Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1958
Title: Quantum neural networks for dynamic route identification to avoid traffic
Authors: Boyapati S
Swarna S.R
Kumar A.
Keywords: Machine Learning
Quantum computing
Deep Learning
Big Data
Spark
Routing
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Computation is the primary task performed for the evaluation of the solution for a specific problem, and in realtime, having better challenges to implementing the solution path with the better computational mechanisms. The concept of quantum computation mechanism using the neural networks is having the highest amount of the success rate in prediction models design and implementation. The idea of a dynamic routing mechanism using quantum computing and neural networks are the main essence. A better prediction model is performed for this specific kind of problem, which needs a particular focus on the latest problem-solving mechanisms. The problem-solving tools like neural networks will dynamically perform with real-time data, but a new add-on is needed to add like big data to implement the live data. The live data can help implement and understand the importance of solving the problem like dynamic routing mechanism. There is a chance of random growth in such a field of computer science. This computational mechanism using quantum computing and the neural network will track the live operations and form the dynamic route changes in the real-time scenario. This real-time scenario worked with a 95% accuracy rate. The accuracy will differ based on the number of connecting nodes are being considered to evaluate the hidden layers of the problem-solving mechanism.
URI: 10.1109/I-SMAC49090.2020.9243322
http://hdl.handle.net/123456789/1958
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