Retracted: A Framework for Hierarchical Big Image Data

Mylavarapu, Bharadwaja Krishnadev; Mylavarapu, Raghu T

doi:10.1109/icicct.2018.8473339

Cited by 1 publication

(1 citation statement)

References 10 publications

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“…Then make a neural network with 500 hidden layers, add input layer, output layer next and implement ANN with all data. Figure 6 is representing the architectural overview [23].…”

Section: Architectural Implementationmentioning

confidence: 99%

Deep Learning Approach for Customer Records Prediction for Future Products Issues

Chandrasekaran¹

2019

JCC

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Artificial neural networks consist of the mechanisms of identifying the solution for the problem with the layers design and implementation, and the hidden layers will have rules to be done by the model. The model consists of different approaches, and based on the priority and the requirement, we need to create a model, and this article is dealing with the prediction related to bank customers and their loan processing, etc. In this article, we have a global dataset collected related to 10000 customers of a single bank and their account and other details related to the customers of the bank. Here, we are implementing using tensor flow and Keras libraries to create an artificial neural network which will work on the model and hidden layers. The hidden layers are the most import part of the presentation, and the virtual environment in the field can be helpful for the better prediction of the related things. Machine learning implementations with the combination of deep learning artificial neural networks and also with tensor flow and Keras will be the most exciting and attractive portion of the research work in any field of science and technology. This architecture and application will help to predict future bank applications, and this can be helpful for the customers to understand their level of applications and products usage based on their account weight.

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“…Then make a neural network with 500 hidden layers, add input layer, output layer next and implement ANN with all data. Figure 6 is representing the architectural overview [23].…”

Section: Architectural Implementationmentioning

confidence: 99%

Deep Learning Approach for Customer Records Prediction for Future Products Issues

Chandrasekaran¹

2019

JCC

View full text Add to dashboard Cite

show abstract

Retracted: A Framework for Hierarchical Big Image Data

Cited by 1 publication

References 10 publications

Deep Learning Approach for Customer Records Prediction for Future Products Issues

Deep Learning Approach for Customer Records Prediction for Future Products Issues

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