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The traditional teaching mode is difficult to fully meet the diversity of modern music education. This paper focuses on exploring new paths to optimize university music teaching models using advanced data algorithm technologies such as embedded neural networks. This exploration is not only an innovation of traditional teaching models, but also a key practice to promote students’ comprehensive development, enhance their overall quality, and stimulate innovative thinking. This paper deeply analyzes the urgency and importance of optimizing the music teaching mode in universities, and points out that in the rapidly changing digital age, music education must keep pace with the times to achieve a comprehensive upgrade of teaching content, methods, and evaluation system through technological means, in order to meet the diverse and high-quality demands of society for music talents. These technological advancements not only provide strong support for the integration of teaching resources and the design of personalized learning paths, but also open up vast space for the implementation of innovative teaching models. This paper introduces a new concept of spectral regression rationality. This concept aims to conduct in-depth analysis of music works through embedded neural networks to ensure the accuracy of music expression. At the same time, guide students to master scientific data analysis methods and cultivate their rational thinking and aesthetic abilities in music creation.

The emergence of Intelligent Education Cloud Platforms has revolutionized how educational resources are shared and utilized, enabling a more inclusive and adaptive approach to learning. In the context of college preschool education, the integration of distributed sharing and personalized recommendation systems addresses critical challenges in resource accessibility and learner engagement. Traditional methodologies often rely on static and generalized frameworks, lacking the flexibility to cater to diverse learning needs and the scalability for dynamic resource allocation. These limitations hinder their capacity to provide tailored educational pathways and real-time adaptability, which are essential in preschool education’s highly individualized context. To overcome these barriers, we propose a Knowledge-Adaptive Education Network (KAEN) augmented by the Adaptive Learning Pathway Strategy (ALPS), tailored for deployment on Intelligent Education Cloud Platforms. KAEN leverages graph-based knowledge representation, dynamic content alignment networks, and reinforcement learning to optimize resource recommendation and personalize learning experiences. ALPS complements this system by generating individualized learning pathways, integrating multi-modal content, and real-time feedback mechanisms to enhance engagement and educational outcomes. Experimental validation demonstrates significant improvements in resource utilization efficiency, learner engagement metrics, and adaptive content delivery quality. These findings underscore the potential of integrating AI-driven frameworks into Intelligent Education environments, offering scalable and effective solutions for preschool education resource sharing and personalization.

Lung cancer is a chronic disease that leads to the most common deaths worldwide because lung cancer is not detected during early detection and classification. Therefore, several research works are being carried out based on early-stage lung cancer prediction using artificial intelligence (AI) technology. With the advancement of machine learning (ML) and deep learning (DL) techniques, several techniques have been developed for the early diagnosis of lung cancer. This survey paper considers the numerous research papers developed to predict lung cancer. The survey paper focused on predicting lung cancer in the following ways: a detailed survey on pre-processing and segmentation techniques used in lung cancer prediction, non-small cell lung cancer (NSCLC) prediction techniques ML, and DL approaches utilized for lung cancer prediction. A detailed survey on the use of various datasets in lung cancer prediction is included in this paper. The application and challenges of the lung cancer prediction model are also explored in this survey paper through the various existing methods. This survey is used to analyze the various limitations of the lung cancer model. The survey analysis is helpful in developing new lung cancer prediction techniques and solving the existing lung cancer model issues.

In the era of cloud computing, businesses are increasingly relying on cloud platforms to streamline their operations and deliver services efficiently. Recommendation systems play a pivotal role in suggesting suitable services and resources to enhance user experience and optimize resource allocation. This paper presents a novel approach, the Multi-Model Fusion Recommendation Algorithm (MMFRA), which integrates multiple recommendation models using advanced fusion techniques to enhance the accuracy of recommendations in cloud platform business scenarios. The implementation process of MMFRA involves combining diverse recommendation models, such as collaborative filtering, content-based filtering, and matrix factorization, into a unified framework that leverages their strengths while mitigating individual limitations. This fusion process is designed to achieve higher precision in service recommendations by considering various aspects of user behavior and preferences. Through a comprehensive evaluation in a simulated cloud platform environment, MMFRA demonstrates superior performance in terms of recommendation accuracy and user satisfaction. The proposed algorithm offers significant potential for enhancing the effectiveness of cloud platform services, ultimately benefiting both service providers and users.

The application of artificial intelligence of things (AIoT) is becoming increasingly widespread. With its rapid development, it has raised many complex network security issues. These have brought crushing security threats and risks to all stakeholders. AIoT cloud platform in the smart home plays a vital role in the security management of devices and is the current research hotspot. This paper discusses the security of the AIoT cloud platform, represented by the smart home. The indicator system describing the protection of the smart home cloud platform is proposed, and the security risk evaluation method of the cloud service is formed. Based on the evaluation method, a lightweight security evaluation framework for smart home cloud service is designed to evaluate the typical cloud platform’s security. The evaluation identifies some technical and administrative issues. Finally, to improve the security of the cloud console of AIoT and discover potential security risks in time, a security self-test model of the scalable cloud console is proposed.

Based on the spring, spring MVC and MyBatis structures of the cloud platform SSM framework, an information management platform for power grid material supply chain is built. The data layer uses a variety of sensors to collect power grid material supply chain information, and the information is fed back to the data storage layer after being integrated by the logical reorganization function of the persistence layer. The data storage layer uses the multi-sensor supply chain information fusion method based on paste progress to fuse the information and store it in the database. The business logic layer calls the information in the database and uses the improved k-means clustering algorithm to detect the abnormal supply chain data information. After calculation and data control by the control layer, the data management results are displayed through the presentation layer. The experimental results show that the absolute error of data fusion is very low. It can effectively cluster data information and distinguish outlier anomaly information at the same time, and the effect of information management is good.

This paper gives a comprehensive review on scientific and economic interests of intelligent computational techniques applied to construction of sustainable circular economy as well as the current methodologies and tools used and their cooperation with other digital tools such as IoT and cloud platform in the context of Industry 4.0. More emphasis has been placed on the areas of environmental impacts evaluation, remanufacturing and resource sustainability management and optimization, which are playing a key role in circular economy beyond classical manufacturing themes. Based on this review, a short analysis has been provided on the perspectives of this research theme in the future.

China's rapid economic development has also led to increased traffic jams, environmental pollution and traffic accidents due to increasing car ownership. This has made traffic management more difficult. To increase transportation efficiency, reduce environmental pollution and reduce energy consumption, an Integrated Transport System can be formed using an Intelligent Transportation System. In this paper, the current traffic situation in China is first introduced along with the functions and structures of the Intelligent Transportation System. Advanced foreign Intelligent Transportation Systems are then introduced, followed by an analysis of the country's current traffic situation. Finally, the paper lists the framework of the Intelligent Transportation System and its proper solutions suitable for the country.

Currently, there is an increasing dependence on the educational administration system in colleges and universities. As the educational administration system supports the school teaching resources, the massive access pressure during course selection is a problem that is becoming increasingly prominent. We propose the construction of a cloud platform to pool all physical servers and the setting up of virtual machines. During course selection, the cloud platform can increase or decrease the number of virtual machines automatically through the load elastic flexible configuration and the monitoring of parameters such as the number of requests and CPU usage. This greatly improves the efficiency of course selectionand student satisfaction while significantly reducing server hardware expenditure and raising the efficiency of maintenance and operations.