Abstract With the rapid development of information technology and the widespread application of computer networks, data security issues have become increasingly prominent, especially in key industries such as finance and medical care, where the protection of sensitive data has become a top priority. In order to meet these challenges, this article proposes a data security protection system based on deep learning (DL) technology, which can monitor and identify potential security threats in real time and greatly enhance data security by automatically analyzing and learning network traffic, user behavior, and intrusion characteristics. The study uses a deep belief network to complete high-intensity protection of personal or corporate private data while automatically countering and reminding some network intrusion behaviors. The performance differences in data security of individuals or enterprises before and after the use of the protection system were compared through experiments. The results show that the new system can improve data security performance by about 54.6% in all aspects. The results show that the new security protection model based on DL technology not only improves detection accuracy and response speed but also effectively resists emerging threats, providing a more solid protection barrier for network data.

Abstract Intelligent manufacturing (IM) is an advanced model that integrates traditional manufacturing with artificial intelligence to achieve automation, intelligence, and flexibility throughout the production process. Chinese enterprises exhibit a relatively low overall innovation efficiency, with a central focus on IM as a powerhouse strategy. The developmental status of IM is pivotal, as it directly affects the quality standards of China’s manufacturing industry. This study employed machine learning techniques to examine whether IM can enhance the innovation efficiency of Chinese enterprises while also exploring the roles of government subsidies and intellectual capital in this process. The empirical findings are as follows. (1) Among Chinese listed companies, a higher level of IM significantly improved innovation efficiency, a result confirmed by robustness tests. (2) Mechanism analysis revealed that IM enhanced innovation efficiency primarily by securing government resource support and fostering intellectual capital. (3) Moderation analysis indicated that the positive correlation between IM and innovation efficiency weakened as the level of financial development increased, whereas intensified market competition strengthened this correlation. This study could provide valuable insights for Chinese manufacturing enterprises aiming to enhance their intelligence levels and promote innovation transformation and upgrading.

Abstract In this work, we propose a novel cross-dataset micro-expression identification method based on facial regions of interest contribution quantification, where the training samples are from the source dataset and the test samples are from the target dataset. Specifically, a micro-expression video clip (i.e., a sample) is first sampled at intervals to capture an image sequence consisting of multiple consecutive video frames. Then, locate and crop the facial area in the image frame, and perform facial landmark detection on the cropped area. Next calculate the optical-flow field of the image sequence, and extract the improved Main Directional Mean Optical-flow feature. Finally, the learned group sparse model is used as the classifier to predict the label information of the unmarked target samples, so as to complete the category recognition of micro-expressions. Extensive cross-dataset comparative experiments on four spontaneous micro-expression datasets, CASME, CASME II, SMIC-HS, and SAMM, show that the recognition strategy proposed in this work is effective. Compared with several other advanced recognition methods, our method has better cross-dataset recognition performance, not only the classification accuracy is higher, but also the classification stability is better when faced with different target datasets and different micro-expression categories. It can be applied in many fields such as clinical diagnosis, interrogation, negotiation, and national security.

Abstract As society develops and educational needs continue to change, the traditional way of teaching ideology and politics is facing challenges in terms of efficiency and effectiveness evaluation. In response to the low efficiency of ideological and political education (IPE) methods and the difficulty in accurately and comprehensively evaluating students’ ideological and political literacy and moral qualities, this article used the Long Short-Term Memory with Self-Attention Mechanism (LSTM-SAM) model to conduct experiments on the evaluation of IPE effectiveness. First, by collecting information on IPE from a research center of a certain university in 2023, and then using the LSTM (Long Short-Term Memory) model to catch the long-term dependencies of IPE, the learning trajectory and changing trends of students can be better understood. The self-attention mechanism was applied to dynamically learn and distinguish the importance of different parts in the input sequence, better weighting key features such as student learning behavior and participation level, thereby enhancing the accuracy and robustness of effectiveness evaluation. Finally, the splicing method was adopted to integrate the LSTM model and self-attention mechanism for the experiment, and the teaching efficiency of different teaching methods was statistically analyzed through a questionnaire survey. The test results indicated that the classification accuracy of the LSTM-SAM model reached 98.41%, which was 1.61% higher than the LSTM model. The teaching efficiency was the highest under the gamified teaching method, providing an effective method for evaluating the effectiveness of IPE and providing useful reference for optimizing teaching methods.

Abstract Concealing sensitive information inside multimedia is very interesting in secure communication due to its wide application. This study discusses the different difficulties involved in embedding the ciphertext inside video frame without affecting the imperceptibility and the strength resistance against different cyberattacks. The main goal of this study is to suggest a novel technique to conceal encrypted data inside video frames securely. This is done by combining the Laplacian of Gaussian (LoG) edge detection algorithm and Chacha20 encryption algorithm. LoG facilitates to detect the suitable location inside video frame for concealing data. While ChaCha20 is used to encrypt data before embedding them inside these locations, which augmenting data security. The proposed method involves sequence of steps involving detecting edges inside video frames, determining the suitable edges for concealing data, encrypting data by XORing it with the encryption key which is generated using ChaCha20 algorithm, embedding the encrypted data inside the determined edges, and then reconstruct the video frame to rebuild the video that involves the concealed encrypted data and keeping the lowest level of visual distortion. The experimental results showed that combining these two approaches provide fast, robust, and secure method, which can be seen by evaluating the system using mean square error, peak signal-to-noise ratio, correlation, number of pixels change rate, unified average changing intensity, and entropy, these evaluation metrics provide excellent results. This study suggests a strong and novel method to embed the encrypted data inside video frames which can be employed in secure communication, copyright protection, and data authentication. Merging LoG and ChaCha20 algorithms produce perfect results in both security and visual perception quality, that provides a means for farther achievement in secure data embedding techniques.

Abstract With the increasing social pressure and academic competition, the mental health (for convenience, abbreviated as MH) problems of college students are becoming increasingly prominent, but there are often challenges that are difficult to accurately predict and intervene in a timely manner. The aim of this article is to address the early warning needs of college students’ MH problems and construct a model that can timely identify the MH problems of college students. The experiment collected MH related data from college students in S city, and analyzed and trained these data using the Long short-term memory (LSTM) network model. By changing the number of hidden layers, learning rate, batch size, and epoch times, the most suitable training effect was achieved. By using the time-series characteristics of the LSTM model, the selected parameters from the experiment can better capture the changing trends of college students’ MH status, thereby improving prediction accuracy. Finally, three stage indicators of low, medium, and high were set up for early warning of the predicted results, in order to effectively and timely take measures. The research results indicated that the constructed model achieved a minimum regularization loss of 0.0674 after training. Finally, the adjusted model was used to predict the test set, with an average accuracy of 0.852 and an average accuracy of 0.906. The LSTM-based MH risk model performed well in predicting college students’ MH problems and could identify potential risk factors in a timely manner.

Abstract In order to solve the problem of the low transmission rate of wireless network sensors (WNSs), this article proposes a WNS visual assistance system based on embedded systems. The system uses WNSs instead of traditional wired sensors, which can achieve synchronous transmission of multi-node data, enabling nodes to work together better, thereby improving the real-time and reliability of the entire system. This article conducts in-depth research on feature extraction algorithms and tests the visual assistance system in the experimental section. The results show that the recognition rate and stability of the visual symbol assistance system implemented using WNSs are higher than those of ordinary systems. In the satisfaction survey, it was found that 87 people were very satisfied with the visual symbol assistance system, accounting for 87%, while only 57 people were very satisfied with the traditional visual symbol assistance system, accounting for 57%. The experimental results show that the system output stability of the design method is good, and the response time and reliability are better.

Abstract In the field of tourism recommendation systems, accurately recommending scenic spots and routes for users is one of the hot research directions. In order to better consider the complex interaction between user preferences and attraction features, as well as the potential connections between different information sources, this study constructed a graph attention network model using knowledge graphs for tourist attraction and route recommendations, and extracted features from visual images using visual geometry group-16. The results indicate that, in Xian, when the learning rate is 0.01, the area under the curve value is 0.916. The area under the curve of New York is 0.909, and the learning rate is 0.001. The area under the curve value of the Tokyo dataset is 0.895. When the learning rate is moderate, the model quickly stabilizes in the first 16 rounds and reaches its optimal state in 26–30 rounds. When the propagation depth is 2, the accuracy is 0.920, 0.905, and 0.895, respectively. After introducing visual features, the accuracy, recall, and F1 score improved by 10 to 15.7%. The multi-layer perceptron further increased the effect by 4–6%. These experimental data fully demonstrate the effectiveness and accuracy of the recommendation algorithm. This study provides a powerful tool for tourism recommendation systems, which helps to further improve user experience.

Abstract Problem Metaheuristics are efficient algorithms designed to address a broad spectrum of optimization challenges and offer satisfactory solutions, even in scenarios of limited processing capability or incomplete information. It has been observed that no single metaheuristic algorithm is universally ideal for all applications. This realization underscores the opportunity for the introduction of new metaheuristic algorithms or enhancements to existing ones. Aim The aim of this work is to propose Quokka swarm optimization (QSO), a novel nature-inspired metaheuristic optimization technique. The QSO simulates the cooperative behavior of quokka animals, which can be used to address optimization issues. Method A group of common unconstrained and constrained test functions is employed to demonstrate the strength of the proposed approach. To test the performance of QSO, 43 popular test functions that are used in the optimization were employed as benchmarks. The solutions have been refining their positions in tandem with the ongoing discovery of the best solution. In addition, QSO can substitute the worst quokka with the best child found so far to improve the solutions. Performance comparisons using the Blue monkey swarm optimization, Gray wolf optimization, Biogeography-based optimizer, Artificial bee colony, Particle swarm optimization, and Gravitational search algorithm were also performed. Results The obtained results showed that QSO is competitive in comparison to the chosen metaheuristic algorithms.

Abstract With the rapid development of China’s economy and society, the income level of Chinese residents is increasing day by day, and the dietary consumption structure of domestic residents is constantly optimized and upgraded. The consumption demand for livestock products such as meat, eggs and milk, and corn starch industrial products is increasing day by day. Therefore, as the main raw material of feed industry and deep processing owners, the consumption scale of China corn market is constantly expanding. However, the corresponding domestic corn production scale has not expanded, and the domestic corn production presents an obvious problem of insufficient supply scale, which leads to a sharp and rapid increase in the number of corn imports in China, which seriously impacts the domestic corn market and affects China’s healthy development of corn industry. In recent years, the continuous spread of the COVID-19 epidemic has seriously affected the global grain production and trade. The fierce military conflict between Russia and Ukraine, the two major grain exporting countries, has once again brought a serious adverse impact on the supply and price of the global grain market, resulting in an increase in the import cost of corn in China and a change in the import pattern of corn in China. In this regard, this study analyzes the current situation of corn production and import trade in China, and based on the relevant trade data between China and major corn importing countries from 2013 to 2022, establishes a trade gravity model to quantitatively evaluate the main factors affecting China’s corn import trade, and analyzes the trade potential of sample countries. It was found that in 2022, China’s import and export trade volume and trade value were 20.62 million tons and 7.11 billion US dollars, respectively, compared with 2013, trade volume and trade volume increased by 6.3 times. Statistics on the potential types of various trading countries showed that the proportion of potential countries was 14.2%, and Brazil had the greatest potential in China’s corn import trade. The proportion of potential expanding countries is about 71.4%, and Laos is a mature country with less opportunities for further development. In order to promote the benign development of the domestic corn industry and the development of China’s corn import trade, this paper puts forward a series of countermeasure suggestions based on the above research.