Advancements in Artificial Intelligence, Machine Learning and Deep Learning, Robotics and Industry 4.0: A Systematic Review on Application, Issues, and Electronic Markets
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© 2024 by IJCTT Journal
Volume-72 Issue-10
Year of Publication : 2024
Authors : Kishorebabu Tenneti, Susmitha Pandula
DOI :  10.14445/22312803/IJCTT-V72I10P109

How to Cite?

Kishorebabu Tenneti, Susmitha Pandula, "Advancements in Artificial Intelligence, Machine Learning and Deep Learning, Robotics and Industry 4.0: A Systematic Review on Application, Issues, and Electronic Markets," International Journal of Computer Trends and Technology, vol. 72, no. 10, pp. 50-56, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I10P109

Abstract
This systematic review discusses state-of-the-art techniques utilizing AI derivatives: machine learning (ML) and deep learning (DL). In the current paper, five selected sources are discussed to demonstrate the significant transition brought by ML and DL in application fields such as robotics, Industry 4.0, and electronic markets. As the review highlights, these technologies improve existence’s independence, effectiveness, and decision-making in intricate procedures. Top issues, including data issues, model explainability, and ethical issues, are noted, stressing the importance of future research and the creation of the Post-RAE XAI systems. The findings of this research propose new avenues for future investigations on the current state-of-the-art and prospects of AI applications unified by ML and DL to improve human lives across the globe.

Keywords
Applications of Machine Learning, Artificial Intelligence, Challenges in Machine Learning, Deep Learning, ML Algorithms.

Reference

[1] J. Matthew Helm et al., “Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions,” Current Reviews in Musculoskeletal Medicine, vol. 13, pp. 69-76, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Rahul Rai et al., “Machine Learning in Manufacturing and Industry 4.0 Applications,” International Journal of Production Research, vol. 59, no. 16, pp. 4773-4778, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Georgios Lampropoulos, “Artificial Intelligence, Big Data, And Machine Learning in Industry 4.0,” In Encyclopedia of Data Science and Machine Learning, pp. 2101-2109, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Marek Nagy, George Lăzăroiu, and Katarina Valaskova, “Machine Intelligence and Autonomous Robotic Technologies in the Corporate Context of Smes: Deep Learning and Virtual Simulation Algorithms, Cyber-Physical Production Networks, And Industry 4.0-Based Manufacturing Systems,” Applied Sciences, vol. 13, no. 3, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Arzam Kotriwala et al., “XAI for Operations in the Process Industry-Applications, Theses, and Research Directions.” In Proceedings AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering, pp. 1-12, 2021.
[Google Scholar] [Publisher Link]
[6] Mohsen Soori, Behrooz Arezoo, and Roza Dastres, “Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, A Review,” Cognitive Robotics, vol. 3, pp. 54-70, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Sergio Cebollada et al., “A State-of-the-Art Review on Mobile Robotics Tasks Using Artificial Intelligence and Visual Data,” Expert Systems with Applications, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Imran Ahmed, Gwanggil Jeon, and Francesco Piccialli, “From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, And Where,” IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5031-5042, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Bo Zhang, Jun Zhu, and Hang Su, “Toward the Third Generation Artificial Intelligence,” Science China Information Sciences, vol. 66, no. 2, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Christian Janiesch, Patrick Zschech, and Kai Heinrich, “Machine Learning and Deep Learning.” Electronic Markets, vol. 31, no. 3, pp. 685-695, 2021.
[CrossRef] [Google Scholar] [Publisher Link]