Explore the vast range of titles available.

In recent years, artificial intelligence (AI) technology has rapidly advanced and found widespread application in corporate management. Leveraging AI to enhance Environmental, Social, and Governance (ESG) performance and promote sustainable development has become a focal point for both academia and industry. This study aims to explore the impact of AI-driven ESG practices on the sustainable development performance of central state-owned enterprises in China. It analyzes the specific effects of AI technology in corporate governance, environmental protection, and social responsibility, and evaluates its contribution to the overall sustainable development of enterprises. The study employs a survey method, targeting 200 managers and employees from Central state-owned enterprises. The questionnaire comprises 15 questions covering three dimensions: corporate governance, environmental protection, and social responsibility. Descriptive statistics and correlation analysis are used to conduct an in-depth analysis of the collected data. The results indicate that respondents positively assess central state-owned enterprises in terms of corporate governance, environmental protection, and social responsibility, with particularly strong performance in social responsibility. Additionally, a regression analysis model is constructed. The results demonstrate that AI technology can enhance the practices and foster the sustainable development of central state-owned enterprises. Furthermore, ESG serves as a mediating factor between AI adoption and improvements in sustainable development performance. The findings provide practical insights for improving corporate management efficiency, enhancing environmental performance transparency, and boosting social image and brand value.

This study proposes a novel artificial intelligence (AI)-assisted design model that combines Variational Autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and efficiency in cultural and creative product design. By introducing AI-driven decision support, the model streamlines the design workflow and significantly improves design quality. The study establishes a comprehensive framework and applies the model to four distinct design tasks, with extensive experiments validating its performance. Key factors, including creativity, cultural adaptability, and practical application, are evaluated through structured surveys and expert feedback. The results reveal that the VAE + RL model surpasses alternative approaches across multiple criteria. Highlights include a user satisfaction rate of 95%, a Structural Similarity Index (SSIM) score of 0.92, model accuracy of 93%, and a loss reduction to 0.07. These findings confirm the model’s superiority in generating high-quality designs and achieving high user satisfaction. Additionally, the model exhibits strong generalization capabilities and operational efficiency, offering valuable insights and data support for future advancements in cultural product design technology.

This paper intends to solve the limitations of the existing methods to deal with the comments of tourist attractions. With the technical support of Artificial Intelligence (AI), an online comment method of tourist attractions based on text mining model and attention mechanism is proposed. In the process of text mining, the attention mechanism is used to calculate the contribution of each topic to text representation on the topic layer of Latent Dirichlet Allocation (LDA). The Bidirectional Recurrent Neural Network (BiGRU) can effectively capture the temporal relationship and semantic dependence in the text through its powerful sequence modeling ability, thus achieving a more accurate classification of emotional tendencies. In order to verify the performance of the proposed ATT-LDA- Bigelow model, online comments about tourist attractions are collected from Ctrip.com, and users’ emotional tendencies towards different scenic spots are analyzed. The results show that this model has the best emotion classification effect in online comments of scenic spots, with the accuracy and F1 value reaching 93.85% and 93.68% respectively, which is superior to other emotion classification models. The proposed method not only improves the accuracy of sentiment analysis, but also provides strong support for the optimization of tourism recommendation system and provides more comprehensive, objective and accurate tourism information for scenic spot managers and tourism enterprises. This achievement is expected to bring new enlightenment and breakthrough to the research and practice in related fields.

This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on big data analysis to enhance the information security protection capability of enterprises. A big data analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models. For different types of security threats, the system uses feature engineering and model training processes to extract key risk indicators and optimize model prediction performance. The experimental results show that the constructed risk prediction model has excellent performance on the test set, and its Area Under the Curve reaches 0.95, indicating that the model has good differentiation ability and high prediction accuracy. In addition, in the multi-class risk identification task, the model achieves an average precision of 0.87. Compared with the traditional method, it has remarkably improved the early warning accuracy and response speed of enterprises to various information security incidents. Therefore, this study confirms the effectiveness and feasibility of applying BDT to EIS risk management, and the successfully constructed prediction model provides strong technical support for EIS protection.

This study proposes a stock price prediction model based on the Residual-enhanced Channel-Spatial Attention Network (R-CSAN), which integrates channel-spatial adaptive attention mechanisms with residual connections to effectively capture the multidimensional complex patterns in financial time series. The R-CSAN adopts an encoder-decoder architecture, where the encoder extracts feature correlations from historical data through multiple layers of channel-spatial attention modules, and the decoder incorporates a masking mechanism to prevent future information leakage and introduces a cross-attention mechanism to model inter-market correlations. Experiments conducted on four cross-market stock datasets, including Amazon, Maotai, Ping An, and Vanke, demonstrate that R-CSAN significantly outperforms not only traditional baseline models such as ARIMA, LSTM, and CNN-LSTM, but also recent Transformer-based approaches like Informer, Autoformer, and iTransformer on metrics including RMSE, MAE, MAPE, , and return on investment. The model reduces RMSE by 17.3–49.3% compared to traditional methods and 6.2–11.6% compared to Transformer variants, with the highest reaching 93.17% and an increase in return on investment to 482.64%. Ablation experiments confirm the critical contributions of each component, with the temporal module removal causing an average increase of 38.6% in RMSE and channel-spatial attention removal resulting in a 21.3% increase. Moreover, the model provides an interpretative analysis of features and temporal dimensions through attention weight visualization, offering insights into both indicator importance and critical time periods for prediction. In practical applications, R-CSAN’s outputs can be integrated into quantitative trading strategies including breakout trading, moving average crossover signals, and portfolio allocation optimization, providing a new paradigm for robust prediction in highly volatile markets.

This work aims to improve the accuracy and efficiency of flood disaster monitoring, including monitoring before, during, and after the flood, to achieve accurate extraction of flood disaster change information. A modified U-Net network model, incorporating the Transformer multi-head attention mechanism (TM), is developed specifically for the characteristics of Synthetic Aperture Radar (SAR) images. By integrating the TM, the model effectively prioritizes image regions relevant to flood disasters. The model is trained on a substantial volume of annotated SAR image data, and its performance is assessed using metrics such as loss function, accuracy, and precision. Experimental findings demonstrate significant improvements in loss value, accuracy, and precision compared to existing models. Specifically, the accuracy of the model algorithm in this work reaches 95.52%, marking a 3.46% improvement over the baseline U-Net network. Additionally, the developed model achieves an accuracy of 90.11% while maintaining a loss value of approximately 0.59, whereas other model algorithms exceed a loss value of 0.74. Thus, this work not only introduces a novel technical approach for flood disaster monitoring but also has the potential to enhance disaster response procedures and provide scientific evidence for disaster management and risk assessment processes.

The purpose of this work is to explore methods of visual communication based on generative artificial intelligence in the context of new media. This work proposes an image automatic generation and recognition model that integrates the Conditional Generative Adversarial Network (CGAN) with the Transformer algorithm. The generator component of the model takes noise vectors and conditional variables as inputs. Subsequently, a Transformer module is incorporated, where the multi-head self-attention mechanism enables the model to establish complex relationships among different data points. This is further refined through linear transformations and activation functions to enhance feature representations. Ultimately, the self-attention mechanism captures the long-range dependencies within images, facilitating the generation of high-quality images that meet specific conditions. The model’s performance is assessed, and the findings show that the accuracy of the proposed model reaches 95.69%, exceeding the baseline algorithm Generative Adversarial Network by more than 4%. Additionally, the Peak Signal-to-Noise Ratio of the model’s algorithm is 33dB, and the Structural Similarity Index is 0.83, indicating higher image generation quality and recognition accuracy. Therefore, the model proposed achieves high recognition and prediction accuracy of generated images, and higher image quality, promising significant application value in visual communication in the new media era.

With the rapid advancement of information and communication technologies, smart tourism has become a crucial means for improving the quality of tourism services and enhancing economic efficiency in the tourism sector. This work proposes an analysis method based on the artificial neural network to predict tourist behavior patterns through big data analysis, thereby optimizing the allocation of tourism resources. The work begins by collecting various data types, including basic visitor information, consumption records, and satisfaction evaluations, from a well-known smart tourism destination as research samples. By carefully configuring and optimizing parameters such as learning rate, batch size, and optimizers, the work develops an efficient artificial neural network model. Experimental validation using real-world data demonstrates that the model excels across several performance metrics, including accuracy, recall, precision, and F1 score, and shows significant advantages over traditional statistical methods. In addition, the survey results show that users are highly satisfied with personalized service recommendations, resource optimization, and the overall user experience, with 75% of users expressing satisfaction. This work not only makes an academic contribution to the field of smart tourism but also demonstrates significant potential in improving tourism economic efficiency and enhancing the visitor experience.

This study proposes an innovative evaluation framework that integrates deep learning with multi-attribute decision-making (MADM) methods to enhance the scientific rigor and accuracy of image evaluation of ice and snow tourism destinations. Compared to traditional evaluation approaches, this framework effectively processes unstructured textual data and conducts comprehensive assessments across multiple dimensions. The study innovatively designs a text feature extraction model based on the Bidirectional Encoder Representations from Transformers (BERT)-Convolutional Neural Network (CNN). Meanwhile, MADM methods are introduced for attribute weight allocation and decision optimization. The model employs BERT for in-depth semantic analysis of tourist reviews, utilizes CNN to extract local textual features, and combines MADM methods to generate comprehensive scores. In the study, the optimized model demonstrates a high consistency, achieving a consistency ratio of only 0.03 in the facilities and services theme. Moreover, this model significantly outperforms the Robustly Optimized Bidirectional Encoder Representations from Transformers Approach (RoBERTa), with a consistency ratio of 0.06. Regarding priority stability, the optimized model reaches 0.91 in comprehensive experience themes. In the aspect of computing time, the inference time of the optimized model is 0.14 s in the facilities and services theme. The experimental results indicate that the optimized model performs well in dealing with complex unstructured text data while showing high efficiency and stability in weight allocation and multidimensional decision-making tasks. Therefore, this study contributes meaningfully to the research in the image evaluation field for ice and snow tourism destinations. It also provides a vital theoretical basis and practical tools for tourism image optimization, precise marketing, and scientific management.

In order to solve the problems of inefficient allocation of teaching resources and inaccurate recommendation of learning paths in higher education, this paper proposes a smart education optimization model (SEOM) by combining the improved random forest algorithm (RFA) based on adaptive enhancement mechanism and the Graph Neural Network (GNN) algorithm. The public data and information such as the national higher education intelligent education platform are collected, and SEOM is trained and verified. The results show that SEOM has high accuracy and generalization ability in three different teaching scenes: online mixed teaching, personalized teaching and project-based teaching. The Root Mean Square Error (RMSE) value in cross-validation is between 0.2 and 0.5, and the Mean Absolute Error (MAE) value is between 0.1 and 0.5. SEOM shows strong stability when dealing with multidimensional educational resources and complex teaching modes. The accuracy rate remains at 85-97%, indicating its reliability in personalized learning path recommendation. Further analysis shows that the chi-square freedom ratio is between 1.0 and 2.5, the fitting index and the adjusted fitting index are both above 0.85, and the comparative fitting index is close to 0.95, which shows that SEOM has high accuracy and rationality in capturing the dependence of knowledge points in different teaching modes. The Root Mean Square Residual (RMR) and Root Mean Square Error of Approximation (RMSEA) are both below 0.05, which indicates that SEOM has small residual and strong scene adaptability. In addition, in the abnormal network environment, the resource allocation efficiency of SEOM is above 60%, and the Shapley value is between 0.1 and 0.4, which shows that SEOM can adapt to the change of network environment and the resource allocation effect is still obvious. Generally speaking, SEOM can optimize the allocation of educational resources and recommend learning paths in a complex environment, and effectively improve the intelligence and efficiency of teaching decision-making, especially for university administrators and educational technology developers.