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With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This paper aims to investigate the preparation of AlN and Al₂O₃, as well as the effect of nano-AlN and nano-Al₂O₃, on friction and wear properties of copper-steel clad plate immersed in the lubricants.

Nano-AlN or nano-Al₂O₃ (0.1, 0.2, 0.3, 0.4 and 0.5 Wt.%) functional fluids were prepared. Their tribological properties were tested by an MRS-10A four-ball friction tester and a ball-on-plate configuration, and scanning electron microscope observed the worn surface of the plate.

An increase in nano-AlN and Al₂O₃ content enhances the extreme pressure and anti-wear performance of the lubricant. The best performance is achieved at 0.5 Wt.% of nano-AlN and 0.3 Wt.% of nano-Al₂O₃ with PB of 834 N and 883 N, a coefficient of friction (COF) of approximately 0.07 and 0.06, respectively. Furthermore, the inclusion of nano-AlN and nano-Al₂O₃ particles in the lubricant enhances its extreme pressure performance and reduces wear, leading to decreased wear spot depth. The lubricating effect of the nano-Al₂O₃ lubricant on the surface of the copper-steel composite plate is slightly superior to that of the nano-AlN lubricant, with a COF reaching 0.07. Both lubricants effectively fill and lubricate the holes on the surface of the copper-steel composite plate.

AlN and Al₂O₃ as water-based lubricants have excellent lubrication performance and can reduce the COF. It can provide some reference for the practical application of nano-water-based lubricants.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0255/