Virtual reality technology (VR) is a computer simulation system that can create and experience virtual worlds. It utilizes computers to generate a simulated environment, allowing users to immerse themselves in the environment. Virtual reality technology has been one of the fastest-developing information technologies in recent years. It, along with multimedia technology and network technology, is known as the three most promising computer technologies. As an emerging science and technology, it has been less than 100 years since its emergence, and there is still great room for development in its theory and practical application. This article focuses on virtual reality technology and its applications, based on existing literature and statistical data. The main content includes its advantages, characteristics, technical composition, development history, and applications in different directions. The development process of virtual reality technology is not long, but it has unique advantages and technologies specifically serving it. It does not require much physical participation and has positive implications in applications such as education, entertainment, and healthcare. Its future direction will be towards replacing real hardware with virtual hardware, becoming more practical, intelligent, and refined.

Sentiment analysis, which tries to examine the emotional information in the provided text data, has always been a popular topic in the community of natural language processing. Sentiment analysis is currently used in many different contexts, including e-commerce platforms, social media platforms, public opinion platforms, and chatbots. These applications are crucial to the advancement of society and the domestic economy. However, due to the personalization of text data, especially comments, and the presence of acronyms, it is a challenging problem to obtain accurate sentiment information from large and complex unstructured text data. This study presents a comparative examination of various text sentiment analysis approaches, including LSTM, CNN, and GRU. These methods are employed to evaluate their respective performance on sentiment analysis tasks, specifically using a dataset of hotel reviews for training the models. The method presented in this research has been extensively validated through numerous experimental results, affirming its efficacy and its potential to offer novel perspectives for the practical implementation of sentiment analysis.

An important task in the study of Natural Language Processing (NLP) is the analysis of movie reviews. It finishes the task of classifying movie review texts into sentiment, such as positive, negative or neutral sentiment. Previous works mainly follow the pipeline of LSTM (Long Short-Term Memory Network). The network model is a variant of Recurrent Neural Network (RNN) and particularly suitable for processing natural language texts. Though existing LSTM-based works have improved the performance significantly, we argue that most of them deal with the problem of analyzing the sentiment of movie reviews while ignore analyze the model performance in different application scenarios, such as different lengths of the reviews and the frequency of sentiment adverbs in the reviews. To alleviate the above issue, in this paper, we constructed a simple LSTM model containing an embedding layer, a batch normalization layer, a dropout layer, a one-dimensional convolutional layer, a maximal pooling layer, a bi-directional LSTM layer and a fully connected layer. We used the existing IMDB movie review dataset to train the model, and selected two research scenarios of movie review length and frequency of occurrence of sentiment adverbs to test the model, respectively. From the experimental results, we proposed a model for the scenarios in which the LSTM model handles the problem of sentiment analysis with respect to the dataset construction, model stability and generalization ability, text fragment processing, data preprocessing and feature extraction, model optimization and improvement.

As the Internet is progressively becoming larger and more intricate, more and more users of various social media choose to post their comments to express their opinions and thinking on those platforms. Analyzing the emotions contained in user comments holds great business value, helping to accurately perceive user consumption habits and improve user service levels. However, the use of emoticons and stickers in comments has increased dramatically in recent years, which brings new challenges to text sentiment analysis based on natural language processing. In this paper, in order to alleviate the above problems, we propose a method for analyzing the sentiment of Chinese comments based on the attention mechanism and BiLSTM. Specifically, we partitioned the original dataset from the Weibo platform according to the number and type of emoticons in the comments. By analyzing the actual data, the specific features of emojis that affect the performance of sentiment analysis are identified, and corresponding explanations are given. In addition, a hypothesis is proposed to quantify the impact of emoticons on model effectiveness. All the results demonstrate the effectiveness of our proposed method.

With the rapid growth of urbanization, the problem of stray animals on the streets is particularly prominent, especially the shortage of food for cats and dogs. This study introduces an automatic feeding system based on the Detectron2 deep learning framework, aiming to accurately identify and provide suitable food for these stray animals. Through training using Detectron2 with a large amount of image data, the system shows extremely high recognition accuracy in single-object images. When dealing with multi-object images, Detectron2 can generate independent recognition frames for each target and make corresponding feeding decisions. Despite the outstanding performance of the model, its potential uncertainties and errors still need to be considered. This research not only offers a practical solution to meet the basic needs of stray animals but also provides a new perspective for urban management and animal welfare. By combining technology with social responsibility, this innovative solution opens up a new path for solving the stray animal problem in cities, with broad application prospects and profound social significance.

Database-manage-system has become a modern management among different enterprises. The enterprise’s data need managed by a well-performed system. Also, the database is an essential need for a modern library. In this case, more and more people start using different way to build the system which is suitable for the enterprises. The library system has become a famous one which need a large database system to encounter the huge amount of data. In this article, it shows the usage of database-manage-system in a local library and how does the system work. Also, the introduction of the function of MySQL usage in this application. Finally, after the test of the data and running the programme in MySQL, the database-manage-system realize the function of list the book, check of availability, make reservations and store different users’ status. In this essay, through the test of data and the code, a database-manage-system is designed. This database-manage-system could complete basic function which contain check status, make appointments and locate books.

With the continuous development of the social economy, countries increasingly pay more attention to mechatronics and put forward various relevant policies for the actual situation of mechatronics to promote mechatronics and achieve sustainable development. However, from the current actual situation of mechatronics, due to the influence of various external factors, its degree of development cannot meet the requirements of modern social development; therefore, the application of advanced intelligent technology to mechatronics design is inevitable. Based on this, this paper expounds the intelligent manufacturing technology and mechatronics system overview as the basis for analysing the actual situation of the development of modern mechanical and electrical equipment digital design, then analyses the advantages of intelligent technology from different aspects and applies them to the daily mechatronics design to bring huge economic benefits to enterprises, and concludes that the development level of social productivity in the future is closely related to the integration level of mechatronics and intelligent manufacturing.

As critical mechanics and artificial intelligence have improved at a fast pace, the application of robots has become a trending research domain. Robot has a golden opportunity to be a game-changer in the education domain. A large group of studies have noted that robots can offer a promising learning design in the classroom to help students boost their studying. This article analysed papers published in the science database between 2012 and 2022 relating to robot settings and tried to conduct a review on robots used in the classroom. The review focuses on features in these studies, including the age of participants, duration of the study, field of discipline, interaction method, and studying strategies, identifying roles of robots and evaluating the performance of students. This study also indicates shortages in robot deployment and provides several suggestions for future research.

With the widespread application of computer network technology, computer network security defense capability has become a focus of attention in various industries. The extensive use of computer information systems in various sectors has significantly improved work efficiency but has also introduced security risks and management issues. The deployment of network security detection and control systems allows for effective security monitoring and management of computer operations and real-time network information. This paper presents a computer network security detection and control system based on human-computer interaction, which enables users to handle daily key business processes. The work principles and overall architecture of the network security detection and control system are analyzed and demonstrated. It offers functions such as filtering options, address rules, network security detection, network unreachability, overall traffic analysis, subnet definition, fault diagnosis, and security analysis. Testing and analysis indicate that the system’s design achieves the intended goals. In the application of network security features, it can effectively combine various forces to enhance the quality and efficiency of network security, making it widely applicable in various industry units.

In the convergence of fashion and artificial intelligence (AI), significant strides have been made in areas such as clothing recognition, retrieval, and classification, enabled by advanced AI technologies and expansive annotated datasets. As the AI in Fashion market continues to surge, the future of the fashion industry promises to be redefined by intelligent, efficient, and more accessible solutions. Image retrieval, one of the important parts in AI, has experienced remarkable growth, empowered by advanced algorithms and vast annotated datasets, making it a crucial component in various domains such as digital libraries, online marketing. Therefore, this report mainly provides an extensive review of image retrieval methods and the emerging paradigm of contrastive learning, underscoring their relevance and applications in the realm of artificial intelligence. This paper primarily reviews the technologies in the amalgamation of the image retrieval field and contrastive learning. It elucidates the history and progression of image retrieval, offers a methodical analysis of the two primary approaches—text-based image retrieval and content-based image retrieval—and examines how contrastive learning is employed in image retrieval systems.