One of the key challenges in financial distress data is class imbalance, where the data are characterized by a highly imbalanced ratio between the number of distressed and non-distressed samples. This study examines eight resampling techniques for improving distress prediction using the XGBoost algorithm. The study was performed on a dataset acquired from the CSMAR database, containing 26,383 firm-quarter samples from 639 Chinese A-share listed companies (2007–2024), with only 12.1% of the cases being distressed. Results show that standard Synthetic Minority Oversampling Technique (SMOTE) enhanced F1-score (up to 0.73) and Matthews Correlation Coefficient (MCC, up to 0.70), while SMOTE-Tomek and Borderline-SMOTE further boosted recall, slightly sacrificing precision. These oversampling and hybrid methods also maintained reasonable computational efficiency. However, Random Undersampling (RUS), though yielding high recall (0.85), suffered from low precision (0.46) and weaker generalization, but was the fastest method. Among all techniques, Bagging-SMOTE achieved balanced performance (AUC 0.96, F1 0.72, PR-AUC 0.80, MCC 0.68) using a minority-to-majority ratio of 0.15, demonstrating that ensemble-based resampling can improve robustness with minimal impact on the original class distribution, albeit with higher computational cost. The compared findings highlight that no single approach fits all use cases, and technique selection should align with specific goals. Techniques favoring recall (e.g., Bagging-SMOTE, SMOTE-Tomek) are suited for early warning, while conservative techniques (e.g., Tomek Links) help reduce false positives in risk-sensitive applications, and efficient methods such as RUS are preferable when computational speed is a priority.

The growth of internet usage during the COVID-19 pandemic creates a new business avenue on e-payment for organizations to expand their business horizon. However, challenges on user-related factors arise with this new avenue. This study aims to investigate the association of these factors on the adoption of e-payment services using machine learning inference. An artificial intelligence-based analysis pipeline is established to study the impact of individual items of the dependent factors on the usage of e-payment. In the analysis pipeline, the important items were extracted using a hybrid artificial intelligence method, and the relationships of these items were inferred using the tree algorithm. The results show that items related to expectancy, facilitating conditions, user attitude, and performance expectancy affect usage of e-payment services. Participants below 25 years old require a gamification solution to adopt e-payment, and participants above 40 years old need social support.

Differential privacy is heavily used in privacy protection due to it provides strong protection against private data. The existing differential privacy scheme mainly implements the privacy protection of nodes or edges in the network by perturbing the data query results. Most of them cannot meet the privacy protection requirements of multiple types of information. In order to overcome these issues, a differential privacy security mechanism with average path length (APL) query is proposed in this paper, which realize the privacy protection of both network vertices and edge weights. Firstly, by describing APL, the reasons for choosing this attribute as the query function are analyzed. Secondly, global sensitivity of APL query under the need of node privacy protection and edge-weighted privacy protection is proved. Finally, the relationship between data availability and privacy control parameters in differential privacy is analyzed through experiments.

Financial distress is when a company faces significant difficulties meeting its financial obligations and maintaining profitability, leading to bankruptcy, insolvency, and severe economic losses. Therefore, early warning for companies at risk of financial distress is vital for business stakeholders to take timely corrective actions and avoid adverse outcomes. Existing financial distress predictions often rely on historical datasets, incorporating various indicators collected through varied methodologies and experts’ opinions. The challenge arises in discerning which indicators are pivotal for predicting corporate distress, as their influence and relevance may vary. This study proposed a machine learning framework to eliminate variations of different experts’ knowledge when selecting pivotal indicators. Data containing 4006 companies and 204 indicators was extracted from CSMAR. The Chi-square test is employed to select significant indicators. The correlation of these selected indicators is modeled using the C4.5 decision tree. Results showed that this selected feature set is closely aligned with those obtained when utilizing all features in the data. A thorough comparison of the indicators selected by the expert revealed notable distinctions. Features chosen by the Chi-square test are related to financial ratios and also exhibit a pronounced focus on societal attention, shareholding concentration, and market dynamics.

The paper pursues to research the security authentication users in espousing a novel image authentication system using a stimulus research model (SRM) that was extended from Technology Acceptance Model (TAM). This research continues to investigate the relationship between the usefulness, ease of use, and security acceptance of novelty authentication image security. The research method based on quantitative is used here to fulfil the requirements of which the security authentication users' acceptance to use were used for the analysis. The results from this research imply that security, usefulness, and ease of use influence the acceptance of novelty authentication image security. This research can set a novel approach and trend to determine the prototype acceptance, especially for practical application in Education. Furthermore, this practical approach for product market mix will contribute to the economic development of the country.

Two key challenges in financial distress prediction are pronounced class imbalance between majority and minority classes and the persistent misclassification of hard-to-learn samples. To tackle these issues, this study proposes an ensemble framework called Dynamic Weight-Adjusted Random Forest Boost (DWARFB). The proposed method incorporates a dynamic sample-weighting mechanism that leverages cumulative misclassification information, adaptive minority–majority class ratios to address class imbalance issue, and a real-time performance-driven strategy to integrate models’ prediction results. The effectiveness of DWARFB is evaluated using a financial dataset from the China Stock Market & Accounting Research (CSMAR) database. Comparative experiments against eight benchmark Random Forest (RF) approaches show that DWARFB delivers superior balanced performance, and a stable precision–recall trade-off, which effectively reduces both false negatives and false positives in the prediction. Moreover, a loss-based feature contribution metric provides economically meaningful insights into the key financial determinants of distress, enhancing model interpretability. Overall, DWARFB demonstrates strong reliability and adaptability and offers a practical solution for early financial distress warning in imbalanced and dynamic financial environments.