Scalability Challenges in Combining AI with Blockchain
A Performance-Centric Analysis
Keywords:
Artificial intelligence, blockchain, scalability, computational overhead, decentralized systemsAbstract
The integration of artificial intelligence (AI) with blockchain technology has garnered significant attention in recent years due to the potential benefits of combining decentralized data management with intelligent processing capabilities. However, this integration presents notable scalability challenges that hinder the performance of decentralized AI systems. This paper analyzes the key challenges associated with scaling AI models within blockchain networks, particularly focusing on computational overhead, transaction throughput, and latency issues. Various strategies for enhancing the performance of decentralized AI systems are discussed, including optimization techniques, hybrid architectures, and the implementation of advanced consensus mechanisms. Ultimately, this research aims to provide insights into the performance-centric considerations necessary for overcoming scalability challenges in the AI-blockchain landscape.
References
Gayam, Swaroop Reddy. "Deep Learning for Autonomous Driving: Techniques for Object Detection, Path Planning, and Safety Assurance in Self-Driving Cars." Journal of AI in Healthcare and Medicine 2.1 (2022): 170-200.
Chitta, Subrahmanyasarma, et al. "Decentralized Finance (DeFi): A Comprehensive Study of Protocols and Applications." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 124-145.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Real-Time Logistics and Transportation Optimization in Retail Supply Chains: Techniques, Models, and Applications." Journal of Machine Learning for Healthcare Decision Support 1.1 (2021): 88-126.
Putha, Sudharshan. "AI-Driven Predictive Analytics for Supply Chain Optimization in the Automotive Industry." Journal of Science & Technology 3.1 (2022): 39-80.
Sahu, Mohit Kumar. "Advanced AI Techniques for Optimizing Inventory Management and Demand Forecasting in Retail Supply Chains." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 190-224.
Kasaraneni, Bhavani Prasad. "AI-Driven Solutions for Enhancing Customer Engagement in Auto Insurance: Techniques, Models, and Best Practices." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 344-376.
Vangoor, Vinay Kumar Reddy, et al. "Energy-Efficient Consensus Mechanisms for Sustainable Blockchain Networks." Journal of Science & Technology 1.1 (2020): 488-510.
Kondapaka, Krishna Kanth. "AI-Driven Inventory Optimization in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 377-409.
Kasaraneni, Ramana Kumar. "AI-Enhanced Supply Chain Collaboration Platforms for Retail: Improving Coordination and Reducing Costs." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 410-450.
Pattyam, Sandeep Pushyamitra. "Artificial Intelligence for Healthcare Diagnostics: Techniques for Disease Prediction, Personalized Treatment, and Patient Monitoring." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 309-343.
Kuna, Siva Sarana. "Utilizing Machine Learning for Dynamic Pricing Models in Insurance." Journal of Machine Learning in Pharmaceutical Research 4.1 (2024): 186-232.
George, Jabin Geevarghese. "Augmenting Enterprise Systems and Financial Processes for transforming Architecture for a Major Genomics Industry Leader." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 242-285.
Katari, Pranadeep, et al. "Cross-Chain Asset Transfer: Implementing Atomic Swaps for Blockchain Interoperability." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 102-123.
Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "SLP (Systematic Layout Planning) for Enhanced Plant Layout Efficiency." International Journal of Science and Research (IJSR) 13.6 (2024): 820-827.
Venkata, Ashok Kumar Pamidi, et al. "Implementing Privacy-Preserving Blockchain Transactions using Zero-Knowledge Proofs." Blockchain Technology and Distributed Systems 3.1 (2023): 21-42.
Namperumal, Gunaseelan, Debasish Paul, and Rajalakshmi Soundarapandiyan. "Deploying LLMs for Insurance Underwriting and Claims Processing: A Comprehensive Guide to Training, Model Validation, and Regulatory Compliance." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 226-263.
Yellepeddi, Sai Manoj, et al. "Blockchain Interoperability: Bridging Different Distributed Ledger Technologies." Blockchain Technology and Distributed Systems 2.1 (2022): 108-129.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
