Photovoltaics are methods used to generate electricity by using solar cells, which convert natural energy from the sun. This generation makes use of unlimited natural energy. However, this generation is irregular because they depend on weather occurrences. For this reason, there is a need to improve their economic efficiency through accurate predictions and reducing their uncertainty. Most researches were conducted to predict photovoltaic generation with various machine learning and deep learning methods that have complicated structures and over-fitted performances. As improving the performance, this paper explores the probabilistic approach to improve the prediction of the photovoltaic rate of power output per hour. This research conducted a variable correlation analysis with output values and a specific EM algorithm (expectation and maximization) made from 6054 observations. A comparison was made between the performance of the EM algorithm with five different machine learning algorithms. The EM algorithm exhibited the best performance compared to other algorithms with an average of 0.75 accuracies. Notably, there is the benefit of performance, stability, the goodness of fit, lightness, and avoiding overfitting issues using the EM algorithm. According to the results, the EM algorithm improves photovoltaic power output prediction with simple weather forecasting services.

This study investigates the utility of GPT-generated text as a training resource in supervised learning, focusing on two perspectives: its effectiveness as an augmentation tool in data-scarce or class-imbalanced settings and its potential as a substitute for human-written data. Using MBTI personality classification as a benchmark task, we conducted controlled experiments under both class imbalance and few-shot learning conditions. Results showed that GPT-generated text could improve classification performance when used to supplement underrepresented classes. However, when synthetic data fully replace real data, performance declines significantly—particularly in tasks requiring fine-grained semantic distinctions. Further analysis reveals that GPT outputs often capture only partial personality traits, enabling coarse-level classification but falling short in nuanced cases. These findings suggest that GPT-generated text can function as a conditional training resource, with its effectiveness closely tied to the granularity of the classification task.

Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships, as in Euclidean data such as time series or image grids. However, real-world data frequently involve irregular, interconnected structures, requiring a shift toward non-Euclidean approaches. This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals. By leveraging graph convolution filters, we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods. Utilizing the Graph Convolutional Networks (GCN) framework, we integrate graph-based embeddings with conventional anomaly detection models, enhancing performance through relational insights. Our method is validated on European credit card transaction data, demonstrating its effectiveness in detecting fraudulent transactions, particularly those with subtle patterns that evade traditional, amount-based detection techniques. The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection, showcasing the robustness and applicability of our approach in complex, real-world scenarios.