Abstract The paper proposes an early warning system for corporate financial risk using an improved BP (Backpropagation) neural network integrated with grey system theory. The author highlights the critical role of small and medium enterprises (SMEs) in China's... The paper proposes an early warning system for corporate financial risk using an improved BP (Backpropagation) neural network integrated with grey system theory. The author highlights the critical role of small and medium enterprises (SMEs) in China's economy and their vulnerability to financial distress due to poor financial management, inadequate internal controls, and external market pressures. To address this, a three layer Grey BP neural network model is constructed, featuring 16 financial input indicators, 9 hidden nodes, and 4 output categories representing different risk levels. The model is trained using financial data from 2000-2004 and validated with 2005-2006 data from 93 firms. Experimental results show high prediction accuracy, with an R-value exceeding 0.889, particularly for one year ahead (T-1) forecasts compared to two year ahead (T-2). The study concludes that the Grey BP neural network effectively identifies emerging financial risks, offering enterprise managers a reliable tool for proactive risk mitigation. Its adaptability, self learning capabilities, and minimal data requirements make it especially suitable for dynamic, complex economic environments. The research contributes to the growing body of work applying artificial intelligence to financial early warning systems and demonstrates the practical value of hybrid models that combine grey theory with neural networks for enhanced forecasting precision and generalization. Read More Read Less

Abstract The paper proposes an intelligent secure education system tailored for college students using personalized recommendation technology. Recognizing the growing security challenges students face ranging from cybersecurity and traffic safety to mental health the system aims to enhance... The paper proposes an intelligent secure education system tailored for college students using personalized recommendation technology. Recognizing the growing security challenges students face ranging from cybersecurity and traffic safety to mental health the system aims to enhance security awareness through customized learning content. It integrates user management, resource management, recommendation algorithms, and user interfaces into a cohesive architecture. Central to the system is a matrix factorization algorithm enhanced with techniques like multi head self attention and feed forward neural networks to analyze user behavior and recommend relevant educational resources. The system preprocesses diverse resource types (text, images, video) and extracts features using methods such as word embeddings and convolutional neural networks. Experimental evaluation compares an experimental group using the personalized system against a control group using traditional methods. Metrics like recommendation accuracy, recall, and user satisfaction are analyzed, with results indicating improved learning effectiveness and engagement. The figures in the paper illustrate the impact of parameters, such as K, on recommendation performance and compare algorithm accuracy using metrics such as MAE. The study concludes that personalized recommendation systems significantly enhance secure education by aligning content with individual student needs, and it calls for further refinement through user feedback and advanced algorithms to address evolving security education demands. Read More Read Less

Abstract The paper investigates common cause failures (CCFs) in secure computer operating systems using data mining techniques. As secure systems underpin critical sectors like government, defense, and healthcare, understanding and mitigating complex, interrelated failures stemming from hardware, software,... The paper investigates common cause failures (CCFs) in secure computer operating systems using data mining techniques. As secure systems underpin critical sectors like government, defense, and healthcare, understanding and mitigating complex, interrelated failures stemming from hardware, software, or human errors is essential. The study proposes a novel Common Cause Failure Score to quantify and compare security performance across systems. Using association rule mining and decision tree algorithms (e.g., C5.0), the research uncovers hidden relationships among failure events. The Naive Bayes classifier is also employed to manage multidimensional data and categorize risk levels. Experiments involve preprocessing real world system failure data, applying statistical and machine learning models, and validating results using tools such as ROC curves and confusion matrices. Health Index (HI) modeling and life prediction techniques further assess system reliability over time. Findings demonstrate that integrating multiple data mining approaches enhances the accuracy of failure prediction, root cause analysis, and risk mitigation strategies. The study concludes that these methods significantly improve system stability, support informed decision making by administrators, and provide a robust framework for evaluating and selecting high performance, secure computing systems in complex network environments. Read More Read Less