The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, regulatory bodies, and investors. Traditional models include the z-score, the logit (logistic regression model), the kernel-based virtual machine (KVM), and neural network approaches. Nevertheless, the outcomes from these methods have often fallen short of expectations. Three major challenges in previous works are feature selection, imbalanced classification, and hyperparameter optimization. This paper presents a method for credit risk prediction for listed companies that uses an off-policy proximal policy optimization (PPO) algorithm for feature selection and imbalanced classification. The off-policy PPO, a reinforcement learning (RL) approach, enhances sample efficiency by more effectively utilizing past experiences during policy updates. This approach improves feature selection and the management of imbalanced classification by optimizing data use, thereby enhancing model training outcomes. Moreover, we use the Bayesian optimization hyperband (BOHB) approach to refine the hyperparameters of the method. BOHB merges Bayesian optimization and Hyperband, significantly speeding up the optimization process. We assess our model using the China Stock Market and Accounting Research (CSMAR), MorningStar, KMV default, Give Me Some Credit (GMSC), and the University of California, Irvine Credit Card Default (UCICCD) datasets. Our experimental findings demonstrate the excellence of the model over existing state-of-the-art models, achieving F-measures of 90.763%, 86.358%, 87.047%, 90.576%, and 89.485% on these datasets. These findings validate the efficiency of the method in economic settings, signifying a major progression in systems for predicting credit risk and enhancing investigative approaches. 

Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband

Herein, a digital–analog hybrid resistive random‐access memory (RRAM) is prepared by integrating the structurally similar SiNx‐based digital‐type RRAM with analog‐type RRAM by heterogeneous integration method. The Pt/SiNx/Ta/Ru is a digital RRAM due to the formation and breakage of the internal silicon dangling bonds conductive filaments, and the TiN/SiNx/Ta/Ru structure is an analog RRAM due to the traps‐filled limit region of the space‐charge limited current. The heterogeneously integrated digital RRAM has good cycling stability and endurance characteristics, and the heterogeneously integrated analog RRAM has high linearity and multistate counting characteristics. This heterogeneous integration approach is useful for the implementation of hybrid digital–analog RRAM.

SiNx‐Based Digital–Analog Hybrid Resistive Random Access Memory via Heterogeneous Integration

A software defect is a common cyberspace security problem, leading to information theft, system crashes, and other network hazards. Software security is a fundamental challenge for cyberspace security defense. However, when researching software defects, the defective code in the software is small compared with the overall code, leading to data imbalance problems in predicting software vulnerabilities. This study proposes a heterogeneous integration algorithm based on imbalance rate threshold drift for the data imbalance problem and for predicting software defects. First, the Decision Tree-based integration algorithm was designed following sample perturbation. Moreover, the Support Vector Machine (SVM)-based integration algorithm was designed based on attribute perturbation. Following the heterogeneous integration algorithm, the primary classifier was trained by sample diversity and model structure diversity. Second, we combined the integration algorithms of two base classifiers to form a heterogeneous integration model. The imbalance rate was designed to achieve threshold transfer and obtain software defect prediction results. Finally, the NASA-MDP and Juliet datasets were used to verify the heterogeneous integration algorithm’s validity, correctness, and generalization based on the Decision Tree and SVM.

Software defect prediction method based on the heterogeneous integration algorithm

(1) Background: To avoid affecting the accuracy and practicability of the security federation model due to the geographical and environmental factors involved in each local model and set the corresponding weights for each local model, the local model parameters and weights participated in the calculation at the same time. (2) Methods: Apply the improved model to the income evaluation of taxi drivers. Multiple linear regression was used to fit the local model parameters, and the loss function value was calculated. Then, according to the improved security federation algorithm, the model parameters and local model weights were encrypted by using the Paillier homomorphic encryption algorithm, and the encrypted model parameter information was uploaded to the aggregation server for aggregation average. (3) Results: The experimental results show that after 1000 iterations, the accuracy curve converges in the interval [0.93, 0.97]; the mean accuracy value was 94.27%, and the mean loss function value was 1.0886. It was the same understanding that the mean value of the loss function calculated by the traditional model was 1.9910. (4) From the model and data, the accuracy of the improved model has been improved. It can better reflect the income of taxi drivers.

/pdf/improved-design-and-application-of-security-federation-dzh7kmfq.pdf

Improved Design and Application of Security Federation Algorithm

Credit risk prediction for small and micro enterprises based on federated transfer learning frozen network parameters

Federated Learning Strategies for Integrating Composite Meta-Consistency Loss With Multi-Head Attention

When calculating participants’ contribution to federated learning, addressing issues such as the inability to collect complete test data and the impact of malicious and dishonest participants on the global model is necessary. This article proposes a federated aggregation method based on cosine similarity approximation Shapley value method contribution degree. Firstly, a participant contribution calculation model combining cosine similarity and the approximate Shapley value method was designed to obtain the contribution values of the participants. Then, based on the calculation model of participant contribution, a federated aggregation algorithm is proposed, and the aggregation weights of each participant in the federated aggregation process are calculated by their contribution values. Finally, the gradient parameters of the global model were determined and propagated to all participants to update the local model. Experiments were conducted under different privacy protection parameters, data noise parameters, and the proportion of malicious participants. The results showed that the accuracy of the algorithm model can be maintained at 90% and 65% on the MNIST and CIFAR-10 datasets, respectively. This method can reasonably and accurately calculate the contribution of participants without a complete test dataset, reducing computational costs to a certain extent and can resist the influence of the aforementioned participants.

Federated aggregation method based on cosine similarity approximation Shapley value method contribution degree

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