With the flourishing development of artificial intelligence and computer animation technologies, there has been an increasing intersection between these two. In the field of computer animation, the use of artificial intelligence significantly reduces the difficulties in design, production, and post-production processes, which has a massive impact on the entire field. The paper attempts to discuss the relationship between artificial intelligence and computer animation. Not only does the paper elaborate on the related applications of artificial intelligence in various subfields of computer animation, but it also analyzes existing problems and future development trends. The research indicates that AI has achieved significant breakthroughs in computer animation, such as auto-generation of animations, real-time character driving, and emotionally responsive animation creation. However, it also faces challenges like handling interactions in complex scenarios, maintaining realism, and animating high-level abstract concepts. Despite these challenges, it is believed that in the future, AI will further propel the development of computer animation, aiding creators in producing animations that are more vibrant, intricate, and personalized.

Dingdong Buying Vegetable is a cohort of emerging entities that have swiftly gained prominence within the business domain in recent times. The data mining function of its data platform APP is undeniably linked to the underlying factor contributing to its commercial success. This article examines the fundamental principles, practical manifestations, and pertinent research instances of data mining. It specifically centers on the primary interface of the Dingdong Buying Vegetable APP, scrutinizing its design and distinctive attributes tailored to specific customers. Furthermore, it conducts an in-depth analysis of the correlation between the platform’s commercial success and its data mining functionality. The primary aspect in which the data mining function of the Dingdong Buying Vegetable APP is expected to be manifested is through extensive data mining. The process of mining client data and conducting a full comparison and analysis of sales data for all products sold is a highly intricate and exhaustive endeavor. The use of these data mining functions serves multiple purposes. Firstly, it efficiently identifies customers’ genuine requirements on the client side, enabling the recommendation of suitable products and fostering customer reliance on the application. Additionally, it facilitates accurate decision-making in marketing products on the product side by comparing and analyzing diverse data pertaining to the sold products. This approach helps prevent the sale of unsought items and effectively minimizes company expenditure.

This paper presents a Ruby on Rails-based grader application system designed to streamline the process of matching students with grading positions within the Computer Science and Engineering (CSE) department. Motivated by the need for efficiency and consistency, the system offers role-based user access, enabling students, instructors, and administrators to engage seamlessly. Leveraging Model-View-Controller (MVC) architecture, the system integrates external tools for enhanced development, while the dynamic database schema efficiently manages data. Key functionalities encompass application submission, administrator interface, and real-time course management. This innovative system fosters collaboration, improves administrative oversight, and adapts to changing academic demands. In conclusion, the presented grader application system has achieved powerful functions which enables users to login, view available courses, and most importantly, accept or decline the applications from students whom want to be a grader for a specific course.

In the context of global education, universities and institutions emphasize the importance of proficient academic information management systems. As traditional approaches to course data management become obsolete, there is a growing need to leverage digital transformation for academic management. This paper proposes a novel course database design that centralizes information about subjects, courses, staff, students, buildings, and grades. The database is designed to facilitate the overall academic progress of students by ensuring efficient record keeping, retrieval and updating. The architecture simplifies administrative tasks, underscores the importance of databases in modern educational institutions by providing in-depth student data to support personalized learning, enhance communication among stakeholders, and aid academic research. The conclusion of the experimental test is that a database designer should always ensure they pay attention to avoiding data redundancy and ensuring data diversity when processing data.

Database technology has always been a focal point of interest for enterprises and organizations in the field of information management. With the continuous growth and diversification of information, effective information management has become crucial. This paper aims to explore the extensive applications of database systems in information management. Firstly, the paper reviews relational and non-relational databases. Subsequently, it delves into the current applications of database systems in various domains, including enterprise management, retail, education, and government and public services. In the realm of enterprise management, database systems provide a solid foundation for information management by ensuring the timeliness, accuracy, and reliability of data. In the retail industry, they support inventory management, sales analysis, and enhance the user experience. In education, database systems are used for student information management, teaching data analysis, and online learning. In the government and public services sector, they facilitate information sharing and data transparency, while playing a critical role in crisis management and emergency response. This paper highlights the significance and diverse applications of database systems in different domains, offering insights into the current research trends and future prospects in this field.

The mihoyo Sales Management System was developed to adjust to the dynamic business environment and maintain a competitive edge. In the context of this era, the mihoyo Sales Management System utilizes a robust database structure to optimize sales operations. In this paper, a management database for Mihiyo company is designed. The customer module stores customer information, linked to orders, payments, and expenses. An order table tracks order details, linked to customers and products. Payment and shipment tables manage transaction and delivery information. The inventory table enables real-time monitoring of stock levels. Financial management tables record sales, payment, and expense data. User tables store information related to different user roles. The system's interface seamlessly interacts with the database, allowing users to access and update information efficiently. The reporting and analytics module analyzes data from the database, facilitating decision-making and performance evaluation. Database testing requires completing relevant test cases and achieving a robust system.

Street view imagery is an emerging form of geographic big data. It presents urban visual environments from the perspective of urban residents and also contains non-visual environment of cities, such as urban human activities and socio-economic development. However, traditional digital image processing has its limitations, and the continuous development of artificial intelligence, especially computer vision and deep learning, provides strong technical support for exploring the rich semantic information in street view imagery. This paper reviews the related research on street view imagery and its artificial intelligence analysis methods and applications. It outlines the acquisition, storage, and common data sources of street view imagery. Then it introduces computer vision, deep learning, and commonly used open-source datasets in street view imagery analysis. It also detailed three aspects of AI-based street view imagery applications, namely quantification of the physical space, urban perception, and spatial semantic speculation. Finally, issues like data acquisition, domain adaption and deep learning black box are discussed. The hotspots and prospects for the development of this research topic are also prospected.

Tired driving is still a significant cause of traffic accidents, often causing huge economic losses to society.Therefore, how to avoid driver fatigue has become a meaningful problem. The purpose of this paper is to review and summarize various research methods of driver fatigue monitoring system at home and abroad, so as to reduce the probability of traffic accidents caused by fatigue driving. The main content of this paper is to review the existing fatigue driving monitoring technologies such as electrocardiogram (ECG), electroencephalogram (EEG), HRV, eye tracking system, CNN, facial state analysis, emotion recognition, 5G and brain wave recognition, and infer the fatigue level of drivers by using these technologies to extract various clues that usually represent the level of human vigilance. Assess cognitive load and identify emotional states to monitor tired driving. By discussing the advantages and disadvantages of various fatigue monitoring systems at home and abroad, this paper proposes an innovative method, which transforms the previous single-mode monitoring system into a multi-mode monitoring system, integrates multiple biosensors, extracts relevant features from the biosensor data and processes the data reasonably in order to understand the driver’s physiological state more comprehensively. This method is expected to improve the accuracy of fatigue monitoring system.

Nowadays, the idea of Artificial Intelligence (AI) medical detection has aroused great interest around the world. AI has the potential to strengthen medicine in both observation and operation. For instance, AI could catch crucial details that are not intuitive to humans. Robots controlled by AI could also do micro-operations that are extremely hard on human hands. In this study, the author utilizes one of the most focused traditional machine-learning methods, that is the Light Gradient Boosting Machine (LightGBM) algorithm for breast cancer prediction. The LightGBM performs both well on accuracy and speed in the study’s experiment. The study applies the bootstrap aggregating (Bagging) method to cope with the over-fitting problem. As the significance of the study, the study shows that the LightGBM can be utilized in designing accurate, fast and cheap medical detection devices. Nevertheless, programmers should handle the over-fitting problem cautiously while building models based on LightGBM. This could help doctors in impoverished areas realize accurate medical detection. People could also do accurate self-diagnosing with a cheap, portable device at home.

One significant assessing criteria of climate change is geometric evolution. The rate of evolution reveals the speed that environment worsens. Advanced space mirror monitors that and generates images timely. However, it might be difficult for human to deal with collected numerous image-related data. In previous research, convolutional neural network is regarded to have specific advantage in resolving image recognition tasks. Hence, a new type of convolutional neural network model is applied to identify different kinds of landscape. Virtually, this model is called Efficient Net which based on landscape recognition dataset with 5 classes of landscapes. The study also introduces the fine-tuning to further improve the performance of the model. To evaluate the model, the precision, recall, F1 score, accuracy and loss are adopted as assessing criteria. The results shows that the model predicts the target dataset to a great extent. However, it has been tested that the class of mountain might not be suitable for predicting because of vague criterion. That is helpful in real-condition geographical applications and environmental governance.