Optimized Deep Neural Network Based Predictive Model for Customer Attrition Analysis in Banking Sector

2019 ◽  
Vol 13 ◽  
Author(s):  
Sandeep Kumar Hegde ◽  
Monica R Mundada
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Deep Neural Network ◽  
Banking Sector ◽  
Customer Attrition ◽  
Attrition Analysis

‘This is a House’: Large Image Collections and Their Platform Embeddings

2021 ◽  
pp. 135485652110520
Author(s):  
Adrian Mackenzie
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Deep Neural Network ◽  
Satellite Images ◽  
Predictive Modelling ◽  
Active Role ◽  
Human Settlement ◽  
The World

This paper analyses the active role of image collections in supporting platforms and their operations. Large image collections are increasingly present on media, scientific and other platforms. A case study of Facebook’s predictive modelling of satellite images of human settlement exemplifies how image collections are changing. The treatment of images in a predictive model – a deep neural network – constructs a condensed indexical field, a field that allows the platform to generate referential statements about the world. Under platform conditions, image collections function less as archives or records and more as densely woven indexical fields that orient, position and embed the platform. In describing the transformation of image collections, the paper points to important changes in how platforms use images to position themselves in the world.

scholarly journals Digital Bank Runs: A Deep Neural Network Approach

2021 ◽  
Vol 13 (3) ◽  
pp. 1513
Author(s):  
Marc Sanchez-Roger ◽  
Esther Puyol-Antón
Keyword(s):  
Neural Network ◽  
Commercial Banks ◽  
Deep Neural Network ◽  
Banking Sector ◽  
Financial System ◽  
Bank Runs ◽  
Neural Network Approach ◽  
Bank Deposits ◽  
System Risk ◽  
The Impact

The introduction of Central Bank Digital Currency (CBDC) could represent a deep structural change to the financial sector, and in particular to the banking sector. This paper proposes a Deep Neural Network (DNN) design to model the introduction of CBDC and its potential impact on commercial banks’ deposits. The model proposed forecasts the likelihood of the occurrence of bank runs as a function of the system characteristics and of the intrinsic features of CBDC. The success rate of CBDC and the impact on the banking sector is highly dependent on its design. Whether CBDC should carry any form of interest, if the amount of CBDC should be capped by account or if convertibility from banks’ deposits should be guaranteed by commercial banks are important features to consider. Further, the design of CBDC needs to contribute to enhancing the sustainability of the financial system, hence a CBDC design that promotes financial inclusion is paramount. The model is initially calibrated with Euro area system data. Results show that an increase in the financial system risk perception would trigger a significant transfer of wealth from bank deposits to CBDC, while the wealth transfer to CBDC is to a lesser extent also sensitive to its interest rate.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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