Keywords
AI
neural networks
How to Cite
Abstract
Neural-symbolic integration represents a compelling frontier in AI, offering the promise of combining the robust learning capabilities of neural networks with the structured, logical reasoning of symbolic AI. This paper presents a comprehensive review and analysis of the current state of neural-symbolic integration, focusing on approaches that bridge the gap between these two paradigms. We discuss key challenges, such as knowledge representation, learning from limited data, and ensuring interpretability, and survey recent advancements in neural-symbolic integration. We also highlight promising directions for future research and discuss potential applications of neural-symbolic systems in various domains.
References
Tatineni, Sumanth. "Customer Authentication in Mobile Banking-MLOps Practices and AI-Driven Biometric Authentication Systems." Journal of Economics & Management Research. SRC/JESMR-266. DOI: doi. org/10.47363/JESMR/2022 (3) 201 (2022): 2-5.
Vemori, Vamsi. "Towards a Driverless Future: A Multi-Pronged Approach to Enabling Widespread Adoption of Autonomous Vehicles-Infrastructure Development, Regulatory Frameworks, and Public Acceptance Strategies." Blockchain Technology and Distributed Systems 2.2 (2022): 35-59.
Mahammad Shaik, et al. “Unveiling the Achilles’ Heel of Decentralized Identity: A Comprehensive Exploration of Scalability and Performance Bottlenecks in Blockchain-Based Identity Management Systems”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019, pp. 1-22, https://dlabi.org/index.php/journal/article/view/3.
Tatineni, Sumanth. "INTEGRATING AI, BLOCKCHAIN AND CLOUD TECHNOLOGIES FOR DATA MANAGEMENT IN HEALTHCARE." Journal of Computer Engineering and Technology (JCET) 5.01 (2022).