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.
By combing the shortcomings of the current quantitative securities trading, a new deep reinforcement learning modeling method is proposed to improve the abstraction of state, action and reward function; on the basis of the traditional DQN algorithm, a deep reinforcement learning algorithm model of RB_DRL is proposed. By improving the network structure and connection mode, and redefining the loss function of the network, the improved model performs well in many groups of comparative experiments. A securities quantitative trading system based on deep reinforcement learning is designed, which organically combines models, strategies and data, visually displays the information to users in the form of web pages to facilitate users' use and seeks the trading rules of the financial market to provide investors with a more stable trading strategy. The research results have important practical value and research significance in the field of financial investment.
Machine learning algorithms have attracted widespread attention in both industry and academia. This article mainly studies the marketing strategy decision-making of private listed enterprises based on Bayesian panel data model. By constructing a Bayesian static panel data model and a Bayesian dynamic panel data model, an empirical analysis was conducted on the debt financing decisions of private enterprises from two aspects: external financial environment and internal governance. The experimental results show that the MC error and standard deviation of parameter estimation for Bayesian static panel data model and Bayesian dynamic panel data model are both very small. This method contains more information, increases observation data and degrees of freedom. This article provides important theoretical guidance for the coordinated development of private listed enterprises and state-owned enterprises. It is conducive to promoting the coordinated development of the entire national economy.
This article takes college students' learning adaptability as the research object, adopts B/S structure to develop a learning adaptive platform, designs a learner data model, a learning style model, a learning resource presentation module, and an ability level test module; tests the platform through simulated data; and analyzes college students' learning style, knowledge level and learner collaboration level. The results show that college students' learning adaptation has the characteristics of flexibility, individuality, initiative, and reflection. A self-adaptive learning platform can understand its learning state and effect through learning evaluation, adjust its learning strategies and methods in time, and help college students better understand and master knowledge. The research results provide theoretical data support for the exploration of college students' learning adaptability under the background of wisdom education.
The research in this article aims to consider low-carbon factors, through reasonable vehicle allocation and optimization of distribution routes, to achieve high satisfaction and low total cost, and to provide an optimized solution for fresh food distribution companies. In this article, cargo damage cost, energy cost, and carbon emission cost are added to the total cost, and customer satisfaction constraints based on time and quality are added, respectively, to construct a multi-vehicle cold chain VRP model under the low-carbon perspective. In order to obtain a good initial path method, a good chromosome is generated and added to the initial chromosome population according to the constraints of the vehicle type and time window, and the local elite retention strategy is combined to speed up the population convergence. Finally, taking the data of A Fresh Food Company as an example, the MATLAB software is used to realize the programming, which verifies the validity and superiority of the multi-vehicle cold chain VRP model under the low-carbon perspective.
This study investigates the method of analyzing emotional tendencies in music courses and its application in lesson plan evaluation. Using a weighted method to analyze emotional tendencies in music curriculum, the study compares the results with existing literature, demonstrating the superior accuracy of the proposed method. To evaluate lesson plan quality, a combination of self-assessment, mutual evaluation, group evaluation, and the middle school music lesson plan evaluation form is recommended for comprehensive assessment. The study's method for comment polarity achieves an accuracy rate of 69.19%, significantly outperforming other methods. Additionally, improvements in lexical feature extraction reduce computation complexity and interference factors in sentiment polarity analysis. In conclusion, this study offers valuable insights for enhancing teaching effectiveness, lesson plan quality, and understanding course feedback.
The comprehensive evaluation and selection of suppliers under the environment of supply chain management has become a key factor affecting the success of supply chain. How to select suppliers and the strategic partnership between suppliers under the environment of supply chain management has become an important challenge. To solve this problem, this paper takes the supplier evaluation and selection of Guangzhou Automobile Toyota Company as the research object, constructs the index system of supplier comprehensive evaluation and selection, uses the RBF neural network algorithm to establish the supplier evaluation and selection model, and makes an experimental study. The results show that radial basis function neural network is a local approximation network, which has a unique and definite solution to the problem, and there is no local minimum problem in BP network. It is a method that enables enterprises and suppliers to have a clear understanding and seek further promotion together. The research provides theoretical data support for enterprise managers to make decisions.
Based on the fuzzy method, this paper establishes a ranking model of the psychological quality of college teachers and an interception model of assessment indicators. On this basis, a quantitative evaluation method of college teachers' psychological quality is proposed by using the principles of fuzzy psychological evaluation and fuzzy recognition. According to empirical study, this evaluation approach is capable of providing a theoretical foundation for the next teacher training as well as a thorough assessment of the psychological qualities of teachers. The research concludes by pointing out that the model and evaluation approach can also be used to introduce and train university teachers, and it makes some sound recommendations for their development. An empirical study on the quantitative evaluation method of college teachers' psychological quality based on fuzzy psychological evaluation and fuzzy recognition principle is beneficial to better build the foundation of college teachers' psychological quality under the concept of harmonious education.
This paper introduces a technology, a data-driven optimization model of manufacturing service in intelligent manufacturing process using deep learning algorithm and resource agent (DDR), and a data-driven resource agent that represents available manufacturing resources. Asset agent is an intelligent module of entity production unit, which has powerful functions of data processing and service management. This paper includes the method of designing expert-based processes, the current process realization model, and the key performance indicators (KPI) used to evaluate the optimization work. The model aims to maximize efficiency, reduce the cost of manufacturing resources, improve the production and maintenance efficiency of network resources, and improve the manufacturing service level. Finally, the efficiency and technical feasibility of the model are evaluated through a typical example of industrial product production process.
How to correctly understand the existence of local government debt, study its risk classification and impact, give full play to the “dual nature” of debt with a full-caliber indicator system, and avoid debt risks to the greatest extent. That is the research direction of this article. In order to improve the accuracy and efficiency of risk assessment and effectively reduce the debt risk of government platform companies, a risk assessment method based on optimized back-propagation (BP) neural network is proposed. First, the method uses quantum genetic algorithm (quantum genetic algorithm, QGA) to adjust and determine the initial weight and threshold of BP neural network and realize the optimization of BP neural network model parameter setting. Then, the QGA-BP debt risk assessment of government platforms is verified that it performs well in the debt risk prediction of government platform companies, and its prediction accuracy and prediction speed are improved.
Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.
In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.
The internet of things (IoT) has become a key support object for Chinese strategic emerging industries. It is of great practical significance to promote the construction of the internet of things. Driven by national policies, the development of the internet of things in China has achieved certain results, but it also faces many problems. For example, there are few theoretical and empirical studies on the internet of things economy. In this context, from the perspective of big data, this paper studies the development model and influencing factors of the internet of things economy, and takes Jiangsu Province as an example to put forward development strategies. This article studies the three-stage development model of the IoT economic big data ecosystem: the primary stage, the growth and maturity stage, and the integration stage. On the basis of the research on the development model, the general evaluation method of the economic development model of the internet of things from the perspective of big data is studied, and the general model of strategy selection is established.
The technology of relay protection in China's power system has gradually changed from the traditional operation mode to the development direction of informatization, intelligence, and automation. As a result, the role of relay protection in the power system has become more and more important. It brings higher requirements to the reliability of relay protection; effective reliability assessment of the relay protection system and the corresponding condition operation, minimize or avoid accidents, and ensure the safety of power grids. Starting from the operating characteristics of relay protection, it is suitable for practical engineering applications. Aiming at the problems of low work efficiency and low inspection quality in manual inspection of relay protection pressure plate switching state, The Faster R-CNN image processing algorithm will be come up with. This method uses grayscale, binarization and filtering techniques to preprocess the platen photos, and uses RPN.
E-learning offers an experience that is not constrained by time or geography. Owing to the advancements in technology and accessible computing, users have several ways to interact with e-learning programs. Therefore, usability approaches are crucial for the success of an e-learning application or a website. This study investigates various user-interface usability evaluation methods (UEM) and distribution of e-learning web-based applications, such as Moodle, Blackboard, Learning Management System (LMS), Zoom, Google Classroom, Facebook, and other online programs that exist for online education. To evaluate the usability features of online educational apps and websites, including their effectiveness and usability for students, a survey was conducted to collect responses on online education.
With the popularization of the internet, cybercrime continues to increase, and traditional blacklist methods have difficulty in coping with new threats. To address this challenge, the authors propose a web domain name security access recognition algorithm based on bidirectional recurrent neural networks, aiming to more effectively combat domain name generation technology. This algorithm extracts richer semantic features at each layer through bidirectional recurrent neural networks to more accurately describe domain name features, thus effectively handling SGD problems in abnormal network traffic detection. The results show that compared with the other three algorithms, the model trained by HCA-BAGD has better performance and higher accuracy, successfully solving the problem of network security detection. This study emphasizes the importance of cybersecurity and emphasizes continuous innovation and the adoption of new technological tools to ensure the safe operation of the internet ecosystem, bringing new perspectives and solutions to research and applications in the field of cybersecurity.
