Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large-scene data-intensive technology puts a high value on automatic target recognition (ATR) which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning-based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network (RNN) and convolutional neural network (CNN)-varied models to deepen the regime's understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer (LViT)-based model to classify SAR images. The entire structure was verified by an openaccessed SAR data set and reco\(\chi\delta\)gnition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.

Artificial Intelligence, specifically, Boost-base methods, has successfully revolutionized the area of stock price forecasting in recent years. This article integrates the idea of the hybrid stacking ensemble learning to forecast the trend of stocks. In the utilized Google stock price dataset, the market features "Open," "High", "Low," and "Volume" are utilized as input variables for a proposed hybrid stacking model in line with various Artificial Intelligence (AI) techniques. Unlike traditional stock price forecasting methods, this hybrid stacking approach incorporates multiple AI models, including XGBoost, CatBoost, and Light Gradient-Boosting Machine (LightGBM), with Lasso regression serving as the meta-learner. By employing the intrinsic pattern hidden in the real-world stock market and the inherent stacking mode of the proposed methodology, the hybrid algorithm in this paper endeavors to enhance the prediction capability and capture the real-world operation mode in financial market. By leveraging distributed stock price data inputs and the stacking methodology, the model aims to enhance forecasting accuracy and better reflect actual stock market conditions. The multistate algorithm outperformed on the widely-employed Google datasets with several typical prediction evaluation criteria, including some classical format. The benchmark experiments on various baseline models demonstrate the effectiveness and the advantages of the developed multi-stage algorithm and enjoy the significant improvement on the Google standard market competition.

The instability of electrical trading prices presents considerable challenges for forecasting and allocating resources in smart grid systems. This paper proposes an electrical price forecasting approach based on hybrid stacking ensemble learning methods. Although weather conditions significantly impact the distribution of electrical loads and prices, different weatherrelated historical data, including temperature and humidity, would be utilized as input features for the proposed hybrid stacking model based on different Artificial Intelligence (AI) methods. Different from the conventional load forecasting method, the proposed hybrid stacking method includes multiple AI models including XGBoost, CatBoost, Neural Oblivious Decision Ensemble (NODE), Light Gradient-Boosting Machine (LightGBM), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), with Lasso regression acting as the meta-learner. Considering distributed weather data inputs and the stacking nature of the proposed method, the model seeks to improve forecasting accuracy and more closely represent actual electrical load and price situations. The performance of the hybrid stacking model was experimented with realistic datasets and evaluated by different time series forecasting metrics, including Mean Absolute Percentage Error (MAPE). Comparisons with single AI models show that the proposed hybrid stacking method improves the performance of electrical load and price forecasting and could be beneficial to the operation and allocation of smart grid systems.

This paper discusses a method to optimize the Transformer model using the Arctic Puffin Optimization Algorithm (APO) for predicting and classifying users' emotional states. The experiment is divided into a main experiment and a comparison experiment. In the main experiment, the APO-optimized Transformer shows significant performance improvement, with the fitness curve stabilizing after two iterations. The model achieved 98.79% accuracy on the training set and 98.10% on the test set, with the accuracy and loss curves further validating its effectiveness. In the comparison experiment, we compared the proposed method with three basic machine learning models, with the random forest model performing best at 94.7% accuracy. However, the APO-optimized Transformer surpassed the random forest, achieving a 3.4% improvement with 98.10% accuracy. These results demonstrate the potential of the APO algorithm in deep learning and its effectiveness for social media sentiment analysis.