Entertainment Computing最新文献

In the era of digital entertainment media, the rapid dissemination of information and the widespread application of social media have a huge impact on user behavior and psychology. For students, the experience of digital entertainment content may have potential adverse effects on their mental health. The aim of this study is to propose a prediction model of university students’ psychological intervention based on optical network transmission based on parallel computing algorithm, in order to improve the computational efficiency and accuracy of the model. Then, a large number of psychological data of college students are collected, and feature extraction and model training are carried out by using machine learning algorithms. Finally, the predictive model of psychological intervention is simulated by parallel computing algorithm. The experimental results show that the optical network transmission based on parallel computing algorithm has high computational efficiency and accuracy in the prediction model of psychological intervention of college students. The model can reliably predict and evaluate the effect of psychological intervention of college students, provide an effective simulation tool for psychological intervention of college students, and help realize personalized and efficient psychological intervention measures.

With the rapid development of digital entertainment technology, people’s demand for green landscape reconstruction and virtual reality (VR) interactive art design is increasing. This study aims to explore the use of digital entertainment technology and entertainment gesture robots, combined with green landscape 3D reconstruction and VR interactive art design, to provide a new art experience. This paper uses digital entertainment technology to reconstruct the green landscape in 3D, and combines the interactive function of entertainment gesture robot to enable users to interact with the virtual environment. Then VR technology is used to present the reconstructed landscape to the user, and through immersive experience, users can feel the immersive art design. Through experimental evaluation and user survey, 3D reconstruction of green landscape and VR interactive art design using digital entertainment technology and entertainment gesture robots can provide a new entertainment experience. Users are more satisfied with the immersive art design expression, and experience the interaction and connection with the natural environment, which provides users with a unique and immersive art experience, further enriching the form and content of digital entertainment.

With the continuous progress of AI intelligent technology, the internet industry has undergone tremendous changes. The application of virtual reality and other digital entertainment technology has changed the classroom mode, and at the same time, current political classrooms place more emphasis on interaction with society. Therefore, this article constructs a political classroom AI intelligent system based on CNN network. This system studies the political classroom from the perspective of AI intelligent construction, using a combination of theory and practice. This article selects some research materials from both domestic and international sources. Firstly, it summarizes and analyzes relevant concepts such as convolutional neural networks, summarizes the importance of political classroom education, and analyzes the problems faced by political classrooms in the context of AI intelligence. Then, experimental analysis was conducted on the AI intelligent system in the political classroom. The experiment proved that the AI intelligent system in the political classroom has good working performance in the actual operation process, and the overall performance is also better than other systems. Implementing a network-based AI intelligent system for the political classroom and applying AI intelligent technology in practical applications can improve the personalization and accuracy of the political classroom.

E-learning is a social network learning method that greatly promotes the dissemination of knowledge. Students can use mobile network terminals for video learning and also share their learning materials, making the learning process interactive and entertaining. This article is based on virtual reality technology and studies the effectiveness of the interactive E-learning mode for political courses in the digital entertainment environment. This online teaching method benefits students and teachers a lot with its unique advantages, and is unanimously favored by experts and scholars. These courses not only provide students with a comprehensive understanding of Marxism, but also help them develop a correct world outlook, values, and ethics. Meanwhile, in the current digital technology environment, interactive online learning has the characteristics of virtualization and entertainment. Students can comment on social hot topics and share their favorite content through various media platforms. The results indicate that the application of artificial intelligence and virtual reality technology in the interactive e-learning mode of political networks has important advantages.

Social robots are intelligent programs that can autonomously run, automatically publish information, and interact on social media platforms. This article focuses on the speech recognition algorithm of interactive artificial intelligence systems, combined with the Hanning window function and the Vitit algorithm, and explores its application value in English video teaching systems through detailed experiments. Adopting speech feature extraction technology provides strong support for the subsequent recognition process. The application of Hanning window function has a positive impact on speech signal preprocessing. The most indispensable algorithm in speech recognition models is the Vitit algorithm, which successfully solves the problem of time variation and continuity of speech signals. This study successfully applied speech recognition algorithms based on interactive artificial intelligence systems to English video teaching systems. By combining speech feature extraction, Hanning window function, and Vitit algorithm, a system with strong recognition ability and interactivity was constructed. The English video teaching system aims to improve students’ learning process. By combining interactive artificial intelligence speech recognition technology with English video teaching, it can improve students’ experience in learning English and also meet their personalized learning habits.

Traditional design methods usually present design ideas through plans and models, but this method can not truly simulate the design effect. Therefore, this research aims to use computer game simulation and virtual robotics technology to provide a more interactive and realistic design experience. The study creates a virtual street environment with elements such as streets, buildings and vegetation through computer game simulation. Participants can move and interact in this environment through virtual robots, experience the process of street landscape design, adjust the street layout, add landscape elements, and observe the design effect in real time according to their own design intentions. Compared with traditional design methods, the experimental results show that the design experience using computer game simulation and virtual robot technology is more vivid, creative and interactive. Participants can better perceive the design effect and adjust the design scheme in real time. This interactive entertainment design experience method can help designers and participants better understand and evaluate the design scheme, and improve the design effect and participation.

The development of digital technology has given music dissemination a technological advantage of high productivity and efficiency. With the continuous improvement of digital technology and the diversification of communication forms, information visualization design pays more attention to the audience’s experience and feelings. This article analyzes the application of pattern recognition based artificial intelligence in music entertainment environments and automatic recognition of music. Digital media can achieve bidirectional information transmission through user participation and feedback, and can provide customers with more entertainment music experiences through entertainment games and other means. This article focuses on the image enhancement processing, note recognition, and automatic recognition conversion of music scores in complex contexts, and develops an automatic music score recognition system to digitize music score information. Through online dissemination, the public can fully experience the beauty of music in the process of communication and interaction, and at the same time, music can be brought into the entertainment environment.

In order to understand the mathematical analysis of human motion vision capture image processing, a mathematical analysis of human motion vision capture image processing based on artificial intelligence is proposed. This paper firstly introduces the research progress, classification and several commonly used vision-based human motion tracking methods of motion capture technology. Secondly, the process and framework of capturing human motion video by using ordinary cameras and marking nodes are proposed, and the automatic tracking algorithm based on Camshift and Kalman filter is adopted. It verifies the effectiveness of the system, changes the traditional way of motion capture, and makes the process of capture more convenient when the capture effect meets the requirements. Finally, the performance of the human motion capture data retrieval algorithm based on video is evaluated comprehensively. It is compared with the latest literature in this field. In terms of time efficiency, for each online retrieval of data set, the proposed algorithm takes 0.056 s, while the methods of other scholars take an average of 1.5 s. Meanwhile, experiments are also conducted on public databases, proving the universality and scalability of the proposed algorithm. The algorithm proposed in this paper has greater advantages than the most advanced method of the same type, which verifies the effectiveness of the algorithm proposed in this paper.

We studied the use of machine learning models for training dance action models, collected dance action data, annotated and classified it, and established a training set for dance action models. We trained the training set to learn the feature representation and pattern recognition capabilities of dance actions. Through training and tuning the model, a model that can accurately recognize and generate dance movements was obtained. Evaluate the similarity between two dance movements and select the appropriate dance movements to form a smooth dance sequence. A planning algorithm was designed based on the kinematic and dynamic characteristics of robots to generate dance action paths suitable for the robot’s body conditions. Considering factors such as joint limitations, body stability, and smooth movement of the robot, generate a reasonable dance motion path while ensuring safety. Through on-site testing and data analysis of the system, it has been verified that it can effectively generate diverse and expressive dance movements, bringing a unique viewing experience to entertainment venues.

In existing English teaching, teachers often find it difficult to accurately understand students’ emotional changes, thus unable to adjust teaching strategies in a timely manner. Therefore, the introduction of computer vision and facial recognition algorithms will help improve the effectiveness of English teaching. This article is based on computer vision technology and facial recognition algorithms. Based on the sample data provided during the learning process, a model is learned and established to recognize facial expressions under different emotions and identify students’ emotional states. Use computer vision technology to capture and analyze students’ facial expressions in real-time. Then, facial recognition algorithms are used to recognize and classify the captured facial features to determine the current emotional state of the students. Finally, based on the students’ emotional state, the system will provide corresponding feedback. This technology based on computer vision and facial recognition algorithms can help teachers better understand students’ emotional changes, adjust teaching strategies in a timely manner, provide personalized learning feedback, and thereby improve students’ learning effectiveness and the quality of English teaching. This technology can also provide students with a better learning experience, enhancing their learning motivation and interest.