A personal credit evaluation algorithm is proposed by the design of a decision tree with a boosting algorithm, and the classification is carried out. By comparison with the conventional decision tree algorithm, it is shown that the boosting algorithm acts to speed up the processing time. The Classification and Regression Tree (CART) algorithm with the boosting algorithm showed 90.95% accuracy, slightly higher than without boosting, 90.31%. To avoid overfitting of the model on the training set due to unreasonable data set division, we consider cross-validation and illustrate the results with simulation; hypermeters of the model have been applied and the model fitting effect is verified. The proposed decision tree model is fitted optimally with the help of a confusion matrix. In this paper, relevant evaluation indicators are also introduced to evaluate the performance of the proposed model. For the comparison with the conventional methods, accuracy rate, error rate, precision, recall, etc. are also illustrated; we comprehensively evaluate the model performance based on the model accuracy after the 10-fold cross-validation. The results show that the boosting algorithm improves the performance of the model in accuracy and precision when CART is applied, but the model fitting time takes much longer, around 2 min. With the obtained result, it is verified that the performance of the decision tree model is improved under the boosting algorithm. At the same time, we test the performance of the proposed verification model with model fitting, and it could be applied to the prediction model for customers’ decisions on subscription to the fixed deposit business.

This study aims to improve the accuracy of forecasting the turnover intention of new college graduates by solving the imbalance data problem. For this purpose, data from the Korea Employment Information Service's Job Mobility Survey (Graduates Occupations Mobility Survey: GOMS) for college graduates were used. This data includes various items such as turnover intention, personal characteristics, and job characteristics of new college graduates, and the class ratio of turnover intention is imbalanced. For solving the imbalance data problem, the synthetic minority over-sampling technique (SMOTE) and generative adversarial networks (GAN) were used to balance class variables to examine the improvement of turnover intention prediction accuracy. After deriving the factors affecting the turnover intention by referring to previous studies, a turnover intention prediction model was constructed, and the model's prediction accuracy was analyzed by reflecting each data. As a result of the analysis, the highest predictive accuracy was found in class balanced data through generative adversarial networks rather than class imbalanced original data and class balanced data through SMOTE. The academic implication of this study is that first, the diversity of data sampling methods was presented by expanding and applying GAN, which are widely used in unstructured data sampling fields such as images and images, to structured data in business administration fields such as this study. Second, two refining processes were performed on data generated using generative adversarial networks to suggest a method for refining only data corresponding to a more minority class. The practical implication of this study is that it suggested a plan to predict the turnover intention of new college graduates early through the establishment of a predictive model using public data and machine learning.

In recent years, deep learning has gained significant traction in the fields of skincare and dermatology, with research primarily focused on two areas: cosmetic skin assessments and clinical diagnoses of skin diseases. Approximately two-thirds of the literature is dedicated to medical conditions such as melanoma and seborrheic keratosis, while the remaining third addresses cosmetic issues like UV damage, oiliness, pore size, and wrinkles. Although both domains utilize a similar methodological framework—training deep learning models on skin images for classification—they remain largely separate. Recent publications from 2024 and 2025 indicate a growing interest in integrating deep learning across these two fields. AI-driven skincare applications have shown promise in delivering rapid, personalized assessments, although challenges like biases in skin tone analysis continue to exist. In contrast, medical applications have made strides with deep learning models for skin cancer detection, achieving high accuracy and proving their potential as decision-support tools for dermatologists. The convergence of these domains presents a significant opportunity for hybrid models that could link cosmetic and medical applications, particularly for early skin cancer detection. By utilizing the extensive data and feature extraction techniques prevalent in skincare analysis, deep learning models could improve predictive accuracy and aid in the early identification of malignant conditions. This integration could facilitate proactive skin health monitoring by incorporating dermatological risk assessments into routine skincare evaluations. However, realizing this potential necessitates addressing challenges related to data diversity, model generalizability, and ethical considerations in AI-driven diagnostics. Bridging deep learning applications in skincare and dermatology not only offers the promise of improved early detection of serious skin conditions but also paves the way for more accessible, AI-powered dermatological care. Continued interdisciplinary collaboration is essential to develop these hybrid models and fully harness their potential in enhancing skin health diagnostics.