Multiclass classification in machine learning often faces significant challenges due to unbalanced datasets. This situation leads to biased predictions and reduced model performance. This research addresses this issue by proposing a novel approach that combines convolutional neural networks (CNNs) with class weights and early-stopping techniques. The motivation behind this study stems from the need to improve model performance, especially for minority classes, which are often neglected in existing methodologies. Although various strategies such as resampling, ensemble methods, and data augmentation have been explored, they frequently have limitations based on the characteristics of the data and the specific model type. Our approach focuses on optimizing the loss function via class weights to give greater importance to minority classes. Therefore, it reduces bias and improves overall accuracy. Furthermore, we implement early stopping to avoid overfitting and improve generalization by continuously monitoring the validation performance during training. This study contributes to the body of knowledge by demonstrating the effectiveness of this combined technique in improving multiclass classification in unbalanced scenarios. The proposed model is tested for oil palm leaves analysis to identify deficiencies in nitrogen (N), boron (B), magnesium (Mg), and potassium (K). The CNN model with three layers and a SoftMax activation function was trained for 200 epochs each. The analysis compared three scenarios: training with the imbalanced dataset, training with class weights, and training with class weights and early stopping. The results showed that applying class weights significantly improved the classification accuracy, with a trade-off in other class predictions. This indicates that, while class weight has a positive overall impact, further strategies are necessary to improve model performance across all categories in this study.

Credit score models are essential tools for evaluating creditworthiness and mitigating financial risks. However, the imbalanced nature of multi-class credit score datasets poses significant challenges for traditional classification algorithms, leading to poor performance in minority classes. This study explores the effectiveness of Generative Adversarial Network (GAN)-based oversampling methods, including CTGAN, CopulaGAN, WGAN-GP, and DraGAN, in addressing this issue. By synthesizing realistic data for minority classes and integrating it with majority class data, the study benchmarks these GAN-based methods across classical (KNN, Decision Tree, Logistic Regression) and ensemble machine learning models (XGBoost, Random Forest, LightGBM). Evaluation metrics such as accuracy and F1-score reveal that WGAN-GP consistently achieves superior performance, especially when combined with Random Forest, outperforming other methods in balancing dataset representation and enhancing classification accuracy. The results showed that WGAN-GP + RF achieved 0.873 in accuracy, 0.936 F1-score in the “good” class, 0.806 F1-score in the “poor” class, and 0.816 F1-score in the “standard” class. The findings underscore the potential of GAN-based oversampling in improving multi-class credit score classification and highlight future directions, including hybrid sampling and cost-sensitive learning, to address remaining challenges.

Children's privacy concerns about smart toys are becoming more and more critical in the toy industry. Parents and guardians continue to strive to protect their children from unnecessary privacy risks such as collection, and unconsented use of or access to their children's information. However, there is still no standardized privacy framework, which focuses on smart toys in this paradigm; making it difficult to determine possible privacy violation in for example determining whether a phrase shared with a smart toy is sensitive or not. To overcome this challenge, we build a privacy identification system through Chatbot technology. We call this system a Children Privacy Identification (CPI) system. To develop CPI system, we divide our research works into two parts: (1) Collect the phrase from the smart toys; and (2) Explore privacy Identification based on Personally Identifiable Information (PII) and Children's Online Privacy Protection Act (COPPA). For illustration, we integrate the CPI system in LINE Chatbot. The result shows that people feel more comfortable in talking to LINE Chatbot with privacy protection.