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7 results on '"Sarin, Saket"'

4. FuzzyBack—A Hybrid Neuro-Fuzzy Ensemble for Concept Drift Adaptation in Stream Mining Using Neural Network

Author

Sarin, Saket, Singh, Sunil K., Kumar, Sudhakar, Chauhan, Utkarsh, Goyal, Shivam, Singh, Tushar, Priyanshu, Gupta, Brij B., Colace, Francesco, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Pant, Millie, editor, Deep, Kusum, editor, and Nagar, Atulya, editor

Published
2024
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5. Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems.

Author

Sarin, Saket, Singh, Sunil K., Kumar, Sudhakar, Goyal, Shivam, Gupta, Brij Bhooshan, Alhalabi, Wadee, and Arya, Varsha

Subjects
DIGITAL asset management, SWARM intelligence, DIGITAL technology, REINFORCEMENT learning, HIGH technology industries
Abstract

In the rapidly evolving landscape of today's digital economy, Financial Technology (Fintech) emerges as a transformative force, propelled by the dynamic synergy between Artificial Intelligence (AI) and Algorithmic Trading. Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning (MARL) and Explainable AI (XAI) within Fintech, aiming to refine Algorithmic Trading strategies. Through meticulous examination, we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm, employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions. These AI-infused Fintech platforms harness collective intelligence to unearth trends, mitigate risks, and provide tailored financial guidance, fostering benefits for individuals and enterprises navigating the digital landscape. Our research holds the potential to revolutionize finance, opening doors to fresh avenues for investment and asset management in the digital age. Additionally, our statistical evaluation yields encouraging results, with metrics such as Accuracy = 0.85, Precision = 0.88, and F1 Score = 0.86, reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess. [ABSTRACT FROM AUTHOR]

Published
2024
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