Scaling AI With Quantum Network Models for Back Pain Genetic Architecture

Maheswari, Uma and Ninawe, Swapnil S. and Pokkuluri, Kiran Sree and Pandit, Shraddha V. and Hariram, Venkatesan and Shankar, R Shiva (2024) Scaling AI With Quantum Network Models for Back Pain Genetic Architecture. Springer. pp. 307-320. ISSN 2327-0411

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Abstract

In this exploratory chapter, the incorporation of amount network models to evaluate artificial intelligence (AI) approaches for the purpose of deciphering the inheritable armature that underpins back pain is investigated. Through the utilisation of amount network models, the purpose of this research is to improve the computational efficiency and effectiveness of artificial intelligence algorithms in the process of analysing enormous inheritable datasets that are linked with reverse pain. Finding detailed inheritable patterns and relationships that contribute to back pain vulnerability can be accomplished through the community that exists between quantity computing and network modelling. This community offers interesting paths for. © 2025 Elsevier B.V., All rights reserved.

Item Type: Article
Subjects: Biochemistry, Genetics and Molecular Biology > Genetics
Divisions: Homoeopathy > Vinayaka Mission's Homoeopathic Medical College & Hospital, Salem > Homoeopathic Pharmacy
Depositing User: Unnamed user with email techsupport@mosys.org
Last Modified: 27 Nov 2025 05:24
URI: https://vmuir.mosys.org/id/eprint/1484

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