Museum Information Systems (MIS) often rely on manual classification and keyword search, limiting accuracy and scalability. Deep learning offers a solution, but effective integration requires alignment with curatorial workflows. This study proposes a model-driven framework for integrating Convolutional Neural Networks (CNNs) into MIS to enhance artifact classification and retrieval. A prototype was built using ReactJS, Django, and TensorFlow, and it was trained on a curated subset of The Met's Open Access Images. The system employs a Hybrid-E Loss for improved classification accuracy. The model achieved 94.3% classification accuracy and real-time retrieval latency below 100 ms, with throughput exceeding 14 queries per second. The framework successfully bridges AI performance with curatorial logic, demonstrating a scalable and interpretable solution for digital heritage systems.
This article explores how Xamarin simplifies cross-platform mobile app development and highlights the importance of design patterns in tackling modern software challenges. As demand grows for adaptable, high-performing apps, Xamarin empowers developers to create seamless solutions for iOS, Android, and Windows using C#. The text introduces key design patterns like Model-View-ViewModel (MVVM), singleton, and dependency injection, showing how they reduce complexity, boost code reuse, and improve maintainability. It also discusses the transition to .NET MAUI, the next-generation framework built on Xamarin's foundation, offering enhanced performance and flexibility. A practical example demonstrates the Model-View-ViewModel (MVVM) pattern in a task list app, illustrating how design patterns solve real-world problems. By focusing on efficient resource management and scalable design, this article provides actionable insights to overcome challenges in cross-platform development, ensuring robust and maintainable applications.
Students enrolled in interdisciplinary programs supported with modern information technologies may face more challenges in learning, because they must learn from at least two curricula, including courses of big data, new media technologies and intelligent platform use, etc. Taking students in these programs as an example, this study aims to investigate relationships among eight variables related to student career development. The research employed the quantitative method of Pearson bivariate analysis, ANOVA and Hayes (2017) regression procedure to test the direct and indirect relationship among variables. The researcher developed a moderated mediation model visualizing the relationship between professional knowledge and confidence for successful employment. Recommendations of early intervention targeting first-year students and different curriculum designing were proposed to facilitate planning in the acquisition of professional knowledge and the establishment of career goals, aiming to enhance the career development and success of tertiary vocational college students.
With the development of computer technology innovation, be able to deal with the media comprehensive information and real-time information interaction with the computer multimedia technology arises at the historic moment, it promotes the application fields of computer widen to industrial all aspects of life. As the product of digital technology, animation technology plays an irreplaceable role in the production of multimedia courseware. However, the existing human-computer interaction methods have shortcomings such as incomplete extraction of video features and poor human-computer interaction effect. In this context, this paper designs a multimedia human-computer interaction method for animation works based on CNN model. First of all, the original video data is collected and preprocessed. Then it is input into the HCI framework based on CNN model for feature extraction. Finally, the effectiveness and practicability of the proposed method are proved by simulation experiments, which provides a reference and basis for the research of modern human-computer interaction.
