In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models are preferred. In this manuscript, an effective Bi-GRU is proposed for accurate sales forecasting related to E-commerce companies. Initially, retail sales data are acquired from two benchmark online datasets: Rossmann dataset and Walmart dataset. From the acquired datasets, the unreliable samples are eliminated by interpolating missing data, outlier’s removal, normalization, and de-normalization. Then, feature engineering is carried out by implementing the Adaptive Particle Swarm Optimization (APSO) algorithm, Recursive Feature Elimination (RFE) technique, and Minimum Redundancy Maximum Relevance (MRMR) technique. Followed by that, the optimized active features from feature engineering are given to the Bi-Directional Gated Recurrent Unit (Bi-GRU) model for precise retail sales forecasting. From the result analysis, it is seen that the proposed Bi-GRU model achieves higher results in terms of an R2 value of 0.98 and 0.99, a Mean Absolute Error (MAE) of 0.05 and 0.07, and a Mean Square Error (MSE) of 0.04 and 0.03 on the Rossmann and Walmart datasets. The proposed method supports the retail sales forecasting by achieving superior results over the conventional models.

With the continuous rise of various social media, the security problems caused by disseminating news in social networks have become increasingly prominent. Among them, the dissemination of false news has brought a significant threat to the security of cyberspace. A method based on influence maximization is proposed to change the premise of the false news in network space. This article employed the Louvain clustered local degree centrality (LCLD) and random maximum degree (RMD) algorithm to extract the most influential node set via influence maximization. Subsequently, TextCNN (convolutional neural network) was used to classify and identify false news and filter out several critical points in the node set. Then, the modified propagation network was repredicted by the prediction model. As a result, the spread of false news was significantly suppressed compared with the network before modification. Finally, the verification was carried out on Buzz Feed News’s real dataset. First, the information-based cascaded prediction model can more accurately fit the actual spread. Then the modified network was input into the prediction model for prediction, and the results show that the spread of false news can be suppressed. Finally, the influence maximization algorithm deletes several nodes containing incorrect information. It can effectively suppress the spread of false news, thus verifying the effectiveness of the proposed method.