The prediction of the “ups and downs” of stock market prices is one of the important undertakings of the financial market. Since accurate prediction helps foster considerable economic benefits, stock market prediction has attracted significant interest by both investors and researchers. Efforts into building an accurate, stable and effective model to predict stock prices’ movements have been proliferating at a fast pace, to meet such a challenge. Firstly, this paper uses a correlation analysis to analyze the attributes of a stock dataset, processing missing values, determining the data attributes to be retained data, then divide it in a training set and a testing set. Then, the LSTM model is subsequently used to predict the retained attributes after the analysis and retention of prediction results to construct a new testing set. At the same time, the Bo-XGBoost model based on XGBoost is used to train the original training set. It uses Bayesian calculation to optimize the parameters which are proved to be difficult to find the best solution in the XGBoost model. Finally, the LSTM-BO-XGBoost model proposed in this paper is applied to “ES=F”, “YM=F”, “CL=F”, “^TNX”, “^N225”, “NQ=F”, “AAPL”, “GC= F”, “JPY=X” and “SI=F” rates with 10 stocks in the forecast and evaluated by four evaluation indexes: root mean square error (RMSE), average absolute error (MAE), accuracy rate, and F1-score. It is found that the LSTM-BO-XGBoost model proposed in this paper performs better than LSTM in stock price prediction. In order to further evaluate the performance of the algorithm, the LSTM-BO-XGBoost mode is compared with the single LSTM network model and RNN network model, the LSTM-BO-XGBoost hybrid model. The results show that the LSTM-BO-XGBoost mode has high performance, stability and feasibility than the others.

After the COVID-19 ended, the global economy gradually recovered. Due to the nonlinearity, complexity, and high noise of financial time series, stock price prediction has become one of the most challenging tasks in the stock market. To tackle this challenge and enhance the prediction performance in the complicated stock markets, we propose a novel integrated approach based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Long Short-Term Memory (LSTM), and ensemble learning algorithm LightGBM to simultaneously improve the fitting and accuracy of stock price prediction. In addition, to prevent overfitting and improve predictive performance, this study adopted the Simulated Annealing (SA) algorithm for optimization. The predictive performance of the proposed hybrid model is comprehensively evaluated by comparing it with single LSTM, RNN, and other popular hybrid models. Three evaluation metrics, namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and accuracy, are used to compare the aforementioned models. The experimental results indicate that the proposed hybrid CEEMDAN-LSTM-SA-LightGBM model outperforms all other comparative models in this study with better fitting and accuracy.

Relation extraction, a crucial task in understanding the intricate relationships between entities in biomedical domains, has predominantly focused on binary relations within single sentences. However, in practical biomedical scenarios, relationships often extend across multiple sentences, leading to extraction errors with potential impacts on clinical decision-making and medical diagnosis. To overcome this limitation, we present a novel cross-sentence relation extraction framework that integrates and enhances coreference resolution and relation extraction models. Coreference resolution serves as the foundation, breaking sentence boundaries and linking entities across sentences. Our framework incorporates pre-trained deep language representations and leverages graph LSTMs to effectively model cross-sentence entity mentions. The use of a self-attentive Transformer architecture and external semantic information further enhances the modeling of intricate relationships. Comprehensive experiments conducted on two standard datasets, namely the BioNLP dataset and THYME dataset, demonstrate the state-of-the-art performance of our proposed approach.

With the rapid development of Internet of Vehicles technology, ensuring the privacy and security of vehicle communications becomes a critical challenge. This paper proposes a novel anonymous communication scheme based on group signatures to address privacy protection and secure authentication issues in vehicular ad-hoc networks. Our approach ensures the privacy of vehicle identities while achieving efficient message authentication and traceability. Through theoretical analysis and experimental simulations, we demonstrate the superiority of our scheme in terms of computational efficiency, communication overhead, and storage requirements, all while maintaining strong resistance against various potential attacks. The evaluation results indicate that the proposed scheme provides an effective solution for secure, anonymous communication within VANET, laying a foundation for the future development of intelligent transportation systems.