Smart EDGE Based Tensor Flow Quantum Learning Model for Rural Electrification of Smart Nation
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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.
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