G communications, in support of smart manufacturing and the dynamical processes in modern industries. In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective towards deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and Big Data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and Big Data methods in smart industries with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. In a nutshell, we have explored the significance of AI and Big data towards Industry 4.0 applications through panoramic reviews and discussions. We believe, this work will provide a baseline for future research in the domain.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 218 references that are in the map. First posted on arXiv 6 Apr 2021.

Builds on

20 of 218 references.

References · 20

  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  2. 2019Federated Machine Learning: Concept and Applicationscited by 409
  3. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 234
  4. 2018PRADA: Protecting against DNN Model Stealing Attackscited by 159
  5. 2020Adversarial Machine Learning -- Industry Perspectivescited by 121
  6. 2020PatchAttack: A Black-box Texture-based Attack with Reinforcement Learningcited by 67
  7. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  8. 2019Adversarial Attacks on Deep Neural Networks for Time Series Classificationcited by 53
  9. 2019Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Surveycited by 27
  10. 2020Explainable Predictive Process Monitoringcited by 27
  11. 2019Failure Modes in Machine Learning Systemscited by 15
  12. 2020Visual Sentiment Analysis from Disaster Images in Social Mediacited by 14
8 more
  1. 2019Sentiment Analysis from Images of Natural Disasterscited by 10
  2. 20195G network slicing using SDN and NFV- A survey of taxonomy, architectures and future challengescited by 9
  3. 2018FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategycited by 7
  4. 2019The Role of Big Data Analytics in Industrial Internet of Thingscited by 6
  5. 2020Private 5G: The Future of Industrial Wirelesscited by 2
  6. 2017Artificial Intelligence and Data Science in the Automotive Industrycited by 1
  7. 2019Scalable and Privacy-preserving Design of On/Off-chain Smart Contractscited by 1
  8. 2020Quality4.0 -- Transparent product quality supervision in the age of Industry 4.0cited by 1

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Cited by year

Citing papers per year: 2022: 3.

3 papers.

  1. 2022An Explainable Decision Support System for Predictive Process Analyticscited by 8
  2. 2022Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Researchcited by 2
  3. 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2