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Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent. [ABSTRACT FROM AUTHOR]

As China's economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China's daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks. [ABSTRACT FROM AUTHOR]

During the last two decades, as computer technology has matured and business scenarios have diversified, the scale of application of computer systems in various industries has continued to expand, resulting in a huge increase in industry data. As for the medical industry, huge unstructured data has been accumulated, so exploring how to use medical image data more effectively to efficiently complete diagnosis has an important practical impact. For a long time, China has been striving to promote the process of medical informatization, and the combination of big data and artificial intelligence and other advanced technologies in the medical field has become a hot industry and a new development trend. This paper focuses on cardiovascular diseases and uses relevant deep learning methods to realize automatic analysis and diagnosis of medical images and verify the feasibility of AI-assisted medical treatment. We have tried to achieve a complete diagnosis of cardiovascular medical imaging and localize the vulnerable lesion area. (1) We tested the classical object based on a convolutional neural network and experiment, explored the region segmentation algorithm, and showed its application scenarios in the field of medical imaging. (2) According to the data and task characteristics, we built a network model containing classification nodes and regression nodes. After the multitask joint drill, the effect of diagnosis and detection was also enhanced. In this paper, a weighted loss function mechanism is used to improve the imbalance of data between classes in medical image analysis, and the effect of the model is enhanced. (3) In the actual medical process, many medical images have the label information of high-level categories but lack the label information of low-level lesions. The proposed system exposes the possibility of lesion localization under weakly supervised conditions by taking cardiovascular imaging data to resolve these issues. Experimental results have verified that the proposed deep learning-enabled model has the capacity to resolve the aforementioned issues with minimum possible changes in the underlined infrastructure. [ABSTRACT FROM AUTHOR]

At present, China's economic development continues to progress and foreign exchanges are also increasingly frequent. Learning to master the world's universal pronunciation, English, has become a more important link. However, people's living habits make it difficult to carry heavy desktop devices for a long time, so mobile English learning platform meets the development needs of people's English teaching. Based on the existing English mobile learning platform, this paper puts forward the concept of integrating artificial intelligence and deep learning technology into it. Through deep learning, the learning status and learning situation of students in the process of English learning are extracted, so as to analyze the needs and learning interests of each student in English learning and then push corresponding English materials to each student, which improves the efficiency of English learning. In addition, deep learning can also model the data of students' behaviors and build a language vector feature extraction mechanism and translation quality evaluation model, so as to carry out certain intelligent auxiliary correction and correction on students' English grammatical expressions and spoken English. The research in this paper has achieved good results through practice. The practice results show that the integration of deep learning into the existing mobile English teaching platform can optimize the functions of the existing platform, provide more ideas for the development of online English learning, and has good theoretical and practical value. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy and the continuous optimization and improvement of Internet information technology, a new thing, new media, has emerged. On this basis, a kind of artistic behavior of creating, carrying, spreading, and appreciating is also called new media art. New media art has developed for a long time in foreign countries, but its development and popularization are relatively late in China, and it is still in a stage of improvement. As far as visual communication design is concerned, the emergence and development of new media have strongly promoted the development of its work and the dissemination of various works. Developing visual communication design based on new media art has become a key issue for all visual communication designers. Nowadays, deep learning is widely used in different fields in different ways, and image art representation is a hot research field in computer vision. Therefore, this paper mainly studies and analyzes the symbolism of new media art expression forms and proposes a new media art symbolism model based on artificial intelligence big data. In order to provide a method of real-time representation of art images, the deep learning method is used to model the artistic expression of images, and spatial reorganization of content features and style features is carried out. This paper designs simulation experiments to verify the effectiveness of the model, which can provide some help for the development and improvement of new media art in China. We compared the results of our model with a number of other models to test its effectiveness. We achieved a matching accuracy of 98.4% which is higher than or equal to the results of the existing models. [ABSTRACT FROM AUTHOR]

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application. [ABSTRACT FROM AUTHOR]

Since the beginning of the 21st century, with the continuous growth and improvement of the comprehensive strength of our national economy and the continuous improvement of our modern industrial machinery and equipment, the level of safety technology, production level, and quality management level see continuous steady improvement, resulting in the rapid economic growth of chemical industries in China in recent years. In recent years, chemicals have been widely used and demand is increasing. However, the physical properties of some chemical substances are very unstable and vary according to process conditions, making it difficult to control. At the same time, safety issues have become a major "red warning" for industrial enterprises. Various safety incidents have attracted social attention, and the chemical industry has also learned from them, sounding the safety alarm for itself and reviewing its own shortcomings, and taking improvement measures. Therefore, it is obviously necessary to always emphasize the concept of "safety first, prevention first, and comprehensive management" to prevent enterprise safety accidents. It is necessary to regularly investigate and analyze the situation of safety accidents to summarize and continuously improve. This is the most important thing for enterprises to prevent the continuous occurrence of safety accidents in a good and effective way. Regularly analyzing, summarizing, and improving safety issues are the best way to prevent accidents. Deep learning mainly relies on letting computers or other electronic devices perform iterative learning on a large number of data samples according to the rules defined by the program. The computer after learning will have the analysis ability similar to humans, but its analysis ability is limited to the range of the sample data to be learned. Artificial intelligence technology realizes an operation system that simulates human thinking through the definition of algorithms and programs. The use of deep learning and artificial intelligence in industrial enterprise safety production supervision can realize enterprise safety production supervision, early warning, and intelligent operation. Without the security of the production chain, a business cannot develop. Based on the basic theory of safety supervision and early warning in the field of enterprise safety production and related knowledge of artificial intelligence, this paper studies the work safety in the supervision and early warning mechanism abides by the selection principle of the management system, safety production indicators and indicator system, and conducts work based on the production work data of industrial plants. Reasonable safety supervision and early warning mechanism provide technical assistance for effectively controlling safety accidents in production and management measures to improve enterprise safety. [ABSTRACT FROM AUTHOR]

The rise of FinTech has been meteoric in China. Investing in mutual funds through robo-advisor has become a new innovation in the wealth management industry. In recent years, machine learning, especially deep learning, has been widely used in the financial industry to solve financial problems. This paper aims to improve the accuracy and timeliness of fund classification through the use of machine learning algorithms, that is, Gaussian hybrid clustering algorithm. At the same time, a deep learning-based prediction model is implemented to predict the price movement of fund classes based on the classification results. Fund classification carried out using 3,625 Chinese mutual funds shows both accurate and efficient results. The cluster-based spatiotemporal ensemble deep learning module shows better prediction accuracy than baseline models with only access to limited data samples. The main contribution of this paper is to provide a new approach to fund classification and price movement prediction to support the decision-making of the next generation robo-advisor assisted by artificial intelligence. [ABSTRACT FROM AUTHOR]

Deep changes are occurring in the components and forms of education as a result of the ongoing integration and development of emerging technologies like cloud computing, mobile computing, and artificial intelligence with teaching and learning, and the digital transformation of education is consistently being pushed to new heights. Simultaneously, China's higher education has concurrently reached the stage of popularization. The digitalization of higher education is related to the development quality and value proposition of higher education and determines whether it can adapt to the needs of quality diversification, lifelong learning, training personalization, and governance modernization in the popularization stage. As a result, the current and future phases of China's higher education reform call for accelerating the pace of higher education's digital transformation and guiding the high-quality growth of higher education with digital innovation. The application potential of intelligent learning systems in higher education is becoming more and more clear in this context. In view of this, this work draws from previous research and experiences to build and implement an embedded voice teaching system based on cloud computing and a deep learning model to meet the development needs of the current digital transformation of higher education. On the one hand, the new system can well compensate for the flaws and shortcomings of the current teaching means in universities and realize the accompanying ubiquitous learning by relying on the powerful storage and computing capacity of the cloud computing platform. On the other hand, this study designs a set of voice recognition methods integrating HMM + LSTM to enhance the embedded voice system's recognition performance, ultimately allowing for the voice recognition feature to be implemented in the pedagogical system. When it comes to processing audio signals, the hybrid model makes use of both the HMM's robust time processing capability and the deep neural network's robust characterization capability and generalization performance. As a result, the voice recognition rate, anti-interference performance, and noise robustness can all be significantly improved. Finally, the embedded voice system is put through its paces in an experimental setting to gauge its performance and functionality. The results of the tests demonstrate that the created hybrid model has high recognition accuracy and good noise immunity, which will be utilized as a foundation for the design and development of the final system. Meanwhile, the new system's functional modules have achieved the expected results with good stability and reliability. Trial results gathered through interviews and questionnaires demonstrate that the new system significantly enhances the intelligence and adaptability of college teaching methods and is conducive to promoting the improvement of college students' cultural literacy and innovation ability. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the Internet in China, network communication models and network services have become more and more complex, and with the increase of data types and diversification of data sources, network operation and maintenance projects have become more and more difficult. Passive statistical testing techniques for outliers are widely used, and many shortcomings have been pointed out, so there is an urgent need for improvement. After the research on the abnormal test of the text, it is determined that each method uses the visualization tool to simulate the data through the classification algorithm of the existing abnormal test and to identify the possibility of the data of each method and the PR diagram. The accuracy is counterintuitive. At present, Hallyu has a greater influence on the Internet, mainly in the fields of fashion and culture in Asian countries. Therefore, Korean language teaching has received widespread attention from the society, and combining cultural education with language teaching is an important way to improve the efficiency of language teaching. In the actual Korean language teaching work, a variety of Korean language materials are essential educational and teaching resources. The simulation results show that a large amount of training can make the DNN generate a better auto-encoder. In addition to strengthening the template for simulation training in deep learning, multiple supervisions of the data can be performed to reduce errors and improve efficiency. [ABSTRACT FROM AUTHOR]

The quality of talent has increased across all fields due to the constant growth of different industries and the growing job saturation. Real-time job information on recruitment platforms can, therefore, accurately reflect the demand for talent from businesses, serving as a basis for the creation of training policies in schools. In international competition, the development of talents, especially top-level talents, will become more and more crucial. Growing in importance is China's economy and social development. The evaluation of higher vocational and technical talents, however, should also be assessed from a variety of angles, given the diversification of talent training objectives and teaching methods, as well as the expansion of teaching functions. An emerging machine learning technology called deep learning (DL) has been developed to bring machine learning closer to the goals of artificial intelligence. This essay offers a thorough evaluation of the depth of deep learning as it relates to the development of innovative talent in schools. The entire school must be strengthened. It is demonstrated that the average execution time is slashed by 0.0024 s, and the learning sample size error of the DL model is reduced by 0.05276 when compared to the Apriori method. As a result, implementing and researching the DL model can significantly improve both the overall teaching quality of schools and their capacity for innovation. [ABSTRACT FROM AUTHOR]

With the development of science and technology and the progress of social civilization, people have entered the era of big data. People's personal information, privacy data, financial status, and financial information have become more and more transparent. Personal privacy has also changed from traditional physical privacy to portrait privacy and further changed to digital privacy based on artificial intelligence, big data, algorithms, and other technologies. At this stage, a new type of privacy problem has been formed, that is, deep learning privacy. In order to better protect the right to privacy, we should effectively block the protection dilemma of privacy and improve the construction of algorithms in the era of artificial intelligence, strengthen our own awareness of prevention, understand the current situation of infringing and protecting the right to privacy, fully protect the right to privacy, and promote the legal construction of the right to privacy in the era of artificial intelligence. However, China's traditional privacy protection system cannot fully adapt to this change, and this highlights the lack of rules and hidden dangers of efficiency of privacy protection under the influence of penetrating monitoring, mandatory sharing, structural discrimination, and interactive assimilation of artificial intelligence. Therefore, based on the infringement mechanism of artificial intelligence on personal privacy, we should build a monitoring and identification function filing system, establish a segmentation and selective agreement rule system, build a comparative antidiscrimination rule system, innovate the "cloud blocking" rule system to protect our privacy, and comprehensively address the major challenges posed by new technologies such as artificial intelligence to China's privacy protection system. [ABSTRACT FROM AUTHOR]