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

arXiv: https://arxiv.org/abs/2104.02425 · HTML: https://mlsanity.com/paper/2104.02425/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 218 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 20 of 20)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [PRADA: Protecting against DNN Model Stealing Attacks](https://mlsanity.com/paper/1805.02628/) (2018)
- [Adversarial Machine Learning -- Industry Perspectives](https://mlsanity.com/paper/2002.05646/) (2020)
- [PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning](https://mlsanity.com/paper/2004.05682/) (2020)
- [Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge](https://mlsanity.com/paper/2101.07511/) (2021)
- [Adversarial Attacks on Deep Neural Networks for Time Series Classification](https://mlsanity.com/paper/1903.07054/) (2019)
- [Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/1901.07947/) (2019)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Failure Modes in Machine Learning Systems](https://mlsanity.com/paper/1911.11034/) (2019)
- [Visual Sentiment Analysis from Disaster Images in Social Media](https://mlsanity.com/paper/2009.03051/) (2020)
- [Sentiment Analysis from Images of Natural Disasters](https://mlsanity.com/paper/1910.04416/) (2019)
- [5G network slicing using SDN and NFV- A survey of taxonomy, architectures and future challenges](https://mlsanity.com/paper/1912.02802/) (2019)
- [FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy](https://mlsanity.com/paper/1802.03750/) (2018)
- [The Role of Big Data Analytics in Industrial Internet of Things](https://mlsanity.com/paper/1904.05556/) (2019)
- [Private 5G: The Future of Industrial Wireless](https://mlsanity.com/paper/2006.01820/) (2020)
- [Artificial Intelligence and Data Science in the Automotive Industry](https://mlsanity.com/paper/1709.01989/) (2017)
- [Scalable and Privacy-preserving Design of On/Off-chain Smart Contracts](https://mlsanity.com/paper/1902.06359/) (2019)
- [Quality4.0 -- Transparent product quality supervision in the age of Industry 4.0](https://mlsanity.com/paper/2011.06502/) (2020)

## Built on by (most cited first, 3 of 3)

- [An Explainable Decision Support System for Predictive Process Analytics](https://mlsanity.com/paper/2207.12782/) (2022)
- [Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research](https://mlsanity.com/paper/2206.14153/) (2022)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)

## 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.