Smart EDGE Based Tensor Flow Quantum Learning Model for Rural Electrification of Smart Nation
Main Article Content
Abstract
The objective of the research paper is to propose a tensor quantum neural model with a smart EDGE (Evolving Dimension and Gradient Enlargement) technique. The conventional machine learning technique like recurrent deep learning addresses the vanishing gradient problem using layer wise learning techniques. When the dimensionality of learning changes a greater number of layers are to be included to completely cover the full level of knowledge across the domain. The paper addresses this critical issue and modify the layer wise learning into an EDGE centred tenser flow quantum based neural model. The application of the model is validated on the generated data set through towards rural electrification processes for a smart nation, India. This energy domain involves energy losses, infrastructure facility, feasibility of electrification and environmental issues to determine an amicable solution for the inter-twined complexities towards social reform need. An EDGE Tensor Flow approach in Keras platform towards quantum level processing is modelled and executed with python programming.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
