Blockchain and Machine Learning as Deep Reinforcement

Authors

  • Hiba Salah Mahdi The Students Affairs and Registration Department. University Baghdad , Iraq

DOI:

https://doi.org/10.31185/wjcm.103

Keywords:

Blockchain, Machine learning

Abstract

Due to its capacity to make wise decisions, deep learning has become extremely popular in recent years. The current generation of deep learning, which heavily rely centralized servers, are unable to offer attributes like operational transparency, stability, security, and reliable data provenance. Additionally, Single point of failure is a problem that deep learning designs are susceptible since they need centralized data to train them. We review the body of research on the application of deep learning to blockchain. We categorize and arrange the literature for developing topic taxonomy based their criteria: Application domain, deep learning-specific consensus mechanisms, goals for deployment and blockchain type. To facilitate meaningful discussions, we list the benefits and drawbacks of the most cutting-edge blockchain-based deep learning frameworks.

References

Y. Wu, Z. Wang, Y. Ma, and V. Leung, “Deep reinforcement learning for blockchain in industrial IoT: A survey,” Computer Networks, vol. 191, 2021.

S. Ayyoubzadeh, S. Ayyoubzadeh, H. Zahedi, M. Ahmadi, and S. Kalhori, “Predicting COVID-19 incidence through analysis of google trends data in Iran: data mining and deep learning pilot study,” JMIR Public Health Surv, vol. 6, no. 2, pp. 18828–18828, 2020.

J. Shuja, E. Alanazi, W. Alasmary, and A. Alashaikh, “COVID-19 open source data sets: a comprehensive survey,” Appl. Intell, vol. 51, no. 3, pp. 1296–1325, 2021.

D. Berman, A. Buczak, J. Chavis, and C. Corbett, “A survey of Deep learning methods for cyber security,” Information, vol. 10, no. 4, pp. 122–122, 2019.

S. Lawrence and C. Giles, “Overfitting and Neural networks: conjugate gradient and backpropagation,” Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN Neural Computing: New Challenges and Perspectives for the New Millenniu, vol. 1, pp. 114–119, 2000.

B. Mohanta, D. Jena, U. Satapathy, and S. Patnaik, “Survey on IoT Security: Challenges and Solution using Machine Learning,” Artificial Intelligence and Blockchain Technology. Internet Things, vol. 11, pp. 100227–100227, 2020.

Y. Sun, L. Zhang, G. Feng, B. Yang, B. Cao, and M. Imran, “Blockchain-enabled wireless Internet of Things: Performance analysis and optimal communication node deployment,” IEEE Internet Things Journal, vol. 6, pp. 5791–5802, 2019.

F. Jameel, Z. Hamid, F. Jabeen, S. Zeadally, and M. Javed, “A survey of device-to-device communications: Research issues and challenges,” IEEE Commun. Surv. Tutorials, vol. 20, pp. 2133–2168, 2018.

K. Bansal, K. Mittal, G. Ahuja, A. Singh, and S. Gill, “Deep Bus: Machine learning based real time pothole detection system for smart transportation using IoT,” Internet Technology, vol. 3, pp. 156–156, 2020.

P. Kaur, A. Singh, and S. Gill, “An Integrated Approach to Improve QoS in AODV, DSR and DSDV Routing Protocols for FANETS Using the Chain Mobility Model Computer Jpurnal,” 2020.

S. Narayan and G. Tagliarini, “An analysis of underfitting in MLP networks,” Proceedings. 2005 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 984–988, 2005.

T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, and P. Dhariwal, “Language models are few-shot learners,” 2020.

D. Mao, Z. Hao, F. Wang, and H. Li, “Innovative Blockchain-Based Approach for Sustainable and Credible Environment in Food Trade: A Case Study in Shandong Province,” China. Sustainability, pp. 10–10, 2018.

M. Shiraz, A. Gani, R. Ahmad, S. Shah, A. Karim, and Z. Rahman, “A lightweight distributed framework for computational offloading in mobile cloud computing,” PLoS ONE, vol. 9, no. 8, pp. 102270–102279, 2014.

R. Ahmad, K. Salah, R. Jayaraman, I. Yaqoob, S. Ellahham, and M. Omar, “The role of blockchain technology in telehealth and telemedicine,” International Journal Med. Inf, vol. 148, pp. 104399–104399, 2021.

L. Bach, B. Mihaljevic, and M. Zagar, “Comparative analysis of blockchain consensus algorithms,” 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE 2018, pp. 1545–1550, 2018.

Z. Zheng, S. Xie, H. Dai, W. Chen, X. Chen, and Weng, “An overview on smart contracts: Challenges, advances and platforms,” Fut. Gen. Comput. Syst, vol. 105, pp. 475–491, 2020.

W. Ren, J. Hu, T. Zhu, Y. Ren, and K. Choo, “A flexible method to defend against computationally resourceful miners in blockchain Proof-ofwork,” Inf. Sci, vol. 507, pp. 161–171, 2020.

R. Ahmad, H. Hasan, I. Yaqoob, K. Salah, R. Jayaraman, and M. Omar, “Blockchain for aerospace and defense: opportunities and open research challenges,” Comput. Ind. Eng, vol. 151, pp. 106982–106982, 2021.

K. Sarpatwar, R. Vaculin, H. Min, G. Su, T. Heath, and D. Dillenberger, Towards enabling trusted artificial intelligence via blockchain. In: Policy-based autonomic data governance. Berlin: Springer, 2019.

R. Shinde, S. Patil, K. Kotecha, and K. Ruikar, “Blockchain for securing ai applications and open innovations,” J. Open Innov, vol. 7, no. 3, pp. 189–189, 2021.

K. Yeow, A. Gani, R. Ahmad, J. Rodrigues, and K. Ko, “Decentralized consensus for edge-centric internet of things: a review, taxonomy, and research issues,” IEEE Access, vol. 6, pp. 1513–1524, 2017.

M. Sookhak, M. Jabbarpour, N. Safa, and F. Yu, “Blockchain and smart contract for access control in healthcare: a survey, issues and challenges, and open issues,” J. Netw. Comput. Appl, vol. 178, pp. 102950–102950, 2021.

R. Ahmad, K. Salah, R. Jayaraman, I. Yaqoob, S. Ellahham, and M. Omar, “Blockchain and COVID-19 pandemic: applications and challenges,” 2020.

J. Benet, “IPFS - Content Addressed, versioned, P2P file system,” 2014.

A. Lakshman and P. Malik, “Cassandra: a decentralized structured storage system,” SIGOPS Oper. Syst. Rev, vol. 44, no. 2, pp. 35–40, 2010.

S. Wilkinson, T. Boshevski, J. Brandoff, and V. Buterin, “Storj: a peer-to-peer cloud storage network,” 2014.

T. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, and Y. Ma, “PCANet: a simple deep learning baseline for image classification?,” IEEE Trans. Image Process, vol. 24, no. 12, pp. 5017–5032, 2015.

Z. Zhao, P. Zheng, S. Xu, and X. Wu, “Object detection with deep learning: a review,” IEEE Trans. Neural Netw. Learn. Syst, vol. 30, no. 11, pp. 3212–3232, 2019.

M. Daily, S. Medasani, R. Behringer, and M. Trivedi, “Self-driving cars,” Computer, vol. 50, pp. 18–23, 2017.

T. Hassan, B. Hassan, A. El-Baz, and N. Werghi, “A dilated residual hierarchically fashioned segmentation framework for extracting Gleason tissues and grading prostate cancer from whole slide images,” 2021.

T. Sheridan, “Human-robot interaction: status and challenges,” Hum Factors, vol. 58, no. 4, pp. 525–532, 2016.

C. Mcclelland, “The Difference between artificial intelligence, machine learning, and deep learning,” 2017.

B. Mcmahan, E. Moore, D. Ramage, S. Hampson, and B. Arcas, “Communication-efficient learning of deep networks from decentralized data,” Artificial intelligence and statistics. PMLR, pp. 1273–1282, 2017.

T. Hassan, B. Hassan, M. Akram, S. Hashmi, A. Taguri, and N. Werghi, “Incremental cross-domain adaptation for robust retinopathy screening via Bayesian deep learning,” IEEE Trans. Instrum. Measur, vol. 70, pp. 1–14, 2021.

T. Hassan, S. Aslam, and J. Jang, “Fully automated multi-resolution channels and multithreaded spectrum allocation protocol for IoT based sensor nets,” IEEE Access, vol. 6, pp. 545–556, 2018.

B. Marr, “Artificial intelligence and blockchain: 3 major benefits of combining these two mega-trends,” 2018.

https://www.forbes.com/sites/bernardmarr/2018/03/02/artificial-intelligenceand-blockchain-3-major-benefits-of-combining-these twomegatrends/?sh=604fcaa04b44.

D. Campbell, “Combining AI and blockchain to push frontiers in healthcare,” 2018. https://www.macadamian.com/learn/combining-ai-andblockchain-in-healthcare/.

K. Hassan, F. Tahir, M. Rehan, C. Ahn, and M. Chadli, “On relative-output feedback approach for group consensus of clusters of multiagent systems,” IEEE Trans. Cybern security, pp. 1–12, 2021.

D. Magazzeni, P. Mcburney, and W. Nash, “Validation and verification of smart contracts: a research agenda,” Computer, vol. 50, no. 9, pp. 50–57, 2017.

S. Nakamoto, “Bitcoin: a peer-to-peer electronic cash system,” Decentralized Business Review, pp. 21260–21260, 2008.

T. Dinh, J. Wang, G. Chen, R. Liu, B. Ooi, and K. Tan, “BLOCKBENCH: A Framework for Analyzing Private Blockchains,” 2017.

A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017.

D. Rumelhart and J. Mcclelland, “Learning Internal Representations by Error Propagation,” 1987. 318-362.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput, vol. 9, no. 8, pp. 1735–1780, 1997.

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” NIPS Workshop on Deep Learning, 2014.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Farley, and S. Ozair, “Generative Adversarial Nets, Ser NIPS’,” vol. 14, pp. 2672–2680, 2014.

V. Mnih, K. Kavukcuoglu, D. Silver, and A. Rusu, “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.

M. Bronstein, J. Bruna, Y. Lecun, A. Szlam, and P. Van-Dergheynst, “Geometric deep learning: going beyond Euclidean data,” IEEE Signal Process. Mag, vol. 34, no. 4, pp. 18–42, 2017.

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Published

2023-03-30

Issue

Section

Computer

How to Cite

[1]
hiba mahdi, “Blockchain and Machine Learning as Deep Reinforcement ”, WJCMS, vol. 2, no. 1, pp. 46–53, Mar. 2023, doi: 10.31185/wjcm.103.