Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis

This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory (PQM) and the possibility to train artificial neural networks (ANN) in superposition. We obtain an exponential quantum speedup in the evaluation of neural networks. We also verify experimentally through a reduced experimental analysis that the proposed algorithm can be used to select near-optimal neural networks.

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Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

Applied Sciences ◽

10.3390/app9173502 ◽

2019 ◽

Vol 9 (17) ◽

pp. 3502 ◽

Cited By ~ 4

Author(s):

Nicola Baldo ◽

Evangelos Manthos ◽

Matteo Miani

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Cross Validation ◽

Laboratory Data ◽

Stiffness Modulus ◽

Artificial Neural ◽

Marshall Stability ◽

Mean Square Errors ◽

Fold Cross Validation

The present paper discusses the analysis and modeling of laboratory data regarding the mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ) were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear transfer function. The effectiveness of such an approach was verified in terms of the coefficients of correlation ( R ) and mean square errors; in particular, R values were within the range 0.965 – 0.919 in the training phase and 0.881 – 0.834 in the CV testing phase, depending on the predicted parameters.

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Comparison of Logistic Regression and Artificial Neural Network based Bankruptcy Prediction Models

International Journal of Business Analytics and Intelligence ◽

10.21863/ijbai/2015.3.1.004 ◽

2015 ◽

Vol 3 (1) ◽

Author(s):

Easwaran Iyer ◽

Vinod Kumar Murti

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Logistic Regression ◽

Artificial Neural Networks ◽

Prediction Models ◽

Bankruptcy Prediction ◽

Manufacturing Companies ◽

Recent Developments ◽

Artificial Neural

Logistic Regression is one of the popular techniques used for bankruptcy prediction and its popularity is attributed due to its robust nature in terms of data characteristics. Recent developments have explored Artificial Neural Networks for bankruptcy prediction. In this study, a paired sample of 174 cases of Indian listed manufacturing companies have been used for building bankruptcy prediction models based on Logistic Regression and Artificial Neural Networks. The time period of study was year 2000 through year 2009. The classification accuracies have been compared for built models and for hold-out sample of 44 paired cases. In analysis and hold-out samples, both the models have shown appreciable classification results, three years prior to bankruptcy. Thus, both the models can be used (by banks, SEBI etc.) for bankruptcy prediction in Indian Context, however, Artificial Neural Network has shown marginal supremacy over Logistic Regression.

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